diff --git a/.github/workflows/quicktest-dev-pr.yml b/.github/workflows/quicktest-dev-pr.yml
new file mode 100644
index 0000000000000000000000000000000000000000..cd59a629405c748187cdf478c0bdb0694c58c79f
--- /dev/null
+++ b/.github/workflows/quicktest-dev-pr.yml
@@ -0,0 +1,21 @@
+name: QuicktestPRAgainstDev
+
+on:
+ pull_request:
+ branches: [ dev ]
+ push:
+ branches: [ dev ]
+
+
+jobs:
+
+ test:
+ name: Run quicktest on PR branch
+ runs-on: ubuntu-latest
+
+ steps:
+ - name: checkout
+ uses: actions/checkout@v2
+
+ - name: DockerRunQuicktest
+ run: sh run-docker.sh quicktest
diff --git a/AUTHORS.rst b/AUTHORS.rst
index e231e61d38991e11e2e43a7c9a3a78c50c878244..eb1e06e54b7eb6deedd3e7f8392bb3aa257e7dc6 100644
--- a/AUTHORS.rst
+++ b/AUTHORS.rst
@@ -6,3 +6,5 @@ Contributors
* Jakoba Petri-Koenig (@auphelia)
* Andrea Rigoni (@AndreaRigoni)
* Hendrik Borras (@HenniOVP)
+* Lucian Petrica (@quetric)
+* Tobias Alonso (@Tobi-Alonso)
diff --git a/run-docker.sh b/run-docker.sh
index e1f17e728204217ff3caa6e486b2daae16d6d271..186efc322a8f437be0371b5a142a9dd524d1abf3 100755
--- a/run-docker.sh
+++ b/run-docker.sh
@@ -96,6 +96,8 @@ gecho "Port-forwarding for Netron $NETRON_PORT:$NETRON_PORT"
gecho "Vivado IP cache dir is at $VIVADO_IP_CACHE"
gecho "Using default PYNQ board $PYNQ_BOARD"
+DOCKER_INTERACTIVE = ""
+
if [ "$1" = "test" ]; then
gecho "Running test suite (all tests)"
DOCKER_CMD="python setup.py test"
@@ -108,6 +110,7 @@ elif [ "$1" = "notebook" ]; then
else
gecho "Running container only"
DOCKER_CMD="bash"
+ DOCKER_INTERACTIVE="-it"
fi
# Build the FINN Docker image
@@ -123,7 +126,7 @@ docker build -f docker/Dockerfile.finn_dev --tag=$DOCKER_TAG \
# Launch container with current directory mounted
# important to pass the --init flag here for correct Vivado operation, see:
# https://stackoverflow.com/questions/55733058/vivado-synthesis-hangs-in-docker-container-spawned-by-jenkins
-docker run -t --rm --name $DOCKER_INST_NAME -it --init \
+docker run -t --rm --name $DOCKER_INST_NAME $DOCKER_INTERACTIVE --init \
--hostname $DOCKER_INST_NAME \
-e "XILINX_VIVADO=$VIVADO_PATH" \
-e "SHELL=/bin/bash" \
diff --git a/src/finn/core/modelwrapper.py b/src/finn/core/modelwrapper.py
index cdf99dc3bd8b698bec60d79ef6e34640ac3b740c..ed32426abcc8ea71428a7f746a99454e8e4a2c17 100644
--- a/src/finn/core/modelwrapper.py
+++ b/src/finn/core/modelwrapper.py
@@ -333,6 +333,22 @@ class ModelWrapper:
else:
return None
+ def is_fork_node(self, node):
+ """Checks if the given node is a fork, that is, the node has multiple
+ direct successors"""
+ direct_successors = self.find_direct_successors(node)
+ is_fork = False if direct_successors is None else (len(direct_successors) > 1)
+ return is_fork
+
+ def is_join_node(self, node):
+ """Checks if the given node is a join, that is, the node has multiple
+ direct predecessors"""
+ direct_predecessors = self.find_direct_predecessors(node)
+ is_join = (
+ False if direct_predecessors is None else (len(direct_predecessors) > 1)
+ )
+ return is_join
+
def get_all_tensor_names(self):
"""Returns a list of all (input, output and value_info) tensor names
in the graph."""
diff --git a/src/finn/custom_op/fpgadataflow/__init__.py b/src/finn/custom_op/fpgadataflow/__init__.py
index a99d62fd18a958d37fef6b3e1939ef97e859b0b2..17a55e519ed0440f68e295aecaab179e6adf632f 100644
--- a/src/finn/custom_op/fpgadataflow/__init__.py
+++ b/src/finn/custom_op/fpgadataflow/__init__.py
@@ -40,6 +40,7 @@ from finn.util.basic import (
from finn.util.fpgadataflow import (
IPGenBuilder,
pyverilate_get_liveness_threshold_cycles,
+ rtlsim_multi_io,
)
from . import templates
@@ -109,6 +110,31 @@ class HLSCustomOp(CustomOp):
)
return verilog_file
+ def get_all_verilog_paths(self):
+ "Return list of all folders containing Verilog code for this node."
+
+ code_gen_dir = self.get_nodeattr("code_gen_dir_ipgen")
+ assert (
+ code_gen_dir != ""
+ ), """Node attribute "code_gen_dir_ipgen" is
+ not set. Please run HLSSynthIP first."""
+ verilog_path = "{}/project_{}/sol1/impl/verilog/".format(
+ code_gen_dir, self.onnx_node.name
+ )
+ # default impl only returns the HLS verilog codegen dir
+ return [verilog_path]
+
+ def get_all_verilog_filenames(self):
+ "Return list of all Verilog files used for this node."
+
+ verilog_files = []
+ verilog_paths = self.get_all_verilog_paths()
+ for verilog_path in verilog_paths:
+ for f in os.listdir(verilog_path):
+ if f.endswith(".v"):
+ verilog_files += [f]
+ return verilog_files
+
def prepare_rtlsim(self):
"""Creates a Verilator emulation library for the RTL code generated
for this node, sets the rtlsim_so attribute to its path and returns
@@ -116,24 +142,15 @@ class HLSCustomOp(CustomOp):
if PyVerilator is None:
raise ImportError("Installation of PyVerilator is required.")
- # ensure that code is generated
- code_gen_dir = self.get_nodeattr("code_gen_dir_ipgen")
- assert (
- code_gen_dir != ""
- ), """Node attribute "code_gen_dir_ipgen" is
- not set. Please run HLSSynthIP first."""
- verilog_file = self.get_verilog_top_filename()
- assert os.path.isfile(verilog_file), "Cannot find top-level Verilog file."
+ verilog_paths = self.get_all_verilog_paths()
+ verilog_files = self.get_all_verilog_filenames()
# build the Verilator emu library
sim = PyVerilator.build(
- verilog_file,
+ verilog_files,
build_dir=make_build_dir("pyverilator_" + self.onnx_node.name + "_"),
- verilog_path=[
- "{}/project_{}/sol1/impl/verilog/".format(
- code_gen_dir, self.onnx_node.name
- )
- ],
+ verilog_path=verilog_paths,
trace_depth=get_rtlsim_trace_depth(),
+ top_module_name=self.get_verilog_top_module_name(),
)
# save generated lib filename in attribute
self.set_nodeattr("rtlsim_so", sim.lib._name)
@@ -302,14 +319,24 @@ Found no codegen dir for this node, did you run the prepare_cppsim transformatio
)
def npy_to_dynamic_output(self, context):
- """Reads the output from a .npy file and saves it at the right place in
- the context dictionary."""
- # TODO support multi-output nodes as needed
+ """Reads the output from an output.npy file generated from cppsim and
+ places its content into the context dictionary."""
node = self.onnx_node
code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
output = np.load("{}/output.npy".format(code_gen_dir))
context[node.output[0]] = output
+ def npy_to_dynamic_outputs(self, context, npy_list):
+ """Reads the output from .npy files generated from cppsim and places
+ their content into the context dictionary.
+ npy_list is a list specifying which files to read, and its order must
+ match the order of node outputs."""
+ node = self.onnx_node
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
+ for i in range(len(npy_list)):
+ output = np.load("{}/{}".format(code_gen_dir, npy_list[i]))
+ context[node.output[i]] = output
+
def exec_precompiled_singlenode_model(self):
"""Executes precompiled executable."""
executable_path = self.get_nodeattr("executable_path")
@@ -405,6 +432,16 @@ compilation transformations?
sim.stop_vcd_trace()
return outputs
+ def rtlsim_multi_io(self, sim, io_dict):
+ "Run rtlsim for this node, supports multiple i/o streams."
+
+ trace_file = self.get_nodeattr("rtlsim_trace")
+ if trace_file == "default":
+ trace_file = self.onnx_node.name + ".vcd"
+ num_out_values = self.get_number_output_values()
+ total_cycle_count = rtlsim_multi_io(sim, io_dict, num_out_values, trace_file)
+ self.set_nodeattr("sim_cycles", total_cycle_count)
+
def execute_node(self, context, graph):
"""Executes single node using cppsim or rtlsim."""
mode = self.get_nodeattr("exec_mode")
diff --git a/src/finn/custom_op/fpgadataflow/duplicatestreams_batch.py b/src/finn/custom_op/fpgadataflow/duplicatestreams_batch.py
new file mode 100644
index 0000000000000000000000000000000000000000..54051af5e0387081a23e1f8fa77ec9e363098830
--- /dev/null
+++ b/src/finn/custom_op/fpgadataflow/duplicatestreams_batch.py
@@ -0,0 +1,361 @@
+# 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 os
+
+import numpy as np
+
+from finn.core.datatype import DataType
+from finn.custom_op.fpgadataflow import HLSCustomOp
+from onnx import TensorProto, helper
+from finn.util.data_packing import npy_to_rtlsim_input, rtlsim_output_to_npy
+
+
+class DuplicateStreams_Batch(HLSCustomOp):
+ """Class that corresponds to finn-hlslib function of the same name."""
+
+ def __init__(self, onnx_node):
+ super().__init__(onnx_node)
+
+ def get_nodeattr_types(self):
+ my_attrs = {
+ "NumChannels": ("i", True, 0),
+ "PE": ("i", True, 0),
+ # FINN DataTypes for input
+ "inputDataType": ("s", True, ""),
+ # number of input vectors, examples:
+ # [1] is a single vector (like a FC layer with batch=1)
+ # [4] is four vectors (like a FC layer with batch=4)
+ # [1, 4, 4] is four * four vectors (like a conv layer with batch=1)
+ "numInputVectors": ("ints", False, [1]),
+ }
+ my_attrs.update(super().get_nodeattr_types())
+ return my_attrs
+
+ def get_normal_input_shape(self):
+ ch = self.get_nodeattr("NumChannels")
+ vecs = list(self.get_nodeattr("numInputVectors"))
+ ishape = tuple(vecs + [ch])
+ return ishape
+
+ def get_folded_input_shape(self):
+ ch = self.get_nodeattr("NumChannels")
+ pe = self.get_nodeattr("PE")
+ vecs = list(self.get_nodeattr("numInputVectors"))
+ assert ch % pe == 0, "PE must divide NumChannels"
+ folds = int(ch / pe)
+ folded_ishape = tuple(vecs + [folds, pe])
+ return folded_ishape
+
+ def get_normal_output_shape(self):
+ return self.get_normal_input_shape()
+
+ def get_folded_output_shape(self):
+ return self.get_folded_input_shape()
+
+ def make_shape_compatible_op(self, model):
+ exp_ishape = self.get_normal_input_shape()
+ oshape = self.get_normal_output_shape()
+ ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
+ assert ishape == exp_ishape, "Unexpected input shape."
+ # implement tensor with correct shape
+ values = np.random.randn(*oshape).astype(np.float32)
+ split_input = np.concatenate((values, values), axis=0)
+ return helper.make_node(
+ "Split",
+ inputs=[split_input],
+ outputs=[self.onnx_node.output[0], self.onnx_node.output[0]],
+ value=helper.make_tensor(
+ name="const_tensor", data_type=TensorProto.FLOAT, axis=0
+ ),
+ )
+
+ def infer_node_datatype(self, model):
+ odt = self.get_output_datatype()
+ model.set_tensor_datatype(self.onnx_node.output[0], odt)
+
+ def verify_node(self):
+ info_messages = []
+ # verify that "domain" is set to "finn"
+ domain_value = self.onnx_node.domain
+ if domain_value == "finn":
+ info_messages.append("Attribute domain is set correctly")
+ else:
+ info_messages.append('Attribute domain should be set to "finn"')
+
+ # verify that "backend" is set to "fpgadataflow"
+ backend_value = self.get_nodeattr("backend")
+ if backend_value == "fpgadataflow":
+ info_messages.append("Attribute backend is set correctly")
+ else:
+ info_messages.append('Attribute backend should be set to "fpgadataflow"')
+
+ # verify that all necessary attributes exist
+ try:
+ self.get_nodeattr("code_gen_dir_cppsim")
+ self.get_nodeattr("executable_path")
+ self.get_nodeattr("NumChannels")
+ self.get_nodeattr("PE")
+ self.get_nodeattr("inputDataType")
+ info_messages.append("All necessary attributes exist")
+ except Exception:
+ info_messages.append(
+ """The required GlobalAccPool_Batch attributes do not exist."""
+ )
+
+ return info_messages
+
+ def get_input_datatype(self):
+ """Returns FINN DataType of input."""
+ return DataType[self.get_nodeattr("inputDataType")]
+
+ def get_output_datatype(self):
+ """Returns FINN DataType of output."""
+ return DataType[self.get_nodeattr("inputDataType")]
+
+ def get_instream_width(self):
+ """Returns input stream width."""
+ ibits = self.get_input_datatype().bitwidth()
+ pe = self.get_nodeattr("PE")
+ in_width = pe * ibits
+ return in_width
+
+ def get_outstream_width(self):
+ """Returns output stream width."""
+ obits = self.get_output_datatype().bitwidth()
+ pe = self.get_nodeattr("PE")
+ out_width = pe * obits
+ return out_width
+
+ def get_number_output_values(self):
+ return 2 * np.prod(self.get_folded_output_shape()[1:-1])
+
+ def execute_node(self, context, graph):
+ mode = self.get_nodeattr("exec_mode")
+ node = self.onnx_node
+ exp_ishape = self.get_normal_input_shape()
+ exp_oshape = self.get_normal_output_shape()
+ folded_ishape = self.get_folded_input_shape()
+ folded_oshape = self.get_folded_output_shape()
+
+ if mode == "cppsim":
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
+ elif mode == "rtlsim":
+ code_gen_dir = self.get_nodeattr("code_gen_dir_ipgen")
+ else:
+ raise Exception(
+ """Invalid value for attribute exec_mode! Is currently set to: {}
+ has to be set to one of the following value ("cppsim", "rtlsim")""".format(
+ mode
+ )
+ )
+
+ inp = context[node.input[0]]
+ assert str(inp.dtype) == "float32", "Input datatype is not float32"
+ assert inp.shape == exp_ishape, """Input shape doesn't match expected shape ."""
+ export_idt = self.get_input_datatype()
+ # reshape input into folded form
+ inp = inp.reshape(folded_ishape)
+ # make copy before saving array
+ reshaped_input = inp.copy()
+ np.save(os.path.join(code_gen_dir, "input_0.npy"), reshaped_input)
+
+ if mode == "cppsim":
+ # 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)
+ elif mode == "rtlsim":
+ sim = self.get_rtlsim()
+ nbits = self.get_instream_width()
+ rtlsim_inp = npy_to_rtlsim_input(
+ "{}/input_0.npy".format(code_gen_dir), export_idt, nbits
+ )
+ super().reset_rtlsim(sim)
+ super().toggle_clk(sim)
+ rtlsim_dict = {
+ "inputs": {"in0": rtlsim_inp},
+ "outputs": {"out0": [], "out1": []},
+ }
+ 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()
+
+ 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: {}
+ has to be set to one of the following value ("cppsim", "rtlsim")""".format(
+ mode
+ )
+ )
+
+ assert (
+ context[node.output[0]].shape == exp_oshape
+ ), """Output0 shape doesn't match expected shape."""
+ assert (
+ context[node.output[1]].shape == exp_oshape
+ ), """Output1 shape doesn't match expected shape."""
+
+ def global_includes(self):
+ self.code_gen_dict["$GLOBALS$"] = ['#include "streamtools.h"']
+
+ def defines(self, var):
+ self.code_gen_dict["$DEFINES$"] = []
+
+ def read_npy_data(self):
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
+ dtype = self.get_input_datatype()
+ elem_bits = dtype.bitwidth()
+ packed_bits = self.get_instream_width()
+ packed_hls_type = "ap_uint<%d>" % packed_bits
+ elem_hls_type = dtype.get_hls_datatype_str()
+ npy_type = "float"
+ npy_in = "%s/input_0.npy" % code_gen_dir
+ self.code_gen_dict["$READNPYDATA$"] = []
+ self.code_gen_dict["$READNPYDATA$"].append(
+ 'npy2apintstream<%s, %s, %d, %s>("%s", in0);'
+ % (packed_hls_type, elem_hls_type, elem_bits, npy_type, npy_in)
+ )
+
+ def strm_decl(self):
+ 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())
+ )
+
+ 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,
+ )
+ ]
+
+ def dataoutstrm(self):
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
+ dtype = self.get_output_datatype()
+ elem_bits = dtype.bitwidth()
+ packed_bits = self.get_outstream_width()
+ 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
+ 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,
+ )
+ ]
+
+ 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,
+ )
+ ]
+
+ def save_as_npy(self):
+ self.code_gen_dict["$SAVEASCNPY$"] = []
+
+ def blackboxfunction(self):
+ self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
+ """void {}(hls::stream<ap_uint<{}>> &in0,
+ hls::stream<ap_uint<{}>> &out0,
+ hls::stream<ap_uint<{}>> &out1)""".format(
+ self.onnx_node.name,
+ self.get_instream_width(),
+ self.get_outstream_width(),
+ self.get_outstream_width(),
+ )
+ ]
+
+ def pragmas(self):
+ 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")
+ self.code_gen_dict["$PRAGMAS$"].append(
+ "#pragma HLS INTERFACE ap_ctrl_none port=return"
+ )
diff --git a/src/finn/custom_op/fpgadataflow/sameresize_batch.py b/src/finn/custom_op/fpgadataflow/sameresize_batch.py
new file mode 100644
index 0000000000000000000000000000000000000000..c459cac1e9c17336200a1fc85aad2af5e14e2c61
--- /dev/null
+++ b/src/finn/custom_op/fpgadataflow/sameresize_batch.py
@@ -0,0 +1,298 @@
+import os
+import numpy as np
+from onnx import TensorProto, helper
+from finn.core.datatype import DataType
+from finn.custom_op.fpgadataflow import HLSCustomOp
+from finn.util.data_packing import npy_to_rtlsim_input, rtlsim_output_to_npy
+
+
+class SameResize_Batch(HLSCustomOp):
+ """Class that corresponds to finn-hlslib SameResize function.
+ Implements 'same' padding on a given input image."""
+
+ def __init__(self, onnx_node):
+ super().__init__(onnx_node)
+
+ def get_nodeattr_types(self):
+ my_attrs = {
+ "ImgDim": ("i", True, 0),
+ "KernelDim": ("i", True, 0),
+ "Stride": ("i", True, 0),
+ "NumChannels": ("i", True, 0),
+ # FINN input datatype
+ "inputDataType": ("s", True, ""),
+ # distribution of added values to achieve "same" padding
+ "PaddingStyle": ("i", True, 2),
+ }
+ my_attrs.update(super().get_nodeattr_types())
+ return my_attrs
+
+ def get_normal_input_shape(self):
+ idim = self.get_nodeattr("ImgDim")
+ num_ch = self.get_nodeattr("NumChannels")
+
+ ishape = (1, idim, idim, num_ch)
+ return ishape
+
+ def get_normal_output_shape(self):
+ idim = self.get_nodeattr("ImgDim")
+ num_ch = self.get_nodeattr("NumChannels")
+ kdim = self.get_nodeattr("KernelDim")
+ stride = self.get_nodeattr("Stride")
+ assert idim % stride == 0, "Stride must divide input dimension."
+ # number of "same" windows over the input data
+ same_windows = idim // stride
+ odim = kdim + stride * (same_windows - 1)
+
+ oshape = (1, odim, odim, num_ch)
+ return oshape
+
+ def get_folded_input_shape(self):
+ # even though there is no folding in the current hlslib op,
+ # insert a time multiplexing axis to remain compatible with the
+ # shapes produced by the rest of the dataflow pipeline
+ ret = list(self.get_normal_input_shape())
+ ret.insert(-1, 1)
+ return tuple(ret)
+
+ def get_folded_output_shape(self):
+ # even though there is no folding in the current hlslib op,
+ # insert a time multiplexing axis to remain compatible with the
+ # shapes produced by the rest of the dataflow pipeline
+ ret = list(self.get_normal_output_shape())
+ ret.insert(-1, 1)
+ return tuple(ret)
+
+ def make_shape_compatible_op(self, model):
+ exp_ishape = self.get_normal_input_shape()
+ oshape = self.get_normal_output_shape()
+ ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
+ assert ishape == exp_ishape, "Unexpect input shape for SameResize."
+ # implement tensor with correct shape
+ values = np.random.randn(*oshape).astype(np.float32)
+ return helper.make_node(
+ "Constant",
+ inputs=[],
+ outputs=[self.onnx_node.output[0]],
+ value=helper.make_tensor(
+ name="const_tensor",
+ data_type=TensorProto.FLOAT,
+ dims=values.shape,
+ vals=values.flatten().astype(float),
+ ),
+ )
+
+ def infer_node_datatype(self, model):
+ node = self.onnx_node
+ # data type stays the same
+ dtype = model.get_tensor_datatype(node.input[0])
+ exp_idtype = self.get_input_datatype()
+ assert dtype == exp_idtype, "Unexpected datatype for SameResize_Batch"
+ model.set_tensor_datatype(node.output[0], dtype)
+
+ def verify_node(self):
+ pass
+
+ def get_input_datatype(self):
+ """Returns FINN DataType of input."""
+ ret = DataType[self.get_nodeattr("inputDataType")]
+ # the hlslib op always pads with zeroes, so ensure that the DataType
+ # is able to represent zeroes
+ assert ret.allowed(0), "SameResize_Batch DataType must support zero"
+ return ret
+
+ def get_output_datatype(self):
+ """Returns FINN DataType of output. (Same as input datatype)"""
+ return self.get_input_datatype()
+
+ def get_instream_width(self):
+ ibits = self.get_input_datatype().bitwidth()
+ num_ch = self.get_nodeattr("NumChannels")
+
+ return ibits * num_ch
+
+ def get_outstream_width(self):
+ obits = self.get_output_datatype().bitwidth()
+ num_ch = self.get_nodeattr("NumChannels")
+
+ return obits * num_ch
+
+ def get_number_output_values(self):
+ folded_oshape = self.get_folded_output_shape()
+ return np.prod(folded_oshape[:-1])
+
+ def global_includes(self):
+ self.code_gen_dict["$GLOBALS$"] = ['#include "streamtools.h"']
+
+ def defines(self, var):
+ numReps = 1
+ assert self.get_nodeattr("PaddingStyle") == 2, "Only PaddingStyle=2 supported"
+ self.code_gen_dict["$DEFINES$"] = [
+ """#define ImgDim1 {}\n #define KernelDim1 {}\n
+ #define Stride1 {}\n #define NumChannels1 {}\n
+ #define PaddingStyle1 {}\n #define numReps {}""".format(
+ self.get_nodeattr("ImgDim"),
+ self.get_nodeattr("KernelDim"),
+ self.get_nodeattr("Stride"),
+ self.get_nodeattr("NumChannels"),
+ self.get_nodeattr("PaddingStyle"),
+ numReps,
+ )
+ ]
+
+ def read_npy_data(self):
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
+ dtype = self.get_input_datatype()
+ if dtype == DataType.BIPOLAR:
+ # use binary for bipolar storage
+ dtype = DataType.BINARY
+ elem_bits = dtype.bitwidth()
+ packed_bits = self.get_instream_width()
+ packed_hls_type = "ap_uint<%d>" % packed_bits
+ elem_hls_type = dtype.get_hls_datatype_str()
+ npy_type = "float"
+ npy_in = "%s/input_0.npy" % code_gen_dir
+ self.code_gen_dict["$READNPYDATA$"] = []
+ self.code_gen_dict["$READNPYDATA$"].append(
+ 'npy2apintstream<%s, %s, %d, %s>("%s", in0);'
+ % (packed_hls_type, elem_hls_type, elem_bits, npy_type, npy_in)
+ )
+
+ def strm_decl(self):
+ 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<{}>> out ("out");'.format(self.get_outstream_width())
+ )
+
+ def docompute(self):
+ in_t = self.get_input_datatype().get_hls_datatype_str()
+ node = self.onnx_node
+ self.code_gen_dict["$DOCOMPUTE$"] = [
+ """{}<ImgDim1, KernelDim1, Stride1, NumChannels1,
+ {}, PaddingStyle1> (in0, out, numReps);""".format(
+ node.op_type, in_t
+ )
+ ]
+
+ def dataoutstrm(self):
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
+ dtype = self.get_output_datatype()
+ if dtype == DataType.BIPOLAR:
+ # use binary for bipolar storage
+ dtype = DataType.BINARY
+ elem_bits = dtype.bitwidth()
+ packed_bits = self.get_outstream_width()
+ packed_hls_type = "ap_uint<%d>" % packed_bits
+ elem_hls_type = dtype.get_hls_datatype_str()
+ npy_type = "float"
+ npy_out = "%s/output.npy" % code_gen_dir
+ oshape = self.get_folded_output_shape()
+ oshape_cpp_str = str(oshape).replace("(", "{").replace(")", "}")
+
+ self.code_gen_dict["$DATAOUTSTREAM$"] = [
+ 'apintstream2npy<%s, %s, %d, %s>(out, %s, "%s");'
+ % (
+ packed_hls_type,
+ elem_hls_type,
+ elem_bits,
+ npy_type,
+ oshape_cpp_str,
+ npy_out,
+ )
+ ]
+
+ def save_as_npy(self):
+ self.code_gen_dict["$SAVEASCNPY$"] = []
+
+ def blackboxfunction(self):
+ packed_bits = self.get_instream_width()
+ packed_hls_type = "ap_uint<%d>" % packed_bits
+ self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
+ "void %s(hls::stream<%s > &in0, hls::stream<%s > &out)"
+ % (self.onnx_node.name, packed_hls_type, packed_hls_type)
+ ]
+
+ def pragmas(self):
+ 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 ap_ctrl_none port=return"
+ )
+
+ def execute_node(self, context, graph):
+ mode = self.get_nodeattr("exec_mode")
+ node = self.onnx_node
+ exp_ishape = self.get_normal_input_shape()
+ exp_oshape = self.get_normal_output_shape()
+ folded_oshape = self.get_folded_output_shape()
+
+ if mode == "cppsim":
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
+ elif mode == "rtlsim":
+ code_gen_dir = self.get_nodeattr("code_gen_dir_ipgen")
+ else:
+ raise Exception(
+ """Invalid value for attribute exec_mode! Is currently set to: {}
+ has to be set to one of the following value ("cppsim", "rtlsim")""".format(
+ mode
+ )
+ )
+
+ inp = context[node.input[0]]
+ assert str(inp.dtype) == "float32", "Input datatype is not float32"
+ assert (
+ inp.shape == exp_ishape
+ ), """Input shape doesn't
+ match expected shape (1, ImgDim, ImgDim, NumChannels)."""
+ export_idt = self.get_input_datatype()
+
+ # no reshaping for input since assuming no folding on input
+ # make copy before saving array
+ inp = inp.copy()
+ np.save(os.path.join(code_gen_dir, "input_0.npy"), inp)
+
+ if mode == "cppsim":
+ # execute the precompiled model
+ super().exec_precompiled_singlenode_model()
+ # load output npy file
+ super().npy_to_dynamic_output(context)
+ assert (
+ context[node.output[0]].shape == folded_oshape
+ ), "cppsim \
+ did not produce expected ofolded utput shape"
+ context[node.output[0]] = context[node.output[0]].reshape(*exp_oshape)
+ elif mode == "rtlsim":
+ sim = self.get_rtlsim()
+ nbits = self.get_instream_width()
+ rtlsim_inp = npy_to_rtlsim_input(
+ "{}/input_0.npy".format(code_gen_dir), export_idt, nbits
+ )
+ super().reset_rtlsim(sim)
+ super().toggle_clk(sim)
+ rtlsim_output = self.rtlsim(sim, rtlsim_inp)
+ odt = export_idt
+ target_bits = odt.bitwidth()
+ packed_bits = self.get_outstream_width()
+ out_npy_path = "{}/output.npy".format(code_gen_dir)
+ out_shape = self.get_folded_output_shape()
+ rtlsim_output_to_npy(
+ rtlsim_output, out_npy_path, odt, out_shape, packed_bits, target_bits
+ )
+ # load and reshape output
+ output = np.load(out_npy_path)
+ output = np.asarray([output], dtype=np.float32).reshape(*exp_oshape)
+ context[node.output[0]] = output
+ else:
+ raise Exception(
+ """Invalid value for attribute exec_mode! Is currently set to: {}
+ has to be set to one of the following value ("cppsim", "rtlsim")""".format(
+ mode
+ )
+ )
+ assert (
+ context[node.output[0]].shape == exp_oshape
+ ), """Output shape doesn't match expected shape
+ (1, OutputDim, OutputDim, NumChannels)."""
diff --git a/src/finn/custom_op/fpgadataflow/thresholding_batch.py b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa33c70218fab16f106da45e296f0d59ae4ea606
--- /dev/null
+++ b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
@@ -0,0 +1,551 @@
+# 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.
+
+from math import ceil
+import os
+
+import numpy as np
+
+from onnx import TensorProto, helper
+from finn.core.datatype import DataType
+from finn.custom_op.fpgadataflow import HLSCustomOp
+from finn.util.basic import interleave_matrix_outer_dim_from_partitions
+from finn.util.data_packing import (
+ npy_to_rtlsim_input,
+ numpy_to_hls_code,
+ rtlsim_output_to_npy,
+)
+from . import templates
+
+# ONNX i/o tensor shape assumptions for Thresholding:
+# input 0 is the input tensor, shape (..., NumChannels)
+# input 1 is the threshold tensor, shape (NumChannels, n_thres)
+# output 0 is the output tensor, shape (..., NumChannels) - same as input
+# the ... here can be any shape (representing groups of vectors)
+
+
+class Thresholding_Batch(HLSCustomOp):
+ """Class that corresponds to finn-hls Thresholding_Batch function."""
+
+ def __init__(self, onnx_node):
+ super().__init__(onnx_node)
+ self.decoupled_wrapper = templates.decoupled_wrapper
+
+ def get_nodeattr_types(self):
+ my_attrs = {
+ "PE": ("i", True, 0),
+ "NumChannels": ("i", True, 0),
+ # string defining memory type
+ "ram_style": ("s", False, "distributed"),
+ # FINN DataTypes for inputs, weights, outputs
+ "inputDataType": ("s", True, ""),
+ "outputDataType": ("s", True, ""),
+ # input and output FIFO depths
+ "inFIFODepth": ("i", False, 0),
+ "outFIFODepth": ("i", False, 0),
+ # number of input vectors, examples:
+ # [1] is a single vector (like a FC layer with batch=1)
+ # [4] is four vectors (like a FC layer with batch=4)
+ # [1, 4, 4] is four * four vectors (like a conv layer with batch=1)
+ "numInputVectors": ("ints", False, [1]),
+ }
+ my_attrs.update(super().get_nodeattr_types())
+ return my_attrs
+
+ def calc_tmem(self):
+ """Calculates and returns TMEM."""
+ mh = self.get_nodeattr("NumChannels")
+ pe = self.get_nodeattr("PE")
+ return mh // pe
+
+ def make_shape_compatible_op(self, model):
+ oshape = self.get_normal_output_shape()
+ # implement tensor with correct shape
+ values = np.random.randn(*oshape).astype(np.float32)
+ return helper.make_node(
+ "Constant",
+ inputs=[],
+ outputs=[self.onnx_node.output[0]],
+ value=helper.make_tensor(
+ name="const_tensor",
+ data_type=TensorProto.FLOAT,
+ dims=values.shape,
+ vals=values.flatten().astype(float),
+ ),
+ )
+
+ def infer_node_datatype(self, model):
+ node = self.onnx_node
+ # check input datatype against property
+ idt_name = self.get_input_datatype().name
+ exp_idt_name = self.get_nodeattr("inputDataType")
+ assert exp_idt_name == idt_name, "Bad input DataType for Thresholding layer"
+ # set output datatype from property
+ odt = self.get_output_datatype()
+ model.set_tensor_datatype(node.output[0], odt)
+
+ def verify_node(self):
+ info_messages = []
+ # verify that "domain" is set to "finn"
+ domain_value = self.onnx_node.domain
+ if domain_value == "finn":
+ info_messages.append("Attribute domain is set correctly")
+ else:
+ info_messages.append('Attribute domain should be set to "finn"')
+
+ # verify that "backend" is set to "fpgadataflow"
+ backend_value = self.get_nodeattr("backend")
+ if backend_value == "fpgadataflow":
+ info_messages.append("Attribute backend is set correctly")
+ else:
+ info_messages.append('Attribute backend should be set to "fpgadataflow"')
+
+ # verify that all necessary attributes exist
+ # TODO collect automatically from get_nodeattr_types
+ try:
+ self.get_nodeattr("code_gen_dir_cppsim")
+ self.get_nodeattr("executable_path")
+ self.get_nodeattr("NumChannels")
+ self.get_nodeattr("PE")
+ self.get_nodeattr("inputDataType")
+ self.get_nodeattr("outputDataType")
+ info_messages.append("All necessary attributes exist")
+ except Exception:
+ info_messages.append(
+ """The required Threshold_Batch attributes do not exist."""
+ )
+
+ return info_messages
+
+ def bram_estimation(self):
+ """Calculates BRAM cost if resource set to BRAM"""
+ style = self.get_nodeattr("ram_style")
+ P = self.get_nodeattr("PE")
+ idt = self.get_input_datatype()
+ A = idt.bitwidth()
+ tmem = self.calc_tmem()
+
+ if style == "block" and tmem > 1:
+ return int(ceil(A * P / 16)) * int(ceil(tmem / 1024))
+ else:
+ return 0
+
+ def lut_estimation(self):
+ """Calculates LUT cost, taking memory resource type into account """
+ # TODO add in/out FIFO contributions
+ style = self.get_nodeattr("ram_style")
+ P = self.get_nodeattr("PE")
+ idt = self.get_input_datatype()
+ A = idt.bitwidth()
+ tmem = self.calc_tmem()
+ # cost of comparators
+ comparator_cost = A * P
+ # cost of LUTRAM
+ if style == "distributed" and tmem > 1:
+ lutram_cost = P * A * int(ceil(tmem / 64))
+ else:
+ lutram_cost = 0
+ # total cost
+ return comparator_cost + lutram_cost
+
+ def get_input_datatype(self):
+ """Returns FINN DataType of input."""
+ return DataType[self.get_nodeattr("inputDataType")]
+
+ def get_output_datatype(self):
+ """Returns FINN DataType of output."""
+ return DataType[self.get_nodeattr("outputDataType")]
+
+ def get_instream_width(self):
+ i_bits = self.get_input_datatype().bitwidth()
+ return i_bits * self.get_nodeattr("PE")
+
+ def get_outstream_width(self):
+ o_bits = self.get_output_datatype().bitwidth()
+ return o_bits * self.get_nodeattr("PE")
+
+ def get_folded_input_shape(self):
+ ich = self.get_nodeattr("NumChannels")
+ pe = self.get_nodeattr("PE")
+ fold = ich // pe
+ vecs = list(self.get_nodeattr("numInputVectors"))
+ folded_input_shape = tuple(vecs + [fold, pe])
+ return folded_input_shape
+
+ def get_folded_output_shape(self):
+ # same shape as input
+ return self.get_folded_input_shape()
+
+ def get_normal_input_shape(self):
+ ich = self.get_nodeattr("NumChannels")
+ vecs = list(self.get_nodeattr("numInputVectors"))
+ normal_input_shape = tuple(vecs + [ich])
+ return normal_input_shape
+
+ def get_normal_output_shape(self):
+ # same shape as input
+ return self.get_normal_input_shape()
+
+ def get_number_output_values(self):
+ nf = np.prod(self.get_folded_output_shape()[:-1])
+ return nf
+
+ def get_template_param_values(self):
+ """Returns the template parameter values according to input, output and weight
+ data types."""
+ ret = dict()
+ inp_hls_str = self.get_input_datatype().get_hls_datatype_str()
+ out_hls_str = self.get_output_datatype().get_hls_datatype_str()
+ # fill in TSrcI
+ ret["TSrcI"] = "Slice<%s>" % inp_hls_str
+ # fill in TDstI
+ ret["TDstI"] = "Slice<%s>" % out_hls_str
+
+ return ret
+
+ def get_hls_compatible_threshold_tensor(self, orig_thres_matrix):
+ """Convert the original numpy weight matrix orig_weight_matrix into
+ a form suitable for passing to the hlslib call:
+ * ensure MH % PE == 0
+ * for unsigned inputs, ensure thresholds are positive
+ * interleave rows between PEs
+ * reshape into (PE, TMEM, n_thres_steps) and return
+ """
+ mh = self.get_nodeattr("NumChannels")
+ pe = self.get_nodeattr("PE")
+ tmem = mh // pe
+ assert mh % pe == 0, "Requirement NumChannels divisable by PE is violated."
+ assert (
+ orig_thres_matrix.ndim == 2
+ ), """Threshold matrix dimension is
+ not as expected (2)."""
+ n_thres_steps = orig_thres_matrix.shape[1]
+ if not self.get_input_datatype().signed():
+ # ensure all thresholds are nonnegative
+ assert (orig_thres_matrix >= 0).all()
+ # ensure all thresholds are integer
+ assert (orig_thres_matrix.astype(np.int32) == orig_thres_matrix).all()
+ ret = orig_thres_matrix
+ # ensure channels = mh , duplicating if necessary
+ if ret.shape[0] == 1:
+ ret = np.tile(ret, (mh, 1))
+ assert (
+ ret.shape[0] == mh
+ ), "Channels of threshold matrix are not as expected (mh)"
+ # distribute rows between PEs
+ ret = interleave_matrix_outer_dim_from_partitions(ret, pe)
+ assert (
+ ret.shape[0] == pe
+ ), """First dimension after distribution of the
+ rows between PEs is not as expected (pe)"""
+ assert (
+ ret.shape[1] == tmem
+ ), """Second dimension after distribution of the
+ rows between PEs is not as expected (tmem)"""
+ assert (
+ ret.shape[2] == n_thres_steps
+ ), """Third dimension after distribution of the
+ rows between PEs is not as expected (n_thres_steps)"""
+ return ret.reshape(1, pe, tmem, n_thres_steps)
+
+ def generate_params(self, model, path):
+ code_gen_dir = path
+ # save thresholds in thresh.h
+ thresholds = model.get_initializer(self.onnx_node.input[1])
+
+ threshold_tensor = self.get_hls_compatible_threshold_tensor(thresholds)
+ tdt = DataType.INT32
+ thresholds_hls_code = numpy_to_hls_code(
+ threshold_tensor, tdt, "thresholds", False, True
+ )
+ # write thresholds into thresh.h
+ f_thresh = open("{}/thresh.h".format(code_gen_dir), "w")
+ tdt_hls = tdt.get_hls_datatype_str()
+ # use binary to export bipolar activations
+ export_odt = self.get_output_datatype()
+ if self.get_output_datatype() == DataType.BIPOLAR:
+ export_odt = DataType.BINARY
+ odt_hls = export_odt.get_hls_datatype_str()
+ f_thresh.write(
+ "static ThresholdsActivation<{},{},{},{},{},{},{}> threshs \
+ = ".format(
+ self.calc_tmem(),
+ self.get_nodeattr("PE"),
+ threshold_tensor.shape[-1],
+ tdt_hls,
+ odt_hls,
+ export_odt.min(),
+ "std::less_equal<%s>" % tdt_hls,
+ )
+ )
+ f_thresh.write(thresholds_hls_code)
+ f_thresh.close()
+
+ def execute_node(self, context, graph):
+ mode = self.get_nodeattr("exec_mode")
+ node = self.onnx_node
+
+ # TODO ensure codegen dir exists
+ if mode == "cppsim":
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
+ elif mode == "rtlsim":
+ code_gen_dir = self.get_nodeattr("code_gen_dir_ipgen")
+ else:
+ raise Exception(
+ """Invalid value for attribute exec_mode! Is currently set to: {}
+ has to be set to one of the following value ("cppsim", "rtlsim")""".format(
+ mode
+ )
+ )
+
+ # create a npy file fore each input of the node (in_ind is input index)
+ in_ind = 0
+ for inputs in node.input:
+ # it is assumed that the first input of the node is the data input
+ # the second input are the weights
+ # the third input are the thresholds
+ if in_ind == 0:
+ assert (
+ str(context[inputs].dtype) == "float32"
+ ), """Input datatype is
+ not float32 as expected."""
+ expected_inp_shape = self.get_folded_input_shape()
+ reshaped_input = context[inputs].reshape(expected_inp_shape)
+ if self.get_input_datatype() == DataType.BIPOLAR:
+ # store bipolar activations as binary
+ reshaped_input = (reshaped_input + 1) / 2
+ export_idt = DataType.BINARY
+ else:
+ export_idt = self.get_input_datatype()
+ # make copy before saving the array
+ reshaped_input = reshaped_input.copy()
+ np.save(
+ os.path.join(code_gen_dir, "input_{}.npy".format(in_ind)),
+ reshaped_input,
+ )
+ elif in_ind > 2:
+ raise Exception("Unexpected input found for StreamingFCLayer")
+ in_ind += 1
+
+ if mode == "cppsim":
+ # execute the precompiled model
+ super().exec_precompiled_singlenode_model()
+ # load output npy file
+ super().npy_to_dynamic_output(context)
+ # reinterpret binary output as bipolar where needed
+ if self.get_output_datatype() == DataType.BIPOLAR:
+ out = context[node.output[0]]
+ out = 2 * out - 1
+ context[node.output[0]] = out
+ assert (
+ context[node.output[0]].shape == self.get_folded_output_shape()
+ ), """Output shape is not as expected"""
+ # reshape output to have expected shape
+ oshape = self.get_normal_output_shape()
+ context[node.output[0]] = context[node.output[0]].reshape(*oshape)
+ elif mode == "rtlsim":
+ sim = self.get_rtlsim()
+ nbits = self.get_instream_width()
+ inp = npy_to_rtlsim_input(
+ "{}/input_0.npy".format(code_gen_dir), export_idt, nbits
+ )
+ super().reset_rtlsim(sim)
+ super().toggle_clk(sim)
+ output = self.rtlsim(sim, inp)
+ odt = self.get_output_datatype()
+ target_bits = odt.bitwidth()
+ packed_bits = self.get_outstream_width()
+ out_npy_path = "{}/output.npy".format(code_gen_dir)
+ out_shape = self.get_folded_output_shape()
+ rtlsim_output_to_npy(
+ output, out_npy_path, odt, out_shape, packed_bits, target_bits
+ )
+
+ # load and reshape output
+ output = np.load(out_npy_path)
+ oshape = self.get_normal_output_shape()
+ output = np.asarray([output], dtype=np.float32).reshape(*oshape)
+ context[node.output[0]] = output
+ else:
+ raise Exception(
+ """Invalid value for attribute exec_mode! Is currently set to: {}
+ has to be set to one of the following value ("cppsim", "rtlsim")""".format(
+ mode
+ )
+ )
+
+ def global_includes(self):
+ self.code_gen_dict["$GLOBALS$"] = ['#include "activations.hpp"']
+ self.code_gen_dict["$GLOBALS$"] += ['#include "thresh.h"']
+
+ # TODO check and add whatever missing
+ def defines(self, var):
+ numInputVectors = list(self.get_nodeattr("numInputVectors"))
+ numReps = numInputVectors[0]
+ self.code_gen_dict["$DEFINES$"] = [
+ """#define NumChannels1 {}\n #define PE1 {}\n #define numReps {}""".format(
+ self.get_nodeattr("NumChannels"), self.get_nodeattr("PE"), numReps,
+ )
+ ]
+
+ def read_npy_data(self):
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
+ dtype = self.get_input_datatype()
+ elem_bits = dtype.bitwidth()
+ packed_bits = self.get_instream_width()
+ packed_hls_type = "ap_uint<%d>" % packed_bits
+ elem_hls_type = dtype.get_hls_datatype_str()
+ npy_type = "float"
+ npy_in = "%s/input_0.npy" % code_gen_dir
+ self.code_gen_dict["$READNPYDATA$"] = []
+ # note: the innermost dim is reversed for the input
+ self.code_gen_dict["$READNPYDATA$"].append(
+ 'npy2apintstream<%s, %s, %d, %s>("%s", in0, false);'
+ % (packed_hls_type, elem_hls_type, elem_bits, npy_type, npy_in)
+ )
+
+ def strm_decl(self):
+ 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<{}>> out ("out");'.format(self.get_outstream_width())
+ )
+
+ def docompute(self):
+ tmpl_args = self.get_template_param_values()
+ # TODO: why put some template parameters into defines and not others?
+ # should ImgDim be defined or just filled in here like we do now?
+ node = self.onnx_node
+ ishape = self.get_folded_input_shape()
+ if len(ishape) == 3:
+ imgdim = 1
+ elif len(ishape) == 5:
+ imgdim = ishape[1]
+ else:
+ raise Exception("""Unexpeted input shape""")
+ self.code_gen_dict["$DOCOMPUTE$"] = [
+ """{}<{}, NumChannels1, PE1, {}, {}>
+ (in0, out, threshs, numReps);""".format(
+ node.op_type, imgdim, tmpl_args["TSrcI"], tmpl_args["TDstI"],
+ )
+ ]
+
+ def dataoutstrm(self):
+ code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
+ dtype = self.get_output_datatype()
+ if dtype == DataType.BIPOLAR:
+ # use binary for bipolar storage
+ dtype = DataType.BINARY
+ elem_bits = dtype.bitwidth()
+ packed_bits = self.get_outstream_width()
+ packed_hls_type = "ap_uint<%d>" % packed_bits
+ elem_hls_type = dtype.get_hls_datatype_str()
+ npy_type = "float"
+ npy_out = "%s/output.npy" % code_gen_dir
+ shape = self.get_folded_output_shape()
+ shape_cpp_str = str(shape).replace("(", "{").replace(")", "}")
+
+ # note: the innermost dim is not reversed for the output
+ self.code_gen_dict["$DATAOUTSTREAM$"] = [
+ 'apintstream2npy<%s, %s, %d, %s>(out, %s, "%s", false);'
+ % (
+ packed_hls_type,
+ elem_hls_type,
+ elem_bits,
+ npy_type,
+ shape_cpp_str,
+ npy_out,
+ )
+ ]
+
+ def save_as_npy(self):
+ self.code_gen_dict["$SAVEASCNPY$"] = []
+
+ def blackboxfunction(self):
+ self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
+ """void {}(hls::stream<ap_uint<{}>> &in0,
+ hls::stream<ap_uint<{}>> &out
+ )""".format(
+ self.onnx_node.name,
+ self.get_instream_width(),
+ self.get_outstream_width(),
+ )
+ ]
+
+ def pragmas(self):
+ 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 ap_ctrl_none port=return"
+ )
+
+ # the threshold tensor is acc_type [PE][TMEM][N_THRES]
+ # partition for parallel access along PE and N_THRES
+ # dimensions (dims 1 and 3)
+ self.code_gen_dict["$PRAGMAS$"].append(
+ (
+ "#pragma HLS ARRAY_PARTITION variable=threshs.m_thresholds "
+ "complete dim=1"
+ )
+ )
+ self.code_gen_dict["$PRAGMAS$"].append(
+ (
+ "#pragma HLS ARRAY_PARTITION variable=threshs.m_thresholds "
+ "complete dim=3"
+ )
+ )
+ # set resource type
+ ram_style = self.get_nodeattr("ram_style")
+ pe = self.get_nodeattr("PE")
+ ich = self.get_nodeattr("NumChannels")
+ # if PE less than NumChannels, assign cores according to ram_style;
+ # otherwise if PE == NumChannels, Vivado HLS will unroll to FFs
+ if pe < ich:
+ if ram_style == "distributed":
+ self.code_gen_dict["$PRAGMAS$"].append(
+ (
+ "#pragma HLS RESOURCE variable=threshs.m_thresholds "
+ "core=ROM_2P_LUTRAM"
+ )
+ )
+ elif ram_style == "block":
+ self.code_gen_dict["$PRAGMAS$"].append(
+ (
+ "#pragma HLS RESOURCE variable=threshs.m_thresholds "
+ "core=ROM_2P_BRAM"
+ )
+ )
+ else:
+ raise Exception(
+ """Invalid value for attribute ram_style! Is currently set to: {}
+ has to be set to one of ("block", "distributed")""".format(
+ ram_style
+ )
+ )
diff --git a/src/finn/custom_op/registry.py b/src/finn/custom_op/registry.py
index ac1f49ee26d1c23bd1b0e67ae4ba7e0c2b55b435..238829e03353d79fab7c51e7d1b9dca6e2a96a11 100644
--- a/src/finn/custom_op/registry.py
+++ b/src/finn/custom_op/registry.py
@@ -44,8 +44,11 @@ from finn.custom_op.fpgadataflow.streamingdatawidthconverter_batch import (
StreamingDataWidthConverter_Batch,
)
from finn.custom_op.fpgadataflow.globalaccpool_batch import GlobalAccPool_Batch
+from finn.custom_op.fpgadataflow.sameresize_batch import SameResize_Batch
+from finn.custom_op.fpgadataflow.thresholding_batch import Thresholding_Batch
from finn.custom_op.fpgadataflow.addstreams_batch import AddStreams_Batch
from finn.custom_op.fpgadataflow.labelselect_batch import LabelSelect_Batch
+from finn.custom_op.fpgadataflow.duplicatestreams_batch import DuplicateStreams_Batch
# create a mapping of all known CustomOp names and classes
custom_op = {}
@@ -62,8 +65,11 @@ custom_op["MaxPoolNHWC"] = MaxPoolNHWC
custom_op["StreamingDataWidthConverter_Batch"] = StreamingDataWidthConverter_Batch
custom_op["StreamingFIFO"] = StreamingFIFO
custom_op["GlobalAccPool_Batch"] = GlobalAccPool_Batch
+custom_op["SameResize_Batch"] = SameResize_Batch
+custom_op["Thresholding_Batch"] = Thresholding_Batch
custom_op["AddStreams_Batch"] = AddStreams_Batch
custom_op["LabelSelect_Batch"] = LabelSelect_Batch
+custom_op["DuplicateStreams_Batch"] = DuplicateStreams_Batch
def getCustomOp(node):
diff --git a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
index dbd98623c4cdf5baca9fa9c137debf8be0f70981..3ff86cab48d365c10e69bc2c764e8083c6a36880 100644
--- a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
+++ b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
@@ -33,6 +33,7 @@ from finn.transformation import Transformation
from finn.custom_op.registry import getCustomOp
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.infer_datatypes import InferDataTypes
+import finn.core.data_layout as DataLayout
class InferConvInpGen(Transformation):
@@ -398,3 +399,59 @@ class InferQuantizedStreamingFCLayer(Transformation):
model = model.transform(InferShapes())
model = model.transform(InferDataTypes())
return (model, graph_modified)
+
+
+class InferThresholdingLayer(Transformation):
+ """Convert any MultiThreshold into a standalone thresholding HLS layer."""
+
+ def apply(self, model):
+ graph = model.graph
+ node_ind = 0
+ graph_modified = False
+ for node in graph.node:
+ node_ind += 1
+ if node.op_type == "MultiThreshold":
+ thl_input = node.input[0]
+ thl_threshold = node.input[1]
+ thl_output = node.output[0]
+ thl_in_shape = model.get_tensor_shape(thl_input)
+ idt = model.get_tensor_datatype(thl_input)
+
+ # skip conversion for layers with float input
+ if not idt.is_integer():
+ continue
+
+ # skip conversion if input is not NHWC or NC
+ thl_in_layout = model.get_tensor_layout(thl_input)
+ if thl_in_layout != DataLayout.NHWC and thl_in_layout != DataLayout.NC:
+ continue
+
+ # now safe to assume number of channels is in last dimension
+ ifc = int(thl_in_shape[-1])
+ # create node with no parallelization first
+ pe = 1
+ assert ifc % pe == 0, "Requirement IFC divisable by PE is violated."
+
+ odt = model.get_tensor_datatype(thl_output)
+ # create and insert new StreamingFCLayer node
+ new_node = helper.make_node(
+ "Thresholding_Batch",
+ [thl_input, thl_threshold],
+ [thl_output],
+ domain="finn",
+ backend="fpgadataflow",
+ NumChannels=ifc,
+ PE=pe,
+ inputDataType=idt.name,
+ outputDataType=odt.name,
+ numInputVectors=list(thl_in_shape[:-1]),
+ )
+ graph.node.insert(node_ind, new_node)
+ # remove old node
+ graph.node.remove(node)
+ graph_modified = True
+
+ if graph_modified:
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+ return (model, graph_modified)
diff --git a/src/finn/transformation/general.py b/src/finn/transformation/general.py
index 53c73e1dc4fe0bfab53e3f126add992cb338c11d..f51ffbcfd9f62e06bf4942409fbb163e92ff6370 100644
--- a/src/finn/transformation/general.py
+++ b/src/finn/transformation/general.py
@@ -81,6 +81,41 @@ class GiveReadableTensorNames(Transformation):
return (model, False)
+class GiveUniqueParameterTensors(Transformation):
+ """Make every parameter tensor unique. The aim is to avoid affecting
+ other nodes apart from the one the system is currently operating on."""
+
+ def apply(self, model):
+ graph = model.graph
+ graph_modified = False
+ seen_parameters = []
+ for n in graph.node:
+ # copy inputs since they may be modified
+ node_inputs_list = [x for x in n.input]
+ for input_idx, node_input in enumerate(node_inputs_list):
+ # check if it's a parameter
+ input_init = model.get_initializer(node_input)
+ if input_init is None:
+ # dynamic input
+ continue
+
+ # check if repeated
+ if node_input not in seen_parameters:
+ # first occurance
+ seen_parameters += [node_input]
+ continue
+
+ new_param_name = model.make_new_valueinfo_name()
+
+ model.set_initializer(new_param_name, input_init)
+ model.set_tensor_datatype(new_param_name, model.get_tensor_datatype(node_input))
+
+ # point node input to new tensor
+ n.input[input_idx] = new_param_name
+
+ return (model, graph_modified)
+
+
class ConvertSubToAdd(Transformation):
"""Convert subtract-a-constant nodes to add-a-constant nodes."""
diff --git a/src/finn/transformation/streamline/absorb.py b/src/finn/transformation/streamline/absorb.py
index 0d709297a9132b15b51435b7ab4b51ce55c7e9f3..dbcf97361017144174f9fbfca35a84361b5abd26 100644
--- a/src/finn/transformation/streamline/absorb.py
+++ b/src/finn/transformation/streamline/absorb.py
@@ -46,7 +46,11 @@ class AbsorbAddIntoMultiThreshold(Transformation):
graph_modified = False
for n in graph.node:
node_ind += 1
- if n.op_type == "Add":
+ if (
+ n.op_type == "Add"
+ and not model.is_fork_node(n)
+ and not model.is_join_node(n)
+ ):
consumer = model.find_consumer(n.output[0])
if consumer is not None and consumer.op_type == "MultiThreshold":
add_weight_name = n.input[1]
@@ -83,7 +87,11 @@ class AbsorbMulIntoMultiThreshold(Transformation):
graph_modified = False
for n in graph.node:
node_ind += 1
- if n.op_type == "Mul":
+ if (
+ n.op_type == "Mul"
+ and not model.is_fork_node(n)
+ and not model.is_join_node(n)
+ ):
mul_weight_name = n.input[1]
A = model.get_initializer(mul_weight_name)
assert A is not None, "Initializer for mul weights is not set."
diff --git a/src/finn/transformation/streamline/collapse_repeated.py b/src/finn/transformation/streamline/collapse_repeated.py
index aa059747b602bc6b659bc8b53b1f18988bba1ef0..67824ad4f633983b93e3178d03118927a1ddd85b 100644
--- a/src/finn/transformation/streamline/collapse_repeated.py
+++ b/src/finn/transformation/streamline/collapse_repeated.py
@@ -48,9 +48,17 @@ class CollapseRepeatedOp(Transformation):
graph_modified = False
for n in graph.node:
node_ind += 1
- if n.op_type == self.op_name:
+ if (
+ n.op_type == self.op_name
+ and not model.is_fork_node(n)
+ and not model.is_join_node(n)
+ ):
consumer = model.find_consumer(n.output[0])
- if consumer is not None and consumer.op_type == self.op_name:
+ if (
+ consumer is not None
+ and consumer.op_type == self.op_name
+ and not model.is_join_node(consumer)
+ ):
op0_param_name = n.input[1]
op1_param_name = consumer.input[1]
op0_param = model.get_initializer(op0_param_name)
diff --git a/src/finn/transformation/streamline/reorder.py b/src/finn/transformation/streamline/reorder.py
index 1886c785705161c3a13493de44dc3f3f86463f4f..0b6259a61d3eb67b7b38d4c6939019ce2893a875 100644
--- a/src/finn/transformation/streamline/reorder.py
+++ b/src/finn/transformation/streamline/reorder.py
@@ -36,8 +36,9 @@ from finn.util.basic import get_by_name
class MoveAddPastMul(Transformation):
- """Move add operations past multiply operations. The aim is to have them
- next to each other such that they can be collapsed into a single add."""
+ """Move add operations past multiply operations on linear segments of the graph.
+ The aim is to have them next to each other such that they can be collapsed into
+ a single add."""
def apply(self, model):
graph = model.graph
@@ -45,9 +46,17 @@ class MoveAddPastMul(Transformation):
graph_modified = False
for n in graph.node:
node_ind += 1
- if n.op_type == "Add":
+ if (
+ n.op_type == "Add"
+ and not model.is_fork_node(n)
+ and not model.is_join_node(n)
+ ):
consumer = model.find_consumer(n.output[0])
- if consumer is not None and consumer.op_type == "Mul":
+ if (
+ consumer is not None
+ and consumer.op_type == "Mul"
+ and not model.is_join_node(consumer)
+ ):
# have: (x) -> add(,B) -> (x+B) -> mul(,A) -> (xA+BA)
# want: (x) -> mul(,A) -> (xA) -> add(,BA) -> (xA+BA)
# assume input 0 is from the previous layer, input 1 is the
@@ -63,12 +72,16 @@ class MoveAddPastMul(Transformation):
end_name = consumer.output[0]
# compute new param value for add
BA = B * A
+
# make and insert new nodes
new_mul = oh.make_node(
- "Mul", [start_name, mul_weight_name], [middle_name]
+ "Mul",
+ [start_name, mul_weight_name],
+ [middle_name],
+ name=consumer.name,
)
new_add = oh.make_node(
- "Add", [middle_name, add_weight_name], [end_name]
+ "Add", [middle_name, add_weight_name], [end_name], name=n.name
)
graph.node.insert(node_ind, new_mul)
graph.node.insert(node_ind + 1, new_add)
@@ -78,6 +91,7 @@ class MoveAddPastMul(Transformation):
graph.node.remove(n)
graph.node.remove(consumer)
graph_modified = True
+
model = model.transform(InferShapes())
return (model, graph_modified)
@@ -92,9 +106,17 @@ class MoveScalarMulPastMatMul(Transformation):
graph_modified = False
for n in graph.node:
node_ind += 1
- if n.op_type == "Mul":
+ if (
+ n.op_type == "Mul"
+ and not model.is_fork_node(n)
+ and not model.is_join_node(n)
+ ):
consumer = model.find_consumer(n.output[0])
- if consumer is not None and consumer.op_type == "MatMul":
+ if (
+ consumer is not None
+ and consumer.op_type == "MatMul"
+ and not model.is_join_node(consumer)
+ ):
mul_weight_name = n.input[1]
matmul_weight_name = consumer.input[1]
A = model.get_initializer(mul_weight_name)
@@ -109,10 +131,16 @@ class MoveScalarMulPastMatMul(Transformation):
# if the mul is scalar, we can simply swap the order of ops
# make and insert new nodes
new_matmul = oh.make_node(
- "MatMul", [start_name, matmul_weight_name], [middle_name]
+ "MatMul",
+ [start_name, matmul_weight_name],
+ [middle_name],
+ name=consumer.name,
)
new_mul = oh.make_node(
- "Mul", [middle_name, mul_weight_name], [end_name]
+ "Mul",
+ [middle_name, mul_weight_name],
+ [end_name],
+ name=n.name,
)
graph.node.insert(node_ind, new_matmul)
graph.node.insert(node_ind + 1, new_mul)
@@ -135,9 +163,17 @@ class MoveScalarAddPastMatMul(Transformation):
graph_modified = False
for n in graph.node:
node_ind += 1
- if n.op_type == "Add":
+ if (
+ n.op_type == "Add"
+ and not model.is_fork_node(n)
+ and not model.is_join_node(n)
+ ):
consumer = model.find_consumer(n.output[0])
- if consumer is not None and consumer.op_type == "MatMul":
+ if (
+ consumer is not None
+ and consumer.op_type == "MatMul"
+ and not model.is_join_node(consumer)
+ ):
add_weight_name = n.input[1]
matmul_weight_name = consumer.input[1]
A = model.get_initializer(add_weight_name)
@@ -155,10 +191,16 @@ class MoveScalarAddPastMatMul(Transformation):
# update the add weight
model.set_initializer(add_weight_name, Anew)
new_matmul = oh.make_node(
- "MatMul", [start_name, matmul_weight_name], [middle_name]
+ "MatMul",
+ [start_name, matmul_weight_name],
+ [middle_name],
+ name=consumer.name,
)
new_add = oh.make_node(
- "Add", [middle_name, add_weight_name], [end_name]
+ "Add",
+ [middle_name, add_weight_name],
+ [end_name],
+ name=n.name,
)
graph.node.insert(node_ind, new_matmul)
graph.node.insert(node_ind + 1, new_add)
@@ -181,9 +223,17 @@ class MoveScalarAddPastConv(Transformation):
graph_modified = False
for n in graph.node:
node_ind += 1
- if n.op_type == "Add":
+ if (
+ n.op_type == "Add"
+ and not model.is_fork_node(n)
+ and not model.is_join_node(n)
+ ):
consumer = model.find_consumer(n.output[0])
- if consumer is not None and consumer.op_type == "Conv":
+ if (
+ consumer is not None
+ and consumer.op_type == "Conv"
+ and not model.is_join_node(consumer)
+ ):
conv_node = consumer
add_node = n
add_weight_name = n.input[1]
@@ -194,7 +244,12 @@ class MoveScalarAddPastConv(Transformation):
start_name = n.input[0]
end_name = consumer.output[0]
conv_out_shape = model.get_tensor_shape(end_name)
- if all(x == 1 for x in A.shape):
+
+ using_padding = True
+ pads = list(get_by_name(consumer.attribute, "pads").ints)
+ if sum(pads) == 0:
+ using_padding = False
+ if all(x == 1 for x in A.shape) and not using_padding:
# create a tensor filled with the add constant, in
# the shape expected by the convolution
conv_in_const = np.zeros(conv_in_shape, dtype=np.float32)
@@ -206,7 +261,8 @@ class MoveScalarAddPastConv(Transformation):
execute_node(conv_node, exec_ctx, model.graph)
# retrieve the conv output
Anew = exec_ctx[end_name]
- # strip out repetition
+
+ # strip out repetition if no padding
Anew = Anew[0, :, 0, 0].reshape(1, -1, 1, 1)
# update the add weight
model.set_initializer(add_weight_name, Anew)
@@ -224,6 +280,7 @@ class MoveScalarAddPastConv(Transformation):
graph.node.remove(add_node)
graph.node.insert(node_ind, add_node)
graph_modified = True
+
model = model.transform(InferShapes())
return (model, graph_modified)
@@ -238,9 +295,17 @@ class MoveScalarMulPastConv(Transformation):
graph_modified = False
for n in graph.node:
node_ind += 1
- if n.op_type == "Mul":
+ if (
+ n.op_type == "Mul"
+ and not model.is_fork_node(n)
+ and not model.is_join_node(n)
+ ):
consumer = model.find_consumer(n.output[0])
- if consumer is not None and consumer.op_type == "Conv":
+ if (
+ consumer is not None
+ and consumer.op_type == "Conv"
+ and not model.is_join_node(consumer)
+ ):
mul_weight_name = n.input[1]
A = model.get_initializer(mul_weight_name)
assert A is not None, "Initializer for mul weights is not set."
@@ -379,3 +444,90 @@ class MakeMaxPoolNHWC(Transformation):
graph.node.insert(node_ind - 1, consumer)
graph_modified = True
return (model, graph_modified)
+
+
+class MoveOpPastFork(Transformation):
+ """Move node operations past graph forks. Used when a node before a fork
+ can be merged with nodes in the branches
+ """
+
+ def __init__(self, op_name_list):
+ super().__init__()
+ self.ops_to_move = op_name_list
+
+ def apply(self, model):
+ graph = model.graph
+ graph_modified = False
+ nodes = [n for n in graph.node]
+ node_ind = 0
+ for n in nodes:
+ node_ind += 1
+ if (
+ n.op_type in self.ops_to_move
+ and model.is_fork_node(n)
+ and not model.is_join_node(n)
+ ):
+
+ # Restrict this transform to operations with constant parameters
+ # Assuming parameters is in input 1
+ op_init_param = model.get_initializer(n.input[1])
+ if op_init_param is None:
+ continue
+
+ # Check case when branches are empty and go
+ # to the same node
+ consumers = model.find_consumers(n.output[0])
+ unique_consumer = True
+ for consum_node in consumers[1:]:
+ if consumers[0] != consum_node:
+ unique_consumer = False
+ break
+
+ if unique_consumer:
+ continue
+
+ for consumer_node in consumers[1:]:
+ # create new node
+ new_param_name = model.make_new_valueinfo_name()
+ new_output_tensor_name = model.make_new_valueinfo_name()
+ new_node = oh.make_node(
+ n.op_type,
+ [n.input[0], new_param_name],
+ [new_output_tensor_name],
+ )
+ graph.node.insert(node_ind, new_node)
+ node_ind += 1
+ model.set_initializer(new_param_name, op_init_param)
+
+ # change consumer input tensor
+ graph.node.remove(consumer_node)
+ for idx, consumer_input in enumerate(consumer_node.input):
+ if consumer_input == n.output[0]:
+ consumer_node.input[idx] = new_output_tensor_name
+ break
+ else:
+ raise Exception(
+ "Consumer should have the current node output as input"
+ )
+
+ graph.node.insert(node_ind, consumer_node)
+
+ graph_modified = True
+
+ model = model.transform(InferShapes())
+ return (model, graph_modified)
+
+
+class MoveAddPastFork(MoveOpPastFork):
+ def __init__(self):
+ super().__init__(["Add"])
+
+
+class MoveMulPastFork(MoveOpPastFork):
+ def __init__(self):
+ super().__init__(["Mul"])
+
+
+class MoveLinearPastFork(MoveOpPastFork):
+ def __init__(self):
+ super().__init__(["Add", "Mul"])
diff --git a/src/finn/util/fpgadataflow.py b/src/finn/util/fpgadataflow.py
index 9a2708439c0fed1e25c0d955af21cd2e9e705446..d1669444e55cb0fddb2690e51849c4603d47d32c 100644
--- a/src/finn/util/fpgadataflow.py
+++ b/src/finn/util/fpgadataflow.py
@@ -83,14 +83,27 @@ def pyverilate_stitched_ip(model):
def file_to_dir(x):
return os.path.dirname(os.path.realpath(x))
+ def file_to_basename(x):
+ return os.path.basename(os.path.realpath(x))
+
all_verilog_dirs = list(map(file_to_dir, all_verilog_srcs))
- top_verilog = model.get_metadata_prop("wrapper_filename")
+ all_verilog_files = list(
+ set(
+ filter(
+ lambda x: x.endswith(".v"),
+ list(map(file_to_basename, all_verilog_srcs)),
+ )
+ )
+ )
+ top_module_name = model.get_metadata_prop("wrapper_filename")
+ top_module_name = file_to_basename(top_module_name).strip(".v")
build_dir = make_build_dir("pyverilator_ipstitched_")
sim = PyVerilator.build(
- top_verilog,
+ all_verilog_files,
verilog_path=all_verilog_dirs,
build_dir=build_dir,
trace_depth=get_rtlsim_trace_depth(),
+ top_module_name=top_module_name,
)
return sim
@@ -114,3 +127,91 @@ def is_fpgadataflow_node(node):
is_node = True
return is_node
+
+
+def rtlsim_multi_io(sim, io_dict, num_out_values, trace_file=""):
+ """Runs the pyverilator simulation by passing the input values to the simulation,
+ toggle the clock and observing the execution time. Function contains also an
+ observation loop that can abort the simulation if no output value is produced
+ after a set number of cycles. Can handle multiple i/o streams. See function
+ implementation for details on how the top-level signals should be named.
+
+ sim: the PyVerilator object for simulation
+ io_dict: a dict of dicts in the following format:
+ {"inputs" : {"in0" : <input_data>, "in1" : <input_data>},
+ "outputs" : {"out0" : [], "out1" : []} }
+ <input_data> is a list of Python arbitrary-precision ints indicating
+ what data to push into the simulation, and the output lists are
+ similarly filled when the simulation is complete
+ num_out_values: number of total values to be read from the simulation to
+ finish the simulation and return.
+
+ returns: number of clock cycles elapsed for completion
+
+ """
+
+ if trace_file != "":
+ sim.start_vcd_trace(trace_file)
+
+ for outp in io_dict["outputs"]:
+ sim.io[outp + "_V_V_TREADY"] = 1
+
+ # observe if output is completely calculated
+ # total_cycle_count will contain the number of cycles the calculation ran
+ output_done = False
+ total_cycle_count = 0
+ output_count = 0
+ old_output_count = 0
+
+ # avoid infinite looping of simulation by aborting when there is no change in
+ # output values after 100 cycles
+ no_change_count = 0
+ liveness_threshold = pyverilate_get_liveness_threshold_cycles()
+
+ while not (output_done):
+ for inp in io_dict["inputs"]:
+ inputs = io_dict["inputs"][inp]
+ sim.io[inp + "_V_V_TVALID"] = 1 if len(inputs) > 0 else 0
+ sim.io[inp + "_V_V_TDATA"] = inputs[0] if len(inputs) > 0 else 0
+ if sim.io[inp + "_V_V_TREADY"] == 1 and sim.io[inp + "_V_V_TVALID"] == 1:
+ inputs = inputs[1:]
+ io_dict["inputs"][inp] = inputs
+
+ for outp in io_dict["outputs"]:
+ outputs = io_dict["outputs"][outp]
+ if sim.io[outp + "_V_V_TVALID"] == 1 and sim.io[outp + "_V_V_TREADY"] == 1:
+ outputs = outputs + [sim.io[outp + "_V_V_TDATA"]]
+ output_count += 1
+ io_dict["outputs"][outp] = outputs
+
+ sim.io.ap_clk = 1
+ sim.io.ap_clk = 0
+
+ total_cycle_count = total_cycle_count + 1
+
+ if output_count == old_output_count:
+ no_change_count = no_change_count + 1
+ else:
+ no_change_count = 0
+ old_output_count = output_count
+
+ # check if all expected output words received
+ if output_count == num_out_values:
+ output_done = True
+
+ # end sim on timeout
+ if no_change_count == liveness_threshold:
+ if trace_file != "":
+ sim.flush_vcd_trace()
+ sim.stop_vcd_trace()
+ raise Exception(
+ "Error in simulation! Takes too long to produce output. "
+ "Consider setting the LIVENESS_THRESHOLD env.var. to a "
+ "larger value."
+ )
+
+ if trace_file != "":
+ sim.flush_vcd_trace()
+ sim.stop_vcd_trace()
+
+ return total_cycle_count
diff --git a/tests/core/test_modelwrapper.py b/tests/core/test_modelwrapper.py
index d1da6934a5db07aabe41a9ca40b5de497b6460a1..4bd9385536bc6721c66726169dfa4c69e5f06772 100644
--- a/tests/core/test_modelwrapper.py
+++ b/tests/core/test_modelwrapper.py
@@ -127,3 +127,45 @@ def test_modelwrapper_graph_order():
assert model.get_node_index(Round_node) == 1
assert model.get_node_index(Ceil_node) == 2
assert model.get_node_index(Add_node) == 3
+
+
+def test_modelwrapper_detect_forks_n_joins():
+ # create small network with properties to be tested
+ Neg_node = onnx.helper.make_node("Neg", inputs=["in1"], outputs=["neg1"])
+ Round_node = onnx.helper.make_node("Round", inputs=["neg1"], outputs=["round1"])
+
+ Ceil_node = onnx.helper.make_node("Ceil", inputs=["neg1"], outputs=["ceil1"])
+ Add_node = onnx.helper.make_node(
+ "Add", inputs=["round1", "ceil1"], outputs=["out1"]
+ )
+
+ in1 = onnx.helper.make_tensor_value_info("in1", onnx.TensorProto.FLOAT, [4, 4])
+ out1 = onnx.helper.make_tensor_value_info("out1", onnx.TensorProto.FLOAT, [4, 4])
+
+ graph = onnx.helper.make_graph(
+ nodes=[Neg_node, Round_node, Ceil_node, Add_node],
+ name="simple_graph",
+ inputs=[in1],
+ outputs=[out1],
+ value_info=[
+ onnx.helper.make_tensor_value_info("neg1", onnx.TensorProto.FLOAT, [4, 4]),
+ onnx.helper.make_tensor_value_info(
+ "round1", onnx.TensorProto.FLOAT, [4, 4]
+ ),
+ onnx.helper.make_tensor_value_info("ceil1", onnx.TensorProto.FLOAT, [4, 4]),
+ ],
+ )
+
+ onnx_model = onnx.helper.make_model(graph, producer_name="simple-model")
+ model = ModelWrapper(onnx_model)
+
+ # test
+ assert model.is_fork_node(Neg_node)
+ assert not model.is_fork_node(Round_node)
+ assert not model.is_fork_node(Ceil_node)
+ assert not model.is_fork_node(Add_node)
+
+ assert not model.is_join_node(Neg_node)
+ assert not model.is_join_node(Round_node)
+ assert not model.is_join_node(Ceil_node)
+ assert model.is_join_node(Add_node)
diff --git a/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py b/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
index e03090f0581eebf68cac7baffb6888a6992df68d..48803c9614f53a3a149c6eaac4289d10086513a5 100644
--- a/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
+++ b/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
@@ -39,6 +39,7 @@ from finn.core.modelwrapper import ModelWrapper
from finn.transformation.fold_constants import FoldConstants
from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_data_layouts import InferDataLayouts
from finn.transformation.streamline import Streamline
from finn.util.test import get_test_model_trained
from finn.transformation.double_to_single_float import DoubleToSingleFloat
@@ -54,7 +55,9 @@ export_onnx_path_cnv = "test_output_cnv.onnx"
@pytest.mark.vivado
-def test_convert_to_hls_layers_cnv_w1a1():
+# Standalone or fused thresholding-based activation
+@pytest.mark.parametrize("fused_activation", [True, False])
+def test_convert_to_hls_layers_cnv_w1a1(fused_activation):
cnv = get_test_model_trained("CNV", 1, 1)
bo.export_finn_onnx(cnv, (1, 3, 32, 32), export_onnx_path_cnv)
model = ModelWrapper(export_onnx_path_cnv)
@@ -69,6 +72,7 @@ def test_convert_to_hls_layers_cnv_w1a1():
model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
model = model.transform(ConvertBipolarMatMulToXnorPopcount())
model = model.transform(Streamline())
+ model = model.transform(InferDataLayouts())
# model.save("golden.onnx")
# load one of the test vectors
fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
@@ -80,6 +84,10 @@ def test_convert_to_hls_layers_cnv_w1a1():
expected_ctx = oxe.execute_onnx(model, input_dict, True)
expected = expected_ctx[model.graph.output[0].name]
+ # if we infer thresholding first, all MultiThresholds get converted to HLS
+ # subsequently, the FC inference will generate passthrough MVAUs
+ if not fused_activation:
+ model = model.transform(to_hls.InferThresholdingLayer())
model = model.transform(to_hls.InferBinaryStreamingFCLayer())
model = model.transform(to_hls.InferQuantizedStreamingFCLayer())
for node in model.graph.node:
@@ -102,7 +110,12 @@ def test_convert_to_hls_layers_cnv_w1a1():
model = model.transform(to_hls.InferStreamingMaxPool())
# check topology status
finn_nodes = model.get_finn_nodes()
- assert len(finn_nodes) == 18
+ if fused_activation:
+ assert len(finn_nodes) == 18
+ else:
+ assert len(finn_nodes) == 26
+ thr_nodes = model.get_nodes_by_op_type("Thresholding_Batch")
+ assert len(thr_nodes) == 8
non_finn_nodes = model.get_non_finn_nodes()
assert len(non_finn_nodes) == 4
exp_non_finn_nodes = ["Transpose", "Reshape", "Mul", "Add"]
diff --git a/tests/fpgadataflow/test_fpgadataflow_duplicatestreams.py b/tests/fpgadataflow/test_fpgadataflow_duplicatestreams.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fb84be59333ef0e696204c9064fcf77e35b5d9b
--- /dev/null
+++ b/tests/fpgadataflow/test_fpgadataflow_duplicatestreams.py
@@ -0,0 +1,127 @@
+# 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 pytest
+
+from onnx import TensorProto, helper
+
+import finn.core.onnx_exec as oxe
+from finn.core.datatype import DataType
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
+from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
+from finn.transformation.general import GiveUniqueNodeNames
+from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
+from finn.util.basic import gen_finn_dt_tensor
+from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
+ ReplaceVerilogRelPaths,
+)
+
+
+def make_dupstreams_modelwrapper(ch, pe, idim, idt):
+ shape = [1, idim, idim, ch]
+ inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, shape)
+ outp0 = helper.make_tensor_value_info("outp0", TensorProto.FLOAT, shape)
+ outp1 = helper.make_tensor_value_info("outp1", TensorProto.FLOAT, shape)
+
+ dupstrm_node = helper.make_node(
+ "DuplicateStreams_Batch",
+ ["inp"],
+ ["outp0", "outp1"],
+ domain="finn",
+ backend="fpgadataflow",
+ NumChannels=ch,
+ PE=pe,
+ inputDataType=idt.name,
+ numInputVectors=[1, idim, idim],
+ )
+ graph = helper.make_graph(
+ nodes=[dupstrm_node], name="graph", inputs=[inp], outputs=[outp0, outp1]
+ )
+
+ model = helper.make_model(graph, producer_name="addstreams-model")
+ model = ModelWrapper(model)
+
+ model.set_tensor_datatype("inp", idt)
+
+ return model
+
+
+def prepare_inputs(input_tensor, idt):
+ return {"inp": input_tensor}
+
+
+# data type
+@pytest.mark.parametrize("idt", [DataType.INT4, DataType.UINT16])
+# channels
+@pytest.mark.parametrize("ch", [64])
+# folding
+@pytest.mark.parametrize("fold", [-1, 2, 1])
+# image dimension
+@pytest.mark.parametrize("imdim", [7])
+# execution mode
+@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
+@pytest.mark.vivado
+def test_fpgadataflow_duplicatestreams(idt, ch, fold, imdim, exec_mode):
+ if fold == -1:
+ pe = 1
+ else:
+ pe = ch // fold
+ assert ch % pe == 0
+
+ # generate input data
+ x = gen_finn_dt_tensor(idt, (1, imdim, imdim, ch))
+
+ model = make_dupstreams_modelwrapper(ch, pe, imdim, idt)
+
+ if exec_mode == "cppsim":
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
+ model = model.transform(SetExecMode("cppsim"))
+ elif exec_mode == "rtlsim":
+ model = model.transform(SetExecMode("rtlsim"))
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(PrepareIP("xc7z020clg400-1", 5))
+ model = model.transform(HLSSynthIP())
+ model = model.transform(ReplaceVerilogRelPaths())
+ model = model.transform(PrepareRTLSim())
+ else:
+ raise Exception("Unknown exec_mode")
+
+ # prepare input data and execute
+ input_dict = prepare_inputs(x, idt)
+ output_dict = oxe.execute_onnx(model, input_dict)
+ y0 = output_dict["outp0"]
+ y1 = output_dict["outp1"]
+ expected_y = x
+
+ assert (y0 == expected_y).all(), exec_mode + " failed"
+ assert (y1 == expected_y).all(), exec_mode + " failed"
diff --git a/tests/fpgadataflow/test_fpgadataflow_sameresize.py b/tests/fpgadataflow/test_fpgadataflow_sameresize.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea6130c3891443595b038460233ebb85799ac461
--- /dev/null
+++ b/tests/fpgadataflow/test_fpgadataflow_sameresize.py
@@ -0,0 +1,125 @@
+import pytest
+import os
+import numpy as np
+
+from onnx import TensorProto, helper
+from finn.core.datatype import DataType
+from finn.core.modelwrapper import ModelWrapper
+from finn.util.basic import gen_finn_dt_tensor
+import finn.core.onnx_exec as oxe
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
+from finn.transformation.general import GiveUniqueNodeNames
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
+from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
+
+from finn.util.basic import pynq_part_map
+
+test_pynq_board = os.getenv("PYNQ_BOARD", default="Pynq-Z1")
+test_fpga_part = pynq_part_map[test_pynq_board]
+target_clk_ns = 10
+
+
+def make_single_sameresize_modelwrapper(
+ idim, odim, kdim, stride, num_ch, idt, pad_style
+):
+ inp = helper.make_tensor_value_info(
+ "inp", TensorProto.FLOAT, [1, idim, idim, num_ch]
+ )
+ outp = helper.make_tensor_value_info(
+ "outp", TensorProto.FLOAT, [1, odim, odim, num_ch]
+ )
+
+ SameResize_node = helper.make_node(
+ "SameResize_Batch",
+ ["inp"],
+ ["outp"],
+ domain="finn",
+ backend="fpgadataflow",
+ ImgDim=idim,
+ KernelDim=kdim,
+ Stride=stride,
+ NumChannels=num_ch,
+ inputDataType=str(idt.name),
+ PaddingStyle=pad_style,
+ )
+
+ graph = helper.make_graph(
+ nodes=[SameResize_node], name="sameresize_graph", inputs=[inp], outputs=[outp]
+ )
+
+ model = helper.make_model(graph, producer_name="sameresize-model")
+ model = ModelWrapper(model)
+
+ model.set_tensor_datatype("inp", idt)
+ model.set_tensor_datatype("outp", idt)
+
+ return model
+
+
+# image dimension
+@pytest.mark.parametrize("idim", [8, 16])
+# kernel dimension
+@pytest.mark.parametrize("kdim", [2, 3])
+# stride
+@pytest.mark.parametrize("stride", [1, 2])
+# number of channels
+@pytest.mark.parametrize("num_ch", [1, 2])
+# FINN input datatype
+@pytest.mark.parametrize("idt", [DataType.INT2, DataType.INT4])
+# execution mode
+@pytest.mark.parametrize("mode", ["cppsim", "rtlsim"])
+@pytest.mark.slow
+@pytest.mark.vivado
+def test_fpgadataflow_sameresize(idim, kdim, stride, num_ch, idt, mode):
+ pad_style = 2
+ assert idim % stride == 0, "Stride must divide input dimension."
+ # number of "same" windows over the input data
+ same_windows = idim // stride
+ odim = kdim + stride * (same_windows - 1)
+
+ # generate input data
+ x = gen_finn_dt_tensor(idt, [1, idim, idim, num_ch])
+ input_dict = {"inp": x}
+
+ model = make_single_sameresize_modelwrapper(
+ idim, odim, kdim, stride, num_ch, idt, pad_style
+ )
+ model = model.transform(InferShapes())
+ model = model.transform(SetExecMode(mode))
+ model = model.transform(GiveUniqueNodeNames())
+ if mode == "cppsim":
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
+ elif mode == "rtlsim":
+ model = model.transform(PrepareIP(test_fpga_part, target_clk_ns))
+ model = model.transform(HLSSynthIP())
+ model = model.transform(PrepareRTLSim())
+ y_produced = oxe.execute_onnx(model, input_dict)["outp"]
+ expected_oshape = (1, odim, odim, num_ch)
+ assert y_produced.shape == expected_oshape
+
+ # calculate reference
+ # calculate correct padding according to parameters
+ pad = odim - idim
+ if pad_style == 2:
+ if pad % 2 == 0:
+ pad_up = pad // 2
+ pad_left = pad // 2
+ else:
+ pad_up = pad // 2 + 1
+ pad_left = pad // 2 + 1
+ else:
+ pad_up = pad // 2
+ pad_left = pad // 2
+ pad_down = pad - pad_up
+ pad_right = pad - pad_left
+
+ y_expected = np.pad(
+ x, ((0, 0), (pad_up, pad_down), (pad_left, pad_right), (0, 0)), "constant"
+ )
+
+ assert (y_produced == y_expected).all()
diff --git a/tests/fpgadataflow/test_fpgadataflow_thresholding.py b/tests/fpgadataflow/test_fpgadataflow_thresholding.py
new file mode 100644
index 0000000000000000000000000000000000000000..50b990f13494f22e985406791445b406e9946147
--- /dev/null
+++ b/tests/fpgadataflow/test_fpgadataflow_thresholding.py
@@ -0,0 +1,154 @@
+# 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 pytest
+
+import numpy as np
+from onnx import TensorProto, helper
+
+import finn.core.onnx_exec as oxe
+from finn.analysis.fpgadataflow.hls_synth_res_estimation import hls_synth_res_estimation
+from finn.core.datatype import DataType
+from finn.core.modelwrapper import ModelWrapper
+from finn.custom_op.multithreshold import multithreshold
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
+from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
+from finn.transformation.general import GiveUniqueNodeNames
+from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
+from finn.util.basic import gen_finn_dt_tensor
+from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
+ ReplaceVerilogRelPaths,
+)
+
+
+def make_single_thresholding_modelwrapper(T, pe, idt, odt):
+ NumChannels = T.shape[0]
+
+ inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, [1, NumChannels])
+ outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, [1, NumChannels])
+
+ node_inp_list = ["inp", "thresh"]
+
+ Thresholding_node = helper.make_node(
+ "Thresholding_Batch",
+ node_inp_list,
+ ["outp"],
+ domain="finn",
+ backend="fpgadataflow",
+ NumChannels=NumChannels,
+ PE=pe,
+ inputDataType=idt.name,
+ outputDataType=odt.name,
+ )
+ graph = helper.make_graph(
+ nodes=[Thresholding_node],
+ name="thresholding_graph",
+ inputs=[inp],
+ outputs=[outp],
+ )
+
+ model = helper.make_model(graph, producer_name="thresholding-model")
+ model = ModelWrapper(model)
+
+ model.set_tensor_datatype("inp", idt)
+ model.set_tensor_datatype("outp", odt)
+
+ model.set_tensor_datatype("thresh", idt)
+ model.set_initializer("thresh", T)
+ return model
+
+
+# activation: None or DataType
+@pytest.mark.parametrize("act", [DataType.INT4, DataType.BIPOLAR])
+# input datatype
+@pytest.mark.parametrize("idt", [DataType.INT16, DataType.UINT16])
+# folding, -1 is maximum possible
+@pytest.mark.parametrize("nf", [-1, 2, 1])
+# number of input features
+@pytest.mark.parametrize("ich", [16])
+# execution mode
+@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
+@pytest.mark.vivado
+@pytest.mark.slow
+def test_fpgadataflow_thresholding(idt, act, nf, ich, exec_mode):
+ if nf == -1:
+ nf = ich
+ pe = ich // nf
+ assert ich % pe == 0
+
+ # generate input data
+ x = gen_finn_dt_tensor(idt, (1, ich))
+
+ odt = act
+ n_steps = act.get_num_possible_values() - 1
+ T = np.random.randint(idt.min(), idt.max() + 1, (ich, n_steps)).astype(np.float32)
+ # provide non-decreasing thresholds
+ T = np.sort(T, axis=1)
+
+ model = make_single_thresholding_modelwrapper(T, pe, idt, odt)
+
+ if exec_mode == "cppsim":
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
+ model = model.transform(SetExecMode("cppsim"))
+ elif exec_mode == "rtlsim":
+ model = model.transform(SetExecMode("rtlsim"))
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(PrepareIP("xc7z020clg400-1", 5))
+ model = model.transform(HLSSynthIP())
+ model = model.transform(ReplaceVerilogRelPaths())
+ model = model.transform(PrepareRTLSim())
+ else:
+ raise Exception("Unknown exec_mode")
+
+ # package input data as dictionary
+ input_dict = {"inp": x}
+
+ y = multithreshold(x, T)
+ if act == DataType.BIPOLAR:
+ # binary to bipolar
+ y = 2 * y - 1
+ else:
+ # signed offset
+ y += act.min()
+
+ oshape = model.get_tensor_shape("outp")
+ y_expected = y.reshape(oshape)
+ # execute model
+ y_produced = oxe.execute_onnx(model, input_dict)["outp"]
+
+ y_produced = y_produced.reshape(y_expected.shape)
+
+ assert (y_produced == y_expected).all(), "cppsim failed"
+
+ if exec_mode == "rtlsim":
+ hls_synt_res_est = model.analysis(hls_synth_res_estimation)
+ assert "Thresholding_Batch_0" in hls_synt_res_est
diff --git a/tests/transformation/test_collapse_repeated_op.py b/tests/transformation/test_collapse_repeated_op.py
index 01d932ece0be4b0beb7ad6094284ec3efb1e525e..b74d868f9b921c35ff9f596c811583f45f761374 100644
--- a/tests/transformation/test_collapse_repeated_op.py
+++ b/tests/transformation/test_collapse_repeated_op.py
@@ -34,6 +34,7 @@ import finn.core.onnx_exec as ox
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline import CollapseRepeatedAdd, CollapseRepeatedMul
+import pytest
def test_collapse_repeated_op():
@@ -67,3 +68,60 @@ def test_collapse_repeated_op():
new_model = new_model.transform(CollapseRepeatedMul())
inp_dict = {"top_in": np.asarray([-1.0, 1.0], dtype=np.float32)}
assert ox.compare_execution(model, new_model, inp_dict)
+ assert len(new_model.graph.node) == 2
+ assert new_model.graph.node[0].op_type == "Add"
+ assert new_model.graph.node[1].op_type == "Mul"
+
+
+@pytest.mark.parametrize(
+ "test_args", [("Add", CollapseRepeatedAdd()), ("Mul", CollapseRepeatedMul())],
+)
+def test_collapse_repeated_only_if_linear(test_args):
+ scalar_op = test_args[0]
+ transf_fxn = test_args[1]
+
+ input_shape = [4, 4]
+ output_shape = input_shape
+
+ top_in = oh.make_tensor_value_info("top_in", TensorProto.FLOAT, input_shape)
+ top_out = oh.make_tensor_value_info("top_out", TensorProto.FLOAT, output_shape)
+
+ value_info = [oh.make_tensor_value_info("p1", TensorProto.FLOAT, [1])]
+ value_info += [oh.make_tensor_value_info("p2", TensorProto.FLOAT, [1])]
+ value_info += [oh.make_tensor_value_info("p3", TensorProto.FLOAT, [1])]
+ value_info += [oh.make_tensor_value_info("p4", TensorProto.FLOAT, [1])]
+ value_info += [oh.make_tensor_value_info("p5", TensorProto.FLOAT, [1])]
+
+ modelproto = oh.make_model(
+ oh.make_graph(
+ name="test",
+ inputs=[top_in],
+ outputs=[top_out],
+ value_info=value_info,
+ nodes=[
+ oh.make_node(scalar_op, ["top_in", "p2"], ["t1"]),
+ oh.make_node(scalar_op, ["t1", "p1"], ["t2"]),
+ oh.make_node(scalar_op, ["t2", "p3"], ["t3"]),
+ oh.make_node(scalar_op, ["t2", "p4"], ["t4"]),
+ oh.make_node(scalar_op, ["t3", "t4"], ["t5"]),
+ oh.make_node(scalar_op, ["t5", "p5"], ["top_out"]),
+ ],
+ )
+ )
+ model = ModelWrapper(modelproto)
+ model = model.transform(InferShapes())
+
+ np.random.seed(0)
+ model.set_initializer("p1", *np.random.rand(1).astype(np.float32))
+ model.set_initializer("p2", *np.random.rand(1).astype(np.float32))
+ model.set_initializer("p3", *np.random.rand(1).astype(np.float32))
+ model.set_initializer("p4", *np.random.rand(1).astype(np.float32))
+ model.set_initializer("p5", *np.random.rand(1).astype(np.float32))
+
+ # Transform
+ new_model = model.transform(transf_fxn)
+
+ # Test
+ inp_dict = {"top_in": np.random.rand(*input_shape).astype(np.float32)}
+ assert ox.compare_execution(model, new_model, inp_dict)
+ assert len(new_model.graph.node) == 5
diff --git a/tests/transformation/test_general_transformation.py b/tests/transformation/test_general_transformation.py
index 33b6041a170f3c0de8f741ef3ecb28682f6429ea..153af378eb3e07d5824f114fd194730048fb4953 100644
--- a/tests/transformation/test_general_transformation.py
+++ b/tests/transformation/test_general_transformation.py
@@ -31,6 +31,12 @@ from pkgutil import get_data
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.general import GiveUniqueNodeNames
+import numpy as np
+import onnx
+import finn.core.onnx_exec as oxe
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.general import GiveUniqueParameterTensors
+
def test_give_unique_node_names():
raw_m = get_data("finn", "data/onnx/mnist-conv/model.onnx")
@@ -39,3 +45,76 @@ def test_give_unique_node_names():
assert model.graph.node[0].name == "Reshape_0"
assert model.graph.node[1].name == "Conv_0"
assert model.graph.node[11].name == "Add_2"
+
+
+def test_give_unique_parameter_tensors():
+
+ # Create model
+ input_shape = [4, 4]
+ in1 = onnx.helper.make_tensor_value_info("in1", onnx.TensorProto.FLOAT, input_shape)
+ out1 = onnx.helper.make_tensor_value_info(
+ "out1", onnx.TensorProto.FLOAT, input_shape
+ )
+
+ graph_nodes = []
+ graph_nodes += [
+ onnx.helper.make_node("Add", inputs=["in1", "param1"], outputs=["t1"])
+ ]
+
+ graph_nodes += [
+ onnx.helper.make_node("Sum", inputs=["t1", "param1", "param1"], outputs=["t2"])
+ ]
+
+ graph_nodes += [
+ onnx.helper.make_node("Sum", inputs=["t2", "param2", "param1"], outputs=["t3"])
+ ]
+
+ graph_nodes += [
+ onnx.helper.make_node("Add", inputs=["t3", "param1"], outputs=["out1"])
+ ]
+
+ onnx_graph = onnx.helper.make_graph(
+ nodes=graph_nodes, name="simple_graph", inputs=[in1], outputs=[out1],
+ )
+
+ onnx_model = onnx.helper.make_model(onnx_graph, producer_name="simple-model")
+ model = ModelWrapper(onnx_model)
+
+ # Set param values
+ np.random.seed(0)
+ param1 = np.random.rand(*input_shape).astype(np.float32)
+ param2 = np.random.rand(*input_shape).astype(np.float32)
+ model.set_initializer("param1", param1)
+ model.set_initializer("param2", param2)
+ model = model.transform(InferShapes())
+
+ # Apply transformation
+ new_model = model.transform(GiveUniqueParameterTensors())
+ new_model = new_model.transform(InferShapes())
+
+ # Test
+ # Breaks the model?
+ input_tensor = np.random.rand(*input_shape).astype(np.float32)
+ input_dict = {"in1": input_tensor}
+
+ # run original
+ expected_context = oxe.execute_onnx(model, input_dict)
+ expected_output = expected_context[model.graph.output[0].name]
+
+ # run modified
+ produced_context = oxe.execute_onnx(new_model, input_dict)
+ produced_output = produced_context[new_model.graph.output[0].name]
+
+ assert np.isclose(
+ expected_output, produced_output, atol=1e-8
+ ).all(), " GiveUniqueParameterTensors() transform breaks the model"
+
+ # Does the job?
+ param_set = set()
+ param_cnt = 0
+ for n in new_model.graph.node:
+ for i in range(1, len(n.input)):
+ param_set |= {n.input[i]}
+ param_cnt += 1
+
+ assert len(param_set) == param_cnt, " There are still parameters reused"
diff --git a/tests/transformation/test_move_add_past_mul.py b/tests/transformation/test_move_add_past_mul.py
index a0516d6fb2ff985fc112185ce99ad8facd841caf..163b9d310a5f12bd0b854f9aa46f53a549bf109e 100644
--- a/tests/transformation/test_move_add_past_mul.py
+++ b/tests/transformation/test_move_add_past_mul.py
@@ -60,6 +60,9 @@ def test_move_add_past_mul_single():
new_model = model.transform(MoveAddPastMul())
inp_dict = {"top_in": np.asarray([-1.0, 1.0], dtype=np.float32)}
assert ox.compare_execution(model, new_model, inp_dict)
+ assert new_model.graph.node[0].op_type == "Mul"
+ assert new_model.graph.node[1].op_type == "Add"
+ assert new_model.graph.node[0].output[0] == new_model.graph.node[1].input[0]
def test_move_add_past_mul_multi():
@@ -92,3 +95,50 @@ def test_move_add_past_mul_multi():
new_model = model.transform(MoveAddPastMul())
inp_dict = {"top_in": np.asarray([-1.0, 1.0], dtype=np.float32)}
assert ox.compare_execution(model, new_model, inp_dict)
+ assert new_model.graph.node[0].op_type == "Mul"
+ assert new_model.graph.node[1].op_type == "Mul"
+ assert new_model.graph.node[2].op_type == "Add"
+ assert new_model.graph.node[3].op_type == "Add"
+ for i in range(len(new_model.graph.node) - 1):
+ assert new_model.graph.node[i].output[0] == new_model.graph.node[i + 1].input[0]
+
+
+def test_move_add_past_mul_only_if_linear():
+ top_in = oh.make_tensor_value_info("top_in", TensorProto.FLOAT, [2])
+ top_out = oh.make_tensor_value_info("top_out", TensorProto.FLOAT, [2])
+
+ value_info = [oh.make_tensor_value_info("add1_param", TensorProto.FLOAT, [1])]
+ value_info += [oh.make_tensor_value_info("mul1_param", TensorProto.FLOAT, [1])]
+ value_info += [oh.make_tensor_value_info("mul2_param", TensorProto.FLOAT, [1])]
+ value_info += [oh.make_tensor_value_info("mul3_param", TensorProto.FLOAT, [1])]
+ modelproto = oh.make_model(
+ oh.make_graph(
+ name="test",
+ inputs=[top_in],
+ outputs=[top_out],
+ value_info=value_info,
+ nodes=[
+ oh.make_node("Add", ["top_in", "add1_param"], ["t1"]),
+ oh.make_node("Mul", ["t1", "mul1_param"], ["fork"]),
+ oh.make_node("Mul", ["fork", "mul2_param"], ["t3"]),
+ oh.make_node("Add", ["t3", "fork"], ["t4"]),
+ oh.make_node("Mul", ["t4", "mul3_param"], ["top_out"]),
+ ],
+ )
+ )
+ model = ModelWrapper(modelproto)
+ model = model.transform(InferShapes())
+
+ np.random.seed(0)
+ model.set_initializer("add1_param", np.random.rand(2).astype(np.float32))
+ model.set_initializer("mul1_param", np.random.rand(2).astype(np.float32))
+ model.set_initializer("mul2_param", np.random.rand(2).astype(np.float32))
+ model.set_initializer("mul3_param", np.random.rand(2).astype(np.float32))
+ new_model = model.transform(MoveAddPastMul())
+ inp_dict = {"top_in": np.random.rand(2).astype(np.float32)}
+ assert ox.compare_execution(model, new_model, inp_dict)
+ assert new_model.graph.node[0].op_type == "Mul"
+ assert new_model.graph.node[1].op_type == "Add"
+ assert new_model.graph.node[2].op_type == "Mul"
+ assert new_model.graph.node[3].op_type == "Add"
+ assert new_model.graph.node[4].op_type == "Mul"
diff --git a/tests/transformation/test_move_past_fork.py b/tests/transformation/test_move_past_fork.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3d37bd60c9e2580ca4499daafa8693f39fec810
--- /dev/null
+++ b/tests/transformation/test_move_past_fork.py
@@ -0,0 +1,79 @@
+from onnx import TensorProto, helper
+import numpy as np
+
+import finn.core.onnx_exec as oxe
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.streamline.reorder import MoveLinearPastFork
+from finn.transformation.infer_shapes import InferShapes
+
+import pytest
+
+
+@pytest.mark.parametrize("ch", [64, 1])
+# ifmdim
+@pytest.mark.parametrize("ifmdim", [-1, 7])
+def test_move_past_fork(ch, ifmdim):
+ # generate test vectors of correct shape
+ if ifmdim == -1:
+ input_shape = (1, ch)
+ else:
+ input_shape = (1, ch, ifmdim, ifmdim)
+
+ top_in = helper.make_tensor_value_info("top_in", TensorProto.FLOAT, input_shape)
+ top_out = helper.make_tensor_value_info("top_out", TensorProto.FLOAT, input_shape)
+
+ num_of_params = 8
+ value_info = []
+ for i in range(num_of_params):
+ value_info += [
+ helper.make_tensor_value_info("p" + str(i), TensorProto.FLOAT, input_shape)
+ ]
+
+ add_1_to_move = helper.make_node("Add", ["top_in", "p0"], ["fork1"])
+ mul_1_to_move = helper.make_node("Mul", ["t5", "p4"], ["fork2"])
+ add_2_to_move = helper.make_node("Add", ["fork2", "p5"], ["t6"])
+ mul_1_not_to_move = helper.make_node("Mul", ["t8", "p7"], ["fork3"])
+ modelproto = helper.make_model(
+ helper.make_graph(
+ name="test",
+ inputs=[top_in],
+ outputs=[top_out],
+ value_info=value_info,
+ nodes=[
+ # fork1
+ add_1_to_move,
+ helper.make_node("Mul", ["fork1", "p1"], ["t2"]),
+ helper.make_node("Mul", ["fork1", "p2"], ["t3"]),
+ helper.make_node("Add", ["t2", "t3"], ["t4"]),
+ helper.make_node("Add", ["t4", "p3"], ["t5"]),
+ # fork2
+ mul_1_to_move,
+ add_2_to_move,
+ helper.make_node("Add", ["fork2", "p6"], ["t7"]),
+ helper.make_node("Add", ["t6", "t7"], ["t8"]),
+ # empty branches: do nothing
+ mul_1_not_to_move,
+ helper.make_node("Add", ["fork3", "fork3"], ["top_out"]),
+ ],
+ )
+ )
+ model = ModelWrapper(modelproto)
+ model = model.transform(InferShapes())
+
+ np.random.seed(0)
+ for i in range(num_of_params):
+ model.set_initializer(
+ "p" + str(i), np.random.rand(*input_shape).astype(np.float32)
+ )
+
+ # Transform
+ new_model = model.transform(MoveLinearPastFork())
+ inp_dict = {"top_in": np.random.rand(*input_shape).astype(np.float32)}
+
+ # Test
+ assert oxe.compare_execution(model, new_model, inp_dict)
+ assert not new_model.is_fork_node(add_1_to_move)
+ assert not new_model.is_fork_node(mul_1_to_move)
+ assert not new_model.is_fork_node(add_2_to_move)
+ assert new_model.is_fork_node(mul_1_not_to_move)
+ assert len(new_model.graph.node) == 14
diff --git a/tests/transformation/test_move_scalar_past_conv.py b/tests/transformation/test_move_scalar_past_conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f50642d2b9d1583030630cb4927c2b86667e71a
--- /dev/null
+++ b/tests/transformation/test_move_scalar_past_conv.py
@@ -0,0 +1,166 @@
+import numpy as np
+import onnx.helper as oh
+import pytest
+from onnx import TensorProto
+
+import finn.core.onnx_exec as ox
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.streamline import (
+ MoveScalarAddPastConv,
+ MoveScalarMulPastConv,
+)
+
+
+@pytest.mark.parametrize("padding", [False, True])
+@pytest.mark.parametrize(
+ "test_args", [("Add", MoveScalarAddPastConv()), ("Mul", MoveScalarMulPastConv())],
+)
+def test_move_scalar_past_conv(test_args, padding):
+ scalar_op = test_args[0]
+ transf_fxn = test_args[1]
+
+ in_feature_dim = 7
+ in_chn = 3
+
+ stages = 2
+ kernel_size = 3
+
+ out_feature_dim = (
+ in_feature_dim if padding else in_feature_dim - (kernel_size // 2 * 2) * stages
+ )
+
+ input_shape = [1, in_chn, in_feature_dim, in_feature_dim]
+ output_shape = [1, in_chn, out_feature_dim, out_feature_dim]
+
+ conv_param_shape = [in_chn, in_chn, kernel_size, kernel_size]
+
+ conv_config = {}
+ conv_config["dilations"] = [1, 1]
+ conv_config["group"] = 1
+ conv_config["kernel_shape"] = [kernel_size, kernel_size]
+ if padding:
+ conv_config["pads"] = [1, 1, 1, 1]
+ else:
+ conv_config["pads"] = [0, 0, 0, 0]
+ conv_config["strides"] = [1, 1]
+
+ top_in = oh.make_tensor_value_info("top_in", TensorProto.FLOAT, input_shape)
+ top_out = oh.make_tensor_value_info("top_out", TensorProto.FLOAT, output_shape)
+
+ value_info = [oh.make_tensor_value_info("p1", TensorProto.FLOAT, [1])]
+ value_info += [oh.make_tensor_value_info("p2", TensorProto.FLOAT, conv_param_shape)]
+ value_info += [oh.make_tensor_value_info("p3", TensorProto.FLOAT, conv_param_shape)]
+
+ modelproto = oh.make_model(
+ oh.make_graph(
+ name="test",
+ inputs=[top_in],
+ outputs=[top_out],
+ value_info=value_info,
+ nodes=[
+ oh.make_node(scalar_op, ["top_in", "p1"], ["t1"]),
+ oh.make_node("Conv", ["t1", "p2"], ["t2"], **conv_config),
+ oh.make_node("Conv", ["t2", "p3"], ["top_out"], **conv_config),
+ ],
+ )
+ )
+ model = ModelWrapper(modelproto)
+ model = model.transform(InferShapes())
+
+ np.random.seed(0)
+ model.set_initializer("p1", *np.random.rand(1).astype(np.float32))
+ model.set_initializer("p2", np.random.rand(*conv_param_shape).astype(np.float32))
+ model.set_initializer("p3", np.random.rand(*conv_param_shape).astype(np.float32))
+ new_model = model.transform(transf_fxn)
+ inp_dict = {"top_in": np.random.rand(*input_shape).astype(np.float32)}
+
+ assert ox.compare_execution(model, new_model, inp_dict)
+ if scalar_op == "Add":
+ if padding:
+ assert new_model.graph.node[0].op_type == scalar_op
+ assert new_model.graph.node[1].op_type == "Conv"
+ assert new_model.graph.node[2].op_type == "Conv"
+ else:
+ assert new_model.graph.node[0].op_type == "Conv"
+ assert new_model.graph.node[1].op_type == scalar_op
+ assert new_model.graph.node[2].op_type == "Conv"
+ else:
+ assert new_model.graph.node[0].op_type == "Conv"
+ assert new_model.graph.node[1].op_type == "Conv"
+ assert new_model.graph.node[2].op_type == scalar_op
+
+
+@pytest.mark.parametrize(
+ "test_args", [("Add", MoveScalarAddPastConv()), ("Mul", MoveScalarMulPastConv())],
+)
+def test_move_scalar_past_conv_only_if_linear(test_args):
+ scalar_op = test_args[0]
+ transf_fxn = test_args[1]
+
+ in_feature_dim = 7
+ in_chn = 1
+ padding = False
+ stages = 3
+ kernel_size = 3
+
+ out_feature_dim = (
+ in_feature_dim if padding else in_feature_dim - (kernel_size // 2 * 2) * stages
+ )
+
+ input_shape = [1, in_chn, in_feature_dim, in_feature_dim]
+ output_shape = [1, in_chn, out_feature_dim, out_feature_dim]
+
+ conv_param_shape = [in_chn, in_chn, kernel_size, kernel_size]
+
+ conv_config = {}
+ conv_config["dilations"] = [1, 1]
+ conv_config["group"] = 1
+ conv_config["kernel_shape"] = [kernel_size, kernel_size]
+ conv_config["pads"] = [0, 0, 0, 0]
+ conv_config["strides"] = [1, 1]
+
+ top_in = oh.make_tensor_value_info("top_in", TensorProto.FLOAT, input_shape)
+ top_out = oh.make_tensor_value_info("top_out", TensorProto.FLOAT, output_shape)
+
+ value_info = [oh.make_tensor_value_info("p1", TensorProto.FLOAT, [1])]
+ value_info += [oh.make_tensor_value_info("p2", TensorProto.FLOAT, conv_param_shape)]
+ value_info += [oh.make_tensor_value_info("p3", TensorProto.FLOAT, conv_param_shape)]
+ value_info += [oh.make_tensor_value_info("p4", TensorProto.FLOAT, conv_param_shape)]
+ value_info += [oh.make_tensor_value_info("p5", TensorProto.FLOAT, conv_param_shape)]
+
+ modelproto = oh.make_model(
+ oh.make_graph(
+ name="test",
+ inputs=[top_in],
+ outputs=[top_out],
+ value_info=value_info,
+ nodes=[
+ oh.make_node("Conv", ["top_in", "p2"], ["t1"], **conv_config),
+ oh.make_node(scalar_op, ["t1", "p1"], ["t2"]),
+ oh.make_node("Conv", ["t2", "p3"], ["t3"], **conv_config),
+ oh.make_node("Conv", ["t2", "p4"], ["t4"], **conv_config),
+ oh.make_node(scalar_op, ["t3", "t4"], ["t5"]),
+ oh.make_node("Conv", ["t5", "p5"], ["top_out"], **conv_config),
+ ],
+ )
+ )
+ model = ModelWrapper(modelproto)
+ model = model.transform(InferShapes())
+
+ np.random.seed(0)
+ model.set_initializer("p1", *np.random.rand(1).astype(np.float32))
+ model.set_initializer("p2", np.random.rand(*conv_param_shape).astype(np.float32))
+ model.set_initializer("p3", np.random.rand(*conv_param_shape).astype(np.float32))
+ model.set_initializer("p4", np.random.rand(*conv_param_shape).astype(np.float32))
+ model.set_initializer("p5", np.random.rand(*conv_param_shape).astype(np.float32))
+ new_model = model.transform(transf_fxn)
+ inp_dict = {"top_in": np.random.rand(*input_shape).astype(np.float32)}
+
+ assert ox.compare_execution(model, new_model, inp_dict)
+ assert new_model.graph.node[0].op_type == "Conv"
+ assert new_model.graph.node[1].op_type == scalar_op
+ assert new_model.graph.node[2].op_type == "Conv"
+ assert new_model.graph.node[3].op_type == "Conv"
+ assert new_model.graph.node[4].op_type == scalar_op
+ assert new_model.graph.node[5].op_type == "Conv"
diff --git a/tests/transformation/test_move_scalar_past_matmul.py b/tests/transformation/test_move_scalar_past_matmul.py
index 896527e82d8cfa869cb979d1102904c70703a14c..e432dbf4ec1a38551609e5914e2d44968a020908 100644
--- a/tests/transformation/test_move_scalar_past_matmul.py
+++ b/tests/transformation/test_move_scalar_past_matmul.py
@@ -27,6 +27,7 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
+import pytest
import onnx.helper as oh
from onnx import TensorProto
@@ -99,3 +100,56 @@ def test_move_scalar_add_past_matmul():
assert new_model.graph.node[0].op_type == "MatMul"
assert new_model.graph.node[1].op_type == "Add"
assert new_model.graph.node[0].output[0] == new_model.graph.node[1].input[0]
+
+
+@pytest.mark.parametrize(
+ "test_args",
+ [("Add", MoveScalarAddPastMatMul()), ("Mul", MoveScalarMulPastMatMul())],
+)
+def test_move_scalar_past_matmul_only_if_linear(test_args):
+ scalar_op = test_args[0]
+ transf_fxn = test_args[1]
+ input_shape = [1, 2]
+ matmul_shape = [2, 2]
+ top_in = oh.make_tensor_value_info("top_in", TensorProto.FLOAT, input_shape)
+ top_out = oh.make_tensor_value_info("top_out", TensorProto.FLOAT, input_shape)
+
+ p1 = oh.make_tensor_value_info("p1", TensorProto.FLOAT, [1, 1])
+ p2 = oh.make_tensor_value_info("p2", TensorProto.FLOAT, matmul_shape)
+ p3 = oh.make_tensor_value_info("p3", TensorProto.FLOAT, matmul_shape)
+ p4 = oh.make_tensor_value_info("p4", TensorProto.FLOAT, matmul_shape)
+ modelproto = oh.make_model(
+ oh.make_graph(
+ name="test",
+ inputs=[top_in],
+ outputs=[top_out],
+ value_info=[p1, p2, p3, p4],
+ nodes=[
+ oh.make_node(scalar_op, ["top_in", "p1"], ["t1"]),
+ oh.make_node("MatMul", ["t1", "p2"], ["fork"]),
+ oh.make_node("MatMul", ["fork", "p3"], ["t3"]),
+ oh.make_node(scalar_op, ["t3", "fork"], ["t4"]),
+ oh.make_node("MatMul", ["t4", "p4"], ["top_out"]),
+ ],
+ )
+ )
+ model = ModelWrapper(modelproto)
+ model = model.transform(InferShapes())
+
+ np.random.seed(0)
+ model.set_initializer("p1", np.random.rand(1, 1).astype(np.float32))
+ model.set_initializer("p2", np.random.rand(*matmul_shape).astype(np.float32))
+ model.set_initializer("p3", np.random.rand(*matmul_shape).astype(np.float32))
+ model.set_initializer("p4", np.random.rand(*matmul_shape).astype(np.float32))
+
+ # Transform
+ new_model = model.transform(transf_fxn)
+
+ # Test
+ inp_dict = {"top_in": np.random.rand(*input_shape).astype(np.float32)}
+ assert ox.compare_execution(model, new_model, inp_dict)
+ assert new_model.graph.node[0].op_type == "MatMul"
+ assert new_model.graph.node[1].op_type == scalar_op
+ assert new_model.graph.node[2].op_type == "MatMul"
+ assert new_model.graph.node[3].op_type == scalar_op
+ assert new_model.graph.node[4].op_type == "MatMul"