diff --git a/.github/workflows/quicktest-dev-pr.yml b/.github/workflows/quicktest-dev-pr.yml
index cd59a629405c748187cdf478c0bdb0694c58c79f..924fbd24a174df49af4b3e259ad57d0a7907d42b 100644
--- a/.github/workflows/quicktest-dev-pr.yml
+++ b/.github/workflows/quicktest-dev-pr.yml
@@ -18,4 +18,6 @@ jobs:
uses: actions/checkout@v2
- name: DockerRunQuicktest
+ env:
+ NUM_DEFAULT_WORKERS: 4
run: sh run-docker.sh quicktest
diff --git a/.gitignore b/.gitignore
index f838c1695130d232ac6a2b888aed0cea31aafaa7..8b3166a44070a4575aac86c445c4504b594cda08 100644
--- a/.gitignore
+++ b/.gitignore
@@ -78,3 +78,6 @@ MANIFEST
# Jenkins cfg dir
/docker/jenkins_home
+
+# SSH key dir mounted into Docker
+/ssh_keys/
diff --git a/docker/Dockerfile.finn_ci b/docker/Dockerfile.finn_ci
index 0d610ec66a5f433d156f4e8da976767ce6458aef..d06ff8521555ccd6d09383cab039850f1565fc61 100644
--- a/docker/Dockerfile.finn_ci
+++ b/docker/Dockerfile.finn_ci
@@ -37,7 +37,7 @@ WORKDIR /workspace
RUN apt-get update
RUN apt-get -y upgrade
RUN apt-get install -y build-essential libglib2.0-0 libsm6 libxext6 libxrender-dev
-RUN apt install verilator
+RUN apt-get install -y verilator zsh
RUN apt-get -y install sshpass
RUN echo "StrictHostKeyChecking no" >> /etc/ssh/ssh_config
@@ -47,11 +47,13 @@ RUN git clone https://github.com/Xilinx/brevitas.git /workspace/brevitas
# CNPY
RUN git clone https://github.com/rogersce/cnpy.git /workspace/cnpy
# FINN hlslib
-RUN git clone https://github.com/maltanar/finn-hlslib.git /workspace/finn-hlslib
+RUN git clone https://github.com/Xilinx/finn-hlslib.git /workspace/finn-hlslib
# PyVerilator
RUN git clone https://github.com/maltanar/pyverilator /workspace/pyverilator
# PYNQ-HelloWorld
RUN git clone https://github.com/maltanar/PYNQ-HelloWorld.git /workspace/PYNQ-HelloWorld
+# oh-my-xilinx
+RUN git clone https://bitbucket.org/maltanar/oh-my-xilinx.git /workspace/oh-my-xilinx
# checkout desired FINN branch for testing
RUN git clone --branch $FINN_CI_BRANCH https://github.com/Xilinx/finn /workspace/finn
@@ -59,11 +61,15 @@ RUN git clone --branch $FINN_CI_BRANCH https://github.com/Xilinx/finn /workspace
RUN pip install -r /workspace/finn/requirements.txt
RUN apt update; apt install nano
RUN pip install pytest-dependency
+RUN pip install pytest-xdist
+RUN pip install pytest-parallel
ENV PYTHONPATH "${PYTHONPATH}:/workspace/finn/src"
ENV PYTHONPATH "${PYTHONPATH}:/workspace/pyverilator"
ENV PYNQSHELL_PATH "/workspace/PYNQ-HelloWorld/boards"
ENV VIVADO_IP_CACHE "$BUILD_PATH/vivado_ip_cache"
+ENV PATH "${PATH}:/workspace/oh-my-xilinx"
+ENV OHMYXILINX "/workspace/oh-my-xilinx"
# colorful terminal output
RUN echo "PS1='\[\033[1;36m\]\u\[\033[1;31m\]@\[\033[1;32m\]\h:\[\033[1;35m\]\w\[\033[1;31m\]\$\[\033[0m\] '" >> /root/.bashrc
diff --git a/docker/Dockerfile.finn_dev b/docker/Dockerfile.finn_dev
index 1c2cb19d14137b866b55417522fdebb8e0d7ad90..f8919d7498e0e8ef08a52d1da0782988b56d6df4 100644
--- a/docker/Dockerfile.finn_dev
+++ b/docker/Dockerfile.finn_dev
@@ -37,16 +37,12 @@ ARG PASSWD
ARG JUPYTER_PORT
ARG NETRON_PORT
-EXPOSE $JUPYTER_PORT
-EXPOSE $NETRON_PORT
-
WORKDIR /workspace
RUN apt-get update
RUN apt-get -y upgrade
RUN apt-get install -y build-essential libglib2.0-0 libsm6 libxext6 libxrender-dev
-RUN apt-get install verilator
-RUN apt-get install nano
+RUN apt-get install -y verilator nano zsh rsync
RUN apt-get -y install sshpass
RUN echo "StrictHostKeyChecking no" >> /etc/ssh/ssh_config
@@ -59,6 +55,8 @@ RUN pip install matplotlib
RUN pip install pytest-dependency
RUN pip install sphinx
RUN pip install sphinx_rtd_theme
+RUN pip install pytest-xdist
+RUN pip install pytest-parallel
# switch user
RUN groupadd -g $GID $GNAME
@@ -76,17 +74,21 @@ RUN git clone https://github.com/Xilinx/brevitas.git /workspace/brevitas
# CNPY
RUN git clone https://github.com/rogersce/cnpy.git /workspace/cnpy
# FINN hlslib
-RUN git clone https://github.com/maltanar/finn-hlslib.git /workspace/finn-hlslib
+RUN git clone https://github.com/Xilinx/finn-hlslib.git /workspace/finn-hlslib
# PyVerilator
RUN git clone https://github.com/maltanar/pyverilator /workspace/pyverilator
# PYNQ-HelloWorld
RUN git clone https://github.com/maltanar/PYNQ-HelloWorld.git /workspace/PYNQ-HelloWorld
+# oh-my-xilinx
+RUN git clone https://bitbucket.org/maltanar/oh-my-xilinx.git /workspace/oh-my-xilinx
# for this developer-oriented Docker container we assume the FINN repo is cloned and mounted from the host
# at /workspace/finn -- see run-docker.sh for an example of how to do this.
ENV PYTHONPATH "${PYTHONPATH}:/workspace/finn/src"
ENV PYTHONPATH "${PYTHONPATH}:/workspace/pyverilator"
ENV PYNQSHELL_PATH "/workspace/PYNQ-HelloWorld/boards"
+ENV PATH "${PATH}:/workspace/oh-my-xilinx"
+ENV OHMYXILINX "/workspace/oh-my-xilinx"
WORKDIR /home/$UNAME/finn
RUN echo "PS1='\[\033[1;36m\]\u\[\033[1;31m\]@\[\033[1;32m\]\h:\[\033[1;35m\]\w\[\033[1;31m\]\$\[\033[0m\] '" >> /home/$UNAME/.bashrc
@@ -100,5 +102,8 @@ RUN chmod 755 /usr/local/bin/finn_entrypoint.sh
RUN chmod 755 /usr/local/bin/quicktest.sh
USER $UNAME
+EXPOSE $JUPYTER_PORT
+EXPOSE $NETRON_PORT
+
ENTRYPOINT ["finn_entrypoint.sh"]
CMD ["bash"]
diff --git a/docker/Jenkinsfile b/docker/Jenkinsfile
index 80be261fb3da057186259598f84d915176577a5d..2215bc79cc7b2c20036d882fdc654fbe8721cab6 100644
--- a/docker/Jenkinsfile
+++ b/docker/Jenkinsfile
@@ -9,7 +9,12 @@ pipeline {
string(name: 'PYNQ_PASSWORD', defaultValue: 'xilinx', description: 'PYNQ board password')
string(name: 'PYNQ_TARGET_DIR', defaultValue: '/home/xilinx/finn', description: 'PYNQ board target deployment directory')
string(name: 'NUM_DEFAULT_WORKERS', defaultValue: '1', description: 'Number of cores for parallel transformations')
- string(name: 'DOCKER_CMD', defaultValue: """python setup.py test""", description: 'Command to run')
+ // main test: everything except rtlsim and end2end tests, parallel run with xdist, no parallel transformations to save on memory
+ string(name: 'DOCKER_CMD_MAIN', defaultValue: """python setup.py test --addopts "-k 'not (rtlsim or end2end)' --dist=loadfile -n auto" """, description: 'Main test command')
+ // rtlsim tests: parallel run with pytest-parallel, no parallel transformations to save on memory
+ string(name: 'DOCKER_CMD_RTLSIM', defaultValue: """python setup.py test --addopts "-k rtlsim --workers auto" """, description: 'rtlsim test command')
+ // end2end tests: no parallel testing, use NUM_DEFAULT_WORKERS for parallel transformations
+ string(name: 'DOCKER_CMD_END2END', defaultValue: """python setup.py test --addopts "-k end2end" """, description: 'end2end test command')
}
environment {
DOCKER_TAG='finn_ci:$BUILD_ID'
@@ -32,10 +37,49 @@ pipeline {
"""
}
}
- stage('Test') {
+ stage('test-main') {
steps {
+ catchError {
sh """
- docker run --name $DOCKER_INST_NAME --init \
+ docker run --init \
+ --hostname $DOCKER_INST_NAME \
+ -v ${params.VIVADO_PATH}:${params.VIVADO_PATH}:ro \
+ -e NUM_DEFAULT_WORKERS=1 \
+ -e FINN_INST_NAME=$DOCKER_INST_NAME \
+ -e VIVADO_PATH=${params.VIVADO_PATH} \
+ -e PYNQ_BOARD=${params.PYNQ_BOARD} \
+ -e PYNQ_IP=${params.PYNQ_IP} \
+ -e PYNQ_USERNAME=${params.PYNQ_USERNAME} \
+ -e PYNQ_PASSWORD=${params.PYNQ_PASSWORD} \
+ -e PYNQ_TARGET_DIR=${params.PYNQ_TARGET_DIR} \
+ $DOCKER_TAG ${params.DOCKER_CMD_MAIN}
+ """}
+ }
+ }
+ stage('test-rtlsim') {
+ steps {
+ catchError {
+ sh """
+ docker run --init \
+ --hostname $DOCKER_INST_NAME \
+ -v ${params.VIVADO_PATH}:${params.VIVADO_PATH}:ro \
+ -e NUM_DEFAULT_WORKERS=1 \
+ -e FINN_INST_NAME=$DOCKER_INST_NAME \
+ -e VIVADO_PATH=${params.VIVADO_PATH} \
+ -e PYNQ_BOARD=${params.PYNQ_BOARD} \
+ -e PYNQ_IP=${params.PYNQ_IP} \
+ -e PYNQ_USERNAME=${params.PYNQ_USERNAME} \
+ -e PYNQ_PASSWORD=${params.PYNQ_PASSWORD} \
+ -e PYNQ_TARGET_DIR=${params.PYNQ_TARGET_DIR} \
+ $DOCKER_TAG ${params.DOCKER_CMD_RTLSIM}
+ """}
+ }
+ }
+ stage('test-end2end') {
+ steps {
+ catchError {
+ sh """
+ docker run --init \
--hostname $DOCKER_INST_NAME \
-v ${params.VIVADO_PATH}:${params.VIVADO_PATH}:ro \
-e NUM_DEFAULT_WORKERS=${params.NUM_DEFAULT_WORKERS} \
@@ -46,8 +90,8 @@ pipeline {
-e PYNQ_USERNAME=${params.PYNQ_USERNAME} \
-e PYNQ_PASSWORD=${params.PYNQ_PASSWORD} \
-e PYNQ_TARGET_DIR=${params.PYNQ_TARGET_DIR} \
- $DOCKER_TAG ${params.DOCKER_CMD}
- """
+ $DOCKER_TAG ${params.DOCKER_CMD_END2END}
+ """ }
}
}
}
diff --git a/docker/finn_entrypoint.sh b/docker/finn_entrypoint.sh
index 81a80155d0b39a247211d5d0c736f33eb5234595..b312737c317517ca0ab19c74cf22284b5977b661 100644
--- a/docker/finn_entrypoint.sh
+++ b/docker/finn_entrypoint.sh
@@ -13,11 +13,12 @@ gecho () {
# checkout the correct dependency repo commits
# the repos themselves are cloned in the Dockerfile
-BREVITAS_COMMIT=7696326e5f279cacffd5b6ac8d9e8d81deec3978
+BREVITAS_COMMIT=f9a27226d4acf1661dd38bc449f71f89e0983cce
CNPY_COMMIT=4e8810b1a8637695171ed346ce68f6984e585ef4
-HLSLIB_COMMIT=6b88db826bb023937506913a23d964775a7606af
-PYVERILATOR_COMMIT=1d89cb0d4e0c97469cc6352c611f876ec13edfa6
+HLSLIB_COMMIT=8f9f2018762f654f196b666838aeaf6fc730ad9a
+PYVERILATOR_COMMIT=c97a5ba41bbc7c419d6f25c74cdf3bdc3393174f
PYNQSHELL_COMMIT=0c82a61b0ec1a07fa275a14146233824ded7a13d
+OMX_COMMIT=1bae737669901e762f581af73348332b5c4b2ada
gecho "Setting up known-good commit versions for FINN dependencies"
@@ -42,6 +43,10 @@ git -C /workspace/pyverilator checkout $PYVERILATOR_COMMIT --quiet
gecho "PYNQ shell @ $PYNQSHELL_COMMIT"
git -C /workspace/PYNQ-HelloWorld pull --quiet
git -C /workspace/PYNQ-HelloWorld checkout $PYNQSHELL_COMMIT --quiet
+# oh-my-xilinx
+gecho "oh-my-xilinx @ $OMX_COMMIT"
+git -C /workspace/oh-my-xilinx pull --quiet
+git -C /workspace/oh-my-xilinx checkout $OMX_COMMIT --quiet
# source Vivado env.vars
source $VIVADO_PATH/settings64.sh
diff --git a/docker/quicktest.sh b/docker/quicktest.sh
index 4f6a2d3e230de9fcbb947d794722294880a7730d..49b7886836ac4e45dad856dfcd49223276bd831a 100755
--- a/docker/quicktest.sh
+++ b/docker/quicktest.sh
@@ -1,4 +1,22 @@
#!/bin/bash
+: ${PYTEST_PARALLEL=auto}
+
cd $FINN_ROOT
-python setup.py test --addopts "-m 'not (vivado or slow)'"
+
+# check if command line argument is empty or not present
+if [ -z $1 ]; then
+ echo "Running quicktest: not (vivado or slow) with pytest-xdist"
+ python setup.py test --addopts "-m 'not (vivado or slow)' --dist=loadfile -n $PYTEST_PARALLEL"
+elif [ $1 = "main" ]; then
+ echo "Running main test suite: not (rtlsim or end2end) with pytest-xdist"
+ python setup.py test --addopts "-k not (rtlsim or end2end) --dist=loadfile -n $PYTEST_PARALLEL"
+elif [ $1 = "rtlsim" ]; then
+ echo "Running rtlsim test suite with pytest-parallel"
+ python setup.py test --addopts "-k rtlsim --workers $PYTEST_PARALLEL"
+elif [ $1 = "end2end" ]; then
+ echo "Running end2end test suite with no parallelism"
+ python setup.py test --addopts "-k end2end"
+else
+ echo "Unrecognized argument to quicktest.sh"
+fi
diff --git a/docs/finn-sheduling-and-folding.pptx b/docs/finn-sheduling-and-folding.pptx
new file mode 100644
index 0000000000000000000000000000000000000000..30bbe4d55b1cda9df25a791227983dc7cb750e58
Binary files /dev/null and b/docs/finn-sheduling-and-folding.pptx differ
diff --git a/docs/finn/example_networks.rst b/docs/finn/example_networks.rst
index 9f221871f09bf655db9d81988d6fa83e53473634..86bb2bd11fd805a23a3bdf6da8a8ed686259ecc1 100644
--- a/docs/finn/example_networks.rst
+++ b/docs/finn/example_networks.rst
@@ -20,17 +20,17 @@ version, this is indicated by an x mark in the table.
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
| Export/Import | x | x | x | x | x | x | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| Streamlining | x | x | x | x | x | | |
+| Streamlining | x | x | x | x | x | | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| Convert to HLS layers | x | x | x | x | x | | |
+| Convert to HLS layers | x | x | x | x | x | | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| Stitched IP | x | x | x | x | x | | |
+| Stitched IP | x | x | x | x | x | | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| Hardware test | x | x | x | | x | | |
+| Hardware test | x | x | x | | x | | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| cppsim | x | x | x | x | x | | |
+| cppsim | x | x | x | x | x | | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| rtlsim node-by-node | x | x | x | x | x | | |
+| rtlsim node-by-node | x | x | x | x | x | | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| rtlsim stitched IP | x | x | x | x | x | | |
+| rtlsim stitched IP | x | x | x | x | x | | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
diff --git a/docs/finn/getting_started.rst b/docs/finn/getting_started.rst
index 95594bb67a2be3a4c3fbba488c75a704f623c136..8b20cebcfc49d14d0afbb26edd678d65425476d3 100644
--- a/docs/finn/getting_started.rst
+++ b/docs/finn/getting_started.rst
@@ -18,6 +18,7 @@ Requirements
* A working Vivado 2019.1 installation
* A `VIVADO_PATH` environment variable pointing to the Vivado installation directory (e.g. the directory where settings64.sh is located)
* (optional) A PYNQ board with a network connection
+ * the ``bitstring`` package must be installed on the PYNQ: ``sudo pip3 install bitstring``
Running FINN in Docker
======================
@@ -30,6 +31,7 @@ Getting an interactive shell for development or experimentation
sh run_docker.sh
Simply running sh run-docker.sh without any additional arguments will clone the dependency repos, create a Docker container and give you a terminal with you can use for development for experimentation.
+If you want a new terminal on an already-running container, you can do this with `docker exec -it finn_dev_<username> bash`.
.. warning:: The Docker container is spawned with the `--rm` option, so make sure that any important files you created inside the container are either in the /workspace/finn folder (which is mounted from the host computer) or otherwise backed up.
@@ -71,8 +73,12 @@ from the FINN root directory* as follows:
python setup.py test --addopts "-k test_end2end_tfc_w1a2"
-Please see the pytest documentation for more about picking tests by marks or
-by name.
+Finally, if you want to run tests in parallel (e.g. to take advantage of a multi-core CPU)
+you can use:
+ * pytest-parallel for any rtlsim tests, e.g. `python setup.py test --addopts "-k rtlsim --workers auto"`
+ * pytest-xdist for anything else, make sure to add `--dist=loadfile` if you have tests in the same file that have dependencies on each other e.g. `python setup.py test --addopts "-k mytest -n auto --dist=loadfile"`
+
+Please see the pytest documentation for more about picking tests by marks or by name.
Environment variables
**********************
diff --git a/docs/finn/internals.rst b/docs/finn/internals.rst
index 7a4bc687eeb827320991f7d3f1ef8cc35e97f3da..dee62f09a9253380e05300dac8fa34915c20dab5 100644
--- a/docs/finn/internals.rst
+++ b/docs/finn/internals.rst
@@ -16,6 +16,10 @@ Custom Quantization Annotations
ONNX does not support datatypes smaller than 8-bit integers, whereas in FINN we are interested in smaller integers down to ternary and bipolar. To make this work, FINN uses the quantization_annotation field in ONNX to annotate tensors with their FINN DataType (:py:mod:`finn.core.datatype.DataType`) information. However, all tensors are expected to use single-precision floating point (float32) storage in FINN. This means we store even a 1-bit value as floating point for the purposes of representation. The FINN compiler flow is responsible for eventually producing a packed representation for the target hardware, where the 1-bit is actually stored as 1-bit.
+Note that FINN uses floating point tensors as a carrier data type to represent integers. Floating point arithmetic can introduce rounding errors, e.g. (int_num * float_scale) / float_scale is not always equal to int_num.
+When using the custom ONNX execution flow, FINN will attempt to sanitize any rounding errors for integer tensors. See (:py:mod:`finn.util.basic.sanitize_quant_values`) for more information.
+This behavior can be disabled (not recommended!) by setting the environment variable SANITIZE_QUANT_TENSORS=0.
+
Custom Operations/Nodes
=======================
diff --git a/docs/finn/verification.rst b/docs/finn/verification.rst
index 391c6f999312839daca0d4161336c7c0ae822f89..c52c0840aa40566d930164490b1fd249d7c07757 100644
--- a/docs/finn/verification.rst
+++ b/docs/finn/verification.rst
@@ -28,4 +28,15 @@ This simulation can be used for a model containing several HLS custom operations
Emulation using PyVerilator
===========================
-The emulation using PyVerilator can be used when IP blocks were generated, either node by node or of a whole design. For that purpose PyVerilator gets the generated verilog files.
+The emulation using PyVerilator can be used when IP blocks were generated, either node by node or of a whole (IP-stitched) design. For that purpose PyVerilator gets the generated verilog files.
+
+For debugging purposes, it's possible to generate .vcd trace files that show the value of external & internal signals as the emuation is running. To enable this:
+ - for node-by-node rtlsim, set the `rtlsim_trace` attribute of each node of interest to either a file name for the vcd or `default` to use the node name as the filename.
+ - for IP-stitched rtlsim, set the `rtlsim_trace` metadata_prop for the graph as per above.
+
+To control the tracing depth in the module hierarchy, use the `RTLSIM_TRACE_DEPTH` environment variable (default is 1):
+ - level 1 shows top-level input/output streams
+ - level 2 shows per-layer input/output streams
+ - level 3 shows per full-layer I/O including FIFO count signals
+
+Note that deeper tracing will take longer to execute and may produce very large .vcd files.
diff --git a/notebooks/end2end_example/tfc_end2end_example.ipynb b/notebooks/end2end_example/tfc_end2end_example.ipynb
index d573061487de204084e0d3242da8ad1b791f44d8..c84efc964b1f57b7ed385521fc5214fdc2396590 100644
--- a/notebooks/end2end_example/tfc_end2end_example.ipynb
+++ b/notebooks/end2end_example/tfc_end2end_example.ipynb
@@ -132,7 +132,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f8890385828>"
+ "<IPython.lib.display.IFrame at 0x7f7cc4290940>"
]
},
"execution_count": 3,
@@ -293,7 +293,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7fe1ad0639e8>"
+ "<IPython.lib.display.IFrame at 0x7f7c6c567f28>"
]
},
"execution_count": 6,
@@ -333,9 +333,10 @@
" ConvertDivToMul(),\n",
" BatchNormToAffine(),\n",
" ConvertSignToThres(),\n",
+ " AbsorbSignBiasIntoMultiThreshold(),\n",
" MoveAddPastMul(),\n",
" MoveScalarAddPastMatMul(),\n",
- " MoveScalarAddPastConv(),\n",
+ " MoveAddPastConv(),\n",
" MoveScalarMulPastMatMul(),\n",
" MoveScalarMulPastConv(),\n",
" MoveAddPastMul(),\n",
@@ -350,6 +351,7 @@
" ]\n",
" for trn in streamline_transformations:\n",
" model = model.transform(trn)\n",
+ " model = model.transform(RemoveIdentityOps())\n",
" model = model.transform(GiveUniqueNodeNames())\n",
" model = model.transform(GiveReadableTensorNames())\n",
" model = model.transform(InferDataTypes())\n",
@@ -400,7 +402,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7fe1346e4ef0>"
+ "<IPython.lib.display.IFrame at 0x7f7c6c0bf898>"
]
},
"execution_count": 8,
@@ -454,7 +456,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7fe1346f7780>"
+ "<IPython.lib.display.IFrame at 0x7f7c6c0e5c18>"
]
},
"execution_count": 9,
diff --git a/requirements.txt b/requirements.txt
index 2427f9490a3dd5a7ffe0e0a8cf2ad19af0934cdf..b15d86ed89f7b0e76b772ce42aba6481937310b0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,4 +8,6 @@ pre-commit
pyverilator
scipy
sphinx
+toposort
+vcdvcd
wget
diff --git a/run-docker.sh b/run-docker.sh
index 186efc322a8f437be0371b5a142a9dd524d1abf3..00ca8f86985a78d8f2af099c51dcd4b80cd2e974 100755
--- a/run-docker.sh
+++ b/run-docker.sh
@@ -65,6 +65,11 @@ DOCKER_INST_NAME="finn_dev_${DOCKER_UNAME}"
# ensure Docker tag and inst. name are all lowercase
DOCKER_TAG=$(echo "$DOCKER_TAG" | tr '[:upper:]' '[:lower:]')
DOCKER_INST_NAME=$(echo "$DOCKER_INST_NAME" | tr '[:upper:]' '[:lower:]')
+# Absolute path to this script, e.g. /home/user/bin/foo.sh
+SCRIPT=$(readlink -f "$0")
+# Absolute path this script is in, thus /home/user/bin
+SCRIPTPATH=$(dirname "$SCRIPT")
+
# the settings below will be taken from environment variables if available,
# otherwise the defaults below will be used
: ${JUPYTER_PORT=8888}
@@ -74,11 +79,7 @@ DOCKER_INST_NAME=$(echo "$DOCKER_INST_NAME" | tr '[:upper:]' '[:lower:]')
: ${PYNQ_BOARD="Pynq-Z1"}
: ${PYNQ_TARGET_DIR="/home/xilinx/$DOCKER_INST_NAME"}
: ${NUM_DEFAULT_WORKERS=1}
-
-# Absolute path to this script, e.g. /home/user/bin/foo.sh
-SCRIPT=$(readlink -f "$0")
-# Absolute path this script is in, thus /home/user/bin
-SCRIPTPATH=$(dirname "$SCRIPT")
+: ${FINN_SSH_KEY_DIR="$SCRIPTPATH/ssh_keys"}
BUILD_LOCAL=/tmp/$DOCKER_INST_NAME
VIVADO_HLS_LOCAL=$VIVADO_PATH
@@ -87,6 +88,7 @@ VIVADO_IP_CACHE=$BUILD_LOCAL/vivado_ip_cache
# ensure build dir exists locally
mkdir -p $BUILD_LOCAL
mkdir -p $VIVADO_IP_CACHE
+mkdir -p $FINN_SSH_KEY_DIR
gecho "Instance is named as $DOCKER_INST_NAME"
gecho "Mounting $BUILD_LOCAL into $BUILD_LOCAL"
@@ -96,7 +98,7 @@ 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 = ""
+DOCKER_INTERACTIVE=""
if [ "$1" = "test" ]; then
gecho "Running test suite (all tests)"
@@ -133,6 +135,7 @@ docker run -t --rm --name $DOCKER_INST_NAME $DOCKER_INTERACTIVE --init \
-v $SCRIPTPATH:/workspace/finn \
-v $BUILD_LOCAL:$BUILD_LOCAL \
-v $VIVADO_PATH:$VIVADO_PATH \
+-v $FINN_SSH_KEY_DIR:/home/$DOCKER_UNAME/.ssh \
-e VIVADO_PATH=$VIVADO_PATH \
-e FINN_INST_NAME=$DOCKER_INST_NAME \
-e FINN_ROOT="/workspace/finn" \
diff --git a/src/finn/core/modelwrapper.py b/src/finn/core/modelwrapper.py
index 2896b09e0f54d6d0492c5330ec5da4110e257d30..646add188c5d475cf37ccd33cf24d29d61754ae1 100644
--- a/src/finn/core/modelwrapper.py
+++ b/src/finn/core/modelwrapper.py
@@ -259,11 +259,10 @@ class ModelWrapper:
def find_producer(self, tensor_name):
"""Finds and returns the node that produces the tensor with given name."""
- ret = None
for x in self._model_proto.graph.node:
if tensor_name in x.output:
- ret = x
- return ret
+ return x
+ return None
def find_upstream(self, tensor_name, finder_fxn):
"""Follow the producer chain upstream, calling finder_fxn on each upstream
diff --git a/src/finn/core/onnx_exec.py b/src/finn/core/onnx_exec.py
index 44787e1d26049e6075e2222316b45ab3898acbc7..7c3123cd5eb29a54dc5cbfb912225ad3fdb0f219 100644
--- a/src/finn/core/onnx_exec.py
+++ b/src/finn/core/onnx_exec.py
@@ -39,6 +39,7 @@ from finn.core.remote_exec import remote_exec
from finn.core.rtlsim_exec import rtlsim_exec
from finn.custom_op.registry import getCustomOp
import finn.analysis.topology as ta
+from finn.util.basic import sanitize_quant_values, get_sanitize_quant_tensors
def execute_node(node, context, graph):
@@ -102,15 +103,14 @@ def execute_node(node, context, graph):
raise Exception(
"""Output shapes disagree after node execution:
found %s vs expected %s"""
- % (
- str(output_list[list_ind].shape.shape),
- str(context[outp].shape),
- )
+ % (str(output_list[list_ind].shape), str(context[outp].shape))
)
context[outp] = output_list[list_ind]
-def execute_onnx(model, input_dict, return_full_exec_context=False):
+def execute_onnx(
+ model, input_dict, return_full_exec_context=False, start_node=None, end_node=None
+):
"""Executes given ONNX ModelWrapper with given named inputs.
If return_full_exec_context is False, a dict of named outputs is returned
@@ -118,7 +118,12 @@ def execute_onnx(model, input_dict, return_full_exec_context=False):
If return return_full_exec_context is True, the full set of tensors used by
the execution (including inputs, weights, activations and final outputs)
- will be returned as a dict."""
+ will be returned as a dict.
+
+ When start_node and end_node are set to None, the whole graph is executed.
+ If they are set to particular ONNX nodes, only the subgraph between (and
+ including) those nodes is executed.
+ """
if not model.check_all_tensor_shapes_specified():
raise Exception("Found unspecified tensor shapes, try infer_shapes")
@@ -161,8 +166,28 @@ def execute_onnx(model, input_dict, return_full_exec_context=False):
# execute the model node by node
# we can simply walk down the list since the ONNX spec guarantees that it is
# topologically sorted
- for node in graph.node:
+ subgraph = []
+ if start_node is None:
+ start_node = model.graph.node[0]
+ if end_node is None:
+ end_node = model.graph.node[-1]
+ # select the nodes between specified start/end nodes
+ start_ind = model.get_node_index(start_node)
+ end_ind = model.get_node_index(end_node) + 1
+ assert end_ind >= start_ind, "Start/end nodes must define valid subgraph"
+ subgraph = graph.node[start_ind:end_ind]
+ for node in subgraph:
+ if get_sanitize_quant_tensors() != 0:
+ # round input values to match quantization annotation
+ execution_context = sanitize_quant_values(
+ model, node.input, execution_context
+ )
execute_node(node, execution_context, graph)
+ if get_sanitize_quant_tensors() != 0:
+ # round output values to quantization annotation
+ execution_context = sanitize_quant_values(
+ model, node.output, execution_context
+ )
elif model_exec_mode == "remote_pynq":
# use remote exec metadata built into model to execute on a remote PYNQ
remote_exec(model, execution_context)
diff --git a/src/finn/core/rtlsim_exec.py b/src/finn/core/rtlsim_exec.py
index e5e6d29bd8d8ed23f6a4958856ed1ddea3617175..1e1bee3aa7435d5cab6cbf5ea23dd37dcdfa4380 100644
--- a/src/finn/core/rtlsim_exec.py
+++ b/src/finn/core/rtlsim_exec.py
@@ -66,6 +66,11 @@ def rtlsim_exec(model, execution_context):
i_stream_w = first_node.get_instream_width()
# convert input into time multiplexed shape
i_folded_shape = first_node.get_folded_input_shape()
+ batchsize = i_tensor.shape[0]
+ # override batch size for input
+ i_folded_shape = list(i_folded_shape)
+ i_folded_shape[0] = batchsize
+ i_folded_shape = tuple(i_folded_shape)
# TODO any other layout transformations need to happen here!
i_tensor = i_tensor.reshape(i_folded_shape)
# extract output shape
@@ -74,21 +79,27 @@ def rtlsim_exec(model, execution_context):
o_dt = model.get_tensor_datatype(o_name)
last_node = getCustomOp(model.find_producer(o_name))
o_folded_shape = last_node.get_folded_output_shape()
+ # override batch size from actual input
+ o_shape = list(o_shape)
+ o_shape[0] = batchsize
+ o_shape = tuple(o_shape)
+ o_folded_shape = list(o_folded_shape)
+ o_folded_shape[0] = batchsize
+ o_folded_shape = tuple(o_folded_shape)
o_stream_w = last_node.get_outstream_width()
packedBits = o_stream_w
targetBits = o_dt.bitwidth()
# pack input
packed_input = npy_to_rtlsim_input(i_tensor, i_dt, i_stream_w)
num_out_values = last_node.get_number_output_values()
+ num_out_values *= batchsize
# prepare pyverilator model
rtlsim_so = model.get_metadata_prop("rtlsim_so")
if (rtlsim_so is None) or (not os.path.isfile(rtlsim_so)):
sim = pyverilate_stitched_ip(model)
model.set_metadata_prop("rtlsim_so", sim.lib._name)
else:
- sim = PyVerilator(rtlsim_so)
- _reset_rtlsim(sim)
- _toggle_clk(sim)
+ sim = PyVerilator(rtlsim_so, auto_eval=False)
ret = _run_rtlsim(sim, packed_input, num_out_values, trace_file)
packed_output = ret[0]
model.set_metadata_prop("sim_cycles", str(ret[1]))
@@ -104,18 +115,22 @@ def _reset_rtlsim(sim):
"""Sets reset input in pyverilator to zero, toggles the clock and set it
back to one"""
sim.io.ap_rst_n_0 = 0
- sim.io.ap_clk_0 = 1
- sim.io.ap_clk_0 = 0
+ _toggle_clk(sim)
+ _toggle_clk(sim)
sim.io.ap_rst_n_0 = 1
+ _toggle_clk(sim)
+ _toggle_clk(sim)
def _toggle_clk(sim):
"""Toggles the clock input in pyverilator once."""
- sim.io.ap_clk_0 = 1
sim.io.ap_clk_0 = 0
+ sim.eval()
+ sim.io.ap_clk_0 = 1
+ sim.eval()
-def _run_rtlsim(sim, inp, num_out_values, trace_file=None):
+def _run_rtlsim(sim, inp, num_out_values, trace_file=None, reset=True):
"""Runs the pyverilator simulation by passing the input values to the simulation,
toggle the clock and observing the execution time. Argument num_out_values contains
the number of expected output values, so the simulation is closed after all
@@ -140,6 +155,8 @@ def _run_rtlsim(sim, inp, num_out_values, trace_file=None):
if trace_file is not None:
sim.start_vcd_trace(trace_file)
+ if reset:
+ _reset_rtlsim(sim)
while not (output_observed):
sim.io.in0_V_V_0_tvalid = 1 if len(inputs) > 0 else 0
@@ -148,8 +165,7 @@ def _run_rtlsim(sim, inp, num_out_values, trace_file=None):
inputs = inputs[1:]
if sim.io.out_r_0_tvalid == 1 and sim.io.out_r_0_tready == 1:
outputs = outputs + [sim.io.out_r_0_tdata]
- sim.io.ap_clk_0 = 1
- sim.io.ap_clk_0 = 0
+ _toggle_clk(sim)
observation_count = observation_count + 1
no_change_count = no_change_count + 1
diff --git a/src/finn/core/throughput_test.py b/src/finn/core/throughput_test.py
index 8d3dabcf8af51327d5d951464c6d9b36e2f67497..4444e7584f843cd0edb016b520d01d71e659b904 100644
--- a/src/finn/core/throughput_test.py
+++ b/src/finn/core/throughput_test.py
@@ -28,6 +28,10 @@
import os
import subprocess
+import numpy as np
+
+from finn.util.basic import gen_finn_dt_tensor
+from finn.core.rtlsim_exec import rtlsim_exec
def throughput_test(model, batchsize=1000):
@@ -88,3 +92,50 @@ def throughput_test(model, batchsize=1000):
return res
except FileNotFoundError:
return None
+
+
+def throughput_test_rtlsim(model, batchsize=100):
+ """Runs a throughput test for the given IP-stitched model. When combined
+ with tracing, useful to determine bottlenecks and required FIFO sizes."""
+
+ assert (
+ model.get_metadata_prop("exec_mode") == "rtlsim"
+ ), """Top-level exec_mode
+ metadata_prop must be set to rtlsim"""
+
+ # create random input
+ iname = model.graph.input[0].name
+ ishape = model.get_tensor_shape(iname)
+ ishape_batch = ishape
+ ishape_batch[0] = batchsize
+ idt = model.get_tensor_datatype(iname)
+ dummy_input = gen_finn_dt_tensor(idt, ishape_batch)
+ # compute input/output sizes
+ oname = model.graph.output[0].name
+ oshape = model.get_tensor_shape(oname)
+ oshape_batch = oshape
+ oshape_batch[0] = batchsize
+ odt = model.get_tensor_datatype(oname)
+ i_bytes = (np.prod(ishape_batch) * idt.bitwidth()) / 8
+ o_bytes = (np.prod(oshape_batch) * odt.bitwidth()) / 8
+ # make empty exec context and insert input
+ ctx = model.make_empty_exec_context()
+ ctx[iname] = dummy_input
+ # remove liveness threshold, launch rtlsim
+ os.environ["LIVENESS_THRESHOLD"] = "-1"
+ rtlsim_exec(model, ctx)
+ # extract metrics
+ cycles = int(model.get_metadata_prop("sim_cycles"))
+ clk_ns = float(model.get_metadata_prop("clk_ns"))
+ fclk_mhz = 1 / (clk_ns * 0.001)
+ runtime_s = (cycles * clk_ns) * (10 ** -9)
+ res = dict()
+ res["cycles"] = cycles
+ res["runtime[ms]"] = runtime_s * 1000
+ res["throughput[images/s]"] = batchsize / runtime_s
+ res["DRAM_in_bandwidth[Mb/s]"] = i_bytes * 0.000001 / runtime_s
+ res["DRAM_out_bandwidth[Mb/s]"] = o_bytes * 0.000001 / runtime_s
+ res["fclk[mhz]"] = fclk_mhz
+ res["N"] = batchsize
+
+ return res
diff --git a/src/finn/custom_op/__init__.py b/src/finn/custom_op/__init__.py
index ab6e03bee65b8bf5c4041dd8021b1a561e7673d2..4ae7b9ebffaab6ca6be04b8d73f647b2db22dc78 100644
--- a/src/finn/custom_op/__init__.py
+++ b/src/finn/custom_op/__init__.py
@@ -56,8 +56,15 @@ class CustomOp(ABC):
ret = ret.decode("utf-8")
return ret
else:
- # not set, return default value
- return def_val
+ if req:
+ raise Exception(
+ """Required attribute %s unspecified in
+ a %s node"""
+ % (name, self.onnx_node.op_type)
+ )
+ else:
+ # not set, return default value
+ return def_val
except KeyError:
raise AttributeError("Op has no such attribute: " + name)
diff --git a/src/finn/custom_op/fpgadataflow/__init__.py b/src/finn/custom_op/fpgadataflow/__init__.py
index 17a55e519ed0440f68e295aecaab179e6adf632f..71c731f96ca45519c443a5f932ead050770e17de 100644
--- a/src/finn/custom_op/fpgadataflow/__init__.py
+++ b/src/finn/custom_op/fpgadataflow/__init__.py
@@ -88,6 +88,8 @@ class HLSCustomOp(CustomOp):
"res_hls": ("s", False, ""),
"res_synth": ("s", False, ""),
"rtlsim_so": ("s", False, ""),
+ # partitioning info
+ "partition_id": ("i", False, 0),
# input and output FIFO depths
"inFIFODepth": ("i", False, 2),
"outFIFODepth": ("i", False, 2),
@@ -171,9 +173,15 @@ class HLSCustomOp(CustomOp):
of the node as a dictionary."""
ret = dict()
ret["BRAM_18K"] = self.bram_estimation()
+ ret["BRAM_efficiency"] = self.bram_efficiency_estimation()
ret["LUT"] = self.lut_estimation()
return ret
+ def bram_efficiency_estimation(self):
+ """Function for BRAM efficiency estimation: actual parameter storage
+ needed divided by the allocated BRAM storage (from estimation)"""
+ return 1
+
def bram_estimation(self):
"""Function for BRAM resource estimation, is member function of
HLSCustomOp class but has to be filled by every node"""
diff --git a/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py b/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad68a4bde29123b2498ac7789048bcd2e13bf3bc
--- /dev/null
+++ b/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
@@ -0,0 +1,576 @@
+# 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.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 channelwise ops:
+# input 0 is the input tensor, shape (..., NumChannels)
+# input 1 is the channelwise parameter tensor, shape (NumChannels, params_per_channel)
+# output 0 is the output tensor, shape (..., NumChannels) - same as input
+# the ... here can be any shape (representing groups of vectors)
+
+
+class ChannelwiseOp_Batch(HLSCustomOp):
+ """Class that corresponds to finn-hls Thresholding_Batch function.
+ It can implement a variety of channel-wise parametrized operations,
+ including Add, Mul and multi-thresholding.
+ """
+
+ def __init__(self, onnx_node):
+ super().__init__(onnx_node)
+ self.decoupled_wrapper = templates.decoupled_wrapper
+
+ def get_nodeattr_types(self):
+ my_attrs = {
+ # channelwise "map" function to apply:
+ # one of cmp_le, cmp_ge, add, mul
+ "Func": ("s", False, "cmp_le"),
+ "PE": ("i", True, 0),
+ "NumChannels": ("i", True, 0),
+ # string defining memory resource type for parameters
+ "ram_style": ("s", False, "distributed"),
+ # FINN DataTypes for inputs, weights, outputs
+ "inputDataType": ("s", True, ""),
+ "paramDataType": ("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, the depth of the memory used
+ to store the channelwise op parameters."""
+ chn = self.get_nodeattr("NumChannels")
+ pe = self.get_nodeattr("PE")
+ return chn // 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 ChannelwiseOp layer"
+ # TODO: dynamically infer/update odt based on idt as done in ConvertToHLSLayers?
+ # 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("paramDataType")
+ 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_parameter_tensor(self, orig_param_vector):
+ """Convert the original numpy weight matrix orig_weight_matrix into
+ a form suitable for passing to the hlslib call:
+ * ensure chn % PE == 0
+ * interleave rows between PEs
+ * reshape into (PE, TMEM) and return
+ """
+ chn = self.get_nodeattr("NumChannels")
+ pe = self.get_nodeattr("PE")
+ tmem = chn // pe
+ assert chn % pe == 0, "Requirement NumChannels divisable by PE is violated."
+ assert (
+ orig_param_vector.ndim == 1
+ ), """Parameter vector dimension is {}.
+ Expected dimension: 1.""".format(
+ orig_param_vector.ndim
+ )
+
+ # if not self.get_input_datatype().signed():
+ # # ensure all thresholds are nonnegative
+ # assert (orig_param_vector >= 0).all()
+
+ # ensure all thresholds are integer
+ assert (orig_param_vector.astype(np.int32) == orig_param_vector).all()
+ ret = orig_param_vector
+
+ assert (
+ ret.shape[0] == chn
+ ), "Cardinality of parameter vector is not as expected (chn)"
+
+ # distribute rows between PEs
+ ret = ret.reshape(tmem, pe).transpose()
+ 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)"""
+
+ return ret.reshape(1, pe, tmem)
+
+ def generate_params(self, model, path):
+ code_gen_dir = path
+ # save thresholds in params.h
+ parameters = model.get_initializer(self.onnx_node.input[1])
+ parameter_tensor = self.get_hls_compatible_parameter_tensor(parameters)
+ pdt = DataType[self.get_nodeattr("paramDataType")]
+
+ parameters_hls_code = numpy_to_hls_code(
+ parameter_tensor, pdt, "parameters", False, True
+ )
+ # get input data type
+ export_idt = self.get_input_datatype()
+ if self.get_input_datatype() == DataType.BIPOLAR:
+ export_idt = DataType.BINARY
+ idt_hls = export_idt.get_hls_datatype_str()
+
+ # write parameters into params.h
+ f_params = open("{}/params.h".format(code_gen_dir), "w")
+ pdt_hls = pdt.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()
+ # get desired function
+ func = self.get_nodeattr("Func")
+ if func == "cmp_le":
+ func_str = "std::less_equal"
+ elif func == "cmp_ge":
+ func_str = "std::greater_equal"
+ elif func == "add":
+ func_str = "std::plus"
+ elif func == "mul":
+ func_str = "std::multiplies"
+ else:
+ raise Exception(
+ """Invalid value for attribute Func! Is currently set to: {}
+ has to be set to one of the following value
+ ("cmp_le", "cmp_ge", "add", "mul")""".format(
+ func
+ )
+ )
+ f_params.write(
+ "static ChannelWiseOperation<{},{},{},{},{},{}> threshs \
+ = ".format(
+ self.calc_tmem(),
+ self.get_nodeattr("PE"),
+ idt_hls,
+ pdt_hls,
+ odt_hls,
+ "%s<%s>" % (func_str, odt_hls),
+ )
+ )
+ f_params.write(parameters_hls_code)
+ f_params.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)
+ 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 ChannelwiseOp_Batch")
+ 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 "params.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?
+ 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["$DOCOMPUTE$"] = [
+ """Thresholding_Batch<{}, NumChannels1, PE1, {}, {}>
+ (in0, out, threshs, numReps);""".format(
+ 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 channelwise parameter tensor is acc_type [PE][TMEM][N_PARAMS_PER_CHANNEL]
+ # partition for parallel access along PE and N_PARAMS_PER_CHANNEL
+ # dimensions (dims 1 and 3)
+ self.code_gen_dict["$PRAGMAS$"].append(
+ (
+ "#pragma HLS ARRAY_PARTITION variable=threshs.parameters "
+ "complete dim=1"
+ )
+ )
+ # self.code_gen_dict["$PRAGMAS$"].append(
+ # (
+ # "#pragma HLS ARRAY_PARTITION variable=threshs.parameters "
+ # "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.parameters "
+ "core=ROM_2P_LUTRAM"
+ )
+ )
+ elif ram_style == "block":
+ self.code_gen_dict["$PRAGMAS$"].append(
+ (
+ "#pragma HLS RESOURCE variable=threshs.parameters "
+ "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/fpgadataflow/convolutioninputgenerator.py b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
index e4d106068d4d128c66b2ce5f3d6c925dfe414b90..3e40ad70208909551365c51324153859ccc79ceb 100644
--- a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
+++ b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
@@ -41,10 +41,19 @@ from finn.util.data_packing import npy_to_rtlsim_input, rtlsim_output_to_npy
# output 0 is the output tensor, shape NHWC:
# = (1, OFMDim, OFMDim, (ConvKernelDim^2)*IFMChannels)
+# note: the actual data layout produced by the hlslib kernels is different
+# for depthwise and non-depthwise ops.
+# * non-depthwise SWG: (1, OFMDim, OFMDim, K, K, IFMChannels/SIMD, SIMD)
+# * depthwise SWG: (1, OFMDim, OFMDim, IFMChannels/SIMD, K, K, SIMD)
+# see test_fpgadataflow_slidingwindow.py for an example of how to transform
+# between the two layouts
+
class ConvolutionInputGenerator(HLSCustomOp):
- """Class that corresponds to finn-hlslib ConvolutionInputGenerator
- (sliding window) function."""
+ """Class that corresponds to one of the finn-hlslib ConvolutionInputGenerator
+ (sliding window) function variants. Depending on the combination of
+ attributes (e.g. depthwise or not, whether k % stride is 0) a different
+ variant will be picked for the actual HLS implementation."""
def __init__(self, onnx_node):
super().__init__(onnx_node)
@@ -60,6 +69,7 @@ class ConvolutionInputGenerator(HLSCustomOp):
# FINN DataTypes for inputs, weights, outputs
"inputDataType": ("s", True, ""),
"outputDataType": ("s", True, ""),
+ "depthwise": ("i", False, 0),
# FPGA resource type for ConvolutionInputGenerator input buffer
# auto -- let Vivado HLS decide
# block -- use BRAM
@@ -106,7 +116,6 @@ class ConvolutionInputGenerator(HLSCustomOp):
pad = 0
ofm_dim = compute_conv_output_dim(ifm_dim, k, stride, pad)
assert ifm_ch % simd == 0, "SIMD must divide IFMChannels"
- assert k % stride == 0, "stride must divide kernel size k"
wf = int((k * k * ifm_ch) // simd)
folded_oshape = (1, ofm_dim, ofm_dim, wf, simd)
return folded_oshape
@@ -305,12 +314,35 @@ class ConvolutionInputGenerator(HLSCustomOp):
def docompute(self):
node = self.onnx_node
- self.code_gen_dict["$DOCOMPUTE$"] = [
- """{}<ConvKernelDim1, IFMChannels1, Input_precision1, IFMDim1,
- OFMDim1, SIMD1, Stride1> (in0, out, numReps);""".format(
- node.op_type
- )
- ]
+ ram_style = self.get_nodeattr("ram_style")
+ map_to_hls_ram_style = {
+ "auto": "ap_resource_dflt()",
+ "block": "ap_resource_bram()",
+ "distributed": "ap_resource_lutram()",
+ "ultra": "ap_resource_uram()",
+ }
+ hls_ram_style = map_to_hls_ram_style[ram_style]
+ hls_call = node.op_type
+ # check if non optimized ConvolutionInputGenerator is needed
+ k = self.get_nodeattr("ConvKernelDim")
+ stride = self.get_nodeattr("Stride")
+ if k % stride != 0:
+ hls_call += "_kernel_stride"
+
+ if self.get_nodeattr("depthwise") == 1:
+ self.code_gen_dict["$DOCOMPUTE$"] = [
+ """{}_dws<ConvKernelDim1, IFMChannels1, Input_precision1, IFMDim1,
+ OFMDim1, SIMD1, Stride1> (in0, out, numReps, {});""".format(
+ hls_call, hls_ram_style
+ )
+ ]
+ else:
+ self.code_gen_dict["$DOCOMPUTE$"] = [
+ """{}<ConvKernelDim1, IFMChannels1, Input_precision1, IFMDim1,
+ OFMDim1, SIMD1, Stride1> (in0, out, numReps, {});""".format(
+ hls_call, hls_ram_style
+ )
+ ]
def dataoutstrm(self):
code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
@@ -356,17 +388,3 @@ class ConvolutionInputGenerator(HLSCustomOp):
self.code_gen_dict["$PRAGMAS$"].append(
"#pragma HLS INTERFACE ap_ctrl_none port=return"
)
-
- def ipgen_extra_directives(self):
- # add directive to control input buffer memory resources
- ram_style = self.get_nodeattr("ram_style")
- map_to_hls_ram_style = {
- "auto": "RAM_2P",
- "block": "RAM_2P_BRAM",
- "distributed": "RAM_2P_LUTRAM",
- "ultra": "RAM_2P_URAM",
- }
- hls_ram_style = map_to_hls_ram_style[ram_style]
- directive = "set_directive_resource -core %s " % hls_ram_style
- directive += "ConvolutionInputGenerator inputBuf"
- return [directive]
diff --git a/src/finn/custom_op/fpgadataflow/downsampler.py b/src/finn/custom_op/fpgadataflow/downsampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ce4379a2c41baa5bc009e9df7623d133ee89a09
--- /dev/null
+++ b/src/finn/custom_op/fpgadataflow/downsampler.py
@@ -0,0 +1,297 @@
+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 DownSampler(HLSCustomOp):
+ """Corresponds to finn-hlslib ConvolutionInputGenerator_kernel1 function.
+ Basically performs a down sampling of the image removing rows and columns."""
+
+ def __init__(self, onnx_node):
+ super().__init__(onnx_node)
+
+ def get_nodeattr_types(self):
+ my_attrs = {
+ # spatial size of input images
+ "ImgDim": ("i", True, 0),
+ # number of channels in input image
+ "NumChannels": ("i", True, 0),
+ # Number of input columns computed in parallel
+ "SIMD": ("i", False, 1),
+ "Stride": ("i", True, 2),
+ # FINN input datatype
+ "inputDataType": ("s", True, ""),
+ # Batch size
+ "numInputVectors": ("i", False, 1),
+ }
+ my_attrs.update(super().get_nodeattr_types())
+ return my_attrs
+
+ def get_downsampled_odim(self):
+ "Return the down sampled spatial size of the output."
+ idim = self.get_nodeattr("ImgDim")
+ stride = self.get_nodeattr("Stride")
+ return int(np.floor((idim - 1) / stride) + 1)
+
+ def get_normal_input_shape(self):
+ idim = self.get_nodeattr("ImgDim")
+ num_ch = self.get_nodeattr("NumChannels")
+ batch = self.get_nodeattr("numInputVectors")
+ ishape = (batch, idim, idim, num_ch)
+ return ishape
+
+ def get_normal_output_shape(self):
+ odim = self.get_downsampled_odim()
+ num_ch = self.get_nodeattr("NumChannels")
+ batch = self.get_nodeattr("numInputVectors")
+ oshape = (batch, odim, odim, num_ch)
+ return oshape
+
+ def get_folded_input_shape(self):
+ normal_ishape = list(self.get_normal_input_shape())
+ ifm_ch = self.get_nodeattr("NumChannels")
+ simd = self.get_nodeattr("SIMD")
+ assert ifm_ch % simd == 0, "SIMD must divide input channels"
+ fold = int(normal_ishape[-1] / simd)
+ folded_ishape = normal_ishape[:-1] + [fold, simd]
+ return tuple(folded_ishape)
+
+ def get_folded_output_shape(self):
+ normal_oshape = list(self.get_normal_output_shape())
+ ifm_ch = self.get_nodeattr("NumChannels")
+ simd = self.get_nodeattr("SIMD")
+ assert ifm_ch % simd == 0, "SIMD must divide input channels"
+ fold = int(normal_oshape[-1] / simd)
+ folded_oshape = normal_oshape[:-1] + [fold, simd]
+ return tuple(folded_oshape)
+
+ 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 DownSampler."
+ # 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 DownSampler"
+ 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")]
+ 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()
+ simd = self.get_nodeattr("SIMD")
+ return ibits * simd
+
+ def get_outstream_width(self):
+ obits = self.get_output_datatype().bitwidth()
+ simd = self.get_nodeattr("SIMD")
+ return obits * simd
+
+ 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 "slidingwindow.h"']
+
+ def defines(self, var):
+ self.code_gen_dict["$DEFINES$"] = []
+
+ ifm_ch = self.get_nodeattr("NumChannels")
+ self.code_gen_dict["$DEFINES$"] += ["#define IFMChannels {}".format(ifm_ch)]
+
+ ibits = self.get_input_datatype().bitwidth()
+ self.code_gen_dict["$DEFINES$"] += ["#define Input_precision {}".format(ibits)]
+
+ idim = self.get_nodeattr("ImgDim")
+ self.code_gen_dict["$DEFINES$"] += ["#define IFMDim {}".format(idim)]
+
+ simd = self.get_nodeattr("SIMD")
+ self.code_gen_dict["$DEFINES$"] += ["#define SIMD {}".format(simd)]
+
+ stride = self.get_nodeattr("Stride")
+ self.code_gen_dict["$DEFINES$"] += ["#define Stride {}".format(stride)]
+
+ batch_size = self.get_nodeattr("numInputVectors")
+ self.code_gen_dict["$DEFINES$"] += ["#define numReps {}".format(batch_size)]
+
+ 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):
+ self.code_gen_dict["$DOCOMPUTE$"] = [
+ """ConvolutionInputGenerator_kernel1<IFMChannels, Input_precision,
+ IFMDim, SIMD,Stride> (in0, out, numReps);"""
+ ]
+
+ 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_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 (numInputVectors, ImgDim, ImgDim, NumChannels)."""
+ export_idt = self.get_input_datatype()
+
+ reshaped_input = inp.reshape(folded_ishape)
+ 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_output(context)
+ assert (
+ context[node.output[0]].shape == folded_oshape
+ ), "cppsim did not produce expected folded output 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/sameresize_batch.py b/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
similarity index 76%
rename from src/finn/custom_op/fpgadataflow/sameresize_batch.py
rename to src/finn/custom_op/fpgadataflow/fmpadding_batch.py
index c459cac1e9c17336200a1fc85aad2af5e14e2c61..d326ae7dfc7830a0081c3b13233d67ef08b12eff 100644
--- a/src/finn/custom_op/fpgadataflow/sameresize_batch.py
+++ b/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
@@ -6,27 +6,42 @@ 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."""
+class FMPadding_Batch(HLSCustomOp):
+ """Corresponds to finn-hlslib FMPadding_Batch function.
+ Pads input image by given amount."""
def __init__(self, onnx_node):
super().__init__(onnx_node)
def get_nodeattr_types(self):
my_attrs = {
+ # spatial size of input images
"ImgDim": ("i", True, 0),
- "KernelDim": ("i", True, 0),
- "Stride": ("i", True, 0),
+ # total padding (per dimension) to apply
+ "Padding": ("i", True, 2),
+ # number of channels in input image
"NumChannels": ("i", True, 0),
+ # SIMD Input parallelism
+ "SIMD": ("i", False, 1),
# FINN input datatype
"inputDataType": ("s", True, ""),
- # distribution of added values to achieve "same" padding
- "PaddingStyle": ("i", True, 2),
+ # controls distribution of padded pixels
+ # in case of uneven padding -- see FMPadding fxn
+ # in hlslib
+ "PaddingStyle": ("i", False, 2),
+ # shape describing input vecs per execution
+ "numInputVectors": ("i", False, 1),
}
my_attrs.update(super().get_nodeattr_types())
return my_attrs
+ def get_padded_odim(self):
+ "Return the padded spatial size of the output."
+
+ idim = self.get_nodeattr("ImgDim")
+ pad = self.get_nodeattr("Padding")
+ return idim + pad
+
def get_normal_input_shape(self):
idim = self.get_nodeattr("ImgDim")
num_ch = self.get_nodeattr("NumChannels")
@@ -35,33 +50,29 @@ class SameResize_Batch(HLSCustomOp):
return ishape
def get_normal_output_shape(self):
- idim = self.get_nodeattr("ImgDim")
+ odim = self.get_padded_odim()
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)
+ normal_ishape = list(self.get_normal_input_shape())
+ ifm_ch = self.get_nodeattr("NumChannels")
+ simd = self.get_nodeattr("SIMD")
+ assert ifm_ch % simd == 0, "SIMD must divide input channels"
+ fold = int(normal_ishape[-1] / simd)
+ folded_ishape = normal_ishape[:-1] + [fold, simd]
+ return tuple(folded_ishape)
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)
+ normal_oshape = list(self.get_normal_output_shape())
+ ifm_ch = self.get_nodeattr("NumChannels")
+ simd = self.get_nodeattr("SIMD")
+ assert ifm_ch % simd == 0, "SIMD must divide input channels"
+ fold = int(normal_oshape[-1] / simd)
+ folded_oshape = normal_oshape[:-1] + [fold, simd]
+ return tuple(folded_oshape)
def make_shape_compatible_op(self, model):
exp_ishape = self.get_normal_input_shape()
@@ -87,7 +98,7 @@ class SameResize_Batch(HLSCustomOp):
# 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"
+ assert dtype == exp_idtype, "Unexpected datatype for FMPadding_Batch"
model.set_tensor_datatype(node.output[0], dtype)
def verify_node(self):
@@ -96,9 +107,9 @@ class SameResize_Batch(HLSCustomOp):
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"
+ # the hlslib op always pads with zeros, so ensure that the DataType
+ # is able to represent zeros
+ assert ret.allowed(0), "FMPadding_Batch DataType must support zero"
return ret
def get_output_datatype(self):
@@ -107,15 +118,13 @@ class SameResize_Batch(HLSCustomOp):
def get_instream_width(self):
ibits = self.get_input_datatype().bitwidth()
- num_ch = self.get_nodeattr("NumChannels")
-
- return ibits * num_ch
+ simd = self.get_nodeattr("SIMD")
+ return ibits * simd
def get_outstream_width(self):
obits = self.get_output_datatype().bitwidth()
- num_ch = self.get_nodeattr("NumChannels")
-
- return obits * num_ch
+ simd = self.get_nodeattr("SIMD")
+ return obits * simd
def get_number_output_values(self):
folded_oshape = self.get_folded_output_shape()
@@ -125,18 +134,18 @@ class SameResize_Batch(HLSCustomOp):
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(
+ """#define ImgDim1 {}\n#define OutputDim1 {}\n
+ #define Padding1 {}\n#define NumChannels1 {}\n
+ #define PaddingStyle1 {}\n#define numReps {}
+ #define SIMD1 {}\n""".format(
self.get_nodeattr("ImgDim"),
- self.get_nodeattr("KernelDim"),
- self.get_nodeattr("Stride"),
+ self.get_padded_odim(),
+ self.get_nodeattr("Padding"),
self.get_nodeattr("NumChannels"),
self.get_nodeattr("PaddingStyle"),
- numReps,
+ self.get_nodeattr("numInputVectors"),
+ self.get_nodeattr("SIMD"),
)
]
@@ -171,8 +180,8 @@ class SameResize_Batch(HLSCustomOp):
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(
+ """{}<ImgDim1, OutputDim1, Padding1, NumChannels1,SIMD1,
+ {}, PaddingStyle1> (in0, out, numReps);""".format(
node.op_type, in_t
)
]
@@ -227,6 +236,7 @@ class SameResize_Batch(HLSCustomOp):
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":
@@ -249,10 +259,8 @@ class SameResize_Batch(HLSCustomOp):
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)
+ reshaped_input = inp.reshape(folded_ishape)
+ np.save(os.path.join(code_gen_dir, "input_0.npy"), reshaped_input)
if mode == "cppsim":
# execute the precompiled model
@@ -261,8 +269,7 @@ class SameResize_Batch(HLSCustomOp):
super().npy_to_dynamic_output(context)
assert (
context[node.output[0]].shape == folded_oshape
- ), "cppsim \
- did not produce expected ofolded utput shape"
+ ), "cppsim did not produce expected folded output shape"
context[node.output[0]] = context[node.output[0]].reshape(*exp_oshape)
elif mode == "rtlsim":
sim = self.get_rtlsim()
diff --git a/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py b/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
index 9e6c63dc510aab5f6baff9cb6326a2d0476f67a9..83152dea6cc494b8464c78605399b21b38d48b80 100644
--- a/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
@@ -75,16 +75,19 @@ class GlobalAccPool_Batch(HLSCustomOp):
def get_normal_output_shape(self):
ch = self.get_nodeattr("NumChannels")
vecs = list(self.get_nodeattr("numInputVectors"))
- oshape = tuple([vecs[0]] + [ch])
+ if len(vecs) == 1:
+ oshape = tuple(vecs + [ch])
+ elif len(vecs) == 3:
+ oshape = tuple([vecs[0]] + [1, 1, ch])
return oshape
def get_folded_output_shape(self):
ch = self.get_nodeattr("NumChannels")
pe = self.get_nodeattr("PE")
- vecs = list(self.get_nodeattr("numInputVectors"))
+ unfolded_shape = list(self.get_normal_output_shape())
assert ch % pe == 0, "PE must divide NumChannels"
folds = int(ch / pe)
- oshape = tuple([vecs[0]] + [folds, pe])
+ oshape = tuple(unfolded_shape[:-1] + [folds, pe])
return oshape
def make_shape_compatible_op(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/iodma.py b/src/finn/custom_op/fpgadataflow/iodma.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b718ecbbc490610790b68871080de23a54f4891
--- /dev/null
+++ b/src/finn/custom_op/fpgadataflow/iodma.py
@@ -0,0 +1,346 @@
+# Copyright (c) 2020, Xilinx
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of FINN nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import numpy as np
+import math
+from onnx import TensorProto, helper
+from finn.core.datatype import DataType
+from finn.custom_op.fpgadataflow import HLSCustomOp
+
+
+# the IODMA inerfaces a memory-mapped AXI interface and an AXI stream
+# direction "in": pulls data from AXI-MM to AXI stream
+# direction "out": pushes data from AXI stream to AXI-MM
+
+# DMA Addressing
+# - burst mode can be "wrap" or "increment"
+# - "increment" bursts will increment the address when moving to the next image
+# - "wrap" bursts will reinitialize the address to the start address,
+# and are useful for e.g. streaming weights, where the same buffer is
+# repeatedly read into the FPGA
+# - no additional alignment restrictions beyond anything specified in the AXI spec
+
+# Interfaces
+# - AXI-MM name specified by intfName unless this is set to "" (empty, the default)
+# in which case output AXI-MM are named "out" and input AXI-MM are named "in0"
+# - AXI-MM interface width (in bits) is specified by intfWidth
+# - AXI-Stream interface width (in bits) is specified by streamWidth
+# - If inftWidth and streamWidth are not equal, the DMA core performs
+# width conversion by going up to the least common multiple of bitwidths
+# e.g. intfWidth=32b -> 96b -> sreamWidth=24b
+# - transfers occur in multiples of the AXI-MM interface width, therefore
+# the total number of bits in the tensor must be a multiple of intfWidth
+# - transfers occur in multiples of the AXI-Stream interface width, therefore
+# the total number of bits in the tensor must be a multiple of streamWidth
+# - both interface widths must be a multiple of 8b (AXI protocol requirement)
+# - in most systems, intfWidth is also restricted to a power of 2 (e.g. Vitis)
+# but this is not universal so we don't check here explicitly
+
+# Input/output tensor sizes shapes
+# - The data being moved is a tensor of shape numInputVectors+[NumChannels]
+# - The data type of the tensor elements is specified by dataType
+# - on the stream side
+# -the normal shape is the same as the ONNX tensor attached to it
+# -the folded shape is computed from the stream width and normal shape
+# - on the AXI-MM side
+# -the normal shape is the same as the one on the stream side
+# -the folded shape is not defined
+
+
+class IODMA(HLSCustomOp):
+ """Class that corresponds to finn-hlslib DMA function(s)."""
+
+ def __init__(self, onnx_node):
+ super().__init__(onnx_node)
+
+ def get_nodeattr_types(self):
+ my_attrs = {
+ "NumChannels": ("i", True, 0),
+ # FINN input datatype
+ "dataType": ("s", True, ""),
+ # Stream parameters
+ "streamWidth": ("i", False, 32),
+ # DMA-specific parameters
+ "intfWidth": ("i", False, 32),
+ "burstMode": ("s", False, "increment"),
+ "direction": ("s", False, "in"),
+ # shape describing input vecs per execution
+ "numInputVectors": ("ints", False, [1]),
+ # name of axi-mm interface
+ "intfName": ("s", False, ""),
+ }
+ my_attrs.update(super().get_nodeattr_types())
+ return my_attrs
+
+ def get_normal_input_shape(self):
+ vecs = list(self.get_nodeattr("numInputVectors"))
+ num_ch = self.get_nodeattr("NumChannels")
+ ishape = tuple(vecs + [num_ch])
+ return ishape
+
+ def get_normal_output_shape(self):
+ return self.get_normal_input_shape()
+
+ def get_folded_input_shape(self):
+ if self.get_nodeattr("direction") == "in":
+ raise ValueError("Folded input shape not defined for input IODMA")
+ else:
+ shape = list(self.get_normal_input_shape())
+ itype_bits = self.get_input_datatype().bitwidth()
+ intfw = self.get_nodeattr("streamWidth")
+ assert (
+ intfw % itype_bits == 0
+ ), "Input stream width must be a multiple of datatype bits"
+ elems_per_word = intfw // itype_bits
+ assert shape[-1] % elems_per_word == 0, "Fold depth must be integer"
+ fold_depth = shape[-1] // elems_per_word
+ shape[-1] = fold_depth
+ shape.append(elems_per_word)
+ return tuple(shape)
+
+ def get_folded_output_shape(self):
+ if self.get_nodeattr("direction") == "out":
+ raise ValueError("Folded output shape not defined for output IODMA")
+ else:
+ shape = list(self.get_normal_output_shape())
+ itype_bits = self.get_output_datatype().bitwidth()
+ intfw = self.get_nodeattr("streamWidth")
+ assert (
+ intfw % itype_bits == 0
+ ), "Input stream width must be a multiple of datatype bits"
+ elems_per_word = intfw // itype_bits
+ assert shape[-1] % elems_per_word == 0, "Fold depth must be integer"
+ fold_depth = shape[-1] // elems_per_word
+ shape[-1] = fold_depth
+ shape.append(elems_per_word)
+ return tuple(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)
+ 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."
+ model.set_tensor_datatype(node.output[0], dtype)
+
+ def verify_node(self):
+ pass
+
+ def get_input_datatype(self):
+ """Returns FINN DataType of input."""
+ return DataType[self.get_nodeattr("dataType")]
+
+ def get_output_datatype(self):
+ """Returns FINN DataType of output. (Same as input datatype)"""
+ return self.get_input_datatype()
+
+ def get_instream_width(self):
+ if self.get_nodeattr("direction") == "in":
+ return self.get_nodeattr("intfWidth")
+ elif self.get_nodeattr("direction") == "out":
+ return self.get_nodeattr("streamWidth")
+ else:
+ raise ValueError("Invalid IODMA direction, please set to in or out")
+
+ def get_outstream_width(self):
+ if self.get_nodeattr("direction") == "out":
+ return self.get_nodeattr("intfWidth")
+ elif self.get_nodeattr("direction") == "in":
+ return self.get_nodeattr("streamWidth")
+ else:
+ raise ValueError("Invalid IODMA direction, please set to in or out")
+
+ def get_number_output_values(self):
+ oshape = self.get_normal_output_shape()
+ itype_bits = self.get_input_datatype().bitwidth()
+ intfw = self.get_nodeattr("intfWidth")
+ nelems = np.prod(oshape)
+ nbits = nelems * itype_bits
+ assert nbits % intfw == 0, "DMA: total transfer size must be word multiple"
+ ovalues = nbits // intfw
+ return ovalues
+
+ def global_includes(self):
+ self.code_gen_dict["$GLOBALS$"] = ['#include "dma.h"']
+ self.code_gen_dict["$GLOBALS$"].append('#include "streamtools.h"')
+
+ def defines(self, var):
+ itype_bits = self.get_input_datatype().bitwidth()
+ total_bits = itype_bits * np.prod(self.get_normal_input_shape())
+ assert total_bits % 8 == 0, "DMA input not a multiple of 1 Byte"
+ total_bytes = total_bits // 8
+ self.code_gen_dict["$DEFINES$"] = [
+ """#define NumBytes1 {}\n#define DataWidth1 {}\n""".format(
+ total_bytes, self.get_nodeattr("intfWidth")
+ )
+ ]
+
+ def get_ap_int_max_w(self):
+ "Return the maximum width of any ap_int used in this module."
+ instream = self.get_instream_width()
+ outstream = self.get_outstream_width()
+ width_lcm = (instream * outstream) // math.gcd(instream, outstream)
+ return width_lcm
+
+ def docompute(self):
+ direction = self.get_nodeattr("direction")
+ mode = self.get_nodeattr("burstMode")
+ if direction == "in":
+ if mode == "wrap":
+ func = "Mem2Stream_Batch_external_wmem"
+ else:
+ func = "Mem2Stream_Batch"
+ dwc_func = "WidthAdjustedOutputStream"
+ elif direction == "out":
+ func = "Stream2Mem_Batch"
+ dwc_func = "WidthAdjustedInputStream"
+ else:
+ raise ValueError("Invalid IODMA direction, please set to in or out")
+ # define templates for instantiation
+ dma_inst_template = func + "<DataWidth1, NumBytes1>(%s, %s, numReps);"
+ dwc_inst_template = dwc_func + "<%d, %d, %d> %s(%s, numReps);"
+ # do stream infrastructure and instantiations
+ intfw = self.get_nodeattr("intfWidth")
+ strmw = self.get_nodeattr("streamWidth")
+ width_lcm = (strmw * intfw) // math.gcd(strmw, intfw)
+ # we always need two streams: one of width_lcm, and one of intfw width
+ # because we use WidthAdjustedInputStream,
+ dtype_bits = self.get_input_datatype().bitwidth()
+ total_bits = dtype_bits * np.prod(self.get_normal_input_shape())
+ if direction == "in":
+ self.code_gen_dict["$DOCOMPUTE$"] = [
+ dwc_inst_template
+ % (width_lcm, strmw, total_bits // width_lcm, "dwc_lcm", "out"),
+ dwc_inst_template
+ % (intfw, width_lcm, total_bits // intfw, "dwc_intfw", "dwc_lcm"),
+ dma_inst_template % ("in0", "dwc_intfw"),
+ ]
+ else:
+ self.code_gen_dict["$DOCOMPUTE$"] = [
+ dwc_inst_template
+ % (strmw, width_lcm, total_bits // strmw, "dwc_lcm", "in0"),
+ dwc_inst_template
+ % (width_lcm, intfw, total_bits // width_lcm, "dwc_intfw", "dwc_lcm"),
+ dma_inst_template % ("dwc_intfw", "out"),
+ ]
+
+ def blackboxfunction(self):
+ packed_ibits = self.get_instream_width()
+ packed_hls_type_in = "ap_uint<%d>" % packed_ibits
+ packed_obits = self.get_outstream_width()
+ packed_hls_type_out = "ap_uint<%d>" % packed_obits
+ direction = self.get_nodeattr("direction")
+ if direction == "in":
+ self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
+ "void %s(%s *in0, hls::stream<%s > &out, unsigned int numReps)"
+ % (self.onnx_node.name, packed_hls_type_in, packed_hls_type_out)
+ ]
+ elif direction == "out":
+ self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
+ "void %s(hls::stream<%s > &in0, %s *out, unsigned int numReps)"
+ % (self.onnx_node.name, packed_hls_type_in, packed_hls_type_out)
+ ]
+ else:
+ raise ValueError("Invalid IODMA direction, please set to in or out")
+
+ def pragmas(self):
+ self.code_gen_dict["$PRAGMAS$"] = [
+ "#pragma HLS INTERFACE s_axilite port=numReps bundle=control"
+ ]
+ self.code_gen_dict["$PRAGMAS$"].append(
+ "#pragma HLS INTERFACE s_axilite port=return bundle=control"
+ )
+ direction = self.get_nodeattr("direction")
+ intfname = self.get_nodeattr("intfName")
+ if direction == "in":
+ if intfname == "":
+ self.code_gen_dict["$PRAGMAS$"].append(
+ "#pragma HLS INTERFACE m_axi offset=slave port=in0"
+ )
+ else:
+ self.code_gen_dict["$PRAGMAS$"].append(
+ "#pragma HLS INTERFACE m_axi offset=slave port=%s" % (intfname)
+ )
+ self.code_gen_dict["$PRAGMAS$"].append(
+ "#pragma HLS INTERFACE s_axilite port=in0 bundle=control"
+ )
+ self.code_gen_dict["$PRAGMAS$"].append(
+ "#pragma HLS INTERFACE axis port=out"
+ )
+ elif direction == "out":
+ self.code_gen_dict["$PRAGMAS$"].append(
+ "#pragma HLS INTERFACE axis port=in0"
+ )
+ if intfname == "":
+ self.code_gen_dict["$PRAGMAS$"].append(
+ "#pragma HLS INTERFACE m_axi offset=slave port=out"
+ )
+ else:
+ self.code_gen_dict["$PRAGMAS$"].append(
+ "#pragma HLS INTERFACE m_axi offset=slave port=%s" % (intfname)
+ )
+ self.code_gen_dict["$PRAGMAS$"].append(
+ "#pragma HLS INTERFACE s_axilite port=out bundle=control"
+ )
+ else:
+ raise ValueError("Invalid IODMA direction, please set to in or out")
+ self.code_gen_dict["$PRAGMAS$"].append("#pragma HLS DATAFLOW")
+
+ def execute_node(self, context, graph):
+ pass
+
+ def dataoutstrm(self):
+ pass
+
+ def read_npy_data(self):
+ pass
+
+ def save_as_npy(self):
+ pass
+
+ def strm_decl(self):
+ pass
diff --git a/src/finn/custom_op/fpgadataflow/labelselect_batch.py b/src/finn/custom_op/fpgadataflow/labelselect_batch.py
index 7591f09d8d0cd1847672fe5aa09616ff1571033d..f61fbf12da889700297006ef2566088d4150c0e4 100644
--- a/src/finn/custom_op/fpgadataflow/labelselect_batch.py
+++ b/src/finn/custom_op/fpgadataflow/labelselect_batch.py
@@ -41,6 +41,13 @@ class LabelSelect_Batch(HLSCustomOp):
def __init__(self, onnx_node):
super().__init__(onnx_node)
+ odt_name = self.get_nodeattr("outputDataType")
+ if odt_name == "":
+ # If not provided compute min size
+ labels = self.get_nodeattr("Labels")
+ odt = DataType.get_smallest_possible(labels - 1)
+ odt_name = odt.name
+ self.set_nodeattr("outputDataType", odt_name)
def get_nodeattr_types(self):
my_attrs = {
@@ -49,6 +56,7 @@ class LabelSelect_Batch(HLSCustomOp):
"K": ("i", True, 0),
# FINN DataTypes for input
"inputDataType": ("s", True, ""),
+ "outputDataType": ("s", False, ""),
# 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)
@@ -69,7 +77,6 @@ class LabelSelect_Batch(HLSCustomOp):
pe = self.get_nodeattr("PE")
vecs = list(self.get_nodeattr("numInputVectors"))
assert nlabels % pe == 0, "PE must divide Labels"
- assert pe == 1, "LabelSelect currently fails with folding"
folds = int(nlabels / pe)
folded_ishape = tuple(vecs + [folds, pe])
return folded_ishape
@@ -90,7 +97,7 @@ class LabelSelect_Batch(HLSCustomOp):
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."
+ assert ishape == exp_ishape, "Unexpected input shape."
# implement tensor with correct shape
values = np.random.randn(*oshape).astype(np.int64)
return helper.make_node(
@@ -106,9 +113,8 @@ class LabelSelect_Batch(HLSCustomOp):
)
def infer_node_datatype(self, model):
- # currently set to uint32 to be compatible with hlslib
- # enhancement: consider finding smallest power-of-two int for reduced output bandwidth
- model.set_tensor_datatype(self.onnx_node.output[0], DataType.UINT32)
+ odt = self.get_output_datatype()
+ model.set_tensor_datatype(self.onnx_node.output[0], odt)
def verify_node(self):
info_messages = []
@@ -134,6 +140,7 @@ class LabelSelect_Batch(HLSCustomOp):
self.get_nodeattr("PE")
self.get_nodeattr("K")
self.get_nodeattr("inputDataType")
+ self.get_nodeattr("outputDataType")
info_messages.append("All necessary attributes exist")
except Exception:
info_messages.append(
@@ -150,12 +157,12 @@ class LabelSelect_Batch(HLSCustomOp):
def get_input_datatype(self):
"""Returns FINN DataType of input."""
ret = DataType[self.get_nodeattr("inputDataType")]
- assert ret.signed() is False, "LabelSelect is currently broken for signed inputs"
return ret
def get_output_datatype(self):
"""Returns FINN DataType of output."""
- return DataType.UINT32
+ ret = DataType[self.get_nodeattr("outputDataType")]
+ return ret
def get_instream_width(self):
"""Returns input stream width."""
@@ -260,8 +267,13 @@ class LabelSelect_Batch(HLSCustomOp):
npy_type = "float"
npy_in = "%s/input_0.npy" % code_gen_dir
self.code_gen_dict["$READNPYDATA$"] = []
+
+ # Calling npy2apintstream with reverse_inner = false to have LE packing
+ # as required by HLS fxn LabelSelect_Batch
+ # Also notice that StreamingDataWidthConverter_Batch performs LE packing
+
self.code_gen_dict["$READNPYDATA$"].append(
- 'npy2apintstream<%s, %s, %d, %s>("%s", in0);'
+ 'npy2apintstream<%s, %s, %d, %s>("%s", in0,false);'
% (packed_hls_type, elem_hls_type, elem_bits, npy_type, npy_in)
)
@@ -277,12 +289,13 @@ class LabelSelect_Batch(HLSCustomOp):
def docompute(self):
node = self.onnx_node
self.code_gen_dict["$DOCOMPUTE$"] = [
- """{}<{}, {}, {}, {}, ap_uint<32>> (in0, out, 1);""".format(
+ """{}<{}, {}, {}, {}, {} > (in0, out, 1);""".format(
node.op_type,
self.get_nodeattr("Labels"),
self.get_nodeattr("PE"),
self.get_nodeattr("K"),
self.get_input_datatype().get_hls_datatype_str(),
+ self.get_output_datatype().get_hls_datatype_str(),
)
]
@@ -316,10 +329,11 @@ class LabelSelect_Batch(HLSCustomOp):
def blackboxfunction(self):
self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
"""void {}(hls::stream<ap_uint<{}*{}>> &in0,
- hls::stream<ap_uint<32>> &out)""".format(
+ hls::stream<ap_uint<{}> > &out)""".format(
self.onnx_node.name,
self.get_nodeattr("PE"),
self.get_input_datatype().bitwidth(),
+ self.get_output_datatype().bitwidth(),
)
]
diff --git a/src/finn/custom_op/fpgadataflow/pool_batch.py b/src/finn/custom_op/fpgadataflow/pool_batch.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7edc24d0e24eef1154293caca2519ab3aa68358
--- /dev/null
+++ b/src/finn/custom_op/fpgadataflow/pool_batch.py
@@ -0,0 +1,395 @@
+# 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.custom_op.fpgadataflow import HLSCustomOp
+from finn.core.datatype import DataType
+from onnx import TensorProto, helper
+from finn.util.data_packing import npy_to_rtlsim_input, rtlsim_output_to_npy
+
+
+class Pool_Batch(HLSCustomOp):
+ """Class that corresponds to finn-hlslib Pool_batch function.
+ Requires ConvolutionInputGenerator(depthwise == 1) to format its input
+
+ TODO: explain input shape (to reuse im2col code)
+ Input shape (BatchSize,OutImgDim,OutImgDim,KernelSize^2*Channels)
+ Output shape (BatchSize,OutImgDim,OutImgDim,Channels)
+
+ # note: the actual data layout produced by the hlslib kernels is different
+ # for depthwise ops.
+ # * depthwise SWG: (1, OFMDim, OFMDim, IFMChannels/PE, K, K, PE)
+
+ Channels can be folded using PE (SIMD from the input perspective)
+ TODO: doc
+ """
+
+ def get_nodeattr_types(self):
+ my_attrs = {
+ "Channels": ("i", True, 0),
+ "PE": ("i", True, 1),
+ "KernelSize": ("i", True, 0),
+ # Function:
+ # - MaxPool
+ # - AvgPool (not yet supported, but HLSLIB does)
+ # - AccPool (not yet supported, but HLSLIB does)
+ "Function": ("s", True, ""),
+ "OutImgDim": ("i", True, 0),
+ # FINN DataTypes for inputs/outputs
+ "dataType": ("s", True, ""),
+ "BatchSize": ("i", False, 1),
+ }
+
+ my_attrs.update(super().get_nodeattr_types())
+ return my_attrs
+
+ def get_input_datatype(self):
+ """Returns FINN DataType of input."""
+ return DataType[self.get_nodeattr("dataType")]
+
+ def get_output_datatype(self):
+ """Returns FINN DataType of output."""
+ fxn = self.get_nodeattr("Function")
+ if fxn == "MaxPool":
+ # Same as input
+ return DataType[self.get_nodeattr("dataType")]
+ else:
+ raise Exception("Pool_Batch doesn't currently support " + fxn)
+
+ def get_normal_input_shape(self):
+ ifm_ch = self.get_nodeattr("Channels")
+ odim = self.get_nodeattr("OutImgDim")
+ batch_size = self.get_nodeattr("BatchSize")
+ k = self.get_nodeattr("KernelSize")
+ ishape = (batch_size, odim, odim, k * k * ifm_ch)
+ return ishape
+
+ def get_folded_input_shape(self):
+ normal_ishape = list(self.get_normal_input_shape())
+ ifm_ch = self.get_nodeattr("Channels")
+ pe = self.get_nodeattr("PE")
+ assert ifm_ch % pe == 0, "PE must divide input channels"
+ fold = int(normal_ishape[-1] / pe)
+ folded_ishape = normal_ishape[:-1] + [fold, pe]
+ return tuple(folded_ishape)
+
+ def get_normal_output_shape(self):
+ ofm_ch = self.get_nodeattr("Channels")
+ odim = self.get_nodeattr("OutImgDim")
+ batch_size = self.get_nodeattr("BatchSize")
+ oshape = (batch_size, odim, odim, ofm_ch)
+ return oshape
+
+ def get_folded_output_shape(self):
+ normal_oshape = list(self.get_normal_output_shape())
+ ifm_ch = self.get_nodeattr("Channels")
+ pe = self.get_nodeattr("PE")
+ assert ifm_ch % pe == 0, "PE must divide input channels"
+ fold = int(ifm_ch / pe)
+ folded_oshape = normal_oshape[:-1] + [fold, pe]
+ return tuple(folded_oshape)
+
+ def get_number_output_values(self):
+ folded_oshape = self.get_folded_output_shape()
+ return np.prod(folded_oshape[1:-1])
+
+ def get_instream_width(self):
+ dt_bits = self.get_input_datatype().bitwidth()
+ pe = self.get_nodeattr("PE")
+ # ofm_ch = self.get_nodeattr("Channels")
+ # k = self.get_nodeattr("KernelSize")
+ # assert ifm_ch % pe == 0, "PE must divide input channels"
+ # simd = int(ifm_ch/pe)
+ in_width = int(dt_bits * pe)
+ return in_width
+
+ def get_outstream_width(self):
+ fxn = self.get_nodeattr("Function")
+ if fxn == "MaxPool":
+ return self.get_instream_width()
+ else:
+ raise Exception("Pool_Batch doesn't currently support " + fxn)
+
+ 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 for Pool_Batch."
+ # 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 = self.get_output_datatype()
+ model.set_tensor_datatype(node.output[0], dtype)
+
+ 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 the number of inputs
+ if len(self.onnx_node.input) == 1:
+ info_messages.append("The number of inputs is correct")
+ else:
+ info_messages.append("""Pool_Batch needs 1 data input""")
+
+ # check supported function
+ fnx = self.get_nodeattr("Function")
+ if fnx == "MaxPool":
+ info_messages.append(
+ "Attribute Function contains a supported pool function"
+ )
+ else:
+ info_messages.append(
+ "Attribute Function contains an unsupported pool function"
+ )
+ return info_messages
+
+ def global_includes(self):
+ self.code_gen_dict["$GLOBALS$"] = ['#include "maxpool.h"']
+ self.code_gen_dict["$GLOBALS$"] += ['#include "pool.hpp"']
+
+ def defines(self, var):
+ self.code_gen_dict["$DEFINES$"] = []
+
+ ifm_ch = self.get_nodeattr("Channels")
+ self.code_gen_dict["$DEFINES$"] += ["#define Channels {}".format(ifm_ch)]
+
+ pe = self.get_nodeattr("PE")
+ self.code_gen_dict["$DEFINES$"] += ["#define PE {}".format(pe)]
+
+ k = self.get_nodeattr("KernelSize")
+ self.code_gen_dict["$DEFINES$"] += ["#define KernelSize {}".format(k)]
+
+ odim = self.get_nodeattr("OutImgDim")
+ self.code_gen_dict["$DEFINES$"] += ["#define OFMDim {}".format(odim)]
+
+ numReps = self.get_nodeattr("BatchSize")
+ self.code_gen_dict["$DEFINES$"] += ["#define numReps {}".format(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,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):
+ idt = self.get_input_datatype()
+ i_hls_dt = idt.get_hls_datatype_str()
+ odt = self.get_output_datatype()
+ o_hls_dt = odt.get_hls_datatype_str()
+
+ self.code_gen_dict["$DOCOMPUTE$"] = []
+
+ fxn = self.get_nodeattr("Function")
+ if fxn == "MaxPool":
+ self.code_gen_dict["$DOCOMPUTE$"] += [
+ "MaxPoolFunction<{},KernelSize> pool_fxn;".format(i_hls_dt)
+ ]
+ else:
+ raise Exception("Pool_Batch doesn't currently support " + fxn)
+
+ self.code_gen_dict["$DOCOMPUTE$"] += [
+ """Pool_batch<Channels, PE, KernelSize,Slice<{} >, Slice< {} > >
+ (in0,out, pool_fxn, OFMDim*OFMDim*numReps);""".format(
+ i_hls_dt, o_hls_dt
+ )
+ ]
+
+ 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",false);'
+ % (
+ 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_ibits = self.get_instream_width()
+ packed_in_hls_type = "ap_uint<%d>" % packed_ibits
+
+ packed_obits = self.get_outstream_width()
+ packed_out_hls_type = "ap_uint<%d>" % packed_obits
+ self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
+ "void %s(hls::stream<%s > &in0, hls::stream<%s > &out)"
+ % (self.onnx_node.name, packed_in_hls_type, packed_out_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()
+ folded_ishape = self.get_folded_input_shape()
+ exp_oshape = self.get_normal_output_shape()
+ folded_oshape = self.get_folded_output_shape()
+
+ # 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
+ )
+ )
+
+ 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 (batch_size,odim,odim,k*k*ifm_ch)."""
+
+ export_idt = self.get_input_datatype()
+ reshaped_input = inp.reshape(folded_ishape)
+
+ 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_output(context)
+ assert (
+ context[node.output[0]].shape == folded_oshape
+ ), "cppsim did not produce expected folded output 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, ofm_dim, ofm_dim, k*k*ifm_ch)."""
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index f650442401b49f1ad0a602b6b2ad3e50fbb5e5c2..a7ebff68749120868cae9ce5ac18d2856fe2cb8a 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -240,11 +240,21 @@ class StreamingFCLayer_Batch(HLSCustomOp):
Q = self.get_nodeattr("SIMD")
wdt = self.get_weight_datatype()
W = wdt.bitwidth()
- D_in = self.get_instream_width()
- D_out = self.get_outstream_width()
+ D_in = self.get_nodeattr("MW")
+ D_out = self.get_nodeattr("MH")
omega = (D_in * D_out) / (Q * P)
return P * (math.ceil(omega / 512)) * (math.ceil((Q * W) / 36))
+ def bram_efficiency_estimation(self):
+ wdt = self.get_weight_datatype()
+ W = wdt.bitwidth()
+ D_in = self.get_nodeattr("MW")
+ D_out = self.get_nodeattr("MH")
+ bram16_est = self.bram_estimation()
+ wbits = W * D_in * D_out
+ bram16_est_capacity = bram16_est * 36 * 512
+ return wbits / bram16_est_capacity
+
def lut_estimation(self):
"""Calculates resource estimations for LUTs based on:
- FINN-R: An End-to-End Deep-Learning Framework for Fast
@@ -290,12 +300,15 @@ class StreamingFCLayer_Batch(HLSCustomOp):
return out_width
def get_weightstream_width(self):
- """Returns weight stream width. Used in decoupled mode."""
- pe = self.get_nodeattr("PE")
- simd = self.get_nodeattr("SIMD")
- wp = self.get_weight_datatype().bitwidth()
- w_width = pe * simd * wp
- return w_width
+ """Returns weight stream width. Used only in decoupled mode."""
+ if self.get_nodeattr("mem_mode") == "decoupled":
+ pe = self.get_nodeattr("PE")
+ simd = self.get_nodeattr("SIMD")
+ wp = self.get_weight_datatype().bitwidth()
+ w_width = pe * simd * wp
+ return w_width
+ else:
+ return 0
def get_weightstream_width_padded(self):
"""Returns weight stream width padded to a multiple of 8. This is required
@@ -513,40 +526,44 @@ class StreamingFCLayer_Batch(HLSCustomOp):
elif mem_mode == "decoupled":
"""Saves weights in corresponding file format for cppsim or rtlsim"""
# transpose weight tensor from (1, PE, WMEM, SIMD) to (1, WMEM, PE, SIMD)
- # and save as unflipped weight tensor to be able to differentiate between
- # flipped an unflipped weight tensor (has to be flipped for cppsim)
-
weight_tensor_unflipped = np.transpose(weight_tensor, (0, 2, 1, 3))
- # flip PE dimension and reverse SIMD flip for saving weights in .npy
- weight_tensor_flipped = np.flip(weight_tensor_unflipped, axis=-2)
- weight_tensor_flipped = np.flip(weight_tensor_flipped, axis=-1)
+ # reverse SIMD flip for saving weights in .npy
+ weight_tensor_simd_flipped = np.flip(weight_tensor_unflipped, axis=-1)
+ # PE flip for saving weights in .dat
+ weight_tensor_pe_flipped = np.flip(weight_tensor_unflipped, axis=-2)
- # reshape weight tensor (flipped and unflipped) to desired shape
+ # reshape weight tensor (simd_flipped and pe_flipped) to desired shape
pe = self.get_nodeattr("PE")
simd = self.get_nodeattr("SIMD")
- # unflipped
- weight_tensor_unflipped = weight_tensor_unflipped.reshape(1, -1, pe * simd)
- weight_tensor_unflipped = weight_tensor_unflipped.copy()
+ # simd_flipped
+ weight_tensor_simd_flipped = weight_tensor_simd_flipped.reshape(
+ 1, -1, pe * simd
+ )
+ weight_tensor_simd_flipped = weight_tensor_simd_flipped.copy()
# flipped
- weight_tensor_flipped = weight_tensor_flipped.reshape(1, -1, pe * simd)
- weight_tensor_flipped = weight_tensor_flipped.copy()
+ weight_tensor_pe_flipped = weight_tensor_pe_flipped.reshape(
+ 1, -1, pe * simd
+ )
+ weight_tensor_pe_flipped = weight_tensor_pe_flipped.copy()
"""Saves weights into .npy file"""
- np.save(os.path.join(code_gen_dir, "weights.npy"), weight_tensor_flipped)
+ np.save(
+ os.path.join(code_gen_dir, "weights.npy"), weight_tensor_simd_flipped
+ )
"""Saves weights into .dat file"""
# convert weight values into hexstring
weight_width = self.get_weightstream_width()
# pad to nearest 4 bits to get hex strings
weight_width_padded = roundup_to_integer_multiple(weight_width, 4)
- weight_tensor_unflipped = pack_innermost_dim_as_hex_string(
- weight_tensor_unflipped, export_wdt, weight_width_padded, prefix=""
+ weight_tensor_pe_flipped = pack_innermost_dim_as_hex_string(
+ weight_tensor_pe_flipped, export_wdt, weight_width_padded, prefix=""
)
- weight_stream_len = np.prod(weight_tensor_unflipped.shape)
+ weight_stream_len = np.prod(weight_tensor_pe_flipped.shape)
factor = math.ceil(weight_stream_len / 1024)
# add zeroes to pad out file to 1024 entries
- weight_stream = weight_tensor_unflipped.flatten()
+ weight_stream = weight_tensor_pe_flipped.flatten()
pad_amt = (factor * 1024) - weight_stream_len
weight_stream = np.pad(
weight_stream, (0, pad_amt), mode="constant", constant_values="0"
diff --git a/src/finn/custom_op/fpgadataflow/streamingfifo.py b/src/finn/custom_op/fpgadataflow/streamingfifo.py
index 66190333ce8d71dafba99aaeae4fb2c973d67410..1f734b548f923341687843c538d1887fcc069bee 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfifo.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfifo.py
@@ -110,6 +110,8 @@ class StreamingFIFO(HLSCustomOp):
]
# make instream width a multiple of 8 for axi interface
in_width = self.get_instream_width_padded()
+ count_width = int(self.get_nodeattr("depth") - 1).bit_length()
+ self.code_gen_dict["$COUNT_RANGE$"] = ["[{}:0]".format(count_width - 1)]
self.code_gen_dict["$IN_RANGE$"] = ["[{}:0]".format(in_width - 1)]
self.code_gen_dict["$OUT_RANGE$"] = ["[{}:0]".format(in_width - 1)]
self.code_gen_dict["$WIDTH$"] = [str(in_width)]
diff --git a/src/finn/custom_op/fpgadataflow/templates.py b/src/finn/custom_op/fpgadataflow/templates.py
index 5f526aa2aa1917144c7a048c9d9314aa9288a2d8..1da60a5124fa86b4336bae8fd1a587672f2f2e6f 100644
--- a/src/finn/custom_op/fpgadataflow/templates.py
+++ b/src/finn/custom_op/fpgadataflow/templates.py
@@ -99,6 +99,7 @@ set_top $config_toplevelfxn
open_solution sol1
set_part $config_proj_part
+config_compile -ignore_long_run_time -disable_unroll_code_size_check
config_interface -m_axi_addr64
config_rtl -auto_prefix
$EXTRA_DIRECTIVES$
@@ -408,6 +409,7 @@ strm_fifo_wrapper = """
module $TOPNAME$(
ap_clk,
ap_rst_n,
+count,
in0_V_V_TDATA,
in0_V_V_TVALID,
in0_V_V_TREADY,
@@ -418,6 +420,7 @@ out_V_V_TREADY
input ap_clk;
input ap_rst_n;
+output $COUNT_RANGE$ count;
input $IN_RANGE$ in0_V_V_TDATA;
input in0_V_V_TVALID;
output in0_V_V_TREADY;
@@ -433,6 +436,7 @@ $LAYER_NAME$
(
.clock(ap_clk),
.reset(!ap_rst_n),
+ .count(count),
.i_d(in0_V_V_TDATA),
.i_v(in0_V_V_TVALID),
.i_r(in0_V_V_TREADY),
diff --git a/src/finn/custom_op/fpgadataflow/tlastmarker.py b/src/finn/custom_op/fpgadataflow/tlastmarker.py
index 25ea05e3607a52731ae1b64de421837bf137ee2b..17ba44b959577faf573d77ae222f7b2a3be6669d 100644
--- a/src/finn/custom_op/fpgadataflow/tlastmarker.py
+++ b/src/finn/custom_op/fpgadataflow/tlastmarker.py
@@ -30,20 +30,30 @@ from finn.custom_op.fpgadataflow import HLSCustomOp
class TLastMarker(HLSCustomOp):
- """Class that corresponds to the TLastMarker node that needs to be
- inserted at the end of the model for rtlsim with stitched IP.
- It marks the end of the current image/input sample."""
+ """Node that adds/removes AXI stream TLAST signals where needed. Its behavior
+ is transparent in node-by-node execution, only visible in IP-stitched rtlsim or
+ actual hardware.
+ This node may be needed at the end of the network to signal a DMA write (needed by the
+ FINN PYNQ shell) or at the beginning to remove the end-of-burst from DMA read."""
def __init__(self, onnx_node):
super().__init__(onnx_node)
def get_nodeattr_types(self):
my_attrs = {
+ # number of (static) iterations until TLAST=1 is generated for Direction=out
"NumIters": ("i", True, 0),
+ # whether static or dynamic (from AXI lite) number of iterations are used
+ "DynIters": ("i", False, 1),
+ # direction: whether to insert or remove TLAST
+ "Direction": ("s", False, "out"),
# width of input-output data streams, in bits
"StreamWidth": ("i", True, 0),
# width of individual element in stream, in bits
"ElemWidth": ("i", True, 0),
+ # Protocol: external or internal
+ # Vitis docs recommend using qdma_axis for external, ap_axiu for internal
+ "Protocol": ("s", False, "external"),
}
my_attrs.update(super().get_nodeattr_types())
return my_attrs
@@ -76,12 +86,33 @@ class TLastMarker(HLSCustomOp):
def defines(self, var):
stream_width = self.get_nodeattr("StreamWidth")
+ direction = self.get_nodeattr("Direction")
+ protocol = self.get_nodeattr("Protocol")
# output stream must have TLAST, so we use this stream data type:
# qdma_axis<stream_data_width,0,0,0 >
- out_stream_dtype = "qdma_axis<%d,0,0,0>" % stream_width
+ if direction == "out":
+ if protocol == "external":
+ out_stream_dtype = "qdma_axis<%d,0,0,0>" % stream_width
+ elif protocol == "internal":
+ out_stream_dtype = "ap_axiu<%d,0,0,0>" % stream_width
+ else:
+ raise Exception("Unrecognized Protocol in TLastMarker")
+ in_stream_dtype = "ap_uint<%d>" % stream_width
+ elif direction == "in":
+ out_stream_dtype = "ap_uint<%d>" % stream_width
+ if protocol == "external":
+ in_stream_dtype = "qdma_axis<%d,0,0,0>" % stream_width
+ elif protocol == "internal":
+ in_stream_dtype = "ap_axiu<%d,0,0,0>" % stream_width
+ else:
+ raise Exception("Unrecognized Protocol in TLastMarker")
+ else:
+ raise Exception("Unrecognized Direction in TLastMarker")
+
self.code_gen_dict["$DEFINES$"] = [
"#define StreamWidth %d" % stream_width,
"#define OutDType %s" % out_stream_dtype,
+ "#define InDType %s" % in_stream_dtype,
"#define NumItersPerImg %d" % self.get_nodeattr("NumIters"),
]
@@ -89,27 +120,60 @@ class TLastMarker(HLSCustomOp):
self.code_gen_dict["$READNPYDATA$"] = []
def docompute(self):
- self.code_gen_dict["$DOCOMPUTE$"] = [
- "unsigned int n = 1;",
- "OutDType t;",
- "t.set_keep(-1);",
- "io_section: { // start of cycle accurate region",
- "#pragma HLS protocol fixed",
- "// do a first read from stream before we decide on numIters",
- "// giving software a chance to set up the numIters prior to startup",
- "t.set_data(in0.read());",
- "n = (numIters == 0 ? NumItersPerImg : numIters);",
- "t.set_last(n==1);",
- "out.write(t);",
- "} // end of cycle accurate region",
- "// do one less iteration than spec since we already did one",
- "for(unsigned int i=1; i<n; i++) {",
- "#pragma HLS PIPELINE II=1",
- "t.set_data(in0.read());",
- "t.set_last(i==(n-1));",
- "out.write(t);",
- "}",
- ]
+ dyn_iters = self.get_nodeattr("DynIters")
+ direction = self.get_nodeattr("Direction")
+ use_qdma_axis = self.get_nodeattr("Protocol") == "external"
+ if direction == "in":
+ # read from input and just pass data along; ignore tlast
+ # no dyn iters on input, it doesnt make sense
+ self.code_gen_dict["$DOCOMPUTE$"] = [
+ "for(unsigned int i=0; i<NumItersPerImg; i++) {",
+ "#pragma HLS PIPELINE II=1",
+ "out.write(in0.read().get_data());"
+ if use_qdma_axis
+ else "out.write(in0.read().data);",
+ "}",
+ ]
+
+ elif dyn_iters == 1:
+ # output, with dynamic iteration counts
+ self.code_gen_dict["$DOCOMPUTE$"] = [
+ "unsigned int n = 1;",
+ "OutDType t;",
+ "t.set_keep(-1);" if use_qdma_axis else "t.keep = -1;",
+ "io_section: { // start of cycle accurate region",
+ "#pragma HLS protocol fixed",
+ "// do a first read from stream before we decide on numIters",
+ "// giving software a chance to set up the numIters prior to startup",
+ "t.set_data(in0.read());" if use_qdma_axis else "t.data = in0.read();",
+ "n = (numIters == 0 ? NumItersPerImg : numIters);",
+ "t.set_last(n==1);" if use_qdma_axis else "t.last = (n==1);",
+ "out.write(t);",
+ "} // end of cycle accurate region",
+ "// do one less iteration than spec since we already did one",
+ "for(unsigned int i=1; i<n; i++) {",
+ "#pragma HLS PIPELINE II=1",
+ "t.set_data(in0.read());" if use_qdma_axis else "t.data = in0.read();",
+ "t.set_last(i==(n-1));" if use_qdma_axis else "t.last = (i==(n-1));",
+ "out.write(t);",
+ "}",
+ ]
+
+ else:
+ # output, with static iteration counts
+ self.code_gen_dict["$DOCOMPUTE$"] = [
+ "unsigned int n = 1;",
+ "OutDType t;",
+ "t.set_keep(-1);" if use_qdma_axis else "t.keep = -1;",
+ "for(unsigned int i=0; i<NumItersPerImg; i++) {",
+ "#pragma HLS PIPELINE II=1",
+ "t.set_data(in0.read());" if use_qdma_axis else "t.data = in0.read();",
+ "t.set_last(i==(NumItersPerImg-1));"
+ if use_qdma_axis
+ else "t.last = (i==(NumItersPerImg-1));",
+ "out.write(t);",
+ "}",
+ ]
def dataoutstrm(self):
self.code_gen_dict["$DATAOUTSTREAM$"] = []
@@ -118,18 +182,30 @@ class TLastMarker(HLSCustomOp):
self.code_gen_dict["$SAVEASCNPY$"] = []
def blackboxfunction(self):
- self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
- """void %s(hls::stream<ap_uint<StreamWidth> > &in0,
- hls::stream<OutDType> &out, unsigned int numIters)"""
- % self.onnx_node.name
- ]
+ dyn_iters = self.get_nodeattr("DynIters")
+
+ if dyn_iters == 1:
+ self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
+ """void %s(hls::stream<InDType> &in0,
+ hls::stream<OutDType> &out, unsigned int numIters)"""
+ % self.onnx_node.name
+ ]
+ else:
+ self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
+ """void %s(hls::stream<InDType> &in0, hls::stream<OutDType> &out)"""
+ % self.onnx_node.name
+ ]
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 s_axilite port=numIters bundle=control"
- )
+
+ dyn_iters = self.get_nodeattr("DynIters")
+ if dyn_iters == 1:
+ self.code_gen_dict["$PRAGMAS$"].append(
+ "#pragma HLS INTERFACE s_axilite port=numIters bundle=control"
+ )
+
self.code_gen_dict["$PRAGMAS$"].append(
"#pragma HLS INTERFACE ap_ctrl_none port=return"
)
@@ -158,7 +234,7 @@ class TLastMarker(HLSCustomOp):
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())
+ 'hls::stream<InDType> in0 ("in0");'
)
self.code_gen_dict["$STREAMDECLARATIONS$"].append(
'hls::stream<OutDType> out ("out");'
diff --git a/src/finn/custom_op/im2col.py b/src/finn/custom_op/im2col.py
index b0d94b158ba0ffe57646847a2bd99fa831e2ae20..8ed0041704d421dab587f08bcbcd9e739e8434e9 100644
--- a/src/finn/custom_op/im2col.py
+++ b/src/finn/custom_op/im2col.py
@@ -21,8 +21,6 @@ def get_im2col_indices_nchw(
"""Returns im2col indices."""
# First figure out what the size of the output should be
N, C, H, W = x_shape
- assert (H + 2 * padding - field_height) % stride_y == 0
- assert (W + 2 * padding - field_width) % stride_x == 0
out_height = compute_conv_output_dim(H, field_height, stride_y, padding)
out_width = compute_conv_output_dim(W, field_width, stride_x, padding)
@@ -70,6 +68,9 @@ def im2col_indices_nchw(
# * ifm is the number of input channels
# * k is the convolutional kernel size
+# note: for the innermost (dot product) dimension of k*k*ifm, we
+# assume an internal ordering (k, k, ifm)
+
class Im2Col(CustomOp):
def get_nodeattr_types(self):
@@ -79,7 +80,8 @@ class Im2Col(CustomOp):
"input_shape": ("s", True, ""),
"pad_amount": ("i", False, 0),
"pad_value": ("i", False, 0),
- "dw": ("i", False, 0),
+ # depthwise: if != 0, infer ConvolutionInputGenerator with depthwise == 1
+ "depthwise": ("i", False, 0),
}
def make_shape_compatible_op(self, model):
diff --git a/src/finn/custom_op/quantavgpool2d.py b/src/finn/custom_op/quantavgpool2d.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb5c78bc0c8419ba519c5c3113d9b0c7ae2dd3b7
--- /dev/null
+++ b/src/finn/custom_op/quantavgpool2d.py
@@ -0,0 +1,128 @@
+import numpy as np
+from onnx import TensorProto, helper
+import onnxruntime as rt
+
+from finn.custom_op import CustomOp
+from finn.core.datatype import DataType
+from finn.custom_op.maxpoolnhwc import compute_pool_output_dim
+
+
+class QuantAvgPool2d(CustomOp):
+ """Class that corresponds to the quantized average pooling
+ layer from brevitas"""
+
+ def get_nodeattr_types(self):
+ return {
+ "stride": ("i", True, 1),
+ "kernel": ("i", True, 1),
+ "ibits": ("i", True, 1),
+ "obits": ("i", True, 1),
+ # determines if values are signed (set to "1") or unsigned ("0")
+ "signed": ("i", True, 0),
+ # data layout attribute can be set to "NCHW" or "NHWC"
+ "data_layout": ("s", False, "NCHW"),
+ }
+
+ def make_shape_compatible_op(self, model):
+ node = self.onnx_node
+ k = self.get_nodeattr("kernel")
+ s = self.get_nodeattr("stride")
+ data_layout = self.get_nodeattr("data_layout")
+ if data_layout == "NCHW":
+ return helper.make_node(
+ "AveragePool",
+ inputs=[node.input[0]],
+ outputs=[node.output[0]],
+ kernel_shape=[k, k],
+ strides=[s, s],
+ )
+ elif data_layout == "NHWC":
+ iname = node.input[0]
+ ishape = model.get_tensor_shape(iname)
+ (n, hi, wi, c) = ishape
+ ho = compute_pool_output_dim(hi, k, s)
+ wo = compute_pool_output_dim(wi, k, s)
+ oshape = (n, ho, wo, c)
+ # implement tensor with correct shape
+ values = np.random.randn(*oshape).astype(np.float32)
+ return helper.make_node(
+ "Constant",
+ inputs=[],
+ outputs=[node.output[0]],
+ value=helper.make_tensor(
+ name="const_tensor",
+ data_type=TensorProto.FLOAT,
+ dims=values.shape,
+ vals=values.flatten().astype(float),
+ ),
+ )
+
+ else:
+ raise Exception(
+ """Datalayout for QuantAvgPool2d is set to an invalid value.
+ Has to be set to "NCHW" or "NHWC"."""
+ )
+
+ def infer_node_datatype(self, model):
+ node = self.onnx_node
+ bw = self.get_nodeattr("obits")
+ if bw in [2, 4, 8, 16, 32]:
+ if self.get_nodeattr("signed") == 0:
+ dtype = DataType["UINT%d" % bw]
+ else:
+ dtype = DataType["INT%d" % bw]
+ else:
+ raise Exception("Unsupported output datatype for QuantAvgPool2d")
+ model.set_tensor_datatype(node.output[0], dtype)
+
+ def execute_node(self, context, graph):
+ # create a standard average pooling node to help calculate the result
+ node = self.onnx_node
+ k = self.get_nodeattr("kernel")
+ s = self.get_nodeattr("stride")
+ inp_values = context[node.input[0]]
+ oshape = context[node.output[0]].shape
+ if self.get_nodeattr("data_layout") == "NHWC":
+ inp_values = inp_values.transpose(0, 3, 1, 2)
+ oshape = (context[node.output[0]]).transpose(0, 3, 1, 2).shape
+ ishape = inp_values.shape
+ inp = helper.make_tensor_value_info(node.input[0], TensorProto.FLOAT, ishape)
+ outp = helper.make_tensor_value_info(node.output[0], TensorProto.FLOAT, oshape)
+ node_avgpool = helper.make_node(
+ "AveragePool",
+ inputs=[node.input[0]],
+ outputs=[node.output[0]],
+ kernel_shape=[k, k],
+ strides=[s, s],
+ )
+ graph_avgpool = helper.make_graph(
+ nodes=[node_avgpool],
+ name="single-avgpool-exec",
+ inputs=[inp],
+ outputs=[outp],
+ )
+ model_avgpool = helper.make_model(graph_avgpool)
+ idict = {node.input[0]: inp_values}
+ sess = rt.InferenceSession(model_avgpool.SerializeToString())
+ result_temp = sess.run(None, idict)
+ # remove scaling introduced by average
+ result_temp = result_temp[0] * (k * k)
+ ibits = self.get_nodeattr("ibits")
+ max_value = 2 ** ibits - 1
+ max_value = max_value * k * k
+ max_bit_width = int(max_value).bit_length()
+ shift_bits = max_bit_width - self.get_nodeattr("obits")
+ result = np.right_shift(result_temp.astype(int), shift_bits)
+ if self.get_nodeattr("data_layout") == "NHWC":
+ result = result.transpose(0, 2, 3, 1)
+ context[node.output[0]] = result.astype(np.float32)
+
+ 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"')
+ return info_messages
diff --git a/src/finn/custom_op/registry.py b/src/finn/custom_op/registry.py
index 238829e03353d79fab7c51e7d1b9dca6e2a96a11..e4317e02d46df90c8fd0c8854262ca6eb0ea4f48 100644
--- a/src/finn/custom_op/registry.py
+++ b/src/finn/custom_op/registry.py
@@ -31,6 +31,7 @@
from finn.custom_op.fpgadataflow.convolutioninputgenerator import (
ConvolutionInputGenerator,
)
+from finn.custom_op.fpgadataflow.downsampler import DownSampler
from finn.custom_op.fpgadataflow.streamingfclayer_batch import StreamingFCLayer_Batch
from finn.custom_op.fpgadataflow.streamingmaxpool_batch import StreamingMaxPool_Batch
from finn.custom_op.fpgadataflow.streamingfifo import StreamingFIFO
@@ -44,16 +45,21 @@ 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.pool_batch import Pool_Batch
+from finn.custom_op.fpgadataflow.fmpadding_batch import FMPadding_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.quantavgpool2d import QuantAvgPool2d
from finn.custom_op.fpgadataflow.duplicatestreams_batch import DuplicateStreams_Batch
+from finn.custom_op.fpgadataflow.channelwise_op_batch import ChannelwiseOp_Batch
+from finn.custom_op.fpgadataflow.iodma import IODMA
# create a mapping of all known CustomOp names and classes
custom_op = {}
custom_op["MultiThreshold"] = MultiThreshold
+custom_op["DownSampler"] = DownSampler
custom_op["XnorPopcountMatMul"] = XnorPopcountMatMul
custom_op["Im2Col"] = Im2Col
custom_op["StreamingMaxPool_Batch"] = StreamingMaxPool_Batch
@@ -65,11 +71,15 @@ 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["Pool_Batch"] = Pool_Batch
+custom_op["FMPadding_Batch"] = FMPadding_Batch
custom_op["Thresholding_Batch"] = Thresholding_Batch
custom_op["AddStreams_Batch"] = AddStreams_Batch
custom_op["LabelSelect_Batch"] = LabelSelect_Batch
+custom_op["QuantAvgPool2d"] = QuantAvgPool2d
custom_op["DuplicateStreams_Batch"] = DuplicateStreams_Batch
+custom_op["ChannelwiseOp_Batch"] = ChannelwiseOp_Batch
+custom_op["IODMA"] = IODMA
def getCustomOp(node):
diff --git a/src/finn/transformation/bipolar_to_xnor.py b/src/finn/transformation/bipolar_to_xnor.py
index 8b65cfee17edd5d89fcca0bd86da12415d38fe78..80f2a73351f8548c99efd8dedd8a04d44c8558a3 100644
--- a/src/finn/transformation/bipolar_to_xnor.py
+++ b/src/finn/transformation/bipolar_to_xnor.py
@@ -27,6 +27,7 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
+import warnings
from onnx import TensorProto
from onnx import helper as oh
@@ -66,26 +67,40 @@ class ConvertBipolarMatMulToXnorPopcount(Transformation):
mt_chain = model.find_upstream(mm_input, find_prod_mt)
if len(mt_chain) == 0:
- raise Exception(
- """Could not find upstream bipolar
- MultiThreshold"""
- )
- graph_modified = True
- mt = mt_chain[-1]
- mt_inst = getCustomOp(mt)
- # ensure old scale/bias were correct for BIPOLAR
- scale_ok = mt_inst.get_nodeattr("out_scale") == 2.0
- bias_ok = mt_inst.get_nodeattr("out_bias") == -1.0
- assert (
- scale_ok and bias_ok
- ), """Unexpected scale/bias
- attributes for BIPOLAR MultiThreshold node."""
- # start conversion, set MT output to binary
- # (this is what XnorPopcountMatMul expects)
- mt_inst.set_nodeattr("out_dtype", "BINARY")
- mt_inst.set_nodeattr("out_scale", 1.0)
- mt_inst.set_nodeattr("out_bias", 0.0)
- model.set_tensor_datatype(mm_input, DataType.BINARY)
+ if mm_input == graph.input[0].name:
+ # change input datatype to BINARY
+ model.set_tensor_datatype(mm_input, DataType.BINARY)
+ graph_modified = True
+ warnings.warn(
+ """IMPORTANT: Changing graph input DataType
+ to BINARY instead of BIPOLAR. Ensure this is respected
+ when checking for correctness.
+ """
+ )
+ else:
+ raise Exception(
+ """Could not find upstream bipolar
+ MultiThreshold, and the MatMul is not the
+ first node on graph input. Unable to convert
+ input tensor from BIPOLAR to BINARY."""
+ )
+ else:
+ graph_modified = True
+ mt = mt_chain[-1]
+ mt_inst = getCustomOp(mt)
+ # ensure old scale/bias were correct for BIPOLAR
+ scale_ok = mt_inst.get_nodeattr("out_scale") == 2.0
+ bias_ok = mt_inst.get_nodeattr("out_bias") == -1.0
+ assert (
+ scale_ok and bias_ok
+ ), """Unexpected scale/bias
+ attributes for BIPOLAR MultiThreshold node."""
+ # start conversion, set MT output to binary
+ # (this is what XnorPopcountMatMul expects)
+ mt_inst.set_nodeattr("out_dtype", "BINARY")
+ mt_inst.set_nodeattr("out_scale", 1.0)
+ mt_inst.set_nodeattr("out_bias", 0.0)
+ model.set_tensor_datatype(mm_input, DataType.BINARY)
# change node type and domain
n.op_type = "XnorPopcountMatMul"
n.domain = "finn"
diff --git a/src/finn/transformation/change_datalayout.py b/src/finn/transformation/change_datalayout.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5b393a25e57122b059a44f70904a6dbe5bbaa3f
--- /dev/null
+++ b/src/finn/transformation/change_datalayout.py
@@ -0,0 +1,110 @@
+# 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 onnx import helper, TensorProto
+
+from finn.transformation import Transformation
+from finn.transformation.infer_shapes import InferShapes
+from finn.util.basic import get_by_name
+
+
+class ChangeDataLayoutQuantAvgPool2d(Transformation):
+ """Replace QuantAvgPool2d with datalayout (N,C,H,W) with Transpose nodes
+ and QuantAvgPool2dNHWC with datalayout (N,H,W,C)"""
+
+ def apply(self, model):
+ graph = model.graph
+ node_ind = 0
+ graph_modified = False
+ for n in graph.node:
+ node_ind += 1
+ if n.op_type == "QuantAvgPool2d" and (
+ get_by_name(n.attribute, "data_layout") is None
+ or get_by_name(n.attribute, "data_layout").s.decode("UTF-8") == "NCHW"
+ ):
+ graph_modified = True
+ node_input = n.input[0]
+ node_output = n.output[0]
+ s = get_by_name(n.attribute, "stride").i
+ k = get_by_name(n.attribute, "kernel").i
+ ibits = get_by_name(n.attribute, "ibits").i
+ obits = get_by_name(n.attribute, "obits").i
+ signed = get_by_name(n.attribute, "signed").i
+ batchsize = model.get_tensor_shape(n.input[0])[0] # assume NCHW
+ channels = model.get_tensor_shape(n.input[0])[1] # assume NCHW
+ idim = model.get_tensor_shape(n.input[0])[-1] # assume NCHW
+ odim = model.get_tensor_shape(n.output[0])[-1] # assume NCHW
+
+ # create new nodes
+ # NCHW -> NHWC
+ # create new intermediate values
+ inp_trans_out = helper.make_tensor_value_info(
+ model.make_new_valueinfo_name(),
+ TensorProto.FLOAT,
+ (batchsize, idim, idim, channels), # NHWC
+ )
+ graph.value_info.append(inp_trans_out)
+ inp_trans_out = inp_trans_out.name
+ quantavg_out = helper.make_tensor_value_info(
+ model.make_new_valueinfo_name(),
+ TensorProto.FLOAT,
+ (batchsize, odim, odim, channels),
+ )
+ graph.value_info.append(quantavg_out)
+ quantavg_out = quantavg_out.name
+ inp_trans_node = helper.make_node(
+ "Transpose", [node_input], [inp_trans_out], perm=[0, 2, 3, 1]
+ )
+ quantavg_node = helper.make_node(
+ "QuantAvgPool2d",
+ [inp_trans_out],
+ [quantavg_out],
+ domain="finn",
+ stride=s,
+ kernel=k,
+ ibits=ibits,
+ obits=obits,
+ signed=signed,
+ data_layout="NHWC",
+ )
+ # NHWC -> NCHW
+ out_trans_node = helper.make_node(
+ "Transpose", [quantavg_out], [node_output], perm=[0, 3, 1, 2]
+ )
+ # insert nodes
+ graph.node.insert(node_ind, inp_trans_node)
+ graph.node.insert(node_ind + 1, quantavg_node)
+ graph.node.insert(node_ind + 2, out_trans_node)
+ # remove old nodes
+ graph.node.remove(n)
+
+ # set shapes
+ model.set_tensor_shape(inp_trans_out, (batchsize, idim, idim, channels))
+ model.set_tensor_shape(quantavg_out, (batchsize, odim, odim, channels))
+ model = model.transform(InferShapes())
+ return (model, graph_modified)
diff --git a/src/finn/transformation/fpgadataflow/annotate_resources.py b/src/finn/transformation/fpgadataflow/annotate_resources.py
index 207075b00de1871da19ea78472125d435449ed6e..62ee92df54eee2b63d84657515d7fbc3a8808b81 100644
--- a/src/finn/transformation/fpgadataflow/annotate_resources.py
+++ b/src/finn/transformation/fpgadataflow/annotate_resources.py
@@ -69,6 +69,9 @@ class AnnotateResources(Transformation):
total_dict[r_type] += r_amount
else:
total_dict[r_type] = r_amount
+ for k in total_dict.keys():
+ if "efficiency" in k:
+ total_dict[k] = total_dict[k] / len(graph.node)
model.set_metadata_prop("res_total_" + self.mode, str(total_dict))
for node in graph.node:
if _is_fpgadataflow_node(node) and node.name in res_dict.keys():
diff --git a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
index f986cce1f02535ef4294ecfc387786d6c898b704..73a729f67510627e2dfeb85c9ee08cd9c6b5c2d7 100644
--- a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
+++ b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
@@ -26,7 +26,8 @@
# 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 onnx import helper
+from onnx import helper, TensorProto
+import numpy as np
from finn.core.datatype import DataType
from finn.transformation import Transformation
@@ -34,6 +35,9 @@ 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
+from finn.util.onnx import nchw_to_nhwc
+from finn.util.basic import get_by_name
+import warnings
class InferConvInpGen(Transformation):
@@ -51,35 +55,94 @@ class InferConvInpGen(Transformation):
i2c_in_shape = model.get_tensor_shape(i2c_input)
i2c_out_shape = model.get_tensor_shape(i2c_output)
dt = model.get_tensor_datatype(i2c_input)
+ if not dt.is_integer():
+ warnings.warn("Input is not int. Can't infer ConvInpGen")
+ continue
i2c_inst = getCustomOp(n)
stride = i2c_inst.get_nodeattr("stride")
k = i2c_inst.get_nodeattr("kernel_size")
pad = i2c_inst.get_nodeattr("pad_amount")
pad_val = i2c_inst.get_nodeattr("pad_value")
+ depthwise = i2c_inst.get_nodeattr("depthwise")
ifm_ch = i2c_in_shape[-1]
ifm_dim = i2c_in_shape[1]
ofm_dim = i2c_out_shape[1]
- # if padding enabled, ensure pad_val supported by DataType
+
+ # default params for ConvolutionInputGenerator
+ ConvInpGen_node_idx = node_ind
+ ConvInpGen_input = i2c_input
+ ConvInpGen_idim = ifm_dim
+
if pad > 0:
- assert dt.allowed(pad_val), "Im2Col DataType must support pad_val"
- # create equivalent ConvolutionInputGenerator node
- # TODO support padding
- new_node = helper.make_node(
- "ConvolutionInputGenerator",
- [i2c_input],
- [i2c_output],
- domain="finn",
- backend="fpgadataflow",
- ConvKernelDim=k,
- IFMChannels=ifm_ch,
- IFMDim=ifm_dim,
- OFMDim=ofm_dim,
- SIMD=ifm_ch,
- Stride=stride,
- inputDataType=dt.name,
- outputDataType=dt.name,
- )
- graph.node.insert(node_ind, new_node)
+ # if padding enabled, ensure pad_val supported by DataType
+ # assert dt.allowed(pad_val),"""FMPadding_Batch DataType
+ # must support pad_val"""
+ assert (
+ pad_val == 0
+ ), "FMPadding_Batch doesn't currently support pad_val!= 0"
+
+ odim_padding = ifm_dim + 2 * pad
+
+ padding_out = helper.make_tensor_value_info(
+ model.make_new_valueinfo_name(),
+ TensorProto.FLOAT,
+ (1, odim_padding, odim_padding, ifm_ch),
+ )
+ graph.value_info.append(padding_out)
+ padding_out = padding_out.name
+ model.set_tensor_datatype(padding_out, dt)
+
+ ConvInpGen_node_idx += 1
+ ConvInpGen_input = padding_out
+ ConvInpGen_idim = odim_padding
+
+ padding_node = helper.make_node(
+ "FMPadding_Batch",
+ [i2c_input],
+ [padding_out],
+ domain="finn",
+ backend="fpgadataflow",
+ ImgDim=ifm_dim,
+ Padding=2 * pad,
+ NumChannels=ifm_ch,
+ inputDataType=dt.name,
+ )
+ graph.node.insert(node_ind, padding_node)
+
+ if stride > 1 and k == 1:
+ # create DownSampler node
+ ConvInpGen_node = helper.make_node(
+ "DownSampler",
+ [ConvInpGen_input],
+ [i2c_output],
+ domain="finn",
+ backend="fpgadataflow",
+ ImgDim=ConvInpGen_idim,
+ NumChannels=ifm_ch,
+ SIMD=ifm_ch,
+ Stride=stride,
+ inputDataType=dt.name,
+ )
+ graph.node.insert(ConvInpGen_node_idx, ConvInpGen_node)
+ else:
+ # create equivalent ConvolutionInputGenerator node
+ ConvInpGen_node = helper.make_node(
+ "ConvolutionInputGenerator",
+ [ConvInpGen_input],
+ [i2c_output],
+ domain="finn",
+ backend="fpgadataflow",
+ ConvKernelDim=k,
+ IFMChannels=ifm_ch,
+ IFMDim=ConvInpGen_idim,
+ OFMDim=ofm_dim,
+ SIMD=ifm_ch,
+ Stride=stride,
+ inputDataType=dt.name,
+ outputDataType=dt.name,
+ depthwise=depthwise,
+ )
+ graph.node.insert(ConvInpGen_node_idx, ConvInpGen_node)
# remove old nodes
graph.node.remove(n)
graph_modified = True
@@ -135,6 +198,137 @@ class InferStreamingMaxPool(Transformation):
return (model, graph_modified)
+class InferPool_Batch(Transformation):
+ """If kernel_shape > strides, replace Pool layer with with of Im2col
+ + pool(with kernel_shape == strides), plus Transpose layers to keep the original
+ data layout."""
+
+ def apply(self, model):
+ graph = model.graph
+ node_ind = 0
+ graph_modified = False
+ for n in graph.node:
+ node_ind += 1
+ if n.op_type in ["MaxPool"]:
+ # extract pool parameters
+ k = get_by_name(n.attribute, "kernel_shape").ints[-1]
+ stride = get_by_name(n.attribute, "strides").ints[-1]
+
+ if k <= stride:
+ continue
+
+ try:
+ pad = get_by_name(n.attribute, "pads").ints[-1]
+ except AttributeError:
+ pad = 0
+
+ node_input = n.input[0]
+ node_output = n.output[0]
+ idt = model.get_tensor_datatype(node_input)
+ if not idt.is_integer():
+ continue
+
+ # odt = model.get_tensor_datatype(node_output)
+
+ ifm_ch = model.get_tensor_shape(n.input[0])[1] # assume NCHW
+ ofm_ch = ifm_ch
+ ifm_dim = model.get_tensor_shape(n.input[0])[-1] # assume NCHW
+ ofm_dim = model.get_tensor_shape(n.output[0])[-1] # assume NCHW
+ # create new intermediate values
+ inp_trans_out = helper.make_tensor_value_info(
+ model.make_new_valueinfo_name(),
+ TensorProto.FLOAT,
+ (1, ifm_dim, ifm_dim, ifm_ch), # NHWC
+ )
+ graph.value_info.append(inp_trans_out)
+ inp_trans_out = inp_trans_out.name
+ model.set_tensor_datatype(inp_trans_out, idt)
+
+ im2col_out = helper.make_tensor_value_info(
+ model.make_new_valueinfo_name(),
+ TensorProto.FLOAT,
+ (1, ofm_dim, ofm_dim, ifm_ch * k * k),
+ )
+ graph.value_info.append(im2col_out)
+ im2col_out = im2col_out.name
+ model.set_tensor_datatype(im2col_out, idt)
+
+ pool_output = helper.make_tensor_value_info(
+ model.make_new_valueinfo_name(),
+ TensorProto.FLOAT,
+ (1, ofm_dim, ofm_dim, ofm_ch),
+ )
+ graph.value_info.append(pool_output)
+ pool_output = pool_output.name
+ # model.set_tensor_datatype(pool_output, odt)
+
+ # create new nodes
+ # NCHW -> NHWC
+ inp_trans_node = helper.make_node(
+ "Transpose", [node_input], [inp_trans_out], perm=[0, 2, 3, 1]
+ )
+
+ if n.op_type == "MaxPool":
+ pool_fxn = "MaxPool"
+ pad_value = idt.min()
+ else:
+ raise Exception(
+ "pad_value and pool_fxn not configured for {}".format(n.op_type)
+ )
+
+ # format input tensor
+ im2col_node = helper.make_node(
+ "Im2Col",
+ [inp_trans_out],
+ [im2col_out],
+ domain="finn",
+ stride=stride,
+ kernel_size=k,
+ pad_amount=pad,
+ pad_value=pad_value,
+ depthwise=1,
+ input_shape="(1,{},{},{})".format(ifm_dim, ifm_dim, ifm_ch),
+ )
+
+ # Warning PE has to be equal to ifm_ch until Im2Col is replaced by
+ # ConvolutionInputGenerator with depthwise=1.
+ # For other settings the output will be incorrect due to incorrect input
+ # data layout
+ pool_node = helper.make_node(
+ "Pool_Batch",
+ [im2col_out],
+ [pool_output],
+ domain="finn",
+ backend="fpgadataflow",
+ dataType=idt.name,
+ Channels=ifm_ch,
+ PE=ifm_ch,
+ KernelSize=k,
+ Function=pool_fxn,
+ OutImgDim=ofm_dim,
+ BatchSize=1,
+ )
+
+ # NHWC -> NCHW
+ out_trans_node = helper.make_node(
+ "Transpose", [pool_output], [node_output], perm=[0, 3, 1, 2]
+ )
+
+ # insert nodes where the conv is to preserve topological ordering
+ graph.node.insert(node_ind, inp_trans_node)
+ graph.node.insert(node_ind + 1, im2col_node)
+ graph.node.insert(node_ind + 2, pool_node)
+ graph.node.insert(node_ind + 3, out_trans_node)
+ # remove old node
+ graph.node.remove(n)
+ graph_modified = True
+
+ if graph_modified:
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+ return (model, graph_modified)
+
+
class InferBinaryStreamingFCLayer(Transformation):
"""Convert XnorPopcountMatMul layers to
StreamingFCLayer_Batch layers. Any immediately following MultiThreshold
@@ -455,3 +649,243 @@ class InferThresholdingLayer(Transformation):
model = model.transform(InferShapes())
model = model.transform(InferDataTypes())
return (model, graph_modified)
+
+
+class InferChannelwiseLinearLayer(Transformation):
+ """Convert any channel-wise Add/Mul into a HLS layer."""
+
+ def get_smallest_possible(self, vals):
+ """Returns smallest (fewest bits) possible DataType that can represent
+ value. Prefers unsigned integers where possible."""
+ vals = np.array(vals)
+ for v in vals:
+ assert int(v) == v, "Error float value"
+
+ for k in DataType.__members__:
+ dt = DataType[k]
+
+ if dt in [DataType.BIPOLAR, DataType.TERNARY, DataType.FLOAT32]:
+ # not currently supported
+ continue
+
+ if (dt.min() <= vals).all() and (vals <= dt.max()).all():
+ return dt
+
+ warnings.warn(
+ """InferChannelwiseLinearLayer: Output values may not be
+ representable with supported data types.
+ Setting maximum width data type available.
+ This will lead to errors if there are no constrains on the input
+ """
+ )
+
+ if (0 <= vals).all():
+ return DataType.UINT32
+ else:
+ return DataType.INT32
+
+ 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 == "Add" or node.op_type == "Mul":
+ # assuming input[0] is dynamic
+ ll_input = node.input[0]
+ ll_output = node.output[0]
+ ll_in_shape = model.get_tensor_shape(ll_input)
+
+ # check if input 1 has an initializer
+ ll_const = node.input[1]
+ if ll_const is not None:
+ ll_cinit = model.get_initializer(ll_const)
+ if ll_cinit is None:
+ # input 1 is also dynamic
+ continue
+ else:
+ continue
+
+ # get number of channels and channel index from input
+ ll_in_layout = model.get_tensor_layout(ll_input)
+ if ll_in_layout == DataLayout.NHWC or ll_in_layout == DataLayout.NC:
+ ch_index = -1
+ ch = ll_in_shape[-1]
+ elif ll_in_layout == DataLayout.NCHW:
+ ch_index = 1
+ ch = ll_in_shape[1]
+ else:
+ continue
+
+ # check if the shape of initializer is compatible
+ ll_cinit_shape = list(ll_cinit.shape)
+ if np.prod(ll_cinit_shape) == 1:
+ warnings.warn(
+ "Broadcasting " + str(node.op_type) + "(" + node.name + ")"
+ )
+ ll_cinit = np.full((ch), ll_cinit.flatten()[0])
+ elif np.prod(ll_cinit_shape) != ch or ll_cinit_shape[ch_index] != ch:
+ # parameter shape not compatible with Channelwise_batch
+ continue
+
+ # check initializer contains integers as floats
+ if not (ll_cinit.astype(np.int32) == ll_cinit).all():
+ continue
+ # all initializer conditions are met
+
+ # check inputs
+ idt = model.get_tensor_datatype(ll_input)
+ if not idt.is_integer():
+ # skip conversion for layers with float input
+ continue
+
+ # check layout of inputs/outputs, and convert if needed
+ # check layout and convert if necessary
+ if ll_in_layout == DataLayout.NCHW:
+ ll_input = nchw_to_nhwc(ll_input, model, node_ind)
+ node_ind += 1
+ ll_in_shape = model.get_tensor_shape(ll_input)
+
+ # keep track of where we need to insert the HLS Op
+ # it has to be ahead of the output transform
+ insert_point = node_ind
+ ll_output_layout = model.get_tensor_layout(ll_output)
+ if ll_output_layout == DataLayout.NCHW:
+ ll_output = nchw_to_nhwc(ll_output, model, node_ind, reverse=True)
+ node_ind += 1
+
+ # get parameter data type
+ param_min = min(ll_cinit.flatten())
+ param_max = max(ll_cinit.flatten())
+ pdt = self.get_smallest_possible([param_min, param_max])
+
+ # set function and determine output data type
+ if node.op_type == "Add":
+ func = "add"
+ out_min = idt.min() + param_min
+ out_max = idt.max() + param_max
+ odt = self.get_smallest_possible([out_min, out_max])
+ elif node.op_type == "Mul":
+ func = "mul"
+ possible_limits = []
+ possible_limits += [idt.min() * param_min]
+ possible_limits += [idt.min() * param_max]
+ possible_limits += [idt.max() * param_min]
+ possible_limits += [idt.max() * param_max]
+ odt = self.get_smallest_possible(possible_limits)
+
+ model.set_initializer(ll_const, ll_cinit.reshape(ch))
+ model.set_tensor_datatype(ll_output, odt)
+
+ # create node with no parallelization first
+ pe = 1
+ assert ch % pe == 0, "Requirement IFC divisable by PE is violated."
+ # create and insert node
+ new_node = helper.make_node(
+ "ChannelwiseOp_Batch",
+ [ll_input, ll_const],
+ [ll_output],
+ domain="finn",
+ backend="fpgadataflow",
+ Func=func,
+ NumChannels=ch,
+ PE=pe,
+ inputDataType=idt.name,
+ paramDataType=pdt.name,
+ outputDataType=odt.name,
+ numInputVectors=list(ll_in_shape[:-1]),
+ )
+ graph.node.insert(insert_point, 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)
+
+
+class InferGlobalAccPoolLayer(Transformation):
+ """Convert any GlobalAveragePool into a GlobalAccPool HLS layer and a scalar Mul."""
+
+ 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 == "GlobalAveragePool":
+ in0 = node.input[0]
+ result = node.output[0]
+ in0_shape = model.get_tensor_shape(in0)
+
+ idt = model.get_tensor_datatype(in0)
+
+ # skip conversion for layers with float input
+ if not idt.is_integer():
+ continue
+
+ # check layout and convert if necessary
+ in0_layout = model.get_tensor_layout(in0)
+ result_layout = model.get_tensor_layout(result)
+
+ if in0_layout == DataLayout.NCHW:
+ in0 = nchw_to_nhwc(in0, model, node_ind)
+ node_ind += 1
+ in0_shape = model.get_tensor_shape(in0)
+
+ # keep track of where we need to insert the HLS Op
+ # it has to be ahead of the output transform
+ insert_point = node_ind
+
+ if result_layout == DataLayout.NCHW:
+ result = nchw_to_nhwc(result, model, node_ind, reverse=True)
+ node_ind += 1
+
+ num_ch = int(in0_shape[-1])
+ vecs = in0_shape[:-1]
+ # create node with no parallelization first
+ pe = 1
+ assert (
+ num_ch % pe == 0
+ ), "Requirement Labels divisable by PE is violated."
+
+ # create an additional tensor of the same shape and layout as result
+ out_shape = model.get_tensor_shape(result)
+ pool_out = helper.make_tensor_value_info(
+ model.make_new_valueinfo_name(), TensorProto.FLOAT, out_shape
+ )
+ model.graph.value_info.append(pool_out)
+ pool_out = pool_out.name
+ model.set_tensor_layout(pool_out, model.get_tensor_layout(result))
+
+ new_pool = helper.make_node(
+ "GlobalAccPool_Batch",
+ [in0],
+ [pool_out],
+ domain="finn",
+ backend="fpgadataflow",
+ NumChannels=num_ch,
+ PE=pe,
+ inputDataType=idt.name,
+ numInputVectors=vecs,
+ )
+
+ mul_value = helper.make_tensor_value_info(
+ model.make_new_valueinfo_name(), TensorProto.FLOAT, [1]
+ )
+ model.graph.value_info.append(mul_value)
+ model.set_initializer(mul_value.name, np.array(1 / (vecs[1] * vecs[2])))
+ new_mul = helper.make_node("Mul", [pool_out, mul_value.name], [result],)
+ graph.node.insert(insert_point, new_pool)
+ graph.node.insert(insert_point + 1, new_mul)
+ node_ind += 1
+ # 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/fpgadataflow/floorplan.py b/src/finn/transformation/fpgadataflow/floorplan.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d9a51875499d77f384c03f54009a9dd1001dea0
--- /dev/null
+++ b/src/finn/transformation/fpgadataflow/floorplan.py
@@ -0,0 +1,80 @@
+# 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 finn.custom_op.registry import getCustomOp
+from finn.transformation import Transformation
+from finn.util.basic import get_by_name
+
+
+class Floorplan(Transformation):
+ """Perform Floorplanning of the dataflow design. Separate DMAs into their own
+ partitions IDs, and TODO: split the design into sections of defined size"""
+
+ def __init__(self, limits=None):
+ super().__init__()
+ self.resource_limits = limits
+
+ def apply(self, model):
+ target_partition_id = 0
+ # we currently assume that all dataflow nodes belonging to the same partition
+ # are connected to each other and there is a single input/output to/from each.
+ all_nodes = list(model.graph.node)
+ df_nodes = list(
+ filter(lambda x: get_by_name(x.attribute, "backend") is not None, all_nodes)
+ )
+ dma_nodes = list(filter(lambda x: x.op_type == "IODMA", df_nodes))
+
+ non_dma_nodes = list(filter(lambda x: x not in dma_nodes, df_nodes))
+ dyn_tlastmarker_nodes = list(
+ filter(
+ lambda x: x.op_type == "TLastMarker"
+ and getCustomOp(x).get_nodeattr("DynIters") == "true",
+ non_dma_nodes,
+ )
+ )
+
+ non_dma_nodes = list(
+ filter(lambda x: x not in dyn_tlastmarker_nodes, non_dma_nodes)
+ )
+
+ for node in dma_nodes:
+ node_inst = getCustomOp(node)
+ node_inst.set_nodeattr("partition_id", target_partition_id)
+ target_partition_id += 1
+
+ for node in dyn_tlastmarker_nodes:
+ node_inst = getCustomOp(node)
+ node_inst.set_nodeattr("partition_id", target_partition_id)
+ target_partition_id += 1
+
+ for node in non_dma_nodes:
+ # TODO: implement proper floorplanning; for now just a single partition
+ node_inst = getCustomOp(node)
+ node_inst.set_nodeattr("partition_id", target_partition_id)
+
+ return (model, False)
diff --git a/src/finn/transformation/fpgadataflow/insert_iodma.py b/src/finn/transformation/fpgadataflow/insert_iodma.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4368edea717f7499481e9b1c6ac20f7d5bb5f58
--- /dev/null
+++ b/src/finn/transformation/fpgadataflow/insert_iodma.py
@@ -0,0 +1,198 @@
+# 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 onnx import TensorProto
+from onnx import helper as oh
+
+from finn.util.basic import get_by_name
+from finn.custom_op.registry import getCustomOp
+from finn.transformation import Transformation
+from finn.transformation.general import SortGraph
+import finn.core.data_layout as DataLayout
+import math
+import numpy as np
+
+
+class InsertIODMA(Transformation):
+ """Insert DMA nodes on all inputs and outputs."""
+
+ def __init__(self, max_intfwidth=32):
+ super().__init__()
+ assert (
+ 2 ** math.log2(max_intfwidth) == max_intfwidth
+ ), "max_intfwidth must be a power of 2"
+ self.max_intfwidth = max_intfwidth
+
+ def apply(self, model):
+ # only makes sense for a pure fpgadataflow graph -- so we check!
+ all_nodes = list(model.graph.node)
+ assert all(
+ get_by_name(x.attribute, "backend").s.decode("UTF-8") == "fpgadataflow"
+ for x in all_nodes
+ )
+ # parse streamingfclayers looking for external weights with no attached IODMA
+ fc_extw_nodes = list(
+ filter(
+ lambda x: x.op_type == "StreamingFCLayer_Batch"
+ and get_by_name(x.attribute, "mem_mode") is not None
+ and get_by_name(x.attribute, "mem_mode").s.decode("UTF-8") == "external"
+ and model.find_producer(x.input[1]) is None,
+ all_nodes,
+ )
+ )
+ graph_in_name = model.graph.input[0].name
+ first_node = model.find_consumer(graph_in_name)
+ graph_out_name = model.graph.output[0].name
+ final_node = model.find_producer(graph_out_name)
+ if (
+ final_node.op_type == "IODMA"
+ and first_node.op_type == "IODMA"
+ and len(fc_extw_nodes) == 0
+ ):
+ # TODO maybe check the correctness of properties
+ return (model, False)
+ else:
+ if final_node.op_type != "IODMA":
+ # check if tensor is NHWC
+ assert (
+ model.get_tensor_layout(graph_out_name) == DataLayout.NHWC
+ or model.get_tensor_layout(graph_in_name) == DataLayout.NC
+ ), "Data layout of tensors must be NHWC or NC"
+ out_shape = model.get_tensor_shape(graph_out_name)
+ out_dtype = model.get_tensor_datatype(graph_out_name)
+ # determine the feasible interface width
+ transfer_bits = np.prod(out_shape) * out_dtype.bitwidth()
+ intfwidth = math.gcd(transfer_bits, self.max_intfwidth)
+ assert (
+ intfwidth % 8 == 0
+ ), "No feasible interface width for transfer size"
+ # get width of stream input to DMA
+ streamWidth = getCustomOp(final_node).get_outstream_width()
+ # make new buffer
+ final_node_out = oh.make_tensor_value_info(
+ model.make_new_valueinfo_name(), TensorProto.FLOAT, out_shape
+ )
+ model.graph.value_info.append(final_node_out)
+ model.set_tensor_datatype(final_node_out.name, out_dtype)
+ # reroute final node output to final_node_out_name
+ final_node.output[0] = final_node_out.name
+ dma_node = oh.make_node(
+ "IODMA",
+ [final_node_out.name],
+ [graph_out_name],
+ numInputVectors=out_shape[:-1],
+ NumChannels=out_shape[-1],
+ dataType=str(out_dtype.name),
+ intfWidth=intfwidth,
+ streamWidth=streamWidth,
+ direction="out",
+ domain="finn",
+ backend="fpgadataflow",
+ )
+ model.graph.node.append(dma_node)
+ if first_node.op_type != "IODMA":
+ # check if tensor is NHWC
+ assert (
+ model.get_tensor_layout(graph_in_name) == DataLayout.NHWC
+ or model.get_tensor_layout(graph_in_name) == DataLayout.NC
+ ), "Data layout of tensors must be NHWC or NC"
+ in_shape = model.get_tensor_shape(graph_in_name)
+ in_dtype = model.get_tensor_datatype(graph_in_name)
+ # determine the feasible interface width
+ transfer_bits = np.prod(in_shape) * in_dtype.bitwidth()
+ intfwidth = math.gcd(transfer_bits, self.max_intfwidth)
+ assert (
+ intfwidth % 8 == 0
+ ), "No feasible interface width for transfer size"
+ # get width of stream output from DMA
+ streamWidth = getCustomOp(first_node).get_instream_width()
+ # make new buffer
+ first_node_in = oh.make_tensor_value_info(
+ model.make_new_valueinfo_name(), TensorProto.FLOAT, in_shape
+ )
+ model.graph.value_info.append(first_node_in)
+ model.set_tensor_datatype(first_node_in.name, in_dtype)
+ # reroute final node output to final_node_out_name
+ first_node.input[0] = first_node_in.name
+ dma_node = oh.make_node(
+ "IODMA",
+ [graph_in_name],
+ [first_node_in.name],
+ numInputVectors=in_shape[:-1],
+ NumChannels=in_shape[-1],
+ dataType=str(in_dtype.name),
+ intfWidth=intfwidth,
+ streamWidth=streamWidth,
+ direction="in",
+ domain="finn",
+ backend="fpgadataflow",
+ )
+ model.graph.node.insert(0, dma_node)
+ for fc_node in fc_extw_nodes:
+ # check if tensor is NHWC
+ assert (
+ model.get_tensor_layout(fc_node.input[1]) == DataLayout.NHWC
+ or model.get_tensor_layout(graph_in_name) == DataLayout.NC
+ ), "Data layout of tensors must be NHWC or NC"
+ fc_w_name = fc_node.input[1]
+ w_shape = model.get_tensor_shape(fc_w_name)
+ w_dtype = model.get_tensor_datatype(fc_w_name)
+ # determine the feasible interface width
+ transfer_bits = np.prod(w_shape) * w_dtype.bitwidth()
+ intfwidth = math.gcd(transfer_bits, self.max_intfwidth)
+ assert (
+ intfwidth % 8 == 0
+ ), "No feasible interface width for transfer size"
+ # calculate width of stream output from DMA
+ pe = get_by_name(fc_node.attribute, "PE").i
+ simd = get_by_name(fc_node.attribute, "SIMD").i
+ streamWidth = simd * pe * w_dtype.bitwidth()
+ # make new buffer
+ fc_node_in = oh.make_tensor_value_info(
+ model.make_new_valueinfo_name(), TensorProto.FLOAT, w_shape
+ )
+ model.graph.value_info.append(fc_node_in)
+ model.set_tensor_datatype(fc_node_in.name, w_dtype)
+ dma_node = oh.make_node(
+ "IODMA",
+ [fc_w_name],
+ [fc_node_in.name],
+ numInputVectors=w_shape[:-1],
+ NumChannels=w_shape[-1],
+ dataType=str(w_dtype.name),
+ intfWidth=intfwidth,
+ streamWidth=streamWidth,
+ direction="in",
+ burstMode="wrap",
+ domain="finn",
+ backend="fpgadataflow",
+ )
+ fc_node.input[1] = fc_node_in.name
+ model.graph.node.insert(0, dma_node)
+ model = model.transform(SortGraph())
+ return (model, True)
diff --git a/src/finn/transformation/fpgadataflow/insert_tlastmarker.py b/src/finn/transformation/fpgadataflow/insert_tlastmarker.py
index 32f32ece585a93465ba32fede45d5eb606a2b0a3..04dd437af27b9fbe18b2255c20a8e4acda03b3d0 100644
--- a/src/finn/transformation/fpgadataflow/insert_tlastmarker.py
+++ b/src/finn/transformation/fpgadataflow/insert_tlastmarker.py
@@ -31,23 +31,34 @@ from onnx import helper as oh
from finn.custom_op.registry import getCustomOp
from finn.transformation import Transformation
+from finn.util.basic import get_by_name
+
+import numpy as np
class InsertTLastMarker(Transformation):
- """Ensure that the graph is terminated with a TLastMarker node, inserting
- one if necessary."""
+ """Ensure that the graph is started/terminated with a TLastMarker node, inserting
+ one if necessary. Use constructor args to determine type of TLastMarker to be inserted.
+ More information available on the TLastMarker documentation.
+ """
- def __init__(self):
+ def __init__(self, both=False, external=True, dynamic=True):
super().__init__()
+ self.dyniters = dynamic
+ self.external = external
+ self.both = both
def apply(self, model):
# TODO only makes sense for a pure fpgadataflow graph -- check!
graph_out_name = model.graph.output[0].name
final_node = model.find_producer(graph_out_name)
- if final_node.op_type == "TLastMarker":
- # TODO maybe check the correctness of properties
- return (model, False)
- else:
+ graph_modified = False
+ if final_node.op_type != "TLastMarker" and not (
+ final_node.op_type == "IODMA"
+ and get_by_name(final_node.attribute, "direction").s.decode("UTF-8")
+ == "out"
+ ):
+
custom_op = getCustomOp(final_node)
num_iters = int(custom_op.get_number_output_values())
stream_width = int(custom_op.get_outstream_width())
@@ -69,8 +80,51 @@ class InsertTLastMarker(Transformation):
NumIters=num_iters,
StreamWidth=stream_width,
ElemWidth=elem_width,
+ DynIters=(1 if self.dyniters else 0),
+ Direction="out",
+ Protocol=("external" if self.external else "internal"),
domain="finn",
backend="fpgadataflow",
)
model.graph.node.append(tlast_node)
- return (model, True)
+ graph_modified = True
+ # if both is True, also insert marker on input
+ if self.both:
+ graph_in_name = model.graph.input[0].name
+ first_node = model.find_consumer(graph_in_name)
+ if first_node.op_type != "TLastMarker" and not (
+ first_node.op_type == "IODMA"
+ and get_by_name(first_node.attribute, "direction").s.decode("UTF-8")
+ == "in"
+ ):
+
+ custom_op = getCustomOp(first_node)
+ num_iters = np.prod(custom_op.get_folded_input_shape()[1:-1])
+ stream_width = int(custom_op.get_instream_width())
+ in_shape = model.get_tensor_shape(graph_in_name)
+ in_dtype = model.get_tensor_datatype(graph_in_name)
+ elem_width = in_dtype.bitwidth()
+ # make new buffer
+ first_node_in = oh.make_tensor_value_info(
+ model.make_new_valueinfo_name(), TensorProto.FLOAT, in_shape
+ )
+ model.graph.value_info.append(first_node_in)
+ model.set_tensor_datatype(first_node_in.name, in_dtype)
+ # reroute final node output to first_node_in_name
+ first_node.input[0] = first_node_in.name
+ tlast_node = oh.make_node(
+ "TLastMarker",
+ [graph_in_name],
+ [first_node_in.name],
+ NumIters=num_iters,
+ StreamWidth=stream_width,
+ ElemWidth=elem_width,
+ DynIters=(1 if self.dyniters else 0),
+ Direction="in",
+ Protocol=("external" if self.external else "internal"),
+ domain="finn",
+ backend="fpgadataflow",
+ )
+ model.graph.node.insert(0, tlast_node)
+ graph_modified = True
+ return (model, graph_modified)
diff --git a/src/finn/transformation/fpgadataflow/prepare_cppsim.py b/src/finn/transformation/fpgadataflow/prepare_cppsim.py
index a1524322ec03a4e96ef41f999144e3eed349c5af..6eae560e1191642cfaf85d92c6d0fcf644630973 100644
--- a/src/finn/transformation/fpgadataflow/prepare_cppsim.py
+++ b/src/finn/transformation/fpgadataflow/prepare_cppsim.py
@@ -29,9 +29,12 @@
import os
import finn.custom_op.registry as registry
-from finn.transformation import Transformation
from finn.util.basic import make_build_dir
from finn.util.fpgadataflow import is_fpgadataflow_node
+from finn.transformation import Transformation
+from finn.util.basic import get_num_default_workers
+import multiprocessing as mp
+import copy
def _codegen_single_node(node, model):
@@ -66,8 +69,39 @@ class PrepareCppSim(Transformation):
that contains generated C++ code that can be used to simulate node using cppsim.
The subsequent transformation is CompileCppSim"""
+ def __init__(self, num_workers=None):
+ super().__init__()
+ if num_workers is None:
+ self._num_workers = get_num_default_workers()
+ else:
+ self._num_workers = num_workers
+ assert self._num_workers >= 0, "Number of workers must be nonnegative."
+ if self._num_workers == 0:
+ self._num_workers = mp.cpu_count()
+
+ def prepareCppSim_node(self, node):
+ if is_fpgadataflow_node(node) is True:
+ _codegen_single_node(node, self.model)
+ return (node, False)
+
def apply(self, model):
- for node in model.graph.node:
- if is_fpgadataflow_node(node) is True:
- _codegen_single_node(node, model)
- return (model, False)
+ # Remove old nodes from the current model
+ self.model = copy.deepcopy(model)
+ old_nodes = []
+ for i in range(len(model.graph.node)):
+ old_nodes.append(model.graph.node.pop())
+
+ # Execute transformation in parallel
+ with mp.Pool(self._num_workers) as p:
+ new_nodes_and_bool = p.map(self.prepareCppSim_node, old_nodes, chunksize=1)
+
+ # extract nodes and check if the transformation needs to run again
+ # Note: .pop() had initially reversed the node order
+ run_again = False
+ for node, run in reversed(new_nodes_and_bool):
+ # Reattach new nodes to old model
+ model.graph.node.append(node)
+ if run is True:
+ run_again = True
+
+ return (model, run_again)
diff --git a/src/finn/transformation/fpgadataflow/synth_ooc.py b/src/finn/transformation/fpgadataflow/synth_ooc.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d49970c819961d1794cc89e998108639ca15593
--- /dev/null
+++ b/src/finn/transformation/fpgadataflow/synth_ooc.py
@@ -0,0 +1,64 @@
+# 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
+from shutil import copy2
+
+from finn.transformation import Transformation
+from finn.util.vivado import out_of_context_synth
+from finn.util.basic import make_build_dir
+
+
+class SynthOutOfContext(Transformation):
+ """Run out-of-context Vivado synthesis on a stitched IP design."""
+
+ def __init__(self, part, clk_period_ns, clk_name="ap_clk_0"):
+ super().__init__()
+ self.part = part
+ self.clk_period_ns = clk_period_ns
+ self.clk_name = clk_name
+
+ def apply(self, model):
+ def file_to_basename(x):
+ return os.path.basename(os.path.realpath(x))
+
+ vivado_stitch_proj_dir = model.get_metadata_prop("vivado_stitch_proj")
+ assert vivado_stitch_proj_dir is not None, "Need stitched IP to run."
+ 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("synth_out_of_context_")
+ with open(vivado_stitch_proj_dir + "/all_verilog_srcs.txt", "r") as f:
+ all_verilog_srcs = f.read().split()
+ for file in all_verilog_srcs:
+ if file.endswith(".v"):
+ copy2(file, build_dir)
+ ret = out_of_context_synth(
+ build_dir, top_module_name, self.part, self.clk_name, self.clk_period_ns
+ )
+ model.set_metadata_prop("res_total_ooc_synth", str(ret))
+ return (model, False)
diff --git a/src/finn/transformation/general.py b/src/finn/transformation/general.py
index f51ffbcfd9f62e06bf4942409fbb163e92ff6370..4303eb17f39a9949f5729e895e449bbb6a633033 100644
--- a/src/finn/transformation/general.py
+++ b/src/finn/transformation/general.py
@@ -28,6 +28,56 @@
import finn.util.basic as util
from finn.transformation import Transformation
+from toposort import toposort_flatten
+
+
+class RemoveUnusedTensors(Transformation):
+ """Remove any unused tensors in the graph by removing any initializers,
+ ValueInfo and tensor annotations associated with it. Unused tensors do not
+ appear as any input/output for any graph nodes.
+ """
+
+ def apply(self, model):
+ graph_modified = False
+ onnx_graph = model.model.graph
+ # build a set of tensors that we actually use in the graph nodes
+ used_tensors = set()
+ for node in model.graph.node:
+ for i in node.input:
+ used_tensors.add(i)
+ for o in node.output:
+ used_tensors.add(o)
+ # remove initializers, value_info and annotations that are not in the
+ # used set of tensors, as determined by the graph node i/o
+ for init in onnx_graph.initializer:
+ if init.name not in used_tensors:
+ onnx_graph.initializer.remove(init)
+ graph_modified = True
+ for vi in onnx_graph.value_info:
+ if vi.name not in used_tensors:
+ onnx_graph.value_info.remove(vi)
+ graph_modified = True
+ for qa in onnx_graph.quantization_annotation:
+ if qa.tensor_name not in used_tensors:
+ onnx_graph.quantization_annotation.remove(qa)
+ graph_modified = True
+
+ return (model, graph_modified)
+
+
+class RemoveStaticGraphInputs(Transformation):
+ "Remove any top-level graph inputs that have initializers."
+
+ def apply(self, model):
+ graph_modified = False
+ for i in model.graph.input:
+ if model.get_initializer(i.name) is not None:
+ # move ValueInfo to internal (value_info) container
+ model.graph.value_info.append(i)
+ model.graph.input.remove(i)
+ graph_modified = True
+
+ return (model, graph_modified)
class GiveUniqueNodeNames(Transformation):
@@ -104,11 +154,13 @@ class GiveUniqueParameterTensors(Transformation):
# 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))
+ 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
@@ -116,6 +168,55 @@ class GiveUniqueParameterTensors(Transformation):
return (model, graph_modified)
+class SortGraph(Transformation):
+ """ Returns the model with its node list sorted topologically.
+ Any ONNX graph to be executed must have a topologically sorted node list,
+ as dictated by the ONNX standard.
+ """
+
+ # Notes on SortGraph performance:
+ # benchmark in tests/transformation/test_sort_graph.py
+ # The algorithm doesn't move initializers so its performance should only depend on
+ # the number of nodes
+ #
+ # Relative order of magnitudes for time per step:
+ # - Gather graph structure: base
+ # - Sort nodes: 0.1 of base
+ # - Remove and insert in order : 0.001 of base
+ #
+ # Notes:
+ # Remove nodes and insert them in order:
+ # Probably this is faster than copying initializers and more robust in general
+
+ def apply(self, model):
+ # Gather graph structure
+ graph_dependencies = {}
+ node_list = [
+ n for n in model.graph.node
+ ] # I also need the list to remove the nodes
+ for node_idx, n in enumerate(node_list):
+ node_pred = model.find_direct_predecessors(n)
+ if node_pred is None:
+ # Will also eliminate nodes that are floating around for some reason
+ continue
+
+ node_dependencies = [node_list.index(pred) for pred in node_pred]
+ graph_dependencies[node_idx] = set(node_dependencies)
+
+ # Sort nodes
+ sorted_node_indexes = toposort_flatten(graph_dependencies)
+
+ # Remove nodes and insert them in order
+ # Can't remove nodes before if I want to use model.find_direct_predecessors()
+ for n in node_list:
+ model.graph.node.remove(n)
+
+ for new_idx, sorted_idx in enumerate(sorted_node_indexes):
+ model.graph.node.insert(new_idx, node_list[sorted_idx])
+
+ return model, False
+
+
class ConvertSubToAdd(Transformation):
"""Convert subtract-a-constant nodes to add-a-constant nodes."""
diff --git a/src/finn/transformation/infer_data_layouts.py b/src/finn/transformation/infer_data_layouts.py
index 9ac75578ffb911cc44cfddc2b2119b55e6abf2dd..e7a6b88239a1735d5379e165333f8356ae6f88a1 100644
--- a/src/finn/transformation/infer_data_layouts.py
+++ b/src/finn/transformation/infer_data_layouts.py
@@ -38,7 +38,7 @@ def _dims_to_layout(model, node, ndims):
return DataLayout.NC
else:
if node.domain == "finn":
- if node.op_type == "MultiThreshold":
+ if node.op_type == "MultiThreshold" or node.op_type == "QuantAvgPool2d":
mt_inst = registry.getCustomOp(node)
layout = mt_inst.get_nodeattr("data_layout")
if layout == "NHWC" and ndims == 4:
diff --git a/src/finn/transformation/infer_datatypes.py b/src/finn/transformation/infer_datatypes.py
index 1acd4e3abe2d77248810cf15c15475e806a3bd32..39b7a787be8c725e7b6d474757dd96fc4848dfe0 100644
--- a/src/finn/transformation/infer_datatypes.py
+++ b/src/finn/transformation/infer_datatypes.py
@@ -71,7 +71,13 @@ def _infer_node_datatype(model, node):
else:
# unknown, assume node produces float32 outputs
for o in node.output:
- model.set_tensor_datatype(o, DataType.FLOAT32)
+ # check if output datatype is already set to a value != FLOAT32
+ odtype = model.get_tensor_datatype(o)
+ if odtype is not None and odtype != DataType.FLOAT32:
+ # don't change data type
+ model.set_tensor_datatype(o, odtype)
+ else:
+ model.set_tensor_datatype(o, DataType.FLOAT32)
# compare old and new output dtypes to see if anything changed
new_odtypes = list(map(lambda x: model.get_tensor_datatype(x), node.output))
graph_modified = new_odtypes != odtypes
diff --git a/src/finn/transformation/lower_convs_to_matmul.py b/src/finn/transformation/lower_convs_to_matmul.py
index 0cee8eae1f26ba8f78c868343883efa33ba47b16..e5a1f778d0cac48925ecd97ae8b970f7bdab9c4f 100644
--- a/src/finn/transformation/lower_convs_to_matmul.py
+++ b/src/finn/transformation/lower_convs_to_matmul.py
@@ -104,14 +104,19 @@ class LowerConvsToMatMul(Transformation):
inp_trans_out = inp_trans_out.name
model.set_tensor_datatype(inp_trans_out, idt)
- im2col_out = helper.make_tensor_value_info(
- model.make_new_valueinfo_name(),
- TensorProto.FLOAT,
- (1, ofm_dim, ofm_dim, ifm_ch * k * k),
- )
- graph.value_info.append(im2col_out)
- im2col_out = im2col_out.name
- model.set_tensor_datatype(im2col_out, idt)
+ need_im2col = True
+ if k == 1 and pad == 0 and stride == 1:
+ need_im2col = False
+
+ if need_im2col:
+ im2col_out = helper.make_tensor_value_info(
+ model.make_new_valueinfo_name(),
+ TensorProto.FLOAT,
+ (1, ofm_dim, ofm_dim, ifm_ch * k * k),
+ )
+ graph.value_info.append(im2col_out)
+ im2col_out = im2col_out.name
+ model.set_tensor_datatype(im2col_out, idt)
matmul_out = helper.make_tensor_value_info(
model.make_new_valueinfo_name(),
@@ -128,20 +133,24 @@ class LowerConvsToMatMul(Transformation):
"Transpose", [cnv_input], [inp_trans_out], perm=[0, 2, 3, 1]
)
# lower input tensor
- im2col_node = helper.make_node(
- "Im2Col",
- [inp_trans_out],
- [im2col_out],
- domain="finn",
- stride=stride,
- kernel_size=k,
- pad_amount=pad,
- input_shape="(1,{},{},{})".format(ifm_dim, ifm_dim, ifm_ch),
- dw=dw,
- )
+ matmul_input = inp_trans_out
+ if need_im2col:
+ matmul_input = im2col_out
+ im2col_node = helper.make_node(
+ "Im2Col",
+ [inp_trans_out],
+ [im2col_out],
+ domain="finn",
+ stride=stride,
+ kernel_size=k,
+ pad_amount=pad,
+ input_shape="(1,{},{},{})".format(ifm_dim, ifm_dim, ifm_ch),
+ depthwise=dw,
+ )
+
# do matmul
matmul_node = helper.make_node(
- "MatMul", [im2col_out, weight_name], [matmul_out]
+ "MatMul", [matmul_input, weight_name], [matmul_out]
)
# NHWC -> NCHW
out_trans_node = helper.make_node(
@@ -149,9 +158,13 @@ class LowerConvsToMatMul(Transformation):
)
# insert nodes where the conv is to preserve topological ordering
graph.node.insert(node_ind, inp_trans_node)
- graph.node.insert(node_ind + 1, im2col_node)
- graph.node.insert(node_ind + 2, matmul_node)
- graph.node.insert(node_ind + 3, out_trans_node)
+ if need_im2col:
+ graph.node.insert(node_ind + 1, im2col_node)
+ graph.node.insert(node_ind + 2, matmul_node)
+ graph.node.insert(node_ind + 3, out_trans_node)
+ else:
+ graph.node.insert(node_ind + 1, matmul_node)
+ graph.node.insert(node_ind + 2, out_trans_node)
# remove old nodes
graph.node.remove(n)
model = model.transform(InferShapes())
diff --git a/src/finn/transformation/merge_onnx_models.py b/src/finn/transformation/merge_onnx_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..5dc6127ed189311c72a119932394aca4745e3608
--- /dev/null
+++ b/src/finn/transformation/merge_onnx_models.py
@@ -0,0 +1,222 @@
+# 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 copy
+import warnings
+from onnx import helper
+
+from finn.transformation import Transformation
+from finn.core.modelwrapper import ModelWrapper
+import finn.util.basic as util
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.general import (
+ GiveReadableTensorNames,
+ GiveUniqueNodeNames,
+ GiveUniqueParameterTensors,
+)
+
+
+class MergeONNXModels(Transformation):
+ """Merges two models. The model passed in the transformation will be inserted before
+ the model the transformation is applied on, the resulting model is returned.
+ This transformation will try to connect graph.output[0] of the pre model and
+ graph.input[0] of the post model.
+ If more than one input or output exists, a warning is raised."""
+
+ def __init__(self, pre_model):
+ super().__init__()
+ # use deep copy of model that should be inserted in the beginning of
+ # the other model to ensure that it stays unchanged
+ self.pre_model = copy.deepcopy(pre_model)
+
+ def apply(self, model):
+ graph_modified = False
+ pre_model = self.pre_model
+ post_model = copy.deepcopy(model)
+
+ # check for dynamic outputs of pre model
+ dyn_outp = []
+ for outp in pre_model.graph.output:
+ init_val = pre_model.get_initializer(outp.name)
+ if init_val is None:
+ dyn_outp.append(outp)
+
+ if len(dyn_outp) != 1:
+ warnings.warn(
+ "The pre model has more than one dynamic output! The transformation "
+ "tries to connect the first dynamic output to the first dynamic input "
+ "of the post model."
+ )
+
+ # check for dynamic inputs of post model
+ dyn_inp = []
+ for inp in post_model.graph.input:
+ init_val = post_model.get_initializer(inp.name)
+ if init_val is None:
+ dyn_inp.append(inp)
+
+ if len(dyn_inp) != 1:
+ warnings.warn(
+ "The post model has more than one dynamic input! The transformation "
+ "tries to connect the first dynamic input to the first dynamic output "
+ "of the pre model."
+ )
+
+ # erase all node names to avoid conflict
+ for n in pre_model.graph.node:
+ n.name = ""
+ for n in post_model.graph.node:
+ n.name = ""
+
+ # randomize all tensor names
+ names1 = pre_model.get_all_tensor_names()
+ names2 = post_model.get_all_tensor_names()
+ used_names = names1 + names2
+
+ # pre_model
+ for tensor_name in names1:
+ new_name = util.random_string()
+ while new_name in used_names:
+ new_name = util.random_string()
+ pre_model.rename_tensor(tensor_name, new_name)
+ used_names.append(new_name)
+
+ # post_model
+ for tensor in names2:
+ new_name = util.random_string()
+ while new_name in used_names:
+ new_name = util.random_string()
+ post_model.rename_tensor(tensor_name, new_name)
+ used_names.append(new_name)
+
+ # check if models can be merged
+ output_model_a = dyn_outp[0].name
+ input_model_b = dyn_inp[0].name
+ output_a_shape = pre_model.get_tensor_shape(output_model_a)
+ input_b_shape = post_model.get_tensor_shape(input_model_b)
+ assert (
+ output_a_shape == input_b_shape
+ ), "Models can't be merged! Shapes don't match."
+
+ # connect output of one model to input of the other
+ for n in pre_model.graph.node:
+ if output_model_a == n.output[0]:
+ n.output[0] = input_model_b
+
+ # extract information for new model
+
+ # nodes
+ node_list_a = pre_model.graph.node
+ node_list_b = post_model.graph.node
+
+ node_list = node_list_a
+ for node in node_list_b:
+ node_list.append(node)
+
+ # in and output
+ inp = pre_model.graph.input[0]
+ outp = post_model.graph.output[0]
+
+ # create new graph and model
+ new_graph = helper.make_graph(
+ nodes=node_list,
+ name="fuse-graph",
+ inputs=[inp],
+ outputs=[outp],
+ value_info=[],
+ )
+
+ new_model = helper.make_model(new_graph, producer_name="fuse_model")
+ new_model = ModelWrapper(new_model)
+
+ # add value info from both models to new model
+ # pre model
+ vi_pre = [x for x in pre_model.graph.input]
+ vi_pre += [x for x in pre_model.graph.output]
+ vi_pre += [x for x in pre_model.graph.value_info]
+ for vi in vi_pre:
+ # preserve intializers, quantization/sparsity annotation, etc.
+ # initializer
+ init_val = pre_model.get_initializer(vi.name)
+ if init_val is not None:
+ new_model.set_initializer(vi.name, init_val)
+ # FINN datatype
+ dtype = pre_model.get_tensor_datatype(vi.name)
+ new_model.set_tensor_datatype(vi.name, dtype)
+ # data layout
+ data_layout = pre_model.get_tensor_layout(vi.name)
+ if data_layout is not None:
+ new_model.set_tensor_layout(vi.name, data_layout)
+ # sparsity
+ sparsity = pre_model.get_tensor_sparsity(vi.name)
+ if sparsity is not None:
+ new_model.set_tensor_sparsity(vi.name, sparsity)
+ # graph input should not be part of graph.value_info, so don't insert
+ # if current vi == inp, but the quantization annotation is preserved
+ if vi == inp:
+ continue
+ new_model.graph.value_info.append(vi)
+
+ # post model
+ vi_model = [x for x in post_model.graph.input]
+ vi_model += [x for x in post_model.graph.output]
+ vi_model += [x for x in post_model.graph.value_info]
+ for vi in vi_model:
+ # preserve intializers, quantization/sparsity annotation, etc.
+ # initializer
+ init_val = post_model.get_initializer(vi.name)
+ if init_val is not None:
+ new_model.set_initializer(vi.name, init_val)
+ # FINN datatype
+ dtype = post_model.get_tensor_datatype(vi.name)
+ new_model.set_tensor_datatype(vi.name, dtype)
+ # data layout
+ data_layout = post_model.get_tensor_layout(vi.name)
+ if data_layout is not None:
+ new_model.set_tensor_layout(vi.name, data_layout)
+ # sparsity
+ sparsity = post_model.get_tensor_sparsity(vi.name)
+ if sparsity is not None:
+ new_model.set_tensor_sparsity(vi.name, sparsity)
+ # graph output should not be part of graph.value_info, so don't insert
+ # if current vi == outp, but the quantization annotation is preserved
+ if vi == outp:
+ continue
+ new_model.graph.value_info.append(vi)
+
+ # tidy-up new model
+ model = new_model
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+ model = model.transform(InferDataLayouts())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(GiveUniqueParameterTensors())
+ model = model.transform(GiveReadableTensorNames())
+
+ return (model, graph_modified)
diff --git a/src/finn/transformation/streamline/__init__.py b/src/finn/transformation/streamline/__init__.py
index c9c73fa4c8303ee28bc1cc6aee879d633740e01e..d7686eaadcbc800542ab96c5f45145857412b773 100644
--- a/src/finn/transformation/streamline/__init__.py
+++ b/src/finn/transformation/streamline/__init__.py
@@ -41,6 +41,7 @@ from finn.transformation.streamline.absorb import (
FactorOutMulSignMagnitude,
Absorb1BitMulIntoMatMul,
Absorb1BitMulIntoConv,
+ AbsorbSignBiasIntoMultiThreshold,
)
from finn.transformation.streamline.collapse_repeated import (
@@ -52,13 +53,14 @@ from finn.transformation.streamline.reorder import (
MoveAddPastMul,
MoveScalarMulPastMatMul,
MoveScalarAddPastMatMul,
- MoveScalarAddPastConv,
+ MoveAddPastConv,
MoveScalarMulPastConv,
)
from finn.transformation.streamline.round_thresholds import RoundAndClipThresholds
from finn.transformation.streamline.sign_to_thres import ConvertSignToThres
from finn.transformation.batchnorm_to_affine import BatchNormToAffine
+from finn.transformation.streamline.remove import RemoveIdentityOps
class Streamline(Transformation):
@@ -70,9 +72,10 @@ class Streamline(Transformation):
ConvertDivToMul(),
BatchNormToAffine(),
ConvertSignToThres(),
+ AbsorbSignBiasIntoMultiThreshold(),
MoveAddPastMul(),
MoveScalarAddPastMatMul(),
- MoveScalarAddPastConv(),
+ MoveAddPastConv(),
MoveScalarMulPastMatMul(),
MoveScalarMulPastConv(),
MoveAddPastMul(),
@@ -87,6 +90,7 @@ class Streamline(Transformation):
]
for trn in streamline_transformations:
model = model.transform(trn)
+ model = model.transform(RemoveIdentityOps())
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveReadableTensorNames())
model = model.transform(InferDataTypes())
diff --git a/src/finn/transformation/streamline/absorb.py b/src/finn/transformation/streamline/absorb.py
index dbcf97361017144174f9fbfca35a84361b5abd26..8398a277443530e84632d26fbfca6d90ea4b0b9e 100644
--- a/src/finn/transformation/streamline/absorb.py
+++ b/src/finn/transformation/streamline/absorb.py
@@ -28,14 +28,81 @@
import numpy as np
from onnx import helper as oh
+import warnings
from finn.core.datatype import DataType
+import finn.core.data_layout as DataLayout
from finn.transformation import Transformation
from finn.util.basic import get_by_name
from finn.custom_op.registry import getCustomOp
+from finn.transformation.infer_shapes import InferShapes
from finn.transformation.infer_datatypes import InferDataTypes
+class AbsorbSignBiasIntoMultiThreshold(Transformation):
+ """Absorb scalar bias originating from signed int export back into
+ MultiThreshold and re-evaluate the output datatype."""
+
+ def apply(self, model):
+ graph = model.graph
+ node_ind = 0
+ graph_modified = False
+ for n in graph.node:
+ # search for (MultiThreshold, Add) pair
+ node_ind += 1
+ if (
+ n.op_type == "MultiThreshold"
+ 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 == "Add":
+ mt_node = n
+ add_node = consumer
+ threshold_name = mt_node.input[1]
+ add_weight_name = add_node.input[1]
+ T = model.get_initializer(threshold_name)
+ A = model.get_initializer(add_weight_name)
+ if (A is None) or (T is None):
+ warnings.warn("Threshold or add bias not constant, skipping")
+ continue
+ end_name = add_node.output[0]
+ # we can only absorb scalar adds
+ is_scalar = A.ndim == 0 or all(x == 1 for x in A.shape)
+ if not is_scalar:
+ continue
+ bias = A.flatten()[0]
+ # set MultiThreshold bias property
+ mt_inst = getCustomOp(mt_node)
+ bias += mt_inst.get_nodeattr("out_bias")
+ mt_inst.set_nodeattr("out_bias", bias)
+ graph_modified = True
+ # compute new DataType for MultiThreshold output
+ steps = T.shape[-1]
+ new_min = bias
+ new_max = steps + bias
+ odt = DataType.get_smallest_possible(steps).name.replace(
+ "UINT", "INT"
+ )
+ odt = DataType[odt]
+ assert odt.allowed(new_max) and odt.allowed(
+ new_min
+ ), """Could
+ not compute new MultiThreshold DataType (min = %d max = %d)""" % (
+ new_min,
+ new_max,
+ )
+ mt_inst.set_nodeattr("out_dtype", odt.name)
+ # remove Add node, rewire MultiThreshold
+ graph.node.remove(add_node)
+ mt_node.output[0] = end_name
+ # set datatype
+ model.set_tensor_datatype(end_name, odt)
+ if graph_modified:
+ model = model.transform(InferDataTypes())
+ return (model, graph_modified)
+
+
class AbsorbAddIntoMultiThreshold(Transformation):
"""Absorb preceding Add ops into MultiThreshold by updating the threshold
values. Only scalar/1D add vectors can be absorbed."""
@@ -250,11 +317,13 @@ class AbsorbTransposeIntoMultiThreshold(Transformation):
graph_modified = False
for n in graph.node:
node_ind += 1
- if n.op_type == "Transpose":
+ if n.op_type == "Transpose" and not model.is_fork_node(n):
perms = list(get_by_name(n.attribute, "perm").ints)
if perms == [0, 3, 1, 2]:
mt_cand = model.find_consumer(n.output[0])
- if mt_cand.op_type == "MultiThreshold":
+ if mt_cand.op_type == "MultiThreshold" and not model.is_fork_node(
+ mt_cand
+ ):
final_t_cand = model.find_consumer(mt_cand.output[0])
if final_t_cand.op_type == "Transpose":
perms = list(
@@ -290,3 +359,182 @@ class AbsorbTransposeIntoMultiThreshold(Transformation):
if graph_modified:
model = model.transform(InferDataTypes())
return (model, graph_modified)
+
+
+class AbsorbTransposeIntoFlatten(Transformation):
+ """Absorb transpose node into succeeding flatten node, if H=W=1 and the first
+ dimension stays the same. Can also be applied if flatten is implemented implicitly
+ by a reshape node with shape [1, -1] and the first input dimension is 1"""
+
+ def apply(self, model):
+ graph = model.graph
+ graph_modified = False
+ node_ind = 0
+ for n in graph.node:
+ node_ind += 1
+ if (
+ n.op_type == "Reshape"
+ and (model.get_initializer(n.input[1]) == [1, -1]).all()
+ ) or n.op_type == "Flatten":
+ prod = model.find_producer(n.input[0])
+ if (
+ prod is not None
+ and prod.op_type == "Transpose"
+ # we ensure that the first dimension is not changed from the
+ # transpose operation
+ and get_by_name(prod.attribute, "perm").ints[0] == 0
+ ):
+ data_layout = model.get_tensor_layout(prod.input[0])
+ # check for the data layout to interpret input shape correctly
+ if data_layout is None:
+ warnings.warn(
+ """Data layout for input tensor of Transpose node is not set.
+ To use AbsorbTransposeIntoFlatten transformation
+ please set tensor data layout."""
+ )
+ continue
+ elif data_layout == DataLayout.NCHW:
+ (b, c, h, w) = model.get_tensor_shape(prod.input[0])
+ # if h=w=1 the transposition can be absorbed, otherwise
+ # the absorption would lead to an error in the behavior
+ if h != 1 or w != 1:
+ continue
+ # the flatten node from onnx keeps by default the first
+ # dim and flattens the rest, that is why this transformation
+ # can only work with b != 1 if the model contains already a
+ # flatten node and not a reshape node with shape = [1, -1].
+ # If the first dim of the input tensor is not 1, flatten and
+ # reshape (with shape = [1, -1]) would lead to different results
+ if n.op_type == "Reshape" and b != 1:
+ continue
+ elif data_layout == DataLayout.NHWC:
+ (b, h, w, c) = model.get_tensor_shape(prod.input[0])
+ if h != 1 or w != 1:
+ continue
+ if n.op_type == "Reshape" and b != 1:
+ continue
+ # create single flatten node and remove obsolete nodes
+ node = oh.make_node("Flatten", [prod.input[0]], [n.output[0]])
+ graph.node.remove(n)
+ graph.node.remove(prod)
+ graph.node.insert(node_ind, node)
+ graph_modified = True
+ if graph_modified:
+ model = model.transform(InferDataTypes())
+ return (model, graph_modified)
+
+
+class AbsorbScalarMulIntoTopK(Transformation):
+ """Absorb a mul node into a suceeding topk node if the mul is scalar."""
+
+ def apply(self, model):
+ graph = model.graph
+ node_ind = 0
+ graph_modified = False
+ for n in graph.node:
+ node_ind += 1
+ if n.op_type == "TopK":
+ prod = model.find_producer(n.input[0])
+ if prod is not None and prod.op_type == "Mul":
+ prod_input = prod.input[0]
+ param_name = prod.input[1]
+ A = model.get_initializer(param_name)
+ if A is None:
+ warnings.warn("Param is not constant, skipping")
+ continue
+ if all(x == 1 for x in A.shape) and A > 0:
+ # if the mul is scalar and positive, we can just delete the
+ # mul node and rewire the top k node. Because the top k node
+ # works with probabilities and their relation to each other
+ # the relation doesn't change if every value is multiplied
+ # with a scalar
+ graph.node.remove(prod)
+ n.input[0] = prod_input
+ # to avoid error the dataype is set to float32
+ model.set_tensor_datatype(n.input[0], DataType.FLOAT32)
+ graph_modified = True
+ if graph_modified:
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+ return (model, graph_modified)
+
+
+class AbsorbConsecutiveTransposes(Transformation):
+ """Remove (Transpose -> Transpose) patterns when the input and output
+ of the pattern have the same layout."""
+
+ def Are_opposite_permutations(self, perms1, perms2):
+ if len(perms1) != len(perms2):
+ return False
+ assert 0 <= max(perms2) < len(perms2), "invalid permutation"
+ assert 0 <= max(perms1) < len(perms1), "invalid permutation"
+
+ for i, p in enumerate(perms2):
+ if perms1[p] != i:
+ return False
+
+ return True
+
+ def apply(self, model):
+ graph = model.graph
+ graph_modified = False
+ for n in graph.node:
+ if n.op_type == "Transpose":
+ if model.is_fork_node(n):
+ next_nodes = model.find_direct_successors(n)
+ perms1 = list(get_by_name(n.attribute, "perm").ints)
+
+ # check if all nodes after fork are opposite transposes
+ all_opposite_transposes = True
+ for next_node in next_nodes:
+ if next_node is not None and next_node.op_type == "Transpose":
+ perms2 = list(get_by_name(next_node.attribute, "perm").ints)
+ if not self.Are_opposite_permutations(perms1, perms2):
+ all_opposite_transposes = False
+ break
+ else:
+ all_opposite_transposes = False
+ break
+
+ if not all_opposite_transposes:
+ continue
+
+ prod = model.find_producer(n.input[0])
+ for next_node in next_nodes:
+ # connect next_node's consumer input to n's producer output
+ # TODO implement this to allow for forks as producers and
+ # joins as consumers
+ cons = model.find_consumer(next_node.output[0])
+ cons.input[0] = prod.output[0]
+
+ # remove consumer transpose
+ graph.node.remove(next_node)
+
+ # remove producer transpose
+ graph.node.remove(n)
+ graph_modified = True
+
+ else:
+ next_node = model.find_consumer(n.output[0])
+ if next_node is not None and next_node.op_type == "Transpose":
+ perms1 = list(get_by_name(n.attribute, "perm").ints)
+ perms2 = list(get_by_name(next_node.attribute, "perm").ints)
+ if self.Are_opposite_permutations(perms1, perms2):
+
+ # connect next_node's consumer input to n's producer output
+ # TODO implement this to allow for forks as producers
+ consumers = model.find_direct_successors(next_node)
+ prod = model.find_producer(n.input[0])
+ for cons in consumers:
+ for cons_in in cons.input:
+ if cons_in == next_node.output[0]:
+ prod.output[0] = cons_in
+ break
+ # remove both transposes
+ graph.node.remove(n)
+ graph.node.remove(next_node)
+
+ graph_modified = True
+ if graph_modified:
+ model = model.transform(InferDataTypes())
+ return (model, graph_modified)
diff --git a/src/finn/transformation/streamline/collapse_repeated.py b/src/finn/transformation/streamline/collapse_repeated.py
index 67824ad4f633983b93e3178d03118927a1ddd85b..769bed841ce07c1c9c62f762de4b2c0937a6d68f 100644
--- a/src/finn/transformation/streamline/collapse_repeated.py
+++ b/src/finn/transformation/streamline/collapse_repeated.py
@@ -30,6 +30,7 @@ from onnx import helper as oh
from finn.transformation import Transformation
from finn.transformation.infer_shapes import InferShapes
+from finn.core.datatype import DataType
class CollapseRepeatedOp(Transformation):
@@ -83,6 +84,9 @@ class CollapseRepeatedOp(Transformation):
graph.node.insert(node_ind, new_node)
# replace parameter value
model.set_initializer(new_node_param_name, new_param)
+ # be conservative with param/output DataTypes
+ model.set_tensor_datatype(new_node_param_name, DataType.FLOAT32)
+ model.set_tensor_datatype(end_name, DataType.FLOAT32)
# remove old nodes
graph.node.remove(n)
graph.node.remove(consumer)
diff --git a/src/finn/transformation/streamline/remove.py b/src/finn/transformation/streamline/remove.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddc4233ddafbc70c4d20d316ea72ea6bba1b82a8
--- /dev/null
+++ b/src/finn/transformation/streamline/remove.py
@@ -0,0 +1,69 @@
+# 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 finn.transformation import Transformation
+from finn.transformation.infer_shapes import InferShapes
+import numpy as np
+
+class RemoveIdentityOps(Transformation):
+ """Remove identity ops like Add/Sub with zero or Mul/Div with one"""
+
+ def apply(self, model):
+ graph = model.graph
+ node_ind = 0
+ graph_modified = False
+ for n in graph.node:
+ node_ind += 1
+ if (
+ n.op_type in ["Add", "Sub"]
+ and not model.is_fork_node(n)
+ and not model.is_join_node(n)
+ ):
+ A = model.get_initializer(n.input[1])
+ if A is not None and (A == np.zeros_like(A)).all():
+ producer = model.find_producer(n.input[0])
+ # remove node and wire output tensor to
+ # output of producer node
+ producer.output[0] = n.output[0]
+ graph.node.remove(n)
+
+ elif (
+ n.op_type in ["Mul", "Div"]
+ and not model.is_fork_node(n)
+ and not model.is_join_node(n)
+ ):
+ A = model.get_initializer(n.input[1])
+ if A is not None and (A == np.ones_like(A)).all():
+ producer = model.find_producer(n.input[0])
+ # remove node and wire output tensor to
+ # output of producer node
+ producer.output[0] = n.output[0]
+ graph.node.remove(n)
+ model = model.transform(InferShapes())
+ return (model, graph_modified)
diff --git a/src/finn/transformation/streamline/reorder.py b/src/finn/transformation/streamline/reorder.py
index 0b6259a61d3eb67b7b38d4c6939019ce2893a875..2b03532ce3ba7d5159e5ae57e61c2af9c8c37fce 100644
--- a/src/finn/transformation/streamline/reorder.py
+++ b/src/finn/transformation/streamline/reorder.py
@@ -27,12 +27,19 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
+import warnings
from onnx import helper as oh
+from onnx import TensorProto
from finn.transformation import Transformation
+import finn.core.data_layout as DataLayout
from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.core.datatype import DataType
from finn.core.onnx_exec import execute_node
from finn.util.basic import get_by_name
+from finn.custom_op.registry import getCustomOp
class MoveAddPastMul(Transformation):
@@ -65,8 +72,11 @@ class MoveAddPastMul(Transformation):
add_weight_name = n.input[1]
A = model.get_initializer(mul_weight_name)
B = model.get_initializer(add_weight_name)
- assert A is not None, "Initializer for mul weights is not set."
- assert B is not None, "Initializer for add weights is not set."
+ if (A is None) or (B is None):
+ warnings.warn(
+ "Mul or add does not have constant params, skipping"
+ )
+ continue
start_name = n.input[0]
middle_name = n.output[0]
end_name = consumer.output[0]
@@ -121,8 +131,9 @@ class MoveScalarMulPastMatMul(Transformation):
matmul_weight_name = consumer.input[1]
A = model.get_initializer(mul_weight_name)
W = model.get_initializer(matmul_weight_name)
- assert A is not None, "Initializer for mul weights is not set."
- assert W is not None, "Initializer for matmul weights is not set."
+ if (A is None) or (W is None):
+ warnings.warn("MatMul or Mul params are not constant, skipping")
+ continue
start_name = n.input[0]
middle_name = n.output[0]
end_name = consumer.output[0]
@@ -178,8 +189,9 @@ class MoveScalarAddPastMatMul(Transformation):
matmul_weight_name = consumer.input[1]
A = model.get_initializer(add_weight_name)
W = model.get_initializer(matmul_weight_name)
- assert A is not None, "Initializer for add weights is not set."
- assert W is not None, "Initializer for matmul weights is not set."
+ if (A is None) or (W is None):
+ warnings.warn("MatMul or Add params are not constant, skipping")
+ continue
start_name = n.input[0]
middle_name = n.output[0]
end_name = consumer.output[0]
@@ -213,8 +225,8 @@ class MoveScalarAddPastMatMul(Transformation):
return (model, graph_modified)
-class MoveScalarAddPastConv(Transformation):
- """Move scalar add operations past conv operations. We want to have adds
+class MoveAddPastConv(Transformation):
+ """Move scalar and channelwise add operations past conv operations. We want to have adds
next to each other such that they can be collapsed into a single add."""
def apply(self, model):
@@ -239,8 +251,12 @@ class MoveScalarAddPastConv(Transformation):
add_weight_name = n.input[1]
conv_in_name = consumer.input[0]
conv_in_shape = model.get_tensor_shape(conv_in_name)
+ # assume datalayout to be NCHW
+ channels = conv_in_shape[1]
A = model.get_initializer(add_weight_name)
- assert A is not None, "Initializer for add weights is not set."
+ if A is None:
+ warnings.warn("Add param is not constant, skipping")
+ continue
start_name = n.input[0]
end_name = consumer.output[0]
conv_out_shape = model.get_tensor_shape(end_name)
@@ -249,11 +265,17 @@ class MoveScalarAddPastConv(Transformation):
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:
+ if (
+ all(x == 1 for x in A.shape) or A.shape == (1, channels, 1, 1)
+ ) 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)
- conv_in_const.fill(A.item())
+ if A.shape == (1, channels, 1, 1):
+ for ch in range(channels):
+ conv_in_const[0][ch].fill(A[0][ch].item())
+ else:
+ conv_in_const.fill(A.item())
# create an execution context and put in const input
exec_ctx = model.make_empty_exec_context()
exec_ctx[conv_in_name] = conv_in_const
@@ -308,7 +330,9 @@ class MoveScalarMulPastConv(Transformation):
):
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."
+ if A is None:
+ warnings.warn("Mul param is not constant, skipping")
+ continue
conv_node = consumer
mul_node = n
start_name = mul_node.input[0]
@@ -336,6 +360,71 @@ class MoveScalarMulPastConv(Transformation):
return (model, graph_modified)
+class MoveMulPastDWConv(Transformation):
+ """Move channelwise mul operations past depthwise conv operations. We want to have muls
+ next to each other such that they can be collapsed into a single mul."""
+
+ def apply(self, model):
+ graph = model.graph
+ node_ind = 0
+ graph_modified = False
+ for n in graph.node:
+ node_ind += 1
+ 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"
+ and not model.is_join_node(consumer)
+ ):
+ mul_weight_name = n.input[1]
+ A = model.get_initializer(mul_weight_name)
+ if A is None:
+ warnings.warn(
+ """Mul weight tensor is not set. If it is a constant,
+ please use set_initializer to set the tensor."""
+ )
+ continue
+ conv_node = consumer
+ mul_node = n
+ start_name = mul_node.input[0]
+ conv_in_name = conv_node.input[0]
+ conv_in_shape = model.get_tensor_shape(conv_in_name)
+ ifm_ch = conv_in_shape[1]
+ group_attribute = get_by_name(consumer.attribute, "group")
+ if group_attribute is None:
+ continue
+ group_attribute = group_attribute.i
+ conv_out_name = conv_node.output[0]
+ conv_out_shape = model.get_tensor_shape(conv_out_name)
+ if A.shape == (1, ifm_ch, 1, 1) and ifm_ch == group_attribute:
+ # if the mul is channelwise and conv is depthwise,
+ # we can simply swap the order of ops
+ # rewire mul input to be conv input
+ conv_node.input[0] = start_name
+ model.set_tensor_shape(start_name, conv_in_shape)
+ model.set_tensor_datatype(start_name, DataType.FLOAT32)
+ # use old conv input tensor as conv output
+ conv_node.output[0] = conv_in_name
+ model.set_tensor_shape(conv_in_name, conv_out_shape)
+ model.set_tensor_datatype(conv_in_name, DataType.FLOAT32)
+ # use new conv output as new mul node input
+ mul_node.input[0] = conv_in_name
+ # use old conv output as new mul node output
+ mul_node.output[0] = conv_out_name
+ model.set_tensor_datatype(conv_out_name, DataType.FLOAT32)
+ # move mul node past conv node
+ graph.node.remove(mul_node)
+ graph.node.insert(node_ind, mul_node)
+ graph_modified = True
+ model = model.transform(InferShapes())
+ return (model, graph_modified)
+
+
class MoveLinearPastEltwiseAdd(Transformation):
"""Move linear operations (mul, add) past elementwise add operations where possible.
Specifically,matches and transforms the following patterns:
@@ -531,3 +620,279 @@ class MoveMulPastFork(MoveOpPastFork):
class MoveLinearPastFork(MoveOpPastFork):
def __init__(self):
super().__init__(["Add", "Mul"])
+
+
+class MoveMaxPoolPastMultiThreshold(Transformation):
+ """Move MaxPool nodes past MultiThreshold nodes on linear segments of the graph."""
+
+ def apply(self, model):
+ graph = model.graph
+ node_ind = 0
+ graph_modified = False
+ nodes = [n for n in graph.node]
+ for n in nodes:
+ node_ind += 1
+ if n.op_type == "MaxPool" and not model.is_fork_node(n):
+ consumer = model.find_consumer(n.output[0])
+ pads = get_by_name(n.attribute, "pads")
+ has_padding = False
+ if pads is not None:
+ pads = list(pads.ints)
+ has_padding = np.prod(pads) != 0
+ if consumer is not None and consumer.op_type == "MultiThreshold":
+ mt_out = consumer.output[0]
+ mt_odt = model.get_tensor_datatype(mt_out)
+ if mt_odt.signed() and has_padding:
+ warnings.warn(
+ "Skipping padded MaxPool + signed-output MultiThreshold"
+ )
+ continue
+ # check for non-decreasing thresholds and nonnegative
+ # scale factor in MultiThreshold
+ # otherwise we cannot do the reordering
+ T = model.get_initializer(consumer.input[1])
+ T_sorted = np.sort(T, axis=1)
+ assert (
+ T == T_sorted
+ ).all(), "MultiThreshold must have non-decreasing thresholds"
+ mt_inst = getCustomOp(consumer)
+ if mt_inst.get_nodeattr("out_scale") < 0:
+ warnings.warn("Skipping MultiThreshold with negative out_scale")
+ continue
+
+ # remove old nodes
+ graph.node.remove(n)
+ graph.node.remove(consumer)
+
+ # swap conections
+ group_in = n.input[0]
+ # new tensor because dims change
+ group_middle = model.make_new_valueinfo_name()
+ group_out = consumer.output[0]
+
+ consumer.input[0] = group_in
+ consumer.output[0] = group_middle
+
+ n.input[0] = group_middle
+ n.output[0] = group_out
+
+ # insert them back in
+ graph.node.insert(node_ind - 1, consumer)
+ graph.node.insert(node_ind, n)
+
+ graph_modified = True
+
+ model = model.transform(InferShapes())
+ return (model, graph_modified)
+
+class MoveFlattenPastTopK(Transformation):
+ """Move flatten node past a succeeding topk node, if the "axis" attribute in topk
+ is set to -1 and the data layout before the flatten is NHWC with H=W=1"""
+
+ def apply(self, model):
+ graph = model.graph
+ node_ind = 0
+ graph_modified = False
+ for n in graph.node:
+ node_ind += 1
+ if n.op_type == "Flatten":
+ consumer = model.find_consumer(n.output[0])
+ if consumer is not None and consumer.op_type == "TopK":
+ axis = get_by_name(consumer.attribute, "axis")
+ if axis is None or axis.i != -1:
+ continue
+ start_name = n.input[0]
+ data_layout = model.get_tensor_layout(start_name)
+ if data_layout != DataLayout.NHWC:
+ warnings.warn(
+ """Transformation can't be applied. The input
+ to flatten has to have DataLayout.NHWC"""
+ )
+ continue
+ (b, h, w, c) = model.get_tensor_shape(start_name)
+ if h != 1 or w != 1:
+ continue
+ # get parameter k from topk
+ k = model.get_tensor_shape(consumer.output[1])[-1]
+
+ # swap conections
+ # new tensor because dims change
+ middle_name = model.make_new_valueinfo_name()
+ topk_indices = oh.make_tensor_value_info(
+ middle_name, TensorProto.INT64, [b, h, w, k]
+ )
+ end_name = consumer.output[1]
+ graph.value_info.append(topk_indices)
+
+ # remove old nodes
+ graph.node.remove(n)
+ graph.node.remove(consumer)
+
+ # set inputs and outputs correctly
+ consumer.input[0] = start_name
+ consumer.output[1] = middle_name
+ model.set_tensor_shape(consumer.output[0], (b, h, w, k))
+
+ n.input[0] = middle_name
+ n.output[0] = end_name
+
+ # insert them back in
+ graph.node.insert(node_ind - 1, consumer)
+ graph.node.insert(node_ind, n)
+
+ graph_modified = True
+
+ model = model.transform(InferShapes())
+ return (model, graph_modified)
+
+class MoveFlattenPastAffine(Transformation):
+ """Moves a node that implements a (1, -1) reshape past a MatMul, Mul or Add node."""
+
+ def apply(self, model):
+ graph = model.graph
+ graph_modified = False
+ node_ind = 0
+ for n in graph.node:
+ node_ind += 1
+ if (
+ n.op_type == "Flatten"
+ 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"
+ or consumer.op_type == "Mul"
+ or consumer.op_type == "Add"
+ )
+ and not model.is_join_node(consumer)
+ ):
+ # move flatten past operation and rewire tensors
+ start_name = n.input[0]
+ # check if datalyout is set to NHWC and H=W=1
+ datalayout = model.get_tensor_layout(start_name)
+ if datalayout == DataLayout.NHWC:
+ (b, h, w, c) = model.get_tensor_shape(start_name)
+ if h != 1 or w != 1:
+ warnings.warn(
+ """The Transformation can only be performed if
+ H=W=1."""
+ )
+ continue
+ else:
+ warnings.warn(
+ """The Transformation can only be performed on
+ operations that operate on data layout NHWC."""
+ )
+ continue
+ middle_name = n.output[0]
+ end_name = consumer.output[0]
+ op_param_name = consumer.input[1]
+ A = model.get_initializer(op_param_name)
+ if A is None:
+ warnings.warn("Param is not constant, skipping")
+ continue
+ op_in_dt = model.get_tensor_datatype(consumer.input[0])
+ op_out_dt = model.get_tensor_datatype(consumer.output[0])
+ start_shape = model.get_tensor_shape(start_name)
+ dummy_in = np.random.uniform(low=0, high=1, size=(start_shape))
+
+ if consumer.op_type == "MatMul":
+ dummy_out = np.matmul(dummy_in, A)
+ elif consumer.op_type == "Mul":
+ dummy_out = dummy_in * A
+ elif consumer.op_type == "Add":
+ dummy_out = dummy_in + A
+
+ new_op = oh.make_node(
+ consumer.op_type,
+ [start_name, op_param_name],
+ [middle_name],
+ name=consumer.name,
+ )
+ new_flatten = oh.make_node("Flatten", [middle_name], [end_name])
+ graph.node.insert(node_ind, new_op)
+ graph.node.insert(node_ind + 1, new_flatten)
+ model.set_tensor_shape(middle_name, dummy_out.shape)
+ # because a flatten node doesn't change the datatype we need
+ # only the datatype of the op node
+ model.set_tensor_datatype(start_name, op_in_dt)
+ model.set_tensor_datatype(middle_name, op_out_dt)
+ model.set_tensor_datatype(end_name, op_out_dt)
+ # set datalayout
+ model.set_tensor_layout(start_name, DataLayout.NHWC)
+ model.set_tensor_layout(middle_name, DataLayout.NHWC)
+ # remove old nodes
+ graph.node.remove(n)
+ graph.node.remove(consumer)
+ graph_modified = True
+
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+ model = model.transform(InferDataLayouts())
+ return (model, graph_modified)
+
+class MoveTransposePastScalarMul(Transformation):
+ """Moves a Transpose node past a scalar Mul node"""
+
+ def apply(self, model):
+ graph = model.graph
+ node_ind = 0
+ graph_modified = False
+ for n in graph.node:
+ node_ind += 1
+ if (
+ n.op_type == "Transpose"
+ 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"
+ and not model.is_join_node(consumer)
+ ):
+ mul_weight_name = consumer.input[1]
+ A = model.get_initializer(mul_weight_name)
+ if A is None:
+ warnings.warn("Mul param is not constant, skipping")
+ continue
+ transp_node = n
+ mul_node = consumer
+ start_name = transp_node.input[0]
+ middle_name = transp_node.output[0]
+ end_name = mul_node.output[0]
+ transp_in_shape = model.get_tensor_shape(start_name)
+ transp_out_shape = model.get_tensor_shape(middle_name)
+ transp_in_layout = model.get_tensor_layout(start_name)
+ transp_out_layout = model.get_tensor_layout(middle_name)
+ if transp_in_layout is None or transp_out_layout is None:
+ warnings.warn(
+ """Datalayout is not set for tensors.
+ Transformation can't be applied."""
+ )
+ continue
+ if all(x == 1 for x in A.shape):
+ # if the mul is scalar, we can simply swap the order of ops
+ # rewire transpose input to be mul input
+ mul_node.input[0] = start_name
+ model.set_tensor_shape(start_name, transp_in_shape)
+ model.set_tensor_layout(start_name, transp_in_layout)
+ mul_node.output[0] = middle_name
+ model.set_tensor_shape(middle_name, transp_in_shape)
+ model.set_tensor_layout(middle_name, transp_in_layout)
+ transp_node.input[0] = middle_name
+ transp_node.output[0] = end_name
+ model.set_tensor_shape(end_name, transp_out_shape)
+ model.set_tensor_layout(end_name, transp_out_layout)
+ graph.node.remove(transp_node)
+ graph.node.insert(node_ind, transp_node)
+ graph_modified = True
+
+ if graph_modified is True:
+ model = model.transform(InferDataLayouts())
+ model = model.transform(InferShapes())
+ return (model, graph_modified)
+
diff --git a/src/finn/util/basic.py b/src/finn/util/basic.py
index 3880bb9591e27af5fe9d063dba2485d304e4db54..4a8277e08d3fc21e0b20668edf2ecad947b36647 100644
--- a/src/finn/util/basic.py
+++ b/src/finn/util/basic.py
@@ -31,6 +31,7 @@ import random
import string
import subprocess
import tempfile
+import warnings
import numpy as np
@@ -56,6 +57,12 @@ def get_rtlsim_trace_depth():
via the RTLSIM_TRACE_DEPTH environment variable. If the env.var. is
undefined, the default value of 1 is returned. A trace depth of 1
will only show top-level signals and yield smaller .vcd files.
+
+ The following depth values are of interest for whole-network stitched IP
+ rtlsim:
+ - level 1 shows top-level input/output streams
+ - level 2 shows per-layer input/output streams
+ - level 3 shows per full-layer I/O including FIFO count signals
"""
try:
@@ -64,6 +71,16 @@ def get_rtlsim_trace_depth():
return 1
+def get_remote_vivado():
+ """Return the address of the remote Vivado synthesis server as set by the,
+ REMOTE_VIVADO environment variable, otherwise return None"""
+
+ try:
+ return os.environ["REMOTE_VIVADO"]
+ except KeyError:
+ return None
+
+
def get_num_default_workers():
"""Return the number of workers for parallel transformations. Controllable
via the NUM_DEFAULT_WORKERS environment variable. If the env.var. is
@@ -89,6 +106,25 @@ def get_finn_root():
)
+def get_execution_error_thresh():
+ "Return the max error that is allowed for rounding in FINN execution."
+ try:
+ return float(os.environ["ERROR_THRESH"])
+ except KeyError:
+ return 1e-2
+
+
+def get_sanitize_quant_tensors():
+ """Return whether tensors with quantization annotations should be sanitized.
+ Enabled by default, disabling will yield faster ONNX execution but may give
+ incorrect results. Use with caution."""
+ try:
+ return int(os.environ["SANITIZE_QUANT_TENSORS"])
+ except KeyError:
+ # enabled by default
+ return 1
+
+
def make_build_dir(prefix=""):
"""Creates a temporary folder with given prefix to be used as a build dir.
Use this function instead of tempfile.mkdtemp to ensure any generated files
@@ -248,6 +284,69 @@ def calculate_signed_dot_prod_range(dt_a, dt_b, len):
return (min_prod, max_prod)
+def sanitize_quant_values(model, node_tensors, execution_context, check_values=False):
+ """ Sanitize given list of tensors in execution_context by rounding values
+ that are supposed to be integers (as indicated by their quantization
+ annotation). Will raise an assertion if the amount of rounding is too large.
+ Returns the sanitized execution context.
+
+ If check_values is specified, an extra DataType.allowed() check will be
+ performed on any rounded tensors.
+
+ Background:
+ FINN uses floating point tensors as a carrier data type to represent
+ integers. Floating point arithmetic can introduce rounding errors, e.g.
+ (int_num * float_scale) / float_scale is not always equal to int_num.
+ We use this function to ensure that the values that are supposed to be
+ integers are indeed integers.
+ """
+
+ for tensor in node_tensors:
+ dtype = model.get_tensor_datatype(tensor)
+ # floats don't need sanitization, skip to next
+ # introduces less quicker runtime
+ if dtype == DataType.FLOAT32:
+ continue
+ current_values = execution_context[tensor]
+ updated_values = current_values
+ has_to_be_rounded = False
+ # TODO: vectorize with numpy
+ for value in np.nditer(current_values):
+ if not dtype.allowed(value):
+ has_to_be_rounded = True
+ break
+ if has_to_be_rounded:
+ updated_values = np.round(current_values)
+ warnings.warn(
+ "The values of tensor {} can't be represented "
+ "with the set FINN datatype ({}), they will be rounded to match the "
+ "FINN datatype.".format(tensor, dtype)
+ )
+ # check if rounded values are not too far from original values
+ max_error = max(np.abs(current_values - updated_values).flatten())
+ if max_error <= get_execution_error_thresh():
+ if check_values is True:
+ # check again if values can now be represented with set finn datatype
+ # TODO: vectorize with numpy
+ for value in np.nditer(updated_values):
+ if not dtype.allowed(value):
+ raise Exception(
+ """Values can't be represented with set
+ finn datatype ({}) for input {}""".format(
+ dtype, tensor
+ )
+ )
+ execution_context[tensor] = updated_values
+ else:
+ raise Exception(
+ """Rounding error is too high to match set FINN
+ datatype ({}) for input {}""".format(
+ dtype, tensor
+ )
+ )
+ return execution_context
+
+
class CppBuilder:
"""Builds the g++ compiler command to produces the executable of the c++ code
in code_gen_dir which is passed to the function build() of this class."""
diff --git a/src/finn/util/create.py b/src/finn/util/create.py
new file mode 100644
index 0000000000000000000000000000000000000000..853cdd0d44a05426b34bf1db3caa58d9289b2e9e
--- /dev/null
+++ b/src/finn/util/create.py
@@ -0,0 +1,178 @@
+# Copyright (c) 2020, Xilinx
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of FINN nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import numpy as np
+from finn.core.modelwrapper import ModelWrapper
+from onnx import TensorProto, helper
+from finn.core.datatype import DataType
+from finn.util.basic import calculate_signed_dot_prod_range, gen_finn_dt_tensor
+
+
+def hls_random_mlp_maker(layer_spec):
+ """Create an MLP of given specification using HLSCustomOp instances.
+ Generate random weights/thresholds of appropriate size."""
+ ret = []
+ for l in layer_spec:
+ idt = l["idt"]
+ wdt = l["wdt"]
+ mw = l["mw"]
+ mh = l["mh"]
+ act = l["act"]
+ l["W"] = gen_finn_dt_tensor(wdt, (mw, mh))
+ if act is None:
+ # no activation, produce accumulators
+ T = None
+ tdt = None
+ if wdt == DataType.BIPOLAR and idt == DataType.BIPOLAR:
+ odt = DataType.UINT32
+ else:
+ odt = DataType.INT32
+ else:
+ odt = act
+ (min, max) = calculate_signed_dot_prod_range(idt, wdt, mw)
+ n_steps = act.get_num_possible_values() - 1
+ T = np.random.randint(min, max - 1, (mh, n_steps)).astype(np.float32)
+ # provide non-decreasing thresholds
+ T = np.sort(T, axis=1)
+ # generate thresholds for activation
+ if wdt == DataType.BIPOLAR and idt == DataType.BIPOLAR:
+ tdt = DataType.UINT32
+ # bias thresholds to be positive
+ T = np.ceil((T + mw) / 2)
+ assert (T >= 0).all()
+ else:
+ tdt = DataType.INT32
+ l["T"] = T
+ l["tdt"] = tdt
+ l["odt"] = odt
+ ret.append(l)
+
+ return hls_mlp_maker(ret)
+
+
+def hls_mlp_maker(layer_spec):
+ """Create an MLP of given specification using HLSCustomOp instances."""
+
+ current_in_name = ""
+ current_out_name = ""
+ i = 0
+
+ graph = helper.make_graph(nodes=[], name="mlp", inputs=[], outputs=[])
+
+ model = helper.make_model(graph, producer_name="finn")
+ model = ModelWrapper(model)
+
+ for l in layer_spec:
+ current_W_name = "W_%d" % i
+ current_T_name = "T_%d" % i
+ current_in_name = "act_%d" % i
+ current_out_name = "act_%d" % (i + 1)
+
+ W = l["W"]
+ (mw, mh) = W.shape
+ T = l["T"]
+ pe = l["pe"]
+ simd = l["simd"]
+ wdt = l["wdt"]
+ idt = l["idt"]
+ tdt = l["tdt"]
+ odt = l["odt"]
+
+ if i == 0:
+ global_in = helper.make_tensor_value_info(
+ current_in_name, TensorProto.FLOAT, [1, mw]
+ )
+ model.graph.input.append(global_in)
+
+ if i == len(layer_spec) - 1:
+ global_out = helper.make_tensor_value_info(
+ current_out_name, TensorProto.FLOAT, [1, mh]
+ )
+ model.graph.output.append(global_out)
+
+ # there are two ways to implement bipolar weights and inputs for
+ # StreamingFC:
+ # - specify their datatypes as such
+ # - specify their datatypes as BINARY as use binaryXnorMode
+ if wdt == DataType.BIPOLAR and idt == DataType.BIPOLAR:
+ # we'll internally convert weights/inputs to binary and specify the
+ # datatypes as such, and also set the binaryXnorMode attribute to 1
+ export_wdt = DataType.BINARY
+ export_idt = DataType.BINARY
+ binary_xnor_mode = 1
+ else:
+ export_wdt = wdt
+ export_idt = idt
+ binary_xnor_mode = 0
+
+ if T is not None:
+ no_act = 0
+ node_inp_list = [current_in_name, current_W_name, current_T_name]
+ if odt == DataType.BIPOLAR:
+ actval = 0
+ else:
+ actval = odt.min()
+ else:
+ # no thresholds
+ node_inp_list = [current_in_name, current_W_name]
+ actval = 0
+ no_act = 1
+ FCLayer_node = helper.make_node(
+ "StreamingFCLayer_Batch",
+ node_inp_list,
+ [current_out_name],
+ domain="finn",
+ backend="fpgadataflow",
+ resType="ap_resource_lut()",
+ MW=mw,
+ MH=mh,
+ SIMD=simd,
+ PE=pe,
+ inputDataType=export_idt.name,
+ weightDataType=export_wdt.name,
+ outputDataType=odt.name,
+ ActVal=actval,
+ binaryXnorMode=binary_xnor_mode,
+ noActivation=no_act,
+ )
+
+ model.graph.node.append(FCLayer_node)
+ model.set_tensor_datatype(current_in_name, idt)
+ model.set_tensor_datatype(current_out_name, odt)
+ model.set_tensor_datatype(current_W_name, wdt)
+ if binary_xnor_mode:
+ # convert bipolar to binary
+ model.set_initializer(current_W_name, (W + 1) / 2)
+ else:
+ model.set_initializer(current_W_name, W)
+ if T is not None:
+ model.set_tensor_datatype(current_T_name, tdt)
+ model.set_initializer(current_T_name, T)
+ i += 1
+
+ return model
diff --git a/src/finn/util/fpgadataflow.py b/src/finn/util/fpgadataflow.py
index d1669444e55cb0fddb2690e51849c4603d47d32c..3fe747a84985b2702ffb1e5855d9071362efebda 100644
--- a/src/finn/util/fpgadataflow.py
+++ b/src/finn/util/fpgadataflow.py
@@ -104,6 +104,7 @@ def pyverilate_stitched_ip(model):
build_dir=build_dir,
trace_depth=get_rtlsim_trace_depth(),
top_module_name=top_module_name,
+ auto_eval=False,
)
return sim
diff --git a/src/finn/util/onnx.py b/src/finn/util/onnx.py
index b9932111d86d7206b23e1d0e49a6aa8451f8ba24..4d7cdd126ededac887639a932c2021ef5f081c02 100644
--- a/src/finn/util/onnx.py
+++ b/src/finn/util/onnx.py
@@ -28,6 +28,7 @@
import numpy as np
import onnx
+import finn.core.data_layout as DataLayout
def valueinfo_to_tensor(vi):
@@ -37,3 +38,38 @@ def valueinfo_to_tensor(vi):
return np.zeros(
dims, dtype=onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[vi.type.tensor_type.elem_type]
)
+
+
+def nchw_to_nhwc(t, model, idx, reverse=False):
+ """Converts between NCHW <-> NHWC layouts for tensor t by inserting a transpose.
+ If reverse=False, t is assumed NCHW and we insert transpose to convert NCHW -> NHWC
+ If reverse=True, t is assumed NHWC and we insert transpose to convert NHWC -> NCHW.
+ """
+ graph = model.graph
+ # create new NHWC tensor
+ t_shape = model.get_tensor_shape(t)
+ bs = t_shape[0]
+ ch = t_shape[1]
+ height = t_shape[2]
+ width = t_shape[3]
+ t_trans = onnx.helper.make_tensor_value_info(
+ model.make_new_valueinfo_name(),
+ onnx.TensorProto.FLOAT,
+ (bs, height, width, ch), # NHWC
+ )
+ graph.value_info.append(t_trans)
+ dt = model.get_tensor_datatype(t)
+ t_trans = t_trans.name
+ model.set_tensor_datatype(t_trans, dt)
+ model.set_tensor_layout(t_trans, DataLayout.NHWC)
+ # NCHW <-> NHWC transpose
+ if reverse:
+ t_trans_node = onnx.helper.make_node(
+ "Transpose", [t_trans], [t], perm=[0, 3, 1, 2]
+ )
+ else:
+ t_trans_node = onnx.helper.make_node(
+ "Transpose", [t], [t_trans], perm=[0, 2, 3, 1]
+ )
+ graph.node.insert(idx, t_trans_node)
+ return t_trans
diff --git a/src/finn/util/vcd.py b/src/finn/util/vcd.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9e244422065314ceb790dc6719b57688ff76828
--- /dev/null
+++ b/src/finn/util/vcd.py
@@ -0,0 +1,184 @@
+# 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 vcdvcd import VCDVCD
+from finn.util.basic import get_num_default_workers
+import multiprocessing as mp
+
+# string patterns to search for to find particular interfaces
+# streaming interfaces
+vname = "TVALID"
+rname = "TREADY"
+# FIFO count signals
+fifo_mod_name = "StreamingFIFO"
+fifo_cname = "count"
+
+
+def list_stream_if(vcd_file):
+ "Return a list of stream interface names from given vcd trace."
+
+ sig_names = VCDVCD(vcd_file, print_dumps=False, only_sigs=True).get_signals()
+ stream_if_names = []
+ for cand_name in filter(lambda x: x.endswith(vname), sig_names):
+ base_name = cand_name.replace(vname, "")
+ if base_name + rname in sig_names:
+ stream_if_names.append(base_name)
+ return stream_if_names
+
+
+def list_fifo_count_signals(vcd_file):
+ "Return a list of FIFO count signal names from given vcd trace."
+
+ sig_names = VCDVCD(vcd_file, print_dumps=False, only_sigs=True).get_signals()
+ fifo_cnt_names = []
+ for cand_name in filter(lambda x: fifo_cname in x, sig_names):
+ if fifo_mod_name in cand_name:
+ fifo_cnt_names.append(cand_name)
+ return fifo_cnt_names
+
+
+def get_fifo_count_max(vcd_file, fifo_count_signal):
+ "Return the maximum value of the given FIFO count signal in vcd trace."
+
+ d = VCDVCD(vcd_file, signals=[fifo_count_signal], store_tvs=True).get_data()
+ assert len(d) != 0, "FIFO count signal not found"
+ events = list(d.values())[0]["tv"]
+ max = 0
+ for (time, val) in events:
+ current = int(val, base=2)
+ if current > max:
+ max = current
+ return max
+
+
+def _get_fifo_max(x):
+ return (x[0], get_fifo_count_max(x[1], x[0]))
+
+
+def get_all_fifo_count_max(vcd_file, fifo_count_signals=None):
+ """Return a list of max FIFO counts. If fifo_count_signals is None,
+ all FIFO count signals will be returned, otherwise treated as a list of
+ signal names to return the stats for."""
+ if fifo_count_signals is None:
+ fifo_count_signals = list_fifo_count_signals(vcd_file)
+
+ with mp.Pool(get_num_default_workers()) as p:
+ fifo_count_signals = map(lambda x: (x, vcd_file), fifo_count_signals)
+ all_stats = p.map(_get_fifo_max, fifo_count_signals)
+
+ return all_stats
+
+
+def get_stream_if_stats(vcd_file, if_base_name):
+ """Return statistics for given streaming interface in vcd trace in the
+ following dict format:
+
+ <stream_state>: (<num_samples>, <fraction_of_time>),
+
+ where <stream_state> is the combination of (V)alid/(R)eady values,
+ <num_samples> is the approximate number of rising clock edges spent in <state>
+ , and <fraction_of_time> is the fraction of <num_samples> to total
+ amount of time recorded by the trace.
+
+ Example:
+ {"{'V': 0, 'R': 0}": (5, 0.0006060606060606061),
+ "{'V': 1, 'R': 0}": (0, 0.0),
+ "{'V': 0, 'R': 1}": (7605, 0.9218181818181819),
+ "{'V': 1, 'R': 1}": (640, 0.07757575757575758)}
+
+ Here we can see the stream was transmitting values 7.7% of the time,
+ and 9.2% of the time there was no incoming data (valid 0, ready 1)
+ """
+ if_valid = if_base_name + vname
+ if_ready = if_base_name + rname
+ v = VCDVCD(vcd_file, signals=[if_valid], store_tvs=True)
+ endtime = v.get_endtime()
+ v = v.get_data()
+ assert len(v) != 0, "Streaming interface not found"
+ v = list(v.values())[0]["tv"]
+ v = list(map(lambda x: ("V", x[0], x[1]), v))
+ v.append(("V", endtime, "0"))
+ r = VCDVCD(vcd_file, signals=[if_ready], store_tvs=True).get_data()
+ assert len(r) != 0, "Streaming interface not found"
+ r = list(r.values())[0]["tv"]
+ r = list(map(lambda x: ("R", x[0], x[1]), r))
+ r.append(("R", endtime, "0"))
+ events = sorted(v + r, key=lambda x: x[1])
+ ret = {
+ "{'V': 0, 'R': 0}": 0,
+ "{'V': 1, 'R': 0}": 0,
+ "{'V': 0, 'R': 1}": 0,
+ "{'V': 1, 'R': 1}": 0,
+ }
+ status = {"V": 0, "R": 0}
+ last_time = 0
+ total_rising_clock_edges = 0
+ for (sig, time, val) in events:
+ # pyverilator generates 5 time units per sample
+ time = time / 5
+ # pyverilator generates 4 samples per clock period
+ n_rising_clock_edges = int((time - last_time) / 4)
+ # note that the calculation of n_rising_clock_edges is approximate
+ # doing this exactly would require a cycle-by-cycle walkthrough of the
+ # trace, which can take very long
+ ret[str(status)] += n_rising_clock_edges
+ total_rising_clock_edges += n_rising_clock_edges
+ status[sig] = int(val)
+ last_time = time
+
+ for state in ret:
+ v = ret[state]
+ ret[state] = (v, v / total_rising_clock_edges)
+
+ return ret
+
+
+def _get_stats(x):
+ return (x[0], get_stream_if_stats(x[1], x[0]))
+
+
+def get_all_stream_if_stats(vcd_file, stream_ifs=None, sort_by="{'V': 1, 'R': 0}"):
+ """Return a list of streaming interface stats, sorted by the percentage
+ for the given sort_by key. If stream_ifs is None, all streamin interface
+ stats will be returned, otherwise treated as a list of interface names to
+ return the stats for."""
+
+ if stream_ifs is None:
+ stream_ifs = list_stream_if(vcd_file)
+
+ with mp.Pool(get_num_default_workers()) as p:
+ stream_ifs = map(lambda x: (x, vcd_file), stream_ifs)
+ all_stats = p.map(_get_stats, stream_ifs)
+
+ def sort_key(x):
+ stat = x[1]
+ (samples, percent) = stat[sort_by]
+ return percent
+
+ ret = sorted(all_stats, key=sort_key)
+ return ret
diff --git a/src/finn/util/vivado.py b/src/finn/util/vivado.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b6df3940cfeeed292345382471719c49f725de6
--- /dev/null
+++ b/src/finn/util/vivado.py
@@ -0,0 +1,147 @@
+# 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 subprocess
+import stat
+from finn.util.basic import get_remote_vivado
+
+
+def which(program):
+ "Python equivalent of the shell cmd 'which'."
+
+ # source:
+ # https://stackoverflow.com/questions/377017/test-if-executable-exists-in-python
+ def is_exe(fpath):
+ return os.path.isfile(fpath) and os.access(fpath, os.X_OK)
+
+ fpath, fname = os.path.split(program)
+ if fpath:
+ if is_exe(program):
+ return program
+ else:
+ for path in os.environ["PATH"].split(os.pathsep):
+ exe_file = os.path.join(path, program)
+ if is_exe(exe_file):
+ return exe_file
+
+ return None
+
+
+def out_of_context_synth(
+ verilog_dir,
+ top_name,
+ fpga_part="xczu3eg-sbva484-1-e",
+ clk_name="ap_clk_0",
+ clk_period_ns=5.0,
+ remote_server=get_remote_vivado(),
+):
+ "Run out-of-context Vivado synthesis, return resources and slack."
+
+ # ensure that the OH_MY_XILINX envvar is set
+ if "OHMYXILINX" not in os.environ:
+ raise Exception("The environment variable OHMYXILINX is not defined.")
+ # ensure that vivado is in PATH: source $VIVADO_PATH/settings64.sh
+ if which("vivado") is None:
+ raise Exception("vivado is not in PATH, ensure settings64.sh is sourced.")
+ omx_path = os.environ["OHMYXILINX"]
+ if remote_server is None:
+ script = "vivadocompile.sh"
+ else:
+ script = "vivadoprojgen.sh"
+ # vivadocompile.sh <top-level-entity> <clock-name (optional)> <fpga-part (optional)>
+ call_omx = "zsh %s/%s %s %s %s %f" % (
+ omx_path,
+ script,
+ top_name,
+ clk_name,
+ fpga_part,
+ float(clk_period_ns),
+ )
+ call_omx = call_omx.split()
+ proc = subprocess.Popen(
+ call_omx, cwd=verilog_dir, stdout=subprocess.PIPE, env=os.environ
+ )
+ proc.communicate()
+
+ vivado_proj_folder = "%s/results_%s" % (verilog_dir, top_name)
+ res_counts_path = vivado_proj_folder + "/res.txt"
+ if remote_server is not None:
+ print("Using remote Vivado OOC synth, remote server %s" % remote_server)
+ run_synth = """
+#!/bin/bash
+which vivado;
+cd %s;
+vivado -mode tcl -source %s.tcl -tclargs %s;
+cat %s
+ """ % (
+ vivado_proj_folder,
+ top_name,
+ top_name,
+ res_counts_path,
+ )
+ with open(vivado_proj_folder + "/run.sh", "w") as f:
+ f.write(run_synth)
+ st = os.stat(vivado_proj_folder + "/run.sh")
+ os.chmod(vivado_proj_folder + "/run.sh", st.st_mode | stat.S_IEXEC)
+ # note that this assumes the same temp folder can be created on the
+ # remote server
+ # note we set target path as / due to use of -R (relative)
+ remote_server_uri = remote_server + ":/"
+ copy_files = "rsync -avzR %s %s" % (verilog_dir + "/", remote_server_uri)
+ copy_files = copy_files.split()
+ proc = subprocess.Popen(copy_files, cwd=verilog_dir, env=os.environ)
+ proc.communicate()
+ vivado_cmd = "bash -ic %s/run.sh" % vivado_proj_folder
+ run_vivado = ["ssh", "-t", remote_server, vivado_cmd]
+ proc = subprocess.Popen(run_vivado, cwd=verilog_dir, env=os.environ)
+ proc.communicate()
+ remote_server_result = remote_server + ":" + res_counts_path
+ copy_results = "rsync -avz %s %s" % (remote_server_result, res_counts_path)
+ copy_results = copy_results.split()
+ proc = subprocess.Popen(copy_results, cwd=verilog_dir, env=os.environ)
+ proc.communicate()
+
+ with open(res_counts_path, "r") as myfile:
+ res_data = myfile.read().split("\n")
+ ret = {}
+ ret["vivado_proj_folder"] = vivado_proj_folder
+ for res_line in res_data:
+ res_fields = res_line.split("=")
+ print(res_fields)
+ try:
+ ret[res_fields[0]] = float(res_fields[1])
+ except ValueError:
+ ret[res_fields[0]] = 0
+ except IndexError:
+ ret[res_fields[0]] = 0
+ if ret["WNS"] == 0:
+ ret["fmax_mhz"] = 0
+ else:
+ ret["fmax_mhz"] = 1000.0 / (clk_period_ns - ret["WNS"])
+ return ret
diff --git a/tests/brevitas/test_brevitas_avg_pool_export.py b/tests/brevitas/test_brevitas_avg_pool_export.py
new file mode 100644
index 0000000000000000000000000000000000000000..e78812b21a03baa6963f1f0efaefdb4c73e4d0db
--- /dev/null
+++ b/tests/brevitas/test_brevitas_avg_pool_export.py
@@ -0,0 +1,103 @@
+import os
+
+import onnx # noqa
+import torch
+import numpy as np
+import brevitas.onnx as bo
+from brevitas.nn import QuantAvgPool2d
+from brevitas.quant_tensor import pack_quant_tensor
+from brevitas.core.quant import QuantType
+from finn.core.modelwrapper import ModelWrapper
+from finn.core.datatype import DataType
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.util.basic import gen_finn_dt_tensor
+import finn.core.onnx_exec as oxe
+
+import pytest
+
+export_onnx_path = "test_brevitas_avg_pool_export.onnx"
+
+
+@pytest.mark.parametrize("kernel_size", [2, 3])
+@pytest.mark.parametrize("stride", [1, 2])
+@pytest.mark.parametrize("signed", [False, True])
+@pytest.mark.parametrize("bit_width", [2, 4])
+@pytest.mark.parametrize("input_bit_width", [4, 8, 32])
+@pytest.mark.parametrize("channels", [2, 4])
+@pytest.mark.parametrize("idim", [7, 8])
+def test_brevitas_avg_pool_export(
+ kernel_size, stride, signed, bit_width, input_bit_width, channels, idim
+):
+ ishape = (1, channels, idim, idim)
+ ibw_tensor = torch.Tensor([input_bit_width])
+
+ b_avgpool = QuantAvgPool2d(
+ kernel_size=kernel_size,
+ stride=stride,
+ signed=signed,
+ min_overall_bit_width=bit_width,
+ max_overall_bit_width=bit_width,
+ quant_type=QuantType.INT,
+ )
+ # call forward pass manually once to cache scale factor and bitwidth
+ input_tensor = torch.from_numpy(np.zeros(ishape)).float()
+ scale = np.ones((1, channels, 1, 1))
+ output_scale = torch.from_numpy(scale).float()
+ input_quant_tensor = pack_quant_tensor(
+ tensor=input_tensor, scale=output_scale, bit_width=ibw_tensor
+ )
+ bo.export_finn_onnx(b_avgpool, ishape, export_onnx_path, input_t=input_quant_tensor)
+ model = ModelWrapper(export_onnx_path)
+
+ # determine input FINN datatype
+ if signed is True:
+ prefix = "INT"
+ else:
+ prefix = "UINT"
+ dt_name = prefix + str(input_bit_width // 2)
+ dtype = DataType[dt_name]
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+
+ # execution with input tensor using integers and scale = 1
+ # calculate golden output
+ inp = gen_finn_dt_tensor(dtype, ishape)
+ input_tensor = torch.from_numpy(inp).float()
+ input_quant_tensor = pack_quant_tensor(
+ tensor=input_tensor, scale=output_scale, bit_width=ibw_tensor
+ )
+ b_avgpool.eval()
+ expected = b_avgpool.forward(input_quant_tensor).tensor.detach().numpy()
+
+ # finn execution
+ idict = {model.graph.input[0].name: inp}
+ odict = oxe.execute_onnx(model, idict, True)
+ produced = odict[model.graph.output[0].name]
+ assert (expected == produced).all()
+
+ # execution with input tensor using float and scale != 1
+ scale = np.random.uniform(low=0, high=1, size=(1, channels, 1, 1)).astype(
+ np.float32
+ )
+ inp_tensor = inp * scale
+ input_tensor = torch.from_numpy(inp_tensor).float()
+ input_scale = torch.from_numpy(scale).float()
+ input_quant_tensor = pack_quant_tensor(
+ tensor=input_tensor, scale=input_scale, bit_width=ibw_tensor
+ )
+ # export again to set the scale values correctly
+ bo.export_finn_onnx(b_avgpool, ishape, export_onnx_path, input_t=input_quant_tensor)
+ model = ModelWrapper(export_onnx_path)
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+ b_avgpool.eval()
+ expected = b_avgpool.forward(input_quant_tensor).tensor.detach().numpy()
+ # finn execution
+ idict = {model.graph.input[0].name: inp_tensor}
+ odict = oxe.execute_onnx(model, idict, True)
+ produced = odict[model.graph.output[0].name]
+
+ assert np.isclose(expected, produced).all()
+
+ os.remove(export_onnx_path)
diff --git a/tests/brevitas/test_brevitas_cnv.py b/tests/brevitas/test_brevitas_cnv.py
index c04e16ad1923609c81240235057cc7a190c90ffb..764671bee13710ef1d9fa21aab5ef600075b9b0d 100644
--- a/tests/brevitas/test_brevitas_cnv.py
+++ b/tests/brevitas/test_brevitas_cnv.py
@@ -38,11 +38,11 @@ import finn.core.onnx_exec as oxe
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.fold_constants import FoldConstants
from finn.transformation.infer_shapes import InferShapes
-from finn.transformation.general import GiveUniqueNodeNames
+from finn.transformation.general import GiveUniqueNodeNames, RemoveStaticGraphInputs
from finn.transformation.double_to_single_float import DoubleToSingleFloat
from finn.util.test import get_test_model_trained
-export_onnx_path = "test_output_cnv.onnx"
+export_onnx_path = "test_brevitas_cnv.onnx"
@pytest.mark.parametrize("abits", [1, 2])
@@ -57,6 +57,9 @@ def test_brevitas_cnv_export_exec(wbits, abits):
model = model.transform(DoubleToSingleFloat())
model = model.transform(InferShapes())
model = model.transform(FoldConstants())
+ model = model.transform(RemoveStaticGraphInputs())
+ assert len(model.graph.input) == 1
+ assert len(model.graph.output) == 1
fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
input_tensor = np.load(fn)["arr_0"].astype(np.float32)
input_tensor = input_tensor / 255
diff --git a/tests/brevitas/test_brevitas_fc.py b/tests/brevitas/test_brevitas_fc.py
index db18d91e3590e896e111c9e38bdc4de43872a98c..9369b25385080875efcb286c02291fc579a15a34 100644
--- a/tests/brevitas/test_brevitas_fc.py
+++ b/tests/brevitas/test_brevitas_fc.py
@@ -39,6 +39,7 @@ import torch
import finn.core.onnx_exec as oxe
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.fold_constants import FoldConstants
+from finn.transformation.general import RemoveStaticGraphInputs
from finn.transformation.infer_shapes import InferShapes
from finn.util.basic import make_build_dir
from finn.util.test import get_test_model_trained
@@ -63,6 +64,9 @@ def test_brevitas_fc_onnx_export_and_exec(size, wbits, abits):
model = ModelWrapper(finn_onnx)
model = model.transform(InferShapes())
model = model.transform(FoldConstants())
+ model = model.transform(RemoveStaticGraphInputs())
+ assert len(model.graph.input) == 1
+ assert len(model.graph.output) == 1
# load one of the test vectors
raw_i = get_data("finn", "data/onnx/mnist-conv/test_data_set_0/input_0.pb")
input_tensor = onnx.load_tensor_from_string(raw_i)
diff --git a/tests/brevitas/test_brevitas_non_scaled_QuantHardTanh_export.py b/tests/brevitas/test_brevitas_non_scaled_QuantHardTanh_export.py
index b66348a9902802bc65b2a35e8bc3e311cc81e0bc..9c7296b7b3b6d36cfb43b6d9e96e7fba6bbce49a 100644
--- a/tests/brevitas/test_brevitas_non_scaled_QuantHardTanh_export.py
+++ b/tests/brevitas/test_brevitas_non_scaled_QuantHardTanh_export.py
@@ -12,7 +12,7 @@ import finn.core.onnx_exec as oxe
from finn.transformation.infer_shapes import InferShapes
from brevitas.core.quant import QuantType
-export_onnx_path = "test_act.onnx"
+export_onnx_path = "test_brevitas_non_scaled_QuantHardTanh_export.onnx"
@pytest.mark.parametrize("abits", [1, 2, 4, 8])
diff --git a/tests/brevitas/test_brevitas_relu_act_export.py b/tests/brevitas/test_brevitas_relu_act_export.py
index c9d8f2d812bc7bea1a2fd2598a7711099ad421e6..77974dacb51aa8746ce33f9a490becd35390db5a 100644
--- a/tests/brevitas/test_brevitas_relu_act_export.py
+++ b/tests/brevitas/test_brevitas_relu_act_export.py
@@ -12,7 +12,7 @@ from finn.core.modelwrapper import ModelWrapper
import finn.core.onnx_exec as oxe
from finn.transformation.infer_shapes import InferShapes
-export_onnx_path = "test_act.onnx"
+export_onnx_path = "test_brevitas_relu_act_export.onnx"
@pytest.mark.parametrize("abits", [1, 2, 4, 8])
@@ -23,6 +23,7 @@ export_onnx_path = "test_act.onnx"
def test_brevitas_act_export_relu(abits, max_val, scaling_impl_type):
min_val = -1.0
ishape = (1, 15)
+
b_act = QuantReLU(
bit_width=abits,
max_val=max_val,
@@ -67,3 +68,60 @@ scaling_impl.learned_value": torch.tensor(
assert np.isclose(produced, expected, atol=1e-3).all()
os.remove(export_onnx_path)
+
+
+@pytest.mark.parametrize("abits", [1, 2, 4, 8])
+@pytest.mark.parametrize("max_val", [1.0, 1.5, 1 - 2 ** (-7)])
+@pytest.mark.parametrize("scaling_per_channel", [True, False])
+def test_brevitas_act_export_relu_imagenet(abits, max_val, scaling_per_channel):
+ out_channels = 32
+ ishape = (1, out_channels, 1, 1)
+ min_val = -1.0
+ b_act = QuantReLU(
+ bit_width=abits,
+ quant_type=QuantType.INT,
+ scaling_impl_type=ScalingImplType.PARAMETER,
+ scaling_per_channel=scaling_per_channel,
+ restrict_scaling_type=RestrictValueType.LOG_FP,
+ scaling_min_val=2e-16,
+ max_val=6.0,
+ return_quant_tensor=True,
+ per_channel_broadcastable_shape=(1, out_channels, 1, 1),
+ )
+ if scaling_per_channel is True:
+ rand_tensor = (2) * torch.rand((1, out_channels, 1, 1))
+ else:
+ rand_tensor = torch.tensor(1.2398)
+ checkpoint = {
+ "act_quant_proxy.fused_activation_quant_proxy.tensor_quant.\
+scaling_impl.learned_value": rand_tensor.type(
+ torch.FloatTensor
+ )
+ }
+ b_act.load_state_dict(checkpoint)
+ bo.export_finn_onnx(b_act, ishape, export_onnx_path)
+ model = ModelWrapper(export_onnx_path)
+ model = model.transform(InferShapes())
+ inp_tensor = np.random.uniform(low=min_val, high=max_val, size=ishape).astype(
+ np.float32
+ )
+ idict = {model.graph.input[0].name: inp_tensor}
+ odict = oxe.execute_onnx(model, idict, True)
+ produced = odict[model.graph.output[0].name]
+ inp_tensor = torch.from_numpy(inp_tensor).float()
+ b_act.eval()
+ expected = b_act.forward(inp_tensor).tensor.detach().numpy()
+ if not np.isclose(produced, expected, atol=1e-3).all():
+ print(abits, max_val)
+ print("scale: ", b_act.quant_act_scale().type(torch.FloatTensor).detach())
+ if abits < 5:
+ print(
+ "thres:",
+ ", ".join(["{:8.4f}".format(x) for x in b_act.export_thres[0]]),
+ )
+ print("input:", ", ".join(["{:8.4f}".format(x) for x in inp_tensor[0]]))
+ print("prod :", ", ".join(["{:8.4f}".format(x) for x in produced[0]]))
+ print("expec:", ", ".join(["{:8.4f}".format(x) for x in expected[0]]))
+
+ assert np.isclose(produced, expected, atol=1e-3).all()
+ os.remove(export_onnx_path)
diff --git a/tests/brevitas/test_brevitas_scaled_QHardTanh_export.py b/tests/brevitas/test_brevitas_scaled_QHardTanh_export.py
index d499f1517341477eca9915245da9ad12c346c5a9..e0ec82ebed44e2e984be9f62e02bc1721a7f9c33 100644
--- a/tests/brevitas/test_brevitas_scaled_QHardTanh_export.py
+++ b/tests/brevitas/test_brevitas_scaled_QHardTanh_export.py
@@ -12,7 +12,7 @@ from finn.core.modelwrapper import ModelWrapper
import finn.core.onnx_exec as oxe
from finn.transformation.infer_shapes import InferShapes
-export_onnx_path = "test_act.onnx"
+export_onnx_path = "test_brevitas_scaled_QHardTanh_export.onnx"
@pytest.mark.parametrize("abits", [2, 4, 8])
diff --git a/tests/core/test_basic_onnx_exec.py b/tests/core/test_basic_onnx_exec.py
index a7b6da9965aa5912870812a8c1f8d6da2ee0d181..ddb2cbfc40c7647970f0c51ecb95340e7d1dddae 100644
--- a/tests/core/test_basic_onnx_exec.py
+++ b/tests/core/test_basic_onnx_exec.py
@@ -35,6 +35,8 @@ import onnx.numpy_helper as np_helper
import finn.core.onnx_exec as oxe
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.infer_shapes import InferShapes
+from finn.core.datatype import DataType
+from finn.util.basic import gen_finn_dt_tensor
def test_mnist_onnx_download_extract_run():
@@ -47,9 +49,50 @@ def test_mnist_onnx_download_extract_run():
raw_o = get_data("finn", "data/onnx/mnist-conv/test_data_set_0/output_0.pb")
input_tensor = onnx.load_tensor_from_string(raw_i)
output_tensor = onnx.load_tensor_from_string(raw_o)
- # run using FINN-based execution
+ # run using FINN-based execution (full graph)
input_dict = {"Input3": np_helper.to_array(input_tensor)}
- output_dict = oxe.execute_onnx(model, input_dict)
+ output_dict = oxe.execute_onnx(model, input_dict, return_full_exec_context=True)
assert np.isclose(
np_helper.to_array(output_tensor), output_dict["Plus214_Output_0"], atol=1e-3
).all()
+ # test subgraph execution
+ start_node = model.graph.node[1]
+ end_node = model.graph.node[3]
+ subgraph_i_dict = {start_node.input[0]: output_dict[start_node.input[0]]}
+ subgraph_o_dict = oxe.execute_onnx(
+ model,
+ subgraph_i_dict,
+ return_full_exec_context=True,
+ start_node=start_node,
+ end_node=end_node,
+ )
+ assert np.isclose(
+ subgraph_o_dict[end_node.output[0]], output_dict[end_node.output[0]], atol=1e-3
+ ).all()
+
+
+def test_onnx_exec_internal_rounding():
+ inp0 = onnx.helper.make_tensor_value_info("inp0", onnx.TensorProto.FLOAT, [2, 2])
+ inp1 = onnx.helper.make_tensor_value_info("inp1", onnx.TensorProto.FLOAT, [1])
+ outp = onnx.helper.make_tensor_value_info("outp", onnx.TensorProto.FLOAT, [2, 2])
+ mul_node = onnx.helper.make_node("Mul", inputs=["inp0", "inp1"], outputs=["outp"])
+ graph = onnx.helper.make_graph(
+ nodes=[mul_node], name="mul_graph", inputs=[inp0, inp1], outputs=[outp]
+ )
+
+ model = onnx.helper.make_model(graph, producer_name="mul-model")
+ model = ModelWrapper(model)
+ idt = DataType.INT2
+ model.set_tensor_datatype("inp0", idt)
+ model.set_tensor_datatype("inp1", idt)
+ model.transform(InferShapes())
+
+ mul_value = np.asarray([-1], dtype=np.float32)
+ inp_int = gen_finn_dt_tensor(idt, [2, 2])
+ scale = np.random.uniform(low=0, high=1, size=(2, 2)).astype(np.float32)
+ inp_rounded = (inp_int * scale) / (scale + 1e-7)
+ input_dict = {"inp0": inp_rounded, "inp1": mul_value}
+ output_dict = oxe.execute_onnx(model, input_dict)
+ produced = output_dict["outp"]
+ expected = np.multiply(inp_int, mul_value)
+ assert (produced == expected).all()
diff --git a/tests/core/test_modelwrapper.py b/tests/core/test_modelwrapper.py
index 5fa9b23bad5c5b67f65530c55f862f889c07b1ac..0fb7ae42f3bd556755f81a02be6c71fd73ffc519 100644
--- a/tests/core/test_modelwrapper.py
+++ b/tests/core/test_modelwrapper.py
@@ -36,7 +36,7 @@ import finn.core.data_layout as DataLayout
from finn.core.modelwrapper import ModelWrapper
from finn.util.test import get_test_model_trained
-export_onnx_path = "test_output_lfc.onnx"
+export_onnx_path = "test_modelwrapper.onnx"
def test_modelwrapper():
diff --git a/tests/custom_op/test_xnorpopcountmatmul.py b/tests/custom_op/test_xnorpopcountmatmul.py
index 37d9b7e5968bdb70023be9b70515410e941f51ce..745b782d418129d96e21c327a49de04d53aa7c48 100644
--- a/tests/custom_op/test_xnorpopcountmatmul.py
+++ b/tests/custom_op/test_xnorpopcountmatmul.py
@@ -47,7 +47,7 @@ from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline.sign_to_thres import ConvertSignToThres
from finn.util.test import get_test_model_trained
-export_onnx_path = "test_output_lfc.onnx"
+export_onnx_path = "test_xnorpopcountmatmul.onnx"
def test_xnorpopcountmatmul():
diff --git a/tests/end2end/test_end2end_cnv_w1a1.py b/tests/end2end/test_end2end_cnv_w1a1.py
index c3359dcc82650bf0e9e8a5bc5276f5ca770ee96c..a2cfcd3a864c12788c2ac73271b5782ddfa336c1 100644
--- a/tests/end2end/test_end2end_cnv_w1a1.py
+++ b/tests/end2end/test_end2end_cnv_w1a1.py
@@ -42,7 +42,12 @@ from finn.transformation.double_to_single_float import DoubleToSingleFloat
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.move_reshape import RemoveCNVtoFCFlatten
from finn.transformation.fold_constants import FoldConstants
-from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
+from finn.transformation.general import (
+ RemoveUnusedTensors,
+ RemoveStaticGraphInputs,
+ GiveReadableTensorNames,
+ GiveUniqueNodeNames,
+)
from finn.transformation.streamline import Streamline
from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
@@ -72,6 +77,7 @@ from finn.util.test import get_test_model_trained, load_test_checkpoint_or_skip
from finn.transformation.fpgadataflow.annotate_resources import AnnotateResources
from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO
+from finn.core.throughput_test import throughput_test_rtlsim
build_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
test_pynq_board = os.getenv("PYNQ_BOARD", default="Pynq-Z1")
@@ -96,6 +102,7 @@ def test_end2end_cnv_w1a1_import_and_tidy():
model = model.transform(FoldConstants())
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveReadableTensorNames())
+ model = model.transform(RemoveStaticGraphInputs())
model.save(build_dir + "/end2end_cnv_w1a1_tidy.onnx")
@@ -107,6 +114,7 @@ def test_end2end_cnv_w1a1_streamline():
model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
model = model.transform(ConvertBipolarMatMulToXnorPopcount())
model = model.transform(Streamline())
+ model = model.transform(RemoveUnusedTensors())
model.save(build_dir + "/end2end_cnv_w1a1_streamlined.onnx")
@@ -142,15 +150,15 @@ def test_end2end_cnv_w1a1_fold_and_tlastmarker():
fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
# each tuple is (PE, SIMD, in_fifo_depth) for a layer
folding = [
- (16, 3, 128),
- (32, 32, 128),
- (16, 32, 128),
- (16, 32, 128),
- (4, 32, 81),
+ (16, 3, 256),
+ (32, 32, 256),
+ (16, 32, 256),
+ (16, 32, 256),
+ (4, 32, 214),
(1, 32, 2),
- (1, 4, 2),
- (1, 8, 128),
- (5, 1, 3),
+ (1, 4, 126),
+ (1, 8, 62),
+ (5, 1, 6),
]
for fcl, (pe, simd, ififodepth) in zip(fc_layers, folding):
fcl_inst = getCustomOp(fcl)
@@ -159,10 +167,12 @@ def test_end2end_cnv_w1a1_fold_and_tlastmarker():
fcl_inst.set_nodeattr("inFIFODepth", ififodepth)
swg_layers = model.get_nodes_by_op_type("ConvolutionInputGenerator")
+ swg_idepth = [2, 51, 9, 106, 2, 2]
for i in range(len(swg_layers)):
swg_inst = getCustomOp(swg_layers[i])
simd = folding[i][1]
swg_inst.set_nodeattr("SIMD", simd)
+ swg_inst.set_nodeattr("inFIFODepth", swg_idepth[i])
model = model.transform(InsertDWC())
model = model.transform(InsertFIFO())
@@ -221,6 +231,20 @@ def test_end2end_cnv_w1a1_verify_dataflow_part():
assert np.isclose(res_cppsim, res_rtlsim_whole).all()
+@pytest.mark.vivado
+def test_end2end_cnv_w1a1_throughput_test_rtlsim():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w1a1_ipstitch_whole_rtlsim.onnx"
+ )
+ model.set_metadata_prop("rtlsim_trace", "rtlsim_trace.vcd")
+ # os.environ["RTLSIM_TRACE_DEPTH"] = "4"
+ # run through IP-stitched rtlsim with increasing batch sizes and
+ # check the number of cycles it takes to execute
+ ret = throughput_test_rtlsim(model, 10)
+ # TODO check for expected performance
+ assert ret["cycles"] > 0
+
+
@pytest.mark.vivado
def test_end2end_cnv_w1a1_verify_all():
# use the streamlined model as the "golden" model for right answers
diff --git a/tests/end2end/test_end2end_cnv_w2a2.py b/tests/end2end/test_end2end_cnv_w2a2.py
new file mode 100644
index 0000000000000000000000000000000000000000..f45b0a3eccd2f52ea144405865a1df06315952d9
--- /dev/null
+++ b/tests/end2end/test_end2end_cnv_w2a2.py
@@ -0,0 +1,384 @@
+# 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
+
+# as of Feb'20 there is a bug that segfaults ONNX shape inference if we
+# import pytorch before onnx, so we make sure to import onnx first
+import onnx # NOQA
+
+import pytest
+import pkg_resources as pk
+from finn.custom_op.registry import getCustomOp
+from finn.core.onnx_exec import execute_onnx
+from finn.transformation.double_to_single_float import DoubleToSingleFloat
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.move_reshape import RemoveCNVtoFCFlatten
+from finn.transformation.fold_constants import FoldConstants
+from finn.transformation.general import (
+ RemoveUnusedTensors,
+ RemoveStaticGraphInputs,
+ GiveReadableTensorNames,
+ GiveUniqueNodeNames,
+)
+from finn.transformation.streamline import Streamline
+from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
+import finn.transformation.streamline.absorb as absorb
+from finn.transformation.streamline.reorder import MakeMaxPoolNHWC
+import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
+from finn.transformation.fpgadataflow.create_dataflow_partition import (
+ CreateDataflowPartition,
+)
+from finn.transformation.fpgadataflow.insert_dwc import InsertDWC
+from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
+from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
+ ReplaceVerilogRelPaths,
+)
+from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP
+from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
+from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver
+from finn.transformation.fpgadataflow.make_pynq_proj import MakePYNQProject
+from finn.transformation.fpgadataflow.synth_pynq_proj import SynthPYNQProject
+from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
+from finn.util.basic import pynq_part_map
+from finn.util.test import get_test_model_trained, load_test_checkpoint_or_skip
+from finn.transformation.fpgadataflow.annotate_resources import AnnotateResources
+from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
+from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO
+from finn.core.throughput_test import throughput_test_rtlsim
+
+build_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
+test_pynq_board = os.getenv("PYNQ_BOARD", default="Pynq-Z1")
+test_fpga_part = pynq_part_map[test_pynq_board]
+target_clk_ns = 10
+mem_mode = "decoupled"
+
+
+def test_end2end_cnv_w2a2_export():
+ import brevitas.onnx as bo
+
+ cnv = get_test_model_trained("CNV", 2, 2)
+ bo.export_finn_onnx(
+ cnv, (1, 3, 32, 32), build_dir + "/end2end_cnv_w2a2_export.onnx"
+ )
+
+
+def test_end2end_cnv_w2a2_import_and_tidy():
+ model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_export.onnx")
+ model = model.transform(DoubleToSingleFloat())
+ model = model.transform(InferShapes())
+ model = model.transform(FoldConstants())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(GiveReadableTensorNames())
+ model = model.transform(RemoveStaticGraphInputs())
+ model.save(build_dir + "/end2end_cnv_w2a2_tidy.onnx")
+
+
+def test_end2end_cnv_w2a2_streamline():
+ model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_tidy.onnx")
+ model = model.transform(Streamline())
+ model = model.transform(LowerConvsToMatMul())
+ model = model.transform(MakeMaxPoolNHWC())
+ model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
+ model = model.transform(Streamline())
+ model = model.transform(RemoveUnusedTensors())
+ model.save(build_dir + "/end2end_cnv_w2a2_streamlined.onnx")
+
+
+def test_end2end_cnv_w2a2_convert_to_hls_layers():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_streamlined.onnx"
+ )
+ model = model.transform(to_hls.InferQuantizedStreamingFCLayer(mem_mode))
+ model = model.transform(to_hls.InferConvInpGen())
+ model = model.transform(to_hls.InferStreamingMaxPool())
+ model = model.transform(RemoveCNVtoFCFlatten())
+ model.save(build_dir + "/end2end_cnv_w2a2_hls_layers.onnx")
+
+
+def test_end2end_cnv_w2a2_create_dataflow_partition():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_hls_layers.onnx"
+ )
+ parent_model = model.transform(CreateDataflowPartition())
+ parent_model.save(build_dir + "/end2end_cnv_w2a2_dataflow_parent.onnx")
+ sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
+ sdp_node = getCustomOp(sdp_node)
+ dataflow_model_filename = sdp_node.get_nodeattr("model")
+ dataflow_model = load_test_checkpoint_or_skip(dataflow_model_filename)
+ dataflow_model.save(build_dir + "/end2end_cnv_w2a2_dataflow_model.onnx")
+
+
+def test_end2end_cnv_w2a2_fold_and_tlastmarker():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_dataflow_model.onnx"
+ )
+ fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
+ # each tuple is (PE, SIMD, in_fifo_depth) for a layer
+ folding = [
+ (8, 3, 256, "auto"),
+ (16, 16, 256, "auto"),
+ (8, 16, 256, "auto"),
+ (8, 16, 256, "block"),
+ (4, 8, 214, "auto"),
+ (1, 8, 2, "auto"),
+ (1, 2, 126, "distributed"),
+ (2, 2, 62, "block"),
+ (5, 1, 6, "distributed"),
+ ]
+ for fcl, (pe, simd, ififodepth, ramstyle) in zip(fc_layers, folding):
+ fcl_inst = getCustomOp(fcl)
+ fcl_inst.set_nodeattr("PE", pe)
+ fcl_inst.set_nodeattr("SIMD", simd)
+ fcl_inst.set_nodeattr("inFIFODepth", ififodepth)
+ fcl_inst.set_nodeattr("ram_style", ramstyle)
+
+ swg_layers = model.get_nodes_by_op_type("ConvolutionInputGenerator")
+ swg_idepth = [2, 51, 9, 106, 2, 2]
+ for i in range(len(swg_layers)):
+ swg_inst = getCustomOp(swg_layers[i])
+ simd = folding[i][1]
+ swg_inst.set_nodeattr("SIMD", simd)
+ swg_inst.set_nodeattr("inFIFODepth", swg_idepth[i])
+
+ model = model.transform(InsertDWC())
+ model = model.transform(InsertFIFO())
+ model = model.transform(InsertTLastMarker())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(AnnotateResources("estimate"))
+ model.save(build_dir + "/end2end_cnv_w2a2_folded.onnx")
+
+
+@pytest.mark.slow
+@pytest.mark.vivado
+def test_end2end_cnv_w2a2_gen_hls_ip():
+ model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_folded.onnx")
+ model = model.transform(PrepareIP(test_fpga_part, target_clk_ns))
+ model = model.transform(HLSSynthIP())
+ model = model.transform(AnnotateResources("hls"))
+ model.save(build_dir + "/end2end_cnv_w2a2_ipgen.onnx")
+
+
+@pytest.mark.vivado
+def test_end2end_cnv_w2a2_ip_stitch():
+ model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_ipgen.onnx")
+ model = model.transform(ReplaceVerilogRelPaths())
+ model = model.transform(CreateStitchedIP(test_fpga_part, target_clk_ns))
+ model.save(build_dir + "/end2end_cnv_w2a2_ipstitch.onnx")
+
+
+@pytest.mark.vivado
+def test_end2end_cnv_w2a2_verify_dataflow_part():
+ model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_ipstitch.onnx")
+ x = np.zeros((1, 32, 32, 3), dtype=np.float32)
+ inp_name = model.graph.input[0].name
+ out_name = model.graph.output[0].name
+ inp_dict = {inp_name: x}
+ # cppsim
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
+ model = model.transform(SetExecMode("cppsim"))
+ model.save(build_dir + "/end2end_cnv_w2a2_ipgen_cppsim.onnx")
+ ret_cppsim = execute_onnx(model, inp_dict, True)
+ res_cppsim = ret_cppsim[out_name]
+ # node-by-node rtlsim
+ model = model.transform(SetExecMode("rtlsim"))
+ model = model.transform(PrepareRTLSim())
+ model.save(build_dir + "/end2end_cnv_w2a2_ipgen_nodebynode_rtlsim.onnx")
+ ret_rtlsim_nodebynode = execute_onnx(model, inp_dict, True)
+ res_rtlsim_nodebynode = ret_rtlsim_nodebynode[out_name]
+ # whole-network (ip-stitched) rtlsim
+ model.set_metadata_prop("exec_mode", "rtlsim")
+ model.save(build_dir + "/end2end_cnv_w2a2_ipstitch_whole_rtlsim.onnx")
+ # this is a particularly long-running test, set liveness thr. to unlimited
+ os.environ["LIVENESS_THRESHOLD"] = "-1"
+ ret_rtlsim_whole = execute_onnx(model, inp_dict, True)
+ res_rtlsim_whole = ret_rtlsim_whole[out_name]
+ assert np.isclose(res_cppsim, res_rtlsim_nodebynode).all()
+ assert np.isclose(res_cppsim, res_rtlsim_whole).all()
+
+
+@pytest.mark.vivado
+def test_end2end_cnv_w2a2_throughput_test_rtlsim():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_ipstitch_whole_rtlsim.onnx"
+ )
+ model.set_metadata_prop("rtlsim_trace", "rtlsim_trace.vcd")
+ # os.environ["RTLSIM_TRACE_DEPTH"] = "4"
+ # run through IP-stitched rtlsim with increasing batch sizes and
+ # check the number of cycles it takes to execute
+ ret = throughput_test_rtlsim(model, 10)
+ # TODO check for expected performance
+ assert ret["cycles"] > 0
+
+
+@pytest.mark.vivado
+def test_end2end_cnv_w2a2_verify_all():
+ # use the streamlined model as the "golden" model for right answers
+ golden = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_streamlined.onnx"
+ )
+ iname = golden.graph.input[0].name
+ oname = golden.graph.output[0].name
+ # load one of the test vectors
+ fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
+ input_tensor = np.load(fn)["arr_0"].astype(np.float32)
+ input_tensor = input_tensor / 255
+ assert input_tensor.shape == (1, 3, 32, 32)
+ x = input_tensor
+ # x = np.zeros(ishape, dtype=np.float32)
+ ret_golden = execute_onnx(golden, {iname: x}, True)
+ y_golden = ret_golden[oname]
+ # set up parent+child graph to test
+ # we'll use models from the previous step as the child model
+ parent_model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_dataflow_parent.onnx"
+ )
+ iname = parent_model.graph.input[0].name
+ oname = parent_model.graph.output[0].name
+ # produce results with cppsim
+ sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
+ sdp_node = getCustomOp(sdp_node)
+ load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_ipgen_cppsim.onnx")
+ sdp_node.set_nodeattr("model", build_dir + "/end2end_cnv_w2a2_ipgen_cppsim.onnx")
+ ret_cppsim = execute_onnx(parent_model, {iname: x}, True)
+ y_cppsim = ret_cppsim[oname]
+ # produce results with node-by-node rtlsim
+ load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_ipgen_nodebynode_rtlsim.onnx"
+ )
+ sdp_node.set_nodeattr(
+ "model", build_dir + "/end2end_cnv_w2a2_ipgen_nodebynode_rtlsim.onnx"
+ )
+ ret_nodebynode_rtlsim = execute_onnx(parent_model, {iname: x}, True)
+ y_nodebynode_rtlsim = ret_nodebynode_rtlsim[oname]
+ # produce results with whole-network (stitched ip) rtlsim
+ load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_ipstitch_whole_rtlsim.onnx"
+ )
+ sdp_node.set_nodeattr(
+ "model", build_dir + "/end2end_cnv_w2a2_ipstitch_whole_rtlsim.onnx"
+ )
+ # this is a particularly long-running test, set liveness thr. to unlimited
+ os.environ["LIVENESS_THRESHOLD"] = "-1"
+ ret_whole_rtlsim = execute_onnx(parent_model, {iname: x}, True)
+ y_whole_rtlsim = ret_whole_rtlsim[oname]
+ assert np.isclose(y_golden, y_cppsim).all()
+ assert np.isclose(y_golden, y_nodebynode_rtlsim).all()
+ assert np.isclose(y_golden, y_whole_rtlsim).all()
+ assert np.argmax(y_golden) == 3
+
+
+@pytest.mark.vivado
+def test_end2end_cnv_w2a2_make_pynq_proj():
+ model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_ipstitch.onnx")
+ model = model.transform(MakePYNQProject(test_pynq_board))
+ model.save(build_dir + "/end2end_cnv_w2a2_pynq_project.onnx")
+
+
+@pytest.mark.slow
+@pytest.mark.vivado
+def test_end2end_cnv_w2a2_synth_pynq_project():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_pynq_project.onnx"
+ )
+ model = model.transform(SynthPYNQProject())
+ model = model.transform(AnnotateResources("synth"))
+ model.save(build_dir + "/end2end_cnv_w2a2_synth.onnx")
+
+
+def test_end2end_cnv_w2a2_make_driver():
+ model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_synth.onnx")
+ model = model.transform(MakePYNQDriver())
+ model.save(build_dir + "/end2end_cnv_w2a2_pynq_driver.onnx")
+
+
+def test_end2end_cnv_w2a2_deploy_on_pynq():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_pynq_driver.onnx"
+ )
+ try:
+ ip = os.environ["PYNQ_IP"] # no fault for this one; skip if not defined
+ if ip == "":
+ pytest.skip("PYNQ board IP address not specified")
+ username = os.getenv("PYNQ_USERNAME", "xilinx")
+ password = os.getenv("PYNQ_PASSWORD", "xilinx")
+ port = os.getenv("PYNQ_PORT", 22)
+ target_dir = os.getenv("PYNQ_TARGET_DIR", "/home/xilinx/finn")
+ model = model.transform(DeployToPYNQ(ip, port, username, password, target_dir))
+ # save the model to be able to link it to the parent
+ model.save(build_dir + "/end2end_cnv_w2a2_pynq_deploy.onnx")
+ except KeyError:
+ pytest.skip("PYNQ board IP address not specified")
+
+
+def test_end2end_cnv_w2a2_run_on_pynq():
+ # use the streamlined model as the "golden" model for right answers
+ golden = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_streamlined.onnx"
+ )
+ iname = golden.graph.input[0].name
+ oname = golden.graph.output[0].name
+ # load one of the test vectors
+ fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
+ input_tensor = np.load(fn)["arr_0"].astype(np.float32)
+ input_tensor = input_tensor / 255
+ assert input_tensor.shape == (1, 3, 32, 32)
+ x = input_tensor
+ # run using FINN-based execution
+ ret_golden = execute_onnx(golden, {iname: x}, True)
+ y_golden = ret_golden[oname]
+ # set up parent+child graph to test
+ # we'll use models from the previous step as the child model
+ parent_model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_dataflow_parent.onnx"
+ )
+ iname = parent_model.graph.input[0].name
+ oname = parent_model.graph.output[0].name
+ try:
+ ip = os.environ["PYNQ_IP"] # NOQA
+ if ip == "":
+ pytest.skip("PYNQ board IP address not specified")
+ # produce results with cppsim
+ sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
+ sdp_node = getCustomOp(sdp_node)
+ load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_pynq_deploy.onnx")
+ sdp_node.set_nodeattr("model", build_dir + "/end2end_cnv_w2a2_pynq_deploy.onnx")
+ ret = execute_onnx(parent_model, {iname: x}, True)
+ y = ret[oname]
+ assert np.isclose(y, y_golden).all()
+ assert np.argmax(y) == 3
+
+ except KeyError:
+ pytest.skip("PYNQ board IP address not specified")
diff --git a/tests/end2end/test_end2end_tfc_w1a1.py b/tests/end2end/test_end2end_tfc_w1a1.py
index 15c1c41b006c6f87d79a0e7eb6a4458838de5fd2..31659df631e8ab489cb63dbef51200f313bca6b3 100644
--- a/tests/end2end/test_end2end_tfc_w1a1.py
+++ b/tests/end2end/test_end2end_tfc_w1a1.py
@@ -63,7 +63,12 @@ from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
)
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
from finn.transformation.fpgadataflow.synth_pynq_proj import SynthPYNQProject
-from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
+from finn.transformation.general import (
+ RemoveUnusedTensors,
+ RemoveStaticGraphInputs,
+ GiveReadableTensorNames,
+ GiveUniqueNodeNames,
+)
from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline import Streamline
@@ -72,6 +77,8 @@ from finn.util.basic import pynq_part_map
from finn.util.test import get_test_model_trained, load_test_checkpoint_or_skip
from finn.transformation.fpgadataflow.annotate_resources import AnnotateResources
from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
+from finn.core.throughput_test import throughput_test_rtlsim
+import finn.util.vcd as vcd
build_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
test_pynq_board = os.getenv("PYNQ_BOARD", default="Pynq-Z1")
@@ -96,12 +103,14 @@ def test_end2end_tfc_w1a1_import_and_tidy():
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveReadableTensorNames())
model = model.transform(InferDataTypes())
+ model = model.transform(RemoveStaticGraphInputs())
model.save(build_dir + "/end2end_tfc_w1a1_tidy.onnx")
def test_end2end_tfc_w1a1_streamline():
model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w1a1_tidy.onnx")
model = model.transform(Streamline())
+ model = model.transform(RemoveUnusedTensors())
model.save(build_dir + "/end2end_tfc_w1a1_streamlined.onnx")
@@ -197,6 +206,8 @@ def test_end2end_tfc_w1a1_verify_dataflow_part():
res_rtlsim_nodebynode = ret_rtlsim_nodebynode[out_name]
# whole-network (ip-stitched) rtlsim
model.set_metadata_prop("exec_mode", "rtlsim")
+ model.set_metadata_prop("rtlsim_trace", build_dir + "/tfc_w1a1.vcd")
+ os.environ["RTLSIM_TRACE_DEPTH"] = "3"
model.save(build_dir + "/end2end_tfc_w1a1_ipstitch_whole_rtlsim.onnx")
ret_rtlsim_whole = execute_onnx(model, inp_dict, True)
res_rtlsim_whole = ret_rtlsim_whole[out_name]
@@ -204,6 +215,39 @@ def test_end2end_tfc_w1a1_verify_dataflow_part():
assert np.isclose(res_cppsim, res_rtlsim_whole).all()
+def test_end2end_tfc_w1a1_verify_fifo_fullness():
+ vcdf = build_dir + "/tfc_w1a1.vcd"
+ if not os.path.isfile(vcdf):
+ pytest.skip("Cannot find %s, skipping" % vcdf)
+ stream_ifs = vcd.list_stream_if(vcdf)
+ fifos = vcd.list_fifo_count_signals(vcdf)
+ assert len(stream_ifs) == 37
+ assert len(fifos) == 6
+ fifo_max = vcd.get_all_fifo_count_max(vcdf)
+ assert fifo_max[0][0] == "TOP.v.finn_design_i.StreamingFIFO_0.count[3:0]"
+ assert fifo_max[0][1] == 3
+ stream_stat = vcd.get_all_stream_if_stats(vcdf)
+ assert (
+ stream_stat[0][0]
+ == "TOP.v.finn_design_i.StreamingDataWidthConverter_Batch_0_out_V_V_"
+ )
+
+
+@pytest.mark.vivado
+def test_end2end_tfc_w1a1_throughput_test_rtlsim():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_tfc_w1a1_ipstitch_whole_rtlsim.onnx"
+ )
+ # run through IP-stitched rtlsim with increasing batch sizes and
+ # check the number of cycles it takes to execute
+ ret = throughput_test_rtlsim(model, 1)
+ assert ret["cycles"] == 205
+ ret = throughput_test_rtlsim(model, 10)
+ assert ret["cycles"] == 844
+ ret = throughput_test_rtlsim(model, 100)
+ assert ret["cycles"] == 7234
+
+
@pytest.mark.vivado
def test_end2end_tfc_w1a1_verify_all():
# use the streamlined model as the "golden" model for right answers
diff --git a/tests/end2end/test_end2end_tfc_w1a2.py b/tests/end2end/test_end2end_tfc_w1a2.py
index d4c005a86580fb36e735beb00717fcfdffff21e5..d5579f625a20ae26e18bcdcba0cfaa3042a71b9a 100644
--- a/tests/end2end/test_end2end_tfc_w1a2.py
+++ b/tests/end2end/test_end2end_tfc_w1a2.py
@@ -61,7 +61,12 @@ from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
)
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
from finn.transformation.fpgadataflow.synth_pynq_proj import SynthPYNQProject
-from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
+from finn.transformation.general import (
+ RemoveUnusedTensors,
+ RemoveStaticGraphInputs,
+ GiveReadableTensorNames,
+ GiveUniqueNodeNames,
+)
from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline import Streamline
@@ -93,12 +98,14 @@ def test_end2end_tfc_w1a2_import_and_tidy():
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveReadableTensorNames())
model = model.transform(InferDataTypes())
+ model = model.transform(RemoveStaticGraphInputs())
model.save(build_dir + "/end2end_tfc_w1a2_tidy.onnx")
def test_end2end_tfc_w1a2_streamline():
model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w1a2_tidy.onnx")
model = model.transform(Streamline())
+ model = model.transform(RemoveUnusedTensors())
model.save(build_dir + "/end2end_tfc_w1a2_streamlined.onnx")
diff --git a/tests/end2end/test_end2end_tfc_w2a2.py b/tests/end2end/test_end2end_tfc_w2a2.py
index 19d3f86e046658c4080d71984df1cff74008adab..470119f3444987f0156caff81bf556bf4f2f2cbb 100644
--- a/tests/end2end/test_end2end_tfc_w2a2.py
+++ b/tests/end2end/test_end2end_tfc_w2a2.py
@@ -61,7 +61,12 @@ from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
)
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
from finn.transformation.fpgadataflow.synth_pynq_proj import SynthPYNQProject
-from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
+from finn.transformation.general import (
+ RemoveUnusedTensors,
+ RemoveStaticGraphInputs,
+ GiveReadableTensorNames,
+ GiveUniqueNodeNames,
+)
from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline import Streamline
@@ -93,12 +98,14 @@ def test_end2end_tfc_w2a2_import_and_tidy():
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveReadableTensorNames())
model = model.transform(InferDataTypes())
+ model = model.transform(RemoveStaticGraphInputs())
model.save(build_dir + "/end2end_tfc_w2a2_tidy.onnx")
def test_end2end_tfc_w2a2_streamline():
model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w2a2_tidy.onnx")
model = model.transform(Streamline())
+ model = model.transform(RemoveUnusedTensors())
model.save(build_dir + "/end2end_tfc_w2a2_streamlined.onnx")
diff --git a/tests/fpgadataflow/test_convert_to_hls_channelwise_layer.py b/tests/fpgadataflow/test_convert_to_hls_channelwise_layer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d09c64a1250f78604c1a0a362cf234712de2cf57
--- /dev/null
+++ b/tests/fpgadataflow/test_convert_to_hls_channelwise_layer.py
@@ -0,0 +1,115 @@
+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
+import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
+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.replace_verilog_relpaths import (
+ ReplaceVerilogRelPaths,
+)
+from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
+
+from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.general import GiveUniqueNodeNames
+from finn.util.basic import gen_finn_dt_tensor
+from finn.transformation.infer_shapes import InferShapes
+import numpy as np
+
+
+def prepare_inputs(input_tensor):
+ return {"inp": input_tensor}
+
+
+def make_single_maxpool_modelwrapper(onnx_op_name, ishape, idt, pdt, pshape):
+
+ inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, ishape)
+ outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, ishape)
+ p0 = helper.make_tensor_value_info("p0", TensorProto.FLOAT, pshape)
+
+ model = helper.make_model(
+ helper.make_graph(
+ name="test",
+ inputs=[inp],
+ outputs=[outp],
+ value_info=[p0],
+ nodes=[helper.make_node(onnx_op_name, ["inp", "p0"], ["outp"])],
+ )
+ )
+
+ model = ModelWrapper(model)
+ model.set_initializer("p0", gen_finn_dt_tensor(pdt, pshape))
+ model.set_tensor_datatype("inp", idt)
+ model.transform(InferDataLayouts(), make_deepcopy=False)
+ model.transform(InferShapes(), make_deepcopy=False)
+ return model
+
+
+# parameter datatype
+@pytest.mark.parametrize("pdt", [DataType.BIPOLAR, DataType.UINT4, DataType.INT2])
+# input datatype
+@pytest.mark.parametrize("idt", [DataType.INT32, DataType.UINT4, DataType.INT4])
+# function
+@pytest.mark.parametrize("onnx_op_name", ["Add", "Mul"])
+# vector parameter or scalar parameter (broadcast)
+@pytest.mark.parametrize("scalar_param", [True, False])
+# execution mode
+@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
+@pytest.mark.vivado
+@pytest.mark.slow
+def test_convert_to_hls_channelwise_layer(
+ pdt, idt, onnx_op_name, scalar_param, exec_mode
+):
+ ifm_ch = 16
+ ifm_dim = 5
+ ishape = (1, ifm_ch, ifm_dim, ifm_dim)
+ if scalar_param:
+ pshape = (1,)
+ else:
+ pshape = (1, ifm_ch, 1, 1)
+
+ np.random.seed(0)
+ model = make_single_maxpool_modelwrapper(onnx_op_name, ishape, idt, pdt, pshape)
+
+ # Since the aren't Data types with a bit width of a non power of 2,
+ # there are cases where the input won't use it full range.
+ if idt == DataType.INT32:
+ x = gen_finn_dt_tensor(DataType.INT16, (1, ifm_ch, ifm_dim, ifm_dim))
+ elif idt == DataType.UINT32:
+ x = gen_finn_dt_tensor(DataType.UINT16, (1, ifm_ch, ifm_dim, ifm_dim))
+ else:
+ x = gen_finn_dt_tensor(idt, (1, ifm_ch, ifm_dim, ifm_dim))
+
+ input_dict = prepare_inputs(x)
+ y_expected = oxe.execute_onnx(model, input_dict)["outp"]
+
+ new_model = model.transform(to_hls.InferChannelwiseLinearLayer())
+ new_model = new_model.transform(GiveUniqueNodeNames())
+
+ if exec_mode == "cppsim":
+ new_model = new_model.transform(PrepareCppSim())
+ new_model = new_model.transform(CompileCppSim())
+ new_model = new_model.transform(SetExecMode("cppsim"))
+ elif exec_mode == "rtlsim":
+ new_model = new_model.transform(SetExecMode("rtlsim"))
+ new_model = new_model.transform(GiveUniqueNodeNames())
+ new_model = new_model.transform(PrepareIP("xc7z020clg400-1", 5))
+ new_model = new_model.transform(HLSSynthIP())
+ new_model = new_model.transform(ReplaceVerilogRelPaths())
+ new_model = new_model.transform(PrepareRTLSim())
+ else:
+ raise Exception("Unknown exec_mode")
+
+ ctx_produced = oxe.execute_onnx(
+ new_model, input_dict, return_full_exec_context=True
+ )
+ y_produced = ctx_produced["outp"]
+
+ assert (y_produced == y_expected).all()
+ assert new_model.graph.node[1].op_type == "ChannelwiseOp_Batch"
diff --git a/tests/fpgadataflow/test_convert_to_hls_conv_layer.py b/tests/fpgadataflow/test_convert_to_hls_conv_layer.py
new file mode 100644
index 0000000000000000000000000000000000000000..22c356a5869b25fcc7ae3ef0164ed61b53ef232c
--- /dev/null
+++ b/tests/fpgadataflow/test_convert_to_hls_conv_layer.py
@@ -0,0 +1,112 @@
+from onnx import TensorProto, helper
+import numpy as np
+import pytest
+
+from finn.core.datatype import DataType
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.general import GiveUniqueNodeNames
+from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
+
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
+from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
+ ReplaceVerilogRelPaths,
+)
+import finn.core.onnx_exec as oxe
+from finn.core.modelwrapper import ModelWrapper
+from finn.util.basic import gen_finn_dt_tensor
+import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
+
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
+from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
+from finn.custom_op.im2col import compute_conv_output_dim
+
+# conv_config kernel_size,stride, pad
+
+
+@pytest.mark.parametrize(
+ "conv_config", [(1, 2, 0), (1, 3, 0), (3, 2, 1), (3, 1, 0), (3, 1, 1), (5, 2, 1)]
+)
+@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
+@pytest.mark.slow
+@pytest.mark.vivado
+def test_convert_to_hls_conv_layer(conv_config, exec_mode):
+ kernel_size, stride, pad = conv_config
+ np.random.seed(0)
+ idt = DataType.UINT4
+
+ in_feature_dim = 7
+ in_chn = 16
+ out_chn = 20
+
+ out_feature_dim = compute_conv_output_dim(in_feature_dim, kernel_size, stride, pad)
+
+ input_shape = [1, in_chn, in_feature_dim, in_feature_dim]
+ output_shape = [1, out_chn, out_feature_dim, out_feature_dim]
+
+ conv_param_shape = [out_chn, in_chn, kernel_size, kernel_size]
+ conv_weight_dt = DataType.UINT4
+
+ conv_config = {}
+ conv_config["dilations"] = [1, 1]
+ conv_config["group"] = 1
+ conv_config["kernel_shape"] = [kernel_size, kernel_size]
+ conv_config["pads"] = [pad, pad, pad, pad]
+ conv_config["strides"] = [stride, stride]
+
+ top_in = helper.make_tensor_value_info("top_in", TensorProto.FLOAT, input_shape)
+ top_out = helper.make_tensor_value_info("top_out", TensorProto.FLOAT, output_shape)
+ value_info = [
+ helper.make_tensor_value_info("p1", TensorProto.FLOAT, conv_param_shape)
+ ]
+
+ modelproto = helper.make_model(
+ helper.make_graph(
+ name="conv_test",
+ inputs=[top_in],
+ outputs=[top_out],
+ value_info=value_info,
+ nodes=[
+ helper.make_node("Conv", ["top_in", "p1"], ["top_out"], **conv_config)
+ ],
+ )
+ )
+
+ model = ModelWrapper(modelproto)
+ model.set_tensor_datatype("top_in", idt)
+ model.set_tensor_datatype("top_out", idt)
+ model.set_tensor_datatype("p1", conv_weight_dt)
+ model.set_initializer("p1", gen_finn_dt_tensor(conv_weight_dt, conv_param_shape))
+
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+
+ new_model = model.transform(LowerConvsToMatMul())
+ new_model = new_model.transform(to_hls.InferConvInpGen())
+
+ new_model = new_model.transform(GiveUniqueNodeNames())
+ new_model = new_model.transform(InferShapes())
+ new_model = new_model.transform(InferDataTypes())
+
+ if exec_mode == "cppsim":
+ new_model = new_model.transform(PrepareCppSim())
+ new_model = new_model.transform(CompileCppSim())
+ new_model = new_model.transform(SetExecMode("cppsim"))
+ elif exec_mode == "rtlsim":
+ new_model = new_model.transform(SetExecMode("rtlsim"))
+ new_model = new_model.transform(GiveUniqueNodeNames())
+ new_model = new_model.transform(PrepareIP("xc7z020clg400-1", 5))
+ new_model = new_model.transform(HLSSynthIP())
+ new_model = new_model.transform(ReplaceVerilogRelPaths())
+ new_model = new_model.transform(PrepareRTLSim())
+ else:
+ raise Exception("Unknown exec_mode")
+
+ x = gen_finn_dt_tensor(idt, input_shape)
+ inp_dict = {model.graph.input[0].name: x}
+ assert oxe.compare_execution(model, new_model, inp_dict)
+ if kernel_size == 1 and stride > 1 and pad == 0:
+ assert new_model.graph.node[1].op_type == "DownSampler"
diff --git a/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py b/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
index 48803c9614f53a3a149c6eaac4289d10086513a5..20e3ee08d7ffdd013a89d26bb71d86ccc554a5b4 100644
--- a/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
+++ b/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
@@ -51,7 +51,7 @@ from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
from finn.custom_op.registry import getCustomOp
-export_onnx_path_cnv = "test_output_cnv.onnx"
+export_onnx_path_cnv = "test_convert_to_hls_layers_cnv.onnx"
@pytest.mark.vivado
diff --git a/tests/fpgadataflow/test_convert_to_hls_layers_fc.py b/tests/fpgadataflow/test_convert_to_hls_layers_fc.py
index e261a3114853bf24bdb4c931c46ff92eea4150dd..d77065ad9396d0cc8dd57a39ed823fffcb30ee47 100644
--- a/tests/fpgadataflow/test_convert_to_hls_layers_fc.py
+++ b/tests/fpgadataflow/test_convert_to_hls_layers_fc.py
@@ -52,8 +52,7 @@ from finn.transformation.streamline.round_thresholds import RoundAndClipThreshol
from finn.util.test import get_test_model_trained
-export_onnx_path = "test_output_tfc.onnx"
-export_onnx_path_cnv = "test_output_cnv.onnx"
+export_onnx_path = "test_convert_to_hls_layers_fc.onnx"
@pytest.mark.vivado
diff --git a/tests/fpgadataflow/test_convert_to_hls_pool_batch.py b/tests/fpgadataflow/test_convert_to_hls_pool_batch.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9f78dcea1a1ce364d0657ad64de7d440d41b822
--- /dev/null
+++ b/tests/fpgadataflow/test_convert_to_hls_pool_batch.py
@@ -0,0 +1,160 @@
+# 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 numpy as np
+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.fpgadataflow.prepare_rtlsim import PrepareRTLSim
+import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
+from finn.transformation.general import GiveUniqueNodeNames
+from finn.custom_op.registry import getCustomOp
+from finn.util.basic import gen_finn_dt_tensor
+from finn.transformation.infer_shapes import InferShapes
+
+
+def make_single_maxpool_modelwrapper(k, stride, pad, ifm_ch, ifm_dim, ofm_dim, idt):
+ odt = idt
+ inp = helper.make_tensor_value_info(
+ "inp", TensorProto.FLOAT, [1, ifm_ch, ifm_dim, ifm_dim]
+ )
+ outp = helper.make_tensor_value_info(
+ "outp", TensorProto.FLOAT, [1, ifm_ch, ofm_dim, ofm_dim]
+ )
+
+ mp_node = helper.make_node(
+ "MaxPool",
+ ["inp"],
+ ["outp"],
+ kernel_shape=[k, k],
+ pads=[pad, pad, pad, pad],
+ strides=[stride, stride],
+ )
+ graph = helper.make_graph(
+ nodes=[mp_node], name="mp_graph", inputs=[inp], outputs=[outp]
+ )
+
+ model = helper.make_model(graph, producer_name="mp-model")
+ model = ModelWrapper(model)
+
+ model.set_tensor_datatype("inp", idt)
+ model.set_tensor_datatype("outp", odt)
+ model = model.transform(InferShapes())
+
+ return model
+
+
+def prepare_inputs(input_tensor):
+ return {"inp": input_tensor}
+
+
+# input datatype
+@pytest.mark.parametrize("idt", [DataType.UINT4, DataType.INT4])
+# pool configuration: ( k,stride, pad, ifm_dim )
+@pytest.mark.parametrize(
+ "pool_config", [(3, 2, 0, 5), (3, 2, 1, 5), (2, 2, 0, 8), (5, 2, 2, 7)]
+)
+# input channels
+@pytest.mark.parametrize("ifm_ch", [1, 4, 20])
+# number of out channel computed in parallel
+@pytest.mark.parametrize("pe", [1, 4, 20])
+# execution mode
+@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
+# pool type
+@pytest.mark.parametrize("op_type", ["MaxPool"])
+@pytest.mark.slow
+@pytest.mark.vivado
+def test_convert_to_hls_pool_batch(idt, pool_config, ifm_ch, pe, exec_mode, op_type):
+ k, stride, pad, ifm_dim = pool_config
+
+ if ifm_ch % pe != 0:
+ pytest.skip("ifm_ch%pe != 0. Skipping")
+
+ if pad != 0 and idt.signed():
+ pytest.skip("No support for pal_val != 0. Skipping")
+
+ np.random.seed(0)
+ ofm_dim = int(((ifm_dim + 2 * pad - k) / stride) + 1)
+
+ x = gen_finn_dt_tensor(idt, (1, ifm_ch, ifm_dim, ifm_dim))
+ # prepare input data
+ input_dict = prepare_inputs(x)
+ if op_type == "MaxPool":
+ model = make_single_maxpool_modelwrapper(
+ k, stride, pad, ifm_ch, ifm_dim, ofm_dim, idt
+ )
+ else:
+ assert False, "{} is not a supported op_type".format(op_type)
+
+ y_expected = oxe.execute_onnx(model, input_dict)["outp"]
+
+ new_model = model.transform(to_hls.InferPool_Batch())
+ new_model = new_model.transform(GiveUniqueNodeNames())
+
+ if ifm_ch != pe:
+ new_model = new_model.transform(to_hls.InferConvInpGen())
+ # Folding
+ for n in new_model.graph.node:
+ if n.op_type == "ConvolutionInputGenerator":
+ inst = getCustomOp(n)
+ inst.set_nodeattr("SIMD", pe)
+ elif n.op_type == "Pool_Batch":
+ inst = getCustomOp(n)
+ inst.set_nodeattr("PE", pe)
+
+ if exec_mode == "cppsim":
+ new_model = new_model.transform(SetExecMode("cppsim"))
+ new_model = new_model.transform(PrepareCppSim())
+ new_model = new_model.transform(CompileCppSim())
+ elif exec_mode == "rtlsim":
+ new_model = new_model.transform(SetExecMode("rtlsim"))
+ new_model = new_model.transform(GiveUniqueNodeNames())
+ new_model = new_model.transform(PrepareIP("xc7z020clg400-1", 5))
+ new_model = new_model.transform(HLSSynthIP())
+ new_model = new_model.transform(PrepareRTLSim())
+ else:
+ raise Exception("Unknown exec_mode")
+
+ # execute new_model
+ y_produced = oxe.execute_onnx(new_model, input_dict)["outp"]
+ assert (y_produced == y_expected).all()
+ if stride != k:
+ if pad == 0 or ifm_ch == pe:
+ assert len(new_model.graph.node) == 4
+ else:
+ assert len(new_model.graph.node) == 5
+ else:
+ assert len(new_model.graph.node) == 1
diff --git a/tests/fpgadataflow/test_fpgadataflow_channelwise_ops.py b/tests/fpgadataflow/test_fpgadataflow_channelwise_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ed352e28981552b186bb778b94dcbc07471e14b
--- /dev/null
+++ b/tests/fpgadataflow/test_fpgadataflow_channelwise_ops.py
@@ -0,0 +1,156 @@
+# 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.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_modelwrapper(C, pe, idt, odt, pdt, func, vecs):
+ NumChannels = C.shape[0]
+
+ inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, vecs + [NumChannels])
+ outp = helper.make_tensor_value_info(
+ "outp", TensorProto.FLOAT, vecs + [NumChannels]
+ )
+
+ node_inp_list = ["inp", "const"]
+
+ node = helper.make_node(
+ "ChannelwiseOp_Batch",
+ node_inp_list,
+ ["outp"],
+ domain="finn",
+ backend="fpgadataflow",
+ NumChannels=NumChannels,
+ Func=func,
+ PE=pe,
+ inputDataType=idt.name,
+ outputDataType=odt.name,
+ paramDataType=pdt.name,
+ numInputVectors=vecs,
+ )
+ graph = helper.make_graph(nodes=[node], name="graph", inputs=[inp], outputs=[outp])
+
+ model = helper.make_model(graph, producer_name="model")
+ model = ModelWrapper(model)
+
+ model.set_tensor_datatype("inp", idt)
+ model.set_tensor_datatype("outp", odt)
+
+ model.set_tensor_datatype("const", idt)
+ model.set_initializer("const", C)
+ return model
+
+
+# activation: None or DataType
+@pytest.mark.parametrize("act", [DataType.INT8])
+# input datatype
+@pytest.mark.parametrize("idt", [DataType.INT4])
+# param datatype
+@pytest.mark.parametrize("pdt", [DataType.INT4])
+# folding, -1 is maximum possible
+@pytest.mark.parametrize("nf", [-1, 2])
+# number of input features
+@pytest.mark.parametrize("ich", [16])
+# vecs
+@pytest.mark.parametrize("vecs", [[1], [1, 7, 7]])
+# function
+@pytest.mark.parametrize("func", ["add", "mul"])
+# execution mode
+@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
+@pytest.mark.vivado
+@pytest.mark.slow
+def test_fpgadataflow_channelwise_ops(idt, act, pdt, nf, ich, func, vecs, exec_mode):
+ if nf == -1:
+ nf = ich
+ pe = ich // nf
+ assert ich % pe == 0
+
+ # generate input and param data
+ x = gen_finn_dt_tensor(idt, tuple(vecs + [ich]))
+ # C = np.random.randint(idt.min(), idt.max() + 1, ich).astype(np.float32)
+ C = gen_finn_dt_tensor(pdt, (ich))
+
+ odt = act
+
+ model = make_modelwrapper(C, pe, idt, odt, pdt, func, vecs)
+
+ 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}
+
+ oshape = model.get_tensor_shape("outp")
+
+ C_reshaped = np.broadcast_to(C.flatten(), x.shape)
+ if func == "add":
+ y = x + C_reshaped
+ elif func == "mul":
+ y = x * C_reshaped
+
+ 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 "ChannelwiseOp_Batch_0" in hls_synt_res_est
diff --git a/tests/fpgadataflow/test_fpgadataflow_convinputgenerator.py b/tests/fpgadataflow/test_fpgadataflow_convinputgenerator.py
index 5051bf34dc690daf8b6186859d3717cc8e217eee..b5fc85caf274edc9e7afc52df962862fa8a99ba3 100644
--- a/tests/fpgadataflow/test_fpgadataflow_convinputgenerator.py
+++ b/tests/fpgadataflow/test_fpgadataflow_convinputgenerator.py
@@ -78,7 +78,7 @@ def make_single_im2col_modelwrapper(k, ifm_ch, ifm_dim, ofm_dim, simd, stride, i
def make_single_slidingwindow_modelwrapper(
- k, ifm_ch, ifm_dim, ofm_dim, simd, stride, idt
+ k, ifm_ch, ifm_dim, ofm_dim, simd, stride, idt, dw=0
):
odt = idt
inp = helper.make_tensor_value_info(
@@ -102,6 +102,7 @@ def make_single_slidingwindow_modelwrapper(
Stride=stride,
inputDataType=idt.name,
outputDataType=odt.name,
+ depthwise=dw,
)
graph = helper.make_graph(
nodes=[SlidingWindow_node],
@@ -126,25 +127,29 @@ def prepare_inputs(input_tensor):
# input datatype
@pytest.mark.parametrize("idt", [DataType.BIPOLAR, DataType.INT2])
# kernel size
-@pytest.mark.parametrize("k", [2, 4])
+@pytest.mark.parametrize("k", [2, 3])
# input dimension
-@pytest.mark.parametrize("ifm_dim", [4, 6, 8])
+@pytest.mark.parametrize("ifm_dim", [6, 8])
# input channels
-@pytest.mark.parametrize("ifm_ch", [2, 4]) # , 2, 3, 4])
+@pytest.mark.parametrize("ifm_ch", [2, 4])
# Stride
@pytest.mark.parametrize("stride", [1, 2])
# execution mode
@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
# input channel parallelism ("SIMD")
@pytest.mark.parametrize("simd", [1, 2])
+# depthwise
+@pytest.mark.parametrize("dw", [0, 1])
@pytest.mark.slow
@pytest.mark.vivado
-def test_fpgadataflow_slidingwindow(idt, k, ifm_dim, ifm_ch, stride, exec_mode, simd):
+def test_fpgadataflow_slidingwindow(
+ idt, k, ifm_dim, ifm_ch, stride, exec_mode, simd, dw
+):
ofm_dim = int(((ifm_dim - k) / stride) + 1)
x = gen_finn_dt_tensor(idt, (1, ifm_dim, ifm_dim, ifm_ch))
model = make_single_slidingwindow_modelwrapper(
- k, ifm_ch, ifm_dim, ofm_dim, simd, stride, idt
+ k, ifm_ch, ifm_dim, ofm_dim, simd, stride, idt, dw
)
if exec_mode == "cppsim":
@@ -168,6 +173,12 @@ def test_fpgadataflow_slidingwindow(idt, k, ifm_dim, ifm_ch, stride, exec_mode,
k, ifm_ch, ifm_dim, ofm_dim, simd, stride, idt
)
y_expected = oxe.execute_onnx(golden, input_dict)["outp"]
- # if idt == DataType.BIPOLAR:
- # y_expected = 2 * y_expected - 1
- assert (y_produced == y_expected).all()
+ if dw == 0:
+ assert (y_produced == y_expected).all()
+ else:
+ y_expected = y_expected.reshape(
+ 1, ofm_dim, ofm_dim, k * k, ifm_ch // simd, simd
+ )
+ y_expected = y_expected.transpose(0, 1, 2, 4, 3, 5)
+ y_expected = y_expected.reshape(1, ofm_dim, ofm_dim, ifm_ch * k * k)
+ assert (y_produced == y_expected).all()
diff --git a/tests/fpgadataflow/test_fpgadataflow_sameresize.py b/tests/fpgadataflow/test_fpgadataflow_fmpadding.py
similarity index 71%
rename from tests/fpgadataflow/test_fpgadataflow_sameresize.py
rename to tests/fpgadataflow/test_fpgadataflow_fmpadding.py
index ea6130c3891443595b038460233ebb85799ac461..5ff3da87228a2a32a41226bb46e0b16b1a44df50 100644
--- a/tests/fpgadataflow/test_fpgadataflow_sameresize.py
+++ b/tests/fpgadataflow/test_fpgadataflow_fmpadding.py
@@ -23,9 +23,11 @@ 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
-):
+def make_single_fmpadding_modelwrapper(idim, padding, num_ch, simd, idt, pad_style):
+ assert pad_style == 2, "only pad_style == 2 supported in hlslib"
+ assert padding > 0, "Output dim should be greater than input dim"
+ odim = idim + padding
+
inp = helper.make_tensor_value_info(
"inp", TensorProto.FLOAT, [1, idim, idim, num_ch]
)
@@ -33,25 +35,26 @@ def make_single_sameresize_modelwrapper(
"outp", TensorProto.FLOAT, [1, odim, odim, num_ch]
)
- SameResize_node = helper.make_node(
- "SameResize_Batch",
+ FMPadding = helper.make_node(
+ "FMPadding_Batch",
["inp"],
["outp"],
domain="finn",
backend="fpgadataflow",
ImgDim=idim,
- KernelDim=kdim,
- Stride=stride,
+ Padding=padding,
NumChannels=num_ch,
inputDataType=str(idt.name),
PaddingStyle=pad_style,
+ numInputVectors=1,
+ SIMD=simd,
)
graph = helper.make_graph(
- nodes=[SameResize_node], name="sameresize_graph", inputs=[inp], outputs=[outp]
+ nodes=[FMPadding], name="fmpadding_graph", inputs=[inp], outputs=[outp]
)
- model = helper.make_model(graph, producer_name="sameresize-model")
+ model = helper.make_model(graph, producer_name="fmpadding-model")
model = ModelWrapper(model)
model.set_tensor_datatype("inp", idt)
@@ -60,34 +63,31 @@ def make_single_sameresize_modelwrapper(
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])
+# input image dimension
+@pytest.mark.parametrize("idim", [8])
+# number of rows and number of cols to add
+@pytest.mark.parametrize("pad", [2, 3])
# number of channels
-@pytest.mark.parametrize("num_ch", [1, 2])
+@pytest.mark.parametrize("num_ch", [2, 4])
+# Input parallelism
+@pytest.mark.parametrize("simd", [1, 2])
+# PaddingStyle: selects behavior when (odim-idim)%2 != 0
+@pytest.mark.parametrize("pad_style", [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)
-
+def test_fpgadataflow_fmpadding(idim, pad, num_ch, simd, pad_style, idt, mode):
+ if num_ch % simd != 0:
+ pytest.skip(" num_ch % simd != 0, skipping")
# generate input data
x = gen_finn_dt_tensor(idt, [1, idim, idim, num_ch])
input_dict = {"inp": x}
+ odim = idim + pad
- model = make_single_sameresize_modelwrapper(
- idim, odim, kdim, stride, num_ch, idt, pad_style
- )
+ model = make_single_fmpadding_modelwrapper(idim, pad, num_ch, simd, idt, pad_style)
model = model.transform(InferShapes())
model = model.transform(SetExecMode(mode))
model = model.transform(GiveUniqueNodeNames())
@@ -103,8 +103,7 @@ def test_fpgadataflow_sameresize(idim, kdim, stride, num_ch, idt, mode):
assert y_produced.shape == expected_oshape
# calculate reference
- # calculate correct padding according to parameters
- pad = odim - idim
+ # calculate correct pad according to parameters
if pad_style == 2:
if pad % 2 == 0:
pad_up = pad // 2
@@ -115,6 +114,7 @@ def test_fpgadataflow_sameresize(idim, kdim, stride, num_ch, idt, mode):
else:
pad_up = pad // 2
pad_left = pad // 2
+
pad_down = pad - pad_up
pad_right = pad - pad_left
diff --git a/tests/fpgadataflow/test_fpgadataflow_ip_stitch.py b/tests/fpgadataflow/test_fpgadataflow_ipstitch.py
similarity index 87%
rename from tests/fpgadataflow/test_fpgadataflow_ip_stitch.py
rename to tests/fpgadataflow/test_fpgadataflow_ipstitch.py
index 16100522aa94fd25d234efa1d03edfdc866ca1bb..a9f5bf5ffa1f816b82ef701800e92249056b7c74 100644
--- a/tests/fpgadataflow/test_fpgadataflow_ip_stitch.py
+++ b/tests/fpgadataflow/test_fpgadataflow_ipstitch.py
@@ -53,6 +53,11 @@ from finn.transformation.general import GiveUniqueNodeNames
from finn.util.basic import gen_finn_dt_tensor, pynq_part_map
from finn.util.fpgadataflow import pyverilate_stitched_ip
from finn.util.test import load_test_checkpoint_or_skip
+from finn.transformation.fpgadataflow.synth_ooc import SynthOutOfContext
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.fpgadataflow.insert_iodma import InsertIODMA
+from finn.transformation.fpgadataflow.floorplan import Floorplan
+
test_pynq_board = os.getenv("PYNQ_BOARD", default="Pynq-Z1")
test_fpga_part = pynq_part_map[test_pynq_board]
@@ -281,6 +286,27 @@ def test_fpgadataflow_ipstitch_rtlsim():
assert (rtlsim_res == x).all()
+@pytest.mark.vivado
+@pytest.mark.slow
+def test_fpgadataflow_ipstitch_synth_ooc():
+ model = load_test_checkpoint_or_skip(
+ ip_stitch_model_dir + "/test_fpgadataflow_ip_stitch.onnx"
+ )
+ model = model.transform(SynthOutOfContext(test_fpga_part, 5))
+ ret = model.get_metadata_prop("res_total_ooc_synth")
+ assert ret is not None
+ # example expected output: (details may differ based on Vivado version etc)
+ # "{'vivado_proj_folder': ...,
+ # 'LUT': 708.0, 'FF': 1516.0, 'DSP': 0.0, 'BRAM': 0.0, 'WNS': 0.152, '': 0,
+ # 'fmax_mhz': 206.27062706270627}"
+ ret = eval(ret)
+ assert ret["LUT"] > 0
+ assert ret["FF"] > 0
+ assert ret["DSP"] == 0
+ assert ret["BRAM"] == 0
+ assert ret["fmax_mhz"] > 100
+
+
@pytest.mark.vivado
def test_fpgadataflow_ipstitch_pynq_projgen():
model = load_test_checkpoint_or_skip(
@@ -368,3 +394,19 @@ def test_fpgadataflow_ipstitch_remote_execution():
assert np.isclose(outp["outp"], x).all()
except KeyError:
pytest.skip("PYNQ board IP address not specified")
+
+
+def test_fpgadataflow_ipstitch_iodma_floorplan():
+ model = create_one_fc_model()
+ if model.graph.node[0].op_type == "StreamingDataflowPartition":
+ sdp_node = getCustomOp(model.graph.node[0])
+ assert sdp_node.__class__.__name__ == "StreamingDataflowPartition"
+ assert os.path.isfile(sdp_node.get_nodeattr("model"))
+ model = load_test_checkpoint_or_skip(sdp_node.get_nodeattr("model"))
+ model = model.transform(InferDataLayouts())
+ model = model.transform(InsertIODMA())
+ model = model.transform(Floorplan())
+ assert getCustomOp(model.graph.node[0]).get_nodeattr("partition_id") == 0
+ assert getCustomOp(model.graph.node[1]).get_nodeattr("partition_id") == 2
+ assert getCustomOp(model.graph.node[2]).get_nodeattr("partition_id") == 1
+ model.save(ip_stitch_model_dir + "/test_fpgadataflow_ipstitch_iodma_floorplan.onnx")
diff --git a/tests/fpgadataflow/test_fpgadataflow_labelselect.py b/tests/fpgadataflow/test_fpgadataflow_labelselect.py
index 2df841728395229dafe33d2804c44a3489ef3e45..9bc77cd47fd6115823f9a35d98e8874ee3f98b2d 100644
--- a/tests/fpgadataflow/test_fpgadataflow_labelselect.py
+++ b/tests/fpgadataflow/test_fpgadataflow_labelselect.py
@@ -27,6 +27,7 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import pytest
+import numpy as np
from onnx import TensorProto, helper
@@ -70,7 +71,8 @@ def make_labelselect_modelwrapper(labels, pe, k, idt):
model = ModelWrapper(model)
model.set_tensor_datatype("inp", idt)
- model.set_tensor_datatype("outp", DataType.UINT32)
+ odt = DataType.get_smallest_possible(labels - 1)
+ model.set_tensor_datatype("outp", odt)
return model
@@ -79,19 +81,18 @@ def prepare_inputs(input_tensor, idt):
return {"inp": input_tensor}
-# TODO: folded inputs fail, likely problem in hlslib
-# input datatype -- checked by assertion in HLSCustomOp
-@pytest.mark.parametrize("idt", [DataType.UINT8, DataType.UINT16])
+@pytest.mark.parametrize("idt", [DataType.UINT8, DataType.UINT16, DataType.INT16])
# labels
-@pytest.mark.parametrize("labels", [10, 1000])
+@pytest.mark.parametrize("labels", [10, 100])
# folding
-@pytest.mark.parametrize("fold", [-1])
+@pytest.mark.parametrize("fold", [-1, 2, 10])
# number of top labels to select
@pytest.mark.parametrize("k", [1, 5])
# execution mode
@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
@pytest.mark.vivado
def test_fpgadataflow_labelselect(idt, labels, fold, k, exec_mode):
+ np.random.seed(0)
if fold == -1:
pe = 1
else:
diff --git a/tests/fpgadataflow/test_fpgadataflow_res_estimate.py b/tests/fpgadataflow/test_fpgadataflow_res_estimate.py
index 38f792ed3cdd52044b28b4c19ac0603da4e502e6..398a17132a2ef6c92e600102ff5c0b71a1f65aaa 100644
--- a/tests/fpgadataflow/test_fpgadataflow_res_estimate.py
+++ b/tests/fpgadataflow/test_fpgadataflow_res_estimate.py
@@ -92,7 +92,7 @@ def test_res_estimate():
model = model.transform(GiveUniqueNodeNames())
prod_resource_estimation = model.analysis(res_estimation)
expect_resource_estimation = {
- "StreamingFCLayer_Batch_0": {"BRAM_18K": 1, "LUT": 304.4}
+ "StreamingFCLayer_Batch_0": {"BRAM_18K": 1, 'BRAM_efficiency': 0.001736111111111111, "LUT": 304.4}
}
assert check_two_dict_for_equality(
diff --git a/tests/pynq/test_pynq_performance_end2end.py b/tests/pynq/test_pynq_performance_end2end.py
index 66a93a190061e0142637be19bb2ea841d192745a..3b6ea86741b8adefce4faaa65b791f1d213cf3ae 100644
--- a/tests/pynq/test_pynq_performance_end2end.py
+++ b/tests/pynq/test_pynq_performance_end2end.py
@@ -10,7 +10,7 @@ from finn.core.throughput_test import throughput_test
build_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
-@pytest.mark.parametrize("end2end_example", ["tfc_w1a1", "cnv_w1a1"])
+@pytest.mark.parametrize("end2end_example", ["tfc_w1a1", "cnv_w1a1", "cnv_w2a2"])
@pytest.mark.slow
def test_pynq_performance_end2end(end2end_example):
model = load_test_checkpoint_or_skip(
diff --git a/tests/transformation/streamline/test_streamline_cnv.py b/tests/transformation/streamline/test_streamline_cnv.py
index 56dcd26076ec0a5fba6e9be6acac7f5e13572c3d..bcb66a2c22eb4d6a998580129881793bbc86b250 100644
--- a/tests/transformation/streamline/test_streamline_cnv.py
+++ b/tests/transformation/streamline/test_streamline_cnv.py
@@ -34,7 +34,12 @@ import pkg_resources as pk
import finn.core.onnx_exec as oxe
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.fold_constants import FoldConstants
-from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
+from finn.transformation.general import (
+ RemoveUnusedTensors,
+ RemoveStaticGraphInputs,
+ GiveReadableTensorNames,
+ GiveUniqueNodeNames,
+)
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline import Streamline
from finn.util.test import get_test_model_trained
@@ -44,9 +49,9 @@ from finn.transformation.double_to_single_float import DoubleToSingleFloat
export_onnx_path = make_build_dir("test_streamline_cnv_")
# act bits
-@pytest.mark.parametrize("abits", [1])
+@pytest.mark.parametrize("abits", [1, 2])
# weight bits
-@pytest.mark.parametrize("wbits", [1])
+@pytest.mark.parametrize("wbits", [1, 2])
# network topology / size
@pytest.mark.parametrize("size", ["CNV"])
def test_streamline_cnv(size, wbits, abits):
@@ -62,6 +67,7 @@ def test_streamline_cnv(size, wbits, abits):
model = model.transform(FoldConstants())
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveReadableTensorNames())
+ model = model.transform(RemoveStaticGraphInputs())
# load one of the test vectors
fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
input_tensor = np.load(fn)["arr_0"].astype(np.float32)
@@ -73,7 +79,11 @@ def test_streamline_cnv(size, wbits, abits):
expected = expected_ctx[model.graph.output[0].name]
# model.save("orig_cnv.onnx")
model = model.transform(Streamline())
+ model = model.transform(RemoveUnusedTensors())
+ assert len(model.graph.initializer) == 21
+ assert len(model.graph.value_info) == 43
# model.save("streamlined_cnv.onnx")
+ assert len(model.graph.node) == 23
produced_ctx = oxe.execute_onnx(model, input_dict, True)
produced = produced_ctx[model.graph.output[0].name]
assert np.isclose(expected, produced, atol=1e-3).all()
diff --git a/tests/transformation/streamline/test_streamline_fc.py b/tests/transformation/streamline/test_streamline_fc.py
index c68561239b7c30973856fa282d20cd2afaa168ae..dd7e756b4021af26c228804d4b509ecff032347e 100644
--- a/tests/transformation/streamline/test_streamline_fc.py
+++ b/tests/transformation/streamline/test_streamline_fc.py
@@ -37,7 +37,12 @@ import pytest
import finn.core.onnx_exec as oxe
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.fold_constants import FoldConstants
-from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
+from finn.transformation.general import (
+ RemoveUnusedTensors,
+ RemoveStaticGraphInputs,
+ GiveReadableTensorNames,
+ GiveUniqueNodeNames,
+)
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline import Streamline
from finn.util.test import get_test_model_trained
@@ -65,6 +70,7 @@ def test_streamline_fc(size, wbits, abits):
model = model.transform(FoldConstants())
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveReadableTensorNames())
+ model = model.transform(RemoveStaticGraphInputs())
# load one of the test vectors
raw_i = get_data("finn", "data/onnx/mnist-conv/test_data_set_0/input_0.pb")
input_tensor = onnx.load_tensor_from_string(raw_i)
@@ -73,6 +79,10 @@ def test_streamline_fc(size, wbits, abits):
expected_ctx = oxe.execute_onnx(model, input_dict, True)
expected = expected_ctx[model.graph.output[0].name]
model = model.transform(Streamline())
+ model = model.transform(RemoveUnusedTensors())
+ assert len(model.graph.initializer) == 11
+ assert len(model.graph.value_info) == 21
+ assert len(model.graph.quantization_annotation) == 18
produced_ctx = oxe.execute_onnx(model, input_dict, True)
produced = produced_ctx[model.graph.output[0].name]
assert np.isclose(expected, produced, atol=1e-3).all()
diff --git a/tests/transformation/test_absorb_mul_into_topk.py b/tests/transformation/test_absorb_mul_into_topk.py
new file mode 100644
index 0000000000000000000000000000000000000000..1394220f7c336ccea8fe9c494734c4175bf2e847
--- /dev/null
+++ b/tests/transformation/test_absorb_mul_into_topk.py
@@ -0,0 +1,108 @@
+# 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
+
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.general import GiveUniqueNodeNames, GiveReadableTensorNames
+from finn.transformation.insert_topk import InsertTopK
+from finn.transformation.streamline.absorb import AbsorbScalarMulIntoTopK
+import finn.core.onnx_exec as oxe
+
+# parameter to indicate if mul parameter is negative or positive
+@pytest.mark.parametrize("mul_positive", [True, False])
+# parameter to indicate if mul parameter is scalar or not
+@pytest.mark.parametrize("scalar", [True, False])
+def test_absorb_mul_into_topk(mul_positive, scalar):
+ if scalar is True:
+ shape = [1]
+ else:
+ shape = [1, 1, 1, 1000]
+ inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, [1, 1, 1, 1000])
+ a0 = helper.make_tensor_value_info("a0", TensorProto.FLOAT, shape)
+ outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, [1, 1, 1, 1000])
+
+ mul_node = helper.make_node("Mul", ["inp", "a0"], ["outp"])
+ mul_graph = helper.make_graph(
+ nodes=[mul_node],
+ name="mul-graph",
+ inputs=[inp],
+ outputs=[outp],
+ value_info=[a0],
+ )
+
+ model = helper.make_model(mul_graph, producer_name="mul_model")
+ model = ModelWrapper(model)
+ # initialize values
+ if mul_positive is True:
+ a0_values = np.random.uniform(low=0.1, high=1, size=tuple(shape)).astype(
+ np.float32
+ )
+ else:
+ a0_values = np.random.uniform(low=-1, high=-0.1, size=tuple(shape)).astype(
+ np.float32
+ )
+ model.set_initializer("a0", a0_values)
+ model = model.transform(InsertTopK())
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(GiveReadableTensorNames())
+ model_transformed = model.transform(AbsorbScalarMulIntoTopK())
+
+ # compare execution results
+ inp_values = np.random.uniform(low=-10, high=10, size=(1, 1, 1, 1000)).astype(
+ np.float32
+ )
+ idict = {"global_in": inp_values}
+ odict = oxe.execute_onnx(model, idict, True)
+ y_indices = odict["global_out"]
+ y_values = odict["TopK_0_out0"]
+ odict = oxe.execute_onnx(model_transformed, idict, True)
+ y_tr_indices = odict["global_out"]
+ y_tr_values = odict["TopK_0_out0"]
+
+ # the indices stay the same, if the model is transformed or not
+ assert (y_indices == y_tr_indices).all()
+
+ if scalar is True and mul_positive is True:
+ # the values change if the model was transformed
+ assert (y_values != y_tr_values).all()
+
+ # check for new order
+ assert model.graph != model_transformed.graph
+ assert len(model.graph.node) - 1 == len(model_transformed.graph.node)
+ assert model_transformed.graph.node[0].op_type == "TopK"
+
+ else:
+ assert (y_values == y_tr_values).all()
+ assert model.graph == model_transformed.graph
diff --git a/tests/transformation/test_absorb_opposite_transposes.py b/tests/transformation/test_absorb_opposite_transposes.py
new file mode 100644
index 0000000000000000000000000000000000000000..859e691277a261f01b559e2e166763e402c5d689
--- /dev/null
+++ b/tests/transformation/test_absorb_opposite_transposes.py
@@ -0,0 +1,76 @@
+# Copyright (c) 2020, Xilinx
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of FINN nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import numpy as np
+import onnx.helper as oh
+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.absorb import AbsorbConsecutiveTransposes
+
+
+def test_absorb_opposite_transposes():
+ np.random.seed(0)
+ input_shape = [1, 3, 4, 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)
+ value_info = [oh.make_tensor_value_info("add_param_0", TensorProto.FLOAT, [1])]
+ value_info += [oh.make_tensor_value_info("add_param_1", TensorProto.FLOAT, [1])]
+ value_info += [oh.make_tensor_value_info("mul_param_0", 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", "add_param_0"], ["t0"]),
+ oh.make_node("Transpose", ["t0"], ["t1"], perm=[0, 2, 3, 1]),
+ oh.make_node("Transpose", ["t1"], ["t2"], perm=[0, 3, 1, 2]),
+ oh.make_node("Add", ["t2", "add_param_1"], ["t3"]),
+ oh.make_node("Transpose", ["t3"], ["t4"], perm=[0, 2, 3, 1]),
+ oh.make_node("Transpose", ["t4"], ["t5"], perm=[0, 3, 1, 2]),
+ oh.make_node("Add", ["t5", "t2"], ["t6"]),
+ oh.make_node("Mul", ["t6", "mul_param_0"], ["top_out"]),
+ ],
+ )
+ )
+ model = ModelWrapper(modelproto)
+ model = model.transform(InferShapes())
+ model.set_initializer("add_param_0", np.asarray([1], dtype=np.float32))
+ model.set_initializer("add_param_1", np.asarray([3], dtype=np.float32))
+ model.set_initializer("mul_param_0", np.asarray([2], dtype=np.float32))
+ new_model = model.transform(AbsorbConsecutiveTransposes())
+ new_model = new_model.transform(InferShapes())
+ inp_dict = {"top_in": np.random.rand(*input_shape).astype(np.float32)}
+ assert ox.compare_execution(model, model, inp_dict)
+ assert len(new_model.graph.node) == 4
+ for n in new_model.graph.node:
+ assert new_model.graph.node[0].op_type != "Transpose"
diff --git a/tests/transformation/test_absorb_transp_into_flatten.py b/tests/transformation/test_absorb_transp_into_flatten.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbfa15277717c554da01e38608601997407803b2
--- /dev/null
+++ b/tests/transformation/test_absorb_transp_into_flatten.py
@@ -0,0 +1,99 @@
+import pytest
+
+import numpy as np
+from onnx import TensorProto, helper
+
+from finn.core.modelwrapper import ModelWrapper
+import finn.core.data_layout as DataLayout
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.general import GiveUniqueNodeNames, GiveReadableTensorNames
+from finn.transformation.streamline.absorb import AbsorbTransposeIntoFlatten
+import finn.core.onnx_exec as oxe
+
+# permutation of transpose node
+@pytest.mark.parametrize("perm", [[0, 2, 3, 1], [0, 1, 3, 2], [3, 2, 0, 1]])
+# reshape or flatten
+@pytest.mark.parametrize("shape", [None, [1, -1], [-1, 1]])
+# input shape
+@pytest.mark.parametrize("ishape", [[1, 1, 1, 4], [2, 4, 1, 1], [1, 2, 2, 4]])
+# datalayout
+@pytest.mark.parametrize("data_layout", ["NCHW", "NHWC"])
+def test_absorb_transp_into_flatten(perm, shape, ishape, data_layout):
+ inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, ishape)
+ transp_node = helper.make_node("Transpose", ["inp"], ["transp_out"], perm=perm)
+ dummy_in = np.random.uniform(low=0, high=1, size=tuple(ishape)).astype(np.float32)
+ if shape is None:
+ shape_node = helper.make_node("Flatten", ["transp_out"], ["outp"])
+ dummy_in = dummy_in.transpose(tuple(perm))
+ oshape = dummy_in.reshape(dummy_in.shape[0], -1).shape
+ outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, oshape)
+ shape0 = None
+ else:
+ shape0 = helper.make_tensor_value_info("shape0", TensorProto.FLOAT, shape)
+ shape_node = helper.make_node("Reshape", ["transp_out", "shape0"], ["outp"])
+ oshape = dummy_in.transpose(tuple(perm)).reshape(tuple(shape)).shape
+ outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, oshape)
+
+ graph = helper.make_graph(
+ nodes=[transp_node, shape_node],
+ name="absorb-transpose-graph",
+ inputs=[inp],
+ outputs=[outp],
+ )
+
+ model = helper.make_model(graph, producer_name="absorb_transpose_model")
+ model = ModelWrapper(model)
+ if shape is not None:
+ model.graph.value_info.append(shape0)
+ model.set_initializer("shape0", np.asarray(shape))
+ if data_layout == "NCHW":
+ model.set_tensor_layout("inp", DataLayout.NCHW)
+ else:
+ model.set_tensor_layout("inp", DataLayout.NHWC)
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+ model = model.transform(InferDataLayouts())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(GiveReadableTensorNames())
+ model.save("test.onnx")
+ model_transformed = model.transform(AbsorbTransposeIntoFlatten())
+ model_transformed.save("test2.onnx")
+
+ # verify transformation
+ inp_values = np.random.uniform(low=-1, high=1, size=tuple(ishape)).astype(
+ np.float32
+ )
+ idict = {model.graph.input[0].name: inp_values}
+ assert oxe.compare_execution(model, model_transformed, idict)
+
+ # only some of the parameter combinations lead to a graph that will be changed when
+ # AbsorbTransposeIntoFlatten is applied
+
+ if shape == [-1, 1]: # not a flatten operation, so the graph will not be changed
+ assert model.graph == model_transformed.graph
+
+ elif perm == [
+ 3,
+ 2,
+ 0,
+ 1,
+ ]: # the first dimension is also part of the transpose operation
+ # so the graph will not be changed
+ assert model.graph == model_transformed.graph
+
+ # the following cases are the ones in which the model is transformed
+ # because we tested the parameters shape and perm befire we can only consider ishape
+ # and data_layout (the transformed model should only contain a "Flatten" node)
+ elif ishape == [1, 1, 1, 4] and data_layout == "NHWC":
+ assert model_transformed.graph.node[0].op_type == "Flatten"
+
+ elif ishape == [2, 4, 1, 1] and data_layout == "NCHW" and shape is None:
+ # If the first dimension of the input tensor is not 1, flatten and
+ # reshape (with shape = [1, -1]) would lead to different results
+ assert model_transformed.graph.node[0].op_type == "Flatten"
+
+ # all other cases lead to an unchanged model
+ else:
+ assert model.graph == model_transformed.graph
diff --git a/tests/transformation/test_change_datalayout.py b/tests/transformation/test_change_datalayout.py
new file mode 100644
index 0000000000000000000000000000000000000000..66459d574957575e61ec1bec631fb7030a27cca1
--- /dev/null
+++ b/tests/transformation/test_change_datalayout.py
@@ -0,0 +1,112 @@
+# 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 helper, TensorProto
+
+from finn.custom_op.maxpoolnhwc import compute_pool_output_dim
+from finn.core.modelwrapper import ModelWrapper
+from finn.core.datatype import DataType
+import finn.core.data_layout as DataLayout
+from finn.transformation.change_datalayout import ChangeDataLayoutQuantAvgPool2d
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.general import GiveUniqueNodeNames, GiveReadableTensorNames
+from finn.util.basic import gen_finn_dt_tensor
+from finn.util.basic import get_by_name
+import finn.core.onnx_exec as oxe
+
+# stride
+@pytest.mark.parametrize("s", [1, 2])
+# kernel
+@pytest.mark.parametrize("k", [3, 4])
+# ibits
+@pytest.mark.parametrize("ibits", [4, 8])
+# obits
+@pytest.mark.parametrize("obits", [2, 4])
+# signed
+@pytest.mark.parametrize("signed", [False, True])
+# channels
+@pytest.mark.parametrize("c", [2, 3])
+# input dimension
+@pytest.mark.parametrize("idim", [6, 7])
+def test_change_datalayout_quantavgpool(s, k, ibits, obits, signed, c, idim):
+ n = 1
+ odim = compute_pool_output_dim(idim, k, s)
+ # determine input FINN datatype
+ if signed is True:
+ prefix = "INT"
+ else:
+ prefix = "UINT"
+ dt_name = prefix + str(ibits)
+ dtype = DataType[dt_name]
+
+ inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, [n, c, idim, idim])
+ outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, [n, c, odim, odim])
+
+ node = helper.make_node(
+ "QuantAvgPool2d",
+ ["inp"],
+ ["outp"],
+ domain="finn",
+ stride=s,
+ kernel=k,
+ ibits=ibits,
+ obits=obits,
+ signed=signed,
+ data_layout="NCHW",
+ )
+ graph = helper.make_graph(
+ nodes=[node], name="single-quantavgpool", inputs=[inp], outputs=[outp]
+ )
+
+ model = helper.make_model(graph)
+ model = ModelWrapper(model)
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+ model = model.transform(InferDataLayouts())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(GiveReadableTensorNames())
+ model_transformed = model.transform(ChangeDataLayoutQuantAvgPool2d())
+ model_transformed = model_transformed.transform(InferShapes())
+ model_transformed = model_transformed.transform(InferDataTypes())
+ model_transformed = model_transformed.transform(InferDataLayouts())
+ model_transformed = model_transformed.transform(GiveUniqueNodeNames())
+ model_transformed = model_transformed.transform(GiveReadableTensorNames())
+ inp_values = gen_finn_dt_tensor(dtype, [n, c, idim, idim])
+ idict = {"inp": inp_values}
+ assert oxe.compare_execution(model, model_transformed, idict)
+ assert len(model.graph.node) + 2 == len(model_transformed.graph.node)
+ assert model_transformed.graph.node[-1].op_type == "Transpose"
+ assert model_transformed.graph.node[0].op_type == "Transpose"
+ # check if QuantAvgPool2d node has datalayout set correctly
+ node = model_transformed.graph.node[1]
+ d_layout = get_by_name(node.attribute, "data_layout").s.decode("UTF-8")
+ assert d_layout == "NHWC"
+ assert model_transformed.get_tensor_layout(node.input[0]) == DataLayout.NHWC
+ assert model_transformed.get_tensor_layout(node.output[0]) == DataLayout.NHWC
diff --git a/tests/transformation/test_conv_lowering.py b/tests/transformation/test_conv_lowering.py
index c06a9a2c9def44b5384d6a87935e0ce85ede1bb2..fe0b58a6adc8278a2be298a6a2c7265c9743a94a 100644
--- a/tests/transformation/test_conv_lowering.py
+++ b/tests/transformation/test_conv_lowering.py
@@ -27,13 +27,13 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import pytest
+import onnx.helper as oh
+from onnx import TensorProto
import os
-from onnx import helper, TensorProto
import pkg_resources as pk
import brevitas.onnx as bo
import numpy as np
-
from finn.core.modelwrapper import ModelWrapper
from finn.core.datatype import DataType
from finn.transformation.fold_constants import FoldConstants
@@ -46,7 +46,7 @@ from finn.custom_op.im2col import compute_conv_output_dim
from finn.util.basic import gen_finn_dt_tensor
from finn.custom_op.registry import getCustomOp
-export_onnx_path = "test_output_cnv.onnx"
+export_onnx_path = "test_conv_lowering.onnx"
def test_conv_lowering_cnv_w1a1():
@@ -91,16 +91,16 @@ def test_depthwise_conv_lowering(idt, k, ifm_dim, ifm_ch, stride, padding):
ofm_dim = compute_conv_output_dim(ifm_dim, k, stride, pad=padding[0])
# set up onnx model
- inp = helper.make_tensor_value_info(
+ inp = oh.make_tensor_value_info(
"inp", TensorProto.FLOAT, [1, ifm_ch, ifm_dim, ifm_dim]
)
- outp = helper.make_tensor_value_info(
+ outp = oh.make_tensor_value_info(
"outp", TensorProto.FLOAT, [1, ofm_ch, ofm_dim, ofm_dim]
)
- W = helper.make_tensor_value_info("W", TensorProto.FLOAT, [ofm_ch, 1, k, k])
+ W = oh.make_tensor_value_info("W", TensorProto.FLOAT, [ofm_ch, 1, k, k])
- dw_cnv = helper.make_node(
+ dw_cnv = oh.make_node(
"Conv",
inputs=["inp", "W"],
outputs=["outp"],
@@ -109,7 +109,7 @@ def test_depthwise_conv_lowering(idt, k, ifm_dim, ifm_ch, stride, padding):
strides=[stride, stride],
group=ifm_ch,
)
- graph = helper.make_graph(
+ graph = oh.make_graph(
nodes=[dw_cnv],
name="dw_cnv_graph",
inputs=[inp],
@@ -117,7 +117,7 @@ def test_depthwise_conv_lowering(idt, k, ifm_dim, ifm_ch, stride, padding):
value_info=[W],
)
- model = helper.make_model(graph, producer_name="dws_cnv-model")
+ model = oh.make_model(graph, producer_name="dws_cnv-model")
model = ModelWrapper(model)
model.set_tensor_datatype("inp", idt)
model.set_tensor_datatype("outp", odt)
@@ -139,4 +139,52 @@ def test_depthwise_conv_lowering(idt, k, ifm_dim, ifm_ch, stride, padding):
# check if created nodes have attributes that indicate depthwise conv
assert model.get_tensor_sparsity("W") is not None
im2col_node = getCustomOp(model.graph.node[1])
- assert im2col_node.get_nodeattr("dw") == 1
+ assert im2col_node.get_nodeattr("depthwise") == 1
+
+
+def test_conv_lowering_conv_1x1():
+ np.random.seed(0)
+
+ in_feature_dim = 7
+ in_chn = 3
+ kernel_size = 1
+ out_feature_dim = in_feature_dim
+
+ 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, 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", "p1"], ["top_out"], **conv_config)],
+ )
+ )
+ model = ModelWrapper(modelproto)
+ model = model.transform(InferShapes())
+ model.set_initializer("p1", np.random.rand(*conv_param_shape).astype(np.float32))
+
+ new_model = model.transform(LowerConvsToMatMul())
+ inp_dict = {"top_in": np.random.rand(*input_shape).astype(np.float32)}
+
+ assert oxe.compare_execution(model, new_model, inp_dict)
+ assert new_model.graph.node[0].op_type == "Transpose"
+ assert new_model.graph.node[1].op_type == "MatMul"
+ assert new_model.graph.node[2].op_type == "Transpose"
+ assert len(new_model.graph.node) == 3
diff --git a/tests/transformation/test_fold_constants.py b/tests/transformation/test_fold_constants.py
index 685c14a98b9031096aaf5b244c4f484d4f308bca..a976ffd62bce744a474a6fac2a61a6478526777f 100644
--- a/tests/transformation/test_fold_constants.py
+++ b/tests/transformation/test_fold_constants.py
@@ -40,7 +40,7 @@ from finn.transformation.fold_constants import FoldConstants
from finn.transformation.infer_shapes import InferShapes
from finn.util.test import get_test_model_untrained
-export_onnx_path = "test_output_lfc.onnx"
+export_onnx_path = "test_fold_constants.onnx"
def test_const_folding():
diff --git a/tests/transformation/test_infer_data_layouts.py b/tests/transformation/test_infer_data_layouts.py
index fccc7813da6f98c8af4ade7ae562c99b32247a8b..d6d9920043114c78e970842aee5955e3150cf526 100644
--- a/tests/transformation/test_infer_data_layouts.py
+++ b/tests/transformation/test_infer_data_layouts.py
@@ -44,7 +44,7 @@ import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
from finn.transformation.infer_data_layouts import InferDataLayouts
import finn.core.data_layout as DataLayout
-export_onnx_path_cnv = "test_output_cnv.onnx"
+export_onnx_path_cnv = "test_infer_data_layouts.onnx"
def test_infer_data_layouts():
diff --git a/tests/transformation/test_infer_datatypes.py b/tests/transformation/test_infer_datatypes.py
index e3db40289c4318894cf5ad41c2f67b3bff501db9..097ae03f6153843fbb7956a72b38431559d5d0f1 100644
--- a/tests/transformation/test_infer_datatypes.py
+++ b/tests/transformation/test_infer_datatypes.py
@@ -38,7 +38,7 @@ from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.infer_shapes import InferShapes
from finn.util.test import get_test_model_trained
-export_onnx_path = "test_output_lfc.onnx"
+export_onnx_path = "test_infer_datatypes.onnx"
def test_infer_datatypes():
diff --git a/tests/transformation/test_linear_past_eltwise.py b/tests/transformation/test_linear_past_eltwise.py
index b77f59779a5e8559f80e017d13b66bcb67249830..4cff5e5e1d40986a006cc02186fce21a907c2ef1 100644
--- a/tests/transformation/test_linear_past_eltwise.py
+++ b/tests/transformation/test_linear_past_eltwise.py
@@ -41,7 +41,7 @@ from finn.transformation.double_to_single_float import DoubleToSingleFloat
import pytest
-export_onnx_path = "test_scalar_past_eltwise.onnx"
+export_onnx_path = "test_linear_past_eltwise.onnx"
# construct a synthetic graph to test:
# topk insertion, topk conversion to hls, add conversion to hls
diff --git a/tests/transformation/test_merge_onnx_models.py b/tests/transformation/test_merge_onnx_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..db7c990baddfb50a39603937a9c5b73f512a0e59
--- /dev/null
+++ b/tests/transformation/test_merge_onnx_models.py
@@ -0,0 +1,126 @@
+# 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 pkgutil import get_data
+
+import numpy as np
+import onnx
+import onnx.numpy_helper as np_helper
+from onnx import TensorProto, helper
+
+from finn.core.modelwrapper import ModelWrapper
+from finn.core.datatype import DataType
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
+from finn.transformation.merge_onnx_models import MergeONNXModels
+import finn.core.onnx_exec as oxe
+
+
+def test_merge_onnx_models():
+ # load pre model
+ raw_m = get_data("finn", "data/onnx/mnist-conv/model.onnx")
+ model1 = ModelWrapper(raw_m)
+ # the input for model1 comes from a uint8 vector so we set the finn datatype
+ # of the input tensor to DataType.UINT8 to verify that the datatypes are correctly
+ # preserved in the transformed model
+ model1.set_tensor_datatype(model1.graph.input[0].name, DataType.UINT8)
+ model1 = model1.transform(InferShapes())
+ model1 = model1.transform(GiveUniqueNodeNames())
+ model1 = model1.transform(GiveReadableTensorNames())
+
+ # set up post model
+ shape = [1, 10]
+ inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, shape)
+ a0 = helper.make_tensor_value_info("a0", TensorProto.FLOAT, [])
+ a1 = helper.make_tensor_value_info("a1", TensorProto.FLOAT, [])
+ outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, shape)
+
+ mul_node = helper.make_node("Mul", ["inp", "a0"], ["mul_out"])
+ div_node = helper.make_node("Div", ["mul_out", "a1"], ["outp"])
+
+ graph = helper.make_graph(
+ nodes=[mul_node, div_node],
+ name="model2-graph",
+ inputs=[inp],
+ outputs=[outp],
+ value_info=[a0, a1],
+ )
+
+ model2 = helper.make_model(graph, producer_name="model2")
+ model2 = ModelWrapper(model2)
+ # initialize model2
+ a0_value = np.random.uniform(low=0, high=1, size=(1)).astype(np.float32)
+ model2.set_initializer("a0", a0_value)
+ a1_value = np.random.uniform(low=0.1, high=1, size=(1)).astype(np.float32)
+ model2.set_initializer("a1", a1_value)
+ # set a dummy sparsity annotation to check if it gets correctly transferred
+ # to the merged model
+ sparsity = {"dw": {"kernel_shape": 0}}
+ model2.set_tensor_sparsity("a1", sparsity)
+ model2 = model2.transform(InferShapes())
+ model2 = model2.transform(InferDataTypes())
+ model2 = model2.transform(InferDataLayouts())
+ model2 = model2.transform(GiveUniqueNodeNames())
+ model2 = model2.transform(GiveReadableTensorNames())
+
+ # simulate the models before the merging and pass the output of model1 to model2
+ # load one of the test vectors
+ raw_i = get_data("finn", "data/onnx/mnist-conv/test_data_set_0/input_0.pb")
+ inp_values = onnx.load_tensor_from_string(raw_i)
+ inp_values = np_helper.to_array(inp_values)
+ idict = {model1.graph.input[0].name: inp_values}
+ odict = oxe.execute_onnx(model1, idict)
+ temp = odict[model1.graph.output[0].name]
+
+ idict = {model2.graph.input[0].name: temp}
+ odict = oxe.execute_onnx(model2, idict)
+ outp = odict[model2.graph.output[0].name]
+ # merge models
+ model_transformed = model2.transform(MergeONNXModels(model1))
+
+ idict = {model_transformed.graph.input[0].name: inp_values}
+ odict = oxe.execute_onnx(model_transformed, idict)
+ outp_transformed = odict[model_transformed.graph.output[0].name]
+
+ assert (outp == outp_transformed).all()
+ assert len(model_transformed.graph.node) == len(model1.graph.node) + len(
+ model2.graph.node
+ )
+ # to test if the value is preserved we set the sparsity annotation of input[1]
+ # of the division block to a dummy value, we can now look for the division block
+ # and check if the sparsity annotation is still the same
+ for n in model_transformed.graph.node:
+ if n.op_type == "Div":
+ tensor_name = n.input[1]
+ set_sparsity = model_transformed.get_tensor_sparsity(tensor_name)
+ assert sparsity == set_sparsity
+
+ # check if finn datatype of graph.input[0] is still set to UINT8
+ assert model_transformed.get_tensor_datatype("global_in") == DataType.UINT8
diff --git a/tests/transformation/test_move_chw_add_past_conv.py b/tests/transformation/test_move_chw_add_past_conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..b626f7e5b8564739ec383aaddfc262d642bf47cc
--- /dev/null
+++ b/tests/transformation/test_move_chw_add_past_conv.py
@@ -0,0 +1,109 @@
+# 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 helper, TensorProto
+
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.streamline.reorder import MoveAddPastConv
+from finn.custom_op.im2col import compute_conv_output_dim
+import finn.core.onnx_exec as oxe
+
+
+# input dimension
+@pytest.mark.parametrize("idim", [4, 7])
+# kernel size
+@pytest.mark.parametrize("k", [2, 3])
+# stride
+@pytest.mark.parametrize("s", [1, 2])
+# input channels
+@pytest.mark.parametrize("ich", [2, 4])
+# output channels
+@pytest.mark.parametrize("och", [2, 3])
+def test_move_chw_add_past_conv(idim, k, s, ich, och):
+ odim = compute_conv_output_dim(idim, k, s)
+
+ ishape = [1, ich, idim, idim]
+ oshape = [1, och, odim, odim]
+ add_param_shape = [1, ich, 1, 1]
+ conv_param_shape = [och, ich, k, k]
+
+ inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, ishape)
+ outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, oshape)
+ a0 = helper.make_tensor_value_info("a0", TensorProto.FLOAT, add_param_shape)
+ a1 = helper.make_tensor_value_info("a1", TensorProto.FLOAT, conv_param_shape)
+
+ conv_config = {}
+ conv_config["dilations"] = [1, 1]
+ conv_config["group"] = 1
+ conv_config["kernel_shape"] = [k, k]
+ conv_config["pads"] = [0, 0, 0, 0]
+ conv_config["strides"] = [s, s]
+
+ add_node = helper.make_node("Add", ["inp", "a0"], ["add_out"])
+ conv_node = helper.make_node("Conv", ["add_out", "a1"], ["outp"], **conv_config)
+
+ model = helper.make_model(
+ helper.make_graph(
+ nodes=[add_node, conv_node],
+ name="move-add-graph",
+ inputs=[inp],
+ outputs=[outp],
+ value_info=[a0, a1],
+ )
+ )
+
+ model = ModelWrapper(model)
+ # initialize model
+ a0_values = np.random.uniform(low=0, high=1, size=tuple(add_param_shape)).astype(
+ np.float32
+ )
+ model.set_initializer("a0", a0_values)
+ a1_values = np.random.uniform(low=0, high=1, size=tuple(conv_param_shape)).astype(
+ np.float32
+ )
+ model.set_initializer("a1", a1_values)
+
+ model = model.transform(InferShapes())
+
+ # execution before transformation
+ inp_values = np.random.uniform(low=0, high=1, size=tuple(ishape)).astype(np.float32)
+ idict = {model.graph.input[0].name: inp_values}
+ odict = oxe.execute_onnx(model, idict)
+ y_before = odict[model.graph.output[0].name]
+
+ model = model.transform(MoveAddPastConv())
+ odict = oxe.execute_onnx(model, idict)
+ y_after = odict[model.graph.output[0].name]
+
+ assert np.isclose(y_before, y_after).all()
+ assert model.graph.node[0].op_type == "Conv"
+ assert model.graph.node[1].op_type == "Add"
diff --git a/tests/transformation/test_move_flatten_past_affine.py b/tests/transformation/test_move_flatten_past_affine.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2d5e51613d41f3f2db3dabcef7b982ec2816b19
--- /dev/null
+++ b/tests/transformation/test_move_flatten_past_affine.py
@@ -0,0 +1,106 @@
+# 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
+
+from finn.core.modelwrapper import ModelWrapper
+from finn.core.datatype import DataType
+import finn.core.data_layout as DataLayout
+from finn.util.basic import gen_finn_dt_tensor
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.general import GiveUniqueNodeNames, GiveReadableTensorNames
+from finn.transformation.streamline.reorder import MoveFlattenPastAffine
+import finn.core.onnx_exec as oxe
+
+# data layout
+@pytest.mark.parametrize("data_layout", [DataLayout.NHWC, DataLayout.NCHW])
+# batch size
+@pytest.mark.parametrize("batch_size", [1, 2])
+def test_move_flatten_past_affine(data_layout, batch_size):
+ if data_layout == DataLayout.NHWC:
+ ishape = [batch_size, 1, 1, 1024]
+ oshape = [batch_size, 1000]
+ else:
+ ishape = [batch_size, 1024, 1, 1]
+ oshape = [batch_size, 1000]
+
+ inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, ishape)
+ a0 = helper.make_tensor_value_info("a1", TensorProto.FLOAT, [1024, 1000])
+ a1 = helper.make_tensor_value_info("a2", TensorProto.FLOAT, [])
+ a2 = helper.make_tensor_value_info("a3", TensorProto.FLOAT, [1000])
+ outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, oshape)
+
+ flatten_node = helper.make_node("Flatten", ["inp"], ["flatten_out"])
+ matmul_node = helper.make_node("MatMul", ["flatten_out", "a0"], ["matmul_out"])
+ mul_node = helper.make_node("Mul", ["matmul_out", "a1"], ["mul_out"])
+ add_node = helper.make_node("Add", ["mul_out", "a2"], ["outp"])
+
+ graph = helper.make_graph(
+ nodes=[flatten_node, matmul_node, mul_node, add_node],
+ name="move-reshape-graph",
+ inputs=[inp],
+ outputs=[outp],
+ value_info=[a0, a1, a2],
+ )
+
+ model = helper.make_model(graph, producer_name="move_reshape_model")
+ model = ModelWrapper(model)
+
+ # initialize values
+ a0_values = gen_finn_dt_tensor(DataType.TERNARY, [1024, 1000])
+ model.set_initializer("a0", a0_values)
+ a1_values = np.random.uniform(low=0.1, high=0.99, size=(1)).astype(np.float32)
+ model.set_initializer("a1", a1_values)
+ a2_values = np.random.uniform(low=-1, high=1, size=(1000)).astype(np.float32)
+ model.set_initializer("a2", a2_values)
+
+ model.set_tensor_datatype("inp", DataType.INT2)
+ model.set_tensor_layout("inp", data_layout)
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+ model = model.transform(InferDataLayouts())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(GiveReadableTensorNames())
+
+ # compare execution before and after transformation
+ inp_values = gen_finn_dt_tensor(DataType.INT2, ishape)
+ idict = {model.graph.input[0].name: inp_values}
+ model_transformed = model.transform(MoveFlattenPastAffine())
+ assert oxe.compare_execution(model, model_transformed, idict)
+
+ # depending on data layout check if graph is transformed or not
+ if data_layout == DataLayout.NHWC:
+ # check if nodes have new order in transformed graph
+ assert model.graph != model_transformed.graph
+ assert model_transformed.graph.node[-1].op_type == "Flatten"
+ else:
+ assert model.graph == model_transformed.graph
diff --git a/tests/transformation/test_move_flatten_past_topk.py b/tests/transformation/test_move_flatten_past_topk.py
new file mode 100644
index 0000000000000000000000000000000000000000..65da92c22dbe9f6b1c5a49172ffae59fa6e98607
--- /dev/null
+++ b/tests/transformation/test_move_flatten_past_topk.py
@@ -0,0 +1,89 @@
+# 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
+
+from finn.core.modelwrapper import ModelWrapper
+from finn.core.datatype import DataType
+import finn.core.data_layout as DataLayout
+from finn.util.basic import gen_finn_dt_tensor
+from finn.transformation.insert_topk import InsertTopK
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.general import GiveUniqueNodeNames, GiveReadableTensorNames
+from finn.transformation.streamline.reorder import MoveFlattenPastTopK
+import finn.core.onnx_exec as oxe
+
+# data layout
+@pytest.mark.parametrize("data_layout", [DataLayout.NHWC, DataLayout.NCHW])
+# batch size
+@pytest.mark.parametrize("batch_size", [1, 2])
+def test_move_flatten_past_affine(data_layout, batch_size):
+ if data_layout == DataLayout.NHWC:
+ ishape = [batch_size, 1, 1, 1024]
+ oshape = [batch_size, 1024]
+ else:
+ ishape = [batch_size, 1024, 1, 1]
+ oshape = [batch_size, 1024]
+
+ inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, ishape)
+ outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, oshape)
+
+ flatten_node = helper.make_node("Flatten", ["inp"], ["outp"])
+
+ graph = helper.make_graph(
+ nodes=[flatten_node], name="move-flatten-graph", inputs=[inp], outputs=[outp],
+ )
+
+ model = helper.make_model(graph, producer_name="move_flatten_model")
+ model = ModelWrapper(model)
+
+ model.set_tensor_datatype("inp", DataType.INT2)
+ model.set_tensor_layout("inp", data_layout)
+ model = model.transform(InsertTopK())
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+ model = model.transform(InferDataLayouts())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(GiveReadableTensorNames())
+
+ # compare execution before and after transformation
+ inp_values = gen_finn_dt_tensor(DataType.INT2, ishape)
+ idict = {model.graph.input[0].name: inp_values}
+ model_transformed = model.transform(MoveFlattenPastTopK())
+ assert oxe.compare_execution(model, model_transformed, idict)
+
+ # depending on data layout check if graph is transformed or not
+ if data_layout == DataLayout.NHWC:
+ # check if nodes have new order in transformed graph
+ assert model.graph != model_transformed.graph
+ assert model_transformed.graph.node[-1].op_type == "Flatten"
+ else:
+ assert model.graph == model_transformed.graph
diff --git a/tests/transformation/test_move_maxpool_past_multithreshold.py b/tests/transformation/test_move_maxpool_past_multithreshold.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fc19debf8d6fc89d15e3d731f1e54daa491c321
--- /dev/null
+++ b/tests/transformation/test_move_maxpool_past_multithreshold.py
@@ -0,0 +1,100 @@
+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 MoveMaxPoolPastMultiThreshold
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+
+
+def get_multithreshold_rand_params(channels, num_of_thres, seed=None):
+ if seed is not None:
+ np.random.seed(seed)
+ steps = np.random.rand(channels, 1) * 2
+ bias = np.random.rand(channels, 1) * 10
+ thres = [np.arange(num_of_thres) for chn in range(channels)]
+ thres = ((thres - bias) * steps).astype(np.float32)
+ return thres
+
+
+def test_move_maxpool_past_multithreshold():
+ # generate test vectors of correct shape
+ ch = 64
+ ifmdim = 16
+ ofmdim = 16 // 4
+ input_shape = (1, ch, ifmdim, ifmdim)
+ output_shape = (1, ch, ofmdim, ofmdim)
+
+ top_in = helper.make_tensor_value_info("top_in", TensorProto.FLOAT, input_shape)
+ top_out = helper.make_tensor_value_info("top_out", TensorProto.FLOAT, output_shape)
+
+ maxpool_config = {}
+ maxpool_config["pads"] = [1, 1, 1, 1]
+ maxpool_config["kernel_shape"] = [3, 3]
+ maxpool_config["strides"] = [2, 2]
+
+ value_info = []
+ thres1_shape = [1, 1]
+ value_info += [
+ helper.make_tensor_value_info("thres1", TensorProto.FLOAT, thres1_shape)
+ ]
+
+ thres2_shape = [ch, 14]
+ value_info += [
+ helper.make_tensor_value_info("thres2", TensorProto.FLOAT, thres2_shape)
+ ]
+
+ nodes = []
+ nodes += [helper.make_node("MaxPool", ["top_in"], ["t1"], **maxpool_config)]
+ nodes += [
+ helper.make_node(
+ "MultiThreshold",
+ ["t1", "thres1"],
+ ["t2"],
+ domain="finn",
+ out_dtype="BIPOLAR",
+ out_bias=-1.0,
+ out_scale=1.0,
+ )
+ ]
+ nodes += [helper.make_node("MaxPool", ["t2"], ["t3"], **maxpool_config)]
+ nodes += [
+ helper.make_node(
+ "MultiThreshold",
+ ["t3", "thres2"],
+ ["top_out"],
+ domain="finn",
+ out_dtype="UINT4",
+ )
+ ]
+
+ modelproto = helper.make_model(
+ helper.make_graph(
+ name="test",
+ inputs=[top_in],
+ outputs=[top_out],
+ value_info=value_info,
+ nodes=nodes,
+ )
+ )
+ model = ModelWrapper(modelproto)
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+
+ model.set_initializer("thres1", np.array([[0]]))
+ model.set_initializer(
+ "thres2", get_multithreshold_rand_params(*thres2_shape, seed=0)
+ )
+
+ # Transform
+ new_model = model.transform(MoveMaxPoolPastMultiThreshold())
+ inp_dict = {"top_in": np.random.rand(*input_shape).astype(np.float32)}
+
+ # Test
+ assert oxe.compare_execution(model, new_model, inp_dict)
+ assert new_model.graph.node[0].op_type == "MaxPool"
+ assert new_model.graph.node[1].op_type == "MultiThreshold"
+ assert new_model.graph.node[2].op_type == "MultiThreshold"
+ assert new_model.graph.node[3].op_type == "MaxPool"
+ assert len(new_model.graph.node) == 4
diff --git a/tests/transformation/test_move_mul_past_dw_conv.py b/tests/transformation/test_move_mul_past_dw_conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ae8fbfe89986d58d3d71f5f8735a98469d9d1e3
--- /dev/null
+++ b/tests/transformation/test_move_mul_past_dw_conv.py
@@ -0,0 +1,93 @@
+import pytest
+
+from onnx import helper, TensorProto
+from finn.custom_op.im2col import compute_conv_output_dim
+import finn.core.onnx_exec as oxe
+from finn.core.datatype import DataType
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.infer_shapes import InferShapes
+from finn.util.basic import gen_finn_dt_tensor
+from finn.transformation.streamline.reorder import MoveMulPastDWConv
+
+
+# input dimension
+@pytest.mark.parametrize("ifm_dim", [4, 7])
+# input channels
+@pytest.mark.parametrize("ifm_ch", [2, 3])
+# kernel size
+@pytest.mark.parametrize("k", [2, 3])
+# stride
+@pytest.mark.parametrize("stride", [1, 2])
+# padding
+@pytest.mark.parametrize("pad_amt", [0, 1])
+# depthwise
+@pytest.mark.parametrize("dw", [0, 1])
+def test_move_mul_past_dw_conv(ifm_dim, ifm_ch, k, stride, pad_amt, dw):
+ if dw == 1:
+ ofm_ch = ifm_ch
+ groups = ifm_ch
+ W_shape = [ofm_ch, 1, k, k]
+ else:
+ ofm_ch = ifm_ch + 2
+ groups = 1
+ W_shape = [ofm_ch, ifm_ch, k, k]
+
+ ofm_dim = compute_conv_output_dim(ifm_dim, k, stride, pad_amt)
+
+ # set up onnx model
+ inp = helper.make_tensor_value_info(
+ "inp", TensorProto.FLOAT, [1, ifm_ch, ifm_dim, ifm_dim]
+ )
+ mul = helper.make_tensor_value_info("mul", TensorProto.FLOAT, [1, ifm_ch, 1, 1])
+ W = helper.make_tensor_value_info("W", TensorProto.FLOAT, W_shape)
+ outp = helper.make_tensor_value_info(
+ "outp", TensorProto.FLOAT, [1, ofm_ch, ofm_dim, ofm_dim]
+ )
+
+ Mul_node = helper.make_node("Mul", ["inp", "mul"], ["mul_out"])
+
+ Conv_node = helper.make_node(
+ "Conv",
+ ["mul_out", "W"],
+ ["outp"],
+ group=groups,
+ kernel_shape=[k, k],
+ pads=[pad_amt, pad_amt, pad_amt, pad_amt],
+ strides=[stride, stride],
+ )
+
+ graph = helper.make_graph(
+ nodes=[Mul_node, Conv_node],
+ name="mulpastconv_graph",
+ inputs=[inp],
+ outputs=[outp],
+ value_info=[mul, W],
+ )
+
+ model = helper.make_model(graph, producer_name="mulpastconv-model")
+ model = ModelWrapper(model)
+ inp_values = gen_finn_dt_tensor(DataType.INT2, [1, ifm_ch, ifm_dim, ifm_dim])
+ mul_values = gen_finn_dt_tensor(DataType.INT2, [1, ifm_ch, 1, 1])
+ W_values = gen_finn_dt_tensor(DataType.INT2, W_shape)
+ model.set_initializer("W", W_values)
+ model.set_initializer("mul", mul_values)
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+ idict = {"inp": inp_values}
+ odict = oxe.execute_onnx(model, idict, True)
+ out_before = odict["outp"]
+
+ # move channelwise multiplication past depthwise conv
+ model_transformed = model.transform(MoveMulPastDWConv())
+ odict = oxe.execute_onnx(model_transformed, idict, True)
+ out_after = odict["outp"]
+
+ assert (out_before == out_after).all()
+
+ if dw == 0:
+ assert model.graph.node[0].op_type == model_transformed.graph.node[0].op_type
+ assert model.graph.node[1].op_type == model_transformed.graph.node[1].op_type
+ else:
+ assert model.graph.node[0].op_type == model_transformed.graph.node[1].op_type
+ assert model.graph.node[1].op_type == model_transformed.graph.node[0].op_type
diff --git a/tests/transformation/test_move_scalar_past_conv.py b/tests/transformation/test_move_scalar_past_conv.py
index 0f50642d2b9d1583030630cb4927c2b86667e71a..94fee7907d1ed1cccbf95520e903c7d9b43d8f7d 100644
--- a/tests/transformation/test_move_scalar_past_conv.py
+++ b/tests/transformation/test_move_scalar_past_conv.py
@@ -7,14 +7,14 @@ 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,
+ MoveAddPastConv,
MoveScalarMulPastConv,
)
@pytest.mark.parametrize("padding", [False, True])
@pytest.mark.parametrize(
- "test_args", [("Add", MoveScalarAddPastConv()), ("Mul", MoveScalarMulPastConv())],
+ "test_args", [("Add", MoveAddPastConv()), ("Mul", MoveScalarMulPastConv())],
)
def test_move_scalar_past_conv(test_args, padding):
scalar_op = test_args[0]
@@ -83,8 +83,8 @@ def test_move_scalar_past_conv(test_args, padding):
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"
+ assert new_model.graph.node[1].op_type == "Conv"
+ assert new_model.graph.node[2].op_type == scalar_op
else:
assert new_model.graph.node[0].op_type == "Conv"
assert new_model.graph.node[1].op_type == "Conv"
@@ -92,7 +92,7 @@ def test_move_scalar_past_conv(test_args, padding):
@pytest.mark.parametrize(
- "test_args", [("Add", MoveScalarAddPastConv()), ("Mul", MoveScalarMulPastConv())],
+ "test_args", [("Add", MoveAddPastConv()), ("Mul", MoveScalarMulPastConv())],
)
def test_move_scalar_past_conv_only_if_linear(test_args):
scalar_op = test_args[0]
diff --git a/tests/transformation/test_move_transpose_past_scalar_mul.py b/tests/transformation/test_move_transpose_past_scalar_mul.py
new file mode 100644
index 0000000000000000000000000000000000000000..e434fc7d4f683120176e18a2bfa9da99d9ee0b0e
--- /dev/null
+++ b/tests/transformation/test_move_transpose_past_scalar_mul.py
@@ -0,0 +1,82 @@
+import pytest
+
+import numpy as np
+from onnx import TensorProto, helper
+
+from finn.core.modelwrapper import ModelWrapper
+import finn.core.data_layout as DataLayout
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.general import GiveUniqueNodeNames, GiveReadableTensorNames
+from finn.transformation.streamline.reorder import MoveTransposePastScalarMul
+import finn.core.onnx_exec as oxe
+
+# permutation of transpose node
+@pytest.mark.parametrize("perm", [[0, 2, 3, 1], [0, 1, 3, 2], [3, 2, 0, 1]])
+# scalar mul
+@pytest.mark.parametrize("scalar", [True, False])
+# data layout
+@pytest.mark.parametrize("data_layout", [None, DataLayout.NHWC, DataLayout.NCHW])
+def test_move_transpose_past_scalar_mul(perm, scalar, data_layout):
+ inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, [1, 2, 3, 4])
+ # to determine out_size we need to calculate with "perm" for this test case
+ dummy_in = np.random.uniform(low=0, high=1, size=(1, 2, 3, 4)).astype(np.float32)
+ out_size = dummy_in.transpose(tuple(perm)).shape
+
+ if scalar is True:
+ a0_size = []
+ else:
+ a0_size = out_size
+ a0 = helper.make_tensor_value_info("a0", TensorProto.FLOAT, a0_size)
+ outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, out_size)
+ transp_node = helper.make_node("Transpose", ["inp"], ["transp_out"], perm=perm)
+ mul_node = helper.make_node("Mul", ["transp_out", "a0"], ["outp"])
+
+ graph = helper.make_graph(
+ nodes=[transp_node, mul_node],
+ name="mv-transpose-graph",
+ inputs=[inp],
+ outputs=[outp],
+ value_info=[a0],
+ )
+
+ model = helper.make_model(graph, producer_name="mv_transpose_model")
+ model = ModelWrapper(model)
+
+ # initialize values
+ a0_values = np.random.uniform(low=0, high=1, size=tuple(a0_size)).astype(np.float32)
+ model.set_initializer("a0", a0_values)
+ if data_layout is not None:
+ model.set_tensor_layout("inp", data_layout)
+ model = model.transform(InferDataLayouts())
+
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(GiveReadableTensorNames())
+
+ # compare execution before and after transformation
+ inp_values = np.random.uniform(low=0, high=1, size=(1, 2, 3, 4)).astype(np.float32)
+ idict = {model.graph.input[0].name: inp_values}
+ model_transformed = model.transform(MoveTransposePastScalarMul())
+ assert oxe.compare_execution(model, model_transformed, idict)
+
+ # check if order changed
+ if scalar is True and data_layout is not None:
+ assert model_transformed.graph.node[0] != model.graph.node[0]
+ assert model_transformed.graph.node[1] != model.graph.node[1]
+ assert model_transformed.graph.node[0].op_type == "Mul"
+ assert model_transformed.graph.node[1].op_type == "Transpose"
+ mul_input = model_transformed.graph.node[0].input[0]
+ mul_output = model_transformed.graph.node[0].output[0]
+ assert model_transformed.get_tensor_layout(mul_input) == data_layout
+ assert model_transformed.get_tensor_layout(mul_output) == data_layout
+ else:
+ assert model_transformed.graph.node[0] == model.graph.node[0]
+ assert model_transformed.graph.node[1] == model.graph.node[1]
+ if data_layout is not None:
+ mul_input = model_transformed.graph.node[1].input[0]
+ mul_output = model_transformed.graph.node[1].output[0]
+ assert model_transformed.get_tensor_layout(mul_input) != data_layout
+ assert model_transformed.get_tensor_layout(mul_output) != data_layout
diff --git a/tests/transformation/test_remove_identity_ops.py b/tests/transformation/test_remove_identity_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..536c1ab0b48fa44388da23f45b528da3c5f3b2f2
--- /dev/null
+++ b/tests/transformation/test_remove_identity_ops.py
@@ -0,0 +1,81 @@
+import pytest
+
+import numpy as np
+from onnx import helper, TensorProto
+import finn.core.onnx_exec as oxe
+from finn.core.datatype import DataType
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.streamline.remove import RemoveIdentityOps
+from finn.util.basic import gen_finn_dt_tensor
+
+
+def insert_identity_op(model, op):
+ if op in ["Add", "Sub"]:
+ val = np.asarray([0.0], dtype=np.float32)
+ elif op in ["Mul", "Div"]:
+ val = np.asarray([1.0], dtype=np.float32)
+ else:
+ return
+
+ identity_node = helper.make_node(op, ["div_out", "value"], ["ident_out"])
+ graph = model.graph
+ graph.node.insert(3, identity_node)
+ graph.node[-1].input[0] = "ident_out"
+ model.set_initializer("value", val)
+
+ return model
+
+
+# identity operations to be inserted
+@pytest.mark.parametrize("op", ["Add", "Sub", "Mul", "Div"])
+def test_remove_identity_ops(op):
+
+ # set up onnx model
+ inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, [1, 4, 1, 1])
+ mul = helper.make_tensor_value_info("mul", TensorProto.FLOAT, [])
+ shape = helper.make_tensor_value_info("shape", TensorProto.FLOAT, [2])
+ div = helper.make_tensor_value_info("div", TensorProto.FLOAT, [])
+ matmul = helper.make_tensor_value_info("matmul", TensorProto.FLOAT, [4, 2])
+ outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, [1, 2])
+
+ mul_node = helper.make_node("Mul", ["inp", "mul"], ["mul_out"])
+ reshape_node = helper.make_node("Reshape", ["mul_out", "shape"], ["reshape_out"])
+ div_node = helper.make_node("Div", ["reshape_out", "div"], ["div_out"])
+ matmul_node = helper.make_node("MatMul", ["div_out", "matmul"], ["outp"])
+
+ graph = helper.make_graph(
+ nodes=[mul_node, reshape_node, div_node, matmul_node],
+ name="identity-graph",
+ inputs=[inp],
+ outputs=[outp],
+ value_info=[mul, shape, div, matmul],
+ )
+
+ model = helper.make_model(graph, producer_name="mulpastconv-model")
+ model = ModelWrapper(model)
+ inp_values = gen_finn_dt_tensor(DataType.INT2, [1, 4, 1, 1])
+ mul_values = np.random.uniform(low=0.1, high=0.99, size=(1)).astype(np.float32)
+ shape_values = np.asarray([1, -1], dtype=np.int64)
+ div_values = np.random.uniform(low=0.1, high=0.99, size=(1)).astype(np.float32)
+ matmul_values = gen_finn_dt_tensor(DataType.INT2, [4, 2])
+ model.set_initializer("mul", mul_values)
+ model.set_initializer("shape", shape_values)
+ model.set_initializer("div", div_values)
+ model.set_initializer("matmul", matmul_values)
+ insert_identity_op(model, op)
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+ idict = {"inp": inp_values}
+ odict = oxe.execute_onnx(model, idict)
+ out_before = odict["outp"]
+ num_of_nodes_before = len(model.graph.node)
+
+ model = model.transform(RemoveIdentityOps())
+ num_of_nodes_after = len(model.graph.node)
+ assert num_of_nodes_before - 1 == num_of_nodes_after
+
+ odict = oxe.execute_onnx(model, idict)
+ out_after = odict["outp"]
+ assert (out_before == out_after).all()
diff --git a/tests/transformation/test_sign_to_thres.py b/tests/transformation/test_sign_to_thres.py
index b10840df37a695986e54c0bdaa68baa0538f90f2..a92f839e5f6ca8b45eadf939fa35973ac153e0b1 100644
--- a/tests/transformation/test_sign_to_thres.py
+++ b/tests/transformation/test_sign_to_thres.py
@@ -40,8 +40,7 @@ from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline import ConvertSignToThres
from finn.util.test import get_test_model_trained
-export_onnx_path = "test_output_lfc.onnx"
-transformed_onnx_path = "test_output_lfc_transformed.onnx"
+export_onnx_path = "test_sign_to_thres.onnx"
def test_sign_to_thres():
diff --git a/tests/transformation/test_sort_graph.py b/tests/transformation/test_sort_graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..05842504c13b144bb34e8084fb12b5086fa84115
--- /dev/null
+++ b/tests/transformation/test_sort_graph.py
@@ -0,0 +1,150 @@
+from onnx import TensorProto, helper
+import numpy as np
+
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.general import SortGraph
+from finn.transformation.infer_shapes import InferShapes
+import pytest
+import finn.analysis.topology as ta
+
+
+def make_randomly_sorted_linear_model(num_of_nodes, seed=None):
+ if seed is not None:
+ np.random.seed(seed)
+
+ ch = 2
+ ifmdim = 16
+ input_shape = (1, ch, ifmdim, ifmdim)
+
+ top_in = helper.make_tensor_value_info("t0", TensorProto.FLOAT, input_shape)
+ top_out = helper.make_tensor_value_info(
+ "t" + str(num_of_nodes), TensorProto.FLOAT, input_shape
+ )
+
+ value_info = []
+ nodes = []
+ for i in range(num_of_nodes):
+ nodes += [
+ helper.make_node("Add", ["t" + str(i), "p" + str(i)], ["t" + str(i + 1)])
+ ]
+ value_info += [
+ helper.make_tensor_value_info("p" + str(i), TensorProto.FLOAT, input_shape)
+ ]
+
+ nodes = np.random.permutation(nodes)
+
+ modelproto = helper.make_model(
+ helper.make_graph(
+ name="test",
+ inputs=[top_in],
+ outputs=[top_out],
+ value_info=value_info,
+ nodes=nodes,
+ )
+ )
+ model = ModelWrapper(modelproto)
+ model = model.transform(InferShapes())
+
+ for i in range(num_of_nodes):
+ model.set_initializer(
+ "p" + str(i), np.random.rand(*input_shape).astype(np.float32)
+ )
+
+ return model
+
+
+@pytest.mark.parametrize("num_of_nodes", [64])
+def test_sort_linear_graph(num_of_nodes):
+ model = make_randomly_sorted_linear_model(num_of_nodes, seed=0)
+ new_model = model.transform(SortGraph())
+
+ # Test
+ ret = new_model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"], "Nodes are not topologically sorted."
+
+
+def test_sort_nonlinear_graph():
+ ch = 2
+ ifmdim = 16
+ 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)
+ ]
+
+ modelproto = helper.make_model(
+ helper.make_graph(
+ name="test",
+ inputs=[top_in],
+ outputs=[top_out],
+ value_info=value_info,
+ nodes=[
+ # Not sorted nodes
+ helper.make_node("Mul", ["fork1", "p2"], ["t3"]),
+ helper.make_node("Add", ["t4", "p3"], ["t5"]),
+ helper.make_node("Add", ["t2", "t3"], ["t4"]),
+ helper.make_node("Add", ["t6", "t7"], ["t8"]),
+ helper.make_node("Add", ["fork3", "fork3"], ["top_out"]),
+ helper.make_node("Mul", ["t5", "p4"], ["fork2"]),
+ helper.make_node("Add", ["top_in", "p0"], ["fork1"]),
+ helper.make_node("Mul", ["fork1", "p1"], ["t2"]),
+ helper.make_node("Add", ["fork2", "p5"], ["t6"]),
+ helper.make_node("Add", ["fork2", "p6"], ["t7"]),
+ helper.make_node("Mul", ["t8", "p7"], ["fork3"]),
+ ],
+ )
+ )
+ 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)
+ )
+
+ new_model = model.transform(SortGraph())
+
+ # Test
+ ret = new_model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"], "Nodes are not topologically sorted."
+
+
+if __name__ == "__main__":
+ import time
+
+ sizes = [10, 50, 100, 500, 1000]
+ times = []
+ reps = 10
+
+ print("SortGraph performance test:")
+ print("Test sizes", sizes)
+ print("Repetitions per size:", reps)
+ for sz in sizes:
+ acc_time = 0
+ print(" Testing size ", sz)
+ for i in range(reps):
+ # it should take the same time even with the sorted one
+ # but better new model each time as it is a more general approach
+ model = make_randomly_sorted_linear_model(sz) # new model as seed is None
+ bef = time.time()
+ new_model = model.transform(SortGraph(), make_deepcopy=False)
+ acc_time += time.time() - bef
+
+ times += [acc_time / reps]
+
+ # print csv
+ print("\nnum_of_nodes, seconds")
+ for sz, tm in zip(sizes, times):
+ print("{:12d}, {:6.4e}".format(sz, tm))
+
+ # plot
+ # import matplotlib.pyplot as plt
+ # plt.plot(sizes,times,"--o")
+ # plt.grid(True)
diff --git a/tests/transformation/test_topk_insert.py b/tests/transformation/test_topk_insert.py
index 1af0f255d8fb1af8a6e571518f18d831aa71298b..a18e63384150f140cb63ec7b438283eb4797266c 100644
--- a/tests/transformation/test_topk_insert.py
+++ b/tests/transformation/test_topk_insert.py
@@ -18,7 +18,7 @@ from pkgutil import get_data
import pytest
-export_onnx_path = "test_output_lfc.onnx"
+export_onnx_path = "test_topk_insert.onnx"
@pytest.mark.parametrize("k", [1, 5, 10])
diff --git a/tests/util/test_create.py b/tests/util/test_create.py
new file mode 100644
index 0000000000000000000000000000000000000000..7173add35abf04a35c33b0ef10b42ffdb296a653
--- /dev/null
+++ b/tests/util/test_create.py
@@ -0,0 +1,64 @@
+# 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 finn.util.create as create
+from finn.core.datatype import DataType
+
+
+@pytest.mark.parametrize("bitwidth", [DataType.BIPOLAR, DataType.INT2, DataType.INT4])
+def test_hls_random_mlp_maker(bitwidth):
+ w = bitwidth
+ a = bitwidth
+ layer_spec = [
+ {
+ "mw": 185,
+ "mh": 100,
+ "simd": 185,
+ "pe": 100,
+ "idt": DataType.BIPOLAR,
+ "wdt": w,
+ "act": a,
+ },
+ {"mw": 100, "mh": 100, "simd": 100, "pe": 100, "idt": a, "wdt": w, "act": a},
+ {"mw": 100, "mh": 100, "simd": 100, "pe": 100, "idt": a, "wdt": w, "act": a},
+ {"mw": 100, "mh": 100, "simd": 100, "pe": 100, "idt": a, "wdt": w, "act": a},
+ {
+ "mw": 100,
+ "mh": 1,
+ "simd": 100,
+ "pe": 1,
+ "idt": a,
+ "wdt": w,
+ "act": DataType.BIPOLAR,
+ },
+ ]
+
+ ret = create.hls_random_mlp_maker(layer_spec)
+ assert len(ret.graph.node) == 5
+ ret.save("mlp-%s.onnx" % str(bitwidth))