diff --git a/docker/Dockerfile.finn_ci b/docker/Dockerfile.finn_ci
index 269fa38b73a2edd904bc6aad52522cddb3d33d25..654caa949d974e8fff0cd292f5651ce48e4d8d6f 100644
--- a/docker/Dockerfile.finn_ci
+++ b/docker/Dockerfile.finn_ci
@@ -27,15 +27,26 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
FROM pytorch/pytorch:1.1.0-cuda10.0-cudnn7.5-devel
-MAINTAINER Yaman Umuroglu <yamanu@xilinx.com>
+LABEL maintainer="Yaman Umuroglu <yamanu@xilinx.com>"
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 -y verilator zsh nano rsync
-RUN apt-get install -y sshpass wget unzip
+RUN apt-get install -y build-essential
+RUN apt-get install -y libglib2.0-0
+RUN apt-get install -y libsm6
+RUN apt-get install -y libxext6
+RUN apt-get install -y libxrender-dev
+RUN apt-get install -y verilator
+RUN apt-get install -y nano
+RUN apt-get install -y zsh
+RUN apt-get install -y rsync
+RUN apt-get install -y git
+RUN apt-get install -y sshpass
+RUN apt-get install -y wget
+RUN apt-get install -y unzip
+RUN apt-get install -y zip
RUN echo "StrictHostKeyChecking no" >> /etc/ssh/ssh_config
# XRT deps
diff --git a/docker/Dockerfile.finn_dev b/docker/Dockerfile.finn_dev
index 5976d0360e63168ed9d20e8cb0ee5de8e69656a5..35190ec883d2afeab3fda7dc914774944fa1a84f 100644
--- a/docker/Dockerfile.finn_dev
+++ b/docker/Dockerfile.finn_dev
@@ -26,8 +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 pytorch/pytorch:1.1.0-cuda10.0-cudnn7.5-devel
-MAINTAINER Yaman Umuroglu <yamanu@xilinx.com>
+FROM pytorch/pytorch:1.7.1-cuda11.0-cudnn8-runtime
+LABEL maintainer="Yaman Umuroglu <yamanu@xilinx.com>"
ARG GID
ARG GNAME
ARG UNAME
@@ -38,9 +38,20 @@ 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 -y verilator nano zsh rsync
-RUN apt-get -y install sshpass wget unzip
+RUN apt-get install -y build-essential
+RUN apt-get install -y libglib2.0-0
+RUN apt-get install -y libsm6
+RUN apt-get install -y libxext6
+RUN apt-get install -y libxrender-dev
+RUN apt-get install -y verilator
+RUN apt-get install -y nano
+RUN apt-get install -y zsh
+RUN apt-get install -y rsync
+RUN apt-get install -y git
+RUN apt-get install -y sshpass
+RUN apt-get install -y wget
+RUN apt-get install -y unzip
+RUN apt-get install -y zip
RUN echo "StrictHostKeyChecking no" >> /etc/ssh/ssh_config
COPY requirements.txt .
@@ -60,16 +71,18 @@ RUN pip install scikit-learn==0.24.1
RUN pip install tqdm==4.31.1
RUN pip install -e git+https://github.com/fbcotter/dataset_loading.git@0.0.4#egg=dataset_loading
+
# switch user
RUN groupadd -g $GID $GNAME
RUN useradd -M -u $UID $UNAME -g $GNAME
RUN usermod -aG sudo $UNAME
RUN echo "$UNAME:$PASSWD" | chpasswd
RUN echo "root:$PASSWD" | chpasswd
+RUN chown -R $UNAME:$GNAME /workspace
RUN ln -s /workspace /home/$UNAME
-RUN chown -R $UNAME:$GNAME /home/$UNAME
USER $UNAME
+
# cloning dependency repos (as user)
# finn-base
RUN git clone https://github.com/fpjentzsch/finn-base.git /workspace/finn-base
diff --git a/docker/finn_entrypoint.sh b/docker/finn_entrypoint.sh
index 7a1a76e24163ca9fc79ffd19f909d9e79983d8ed..c1a557bb35522a33b3520ed2c8937a521201a86f 100644
--- a/docker/finn_entrypoint.sh
+++ b/docker/finn_entrypoint.sh
@@ -14,7 +14,7 @@ gecho () {
# the repos themselves are cloned in the Dockerfile
FINN_BASE_COMMIT=4b40ff84e7c9210325a11bf73b8b9142b776f94c
FINN_EXP_COMMIT=e9f97dcdb4db2f889b0f36af079a6a1792b7d4de
-BREVITAS_COMMIT=aff49758ec445d77c75721c7de3091a2a1797ca8
+BREVITAS_COMMIT=14abbe1e7ef82485d79415871fcf5766b0a40a00
CNPY_COMMIT=4e8810b1a8637695171ed346ce68f6984e585ef4
HLSLIB_COMMIT=2e49322d1bbc4969ca293843bda1f3f9c05456fc
PYVERILATOR_COMMIT=e2ff74030de3992dcac54bf1b6aad2915946e8cb
diff --git a/notebooks/end2end_example/cybersecurity/1-train-mlp-with-brevitas.ipynb b/notebooks/end2end_example/cybersecurity/1-train-mlp-with-brevitas.ipynb
index ff4c5704002219ca18bb07eeb8c768f860f3ffbf..e0ce00c1beefe8172ac5fd2aeaaa076b9bb574c1 100644
--- a/notebooks/end2end_example/cybersecurity/1-train-mlp-with-brevitas.ipynb
+++ b/notebooks/end2end_example/cybersecurity/1-train-mlp-with-brevitas.ipynb
@@ -103,23 +103,23 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "--2021-02-24 16:57:33-- https://zenodo.org/record/4519767/files/unsw_nb15_binarized.npz?download=1\n",
+ "--2021-05-10 18:14:00-- https://zenodo.org/record/4519767/files/unsw_nb15_binarized.npz?download=1\n",
"Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
"Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 13391907 (13M) [application/octet-stream]\n",
- "Saving to: 'unsw_nb15_binarized.npz'\n",
+ "Saving to: ‘unsw_nb15_binarized.npz’\n",
"\n",
- "unsw_nb15_binarized 100%[===================>] 12.77M 2.17MB/s in 8.9s \n",
+ "unsw_nb15_binarized 100%[===================>] 12.77M 3.96MB/s in 3.4s \n",
"\n",
- "2021-02-24 16:57:44 (1.44 MB/s) - 'unsw_nb15_binarized.npz' saved [13391907/13391907]\n",
+ "2021-05-10 18:14:04 (3.77 MB/s) - ‘unsw_nb15_binarized.npz’ saved [13391907/13391907]\n",
"\n"
]
}
@@ -422,7 +422,9 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "Training loss = 0.132480 test accuracy = 0.797989: 100%|██████████| 10/10 [00:58<00:00, 5.70s/it]\n"
+ "Training loss: 0%| | 0/10 [00:00<?, ?it/s]/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py:130: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at /opt/conda/conda-bld/pytorch_1607370172916/work/c10/cuda/CUDAFunctions.cpp:100.)\n",
+ " Variable._execution_engine.run_backward(\n",
+ "Training loss = 0.131708 test accuracy = 0.805398: 100%|██████████| 10/10 [01:04<00:00, 6.42s/it]\n"
]
}
],
@@ -457,7 +459,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -483,7 +485,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -507,7 +509,7 @@
{
"data": {
"text/plain": [
- "0.7979886313948404"
+ "0.8053976582616722"
]
},
"execution_count": 15,
@@ -546,7 +548,7 @@
{
"data": {
"text/plain": [
- "IncompatibleKeys(missing_keys=[], unexpected_keys=[])"
+ "<All keys matched successfully>"
]
},
"execution_count": 17,
@@ -773,12 +775,14 @@
"# Export to FINN-ONNX <a id=\"export_finn_onnx\" ></a>\n",
"\n",
"\n",
- "[ONNX](https://onnx.ai/) is an open format built to represent machine learning models, and the FINN compiler expects an ONNX model as input. We'll now export our network into ONNX to be imported and used in FINN for the next notebooks. Note that the particular ONNX representation used for FINN differs from standard ONNX, you can read more about this [here](https://finn.readthedocs.io/en/latest/internals.html#intermediate-representation-finn-onnx)."
+ "[ONNX](https://onnx.ai/) is an open format built to represent machine learning models, and the FINN compiler expects an ONNX model as input. We'll now export our network into ONNX to be imported and used in FINN for the next notebooks. Note that the particular ONNX representation used for FINN differs from standard ONNX, you can read more about this [here](https://finn.readthedocs.io/en/latest/internals.html#intermediate-representation-finn-onnx).\n",
+ "\n",
+ "You can see below how we export a trained network in Brevitas into a FINN-compatible ONNX representation. Note how we create a `QuantTensor` instance with dummy data to tell Brevitas how our inputs look like, which will be used to set the input quantization annotation on the exported model."
]
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 27,
"metadata": {
"scrolled": true
},
@@ -787,69 +791,38 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Model saved to cybsec-mlp.onnx\n"
+ "Model saved to cybsec-mlp-ready.onnx\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:15: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
- " from ipykernel import kernelapp as app\n"
+ "<ipython-input-22-78c27bb59095>:15: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
+ " x = (x + torch.tensor([1.0])) / 2.0\n"
]
}
],
"source": [
"import brevitas.onnx as bo\n",
+ "from brevitas.quant_tensor import QuantTensor\n",
"\n",
- "export_onnx_path = \"cybsec-mlp.onnx\"\n",
+ "ready_model_filename = \"cybsec-mlp-ready.onnx\"\n",
"input_shape = (1, 600)\n",
- "bo.export_finn_onnx(model_for_export, input_shape, export_onnx_path)\n",
- "\n",
- "print(\"Model saved to %s\" % export_onnx_path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## One final fix: input datatype\n",
- "\n",
- "There's one more thing we'll do: we will mark the input tensor datatype as `DataType.BIPOLAR`, which will be used by the compiler later on. To do this, we'll utilize the `ModelWrapper` component from FINN, which lets us examine and manipulate the ONNX graph in an easier way.\n",
- "\n",
- "*In the near future it will be possible to add this information to the model [while exporting](https://github.com/Xilinx/brevitas/issues/232), instead of having to add it manually.*"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Input tensor name: 0\n",
- "Input tensor shape: [1, 600]\n",
- "Input tensor datatype: DataType.BIPOLAR\n"
- ]
- }
- ],
- "source": [
- "from finn.core.modelwrapper import ModelWrapper\n",
- "from finn.core.datatype import DataType\n",
+ "# create a QuantTensor instance to mark input as bipolar during export\n",
+ "input_a = np.random.randint(0, 1, size=input_shape).astype(np.float32)\n",
+ "input_a = 2 * input_a - 1\n",
+ "scale = 1.0\n",
+ "input_t = torch.from_numpy(input_a * scale)\n",
+ "input_qt = QuantTensor(\n",
+ " input_t, scale=torch.tensor(scale), bit_width=torch.tensor(1.0), signed=True\n",
+ ")\n",
"\n",
- "finn_model = ModelWrapper(export_onnx_path)\n",
+ "bo.export_finn_onnx(\n",
+ " model_for_export, export_path=ready_model_filename, input_t=input_qt\n",
+ ")\n",
"\n",
- "finnonnx_in_tensor_name = finn_model.graph.input[0].name\n",
- "finnonnx_model_in_shape = finn_model.get_tensor_shape(finnonnx_in_tensor_name)\n",
- "finn_model.set_tensor_datatype(finnonnx_in_tensor_name, DataType.BIPOLAR)\n",
- "print(\"Input tensor name: %s\" % finnonnx_in_tensor_name)\n",
- "print(\"Input tensor shape: %s\" % str(finnonnx_model_in_shape))\n",
- "print(\"Input tensor datatype: %s\" % str(finn_model.get_tensor_datatype(finnonnx_in_tensor_name)))\n",
- "\n",
- "ready_model_filename = \"cybsec-mlp-ready.onnx\"\n",
- "finn_model.save(ready_model_filename)"
+ "print(\"Model saved to %s\" % ready_model_filename)"
]
},
{
@@ -870,7 +843,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
@@ -894,10 +867,10 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f77214fa630>"
+ "<IPython.lib.display.IFrame at 0x7f49738bffa0>"
]
},
- "execution_count": 27,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
@@ -940,7 +913,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.8"
+ "version": "3.8.5"
}
},
"nbformat": 4,
diff --git a/src/finn/builder/build_dataflow_steps.py b/src/finn/builder/build_dataflow_steps.py
index 1c1861e5286e92abcf983056f8263daae14334e8..4c9b076b6acb1e725421d5067ac74ad42c3dbafb 100644
--- a/src/finn/builder/build_dataflow_steps.py
+++ b/src/finn/builder/build_dataflow_steps.py
@@ -158,6 +158,7 @@ def step_streamline(model: ModelWrapper, cfg: DataflowBuildConfig):
topologies.
"""
+ model = model.transform(absorb.AbsorbSignBiasIntoMultiThreshold())
model = model.transform(MoveScalarLinearPastInvariants())
model = model.transform(Streamline())
need_lowering = len(model.get_nodes_by_op_type("Conv")) > 0
diff --git a/src/finn/transformation/streamline/remove.py b/src/finn/transformation/streamline/remove.py
index 12c6984c6e66e1917d2a1e0a74c8620ccb6afabc..0a36b8bbe5c05a8226ae647e0061c1551f3b1cbf 100644
--- a/src/finn/transformation/streamline/remove.py
+++ b/src/finn/transformation/streamline/remove.py
@@ -32,6 +32,23 @@ from finn.transformation.infer_shapes import InferShapes
import numpy as np
+def _remove_node_and_rewire(model, node):
+ producer = model.find_producer(node.input[0])
+ if producer is not None:
+ # wire output tensor to
+ # output of producer node
+ producer.output[0] = node.output[0]
+ else:
+ # node is first in graph
+ consumer = model.find_consumer(node.output[0])
+ assert consumer is not None, "Whole graph is identity"
+ assert consumer.input[0] == node.output[0]
+ # rewire consumer's input directly to graph input
+ consumer.input[0] = node.input[0]
+ # remove node
+ model.graph.node.remove(node)
+
+
class RemoveIdentityOps(Transformation):
"""Remove identity ops like Add/Sub with zero or Mul/Div with one"""
@@ -48,11 +65,7 @@ class RemoveIdentityOps(Transformation):
):
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)
+ _remove_node_and_rewire(model, n)
elif (
n.op_type in ["Mul", "Div"]
@@ -61,10 +74,6 @@ class RemoveIdentityOps(Transformation):
):
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)
+ _remove_node_and_rewire(model, n)
model = model.transform(InferShapes())
return (model, graph_modified)
diff --git a/tests/brevitas/test_brevitas_QConv2d.py b/tests/brevitas/test_brevitas_QConv2d.py
index 198f1e7961a9e160589989b8b34b45b5fda53817..21de8863d3f23316265b075ce529cf2249764a64 100644
--- a/tests/brevitas/test_brevitas_QConv2d.py
+++ b/tests/brevitas/test_brevitas_QConv2d.py
@@ -1,3 +1,31 @@
+# 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 os
import numpy as np
@@ -19,8 +47,11 @@ export_onnx_path = "test_brevitas_conv.onnx"
@pytest.mark.parametrize("dw", [False, True])
+@pytest.mark.parametrize("bias", [True, False])
@pytest.mark.parametrize("in_channels", [32])
-def test_brevitas_QConv2d(dw, in_channels):
+def test_brevitas_QConv2d(dw, bias, in_channels):
+ if bias:
+ pytest.xfail("bias export bug")
ishape = (1, 32, 111, 111)
if dw is True:
groups = in_channels
@@ -45,10 +76,8 @@ def test_brevitas_QConv2d(dw, in_channels):
kernel_size=kernel_size,
padding=padding,
stride=stride,
- bias=False,
+ bias=bias,
bias_quant_type=QuantType.FP,
- compute_output_bit_width=False,
- compute_output_scale=False,
weight_bit_width=4,
weight_quant_type=QuantType.INT,
weight_scaling_impl_type=ScalingImplType.STATS,
@@ -60,7 +89,7 @@ def test_brevitas_QConv2d(dw, in_channels):
)
weight_tensor = gen_finn_dt_tensor(DataType.INT4, w_shape)
b_conv.weight = torch.nn.Parameter(torch.from_numpy(weight_tensor).float())
-
+ b_conv.eval()
bo.export_finn_onnx(b_conv, ishape, export_onnx_path)
model = ModelWrapper(export_onnx_path)
model = model.transform(InferShapes())
@@ -69,7 +98,6 @@ def test_brevitas_QConv2d(dw, in_channels):
odict = oxe.execute_onnx(model, idict, True)
produced = odict[model.graph.output[0].name]
inp_tensor = torch.from_numpy(inp_tensor).float()
- b_conv.eval()
expected = b_conv.forward(inp_tensor).detach().numpy()
assert np.isclose(produced, expected, atol=1e-3).all()
diff --git a/tests/brevitas/test_brevitas_avg_pool_export.py b/tests/brevitas/test_brevitas_avg_pool_export.py
index f3d6c5dde7179bec8fe97e2a6c791afb5733514c..4b88b0f787fb7780079ee82d6802d5cbff410748 100644
--- a/tests/brevitas/test_brevitas_avg_pool_export.py
+++ b/tests/brevitas/test_brevitas_avg_pool_export.py
@@ -1,27 +1,53 @@
+# 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 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
+import pytest
+import finn.core.onnx_exec as oxe
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
+from brevitas.export import FINNManager
+from brevitas.nn import QuantAvgPool2d
+from brevitas.quant_tensor import QuantTensor
+
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("signed", [True, False])
@pytest.mark.parametrize("bit_width", [2, 4])
@pytest.mark.parametrize("input_bit_width", [4, 8, 16])
@pytest.mark.parametrize("channels", [2, 4])
@@ -29,73 +55,46 @@ export_onnx_path = "test_brevitas_avg_pool_export.onnx"
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,
- 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, signed=signed
+ quant_avgpool = QuantAvgPool2d(
+ kernel_size=kernel_size, stride=stride, bit_width=bit_width
)
- bo.export_finn_onnx(b_avgpool, ishape, export_onnx_path, input_t=input_quant_tensor)
- model = ModelWrapper(export_onnx_path)
+ quant_avgpool.eval()
- # determine input FINN datatype
- if signed is True:
- prefix = "INT"
- else:
- prefix = "UINT"
+ # determine input
+ prefix = "INT" if signed else "UINT"
dt_name = prefix + str(input_bit_width)
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, signed=signed
- )
- 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(
+ input_shape = (1, channels, idim, idim)
+ input_array = gen_finn_dt_tensor(dtype, input_shape)
+ # Brevitas QuantAvgPool layers need QuantTensors to export correctly
+ # which requires setting up a QuantTensor instance with the scale
+ # factor, zero point, bitwidth and signedness
+ scale_array = 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, signed=signed
+ input_tensor = torch.from_numpy(input_array * scale_array).float()
+ scale_tensor = torch.from_numpy(scale_array).float()
+ zp = torch.tensor(0.0)
+ input_quant_tensor = QuantTensor(
+ input_tensor, scale_tensor, zp, input_bit_width, signed
+ )
+
+ # export
+ FINNManager.export(
+ quant_avgpool, export_path=export_onnx_path, input_t=input_quant_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()
+ # reference brevitas output
+ ref_output_array = quant_avgpool(input_quant_tensor).tensor.detach().numpy()
+ # finn output
+ idict = {model.graph.input[0].name: input_array}
+ odict = oxe.execute_onnx(model, idict, True)
+ finn_output = odict[model.graph.output[0].name]
+ # compare outputs
+ assert np.isclose(ref_output_array, finn_output).all()
+ # cleanup
os.remove(export_onnx_path)
diff --git a/tests/brevitas/test_brevitas_mobilenet.py b/tests/brevitas/test_brevitas_mobilenet.py
index 94f937ef2afc9eb86665e26d703be9f01e2163a0..98a18403e79366aca184559a8b868f30aa27c35e 100644
--- a/tests/brevitas/test_brevitas_mobilenet.py
+++ b/tests/brevitas/test_brevitas_mobilenet.py
@@ -1,3 +1,31 @@
+# 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 PIL import Image
import numpy as np
import brevitas.onnx as bo
diff --git a/tests/brevitas/test_brevitas_non_scaled_QuantHardTanh_export.py b/tests/brevitas/test_brevitas_non_scaled_QuantHardTanh_export.py
index 9c7296b7b3b6d36cfb43b6d9e96e7fba6bbce49a..37ea12ac0f6387b1ab6669bce4aaaaaa60e87b58 100644
--- a/tests/brevitas/test_brevitas_non_scaled_QuantHardTanh_export.py
+++ b/tests/brevitas/test_brevitas_non_scaled_QuantHardTanh_export.py
@@ -1,3 +1,31 @@
+# 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 onnx # noqa
import numpy as np
diff --git a/tests/brevitas/test_brevitas_qlinear.py b/tests/brevitas/test_brevitas_qlinear.py
new file mode 100644
index 0000000000000000000000000000000000000000..e389bc8c1223510e5f89beed1e973e2d1c7dad35
--- /dev/null
+++ b/tests/brevitas/test_brevitas_qlinear.py
@@ -0,0 +1,80 @@
+# Copyright (c) 2021, 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 os
+import numpy as np
+import torch
+import brevitas.onnx as bo
+from brevitas.nn import QuantLinear
+from brevitas.core.quant import QuantType
+from finn.core.modelwrapper import ModelWrapper
+from finn.core.datatype import DataType
+import finn.core.onnx_exec as oxe
+from finn.transformation.infer_shapes import InferShapes
+from finn.util.basic import gen_finn_dt_tensor
+
+export_onnx_path = "test_brevitas_qlinear.onnx"
+
+
+@pytest.mark.parametrize("bias", [False, True])
+@pytest.mark.parametrize("out_features", [4])
+@pytest.mark.parametrize("in_features", [3])
+@pytest.mark.parametrize("w_bits", [4])
+@pytest.mark.parametrize("i_dtype", [DataType.UINT4])
+def test_brevitas_qlinear(bias, out_features, in_features, w_bits, i_dtype):
+ if bias:
+ pytest.xfail("bias export bug")
+ i_shape = (1, in_features)
+ w_shape = (out_features, in_features)
+ b_linear = QuantLinear(
+ out_features=out_features,
+ in_features=in_features,
+ bias=bias,
+ bias_quant_type=QuantType.FP,
+ weight_bit_width=w_bits,
+ weight_quant_type=QuantType.INT,
+ weight_scaling_per_output_channel=True,
+ )
+ weight_tensor_fp = np.random.uniform(low=-1.0, high=1.0, size=w_shape).astype(
+ np.float32
+ )
+ b_linear.weight.data = torch.from_numpy(weight_tensor_fp)
+ b_linear.eval()
+ bo.export_finn_onnx(b_linear, i_shape, export_onnx_path)
+ model = ModelWrapper(export_onnx_path)
+ model = model.transform(InferShapes())
+ inp_tensor = gen_finn_dt_tensor(i_dtype, i_shape)
+ 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()
+ expected = b_linear.forward(inp_tensor).detach().numpy()
+
+ assert np.isclose(produced, expected, atol=1e-3).all()
+ os.remove(export_onnx_path)
diff --git a/tests/brevitas/test_brevitas_relu_act_export.py b/tests/brevitas/test_brevitas_relu_act_export.py
index fa114585d31fca629aa759e386aa3fbd04280a2e..278f05a4a9f264d69461e555d28437cdc83e1a71 100644
--- a/tests/brevitas/test_brevitas_relu_act_export.py
+++ b/tests/brevitas/test_brevitas_relu_act_export.py
@@ -1,3 +1,31 @@
+# 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 onnx # noqa
import numpy as np
diff --git a/tests/brevitas/test_brevitas_scaled_QHardTanh_export.py b/tests/brevitas/test_brevitas_scaled_QHardTanh_export.py
index e0ec82ebed44e2e984be9f62e02bc1721a7f9c33..b1652c1cdce881157c6e97dcfde4257902678c8f 100644
--- a/tests/brevitas/test_brevitas_scaled_QHardTanh_export.py
+++ b/tests/brevitas/test_brevitas_scaled_QHardTanh_export.py
@@ -1,3 +1,31 @@
+# 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 onnx # noqa
import os
import numpy as np
diff --git a/tests/brevitas/test_brevitas_validate_mobilenet.py b/tests/brevitas/test_brevitas_validate_mobilenet.py
index 42bc3942d1a4f5fbdf70dbb1f1b5e853357abff8..dd079fe2e27ace85b9a08e699fa437e93f8e7f3d 100644
--- a/tests/brevitas/test_brevitas_validate_mobilenet.py
+++ b/tests/brevitas/test_brevitas_validate_mobilenet.py
@@ -1,3 +1,31 @@
+# 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 csv
import numpy as np
diff --git a/tests/end2end/test_end2end_bnn_pynq.py b/tests/end2end/test_end2end_bnn_pynq.py
index ddea2dafce02c181a279d9c95759b97dee00a504..6d0e028a1dc499154e6526be9384f92438a4b98a 100644
--- a/tests/end2end/test_end2end_bnn_pynq.py
+++ b/tests/end2end/test_end2end_bnn_pynq.py
@@ -129,12 +129,7 @@ def update_dashboard_data(topology, wbits, abits, key, val):
def fold_tfc(model):
fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
# (PE, SIMD, ramstyle) for each layer
- config = [
- (16, 49, "block"),
- (8, 8, "auto"),
- (8, 8, "auto"),
- (10, 8, "distributed"),
- ]
+ config = [(16, 49, "block"), (8, 8, "auto"), (8, 8, "auto"), (10, 8, "distributed")]
for fcl, (pe, simd, ramstyle) in zip(fc_layers, config):
fcl_inst = getCustomOp(fcl)
fcl_inst.set_nodeattr("PE", pe)
@@ -372,6 +367,7 @@ class TestEnd2End:
def test_streamline(self, topology, wbits, abits):
prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "pre_post")
model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ model = model.transform(absorb.AbsorbSignBiasIntoMultiThreshold())
# move past any reshapes to be able to streamline input scaling
model = model.transform(MoveScalarLinearPastInvariants())
model = model.transform(Streamline())
diff --git a/tests/end2end/test_end2end_cybsec_mlp.py b/tests/end2end/test_end2end_cybsec_mlp.py
index 4dba19f586e3235a582e72d4c3936a60ebc4a703..63d6a91e37586030a15c9a7c2523679875444f3f 100644
--- a/tests/end2end/test_end2end_cybsec_mlp.py
+++ b/tests/end2end/test_end2end_cybsec_mlp.py
@@ -28,6 +28,7 @@
import torch
from brevitas.nn import QuantLinear, QuantReLU
+from brevitas.quant_tensor import QuantTensor
import torch.nn as nn
import numpy as np
from brevitas.core.quant import QuantType
@@ -115,19 +116,32 @@ def test_end2end_cybsec_mlp_export():
model_for_export = CybSecMLPForExport(model)
export_onnx_path = get_checkpoint_name("export")
input_shape = (1, 600)
- bo.export_finn_onnx(model_for_export, input_shape, export_onnx_path)
+ # create a QuantTensor instance to mark the input as bipolar during export
+ input_a = np.random.randint(0, 1, size=input_shape).astype(np.float32)
+ input_a = 2 * input_a - 1
+ scale = 1.0
+ input_t = torch.from_numpy(input_a * scale)
+ input_qt = QuantTensor(
+ input_t, scale=torch.tensor(scale), bit_width=torch.tensor(1.0), signed=True
+ )
+
+ bo.export_finn_onnx(
+ model_for_export, export_path=export_onnx_path, input_t=input_qt
+ )
assert os.path.isfile(export_onnx_path)
# fix input datatype
finn_model = ModelWrapper(export_onnx_path)
finnonnx_in_tensor_name = finn_model.graph.input[0].name
- finn_model.set_tensor_datatype(finnonnx_in_tensor_name, DataType.BIPOLAR)
- finn_model.save(export_onnx_path)
assert tuple(finn_model.get_tensor_shape(finnonnx_in_tensor_name)) == (1, 600)
- assert len(finn_model.graph.node) == 30
- assert finn_model.graph.node[0].op_type == "Add"
- assert finn_model.graph.node[1].op_type == "Div"
- assert finn_model.graph.node[2].op_type == "MatMul"
+ # verify a few exported ops
+ assert finn_model.graph.node[1].op_type == "Add"
+ assert finn_model.graph.node[2].op_type == "Div"
+ assert finn_model.graph.node[3].op_type == "MatMul"
assert finn_model.graph.node[-1].op_type == "MultiThreshold"
+ # verify datatypes on some tensors
+ assert finn_model.get_tensor_datatype(finnonnx_in_tensor_name) == DataType.BIPOLAR
+ first_matmul_w_name = finn_model.graph.node[3].input[1]
+ assert finn_model.get_tensor_datatype(first_matmul_w_name) == DataType.INT2
@pytest.mark.slow
diff --git a/tests/transformation/streamline/test_remove_identity_ops.py b/tests/transformation/streamline/test_remove_identity_ops.py
index 536c1ab0b48fa44388da23f45b528da3c5f3b2f2..98430fad0e0f4c17d77ddbf44afeeccd44372047 100644
--- a/tests/transformation/streamline/test_remove_identity_ops.py
+++ b/tests/transformation/streamline/test_remove_identity_ops.py
@@ -11,7 +11,7 @@ from finn.transformation.streamline.remove import RemoveIdentityOps
from finn.util.basic import gen_finn_dt_tensor
-def insert_identity_op(model, op):
+def insert_identity_op(model, op, as_first_node):
if op in ["Add", "Sub"]:
val = np.asarray([0.0], dtype=np.float32)
elif op in ["Mul", "Div"]:
@@ -19,10 +19,15 @@ def insert_identity_op(model, op):
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"
+ if as_first_node:
+ identity_node = helper.make_node(op, ["inp", "value"], ["ident_out"])
+ graph.node.insert(0, identity_node)
+ graph.node[1].input[0] = "ident_out"
+ else:
+ identity_node = helper.make_node(op, ["div_out", "value"], ["ident_out"])
+ graph.node.insert(3, identity_node)
+ graph.node[-1].input[0] = "ident_out"
model.set_initializer("value", val)
return model
@@ -30,7 +35,8 @@ def insert_identity_op(model, op):
# identity operations to be inserted
@pytest.mark.parametrize("op", ["Add", "Sub", "Mul", "Div"])
-def test_remove_identity_ops(op):
+@pytest.mark.parametrize("as_first_node", [False, True])
+def test_remove_identity_ops(op, as_first_node):
# set up onnx model
inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, [1, 4, 1, 1])
@@ -64,7 +70,7 @@ def test_remove_identity_ops(op):
model.set_initializer("shape", shape_values)
model.set_initializer("div", div_values)
model.set_initializer("matmul", matmul_values)
- insert_identity_op(model, op)
+ insert_identity_op(model, op, as_first_node)
model = model.transform(InferShapes())
model = model.transform(InferDataTypes())
idict = {"inp": inp_values}