diff --git a/docker/Dockerfile.finn_ci b/docker/Dockerfile.finn_ci
index d1ca2e891b735c8be04418f469984618fcff072a..1e8c3f329f52cf965683569bb9404b3b35bdb11e 100644
--- a/docker/Dockerfile.finn_ci
+++ b/docker/Dockerfile.finn_ci
@@ -55,8 +55,6 @@ RUN git clone https://github.com/rogersce/cnpy.git /workspace/cnpy
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
@@ -70,7 +68,6 @@ 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"
diff --git a/docker/Dockerfile.finn_dev b/docker/Dockerfile.finn_dev
index 4b52d7886b8ab58542cc6ff17bcb65a48ca66237..15f94b3cefb316c1906fcd70f2a2a1246d65ff13 100644
--- a/docker/Dockerfile.finn_dev
+++ b/docker/Dockerfile.finn_dev
@@ -83,8 +83,6 @@ RUN git clone https://github.com/rogersce/cnpy.git /workspace/cnpy
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
@@ -92,7 +90,6 @@ RUN git clone https://bitbucket.org/maltanar/oh-my-xilinx.git /workspace/oh-my-x
# 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 "${PATH}:/workspace/oh-my-xilinx:/home/$UNAME/.local/bin"
ENV OHMYXILINX "/workspace/oh-my-xilinx"
diff --git a/docker/finn_entrypoint.sh b/docker/finn_entrypoint.sh
index 6ca0e7164e82b62719d52097e8c6f7960d341ccb..0867dc189b40ca53e6a1f3da25281194e3847ec2 100644
--- a/docker/finn_entrypoint.sh
+++ b/docker/finn_entrypoint.sh
@@ -12,11 +12,10 @@ gecho () {
# checkout the correct dependency repo commits
# the repos themselves are cloned in the Dockerfile
-BREVITAS_COMMIT=85e28ac2e6570e91216d042212a7d1a28ec6e394
+BREVITAS_COMMIT=feff8780e4ad15e8ed59b1d8facd9ee4132166b4
CNPY_COMMIT=4e8810b1a8637695171ed346ce68f6984e585ef4
HLSLIB_COMMIT=cfafe11a93b79ab1af7529d68f08886913a6466e
PYVERILATOR_COMMIT=c97a5ba41bbc7c419d6f25c74cdf3bdc3393174f
-PYNQSHELL_COMMIT=bf281fc3a44eca29efbcbefd63f1196d82c7c255
OMX_COMMIT=1bae737669901e762f581af73348332b5c4b2ada
gecho "Setting up known-good commit versions for FINN dependencies"
@@ -37,10 +36,6 @@ git -C /workspace/finn-hlslib checkout $HLSLIB_COMMIT --quiet
gecho "PyVerilator @ $PYVERILATOR_COMMIT"
git -C /workspace/pyverilator pull --quiet
git -C /workspace/pyverilator checkout $PYVERILATOR_COMMIT --quiet
-# PYNQ-HelloWorld
-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
diff --git a/docs/finn/end_to_end_flow.rst b/docs/finn/end_to_end_flow.rst
index fd6960a2f94774ab783cf1cff220ac604d9fbe92..529df57a5efe636cdd508efc3297bd05013a6b71 100644
--- a/docs/finn/end_to_end_flow.rst
+++ b/docs/finn/end_to_end_flow.rst
@@ -20,6 +20,5 @@ For a more detailed overview about the different flow sections, please have a lo
brevitas_export
nw_prep
- vivado_synth
- pynq_deploy
+ hw_build
verification
diff --git a/docs/finn/hw_build.rst b/docs/finn/hw_build.rst
new file mode 100644
index 0000000000000000000000000000000000000000..9f292a91c352d3c0d4f7adb340c85f15c1b52a53
--- /dev/null
+++ b/docs/finn/hw_build.rst
@@ -0,0 +1,100 @@
+.. _hw_build:
+
+*****************************
+Hardware Build and Deployment
+*****************************
+
+.. image:: /img/finn-hw-build.png
+ :scale: 70%
+ :align: center
+
+A model where all layers have been converted to HLS layers can be processed by
+FINN to build a bitfile targeting either a Zynq or Alveo system.
+
+Hardware Build
+==============
+
+Internally, the hardware build consists of the following steps:
+
+1. Driver generation
+2. DMA and DWC node insertion
+3. Partitioning for floorplanning
+4. FIFO insertion and IP generation
+5. Vivado/Vitis project generation and synthesis
+
+.. note:: **In previous FINN releases it was necessary to step through the
+individual sub-steps for hardware build manually by calling each transformation.
+The hardware build transformations `ZynqBuild` and `VitisBuild` now execute all
+necessary sub-transformations. For more control over the build process, the
+transformations listed below can still be called individually.
+**
+
+Driver Generation
+------------------
+
+To rapidly test the generated design on PYNQ platforms, FINN is capable of
+generating a Python driver for the given design. This driver packs/unpacks the
+input/output tensors in the expected format, then uses PYNQ APIs to initiate
+data movement and transfer back the results to the host CPU. The generation of
+the driver is done by transformation pass :py:mod:`finn.transformation.fpgadataflow.make_pynq_driver.MakePYNQDriver`.
+
+DMA and DWC Node Insertion
+---------------------------
+
+At this step, FINN will insert custom hardware-oriented ONNX nodes into the graph.
+These are DMA engines for moving data into and out of the accelerator (from DRAM),
+and data width converters between consecutive nodes where required.
+These steps are accomplished by calling the :py:mod:`finn.transformation.fpgadataflow.insert_iodma.InsertIODMA`
+and :py:mod:`finn.transformation.fpgadataflow.insert_dwc.InsertDWC` transformations,
+respectively.
+
+Partitioning for Floorplanning
+-------------------------------
+
+FINN will now partition the graph into several StreamingDataflowPartitions.
+This capability is most to facilitate floorplanning for future FINN releases
+and does not alter the functioning of the model itself. Each DMA node will be
+placed into its own partition. If no partition number attributes are specified,
+all the regular network nodes will become a single partition.
+This is accomplished by the :py:mod:`finn.transformation.fpgadataflow.floorplan.Floorplan`
+and :py:mod:`finn.transformation.fpgadataflow.create_dataflow_partition.CreateDataflowPartition`
+transformations.
+
+.. note:: **For Vitis, each partition will be compiled as a separate kernel,
+and linked together afterwards. For Zynq, each partition will become an IP
+block. **
+
+
+FIFO Insertion and IP Generation
+-------------------------------
+
+FINN will descend into each partition and insert FIFO nodes between streaming nodes,
+where FIFO depths dictated by the node attributes, using the :py:mod:`finn.transformation.fpgadataflow.insert_fifo.InsertFIFO`
+transformation.
+Afterwards, IP blocks will be created for each partition, which in turn contain the
+IP blocks for each layer stitched together. The layer-level IP blocks
+are generated by Vivado HLS, using a sequence of :py:mod:`finn.transformation.fpgadataflow.prepare_ip.PrepareIP`
+and :py:mod:`finn.transformation.fpgadataflow.hlssynth_ip.HLSSynthIP` transformations.
+The top-level IP blocks are generated in Vivado IPI, using the :py:mod:`finn.transformation.fpgadataflow.create_stitched_ip.CreateStitchedIP` transformation.
+
+Vivado/Vitis Project Generation and Synthesis
+---------------------------------------------
+
+The final step in the hardware build flow is to generate a Vivado (for Zynq) or Vitis (for Alveo)
+project, and run synthesis to generate a bitfile. This is done using the `MakeZYNQProject`
+transformation for Zynq, and the `VitisLink` transformation for Alveo.
+
+
+Deployment
+==========
+
+
+Deployment and Remote Execution
+-------------------------------
+
+The bitfile and the driver file(s) are copied to the PYNQ board and can be executed there using the *onnx_exec* function with the right *exec_mode* settings. For details please have a look at transformation :py:mod:`finn.transformation.fpgadataflow.make_deployment.DeployToPYNQ` and the execution function :py:mod:`finn.core.onnx_exec`.
+
+Throughput Test
+---------------
+
+FINN also offers the possibility to measure the network performance directly on the PYNQ board. This can be done by using :py:mod:`finn.core.throughput_test`. When running this function the metrics of the network are returned as dictionary.
diff --git a/docs/finn/img/finn-hw-build.png b/docs/finn/img/finn-hw-build.png
new file mode 100644
index 0000000000000000000000000000000000000000..f3a591fa8f9e25f99b44d2bd9502bf3ae979818b
Binary files /dev/null and b/docs/finn/img/finn-hw-build.png differ
diff --git a/docs/finn/pynq_deploy.rst b/docs/finn/pynq_deploy.rst
deleted file mode 100644
index ef36da9e7f8ceee19abfd061fde10bf06d3afb62..0000000000000000000000000000000000000000
--- a/docs/finn/pynq_deploy.rst
+++ /dev/null
@@ -1,41 +0,0 @@
-.. _pynq_deploy:
-
-***************
-PYNQ Deployment
-***************
-
-.. note:: **This website is currently under construction.**
-
-.. image:: /img/pynq-deploy.png
- :scale: 70%
- :align: center
-
-This chapter is about the hardware generation and deployment on PYNQ. If you need more information about PYNQ, please have a look at the `PYNQ website <https://pynq.readthedocs.io/en/v2.5.1/>`_.
-
-Create PYNQ Shell Project
-=========================
-
-To deploy the network on A PYNQ platform, it needs to be put inside an appropriate *shell*. This *shell* bridges the network with the interfaces the underlying system exposes. This can be done using the transformation MakePYNQProject, see :py:mod:`finn.transformation.fpgadataflow.make_pynq_proj.MakePYNQProject`.
-
-Test on Hardware
-================
-
-Synthesis, Place and Route
---------------------------
-
-After integrating the model into the PYNQ shell, Vivado *Synthesis, Place and Route* can be launched. The result is a bitfile which can be used for the PYNQ board. In FINN this can be done using a transformation pass. For details, please have a look at :py:mod:`finn.transformation.fpgadataflow.synth_pynq_proj.SynthPYNQProject`.
-
-Generate PYNQ runtime code
---------------------------
-
-Additionally, a Python code is necessary to execute the model on the board. This is done by transformation pass :py:mod:`finn.transformation.fpgadataflow.make_pynq_driver.MakePYNQDriver`.
-
-Deployment and Remote Execution
--------------------------------
-
-The bitfile and the driver file(s) are copied to the PYNQ board and can be executed there using the *onnx_exec* function with the right *exec_mode* settings. For details please have a look at transformation :py:mod:`finn.transformation.fpgadataflow.make_deployment.DeployToPYNQ` and the execution function :py:mod:`finn.core.onnx_exec`.
-
-Throughput Test
----------------
-
-Finn also offers the possibility to measure the network performance directly on the PYNQ board. This can be done by using :py:mod:`finn.core.throughput_test`. When running this function the metrics of the network are returned as dictionary.
diff --git a/notebooks/end2end_example/cnv_end2end_example.ipynb b/notebooks/end2end_example/cnv_end2end_example.ipynb
index c1ae0caea9e911064875268ac7aa79fb07bbaf3d..cecb1c9be6d59262e13d77d3d6b3242a013f5ee3 100644
--- a/notebooks/end2end_example/cnv_end2end_example.ipynb
+++ b/notebooks/end2end_example/cnv_end2end_example.ipynb
@@ -55,7 +55,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -76,26 +76,14 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 5,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Downloading: \"https://github.com/Xilinx/brevitas/releases/download/bnn_pynq-r0/cnv_1w1a-758c8fef.pth\" to /home/maltanar/.cache/torch/checkpoints/cnv_1w1a-758c8fef.pth\n",
- "100%|██████████| 6227617/6227617 [00:01<00:00, 4429264.32it/s]\n",
- "/workspace/brevitas/brevitas_examples/bnn_pynq/models/CNV.py:107: 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 = 2.0 * x - torch.tensor([1.0], device=x.device)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import onnx\n",
"from finn.util.test import get_test_model_trained\n",
"import brevitas.onnx as bo\n",
"from finn.core.modelwrapper import ModelWrapper\n",
- "from finn.transformation.double_to_single_float import DoubleToSingleFloat\n",
"from finn.transformation.infer_shapes import InferShapes\n",
"from finn.transformation.fold_constants import FoldConstants\n",
"from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames\n",
@@ -103,7 +91,6 @@
"cnv = get_test_model_trained(\"CNV\", 1, 1)\n",
"bo.export_finn_onnx(cnv, (1, 3, 32, 32), build_dir + \"/end2end_cnv_w1a1_export.onnx\")\n",
"model = ModelWrapper(build_dir + \"/end2end_cnv_w1a1_export.onnx\")\n",
- "model = model.transform(DoubleToSingleFloat())\n",
"model = model.transform(InferShapes())\n",
"model = model.transform(FoldConstants())\n",
"model = model.transform(GiveUniqueNodeNames())\n",
@@ -120,7 +107,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -144,10 +131,10 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f7b24ef8b00>"
+ "<IPython.lib.display.IFrame at 0x7fc303685940>"
]
},
- "execution_count": 3,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -181,7 +168,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -217,7 +204,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -243,10 +230,10 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f0cb1098f28>"
+ "<IPython.lib.display.IFrame at 0x7fc295ad28d0>"
]
},
- "execution_count": 5,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -266,7 +253,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -276,6 +263,7 @@
")\n",
"from finn.transformation.move_reshape import RemoveCNVtoFCFlatten\n",
"from finn.custom_op.registry import getCustomOp\n",
+ "from finn.transformation.infer_data_layouts import InferDataLayouts\n",
"\n",
"# choose the memory mode for the MVTU units, decoupled or const\n",
"mem_mode = \"decoupled\"\n",
@@ -287,6 +275,8 @@
"model = model.transform(to_hls.InferStreamingMaxPool())\n",
"# get rid of Reshape(-1, 1) operation between hlslib nodes\n",
"model = model.transform(RemoveCNVtoFCFlatten())\n",
+ "# infer tensor data layouts\n",
+ "model = model.transform(InferDataLayouts())\n",
"parent_model = model.transform(CreateDataflowPartition())\n",
"parent_model.save(build_dir + \"/end2end_cnv_w1a1_dataflow_parent.onnx\")\n",
"sdp_node = parent_model.get_nodes_by_op_type(\"StreamingDataflowPartition\")[0]\n",
@@ -306,7 +296,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -332,10 +322,10 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f0cb063e208>"
+ "<IPython.lib.display.IFrame at 0x7fc295ec6f60>"
]
},
- "execution_count": 7,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -353,14 +343,10 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
- "from finn.transformation.fpgadataflow.insert_dwc import InsertDWC\n",
- "from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker\n",
- "from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO\n",
- "\n",
"model = ModelWrapper(build_dir + \"/end2end_cnv_w1a1_dataflow_model.onnx\")\n",
"fc_layers = model.get_nodes_by_op_type(\"StreamingFCLayer_Batch\")\n",
"# each tuple is (PE, SIMD, in_fifo_depth) for a layer\n",
@@ -388,9 +374,6 @@
" simd = folding[i][1]\n",
" swg_inst.set_nodeattr(\"SIMD\", simd)\n",
"\n",
- "model = model.transform(InsertDWC())\n",
- "model = model.transform(InsertFIFO())\n",
- "model = model.transform(InsertTLastMarker())\n",
"model = model.transform(GiveUniqueNodeNames())\n",
"model.save(build_dir + \"/end2end_cnv_w1a1_folded.onnx\")"
]
@@ -404,7 +387,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -430,10 +413,10 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f0cb1098748>"
+ "<IPython.lib.display.IFrame at 0x7fc295b3d3c8>"
]
},
- "execution_count": 9,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -455,94 +438,21 @@
"source": [
"## 4. Hardware Generation\n",
"\n",
- "From this point onward, the steps we have to follow do not depend on the particular network and will be exactly the same as the TFC-w1a1 example. We first proceed with HLS synthesis, **which may take 10-20 minutes depending on your host computer**."
+ "From this point onward, the steps we have to follow do not depend on the particular network and will be exactly the same as the TFC-w1a1 example. **which may take about 30 minutes depending on your host computer**."
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
- "from finn.transformation.fpgadataflow.prepare_ip import PrepareIP\n",
- "from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP\n",
- "from finn.util.basic import pynq_part_map\n",
- "\n",
"test_pynq_board = \"Pynq-Z1\"\n",
- "test_fpga_part = pynq_part_map[test_pynq_board]\n",
- "target_clk_ns = 5\n",
- "\n",
- "model = ModelWrapper(build_dir + \"/end2end_cnv_w1a1_folded.onnx\")\n",
- "model = model.transform(PrepareIP(test_fpga_part, target_clk_ns))\n",
- "model = model.transform(HLSSynthIP())\n",
- "model.save(build_dir + \"/end2end_cnv_w1a1_ipgen.onnx\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Once the HLS synthesis is complete, we can stitch together the generated IP blocks into a larger IP that is the implementation of our network:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "from finn.transformation.fpgadataflow.replace_verilog_relpaths import (\n",
- " ReplaceVerilogRelPaths,\n",
- ")\n",
- "from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP\n",
- "\n",
- "model = ModelWrapper(build_dir + \"/end2end_cnv_w1a1_ipgen.onnx\")\n",
- "model = model.transform(ReplaceVerilogRelPaths())\n",
- "model = model.transform(CreateStitchedIP(test_fpga_part))\n",
- "model.save(build_dir + \"/end2end_cnv_w1a1_ipstitch.onnx\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally, we create a PYNQ project that includes the hardware \"shell\" that will support our accelerator, including the data movers, and run Vivado synthesis, **which may take around 30 minutes depending on your host computer.**\n",
+ "target_clk_ns = 10\n",
"\n",
- "*If you'd like to watch the progress, you can open the generated project file (printed below) with the Vivado GUI.*"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Vivado synthesis project is at /tmp/finn_dev_maltanar/vivado_pynq_proj_96qtjweo/resizer.xpr\n"
- ]
- }
- ],
- "source": [
- "from finn.transformation.fpgadataflow.make_pynq_proj import MakePYNQProject\n",
- "from finn.transformation.fpgadataflow.synth_pynq_proj import SynthPYNQProject\n",
- "\n",
- "model = ModelWrapper(build_dir + \"/end2end_cnv_w1a1_ipstitch.onnx\")\n",
- "model = model.transform(MakePYNQProject(test_pynq_board))\n",
- "vivado_proj = model.get_metadata_prop(\"vivado_pynq_proj\")\n",
- "print(\"Vivado synthesis project is at %s/resizer.xpr\" % vivado_proj)\n",
- "model.save(build_dir + \"/end2end_cnv_w1a1_pynqproj.onnx\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "model = ModelWrapper(build_dir + \"/end2end_cnv_w1a1_pynqproj.onnx\")\n",
- "model = model.transform(SynthPYNQProject())\n",
+ "from finn.transformation.fpgadataflow.make_zynq_proj import ZynqBuild\n",
+ "model = ModelWrapper(build_dir+\"/end2end_cnv_w1a1_folded.onnx\")\n",
+ "model = model.transform(ZynqBuild(platform = test_pynq_board, period_ns = target_clk_ns))\n",
"model.save(build_dir + \"/end2end_cnv_w1a1_synth.onnx\")"
]
},
@@ -557,42 +467,839 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
- "from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver\n",
"from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ\n",
"\n",
"# set up the following values according to your own environment\n",
"# FINN will use ssh to deploy and run the generated accelerator\n",
- "ip = os.getenv(\"PYNQ_IP\", \"192.168.1.99\")\n",
+ "ip = os.getenv(\"PYNQ_IP\", \"192.168.2.99\")\n",
"username = os.getenv(\"PYNQ_USERNAME\", \"xilinx\")\n",
"password = os.getenv(\"PYNQ_PASSWORD\", \"xilinx\")\n",
"port = os.getenv(\"PYNQ_PORT\", 22)\n",
- "target_dir = os.getenv(\"PYNQ_TARGET_DIR\", \"/home/xilinx/finn\")\n",
+ "target_dir = os.getenv(\"PYNQ_TARGET_DIR\", \"/home/xilinx/finn_cnv_end2end_example\")\n",
"\n",
"model = ModelWrapper(build_dir + \"/end2end_cnv_w1a1_synth.onnx\")\n",
- "model = model.transform(MakePYNQDriver(platform=\"zynq\"))\n",
"model = model.transform(DeployToPYNQ(ip, port, username, password, target_dir))\n",
"model.save(build_dir + \"/end2end_cnv_w1a1_pynq_deploy.onnx\")"
]
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_01nxn6ne:\r\n",
+ "total 4236\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9171 Aug 28 01:28 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 28 01:28 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 28 01:29 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 28 01:29 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 28 01:28 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 28 01:28 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 28 01:29 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_06f3kimz:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9154 Aug 27 13:30 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 27 13:30 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 27 13:45 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 27 13:45 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 27 13:30 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 27 13:30 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 27 13:45 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_06t_joa5:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9152 Aug 28 16:11 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 28 16:11 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 28 16:12 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 28 16:12 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 28 16:11 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246215 Aug 28 16:11 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 28 16:12 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_0qvh6x0y:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9154 Aug 29 04:50 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 29 04:50 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 29 04:50 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 29 04:51 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 29 04:50 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 29 04:50 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 29 04:51 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_1axnzb_3:\r\n",
+ "total 4288\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9230 Aug 21 09:39 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 21 09:39 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 21 09:39 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 21 09:40 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 Aug 21 09:39 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 301904 Aug 21 09:39 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 21 09:40 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_21w59oae:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9152 Aug 29 04:50 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 29 04:50 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 29 04:51 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 29 04:51 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 29 04:50 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246215 Aug 29 04:50 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 29 04:51 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_28bro9rw:\r\n",
+ "total 4220\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9409 Sep 4 03:35 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Sep 4 03:35 finn\r\n",
+ "-rw-r--r-- 1 root root 207 Sep 4 03:39 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Sep 4 03:35 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Sep 4 03:35 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Sep 4 03:39 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_3_7bkkrt:\r\n",
+ "total 4272\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9153 Aug 30 10:55 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 30 10:55 finn\r\n",
+ "-rw-r--r-- 1 root root 205 Aug 30 10:55 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 Aug 30 10:55 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 301916 Aug 30 10:55 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 30 10:55 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_3tl3zhf1:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8827 Aug 19 10:38 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 10:38 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 19 10:38 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 19 10:38 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 19 10:38 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 19 10:38 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 10:38 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_4cf4kfxo:\r\n",
+ "total 4236\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9172 Aug 29 04:50 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 29 04:50 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 29 04:51 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 29 04:51 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 29 04:50 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 29 04:50 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 29 04:51 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment__6inegxp:\r\n",
+ "total 4260\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9147 Aug 30 09:52 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 30 09:52 finn\r\n",
+ "-rw-r--r-- 1 root root 206 Aug 30 09:54 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 Aug 30 09:52 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 287145 Aug 30 09:52 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 30 09:54 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_7887i7c6:\r\n",
+ "total 4236\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9172 Aug 28 01:28 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 28 01:28 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 28 01:28 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 28 01:28 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 28 01:28 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 28 01:28 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 28 01:28 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_7co5tvv7:\r\n",
+ "total 4272\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8826 Aug 19 08:34 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 08:34 finn\r\n",
+ "-rw-r--r-- 1 root root 205 Aug 19 08:35 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 Aug 19 08:34 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 301916 Aug 19 08:34 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 08:35 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_7rx94isg:\r\n",
+ "total 4228\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9154 Aug 30 12:29 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 30 12:29 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 30 12:29 input.npy\r\n",
+ "-rw-r--r-- 1 root root 205 Aug 30 12:49 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 30 12:30 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 30 12:29 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 30 12:29 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 30 12:49 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_7uu4dz03:\r\n",
+ "total 4240\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9172 Aug 29 16:14 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 29 16:14 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 29 16:14 input.npy\r\n",
+ "-rw-r--r-- 1 root root 205 Aug 29 16:50 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 29 16:14 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 29 16:14 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 29 16:14 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 29 16:51 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_81y7pbz6:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8827 Aug 19 00:36 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 00:36 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 19 00:36 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 19 00:36 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 19 00:36 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246647 Aug 19 00:36 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 00:36 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_8oclwrec:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9154 Aug 28 16:11 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 28 16:11 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 28 16:11 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 28 16:11 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 28 16:11 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 28 16:11 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 28 16:11 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_8xmofsvg:\r\n",
+ "total 4228\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9219 Aug 16 13:19 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 16 13:19 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 16 13:19 input.npy\r\n",
+ "-rw-r--r-- 1 root root 204 Aug 16 13:43 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 16 13:19 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 16 13:19 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246647 Aug 16 13:19 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 16 13:43 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_901qgs6r:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9154 Aug 27 06:06 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 27 06:06 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 27 06:06 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 27 06:06 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 27 06:06 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 27 06:06 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 27 06:06 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_93g2nqtq:\r\n",
+ "total 4260\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8820 Aug 19 07:32 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 07:32 finn\r\n",
+ "-rw-r--r-- 1 root root 203 Aug 19 07:35 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 Aug 19 07:32 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 287145 Aug 19 07:32 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 07:35 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_9ksrgg2h:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8820 Aug 19 07:42 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 07:42 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 144 Aug 19 07:42 input.npy\r\n",
+ "-rw-r--r-- 1 root root 96 Aug 19 07:42 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 19 07:42 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246209 Aug 19 07:42 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 07:42 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_a6wquogd:\r\n",
+ "total 4236\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9171 Aug 28 01:28 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 28 01:28 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 28 01:28 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 28 01:28 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 28 01:28 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 28 01:28 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 28 01:28 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_ale2myrj:\r\n",
+ "total 4276\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8819 Aug 19 09:47 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 09:47 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 19 09:47 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 19 09:47 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 Aug 19 09:47 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 301911 Aug 19 09:47 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 09:47 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_ao3q4u89:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9152 Aug 27 15:49 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 27 15:49 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 27 23:39 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 27 23:39 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 27 15:49 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246215 Aug 27 15:49 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 27 23:39 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_cam682qh:\r\n",
+ "total 4220\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9389 Sep 4 03:35 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Sep 4 03:35 finn\r\n",
+ "-rw-r--r-- 1 root root 204 Sep 4 03:38 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Sep 4 03:35 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246215 Sep 4 03:35 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Sep 4 03:38 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_diprgb3z:\r\n",
+ "total 4236\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8841 Aug 20 02:05 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 20 02:05 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 20 04:11 input.npy\r\n",
+ "-rw-r--r-- 1 root root 84 Aug 20 04:12 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 20 02:05 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246207 Aug 20 02:05 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 20 04:12 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment__e5pd_a_:\r\n",
+ "total 4236\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9172 Aug 27 05:50 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 27 05:50 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 27 05:51 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 27 05:51 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 27 05:50 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 27 05:50 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 27 05:51 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_ecyflrb3:\r\n",
+ "total 4212\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9154 Aug 29 13:11 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 29 13:11 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 29 13:11 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 29 13:11 resizer.hwh\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_eh6_czd4:\r\n",
+ "total 4236\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9171 Aug 28 16:11 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 28 16:11 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 28 16:12 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 28 16:12 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 28 16:11 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 28 16:11 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 28 16:12 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_f_bruffc:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9219 Aug 15 11:46 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 15 11:46 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 15 11:46 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 15 11:46 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 15 11:46 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 15 11:46 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 15 11:46 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_fgopkgby:\r\n",
+ "total 4272\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9153 Aug 29 15:17 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 29 15:17 finn\r\n",
+ "-rw-r--r-- 1 root root 203 Aug 29 15:17 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 Aug 29 15:17 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 301916 Aug 29 15:17 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 29 15:17 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_fu_jf95u:\r\n",
+ "total 4220\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9408 Sep 4 03:35 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Sep 4 03:35 finn\r\n",
+ "-rw-r--r-- 1 root root 205 Sep 4 03:40 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Sep 4 03:35 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Sep 4 03:35 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Sep 4 03:40 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_gsss7jov:\r\n",
+ "total 4236\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9171 Aug 29 04:50 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 29 04:50 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 29 04:51 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 29 04:51 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 29 04:50 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 29 04:50 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 29 04:51 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_h8o7knmw:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9147 Aug 29 14:19 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 29 14:19 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 144 Aug 29 14:19 input.npy\r\n",
+ "-rw-r--r-- 1 root root 96 Aug 29 14:19 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 29 14:19 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246209 Aug 29 14:19 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 29 14:19 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_i705jzvl:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9152 Aug 29 04:50 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 29 04:50 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 29 04:51 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 29 04:51 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 29 04:50 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246215 Aug 29 04:50 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 29 04:51 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_i7w9f7gi:\r\n",
+ "total 4236\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9171 Aug 29 04:50 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 29 04:50 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 29 04:51 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 29 04:52 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 29 04:50 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 29 04:50 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 29 04:52 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_ih88epbh:\r\n",
"total 4260\r\n",
- "-rw-r--r-- 1 xilinx xilinx 6380 May 7 15:14 driver.py\r\n",
- "drwxr-xr-x 4 xilinx xilinx 4096 May 7 15:14 finn\r\n",
- "-rw-r--r-- 1 xilinx xilinx 4045675 May 7 15:14 resizer.bit\r\n",
- "-rw-r--r-- 1 xilinx xilinx 302015 May 7 15:14 resizer.hwh\r\n"
+ "-rw-r--r-- 1 xilinx xilinx 9147 Aug 29 14:15 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 29 14:15 finn\r\n",
+ "-rw-r--r-- 1 root root 207 Aug 29 14:18 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 Aug 29 14:15 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 287145 Aug 29 14:15 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 29 14:18 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_ii6xl1b5:\r\n",
+ "total 4240\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9237 Aug 16 13:02 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 16 13:02 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 16 13:02 input.npy\r\n",
+ "-rw-r--r-- 1 root root 207 Aug 16 13:45 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 16 13:02 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 16 13:02 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246647 Aug 16 13:02 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 16 13:45 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_ilfexhs3:\r\n",
+ "total 4288\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8838 Aug 19 08:57 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 08:57 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 19 08:57 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 19 08:57 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 Aug 19 08:57 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 301904 Aug 19 08:57 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 11:00 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_it7n5qfu:\r\n",
+ "total 4236\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9172 Aug 27 15:49 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 27 15:49 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 27 23:40 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 27 23:40 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 27 15:49 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 27 15:49 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 27 23:40 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_iuszsd4p:\r\n",
+ "total 4212\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9409 Sep 4 13:08 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Sep 4 13:08 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Sep 4 13:08 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Sep 4 13:08 resizer.hwh\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_j8ixf__1:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9152 Aug 27 15:49 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 27 15:49 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 27 23:21 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 27 23:21 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 27 15:49 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246215 Aug 27 15:49 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 27 23:21 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_jdmw5vhc:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9219 Aug 16 02:18 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 16 02:18 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 16 02:19 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 16 02:19 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 16 02:18 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246647 Aug 16 02:18 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 16 02:19 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_jk8k3kdu:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8825 Aug 19 10:56 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 10:56 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 19 10:56 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 19 10:56 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 19 10:56 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246215 Aug 19 10:56 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 10:56 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_jlcdo6uh:\r\n",
+ "total 4236\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8845 Aug 19 00:19 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 00:19 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 19 00:19 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 19 00:19 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 19 00:19 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246647 Aug 19 00:19 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 00:19 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_ke08eze6:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9154 Aug 25 05:14 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 25 05:14 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 25 05:14 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 25 05:15 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 25 05:14 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 25 05:14 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 25 05:15 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_lga6n0hz:\r\n",
+ "total 4236\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9172 Aug 28 16:11 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 28 16:11 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 28 16:12 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 28 16:12 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 28 16:11 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 28 16:11 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 28 16:12 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_lyzs0vjq:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9152 Aug 29 16:46 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 29 16:46 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 29 16:46 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 29 16:46 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 29 16:46 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246215 Aug 29 16:46 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 29 16:46 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_n1bf2coh:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9154 Aug 28 07:16 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 28 07:16 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 28 07:16 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 28 07:16 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 28 07:16 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 28 07:16 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 28 07:16 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_n6az3jmc:\r\n",
+ "total 4220\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9391 Sep 4 03:35 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Sep 4 03:35 finn\r\n",
+ "-rw-r--r-- 1 root root 205 Sep 4 03:36 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Sep 4 03:35 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Sep 4 03:35 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Sep 4 03:36 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_n_yhs0wi:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9265 Aug 15 04:42 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 15 02:25 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 15 02:25 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 15 04:42 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 15 02:25 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 15 02:25 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 15 04:42 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_om0rv8kw:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9219 Aug 16 02:01 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 16 02:01 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 16 02:01 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 16 02:01 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 16 02:01 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246647 Aug 16 02:01 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 16 02:01 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_p8vv_rbe:\r\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8820 Aug 19 13:19 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 13:19 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 144 Aug 19 13:19 input.npy\r\n",
+ "-rw-r--r-- 1 root root 96 Aug 19 13:19 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 19 13:19 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246645 Aug 19 13:19 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 13:19 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_ph6e_tf5:\r\n",
+ "total 4288\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9229 Aug 21 10:09 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 21 10:09 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 21 10:10 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 21 10:10 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 Aug 21 10:09 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 301904 Aug 21 10:09 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 21 10:10 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_pt546ihb:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9152 Aug 27 06:25 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 27 06:25 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 27 06:25 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 27 06:25 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 27 06:25 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246215 Aug 27 06:25 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 27 06:25 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_rkgdftut:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8820 Aug 18 12:54 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 18 12:54 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 144 Aug 18 12:54 input.npy\r\n",
+ "-rw-r--r-- 1 root root 96 Aug 18 12:54 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 18 12:54 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246645 Aug 18 12:54 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 18 12:54 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_rpurccjo:\r\n",
+ "total 4288\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9230 Aug 15 08:31 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 15 08:31 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 15 08:31 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 15 08:31 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 Aug 15 08:31 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 301904 Aug 15 08:31 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 15 08:31 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_sal6h506:\r\n",
+ "total 4220\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9389 Sep 4 03:35 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Sep 4 03:35 finn\r\n",
+ "-rw-r--r-- 1 root root 205 Sep 4 03:37 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Sep 4 03:35 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246215 Sep 4 03:35 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Sep 4 03:37 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_sd_7cw4n:\r\n",
+ "total 4280\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8821 Aug 19 09:36 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 09:36 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 19 09:36 input.npy\r\n",
+ "-rw-r--r-- 1 root root 205 Aug 19 10:58 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 19 09:36 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 Aug 19 09:36 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 301904 Aug 19 09:36 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 10:58 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_ss_xv2xy:\r\n",
+ "total 4220\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9408 Sep 4 03:35 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Sep 4 03:35 finn\r\n",
+ "-rw-r--r-- 1 root root 205 Sep 4 03:40 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Sep 4 03:35 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Sep 4 03:35 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Sep 4 03:40 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_t9yxn329:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9212 Aug 16 08:11 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 16 08:11 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 144 Aug 16 08:11 input.npy\r\n",
+ "-rw-r--r-- 1 root root 96 Aug 16 08:11 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 16 08:11 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246645 Aug 16 08:11 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 16 08:11 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_tc39mytd:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9219 Aug 16 05:44 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 16 05:44 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 16 05:44 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 16 05:44 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 16 05:44 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246647 Aug 16 05:44 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 16 05:44 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_tgt4myfy:\r\n",
+ "total 4276\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8814 Aug 19 07:32 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 07:32 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 144 Aug 19 07:32 input.npy\r\n",
+ "-rw-r--r-- 1 root root 96 Aug 19 07:32 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 Aug 19 07:32 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 301916 Aug 19 07:32 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 07:32 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_tp9a5_fy:\r\n",
+ "total 4236\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9171 Aug 28 16:11 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 28 16:11 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 28 16:13 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 28 16:13 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 28 16:11 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 28 16:11 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 28 16:13 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_tucd1097:\r\n",
+ "total 4228\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9154 Aug 29 16:29 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 29 16:29 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 29 16:29 input.npy\r\n",
+ "-rw-r--r-- 1 root root 206 Aug 29 16:48 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 29 16:29 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 29 16:29 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 29 16:29 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 29 16:48 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_ub1tgpmc:\r\n",
+ "total 4228\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9219 Aug 22 12:43 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 22 12:43 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 22 12:43 input.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 120 Aug 23 04:02 output_golden.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 23 04:02 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 22 12:43 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 22 12:43 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 23 04:02 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_ucr_973r:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9152 Aug 30 12:47 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 30 12:47 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 30 12:47 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 30 12:47 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 30 12:47 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246215 Aug 30 12:47 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 30 12:47 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_uxodq9a4:\r\n",
+ "total 20\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8820 Aug 19 13:04 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 13:04 finn\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 13:04 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_w0ou6nez:\r\n",
+ "total 4220\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9391 Sep 4 01:13 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Sep 4 01:13 finn\r\n",
+ "-rw-r--r-- 1 root root 204 Sep 4 01:14 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Sep 4 01:13 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Sep 4 01:13 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Sep 4 01:14 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_wr1hljxk:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9219 Aug 15 10:07 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 15 10:07 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 15 10:07 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 15 10:07 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 15 10:07 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 15 10:07 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 15 10:07 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_wvxkayc8:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9152 Aug 28 16:11 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 28 16:11 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 28 16:12 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 28 16:12 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 28 16:11 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246215 Aug 28 16:11 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 28 16:12 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_x1yyq4s3:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9154 Aug 27 15:49 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 27 15:49 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 27 23:07 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 27 23:07 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 27 15:49 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 27 15:49 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 27 23:07 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_xec0no1y:\r\n",
+ "total 4276\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8819 Aug 19 09:59 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 09:59 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 19 09:59 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 19 09:59 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 Aug 19 09:59 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 301911 Aug 19 09:59 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 09:59 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_xf594c4i:\r\n",
+ "total 4288\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8837 Aug 19 09:27 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 09:27 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 19 09:27 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 19 09:27 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045675 Aug 19 09:27 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 301904 Aug 19 09:27 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 11:02 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_xuytd3ej:\r\n",
+ "total 4236\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8845 Aug 19 10:23 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 10:23 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 19 10:23 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 19 10:23 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 19 10:23 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 19 10:23 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 10:23 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_xxozg07g:\r\n",
+ "total 4240\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9172 Aug 30 12:14 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 30 12:14 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 12416 Aug 30 12:14 input.npy\r\n",
+ "-rw-r--r-- 1 root root 208 Aug 30 12:51 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 30 12:15 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 30 12:14 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 30 12:14 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 30 12:51 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_y5v0yh_k:\r\n",
+ "total 4228\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8827 Aug 15 00:25 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 15 00:25 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 15 00:25 input.npy\r\n",
+ "-rw-r--r-- 1 root root 206 Aug 15 00:34 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 15 00:26 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 15 00:25 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Aug 15 00:25 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 15 00:34 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_ybgtgj39:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9147 Aug 30 09:56 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 30 09:56 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 144 Aug 30 09:56 input.npy\r\n",
+ "-rw-r--r-- 1 root root 96 Aug 30 09:56 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 30 09:56 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246209 Aug 30 09:56 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 30 09:56 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_yqc16_8w:\r\n",
+ "total 20\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8820 Aug 18 12:40 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 18 12:40 finn\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 18 12:40 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_yt2dpnht:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9217 Aug 16 13:40 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 16 13:40 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 16 13:40 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 16 13:41 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 16 13:40 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246651 Aug 16 13:40 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 16 13:41 sds_trace_data.dat\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_z5zzp4mm:\r\n",
+ "total 4212\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9409 Sep 4 13:07 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Sep 4 13:07 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Sep 4 13:07 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Sep 4 13:07 resizer.hwh\r\n",
+ "\r\n",
+ "/home/xilinx/finn_dev_maltanar/pynq_deployment_zn65nxuo:\r\n",
+ "total 4224\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8825 Aug 19 00:58 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Aug 19 00:58 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Aug 19 00:58 input.npy\r\n",
+ "-rw-r--r-- 1 root root 120 Aug 19 00:59 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Aug 19 00:58 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246651 Aug 19 00:58 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Aug 19 00:59 sds_trace_data.dat\r\n"
]
}
],
@@ -609,30 +1316,18 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.image.AxesImage at 0x7f0c2b2c6908>"
+ "<matplotlib.image.AxesImage at 0x7fc315a36198>"
]
},
- "execution_count": 19,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
}
],
"source": [
@@ -655,7 +1350,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
@@ -676,7 +1371,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
@@ -698,7 +1393,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -707,13 +1402,13 @@
"<BarContainer object of 10 artists>"
]
},
- "execution_count": 28,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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VB8ZtfXlV/fq46PlV9Zlx/9h67rn9Tu0a+uqvH/veX6iqf71qf8S+5RVJDh/PGxeN5/5NSa6sbe6wq6oXV9XLx9c/M/bbLh3b7fDZlY7no89uO5/FEgKtbc/p7kcl2ZjkBVV1322WH5Hkz7r7wd39lSQHJtnc3Q9O8rEk/3GJdb6uu4/s7ockuUeS2QPh/t19VJIXzrz3uUlu6u4jkxyZ5Ler6rBF/YFr3HLbP0nS3X+TZFOSl3T3w7v7S0n+Msnvd/fPJbksd26LW7p7Y5LXJ/mfSZ6X5CFJnlVV962qRyV5dpJHJ3lMhm3+iPG9D8jQ5g9K8q0skcBvVVUPSvLrSR7X3Q9P8qMkT99e+QlbbpsemOTC7n5Yki8l+U8Zwp9fyCre/TUlVfXgJC9L8sSxHU5L8skkj+nuRyQ5J8l/6O5rM+xXrxn3xU+sVp33VePx6cQkD0/yqxnOC8nwix/PH4+ZL07yZ+P812ZojyOT/FqSN82sbkOSJ3f3SXui7lOyg3ba3rnpL5L8zsy5ghXS3ack+bskv5zkNbnzfvCnSd46ts87kvy38W2vTfLa7n5okuv3fK33eUckOWvsP38zw7FqR/24A7p7Y3f/SZIzkvyr8dx07Lh8yn3nlXR0kr/r7oeN32M+NM6/sbsfmeTPM5x/lrI+yVFJjkny+qq6+0pXdgJOT/Kl8bzxkiSPTHJad//sMu97R4b97WEZ7tz+2tYFNVxYf32S48bvU6wQIdDa9oLx7oMLkhya4SQ16yvdfcHM9G1J3jW+fnuGL6nb+uXx6vllSZ6Y5MEzy/56/PfiDAfLJPmXSX6zqi5JcmGS+y5Rj33Vctt/SVV17yT36e6PjbPemuQXZ4psGv+9LMkV3f217v5BkmvGz/mFJO/r7u9293cytMvWOxqu6+5Pja+318ZbPSnJo5JcNLbfkzJcfeTOltumP0ryV+PrRyf5aHdv6e5bcsf+xsp6YpL3dPeNSdLd/5jkkCTnjceyl+TOxzJWzuMzHJ9u7u5vZTie3T1DR+4947HmDUl+aiz/5CSvG+dvSvLjVXXPcdmm7v7enq3+ZCzVTgdmiXNTDeMM3qu7/3ac/849X91Jm90PHps7tv/bcsf56LFJ3jO+1j6L9+XuvmR8fXGSw7Pjftzsuf9TSd5SVb+dZOvd1lPuO6+ky5L8Sg1PLjy+u28a5y/1/WVb7+7u27r7ixn62w9c2apO0qe7+8s7KlBV90pycHe/L0m6+/vdffO4+EEZLig9tbu/urJVxTN7a1RVPSFD5/mx3X1zVX00Q0d71neXWU1vs867Z7g6u7G7rxtvy5td5w/Gf3+UO/5vVIaru+ft7N+wN9vB9p/dprt6FWHrdr5t5vXW6eX2yV5melZluKL4BztXvclZbpt+v7tdGV97/jTJq7t707i/vnx1qzNpd0nyzfFq4FLLHtPd35+dWcOTyMudw2AK7Aerb7Yv9qMMY53syO1t1t2nVNWjM9xhcvF4F94k+84rrbu/UFWPzHB34x9W1fnjoqW+v/yTty8zze6bPZbdmjvfbDLPd6avjeUekeFuSVaQO4HWrnsn+cYYQDwww2NBy7lLkq2/AvZvMzwuMWvrDnjjeCV2nl8MOy/J71bVXZOkqn62qg6c4317u+1t/69X1YNqGIj738yU/3aSeyXJeGXiG3XHeCTPyPB43rw+keRpNYxNc+D4OVsfa7lfVT12fL1UG886P8nxVfWTSVJVP1FV99+JekzFzmzTC5P80vjY3l2TnLDitSNJPpLkhK2PZFbVT2TYR28Ylz9zpuzt+yIr4uMZjk/3GK/oPTXJzUm+XFUnJLf/WMHDxvIfTvL8rW+uqqWCIhZvqXb6bpY4N40/NvHt8YtsMjxGxur4m9yx/Z+eO879F2R4RCnRPnvC3P24qjq8uy/s7jOSbMlwR/dU+84rqqr+RZKbu/vtSV6V4fGjeZ1QVXcZx5n56SRXrUQdJ2ZH/a2vJ/nJsb98t4zDj3T3t5NcX1VPS5KquluN45tmeBTzmCT/Zby4xwpyJ9Da9aEkp1TV5zMcqC5YpnwydPCOqqqXJfn7DOPB3K67v1lVb8wwkPT/S3LRHOt8U4ZbKz9Tw6XbLRl+CWtft73tf3qS92fYDpuTbH2s4Zwkb6yqF2QI156Z4ZnjH8tw2+mz5/3g7v5MVb0lyafHWW/q7s/WMOjtVUmeV1VvTnJlhueft7eeK8f/Cx8eQ6sfZhh/6Cvz1mUiltqmT12qYHd/bbyD7m8znKwuWaoci9XdV1TVHyX5WFX9KMlnM9z5856q+kaGkGjreAv/K8l7q+q4DFdijQu0QOPx6V1JLs1wntl6Hnl6kj8fjzl3zXBMvDTJC5KcVVWfy9Dn+HiGwbtZQTtop+2dm56b4Rx2W4YvuzeF1fD8JH9RVS/J0M/Y2j4vTPL2qnpphv6J9ll58/bjXlVVR2S4++f8DPvc5zLNvvNKe2iG7X1bhj7t7yZ575zv/WqGfvWPJzll27tT2Xnd/Q9V9akaBoD+XobgZ+uyH1bVmRm2+Q1J/u/MW5+R5A3j8h9m5oJqd3+9hoG7P1hVz+nuC/fE3zJF1e1uuH1FVX2nu++5fEkAgEFV3XMcgy5VdXqSn+ru01a5WozGIOJ73d1VdWKSk7r7uNWuF+wNxgur7+/ueQMj2Oe5EwgAYNqOqao/yNAv/EqSZ61uddjGozIMsF4Z7kJ9zirXB4C9mDuBAAAAACbAwNAAAAAAEyAEAgAAAJgAIRAAAADABAiBAAAAACZACAQAAAAwAUIgAAAAgAn4/ybos9quolFvAAAAAElFTkSuQmCC\n",
+ "image/png": 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MuH9sPffcfqd2DX3114997y9U1b9etT9i3/KKJIeP542LxnP/piRX1jZ32FXVi6rqZePrnxn7bZeO7Xb47ErH89Fnt53PYgmB1rZnd/cjk2xM8vyquu82y49I8mfd/eDu/kqSA5Ns7u4HJ/lYkv+4xDpf191HdfdDktwjyeyBcP/uPjrJC2be+5wkN3X3UUmOSvLbVXXYgv6+tW657Z8k6e6/SbIpyYu7+8ju/lKSv0zy+939c0kuy53b4pbu3pjk9Un+Z5LnJnlIkmdW1X2r6pFJnpXkUUkenWGbP3x87wMytPmDknwrSyTwW1XVg5L8epLHdveRSX6U5GnbKz9hy23TA5Nc2N0PS/KlJP8pQ/jzC1nFu7+mpKoenOSlSZ4wtsPpST6Z5NHd/fAk5yT5D919bYb96jXjvviJ1arzvmo8Pp2U5Mgkv5rhvJAMv/jxvPGY+aIkfzbOf22G9jgqya8ledPM6jYkeVJ3n7wHqj4pO2in7Z2b/iLJ78ycK1gh3X1qkr9L8stJXpM77wd/muStY/u8I8l/G9/22iSv7e6HJrl+z9d6n3dEkrPG/vM3MxyrdtSPO6C7N3b3nyQ5M8m/Gs9Nx43Lp9x3XknHJPm77n7Y+D3mQ+P8G7v7EUn+PMP5Zynrkxyd5Ngkr6+qu690ZSfgjCRfGs8bL07yiCSnd/fPLvO+d2TY3x6W4c7tr21dUMOF9dcnOX78PsUKEQKtbc+v4e6DC5IcmuEkNesr3X3BzPRtSd41vn57hi+p2/rl8er5ZUmekOTBM8v+evz34gwHyyT5l0l+s6ouSXJhkvsuUY991XLbf0lVde8k9+nuj42z3prkF2eKbBr/vSzJFd39te7+QZJrxs/5hSTv6+7vdvd3MrTL1jsaruvuT42vt9fGWz0xySOTXDS23xMzXH3kzpbbpj9K8lfj60cl+Wh3b+nuW3LH/sbKekKS93T3jUnS3f+Y5JAk543HshfnzscyVs7jMhyfbu7ub2U4nt09Q0fuPeOx5g1Jfmos/6Qkrxvnb0ry41V1z3HZpu7+3p6s/IQs1U4HZolzUw3jDN6ru/92nP/OPV7baZvdDx6TO7b/23LH+egxSd4zvtY+i/fl7r5kfH1xksOz437c7Ln/U0neUlW/nWTr3dZT7juvpMuS/EoNTy48rrtvGucv9f1lW+/u7tu6+4sZ+tsPXNmqTtKnu/vLOypQVfdKcnB3vy9Juvv73X3zuPhBGS4oPaW7v7qyVcUze2tUVT0+Q+f5Md19c1V9NENHe9Z3l1lNb7POu2e4Oruxu68bb8ubXecPxn9/lDv+b1SGq7vn7eSfsFfbwfaf3aa7ehVh63a+beb11unl9sleZnpWZbii+Ac7V73JWW6bfr+7XRlfe/40yau7e9O4v75sVWszbXdJ8s3xauBSyx7d3d+fnVnDk8jLncNgCuwHq2+2L/ajDGOd7Mjtbdbdp1bVozLcYXLxeBfeJPvOK627v1BVj8hwd+MfVtX546Klvr/8k7cvM83umz2W3Zo732wyz3emr43lHp7hbklWkDuB1q57J/nGGEA8MMNjQcu5S5KtvwL2bzM8LjFr6w5443gldp5fDDsvye9W1V2TpKp+tqoOnON9e7vtbf+vV9WDahiI+9/MlP92knslyXhl4ht1x3gkT8/weN68PpHkqTWMTXPg+DlbH2u5X1U9Zny9VBvPOj/JCVX1k0lSVT9RVfffiXpMxc5s0wuT/NL42N5dk5y44rUjST6S5MStj2RW1U9k2EdvGJc/Y6bs7fsiK+LjGY5P9xiv6D0lyc1JvlxVJya3/1jBw8byH07yvK1vrqoj93B9p2qpdvpuljg3jT828e3xi2wyPEbG6vib3LH9n5Y7zv0XZHhEKdE+e8Lc/biqOry7L+zuM5NsyXBH91T7ziuqqv5Fkpu7++1JXpXh8aN5nVhVdxnHmfnpJFetRB0nZkf9ra8n+cmxv3y3jMOPdPe3k1xfVU9Nkqq6W43jm2Z4FPPYJP9lvLjHCnIn0Nr1oSSnVtXnMxyoLlimfDJ08I6uqpcm+fsM48Hcrru/WVVvzDCQ9P9LctEc63xThlsrP1PDpdstGX4Ja1+3ve1/RpL3Z9gOm5NsfazhnCRvrKrnZwjXnpHhmeMfy3Db6bPm/eDu/kxVvSXJp8dZb+ruz9Yw6O1VSZ5bVW9OcmWG55+3t54rx/8LHx5Dqx9mGH/oK/PWZSKW2qZPWapgd39tvIPubzOcrC7ZM1Wctu6+oqr+KMnHqupHST6b4c6f91TVNzKERFvHW/hfSd5bVcdnuBJrXKAFGo9P70pyaYbzzNbzyNOS/Pl4zLlrhmPipUmen+Ssqvpchj7HxzMM3s0K2kE7be/c9JwM57DbMnzZvSmshucl+YuqenGGfsbW9nlBkrdX1Usy9E+0z8qbtx/3qqo6IsPdP+dn2Oc+l2n2nVfaQzNs79sy9Gl/N8l753zvVzP0q388yanb3p3Kzuvuf6iqT9UwAPT3MgQ/W5f9sKpenmGb35Dk/8689elJ3jAu/2FmLqh299drGLj7g1X17O6+cE/8LVNU3e6G21dU1Xe6+57LlwQAGFTVPccx6FJVZyT5qe4+fZWrxWgMIr7X3V1VJyU5ubuPX+16wd5gvLD6/u6eNzCCfZ47gQAApu3YqvqDDP3CryR55upWh208MsMA65XhLtRnr251ANibuRMIAAAAYAIMDA0AAAAwAUIgAAAAgAkQAgEAAABMgBAIAAAAYAKEQAAAAAATIAQCAAAAmID/Dybos9rT5YM3AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1440x216 with 1 Axes>"
]
diff --git a/notebooks/end2end_example/finn-design-flow-example.svg b/notebooks/end2end_example/finn-design-flow-example.svg
index 04f568c1f77cdbdc0dfbc8cd50b76f4c5d45d954..fa36be96c52dad740cef18bca9b81903e1fba3db 100755
--- a/notebooks/end2end_example/finn-design-flow-example.svg
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diff --git a/notebooks/end2end_example/tfc_end2end_example.ipynb b/notebooks/end2end_example/tfc_end2end_example.ipynb
index 6aebd9bd17d4d9c5b012a193a858d1c2df22ce1e..6b7c6308772435bf213715534438a323e0705841 100644
--- a/notebooks/end2end_example/tfc_end2end_example.ipynb
+++ b/notebooks/end2end_example/tfc_end2end_example.ipynb
@@ -84,7 +84,9 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/workspace/brevitas/brevitas_examples/bnn_pynq/models/TFC.py:80: 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",
+ "Downloading: \"https://github.com/Xilinx/brevitas/releases/download/bnn_pynq-r0/tfc_1w1a-ff8140dc.pth\" to /home/maltanar/.cache/torch/checkpoints/tfc_1w1a-ff8140dc.pth\n",
+ "100%|██████████| 249052/249052 [00:00<00:00, 759439.97it/s]\n",
+ "/workspace/brevitas/brevitas_examples/bnn_pynq/models/FC.py:83: 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 = 2.0 * x - torch.tensor([1.0], device=x.device)\n"
]
}
@@ -132,7 +134,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f7cc4290940>"
+ "<IPython.lib.display.IFrame at 0x7f1b8e6db128>"
]
},
"execution_count": 3,
@@ -293,7 +295,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f7c6c567f28>"
+ "<IPython.lib.display.IFrame at 0x7f1add273cf8>"
]
},
"execution_count": 6,
@@ -402,7 +404,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f7c6c0bf898>"
+ "<IPython.lib.display.IFrame at 0x7f1adc25df60>"
]
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@@ -456,7 +458,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f7c6c0e5c18>"
+ "<IPython.lib.display.IFrame at 0x7f1adc2548d0>"
]
},
"execution_count": 9,
@@ -532,7 +534,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7fe1346f1080>"
+ "<IPython.lib.display.IFrame at 0x7f1adc254630>"
]
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"execution_count": 10,
@@ -592,7 +594,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7fe1ad0b6e48>"
+ "<IPython.lib.display.IFrame at 0x7f1add27eba8>"
]
},
"execution_count": 11,
@@ -627,7 +629,7 @@
"text": [
"\n",
"Stopping http://0.0.0.0:8081\n",
- "Serving '/tmp/finn_dev_jakobap/dataflow_partition_pbrjefjg/df_model.onnx' at http://0.0.0.0:8081\n"
+ "Serving '/tmp/finn_dev_maltanar/dataflow_partition0_8y5bzo4x/df_model.onnx' at http://0.0.0.0:8081\n"
]
},
{
@@ -644,7 +646,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7fe1346f3550>"
+ "<IPython.lib.display.IFrame at 0x7f1b7af8d240>"
]
},
"execution_count": 12,
@@ -679,7 +681,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Folding and Datawidth Converter, FIFO and TLastMarker Insertion <a id='folding'></a>\n",
+ "### Folding: Adjusting the Parallelism <a id='folding'></a>\n",
"\n",
"*Folding* in FINN describes how much a layer is time-multiplexed in terms of execution resources. There are several *folding factors* for each layer, controlled by the PE (parallelization over outputs) and SIMD (parallelization over inputs) parameters as described by the original [FINN paper](https://arxiv.org/pdf/1612.07119). The higher the PE and SIMD values are set, the faster the generated accelerator will run, and the more FPGA resources it will consume. \n",
"\n",
@@ -717,6 +719,7 @@
" 'inputDataType': ('s', True, ''),\n",
" 'weightDataType': ('s', True, ''),\n",
" 'outputDataType': ('s', True, ''),\n",
+ " 'accDataType': ('s', False, 'INT32'),\n",
" 'binaryXnorMode': ('i', False, 0),\n",
" 'noActivation': ('i', False, 0),\n",
" 'numInputVectors': ('ints', False, [1]),\n",
@@ -731,11 +734,13 @@
" 'ip_vlnv': ('s', False, ''),\n",
" 'exec_mode': ('s', False, ''),\n",
" 'cycles_rtlsim': ('i', False, 0),\n",
+ " 'cycles_estimate': ('i', False, 0),\n",
" 'rtlsim_trace': ('s', False, ''),\n",
" 'res_estimate': ('s', False, ''),\n",
" 'res_hls': ('s', False, ''),\n",
" 'res_synth': ('s', False, ''),\n",
" 'rtlsim_so': ('s', False, ''),\n",
+ " 'partition_id': ('i', False, 0),\n",
" 'inFIFODepth': ('i', False, 2),\n",
" 'outFIFODepth': ('i', False, 2)}"
]
@@ -797,36 +802,14 @@
"metadata": {},
"source": [
"Besides PE and SIMD three other node attributes are set. `ram_style` specifies how the weights are to be stored (BRAM, LUTRAM, and so on). It can be selected explicitly or with the option `auto` you can let Vivado decide.\n",
- "`inFIFODepth` and `outFIFODepth` specifies the FIFO depths that is needed by the node from the surrounding FIFOs. These attributes are used in the transformation 'InsertFIFO' to insert the appropriate FIFOs between the nodes.\n",
+ "`inFIFODepth` and `outFIFODepth` specifies the FIFO depths that is needed by the node from the surrounding FIFOs. These attributes are used in the transformation 'InsertFIFO' to insert the appropriate FIFOs between the nodes, which will be automatically called as part of the hardware build process.\n",
"\n",
- "But before FIFOs can be added, it must be determined whether datawidth converters (DWC) are required and they must be inserted correctly. Because by setting the folding, the folded output shape of one node may not match the folded input shape of the next node. \n",
- "\n",
- "In the following, first DWCs and then FIFOs are inserted using the corresponding transformations in FINN."
+ "In previous versions of FINN we had to call transformations to insert data width converters, FIFOs and `TLastMarker` manually at this step. This is no longer needed, as all this is taken care of by the `ZynqBuild` or `VitisBuild` transformations."
]
},
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "from finn.transformation.fpgadataflow.insert_dwc import InsertDWC\n",
- "from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO\n",
- "\n",
- "model = model.transform(InsertDWC())\n",
- "model = model.transform(InsertFIFO())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally, we will run the `InsertTLastMarker` transformation to get a `TLastMarker` node at the output of this graph, which is necessary to run the DMA engines correctly. Using netron we can observe that now the nodes contain the set folding, if necessary a DWC is inserted, inbetween the nodes are FIFOs inserted and the last node is the `TLastMarker` node we insert in the following."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
"metadata": {
"scrolled": true
},
@@ -854,17 +837,15 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7fe135b84780>"
+ "<IPython.lib.display.IFrame at 0x7f1adc266ba8>"
]
},
- "execution_count": 17,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker\n",
- "model = model.transform(InsertTLastMarker())\n",
"model.save(build_dir+\"/tfc_w1_a1_set_folding_factors.onnx\")\n",
"showInNetron(build_dir+\"/tfc_w1_a1_set_folding_factors.onnx\")"
]
@@ -880,24 +861,23 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3. Vivado HLS and IPI <a id='vivado'></a>\n",
- "* [Generating HLS Code](#hls_per_layer)\n",
- "* [Synthesizing HLS to IP Blocks](#hls_synth)\n",
- "* [IP Stitching](#ip_stitching)\n",
+ "## 3. Hardware Build <a id='vivado'></a>\n",
+ "\n",
+ "We're finally ready to start generating hardware from our network. Depending on whether you want to target a Zynq or Alveo platform, FINN offers two transformations to build the accelerator, integrate into an appropriate shell and build a bitfile. These are `ZynqBuild` and `VitisBuild` for Zynq and Alveo, respectively. In this notebook we'll demonstrate the `ZynqBuild` as these boards are more common and it's much faster to complete bitfile generation for the smaller FPGAs found on them.\n",
"\n",
"As we will be dealing with FPGA synthesis tools in these tasks, we'll define two helper variables that describe the Xilinx FPGA part name and the PYNQ board name that we are targeting."
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "dict_keys(['Ultra96', 'Pynq-Z1', 'Pynq-Z2', 'ZCU104'])\n"
+ "dict_keys(['Ultra96', 'Pynq-Z1', 'Pynq-Z2', 'ZCU102', 'ZCU104'])\n"
]
}
],
@@ -909,7 +889,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
@@ -923,342 +903,53 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Generating HLS Code <a id='hls_per_layer'></a>\n",
- "This section deals with the generation of an IP block from the different layers. These can then be stitched to a block design that corresponds to the complete model. The single conversion into IP blocks allows a good transparency and we can check the functionality of each IP block and compare it with the behaviour of the corresponding ONNX node. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Two transformations are required to generate HLS IP blocks for each layer: \n",
- "* `PrepareIP` which generates the HLS C++ code for the node and a tcl-script which starts the HLS synthesis and exports the design as IP. \n",
- "* `HLSSynthIP` which passes the tcl-script to Vivado HLS and thus performs the actual IP generation. \n",
- "\n",
- "We start off by giving unique node names using the basic transformation `GiveUniqueNodeNames`, and then proceed with the HLS C++ code generation with `PrepareIP`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [],
- "source": [
- "model = ModelWrapper(build_dir+\"/tfc_w1_a1_set_folding_factors.onnx\")\n",
- "model = model.transform(GiveUniqueNodeNames())\n",
- "\n",
- "from finn.transformation.fpgadataflow.prepare_ip import PrepareIP\n",
- "model = model.transform(PrepareIP(fpga_part, target_clk_ns))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Synthesizing HLS to IP Blocks <a id='hls_synth'></a>\n",
- "\n",
- "Now that we have generated the HLS code for each layer, we can call the `HLSSynthIP` transformation to convert the generated HLS into Vivado IP blocks. **As this involves calling HLS synthesis, this transformation will run for some time (several minutes).**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [],
- "source": [
- "from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP\n",
- "\n",
- "model = model.transform(HLSSynthIP())\n",
- "model.save(build_dir+\"/tfc_w1_a1_ipgen.onnx\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Each `StreamingFCLayer_Batch` node now has new attributes which can be examined more closely with netron."
+ "In previous versions of FINN, we had to manually go through several steps to generate HLS code, stitch IP, create a PYNQ project and run synthesis. All these steps are now performed by the `ZynqBuild` transform (or the `VitisBuild` transform for Alveo). **As this involves calling HLS synthesis and Vivado synthesis, this transformation will run for some time (up to half an hour depending on your PC).**"
]
},
{
"cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Stopping http://0.0.0.0:8081\n",
- "Serving '/workspace/finn/tfc_w1_a1_ipgen.onnx' at http://0.0.0.0:8081\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- " <iframe\n",
- " width=\"100%\"\n",
- " height=\"400\"\n",
- " src=\"http://0.0.0.0:8081/\"\n",
- " frameborder=\"0\"\n",
- " allowfullscreen\n",
- " ></iframe>\n",
- " "
- ],
- "text/plain": [
- "<IPython.lib.display.IFrame at 0x7fe1346f7588>"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "showInNetron(build_dir+\"/tfc_w1_a1_ipgen.onnx\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "There are two additional attributes: \n",
- "* `code_gen_dir_ipgen` which contains the directory path where all the files generated by the ipgen transformations are stored\n",
- "* `ipgen_path` which contains the path to the project directory in which the generated IP block is stored\n",
- "\n",
- "We can further investigate which files are produced by taking a look in this directory. For example for the first StreamingFCLayer_Batch node."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "StreamingFCLayer_Batch_0_memstream.v thresh.h\r\n",
- "hls_syn_StreamingFCLayer_Batch_0.tcl top_StreamingFCLayer_Batch_0.cpp\r\n",
- "ipgen.sh\t\t\t vivado_hls.log\r\n",
- "memblock_0.dat\t\t\t weights.npy\r\n",
- "project_StreamingFCLayer_Batch_0\r\n"
- ]
- }
- ],
- "source": [
- "fc0w = getCustomOp(model.graph.node[1])\n",
- "code_gen_dir = fc0w.get_nodeattr(\"code_gen_dir_ipgen\")\n",
- "!ls {code_gen_dir}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Directory *project_StreamingFCLayer_Batch_0* contains the project created by Vivado HLS into which the IP Block is exported, along with other files generated by Vivado HLS. If we compare it to the above visualization of the network with netron, this is exactly the name of the folder stored in the node attribute `ipgen_path`. The .cpp code that is passed to Vivado HLS can be found in the file *top_StreamingFCLayer_Batch_0.cpp*. The file *thresh.h* belongs to that as well, it contains the value for the thresholds. The weights are stored as .npy file and as .dat file (*memblock_0.dat*). *vivado_hls.log* is the log file from Vivado HLS. Besides these files, the folder contains *ipgen.sh* and *hls_syn_StreamingFCLayer_Batch_0.tcl* and because we use the StreamingFCLayer in \"decoupled\" mode a verilog wrapper (*StreamingFCLayer_Batch_0_memstream.v*) is produced, for more details on \"decoupled\" and \"const\" mode please see on the [FINN readthedocs website](https://finn.readthedocs.io/) under Internals. \n",
- "\n",
- "In the following we take a closer look at the two generated scripts. We start with *ipgen.sh*."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "#!/bin/bash \r\n",
- "cd /tmp/finn_dev_jakobap/code_gen_ipgen_StreamingFCLayer_Batch_0_edb__5oc\r\n",
- "vivado_hls /tmp/finn_dev_jakobap/code_gen_ipgen_StreamingFCLayer_Batch_0_edb__5oc/hls_syn_StreamingFCLayer_Batch_0.tcl\r\n",
- "cd /workspace/finn\r\n"
- ]
- }
- ],
- "source": [
- "shell_script = code_gen_dir + \"/ipgen.sh\"\n",
- "!cat {shell_script}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The script consists only of two framing `cd` commands and a command to pass the tcl script to *vivado_hls*. The directory has to be changed to create the files in the correct folder and will then be changed back to the original directory. \n",
- "\n",
- "Below is the tcl script which is passed to *vivado_hls*."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "\r\n",
- "set config_proj_name project_StreamingFCLayer_Batch_0\r\n",
- "puts \"HLS project: $config_proj_name\"\r\n",
- "set config_hwsrcdir \"/tmp/finn_dev_jakobap/code_gen_ipgen_StreamingFCLayer_Batch_0_edb__5oc\"\r\n",
- "puts \"HW source dir: $config_hwsrcdir\"\r\n",
- "set config_proj_part \"xc7z020clg400-1\"\r\n",
- "\r\n",
- "set config_bnnlibdir \"/workspace/finn-hlslib\"\r\n",
- "\r\n",
- "set config_toplevelfxn \"StreamingFCLayer_Batch_0\"\r\n",
- "set config_clkperiod 10\r\n",
- "\r\n",
- "open_project $config_proj_name\r\n",
- "add_files $config_hwsrcdir/top_StreamingFCLayer_Batch_0.cpp -cflags \"-std=c++0x -I$config_bnnlibdir\"\r\n",
- "\r\n",
- "set_top $config_toplevelfxn\r\n",
- "open_solution sol1\r\n",
- "set_part $config_proj_part\r\n",
- "\r\n",
- "config_interface -m_axi_addr64\r\n",
- "config_rtl -auto_prefix\r\n",
- "\r\n",
- "\r\n",
- "create_clock -period $config_clkperiod -name default\r\n",
- "csynth_design\r\n",
- "export_design -format ip_catalog\r\n",
- "exit 0\r\n"
+ "/workspace/finn/src/finn/transformation/infer_data_layouts.py:107: UserWarning: Assuming 2D input is NC\n",
+ " warnings.warn(\"Assuming 2D input is NC\")\n"
]
}
],
"source": [
- "tcl_script = code_gen_dir + \"/hls_syn_StreamingFCLayer_Batch_0.tcl\"\n",
- "!cat {tcl_script}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In the first part of the script the project is configured. For example the FPGA part and the clock are set. Then the project is opened and the files are added. The toplevel function is set and after creating a clock, the design is first synthesized with `csynth` and then exported as an IP block.\n",
- "\n",
- "Now that all IP blocks are in place, they can be stitched together to create an IP design that matches the ONNX model. This is covered in the next section."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### IP Stitching <a id='ip_stitching'></a>\n",
- "\n",
- "We now have IP blocks for each of our layers, and will stitch them together into a larger IP that implements the whole network using the `CreateStitchedIP` transformation. Bear in mind that this transformation can only be applied on a graph that only contains HLS nodes that already have been through the `HLSSynthIP` transformation, which is the last step we performed. Prior to calling IP stitching, we'll also use the `ReplaceVerilogRelPaths` transformation to convert any relative `$readmemh` paths in the generated IP blocks to absolute ones, which prevents errors later on. **This step invokes Vivado and may take a few minutes to run.**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [],
- "source": [
- "from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP\n",
- "from finn.transformation.fpgadataflow.replace_verilog_relpaths import ReplaceVerilogRelPaths\n",
- "model = ModelWrapper(build_dir+\"/tfc_w1_a1_ipgen.onnx\")\n",
- "model = model.transform(ReplaceVerilogRelPaths())\n",
- "model = model.transform(CreateStitchedIP(fpga_part))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If you examine the nodes themselves on the transformed model you won't see a difference, because the IP stitching adds model-level metadata to the graph. This can be accessed using the `.model.metadata_props`, the `get_metadata_prop` function in `ModelWrapper`, or by clicking on the global input/output tensors in Netron."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[key: \"vivado_stitch_proj\"\n",
- "value: \"/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl\"\n",
- ", key: \"vivado_stitch_vlnv\"\n",
- "value: \"xilinx_finn:finn:finn_design:1.0\"\n",
- ", key: \"wrapper_filename\"\n",
- "value: \"/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl/finn_vivado_stitch_proj.srcs/sources_1/bd/finn_design/hdl/finn_design_wrapper.v\"\n",
- "]"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model.model.metadata_props"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl'"
- ]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model.get_metadata_prop(\"vivado_stitch_proj\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If you navigate to the folder above (remember the /tmp/finn_xxx folder is mounted on the host as well as inside Docker) you can open the Vivado project (.xpr) file there using Vivado, and view the following stitched IP block design:"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
+ "from finn.transformation.fpgadataflow.make_zynq_proj import ZynqBuild\n",
+ "model = ModelWrapper(build_dir+\"/tfc_w1_a1_set_folding_factors.onnx\")\n",
+ "model = model.transform(ZynqBuild(platform = pynq_board, period_ns = target_clk_ns))"
]
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
- "model.save(build_dir+\"/tfc_w1_a1_ipstitch.onnx\")"
+ "model.save(build_dir + \"/tfc_w1_a1_post_synthesis.onnx\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "At this point, one could take the generated stitched IP and integrate it into your own project using Vivado IP Integrator if desired. Here, we will continue the tutorial by assuming that we want to do a stand-alone deployment for this accelerator for a PYNQ board."
+ "### Examining the generated outputs <a id='gen_outputs'></a>\n",
+ "\n",
+ "TODO"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4. PYNQ hardware generation and deployment <a id='hw_test'></a>\n",
+ "## 4. PYNQ deployment <a id='hw_test'></a>\n",
"\n",
- "* [Inserting the IP into a PYNQ Overlay Shell](#pynq_shell)\n",
- "* [Synthesis, Place and Route](#synth_pl_ro)\n",
- "* [Driver Generation](#driver_gen)\n",
"* [Deployment and Remote Execution](#deploy)\n",
"* [Throughput Test on PYNQ Board](#throughput)\n",
"\n",
@@ -1266,360 +957,6 @@
"We are almost done preparing our hardware design. We'll now put it in a form suitable for use as a PYNQ overlay, synthesize and deploy it."
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Inserting the IP into a PYNQ Overlay Shell <a id='pynq_shell'></a>\n",
- "\n",
- "To deploy our accelerator on a PYNQ platform, it needs to be put inside an appropriate *shell* that bridges it with the interfaces that the underlying system exposes. FINN makes it easy to create a PYNQ-compatible overlay by inserting the stitched IP into an appropriate PYNQ shell with the `MakePYNQProject` transformation, and view the created PYNQ shell project directory using the `metadata_props`. **This invokes Vivado and may take a few minutes to run.**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[key: \"vivado_stitch_proj\"\n",
- "value: \"/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl\"\n",
- ", key: \"vivado_stitch_vlnv\"\n",
- "value: \"xilinx_finn:finn:finn_design:1.0\"\n",
- ", key: \"wrapper_filename\"\n",
- "value: \"/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl/finn_vivado_stitch_proj.srcs/sources_1/bd/finn_design/hdl/finn_design_wrapper.v\"\n",
- ", key: \"vivado_pynq_proj\"\n",
- "value: \"/tmp/finn_dev_jakobap/vivado_pynq_proj_ljn53hfs\"\n",
- ", key: \"vivado_synth_rpt\"\n",
- "value: \"/tmp/finn_dev_jakobap/vivado_pynq_proj_ljn53hfs/synth_report.xml\"\n",
- "]"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from finn.transformation.fpgadataflow.make_pynq_proj import MakePYNQProject\n",
- "model = ModelWrapper(build_dir+\"/tfc_w1_a1_ipstitch.onnx\")\n",
- "model = model.transform(MakePYNQProject(pynq_board))\n",
- "model.model.metadata_props"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "ip_config.tcl\t resizer.cache\tresizer.ip_user_files resizer.xpr\r\n",
- "make_project.sh resizer.hw\tresizer.srcs\t synth_project.sh\r\n"
- ]
- }
- ],
- "source": [
- "! ls {model.get_metadata_prop(\"vivado_pynq_proj\")}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If we open the created Vivado project (.xpr) under the `vivado_pynq_proj` directory above, we can see the system-level block design as below, with the FINN-generated part of the design highlighted. Various other components, such as the DMA engine and data width converters, have also been instantiated.\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [],
- "source": [
- "model.save(build_dir + \"/tfc_w1_a1_pynq_project.onnx\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Synthesis, Place and Route <a id='synth_pl_ro'></a>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We are now ready for the final hardware generation step, which is synthesis, place and route to generate an FPGA bitfile. This can be done by either running the `synth_project.sh` script in the generated Vivado PYNQ project directory inside Docker, or by executing the `SynthPYNQProject` transformation. **This step involves launching Vivado for synthesis and may take a few hours.**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[key: \"vivado_stitch_proj\"\n",
- "value: \"/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl\"\n",
- ", key: \"vivado_stitch_vlnv\"\n",
- "value: \"xilinx_finn:finn:finn_design:1.0\"\n",
- ", key: \"wrapper_filename\"\n",
- "value: \"/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl/finn_vivado_stitch_proj.srcs/sources_1/bd/finn_design/hdl/finn_design_wrapper.v\"\n",
- ", key: \"vivado_pynq_proj\"\n",
- "value: \"/tmp/finn_dev_jakobap/vivado_pynq_proj_ljn53hfs\"\n",
- ", key: \"vivado_synth_rpt\"\n",
- "value: \"/tmp/finn_dev_jakobap/vivado_pynq_proj_ljn53hfs/synth_report.xml\"\n",
- ", key: \"bitfile\"\n",
- "value: \"/tmp/finn_dev_jakobap/vivado_pynq_proj_ljn53hfs/resizer.bit\"\n",
- "]"
- ]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from finn.transformation.fpgadataflow.synth_pynq_proj import SynthPYNQProject\n",
- "model = ModelWrapper(build_dir + \"/tfc_w1_a1_pynq_project.onnx\")\n",
- "model = model.transform(SynthPYNQProject())\n",
- "model.model.metadata_props"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [],
- "source": [
- "model.save(build_dir + \"/tfc_w1_a1_post_synthesis.onnx\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Driver Generation <a id='driver_gen'></a>\n",
- "\n",
- "Now that we have synthesized a bitfile for our network, we will generate some Python code for PYNQ that will act as the driver for this bitfile, package everything into a deployment folder and copy that to our PYNQ board."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [],
- "source": [
- "from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver\n",
- "model = ModelWrapper(build_dir + \"/tfc_w1_a1_post_synthesis.onnx\")\n",
- "model = model.transform(MakePYNQDriver(platform=\"zynq\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The generated driver is placed in a folder that is indicated by the `pynq_driver_dir` top-level metadata. We can examine the generated PYNQ Python driver code as follows:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r\n",
- "import argparse\r\n",
- "\r\n",
- "from pynq import Overlay\r\n",
- "import numpy as np\r\n",
- "from pynq import allocate\r\n",
- "import time\r\n",
- "from finn.util.data_packing import (\r\n",
- " finnpy_to_packed_bytearray,\r\n",
- " packed_bytearray_to_finnpy\r\n",
- ")\r\n",
- "from finn.core.datatype import DataType\r\n",
- "\r\n",
- "class FINNAccelDriver():\r\n",
- " def __init__(self, N, bitfile):\r\n",
- " \"\"\"Instantiate the FINN accelerator driver.\r\n",
- " Gets batchsize (N) as integer and path to bitfile as string.\"\"\"\r\n",
- " self.N = N\r\n",
- " # input FINN DataType\r\n",
- " self.idt = DataType.BINARY\r\n",
- " # output FINN DataType\r\n",
- " self.odt = DataType.UINT32\r\n",
- " # input and output shapes\r\n",
- " self.ishape_normal = (N, 784)\r\n",
- " self.oshape_normal = (N, 10)\r\n",
- " self.ishape_folded = (N, 16, 49)\r\n",
- " self.oshape_folded = (N, 1, 10)\r\n",
- " self.ishape_packed = (N, 16, 7) # datatype np.uint8\r\n",
- " self.oshape_packed = (N, 1, 40) # datatype np.uint8\r\n",
- " # load bitfile and set up accelerator\r\n",
- " self.ol = Overlay(bitfile)\r\n",
- " self.dma = self.ol.axi_dma_0\r\n",
- " self.ctrl_regs = self.ol.resize_accel_0\r\n",
- " # neuron folding factor of output = iterations per sample\r\n",
- " self.itersPerSample = self.oshape_packed[-2]\r\n",
- " # AXI lite register offset for number of iterations\r\n",
- " # used by TLastMarker to signal end of transmission for AXI CDMA\r\n",
- " self.REG_OFFSET_NUM_ITERS = 0x10\r\n",
- " # set up TLastMarker with correct num. samples\r\n",
- " self.ctrl_regs.write(self.REG_OFFSET_NUM_ITERS, self.N*self.itersPerSample)\r\n",
- "\r\n",
- " # allocate a PYNQ buffer for the packed input and buffer\r\n",
- " self.ibuf_packed_device = allocate(shape=self.ishape_packed, dtype=np.uint8)\r\n",
- " self.obuf_packed_device = allocate(shape=self.oshape_packed, dtype=np.uint8)\r\n",
- "\r\n",
- " def fold_input(self, ibuf_normal):\r\n",
- " \"\"\"Reshapes input in desired shape.\r\n",
- " Gets input data (ibuf_normal), checks if data is in expected normal shape.\r\n",
- " Returns folded input.\"\"\"\r\n",
- " # ensure that shape is as expected\r\n",
- " assert ibuf_normal.shape == self.ishape_normal\r\n",
- " # convert to folded form\r\n",
- " ibuf_folded = ibuf_normal.reshape(self.ishape_folded)\r\n",
- " return ibuf_folded\r\n",
- "\r\n",
- " def pack_input(self, ibuf_folded):\r\n",
- " \"\"\"Packs folded input and reverses both SIMD dim and endianness.\r\n",
- " Gets input data in folded shape and returns packed input data.\"\"\"\r\n",
- " ibuf_packed = finnpy_to_packed_bytearray(\r\n",
- " ibuf_folded, self.idt, reverse_endian=True, reverse_inner=True\r\n",
- " )\r\n",
- " return ibuf_packed\r\n",
- "\r\n",
- " def unpack_output(self, obuf_packed):\r\n",
- " \"\"\"Unpacks the packed output buffer from accelerator.\r\n",
- " Gets packed output and returns output data in folded shape.\"\"\"\r\n",
- " obuf_folded = packed_bytearray_to_finnpy(\r\n",
- " obuf_packed, self.odt, self.oshape_folded, reverse_endian=True, reverse_inner=True\r\n",
- " )\r\n",
- " return obuf_folded\r\n",
- "\r\n",
- " def unfold_output(self, obuf_folded):\r\n",
- " \"\"\"Unfolds output data to normal shape.\r\n",
- " Gets folded output data and returns output data in normal shape.\"\"\"\r\n",
- " obuf_normal = obuf_folded.reshape(self.oshape_normal)\r\n",
- " return obuf_normal\r\n",
- "\r\n",
- " def copy_input_data_to_device(self, data):\r\n",
- " \"\"\"Copies given input data to PYNQ buffer.\"\"\"\r\n",
- " np.copyto(self.ibuf_packed_device, data)\r\n",
- "\r\n",
- " def execute(self):\r\n",
- " \"\"\"Executes accelerator by setting up the DMA and\r\n",
- " waiting until all transfers complete. Uses only member variables and\r\n",
- " returns nothing.\"\"\"\r\n",
- " dma = self.dma\r\n",
- " dma.sendchannel.transfer(self.ibuf_packed_device)\r\n",
- " dma.recvchannel.transfer(self.obuf_packed_device)\r\n",
- " dma.sendchannel.wait()\r\n",
- " dma.recvchannel.wait()\r\n",
- "\r\n",
- "\r\n",
- "if __name__ == \"__main__\":\r\n",
- " parser = argparse.ArgumentParser(description='Set exec mode, batchsize N, bitfile name, inputfile name and outputfile name')\r\n",
- " parser.add_argument('--exec_mode', help='Please select functional verification (\"execute\") or throughput test (\"throughput_test\")', default=\"execute\")\r\n",
- " parser.add_argument('--batchsize', help='number of samples for inference', type=int, default=1)\r\n",
- " parser.add_argument('--bitfile', help='name of bitfile (i.e. \"resizer.bit\")', default=\"resizer.bit\")\r\n",
- " parser.add_argument('--inputfile', help='name of input npy file (i.e. \"input.npy\")', default=\"input.npy\")\r\n",
- " parser.add_argument('--outputfile', help='name of output npy file (i.e. \"output.npy\")', default=\"output.npy\")\r\n",
- " # parse arguments\r\n",
- " args = parser.parse_args()\r\n",
- " exec_mode = args.exec_mode\r\n",
- " N = args.batchsize\r\n",
- " bitfile = args.bitfile\r\n",
- " inputfile = args.inputfile\r\n",
- " outputfile = args.outputfile\r\n",
- "\r\n",
- " # instantiate FINN accelerator driver and pass batchsize and bitfile\r\n",
- " finnDriver = FINNAccelDriver(N, bitfile)\r\n",
- "\r\n",
- " # for the remote execution the data from the input npy file has to be loaded,\r\n",
- " # packed and copied to the PYNQ buffer\r\n",
- " if exec_mode == \"execute\":\r\n",
- " # load desired input .npy file\r\n",
- " ibuf_normal = np.load(inputfile)\r\n",
- " ibuf_folded = finnDriver.fold_input(ibuf_normal)\r\n",
- " ibuf_packed = finnDriver.pack_input(ibuf_folded)\r\n",
- " finnDriver.copy_input_data_to_device(ibuf_packed)\r\n",
- " elif exec_mode != \"throughput_test\":\r\n",
- " raise Exception(\"Exec mode has to be set to remote_pynq or throughput_test\")\r\n",
- "\r\n",
- " # for the throughput test the runtime of the network has to be measured\r\n",
- " if exec_mode == \"throughput_test\":\r\n",
- " # measure runtime of network\r\n",
- " start = time.time()\r\n",
- " # dictionary for results of throughput test\r\n",
- " res={}\r\n",
- "\r\n",
- " # execute accelerator\r\n",
- " finnDriver.execute()\r\n",
- "\r\n",
- " # measure run time and fill dictionary with results of the throughput test\r\n",
- " if exec_mode == \"throughput_test\":\r\n",
- " end = time.time()\r\n",
- " runtime = end - start\r\n",
- " res[\"runtime[ms]\"] = runtime*1000\r\n",
- " res[\"throughput[images/s]\"] = N / runtime\r\n",
- " res[\"DRAM_in_bandwidth[Mb/s]\"] = np.prod(finnDriver.ishape_packed)*0.000001 / runtime\r\n",
- " res[\"DRAM_out_bandwidth[Mb/s]\"] = np.prod(finnDriver.oshape_packed)*0.000001 / runtime\r\n",
- " file = open(\"nw_metrics.txt\", \"w\")\r\n",
- " file.write(str(res))\r\n",
- " file.close()\r\n",
- "\r\n",
- " # if execution is selected unpack, unfold and save output to output npy file\r\n",
- " else:\r\n",
- " obuf_folded = finnDriver.unpack_output(finnDriver.obuf_packed_device)\r\n",
- " obuf_normal = finnDriver.unfold_output(obuf_folded)\r\n",
- " np.save(outputfile, obuf_normal)\r\n",
- "\r\n",
- "\r\n"
- ]
- }
- ],
- "source": [
- "driver_dir = model.get_metadata_prop(\"pynq_driver_dir\")\n",
- "! cat {driver_dir}/driver.py"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can see that in the generated driver a class is implemented which implements the FINN accelerator. The constructor gets the batchsize (N) as integer and the bitfile as string. It also contains the expected input/output shapes, and takes care of the instantiation of the accelerator by loading the bitfile and setting up dma and buffer. Several member functions take care of the data folding and packing. The function `copy_input_data_to_device` copies the input data into the PYNQ buffer and `execute` sets up the dma channels and waits until the transfer is completed. This class is used in the main function. But first the arguments are parsed, which are passed to the script. The driver can be used in two modes: \"execute\" and \"throughput_test\". By default all arguments are set to \"execute\" mode. In this mode the batch size is 1, and the passed files are set to the names used by the FINN transformations.\n",
- "\n",
- "In the \"execute\" mode works as follows:\n",
- "1. the data is loaded from the \"inputfile\"\n",
- "2. the data is folded using `fold_input`\n",
- "3. the data is packed using `pack_input`\n",
- "4. the data is copied to the device using `copy_input_data_to_device`\n",
- "5. FINNAccelDriver is executed using `execute`\n",
- "6. the data is unpacked using `unpack_output`\n",
- "7. the data is unfolded using `unfold_output`\n",
- "8. the data is stored in the \"outputfile\"\n",
- "\n",
- "If \"throughput_test\" is selected as `exec_mode`, no actual data needs to be loaded. The batchsize N should be set to a high value (i.e. 1000) and a time measurement is implemented in python. An empty dictionary (`res`) is created and after running the accelerator with the measured runtime it is filled with the metrics and saved in a .txt file.\n",
- "\n",
- "You can build your own applications around the accelerator by modifying the driver, or use the remote execution capabilities that FINN provides just to check if it is working, which will be our next step."
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -1631,12 +968,12 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ\n",
- "ip = \"192.168.3.1\"\n",
+ "ip = \"192.168.2.99\"\n",
"port = \"22\"\n",
"username = \"xilinx\"\n",
"password = \"xilinx\"\n",
@@ -1654,46 +991,44 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[key: \"vivado_stitch_proj\"\n",
- "value: \"/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl\"\n",
- ", key: \"vivado_stitch_vlnv\"\n",
- "value: \"xilinx_finn:finn:finn_design:1.0\"\n",
- ", key: \"wrapper_filename\"\n",
- "value: \"/tmp/finn_dev_jakobap/vivado_stitch_proj_oa43bqzl/finn_vivado_stitch_proj.srcs/sources_1/bd/finn_design/hdl/finn_design_wrapper.v\"\n",
+ "[key: \"pynq_driver_dir\"\n",
+ "value: \"/tmp/finn_dev_maltanar/pynq_driver_ilfzbags\"\n",
", key: \"vivado_pynq_proj\"\n",
- "value: \"/tmp/finn_dev_jakobap/vivado_pynq_proj_ljn53hfs\"\n",
- ", key: \"vivado_synth_rpt\"\n",
- "value: \"/tmp/finn_dev_jakobap/vivado_pynq_proj_ljn53hfs/synth_report.xml\"\n",
+ "value: \"/tmp/finn_dev_maltanar/vivado_zynq_proj_erwcr5nk\"\n",
", key: \"bitfile\"\n",
- "value: \"/tmp/finn_dev_jakobap/vivado_pynq_proj_ljn53hfs/resizer.bit\"\n",
- ", key: \"pynq_driver_dir\"\n",
- "value: \"/tmp/finn_dev_jakobap/pynq_driver_j_9suyqm\"\n",
+ "value: \"/tmp/finn_dev_maltanar/vivado_zynq_proj_erwcr5nk/resizer.bit\"\n",
+ ", key: \"hw_handoff\"\n",
+ "value: \"/tmp/finn_dev_maltanar/vivado_zynq_proj_erwcr5nk/resizer.hwh\"\n",
+ ", key: \"vivado_synth_rpt\"\n",
+ "value: \"/tmp/finn_dev_maltanar/vivado_zynq_proj_erwcr5nk/synth_report.xml\"\n",
+ ", key: \"platform\"\n",
+ "value: \"zynq-iodma\"\n",
", key: \"pynq_ip\"\n",
- "value: \"51.37.47.42\"\n",
+ "value: \"192.168.2.99\"\n",
", key: \"pynq_port\"\n",
- "value: \"23\"\n",
+ "value: \"22\"\n",
", key: \"pynq_username\"\n",
"value: \"xilinx\"\n",
", key: \"pynq_password\"\n",
- "value: \"x1l1nx_f!nn\"\n",
+ "value: \"xilinx\"\n",
", key: \"pynq_target_dir\"\n",
"value: \"/home/xilinx/finn_tfc_end2end_example\"\n",
", key: \"pynq_deployment_dir\"\n",
- "value: \"/tmp/finn_dev_jakobap/pynq_deployment_962qxwkv\"\n",
+ "value: \"/tmp/finn_dev_maltanar/pynq_deployment_eyiu4sxk\"\n",
", key: \"pynq_deploy_dir\"\n",
- "value: \"/tmp/finn_dev_jakobap/pynq_deployment_962qxwkv\"\n",
+ "value: \"/tmp/finn_dev_maltanar/pynq_deployment_eyiu4sxk\"\n",
", key: \"exec_mode\"\n",
"value: \"remote_pynq\"\n",
"]"
]
},
- "execution_count": 38,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -1704,63 +1039,30 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "/home/xilinx/finn_tfc_end2end_example/pynq_deployment_26e8h5jo:\r\n",
- "total 4276\r\n",
- "-rw-r--r-- 1 xilinx xilinx 6363 May 7 10:35 driver.py\r\n",
- "drwxr-xr-x 4 xilinx xilinx 4096 May 7 10:35 finn\r\n",
- "-rw-r--r-- 1 xilinx xilinx 3264 May 7 10:55 input.npy\r\n",
- "-rw-r--r-- 1 root root 172 May 7 10:37 nw_metrics.txt\r\n",
- "-rw-r--r-- 1 root root 120 May 7 10:55 output.npy\r\n",
- "-rw-r--r-- 1 xilinx xilinx 4045675 May 7 10:35 resizer.bit\r\n",
- "-rw-r--r-- 1 xilinx xilinx 302015 May 7 10:35 resizer.hwh\r\n",
- "-rw-r--r-- 1 root root 32 May 7 10:55 sds_trace_data.dat\r\n",
- "\r\n",
- "/home/xilinx/finn_tfc_end2end_example/pynq_deployment_962qxwkv:\r\n",
- "total 4260\r\n",
- "-rw-r--r-- 1 xilinx xilinx 6363 May 7 17:44 driver.py\r\n",
- "drwxr-xr-x 4 xilinx xilinx 4096 May 7 17:44 finn\r\n",
- "-rw-r--r-- 1 xilinx xilinx 4045675 May 7 17:44 resizer.bit\r\n",
- "-rw-r--r-- 1 xilinx xilinx 302015 May 7 17:44 resizer.hwh\r\n",
- "\r\n",
- "/home/xilinx/finn_tfc_end2end_example/pynq_deployment_kvurnk0c:\r\n",
- "total 4300\r\n",
- "-rw-r--r-- 1 xilinx xilinx 3861 Apr 27 12:36 driver.py\r\n",
- "drwxr-xr-x 4 xilinx xilinx 4096 Apr 27 12:37 finn\r\n",
- "-rw-r--r-- 1 xilinx xilinx 3264 Apr 27 12:37 input.npy\r\n",
- "-rw-r--r-- 1 root root 78 Apr 27 12:38 nw_metrics.txt\r\n",
- "-rw-r--r-- 1 root root 120 Apr 27 12:37 output.npy\r\n",
- "-rw-r--r-- 1 xilinx xilinx 4045675 Apr 27 12:36 resizer.bit\r\n",
- "-rw-r--r-- 1 xilinx xilinx 329531 Apr 27 12:36 resizer.hwh\r\n",
- "-rw-r--r-- 1 root root 32 Apr 27 12:38 sds_trace_data.dat\r\n",
- "\r\n",
- "/home/xilinx/finn_tfc_end2end_example/pynq_deployment__tnbutz_:\r\n",
- "total 4276\r\n",
- "-rw-r--r-- 1 xilinx xilinx 6363 May 6 17:34 driver.py\r\n",
- "drwxr-xr-x 4 xilinx xilinx 4096 May 6 17:34 finn\r\n",
- "-rw-r--r-- 1 xilinx xilinx 3264 May 6 17:34 input.npy\r\n",
- "-rw-r--r-- 1 root root 173 May 6 17:35 nw_metrics.txt\r\n",
- "-rw-r--r-- 1 root root 120 May 6 17:34 output.npy\r\n",
- "-rw-r--r-- 1 xilinx xilinx 4045675 May 6 17:34 resizer.bit\r\n",
- "-rw-r--r-- 1 xilinx xilinx 302015 May 6 17:34 resizer.hwh\r\n",
- "-rw-r--r-- 1 root root 32 May 6 17:35 sds_trace_data.dat\r\n",
+ "/home/xilinx/finn_tfc_end2end_example/pynq_deployment_624bd_nc:\r\n",
+ "total 4228\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 9391 Sep 4 10:37 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Sep 4 10:37 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 3264 Sep 4 10:38 input.npy\r\n",
+ "-rw-r--r-- 1 root root 205 Sep 4 10:39 nw_metrics.txt\r\n",
+ "-rw-r--r-- 1 root root 120 Sep 4 10:38 output.npy\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Sep 4 10:37 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Sep 4 10:37 resizer.hwh\r\n",
+ "-rw-r--r-- 1 root root 32 Sep 4 10:39 sds_trace_data.dat\r\n",
"\r\n",
- "/home/xilinx/finn_tfc_end2end_example/pynq_deployment_w4aa1r9k:\r\n",
- "total 4276\r\n",
- "-rw-r--r-- 1 xilinx xilinx 6363 May 7 15:05 driver.py\r\n",
- "drwxr-xr-x 4 xilinx xilinx 4096 May 7 15:05 finn\r\n",
- "-rw-r--r-- 1 xilinx xilinx 3264 May 7 15:06 input.npy\r\n",
- "-rw-r--r-- 1 root root 172 May 7 15:11 nw_metrics.txt\r\n",
- "-rw-r--r-- 1 root root 120 May 7 15:06 output.npy\r\n",
- "-rw-r--r-- 1 xilinx xilinx 4045675 May 7 15:05 resizer.bit\r\n",
- "-rw-r--r-- 1 xilinx xilinx 302015 May 7 15:05 resizer.hwh\r\n",
- "-rw-r--r-- 1 root root 32 May 7 15:11 sds_trace_data.dat\r\n"
+ "/home/xilinx/finn_tfc_end2end_example/pynq_deployment_eyiu4sxk:\r\n",
+ "total 4212\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 8493 Sep 5 01:24 driver.py\r\n",
+ "drwxr-xr-x 4 xilinx xilinx 4096 Sep 5 01:24 finn\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 4045671 Sep 5 01:24 resizer.bit\r\n",
+ "-rw-r--r-- 1 xilinx xilinx 246211 Sep 5 01:24 resizer.hwh\r\n"
]
}
],
@@ -1777,30 +1079,18 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.image.AxesImage at 0x7fe11dda48d0>"
+ "<matplotlib.image.AxesImage at 0x7f1b5b7cddd8>"
]
},
- "execution_count": 41,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
}
],
"source": [
@@ -1822,7 +1112,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
@@ -1842,7 +1132,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
@@ -1864,7 +1154,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -1873,13 +1163,13 @@
"<BarContainer object of 10 artists>"
]
},
- "execution_count": 44,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1920,7 +1210,7 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
@@ -1928,10 +1218,12 @@
"output_type": "stream",
"text": [
"Network metrics:\n",
- "runtime[ms]: 1.4772415161132812\n",
- "throughput[images/s]: 676937.378954164\n",
- "DRAM_in_bandwidth[Mb/s]: 75.81698644286635\n",
- "DRAM_out_bandwidth[Mb/s]: 27.07749515816656\n"
+ "runtime[ms]: 7.472753524780273\n",
+ "throughput[images/s]: 1338194.8122387773\n",
+ "DRAM_in_bandwidth[Mb/s]: 131.14309159940018\n",
+ "DRAM_out_bandwidth[Mb/s]: 13.381948122387772\n",
+ "fclk[mhz]: 100.0\n",
+ "N: 10000\n"
]
}
],
@@ -1939,7 +1231,7 @@
"from finn.core.throughput_test import throughput_test_remote\n",
"\n",
"child_model = ModelWrapper(getCustomOp(sdp_node).get_nodeattr(\"model\"))\n",
- "res = throughput_test_remote(child_model)\n",
+ "res = throughput_test_remote(child_model, 10000)\n",
"print(\"Network metrics:\")\n",
"for key in res:\n",
" print(str(key) + \": \" + str(res[key]))"
@@ -1954,14 +1246,14 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "We reach approximately 43% of the ideal performance.\n"
+ "We reach approximately 86% of the ideal performance.\n"
]
}
],
@@ -1981,8 +1273,15 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The measured values were recorded with a batch size of 1000 and at a frequency of 100 MHz. We will be improving the efficiency of the generated accelerator examples in the coming FINN releases."
+ "The measured values were recorded with a batch size of 10000 and at a frequency of 100 MHz. We will be improving the efficiency of the generated accelerator examples in the coming FINN releases."
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
diff --git a/notebooks/end2end_example/tfc_end2end_verification.ipynb b/notebooks/end2end_example/tfc_end2end_verification.ipynb
index 1ce17d3da3e4313f2b89eb6a790efcee704f70e9..92de7fb7e42b5d0013af31cc0fd88e34d354def8 100644
--- a/notebooks/end2end_example/tfc_end2end_verification.ipynb
+++ b/notebooks/end2end_example/tfc_end2end_verification.ipynb
@@ -264,7 +264,7 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f776245ac18>"
+ "<IPython.lib.display.IFrame at 0x7f3cac09d978>"
]
},
"execution_count": 8,
@@ -409,7 +409,16 @@
"outputs": [],
"source": [
"from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim\n",
- "child_model = ModelWrapper(build_dir + \"/tfc_w1_a1_ipgen.onnx\")\n",
+ "from finn.transformation.fpgadataflow.prepare_ip import PrepareIP\n",
+ "from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP\n",
+ "\n",
+ "test_fpga_part = \"xc7z020clg400-1\"\n",
+ "target_clk_ns = 10\n",
+ "\n",
+ "child_model = ModelWrapper(build_dir + \"/tfc_w1_a1_set_folding_factors.onnx\")\n",
+ "child_model = child_model.transform(GiveUniqueNodeNames())\n",
+ "child_model = child_model.transform(PrepareIP(test_fpga_part, target_clk_ns))\n",
+ "child_model = child_model.transform(HLSSynthIP())\n",
"child_model = child_model.transform(SetExecMode(\"rtlsim\"))\n",
"child_model = child_model.transform(PrepareRTLSim())\n",
"child_model.save(build_dir + \"/tfc_w1_a1_dataflow_child.onnx\")"
@@ -478,18 +487,44 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 18,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/workspace/finn/src/finn/transformation/fpgadataflow/hlssynth_ip.py:70: UserWarning: Using pre-existing IP for StreamingFCLayer_Batch_3\n",
+ " warnings.warn(\"Using pre-existing IP for %s\" % node.name)\n",
+ "/workspace/finn/src/finn/transformation/fpgadataflow/hlssynth_ip.py:70: UserWarning: Using pre-existing IP for StreamingFCLayer_Batch_1\n",
+ " warnings.warn(\"Using pre-existing IP for %s\" % node.name)\n",
+ "/workspace/finn/src/finn/transformation/fpgadataflow/hlssynth_ip.py:70: UserWarning: Using pre-existing IP for StreamingFCLayer_Batch_2\n",
+ " warnings.warn(\"Using pre-existing IP for %s\" % node.name)\n",
+ "/workspace/finn/src/finn/transformation/fpgadataflow/hlssynth_ip.py:70: UserWarning: Using pre-existing IP for StreamingFCLayer_Batch_0\n",
+ " warnings.warn(\"Using pre-existing IP for %s\" % node.name)\n"
+ ]
+ }
+ ],
"source": [
- "child_model = ModelWrapper(build_dir + \"/tfc_w1_a1_ipstitch.onnx\")\n",
+ "from finn.transformation.fpgadataflow.insert_dwc import InsertDWC\n",
+ "from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO\n",
+ "from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP\n",
+ "\n",
+ "child_model = ModelWrapper(build_dir + \"/tfc_w1_a1_dataflow_child.onnx\")\n",
+ "child_model = child_model.transform(InsertDWC())\n",
+ "child_model = child_model.transform(InsertFIFO())\n",
+ "child_model = child_model.transform(GiveUniqueNodeNames())\n",
+ "child_model = child_model.transform(PrepareIP(test_fpga_part, target_clk_ns))\n",
+ "child_model = child_model.transform(HLSSynthIP())\n",
+ "child_model = child_model.transform(CreateStitchedIP(test_fpga_part, target_clk_ns))\n",
+ "child_model = child_model.transform(PrepareRTLSim())\n",
"child_model.set_metadata_prop(\"exec_mode\",\"rtlsim\")\n",
"child_model.save(build_dir + \"/tfc_w1_a1_dataflow_child.onnx\")"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
@@ -502,7 +537,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -522,6 +557,13 @@
"else:\n",
" print(\"The results are not the same!\")"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
diff --git a/src/finn/analysis/fpgadataflow/dataflow_performance.py b/src/finn/analysis/fpgadataflow/dataflow_performance.py
new file mode 100644
index 0000000000000000000000000000000000000000..e678630ae97318af47dd432a7c68442a6642b65f
--- /dev/null
+++ b/src/finn/analysis/fpgadataflow/dataflow_performance.py
@@ -0,0 +1,78 @@
+# 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.util.fpgadataflow import is_fpgadataflow_node
+
+
+def dataflow_performance(model):
+ """Extract key performance indicators from given model with dataflow nodes.
+ Note that the latency (critical path) analysis is very pessimistic, it
+ assumes no overlap between executions and simply sums the expected cycles
+ for each node along the critical path.
+
+ Preconditions:
+ - model consists of fpgadataflow nodes
+ - model has cycle estimates annotated (see AnnotateCycles transformation)
+ - nodes have unique names (see GiveUniqueNodeNames)
+
+ Returns:
+ - max_cycles : number of cycles for slowest node
+ - max_cycles_node_name : name of slowest node
+ - critical_path_cycles : pessimistic expected latency from input to output
+ """
+ latency_at_node_output = {}
+ max_cycles = 0
+ max_node_name = ""
+
+ for node in model.graph.node:
+ if is_fpgadataflow_node(node) is True:
+ inst = getCustomOp(node)
+ node_cycles = inst.get_nodeattr("cycles_estimate")
+ if node_cycles > max_cycles:
+ max_cycles = node_cycles
+ max_node_name = node.name
+ if node.name not in latency_at_node_output:
+ # calculate based on input(s)
+ predecessors = model.find_direct_predecessors(node)
+ if predecessors is None:
+ # no predecessors, node is first node
+ max_pred_latency = 0
+ else:
+ # find max of any of predecessors
+ pred_latencies = map(
+ lambda x: latency_at_node_output[x.name], predecessors
+ )
+ max_pred_latency = max(pred_latencies)
+ latency_at_node_output[node.name] = node_cycles + max_pred_latency
+ critical_path_cycles = max(latency_at_node_output.values())
+ return {
+ "critical_path_cycles": critical_path_cycles,
+ "max_cycles": max_cycles,
+ "max_cycles_node_name": max_node_name,
+ }
diff --git a/src/finn/core/modelwrapper.py b/src/finn/core/modelwrapper.py
index 98b234592ebe0c704fafd1eed980325d8566e7e2..42acc6fd277c9419920edd534e5660e85c7626b0 100644
--- a/src/finn/core/modelwrapper.py
+++ b/src/finn/core/modelwrapper.py
@@ -27,7 +27,7 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import copy
-
+import os
import onnx
import onnx.helper as oh
import onnx.numpy_helper as np_helper
@@ -41,6 +41,7 @@ from finn.transformation.general import (
RemoveStaticGraphInputs,
SortGraph,
)
+from finn.transformation.double_to_single_float import DoubleToSingleFloat
class ModelWrapper:
@@ -51,10 +52,12 @@ class ModelWrapper:
"""Creates a ModelWrapper instance.
onnx_model_proto can be either a ModelProto instance, or a string
with the path to a stored .onnx file on disk, or serialized bytes.
- The make_deepcopy option controls whether a deep copy of the ModelProto
+
+ - make_deepcopy : controls whether a deep copy of the ModelProto
is made internally.
"""
if isinstance(onnx_model_proto, str):
+ assert os.path.isfile(onnx_model_proto)
self._model_proto = onnx.load(onnx_model_proto)
elif isinstance(onnx_model_proto, bytes):
self._model_proto = onnx.load_from_string(onnx_model_proto)
@@ -92,15 +95,23 @@ class ModelWrapper:
"""Runs given anaylsis_fxn on this model and return resulting dict."""
return analysis_fxn(self)
- def transform(self, transformation, make_deepcopy=True, cleanup=True):
+ def transform(
+ self, transformation, make_deepcopy=True, cleanup=True, fix_float64=True
+ ):
"""Applies given Transformation repeatedly until no more changes can be made
and returns a transformed ModelWrapper instance.
- If make_deepcopy is specified, operates on a new (deep)copy of model.
+ - make_deepcopy : operates on a new (deep)copy of model.
+ - fix_float64 : DoubleToSingleFloat correction before starting
+ - cleanup : execute cleanup transformations before returning
"""
transformed_model = self
if make_deepcopy:
transformed_model = copy.deepcopy(self)
+ if fix_float64:
+ (transformed_model, model_was_changed) = DoubleToSingleFloat().apply(
+ transformed_model
+ )
model_was_changed = True
while model_was_changed:
(transformed_model, model_was_changed) = transformation.apply(
diff --git a/src/finn/core/onnx_exec.py b/src/finn/core/onnx_exec.py
index 0c01a48a07608dcd760447e8f569128f58d86f28..15e2a69cd3f61c59287264b76b2229a7da3a3734 100644
--- a/src/finn/core/onnx_exec.py
+++ b/src/finn/core/onnx_exec.py
@@ -60,6 +60,10 @@ def execute_node(node, context, graph):
inp_ctx[new_iname] = inp_ctx[old_iname]
del inp_ctx[old_iname]
ret = execute_onnx(model, inp_ctx, False)
+ # if the model was in ip-stitched rtlsim mode, may get annotation
+ # for numbet of elapsed cycles, save again
+ if model.get_metadata_prop("exec_mode") == "rtlsim":
+ model.save(sdp_node.get_nodeattr("model"))
# output may have been renamed in partition
assert len(ret) == 1
node_oname = node.output[0]
diff --git a/src/finn/core/remote_exec.py b/src/finn/core/remote_exec.py
index ee07201315db23a9a1f8d0b7f1392d99517a8b63..2e139065ec0eff8cdbdb402f80113b039deed4da 100644
--- a/src/finn/core/remote_exec.py
+++ b/src/finn/core/remote_exec.py
@@ -44,7 +44,7 @@ def remote_exec(model, execution_context):
pynq_target_dir = model.get_metadata_prop("pynq_target_dir")
deployment_dir = model.get_metadata_prop("pynq_deploy_dir")
platform = model.get_metadata_prop("platform")
- assert platform in ["alveo", "zynq", "zynq-iodma"]
+ assert platform in ["alveo", "zynq-iodma"]
bitfile = model.get_metadata_prop("bitfile")
bitfile = os.path.basename(bitfile)
if pynq_password == "":
diff --git a/src/finn/core/throughput_test.py b/src/finn/core/throughput_test.py
index 77c7b6c00179343048c52494fbffacd4c7447d7f..1306edfa23a9b25de41d0592796b4a03ad4e6508 100644
--- a/src/finn/core/throughput_test.py
+++ b/src/finn/core/throughput_test.py
@@ -49,7 +49,7 @@ def throughput_test_remote(model, batchsize=1000):
# extracting last folder of absolute path (deployment_dir)
deployment_folder = os.path.basename(os.path.normpath(deployment_dir))
platform = model.get_metadata_prop("platform")
- assert platform in ["alveo", "zynq", "zynq-iodma"]
+ assert platform in ["alveo", "zynq-iodma"]
bitfile = model.get_metadata_prop("bitfile")
bitfile = os.path.basename(bitfile)
if pynq_password == "":
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index 75be2bec869959cdd8fc227677f80a5e40c2a56e..37c6ad4894a1a82878f68c92501844d7fd45d353 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -745,7 +745,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
)
num_w_reps = np.prod(self.get_nodeattr("numInputVectors"))
io_dict = {
- "inputs": {"in0": inp, "weights": wei*num_w_reps},
+ "inputs": {"in0": inp, "weights": wei * num_w_reps},
"outputs": {"out": []},
}
self.rtlsim_multi_io(sim, io_dict)
@@ -1126,6 +1126,11 @@ class StreamingFCLayer_Batch(HLSCustomOp):
% (node_name, dout_name, node_name, node_name, dout_name)
)
cmd.append("save_bd_design")
+ elif mem_mode == "const":
+ # base class impl sufficient for const mode
+ return super().code_generation_ipi()
+ else:
+ raise Exception("Unrecognized mem_mode for StreamingFCLayer")
return cmd
def get_verilog_top_module_intf_names(self):
diff --git a/src/finn/transformation/fpgadataflow/create_stitched_ip.py b/src/finn/transformation/fpgadataflow/create_stitched_ip.py
index aa5de589a75d81ddfa7924a123630d721368fec0..334507affba51e948bc5a907af3003152821a3f9 100644
--- a/src/finn/transformation/fpgadataflow/create_stitched_ip.py
+++ b/src/finn/transformation/fpgadataflow/create_stitched_ip.py
@@ -35,6 +35,9 @@ from finn.util.basic import get_by_name, make_build_dir
from finn.custom_op.registry import getCustomOp
from finn.util.basic import get_num_default_workers
import multiprocessing as mp
+from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
+ ReplaceVerilogRelPaths,
+)
class CreateStitchedIP(Transformation):
@@ -181,6 +184,8 @@ class CreateStitchedIP(Transformation):
self.s_axis_idx += 1
def apply(self, model):
+ # ensure non-relative readmemh .dat files
+ model = model.transform(ReplaceVerilogRelPaths())
ip_dirs = ["list"]
# add RTL streamer IP
ip_dirs.append("/workspace/finn/finn-rtllib/memstream")
@@ -408,7 +413,10 @@ class CreateStitchedIP(Transformation):
tcl.append("ipx::update_checksums [ipx::find_open_core %s]" % block_vlnv)
tcl.append("ipx::save_core [ipx::find_open_core %s]" % block_vlnv)
# export list of used Verilog files (for rtlsim later on)
- tcl.append("set all_v_files [get_files -filter {FILE_TYPE == Verilog && USED_IN_SYNTHESIS == 1} ]")
+ tcl.append(
+ "set all_v_files [get_files -filter {FILE_TYPE == Verilog "
+ + "&& USED_IN_SYNTHESIS == 1} ]"
+ )
v_file_list = "%s/all_verilog_srcs.txt" % vivado_stitch_proj_dir
tcl.append("set fp [open %s w]" % v_file_list)
# write each verilog filename to all_verilog_srcs.txt
diff --git a/src/finn/transformation/fpgadataflow/hlssynth_ip.py b/src/finn/transformation/fpgadataflow/hlssynth_ip.py
index 6d7c4025653948d6958672177ae5e36ab08bf279..8315b6ec11e3d0bc5e2bf97e7c11817ae8b5a5b1 100644
--- a/src/finn/transformation/fpgadataflow/hlssynth_ip.py
+++ b/src/finn/transformation/fpgadataflow/hlssynth_ip.py
@@ -26,9 +26,11 @@
# 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 finn.custom_op.registry as registry
from finn.util.fpgadataflow import is_fpgadataflow_node
from finn.transformation import NodeLocalTransformation
+import warnings
class HLSSynthIP(NodeLocalTransformation):
@@ -36,9 +38,11 @@ class HLSSynthIP(NodeLocalTransformation):
that is referenced in node attribute "code_gen_dir_ipgen"
and save path of generated project in node attribute "ipgen_path".
All nodes in the graph must have the fpgadataflow backend attribute.
+ Any nodes that already have a ipgen_path attribute pointing to a valid path
+ will be skipped.
This transformation calls Vivado HLS for synthesis, so it will run for
- some time (several minutes)
+ some time (minutes to hours depending on configuration).
* num_workers (int or None) number of parallel workers, see documentation in
NodeLocalTransformation for more details.
@@ -59,8 +63,11 @@ class HLSSynthIP(NodeLocalTransformation):
), """Node
attribute "code_gen_dir_ipgen" is empty. Please run
transformation PrepareIP first."""
- # call the compilation function for this node
- inst.ipgen_singlenode_code()
+ if not os.path.isdir(inst.get_nodeattr("ipgen_path")):
+ # call the compilation function for this node
+ inst.ipgen_singlenode_code()
+ else:
+ warnings.warn("Using pre-existing IP for %s" % node.name)
# ensure that executable path is now set
assert (
inst.get_nodeattr("ipgen_path") != ""
diff --git a/src/finn/transformation/fpgadataflow/make_pynq_driver.py b/src/finn/transformation/fpgadataflow/make_pynq_driver.py
index fc326b4a25a9784f3919b4246ec2b8f54fb881f4..0e50213ee6feee5f45c18f87cb31a5faf5fb1c50 100644
--- a/src/finn/transformation/fpgadataflow/make_pynq_driver.py
+++ b/src/finn/transformation/fpgadataflow/make_pynq_driver.py
@@ -28,7 +28,6 @@
import shutil
-from finn.custom_op.registry import getCustomOp
from finn.transformation import Transformation
from finn.util.basic import gen_finn_dt_tensor, get_finn_root, make_build_dir
from finn.util.data_packing import finnpy_to_packed_bytearray
@@ -41,7 +40,7 @@ class MakePYNQDriver(Transformation):
accelerator, including data packing/unpacking. The MakePYNQProject
transformation must have been already applied.
- platform: one of ["zynq", "zynq-iodma", "alveo"]
+ platform: one of ["zynq-iodma", "alveo"]
Outcome if successful: sets the pynq_driver_dir attribute in the ONNX
ModelProto's metadata_props field, with the created driver dir as the
@@ -65,20 +64,15 @@ class MakePYNQDriver(Transformation):
o_tensor_shape_normal = tuple(model.get_tensor_shape(o_tensor_name))
i_tensor_dt = model.get_tensor_datatype(i_tensor_name)
o_tensor_dt = model.get_tensor_datatype(o_tensor_name)
- # handle folded i/o shapes due to differences in DMA engines
- if self.platform == "zynq":
- # extract HLSCustomOp instances to get folded i/o shapes
- first_node = getCustomOp(model.find_consumer(i_tensor_name))
- last_node = getCustomOp(model.find_producer(o_tensor_name))
- i_tensor_shape_folded = tuple(first_node.get_folded_input_shape())
- o_tensor_shape_folded = tuple(last_node.get_folded_output_shape())
- else:
- i_tensor_shape_folded = list(i_tensor_shape_normal)
- i_tensor_shape_folded.insert(-1, 1)
- i_tensor_shape_folded = tuple(i_tensor_shape_folded)
- o_tensor_shape_folded = list(o_tensor_shape_normal)
- o_tensor_shape_folded.insert(-1, 1)
- o_tensor_shape_folded = tuple(o_tensor_shape_folded)
+ # folded shapes for i/o simply derived from regular tensor shapes
+ # this used to be extracted from first/last node folded shapes, but
+ # can't do this anymore due to IODMAs
+ i_tensor_shape_folded = list(i_tensor_shape_normal)
+ i_tensor_shape_folded.insert(-1, 1)
+ i_tensor_shape_folded = tuple(i_tensor_shape_folded)
+ o_tensor_shape_folded = list(o_tensor_shape_normal)
+ o_tensor_shape_folded.insert(-1, 1)
+ o_tensor_shape_folded = tuple(o_tensor_shape_folded)
# generate dummy folded i/o tensors and their packed versions
i_tensor_dummy_folded = gen_finn_dt_tensor(i_tensor_dt, i_tensor_shape_folded)
diff --git a/src/finn/transformation/fpgadataflow/make_pynq_proj.py b/src/finn/transformation/fpgadataflow/make_pynq_proj.py
deleted file mode 100644
index 5e45d6f230503668a15d784e3c6afa45560fe004..0000000000000000000000000000000000000000
--- a/src/finn/transformation/fpgadataflow/make_pynq_proj.py
+++ /dev/null
@@ -1,182 +0,0 @@
-# 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 warnings
-import subprocess
-
-from finn.custom_op.registry import getCustomOp
-from finn.transformation import Transformation
-from finn.util.basic import get_by_name, make_build_dir, roundup_to_integer_multiple
-
-from . import templates
-
-
-class MakePYNQProject(Transformation):
- """Create a Vivado PYNQ overlay project (including the shell infrastructure)
- from the already-stitched IP block for this graph.
- All nodes in the graph must have the fpgadataflow backend attribute,
- and the CreateStitchedIP transformation must have been previously run on
- the graph.
-
- Outcome if successful: sets the vivado_pynq_proj attribute in the ONNX
- ModelProto's metadata_props field, with the created project dir as the
- value.
- """
-
- def __init__(self, platform):
- super().__init__()
- self.platform = platform
-
- def apply(self, model):
- pynq_shell_path = os.environ["PYNQSHELL_PATH"]
- if not os.path.isdir(pynq_shell_path):
- raise Exception("Ensure the PYNQ-HelloWorld utility repo is cloned.")
- ipstitch_path = model.get_metadata_prop("vivado_stitch_proj")
- if ipstitch_path is None or (not os.path.isdir(ipstitch_path)):
- raise Exception(
- "No stitched IPI design found, apply CreateStitchedIP first."
- )
- vivado_stitch_vlnv = model.get_metadata_prop("vivado_stitch_vlnv")
- if vivado_stitch_vlnv is None:
- raise Exception(
- "No vlnv for stitched IP found, apply CreateStitchedIP first."
- )
- vivado_stitch_ifnames = model.get_metadata_prop("vivado_stitch_ifnames")
- if vivado_stitch_ifnames is None:
- raise Exception("No IF name metadata found, apply CreateStitchedIP first.")
- vivado_stitch_ifnames = eval(vivado_stitch_ifnames)
- # recover interface names from dict
- self.clk_name = vivado_stitch_ifnames["clk"][0]
- self.rst_name = vivado_stitch_ifnames["rst"][0]
- self.s_axis_if_name = vivado_stitch_ifnames["s_axis"][0]
- self.m_axis_if_name = vivado_stitch_ifnames["m_axis"][0]
- self.s_aximm_if_name = vivado_stitch_ifnames["axilite"][0]
-
- # collect list of all IP dirs
- ip_dirs = ["list"]
- for node in model.graph.node:
- ip_dir_attribute = get_by_name(node.attribute, "ip_path")
- assert (
- ip_dir_attribute is not None
- ), """Node attribute "ip_path" is
- empty. Please run transformation HLSSynth_ipgen first."""
- ip_dir_value = ip_dir_attribute.s.decode("UTF-8")
- assert os.path.isdir(
- ip_dir_value
- ), """The directory that should
- contain the generated ip blocks doesn't exist."""
- ip_dirs += [ip_dir_value]
- ip_dirs += [ipstitch_path + "/ip"]
- ip_dirs_str = "[%s]" % (" ".join(ip_dirs))
-
- # extract HLSCustomOp instances to get i/o stream widths
- i_tensor_name = model.graph.input[0].name
- o_tensor_name = model.graph.output[0].name
- first_node = getCustomOp(model.find_consumer(i_tensor_name))
- last_node = getCustomOp(model.find_producer(o_tensor_name))
- i_bits_per_cycle = first_node.get_instream_width()
- o_bits_per_cycle = last_node.get_outstream_width()
- # ensure i/o is padded to bytes
- i_bits_per_cycle_padded = roundup_to_integer_multiple(i_bits_per_cycle, 8)
- o_bits_per_cycle_padded = roundup_to_integer_multiple(o_bits_per_cycle, 8)
- assert (
- i_bits_per_cycle_padded % 8 == 0
- ), """Padded input bits are not a
- multiple of 8."""
- assert (
- o_bits_per_cycle_padded % 8 == 0
- ), """Padded output bits are not a
- multiple of 8."""
- in_bytes = i_bits_per_cycle_padded / 8
- out_bytes = o_bits_per_cycle_padded / 8
- in_if_name = self.s_axis_if_name
- out_if_name = self.m_axis_if_name
- clk_name = self.clk_name
- nrst_name = self.rst_name
- axi_lite_if_name = self.s_aximm_if_name
- vivado_ip_cache = os.getenv("VIVADO_IP_CACHE", default="")
-
- # create a temporary folder for the project
- vivado_pynq_proj_dir = make_build_dir(prefix="vivado_pynq_proj_")
- model.set_metadata_prop("vivado_pynq_proj", vivado_pynq_proj_dir)
- # filename for the synth utilization report
- synth_report_filename = vivado_pynq_proj_dir + "/synth_report.xml"
- model.set_metadata_prop("vivado_synth_rpt", synth_report_filename)
- # set platform attribute for correct remote execution
- model.set_metadata_prop("platform", "zynq")
-
- # get metadata property clk_ns to calculate clock frequency
- clk_ns = float(model.get_metadata_prop("clk_ns"))
- if clk_ns not in [5.0, 10.0, 20.0]:
- warnings.warn(
- """The chosen frequency may lead to failure due to clock divider
- constraints."""
- )
- fclk_mhz = 1 / (clk_ns * 0.001)
-
- ip_config_tcl = templates.ip_config_tcl_template % (
- vivado_pynq_proj_dir,
- ip_dirs_str,
- vivado_pynq_proj_dir,
- synth_report_filename,
- vivado_stitch_vlnv,
- in_bytes,
- out_bytes,
- in_if_name,
- out_if_name,
- clk_name,
- nrst_name,
- axi_lite_if_name,
- vivado_ip_cache,
- fclk_mhz,
- )
-
- with open(vivado_pynq_proj_dir + "/ip_config.tcl", "w") as f:
- f.write(ip_config_tcl)
- # create a shell script for project creation and synthesis
- make_project_sh = vivado_pynq_proj_dir + "/make_project.sh"
- working_dir = os.environ["PWD"]
- ipcfg = vivado_pynq_proj_dir + "/ip_config.tcl"
- with open(make_project_sh, "w") as f:
- f.write(
- templates.call_pynqshell_makefile_template
- % (pynq_shell_path, self.platform, ipcfg, "block_design", working_dir)
- )
- synth_project_sh = vivado_pynq_proj_dir + "/synth_project.sh"
- with open(synth_project_sh, "w") as f:
- f.write(
- templates.call_pynqshell_makefile_template
- % (pynq_shell_path, self.platform, ipcfg, "bitstream", working_dir)
- )
- # call the project creation script
- # synthesis script will be called with a separate transformation
- bash_command = ["bash", make_project_sh]
- process_compile = subprocess.Popen(bash_command, stdout=subprocess.PIPE)
- process_compile.communicate()
- return (model, False)
diff --git a/src/finn/transformation/fpgadataflow/make_zynq_proj.py b/src/finn/transformation/fpgadataflow/make_zynq_proj.py
index b79850abad8f63ade326103764f6d62121f401cc..e263c450af5d2dca09a79b7757a0c0d67bdf86ff 100644
--- a/src/finn/transformation/fpgadataflow/make_zynq_proj.py
+++ b/src/finn/transformation/fpgadataflow/make_zynq_proj.py
@@ -44,9 +44,6 @@ from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO
from finn.transformation.fpgadataflow.insert_iodma import InsertIODMA
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.floorplan import Floorplan
from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
@@ -304,7 +301,6 @@ class ZynqBuild(Transformation):
PrepareIP(self.fpga_part, self.period_ns)
)
kernel_model = kernel_model.transform(HLSSynthIP())
- kernel_model = kernel_model.transform(ReplaceVerilogRelPaths())
kernel_model = kernel_model.transform(
CreateStitchedIP(
self.fpga_part, self.period_ns, sdp_node.onnx_node.name, True
diff --git a/src/finn/transformation/fpgadataflow/prepare_ip.py b/src/finn/transformation/fpgadataflow/prepare_ip.py
index 7d5da940010896cde3a49bd45a79c69ef455bc72..21f8e0052d5f2d60f11f33846b483d3f556d1188 100644
--- a/src/finn/transformation/fpgadataflow/prepare_ip.py
+++ b/src/finn/transformation/fpgadataflow/prepare_ip.py
@@ -31,6 +31,7 @@ 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
+import warnings
def _codegen_single_node(node, model, fpgapart, clk):
@@ -49,8 +50,10 @@ def _codegen_single_node(node, model, fpgapart, clk):
prefix="code_gen_ipgen_" + str(node.name) + "_"
)
inst.set_nodeattr("code_gen_dir_ipgen", code_gen_dir)
- # ensure that there is generated code inside the dir
- inst.code_generation_ipgen(model, fpgapart, clk)
+ # ensure that there is generated code inside the dir
+ inst.code_generation_ipgen(model, fpgapart, clk)
+ else:
+ warnings.warn("Using pre-existing code for %s" % node.name)
except KeyError:
# exception if op_type is not supported
raise Exception("Custom op_type %s is currently not supported." % op_type)
@@ -66,6 +69,9 @@ class PrepareIP(Transformation):
* clk in ns (int)
+ Any nodes that already have a code_gen_dir_ipgen attribute pointing to a valid path
+ will be skipped.
+
Outcome if succesful: Node attribute "code_gen_dir_ipgen" contains path to folder
that contains generated C++ code that can be used to generate a Vivado IP block.
The subsequent transformation is HLSSynthIP"""
diff --git a/src/finn/transformation/fpgadataflow/prepare_rtlsim.py b/src/finn/transformation/fpgadataflow/prepare_rtlsim.py
index 5f0b89e85dc5f33319f64ef885db20ed9c4046af..8c28ab7e2376de392b0fdd628c70e854011dc406 100644
--- a/src/finn/transformation/fpgadataflow/prepare_rtlsim.py
+++ b/src/finn/transformation/fpgadataflow/prepare_rtlsim.py
@@ -28,7 +28,9 @@
import finn.custom_op.registry as registry
from finn.util.fpgadataflow import is_fpgadataflow_node
-
+from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
+ ReplaceVerilogRelPaths,
+)
from finn.transformation import NodeLocalTransformation
try:
@@ -54,6 +56,10 @@ class PrepareRTLSim(NodeLocalTransformation):
def __init__(self, num_workers=None):
super().__init__(num_workers=num_workers)
+ def apply(self, model):
+ model = model.transform(ReplaceVerilogRelPaths())
+ return super().apply(model)
+
def applyNodeLocal(self, node):
op_type = node.op_type
if is_fpgadataflow_node(node) is True:
diff --git a/src/finn/transformation/fpgadataflow/synth_pynq_proj.py b/src/finn/transformation/fpgadataflow/synth_pynq_proj.py
deleted file mode 100644
index 7f9bbaf5665f872d4393fa173b60b2cc3a6655e9..0000000000000000000000000000000000000000
--- a/src/finn/transformation/fpgadataflow/synth_pynq_proj.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# 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
-
-from finn.transformation import Transformation
-
-
-class SynthPYNQProject(Transformation):
- """Run synthesis for the PYNQ project for this graph. The MakePYNQProject
- transformation must be applied prior to this transformation."""
-
- def __init__(self):
- super().__init__()
-
- def apply(self, model):
- vivado_pynq_proj_dir = model.get_metadata_prop("vivado_pynq_proj")
- if vivado_pynq_proj_dir is None or (not os.path.isdir(vivado_pynq_proj_dir)):
- raise Exception("No synthesis project, apply MakePYNQProject first.")
- synth_project_sh = vivado_pynq_proj_dir + "/synth_project.sh"
- if not os.path.isfile(synth_project_sh):
- raise Exception("No synthesis script, apply MakePYNQProject first.")
- bash_command = ["bash", synth_project_sh]
- process_compile = subprocess.Popen(bash_command, stdout=subprocess.PIPE)
- process_compile.communicate()
- # set bitfile attribute
- model.set_metadata_prop("bitfile", vivado_pynq_proj_dir + "/resizer.bit")
- model.set_metadata_prop("hw_handoff", vivado_pynq_proj_dir + "/resizer.hwh")
- # TODO pull out synthesis statistics and put them in as attributes
- return (model, False)
diff --git a/src/finn/transformation/fpgadataflow/templates.py b/src/finn/transformation/fpgadataflow/templates.py
index 5a4ee2c7db6382dd032aa4f1e52a16cbcf421ecb..66580c70d23a2d2b19bfe8d94fefcd39a3208bcb 100644
--- a/src/finn/transformation/fpgadataflow/templates.py
+++ b/src/finn/transformation/fpgadataflow/templates.py
@@ -126,18 +126,7 @@ class FINNAccelDriver():
self.itersPerSample = self.oshape_packed[-2]
# clock frequency as specified by user
self.fclk_mhz = $CLOCK_FREQ_MHZ$
- if self.platform == "zynq":
- # set the clock frequency as specified by user during transformations
- if self.fclk_mhz > 0:
- Clocks.$CLK_NAME$ = self.fclk_mhz
- self.dma = self.ol.axi_dma_0
- self.ctrl_regs = self.ol.resize_accel_0
- # AXI lite register offset for number of iterations
- # used by TLastMarker to signal end of transmission for AXI CDMA
- self.REG_OFFSET_NUM_ITERS = 0x10
- # set up TLastMarker with correct num. samples
- self.ctrl_regs.write(self.REG_OFFSET_NUM_ITERS, self.N*self.itersPerSample)
- elif self.platform == "alveo":
+ if self.platform == "alveo":
self.idma = self.ol.idma0
self.odma = self.ol.odma0
elif self.platform == "zynq-iodma":
@@ -147,11 +136,15 @@ class FINNAccelDriver():
if self.fclk_mhz > 0:
Clocks.$CLK_NAME$ = self.fclk_mhz
else:
- raise ValueError("Supported platforms are zynq zynq-iodma alveo")
+ raise ValueError("Supported platforms are zynq-iodma alveo")
# allocate a PYNQ buffer for the packed input and buffer
- self.ibuf_packed_device = allocate(shape=self.ishape_packed, dtype=np.uint8, cacheable=True)
- self.obuf_packed_device = allocate(shape=self.oshape_packed, dtype=np.uint8, cacheable=True)
+ if self.platform == "alveo":
+ self.ibuf_packed_device = allocate(shape=self.ishape_packed, dtype=np.uint8)
+ self.obuf_packed_device = allocate(shape=self.oshape_packed, dtype=np.uint8)
+ else:
+ self.ibuf_packed_device = allocate(shape=self.ishape_packed, dtype=np.uint8, cacheable=True)
+ self.obuf_packed_device = allocate(shape=self.oshape_packed, dtype=np.uint8, cacheable=True)
def fold_input(self, ibuf_normal):
\"\"\"Reshapes input in desired shape.
@@ -199,13 +192,7 @@ class FINNAccelDriver():
\"\"\"Executes accelerator by setting up the DMA(s) and
waiting until all transfers/calls complete. Uses only member variables and
returns nothing.\"\"\"
- if self.platform == "zynq":
- dma = self.dma
- dma.sendchannel.transfer(self.ibuf_packed_device)
- dma.recvchannel.transfer(self.obuf_packed_device)
- dma.sendchannel.wait()
- dma.recvchannel.wait()
- elif self.platform == "zynq-iodma":
+ if self.platform == "zynq-iodma":
# manually launch IODMAs since signatures are missing
self.idma.write(0x10, self.ibuf_packed_device.device_address)
self.idma.write(0x1c, self.N)
@@ -227,7 +214,7 @@ class FINNAccelDriver():
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Set exec mode, batchsize N, bitfile name, inputfile name and outputfile name')
parser.add_argument('--exec_mode', help='Please select functional verification ("execute") or throughput test ("throughput_test")', default="execute")
- parser.add_argument('--platform', help='Target platform: zynq zynq-iodma alveo', default="zynq")
+ parser.add_argument('--platform', help='Target platform: zynq-iodma alveo', default="zynq")
parser.add_argument('--batchsize', help='number of samples for inference', type=int, default=1)
parser.add_argument('--bitfile', help='name of bitfile (i.e. "resizer.bit")', default="resizer.bit")
parser.add_argument('--inputfile', help='name of input npy file (i.e. "input.npy")', default="input.npy")
diff --git a/src/finn/transformation/fpgadataflow/vitis_build.py b/src/finn/transformation/fpgadataflow/vitis_build.py
index 047480897b435a3de0f9746e52aa7c3eb634385c..482dc8d784c66faef9392093c7c857630304eef3 100644
--- a/src/finn/transformation/fpgadataflow/vitis_build.py
+++ b/src/finn/transformation/fpgadataflow/vitis_build.py
@@ -42,9 +42,6 @@ from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarke
from finn.transformation.fpgadataflow.insert_iodma import InsertIODMA
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.floorplan import Floorplan
from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver
@@ -349,7 +346,6 @@ class VitisBuild(Transformation):
PrepareIP(self.fpga_part, self.period_ns)
)
kernel_model = kernel_model.transform(HLSSynthIP())
- kernel_model = kernel_model.transform(ReplaceVerilogRelPaths())
kernel_model = kernel_model.transform(
CreateStitchedIP(
self.fpga_part, self.period_ns, sdp_node.onnx_node.name, True
diff --git a/src/finn/transformation/general.py b/src/finn/transformation/general.py
index 8ad59d2baf3015cfebffeff88a059f48d9428371..02f95b14e7944b828ae4c71ebf26851dc90b755d 100644
--- a/src/finn/transformation/general.py
+++ b/src/finn/transformation/general.py
@@ -111,8 +111,9 @@ class GiveRandomTensorNames(Transformation):
class GiveReadableTensorNames(Transformation):
- """Give more human-readable names to all internal tensors. It's recommended
- to apply give_unique_node_names prior to this transform."""
+ """Give more human-readable names to all internal tensors. You should
+ apply GiveUniqueNodeNames prior to this transform to avoid empty node names,
+ as the readable names are based on the node names."""
def apply(self, model):
# to ensure we can use rename_tensor safely (without renaming existing
@@ -120,6 +121,7 @@ class GiveReadableTensorNames(Transformation):
model = model.transform(GiveRandomTensorNames())
graph = model.graph
for n in graph.node:
+ assert n.name != "", "Found empty node name"
out_num = 0
for o in n.output:
model.rename_tensor(o, "%s_out%d" % (n.name, out_num))
diff --git a/src/finn/util/test.py b/src/finn/util/test.py
index b0985bddd3306f81ad0e81f0fb582a0ae7fdaf3d..3cd4248c5fbf438ac7dd7974adb38d251d389a07 100644
--- a/src/finn/util/test.py
+++ b/src/finn/util/test.py
@@ -26,11 +26,21 @@
# 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
+import onnx.numpy_helper as nph
+import pkg_resources as pk
+from pkgutil import get_data
from brevitas_examples import bnn_pynq
import numpy as np
import pytest
import warnings
from finn.core.modelwrapper import ModelWrapper
+import os
+from finn.util.basic import pynq_part_map, alveo_part_map, alveo_default_platform
+from finn.transformation.fpgadataflow.make_zynq_proj import ZynqBuild
+from finn.transformation.fpgadataflow.vitis_build import VitisBuild, VitisOptStrategy
+from finn.custom_op.registry import getCustomOp
+from finn.core.onnx_exec import execute_onnx
# map of (wbits,abits) -> model
example_map = {
@@ -78,9 +88,86 @@ def soft_verify_topk(invec, idxvec, k):
def load_test_checkpoint_or_skip(filename):
"Try to load given .onnx and return ModelWrapper, else skip current test."
- try:
+ if os.path.isfile(filename):
model = ModelWrapper(filename)
return model
- except FileNotFoundError:
+ else:
warnings.warn(filename + " not found from previous test step, skipping")
pytest.skip(filename + " not found from previous test step, skipping")
+
+
+def get_build_env(kind, target_clk_ns):
+ """Get board-related build environment for testing.
+ - kind = either zynq or alveo.
+ """
+ ret = {}
+ if kind == "zynq":
+ ret["board"] = os.getenv("PYNQ_BOARD", default="Pynq-Z1")
+ ret["part"] = pynq_part_map[ret["board"]]
+ ret["ip"] = os.getenv("PYNQ_IP", "")
+ ret["username"] = os.getenv("PYNQ_USERNAME", "xilinx")
+ ret["password"] = os.getenv("PYNQ_PASSWORD", "xilinx")
+ ret["port"] = os.getenv("PYNQ_PORT", 22)
+ ret["target_dir"] = os.getenv("PYNQ_TARGET_DIR", "/home/xilinx/finn")
+ ret["build_fxn"] = ZynqBuild(ret["board"], target_clk_ns)
+ elif kind == "alveo":
+ ret["board"] = os.getenv("ALVEO_BOARD", default="U250")
+ ret["part"] = alveo_part_map[ret["board"]]
+ ret["platform"] = alveo_default_platform[ret["board"]]
+ ret["ip"] = os.getenv("ALVEO_IP", "")
+ ret["username"] = os.getenv("ALVEO_USERNAME", "")
+ ret["password"] = os.getenv("ALVEO_PASSWORD", "")
+ ret["port"] = os.getenv("ALVEO_PORT", 22)
+ ret["target_dir"] = os.getenv("ALVEO_TARGET_DIR", "/tmp/finn_alveo_deploy")
+ ret["build_fxn"] = VitisBuild(
+ ret["part"],
+ target_clk_ns,
+ ret["platform"],
+ strategy=VitisOptStrategy.BUILD_SPEED,
+ )
+ else:
+ raise Exception("Unknown test build environment spec")
+ return ret
+
+
+def get_example_input(topology):
+ "Get example numpy input tensor for given topology."
+
+ if "fc" in topology:
+ raw_i = get_data("finn", "data/onnx/mnist-conv/test_data_set_0/input_0.pb")
+ onnx_tensor = onnx.load_tensor_from_string(raw_i)
+ return nph.to_array(onnx_tensor)
+ elif topology == "cnv":
+ 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
+ return input_tensor
+ else:
+ raise Exception("Unknown topology, can't return example input")
+
+
+def get_trained_network_and_ishape(topology, wbits, abits):
+ "Return (trained_model, shape) for given BNN-PYNQ test config."
+
+ topology_to_ishape = {
+ "tfc": (1, 1, 28, 28),
+ "cnv": (1, 3, 32, 32),
+ }
+ ishape = topology_to_ishape[topology]
+ model = get_test_model_trained(topology.upper(), wbits, abits)
+ return (model, ishape)
+
+
+def execute_parent(parent_path, child_path, input_tensor_npy):
+ """Execute parent model containing a single StreamingDataflowPartition by
+ replacing it with the model at child_path and return result."""
+
+ parent_model = load_test_checkpoint_or_skip(parent_path)
+ iname = parent_model.graph.input[0].name
+ oname = parent_model.graph.output[0].name
+ sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
+ sdp_node = getCustomOp(sdp_node)
+ sdp_node.set_nodeattr("model", child_path)
+ ret = execute_onnx(parent_model, {iname: input_tensor_npy}, True)
+ y = ret[oname]
+ return y
diff --git a/tests/brevitas/test_brevitas_cnv.py b/tests/brevitas/test_brevitas_cnv.py
index 764671bee13710ef1d9fa21aab5ef600075b9b0d..120c67646de08a1a9875b76bedd3a0130792b487 100644
--- a/tests/brevitas/test_brevitas_cnv.py
+++ b/tests/brevitas/test_brevitas_cnv.py
@@ -39,7 +39,6 @@ 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, RemoveStaticGraphInputs
-from finn.transformation.double_to_single_float import DoubleToSingleFloat
from finn.util.test import get_test_model_trained
export_onnx_path = "test_brevitas_cnv.onnx"
@@ -54,7 +53,6 @@ def test_brevitas_cnv_export_exec(wbits, abits):
bo.export_finn_onnx(cnv, (1, 3, 32, 32), export_onnx_path)
model = ModelWrapper(export_onnx_path)
model = model.transform(GiveUniqueNodeNames())
- model = model.transform(DoubleToSingleFloat())
model = model.transform(InferShapes())
model = model.transform(FoldConstants())
model = model.transform(RemoveStaticGraphInputs())
diff --git a/tests/brevitas/test_brevitas_debug.py b/tests/brevitas/test_brevitas_debug.py
index 7d2a9b6e5e01acfc218b6d8b6d0a8a0e73d7897d..50d0ca44cd0befe5d08b5c1b45edf602457bda19 100644
--- a/tests/brevitas/test_brevitas_debug.py
+++ b/tests/brevitas/test_brevitas_debug.py
@@ -75,5 +75,5 @@ def test_brevitas_debug():
for dbg_name in names_common:
tensor_pytorch = dbg_hook.values[dbg_name].detach().numpy()
tensor_finn = output_dict[dbg_name]
- assert np.isclose(tensor_finn, tensor_pytorch).all()
+ assert np.isclose(tensor_finn, tensor_pytorch, atol=1e-5).all()
os.remove(finn_onnx)
diff --git a/tests/end2end/test_end2end_bnn_pynq.py b/tests/end2end/test_end2end_bnn_pynq.py
new file mode 100644
index 0000000000000000000000000000000000000000..b997fbae195f9226a727ea82f51f3903ec0699de
--- /dev/null
+++ b/tests/end2end/test_end2end_bnn_pynq.py
@@ -0,0 +1,464 @@
+# 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 pytest
+
+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 torch
+import brevitas.onnx as bo
+
+import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
+import finn.transformation.streamline.absorb as absorb
+from finn.core.onnx_exec import execute_onnx
+from finn.custom_op.registry import getCustomOp
+from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
+from finn.transformation.fold_constants import FoldConstants
+
+from finn.transformation.fpgadataflow.create_dataflow_partition import (
+ CreateDataflowPartition,
+)
+from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
+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
+from finn.util.test import (
+ get_build_env,
+ load_test_checkpoint_or_skip,
+ get_example_input,
+ get_trained_network_and_ishape,
+ execute_parent,
+)
+from finn.transformation.fpgadataflow.annotate_resources import AnnotateResources
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.move_reshape import RemoveCNVtoFCFlatten
+from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
+from finn.transformation.streamline.reorder import MakeMaxPoolNHWC
+import warnings
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
+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.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP
+from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
+from finn.transformation.fpgadataflow.insert_dwc import InsertDWC
+from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO
+from finn.transformation.fpgadataflow.annotate_cycles import AnnotateCycles
+from finn.analysis.fpgadataflow.dataflow_performance import dataflow_performance
+from finn.core.modelwrapper import ModelWrapper
+from scipy.stats import linregress
+from finn.core.throughput_test import throughput_test_remote, throughput_test_rtlsim
+
+build_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
+target_clk_ns = 10
+mem_mode = "decoupled"
+rtlsim_trace = False
+
+
+def get_checkpoint_name(topology, wbits, abits, step):
+ return build_dir + "/end2end_%s_w%da%d_%s.onnx" % (topology, wbits, abits, step)
+
+
+def fold_tfc(model):
+ fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
+ # (PE, SIMD, in_fifo_depth, out_fifo_depth, ramstyle) for each layer
+ config = [
+ (16, 49, 16, 64, "block"),
+ (8, 8, 64, 64, "auto"),
+ (8, 8, 64, 64, "auto"),
+ (10, 8, 64, 10, "distributed"),
+ ]
+ for fcl, (pe, simd, ififo, ofifo, ramstyle) in zip(fc_layers, config):
+ fcl_inst = getCustomOp(fcl)
+ fcl_inst.set_nodeattr("PE", pe)
+ fcl_inst.set_nodeattr("SIMD", simd)
+ fcl_inst.set_nodeattr("inFIFODepth", ififo)
+ fcl_inst.set_nodeattr("outFIFODepth", ofifo)
+ fcl_inst.set_nodeattr("ram_style", ramstyle)
+ return model
+
+
+def fold_cnv_large(model):
+ fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
+ # each tuple is (PE, SIMD, in_fifo_depth) for a layer
+ folding = [
+ (16, 3, 256),
+ (32, 32, 256),
+ (16, 32, 256),
+ (16, 32, 256),
+ (4, 32, 214),
+ (1, 32, 2),
+ (1, 4, 126),
+ (1, 8, 62),
+ (5, 1, 6),
+ ]
+ for fcl, (pe, simd, ififodepth) 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)
+
+ 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])
+ return model
+
+
+def fold_cnv_small(model):
+ 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])
+ return model
+
+
+def get_folding_function(topology, wbits, abits):
+ if "tfc" in topology:
+ return fold_tfc
+ elif "cnv" in topology:
+ if wbits == 1 and abits == 1:
+ return fold_cnv_large
+ else:
+ return fold_cnv_small
+ else:
+ raise Exception("Unknown topology/quantization combo for predefined folding")
+
+
+def get_golden_io_pair(topology, wbits, abits):
+ (model, ishape) = get_trained_network_and_ishape(topology, wbits, abits)
+ input_tensor_npy = get_example_input(topology)
+ input_tensor_torch = torch.from_numpy(input_tensor_npy).float()
+ output_tensor_npy = model.forward(input_tensor_torch).detach().numpy()
+ return (input_tensor_npy, output_tensor_npy)
+
+
+@pytest.mark.parametrize("wbits", [1, 2])
+@pytest.mark.parametrize("abits", [1, 2])
+@pytest.mark.parametrize("topology", ["tfc", "cnv"])
+class TestEnd2End:
+ def test_export(self, topology, wbits, abits):
+ if wbits > abits:
+ pytest.skip("No wbits > abits end2end network configs for now")
+ (model, ishape) = get_trained_network_and_ishape(topology, wbits, abits)
+ chkpt_name = get_checkpoint_name(topology, wbits, abits, "export")
+ bo.export_finn_onnx(model, ishape, chkpt_name)
+ assert os.path.isfile(chkpt_name)
+
+ def test_import_and_tidy(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "export")
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ model = model.transform(InferShapes())
+ model = model.transform(FoldConstants())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(GiveReadableTensorNames())
+ model = model.transform(InferDataTypes())
+ model = model.transform(RemoveStaticGraphInputs())
+ model.save(get_checkpoint_name(topology, wbits, abits, "import_and_tidy"))
+
+ def test_streamline(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "import_and_tidy")
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ model = model.transform(Streamline())
+ if "fc" not in topology:
+ model = model.transform(LowerConvsToMatMul())
+ model = model.transform(MakeMaxPoolNHWC())
+ model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
+ model = model.transform(ConvertBipolarMatMulToXnorPopcount())
+ model = model.transform(Streamline())
+ model = model.transform(RemoveUnusedTensors())
+ model.save(get_checkpoint_name(topology, wbits, abits, "streamline"))
+
+ def test_convert_to_hls_layers(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "streamline")
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ # needed for bipolar MatMul layers
+ model = model.transform(to_hls.InferBinaryStreamingFCLayer(mem_mode))
+ # needed for non-bipolar MatMul layers
+ model = model.transform(to_hls.InferQuantizedStreamingFCLayer(mem_mode))
+ # needed for convolutions
+ if "fc" not in topology:
+ model = model.transform(to_hls.InferConvInpGen())
+ model = model.transform(to_hls.InferStreamingMaxPool())
+ model = model.transform(RemoveCNVtoFCFlatten())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(InferDataLayouts())
+ model.save(get_checkpoint_name(topology, wbits, abits, "convert_to_hls_layers"))
+
+ def test_create_dataflow_partition(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(
+ topology, wbits, abits, "convert_to_hls_layers"
+ )
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ parent_model = model.transform(CreateDataflowPartition())
+ parent_model_chkpt = get_checkpoint_name(
+ topology, wbits, abits, "dataflow_parent"
+ )
+ parent_model.save(parent_model_chkpt)
+ 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_chkpt = get_checkpoint_name(
+ topology, wbits, abits, "dataflow_model"
+ )
+ dataflow_model.save(dataflow_model_chkpt)
+
+ def test_fold(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "dataflow_model")
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ folding_fxn = get_folding_function(topology, wbits, abits)
+ model = folding_fxn(model)
+ model.save(get_checkpoint_name(topology, wbits, abits, "fold"))
+
+ @pytest.mark.slow
+ @pytest.mark.vivado
+ def test_cppsim(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "fold")
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
+ model = model.transform(SetExecMode("cppsim"))
+ cppsim_chkpt = get_checkpoint_name(topology, wbits, abits, "cppsim")
+ model.save(cppsim_chkpt)
+ parent_chkpt = get_checkpoint_name(topology, wbits, abits, "dataflow_parent")
+ (input_tensor_npy, output_tensor_npy) = get_golden_io_pair(
+ topology, wbits, abits
+ )
+ y = execute_parent(parent_chkpt, cppsim_chkpt, input_tensor_npy)
+ assert np.isclose(y, output_tensor_npy).all()
+
+ @pytest.mark.slow
+ @pytest.mark.vivado
+ def test_ipgen(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "fold")
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ test_fpga_part = get_build_env("zynq", target_clk_ns)["part"]
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(PrepareIP(test_fpga_part, target_clk_ns))
+ model = model.transform(HLSSynthIP())
+ model.save(get_checkpoint_name(topology, wbits, abits, "ipgen"))
+
+ @pytest.mark.slow
+ @pytest.mark.vivado
+ def test_ipstitch_rtlsim(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "ipgen")
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ test_fpga_part = get_build_env("zynq", target_clk_ns)["part"]
+ model = model.transform(InsertDWC())
+ model = model.transform(InsertFIFO())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(AnnotateCycles())
+ perf = model.analysis(dataflow_performance)
+ latency = perf["critical_path_cycles"]
+ model = model.transform(PrepareIP(test_fpga_part, target_clk_ns))
+ model = model.transform(HLSSynthIP())
+ model = model.transform(CreateStitchedIP(test_fpga_part, target_clk_ns))
+ model = model.transform(PrepareRTLSim())
+ model.set_metadata_prop("exec_mode", "rtlsim")
+ os.environ["LIVENESS_THRESHOLD"] = str(int(latency * 1.1))
+ if rtlsim_trace:
+ model.set_metadata_prop(
+ "rtlsim_trace", "%s_w%da%d.vcd" % (topology, wbits, abits)
+ )
+ os.environ["RTLSIM_TRACE_DEPTH"] = "3"
+ rtlsim_chkpt = get_checkpoint_name(topology, wbits, abits, "ipstitch_rtlsim")
+ model.save(rtlsim_chkpt)
+ parent_chkpt = get_checkpoint_name(topology, wbits, abits, "dataflow_parent")
+ (input_tensor_npy, output_tensor_npy) = get_golden_io_pair(
+ topology, wbits, abits
+ )
+ y = execute_parent(parent_chkpt, rtlsim_chkpt, input_tensor_npy)
+ model = ModelWrapper(rtlsim_chkpt)
+ perf["cycles_rtlsim"] = model.get_metadata_prop("cycles_rtlsim")
+ warnings.warn("Estimated & rtlsim performance: " + str(perf))
+ assert np.isclose(y, output_tensor_npy).all()
+
+ @pytest.mark.slow
+ @pytest.mark.vivado
+ def test_throughput_rtlsim(self, topology, wbits, abits):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "ipstitch_rtlsim")
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ n_nodes = len(model.graph.node)
+ perf_est = model.analysis(dataflow_performance)
+ latency = int(model.get_metadata_prop("cycles_rtlsim"))
+ cycles_per_sample_est = perf_est["max_cycles"]
+ batchsize = 2 * n_nodes
+ ret = throughput_test_rtlsim(model, batchsize=batchsize)
+ res_cycles = ret["cycles"]
+ est_cycles = latency + cycles_per_sample_est * batchsize
+ assert (abs(res_cycles - est_cycles) / res_cycles) < 0.15
+
+ @pytest.mark.slow
+ @pytest.mark.vivado
+ @pytest.mark.vitis
+ @pytest.mark.parametrize("kind", ["zynq", "alveo"])
+ def test_build(self, topology, wbits, abits, kind):
+ if kind == "alveo" and ("VITIS_PATH" not in os.environ):
+ pytest.skip("VITIS_PATH not set")
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "ipgen")
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ cfg = get_build_env(kind, target_clk_ns)
+ model = model.transform(cfg["build_fxn"])
+ model = model.transform(AnnotateResources("synth"))
+ warnings.warn(
+ "Post-synthesis resources (excluding shell): "
+ + model.get_metadata_prop("res_total_synth")
+ )
+ warnings.warn(
+ "Post-synthesis resources (all inclusive): "
+ + model.get_metadata_prop("res_total_top_synth")
+ )
+ model.save(get_checkpoint_name(topology, wbits, abits, "build_" + kind))
+
+ @pytest.mark.parametrize("kind", ["zynq", "alveo"])
+ def test_deploy(self, topology, wbits, abits, kind):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "build_" + kind)
+ model = load_test_checkpoint_or_skip(prev_chkpt_name)
+ cfg = get_build_env(kind, target_clk_ns)
+ if cfg["ip"] == "":
+ pytest.skip("PYNQ board IP address not specified")
+ model = model.transform(
+ DeployToPYNQ(
+ cfg["ip"],
+ cfg["port"],
+ cfg["username"],
+ cfg["password"],
+ cfg["target_dir"],
+ )
+ )
+ # save the model to be able to link it to the parent
+ model.save(get_checkpoint_name(topology, wbits, abits, "deploy_" + kind))
+
+ @pytest.mark.parametrize("kind", ["zynq", "alveo"])
+ def test_run_on_pynq(self, topology, wbits, abits, kind):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "deploy_" + kind)
+ model = load_test_checkpoint_or_skip(prev_chkpt_name) # NOQA
+ cfg = get_build_env(kind, target_clk_ns)
+ if cfg["ip"] == "":
+ pytest.skip("PYNQ board IP address not specified")
+ (input_tensor_npy, output_tensor_npy) = get_golden_io_pair(
+ topology, wbits, abits
+ )
+ parent_model = load_test_checkpoint_or_skip(
+ get_checkpoint_name(topology, wbits, abits, "dataflow_parent")
+ )
+ iname = parent_model.graph.input[0].name
+ oname = parent_model.graph.output[0].name
+ sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
+ sdp_node = getCustomOp(sdp_node)
+ sdp_chkpt = get_checkpoint_name(topology, wbits, abits, "deploy")
+ load_test_checkpoint_or_skip(sdp_chkpt)
+ sdp_node.set_nodeattr("model", sdp_chkpt)
+ ret = execute_onnx(parent_model, {iname: input_tensor_npy}, True)
+ y = ret[oname]
+ assert np.isclose(y, output_tensor_npy).all()
+
+ @pytest.mark.parametrize("kind", ["zynq", "alveo"])
+ def test_throughput_hw(self, topology, wbits, abits, kind):
+ prev_chkpt_name = get_checkpoint_name(topology, wbits, abits, "deploy_" + kind)
+ end2end_example = "%s_w%da%d_%s" % (topology, wbits, abits, kind)
+ model = load_test_checkpoint_or_skip(prev_chkpt_name) # NOQA
+ cfg = get_build_env(kind, target_clk_ns)
+ if cfg["ip"] == "":
+ pytest.skip("PYNQ board IP address not specified")
+ ret = dict()
+ # try a range of batch sizes, some may fail due to insufficient DMA
+ # buffers
+ bsize_range_in = [8 ** i for i in range(5)]
+ bsize_range = []
+ for bsize in bsize_range_in:
+ res = throughput_test_remote(model, bsize)
+ if res is not None:
+ ret[bsize] = res
+ bsize_range.append(bsize)
+ else:
+ # assume we reached largest possible N
+ break
+ y = [ret[key]["runtime[ms]"] for key in bsize_range]
+ lrret = linregress(bsize_range, y)
+ ret_str = ""
+ ret_str += "\n" + "%s Throughput Test Results" % end2end_example
+ ret_str += "\n" + "-----------------------------"
+ ret_str += "\n" + "From linear regression:"
+ ret_str += "\n" + "Invocation overhead: %f ms" % lrret.intercept
+ ret_str += "\n" + "Time per sample: %f ms" % lrret.slope
+ ret_str += "\n" + "Raw data:"
+
+ ret_str += "\n" + "{:<8} {:<16} {:<16} {:<16} {:<16} {:<16}".format(
+ "N", "runtime[ms]", "fclk[mhz]", "fps", "DRAM rd[Mb/s]", "DRAM wr[Mb/s]"
+ )
+ for k in bsize_range:
+ v = ret[k]
+ ret_str += "\n" + "{:<8} {:<16} {:<16} {:<16} {:<16} {:<16}".format(
+ k,
+ np.round(v["runtime[ms]"], 4),
+ v["fclk[mhz]"],
+ np.round(v["throughput[images/s]"], 2),
+ np.round(v["DRAM_in_bandwidth[Mb/s]"], 2),
+ np.round(v["DRAM_out_bandwidth[Mb/s]"], 2),
+ )
+ ret_str += "\n" + "-----------------------------"
+ warnings.warn(ret_str)
diff --git a/tests/end2end/test_end2end_cnv_w1a1.py b/tests/end2end/test_end2end_cnv_w1a1.py
deleted file mode 100644
index f931f91c89f738899ff9e6584be81a3b2d542227..0000000000000000000000000000000000000000
--- a/tests/end2end/test_end2end_cnv_w1a1.py
+++ /dev/null
@@ -1,391 +0,0 @@
-# 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
-from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
-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
-import warnings
-
-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_w1a1_export():
- import brevitas.onnx as bo
-
- cnv = get_test_model_trained("CNV", 1, 1)
- bo.export_finn_onnx(
- cnv, (1, 3, 32, 32), build_dir + "/end2end_cnv_w1a1_export.onnx"
- )
-
-
-def test_end2end_cnv_w1a1_import_and_tidy():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w1a1_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_w1a1_tidy.onnx")
-
-
-def test_end2end_cnv_w1a1_streamline():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w1a1_tidy.onnx")
- model = model.transform(Streamline())
- model = model.transform(LowerConvsToMatMul())
- model = model.transform(MakeMaxPoolNHWC())
- 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")
-
-
-def test_end2end_cnv_w1a1_convert_to_hls_layers():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_cnv_w1a1_streamlined.onnx"
- )
- model = model.transform(to_hls.InferBinaryStreamingFCLayer(mem_mode))
- 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_w1a1_hls_layers.onnx")
-
-
-def test_end2end_cnv_w1a1_create_dataflow_partition():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_cnv_w1a1_hls_layers.onnx"
- )
- parent_model = model.transform(CreateDataflowPartition())
- parent_model.save(build_dir + "/end2end_cnv_w1a1_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_w1a1_dataflow_model.onnx")
-
-
-def test_end2end_cnv_w1a1_fold_and_tlastmarker():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_cnv_w1a1_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 = [
- (16, 3, 256),
- (32, 32, 256),
- (16, 32, 256),
- (16, 32, 256),
- (4, 32, 214),
- (1, 32, 2),
- (1, 4, 126),
- (1, 8, 62),
- (5, 1, 6),
- ]
- for fcl, (pe, simd, ififodepth) 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)
-
- 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_w1a1_folded.onnx")
-
-
-@pytest.mark.slow
-@pytest.mark.vivado
-def test_end2end_cnv_w1a1_gen_hls_ip():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w1a1_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_w1a1_ipgen.onnx")
-
-
-@pytest.mark.vivado
-def test_end2end_cnv_w1a1_ip_stitch():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w1a1_ipgen.onnx")
- model = model.transform(ReplaceVerilogRelPaths())
- model = model.transform(CreateStitchedIP(test_fpga_part, target_clk_ns))
- model.save(build_dir + "/end2end_cnv_w1a1_ipstitch.onnx")
-
-
-@pytest.mark.vivado
-def test_end2end_cnv_w1a1_verify_dataflow_part():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w1a1_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_w1a1_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_w1a1_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_w1a1_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_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
- golden = load_test_checkpoint_or_skip(
- build_dir + "/end2end_cnv_w1a1_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_w1a1_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_w1a1_ipgen_cppsim.onnx")
- sdp_node.set_nodeattr("model", build_dir + "/end2end_cnv_w1a1_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_w1a1_ipgen_nodebynode_rtlsim.onnx"
- )
- sdp_node.set_nodeattr(
- "model", build_dir + "/end2end_cnv_w1a1_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_w1a1_ipstitch_whole_rtlsim.onnx"
- )
- sdp_node.set_nodeattr(
- "model", build_dir + "/end2end_cnv_w1a1_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_w1a1_make_pynq_proj():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w1a1_ipstitch.onnx")
- model = model.transform(MakePYNQProject(test_pynq_board))
- model.save(build_dir + "/end2end_cnv_w1a1_pynq_project.onnx")
-
-
-@pytest.mark.slow
-@pytest.mark.vivado
-def test_end2end_cnv_w1a1_synth_pynq_project():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_cnv_w1a1_pynq_project.onnx"
- )
- model = model.transform(SynthPYNQProject())
- model = model.transform(AnnotateResources("synth"))
- warnings.warn(
- "Post-synthesis resources (excluding shell): "
- + model.get_metadata_prop("res_total_synth")
- )
- model.save(build_dir + "/end2end_cnv_w1a1_synth.onnx")
-
-
-def test_end2end_cnv_w1a1_make_driver():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w1a1_synth.onnx")
- model = model.transform(MakePYNQDriver(platform="zynq"))
- model.save(build_dir + "/end2end_cnv_w1a1_pynq_driver.onnx")
-
-
-def test_end2end_cnv_w1a1_deploy_on_pynq():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_cnv_w1a1_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_w1a1_pynq_deploy.onnx")
- except KeyError:
- pytest.skip("PYNQ board IP address not specified")
-
-
-def test_end2end_cnv_w1a1_run_on_pynq():
- # use the streamlined model as the "golden" model for right answers
- golden = load_test_checkpoint_or_skip(
- build_dir + "/end2end_cnv_w1a1_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_w1a1_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_w1a1_pynq_deploy.onnx")
- sdp_node.set_nodeattr("model", build_dir + "/end2end_cnv_w1a1_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_cnv_w2a2.py b/tests/end2end/test_end2end_cnv_w2a2.py
deleted file mode 100644
index 239094a3c931c16b3afe8d1874345e4dc90334ef..0000000000000000000000000000000000000000
--- a/tests/end2end/test_end2end_cnv_w2a2.py
+++ /dev/null
@@ -1,389 +0,0 @@
-# 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
-import warnings
-
-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"))
- warnings.warn(
- "Post-synthesis resources (excluding shell): "
- + model.get_metadata_prop("res_total_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(platform="zynq"))
- 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
deleted file mode 100644
index 1a3cc4f1bb9232809e864bb0c784498534f63631..0000000000000000000000000000000000000000
--- a/tests/end2end/test_end2end_tfc_w1a1.py
+++ /dev/null
@@ -1,385 +0,0 @@
-# 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 pkgutil import get_data
-
-import pytest
-
-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 onnx.numpy_helper as nph
-
-import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
-import finn.transformation.streamline.absorb as absorb
-from finn.core.onnx_exec import execute_onnx
-from finn.custom_op.registry import getCustomOp
-from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
-from finn.transformation.fold_constants import FoldConstants
-from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
-from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP
-from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
-from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
-from finn.transformation.fpgadataflow.create_dataflow_partition import (
- CreateDataflowPartition,
-)
-from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
-from finn.transformation.fpgadataflow.insert_dwc import InsertDWC
-from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker
-from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO
-from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
-from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver
-from finn.transformation.fpgadataflow.make_pynq_proj import MakePYNQProject
-from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
- ReplaceVerilogRelPaths,
-)
-from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
-from finn.transformation.fpgadataflow.synth_pynq_proj import SynthPYNQProject
-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
-from finn.transformation.streamline.round_thresholds import RoundAndClipThresholds
-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
-import warnings
-
-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_tfc_w1a1_export():
- import brevitas.onnx as bo
-
- tfc = get_test_model_trained("TFC", 1, 1)
- bo.export_finn_onnx(
- tfc, (1, 1, 28, 28), build_dir + "/end2end_tfc_w1a1_export.onnx"
- )
-
-
-def test_end2end_tfc_w1a1_import_and_tidy():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w1a1_export.onnx")
- model = model.transform(InferShapes())
- model = model.transform(FoldConstants())
- 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")
-
-
-def test_end2end_tfc_w1a1_convert_to_hls_layers():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w1a1_streamlined.onnx"
- )
- model = model.transform(ConvertBipolarMatMulToXnorPopcount())
- model = model.transform(absorb.AbsorbAddIntoMultiThreshold())
- model = model.transform(absorb.AbsorbMulIntoMultiThreshold())
- model = model.transform(RoundAndClipThresholds())
- model = model.transform(to_hls.InferBinaryStreamingFCLayer(mem_mode))
- model.save(build_dir + "/end2end_tfc_w1a1_hls_layers.onnx")
-
-
-def test_end2end_tfc_w1a1_create_dataflow_partition():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w1a1_hls_layers.onnx"
- )
- parent_model = model.transform(CreateDataflowPartition())
- parent_model.save(build_dir + "/end2end_tfc_w1a1_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_tfc_w1a1_dataflow_model.onnx")
-
-
-def test_end2end_tfc_w1a1_fold_and_tlastmarker():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w1a1_dataflow_model.onnx"
- )
- fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
- # (PE, SIMD, in_fifo_depth, out_fifo_depth, ramstyle) for each layer
- config = [
- (16, 49, 16, 64, "block"),
- (8, 8, 64, 64, "auto"),
- (8, 8, 64, 64, "auto"),
- (10, 8, 64, 10, "distributed"),
- ]
- for fcl, (pe, simd, ififo, ofifo, ramstyle) in zip(fc_layers, config):
- fcl_inst = getCustomOp(fcl)
- fcl_inst.set_nodeattr("PE", pe)
- fcl_inst.set_nodeattr("SIMD", simd)
- fcl_inst.set_nodeattr("inFIFODepth", ififo)
- fcl_inst.set_nodeattr("outFIFODepth", ofifo)
- fcl_inst.set_nodeattr("ram_style", ramstyle)
- 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_tfc_w1a1_folded.onnx")
-
-
-@pytest.mark.slow
-@pytest.mark.vivado
-def test_end2end_tfc_w1a1_gen_hls_ip():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w1a1_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_tfc_w1a1_ipgen.onnx")
-
-
-@pytest.mark.vivado
-def test_end2end_tfc_w1a1_ip_stitch():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w1a1_ipgen.onnx")
- model = model.transform(ReplaceVerilogRelPaths())
- model = model.transform(CreateStitchedIP(test_fpga_part, target_clk_ns))
- model.save(build_dir + "/end2end_tfc_w1a1_ipstitch.onnx")
-
-
-@pytest.mark.vivado
-def test_end2end_tfc_w1a1_verify_dataflow_part():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w1a1_ipstitch.onnx")
- x = np.zeros((1, 784), 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_tfc_w1a1_ipstitch_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_tfc_w1a1_ipstitch_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.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]
- assert np.isclose(res_cppsim, res_rtlsim_nodebynode).all()
- 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 np.isclose(ret["cycles"], 205, atol=5)
- ret = throughput_test_rtlsim(model, 10)
- assert np.isclose(ret["cycles"], 844, atol=10)
- ret = throughput_test_rtlsim(model, 100)
- assert np.isclose(ret["cycles"], 7234, atol=100)
-
-
-@pytest.mark.vivado
-def test_end2end_tfc_w1a1_verify_all():
- # use the streamlined model as the "golden" model for right answers
- golden = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w1a1_streamlined.onnx"
- )
- iname = golden.graph.input[0].name
- oname = golden.graph.output[0].name
- 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)
- x = nph.to_array(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_tfc_w1a1_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_tfc_w1a1_ipstitch_cppsim.onnx")
- sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w1a1_ipstitch_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_tfc_w1a1_ipstitch_nodebynode_rtlsim.onnx"
- )
- sdp_node.set_nodeattr(
- "model", build_dir + "/end2end_tfc_w1a1_ipstitch_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_tfc_w1a1_ipstitch_whole_rtlsim.onnx"
- )
- sdp_node.set_nodeattr(
- "model", build_dir + "/end2end_tfc_w1a1_ipstitch_whole_rtlsim.onnx"
- )
- 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()
-
-
-@pytest.mark.vivado
-def test_end2end_tfc_w1a1_make_pynq_proj():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w1a1_ipstitch.onnx")
- model = model.transform(MakePYNQProject(test_pynq_board))
- model.save(build_dir + "/end2end_tfc_w1a1_pynq_project.onnx")
-
-
-@pytest.mark.slow
-@pytest.mark.vivado
-def test_end2end_tfc_w1a1_synth_pynq_project():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w1a1_pynq_project.onnx"
- )
- model = model.transform(SynthPYNQProject())
- model = model.transform(AnnotateResources("synth"))
- warnings.warn(
- "Post-synthesis resources (excluding shell): "
- + model.get_metadata_prop("res_total_synth")
- )
- model.save(build_dir + "/end2end_tfc_w1a1_synth.onnx")
-
-
-def test_end2end_tfc_w1a1_make_driver():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w1a1_synth.onnx")
- model = model.transform(MakePYNQDriver(platform="zynq"))
- model.save(build_dir + "/end2end_tfc_w1a1_pynq_driver.onnx")
-
-
-def test_end2end_tfc_w1a1_deploy_on_pynq():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w1a1_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_tfc_w1a1_pynq_deploy.onnx")
- except KeyError:
- pytest.skip("PYNQ board IP address not specified")
-
-
-def test_end2end_tfc_w1a1_run_on_pynq():
- # use the streamlined model as the "golden" model for right answers
- golden = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w1a1_streamlined.onnx"
- )
- iname = golden.graph.input[0].name
- oname = golden.graph.output[0].name
- 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)
- x = nph.to_array(input_tensor)
- # x = np.zeros(ishape, dtype=np.float32)
- # 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_tfc_w1a1_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_tfc_w1a1_pynq_deploy.onnx")
- sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w1a1_pynq_deploy.onnx")
- ret = execute_onnx(parent_model, {iname: x}, True)
- y = ret[oname]
- assert np.isclose(y, y_golden).all()
-
- except KeyError:
- pytest.skip("PYNQ board IP address not specified")
diff --git a/tests/end2end/test_end2end_tfc_w1a2.py b/tests/end2end/test_end2end_tfc_w1a2.py
deleted file mode 100644
index 0f066cb06c53ce118d0a357fce0999299d7f3305..0000000000000000000000000000000000000000
--- a/tests/end2end/test_end2end_tfc_w1a2.py
+++ /dev/null
@@ -1,341 +0,0 @@
-# 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 pkgutil import get_data
-
-import pytest
-
-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 onnx.numpy_helper as nph
-
-import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
-from finn.core.onnx_exec import execute_onnx
-from finn.custom_op.registry import getCustomOp
-from finn.transformation.fold_constants import FoldConstants
-from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
-from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP
-from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
-from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
-from finn.transformation.fpgadataflow.create_dataflow_partition import (
- CreateDataflowPartition,
-)
-from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
-from finn.transformation.fpgadataflow.insert_dwc import InsertDWC
-from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO
-from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker
-from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
-from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver
-from finn.transformation.fpgadataflow.make_pynq_proj import MakePYNQProject
-from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
- ReplaceVerilogRelPaths,
-)
-from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
-from finn.transformation.fpgadataflow.synth_pynq_proj import SynthPYNQProject
-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
-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
-import warnings
-
-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_tfc_w1a2_export():
- import brevitas.onnx as bo
-
- tfc = get_test_model_trained("TFC", 1, 2)
- bo.export_finn_onnx(
- tfc, (1, 1, 28, 28), build_dir + "/end2end_tfc_w1a2_export.onnx"
- )
-
-
-def test_end2end_tfc_w1a2_import_and_tidy():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w1a2_export.onnx")
- model = model.transform(InferShapes())
- model = model.transform(FoldConstants())
- 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")
-
-
-def test_end2end_tfc_w1a2_convert_to_hls_layers():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w1a2_streamlined.onnx"
- )
- model = model.transform(to_hls.InferQuantizedStreamingFCLayer(mem_mode))
- model.save(build_dir + "/end2end_tfc_w1a2_hls_layers.onnx")
-
-
-def test_end2end_tfc_w1a2_create_dataflow_partition():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w1a2_hls_layers.onnx"
- )
- parent_model = model.transform(CreateDataflowPartition())
- parent_model.save(build_dir + "/end2end_tfc_w1a2_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_tfc_w1a2_dataflow_model.onnx")
-
-
-def test_end2end_tfc_w1a2_fold_and_tlastmarker():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w1a2_dataflow_model.onnx"
- )
- fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
- # (PE, SIMD, in_fifo_depth, out_fifo_depth, ramstyle) for each layer
- config = [
- (16, 49, 16, 64, "block"),
- (8, 8, 64, 64, "auto"),
- (8, 8, 64, 64, "auto"),
- (10, 8, 64, 10, "distributed"),
- ]
- for fcl, (pe, simd, ififo, ofifo, ramstyle) in zip(fc_layers, config):
- fcl_inst = getCustomOp(fcl)
- fcl_inst.set_nodeattr("PE", pe)
- fcl_inst.set_nodeattr("SIMD", simd)
- fcl_inst.set_nodeattr("inFIFODepth", ififo)
- fcl_inst.set_nodeattr("outFIFODepth", ofifo)
- fcl_inst.set_nodeattr("ram_style", ramstyle)
- 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_tfc_w1a2_folded.onnx")
-
-
-@pytest.mark.slow
-@pytest.mark.vivado
-def test_end2end_tfc_w1a2_gen_hls_ip():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w1a2_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_tfc_w1a2_ipgen.onnx")
-
-
-@pytest.mark.vivado
-def test_end2end_tfc_w1a2_ip_stitch():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w1a2_ipgen.onnx")
- model = model.transform(ReplaceVerilogRelPaths())
- model = model.transform(CreateStitchedIP(test_fpga_part, target_clk_ns))
- model.save(build_dir + "/end2end_tfc_w1a2_ipstitch.onnx")
-
-
-@pytest.mark.vivado
-def test_end2end_tfc_w1a2_verify_dataflow_part():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w1a2_ipstitch.onnx")
- x = np.zeros((1, 784), 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_tfc_w1a2_ipstitch_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_tfc_w1a2_ipstitch_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_tfc_w1a2_ipstitch_whole_rtlsim.onnx")
- 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_tfc_w1a2_verify_all():
- # use the streamlined model as the "golden" model for right answers
- golden = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w1a2_streamlined.onnx"
- )
- iname = golden.graph.input[0].name
- oname = golden.graph.output[0].name
- 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)
- x = nph.to_array(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_tfc_w1a2_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_tfc_w1a2_ipstitch_cppsim.onnx")
- sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w1a2_ipstitch_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_tfc_w1a2_ipstitch_nodebynode_rtlsim.onnx"
- )
- sdp_node.set_nodeattr(
- "model", build_dir + "/end2end_tfc_w1a2_ipstitch_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_tfc_w1a2_ipstitch_whole_rtlsim.onnx"
- )
- sdp_node.set_nodeattr(
- "model", build_dir + "/end2end_tfc_w1a2_ipstitch_whole_rtlsim.onnx"
- )
- 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()
-
-
-@pytest.mark.vivado
-def test_end2end_tfc_w1a2_make_pynq_proj():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w1a2_ipstitch.onnx")
- model = model.transform(MakePYNQProject(test_pynq_board))
- model.save(build_dir + "/end2end_tfc_w1a2_pynq_project.onnx")
-
-
-@pytest.mark.slow
-@pytest.mark.vivado
-def test_end2end_tfc_w1a2_synth_pynq_project():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w1a2_pynq_project.onnx"
- )
- model = model.transform(SynthPYNQProject())
- model = model.transform(AnnotateResources("synth"))
- warnings.warn(
- "Post-synthesis resources (excluding shell): "
- + model.get_metadata_prop("res_total_synth")
- )
- model.save(build_dir + "/end2end_tfc_w1a2_synth.onnx")
-
-
-def test_end2end_tfc_w1a2_make_driver():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w1a2_synth.onnx")
- model = model.transform(MakePYNQDriver(platform="zynq"))
- model.save(build_dir + "/end2end_tfc_w1a2_pynq_driver.onnx")
-
-
-def test_end2end_tfc_w1a2_deploy_on_pynq():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w1a2_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_tfc_w1a2_pynq_deploy.onnx")
- except KeyError:
- pytest.skip("PYNQ board IP address not specified")
-
-
-def test_end2end_tfc_w1a2_run_on_pynq():
- # use the streamlined model as the "golden" model for right answers
- golden = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w1a2_streamlined.onnx"
- )
- iname = golden.graph.input[0].name
- oname = golden.graph.output[0].name
- 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)
- x = nph.to_array(input_tensor)
- # x = np.zeros(ishape, dtype=np.float32)
- # 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_tfc_w1a2_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_tfc_w1a2_pynq_deploy.onnx")
- sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w1a2_pynq_deploy.onnx")
- ret = execute_onnx(parent_model, {iname: x}, True)
- y = ret[oname]
- assert np.isclose(y, y_golden).all()
-
- except KeyError:
- pytest.skip("PYNQ board IP address not specified")
diff --git a/tests/end2end/test_end2end_tfc_w2a2.py b/tests/end2end/test_end2end_tfc_w2a2.py
deleted file mode 100644
index 6eb613fc877b6e6801140f2a03c3a9509c08c0cb..0000000000000000000000000000000000000000
--- a/tests/end2end/test_end2end_tfc_w2a2.py
+++ /dev/null
@@ -1,341 +0,0 @@
-# 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 pkgutil import get_data
-
-import pytest
-
-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 onnx.numpy_helper as nph
-
-import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
-from finn.core.onnx_exec import execute_onnx
-from finn.custom_op.registry import getCustomOp
-from finn.transformation.fold_constants import FoldConstants
-from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
-from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP
-from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
-from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
-from finn.transformation.fpgadataflow.create_dataflow_partition import (
- CreateDataflowPartition,
-)
-from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
-from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker
-from finn.transformation.fpgadataflow.insert_dwc import InsertDWC
-from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO
-from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
-from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver
-from finn.transformation.fpgadataflow.make_pynq_proj import MakePYNQProject
-from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
- ReplaceVerilogRelPaths,
-)
-from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
-from finn.transformation.fpgadataflow.synth_pynq_proj import SynthPYNQProject
-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
-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
-import warnings
-
-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_tfc_w2a2_export():
- import brevitas.onnx as bo
-
- tfc = get_test_model_trained("TFC", 2, 2)
- bo.export_finn_onnx(
- tfc, (1, 1, 28, 28), build_dir + "/end2end_tfc_w2a2_export.onnx"
- )
-
-
-def test_end2end_tfc_w2a2_import_and_tidy():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w2a2_export.onnx")
- model = model.transform(InferShapes())
- model = model.transform(FoldConstants())
- 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")
-
-
-def test_end2end_tfc_w2a2_convert_to_hls_layers():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w2a2_streamlined.onnx"
- )
- model = model.transform(to_hls.InferQuantizedStreamingFCLayer(mem_mode))
- model.save(build_dir + "/end2end_tfc_w2a2_hls_layers.onnx")
-
-
-def test_end2end_tfc_w2a2_create_dataflow_partition():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w2a2_hls_layers.onnx"
- )
- parent_model = model.transform(CreateDataflowPartition())
- parent_model.save(build_dir + "/end2end_tfc_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_tfc_w2a2_dataflow_model.onnx")
-
-
-def test_end2end_tfc_w2a2_fold_and_tlastmarker():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w2a2_dataflow_model.onnx"
- )
- fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
- # (PE, SIMD, in_fifo_depth, out_fifo_depth, ramstyle) for each layer
- config = [
- (16, 49, 16, 64, "block"),
- (8, 8, 64, 64, "auto"),
- (8, 8, 64, 64, "auto"),
- (10, 8, 64, 10, "distributed"),
- ]
- for fcl, (pe, simd, ififo, ofifo, ramstyle) in zip(fc_layers, config):
- fcl_inst = getCustomOp(fcl)
- fcl_inst.set_nodeattr("PE", pe)
- fcl_inst.set_nodeattr("SIMD", simd)
- fcl_inst.set_nodeattr("inFIFODepth", ififo)
- fcl_inst.set_nodeattr("outFIFODepth", ofifo)
- fcl_inst.set_nodeattr("ram_style", ramstyle)
- 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_tfc_w2a2_folded.onnx")
-
-
-@pytest.mark.slow
-@pytest.mark.vivado
-def test_end2end_tfc_w2a2_gen_hls_ip():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_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_tfc_w2a2_ipgen.onnx")
-
-
-@pytest.mark.vivado
-def test_end2end_tfc_w2a2_ip_stitch():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w2a2_ipgen.onnx")
- model = model.transform(ReplaceVerilogRelPaths())
- model = model.transform(CreateStitchedIP(test_fpga_part, target_clk_ns))
- model.save(build_dir + "/end2end_tfc_w2a2_ipstitch.onnx")
-
-
-@pytest.mark.vivado
-def test_end2end_tfc_w2a2_verify_dataflow_part():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w2a2_ipstitch.onnx")
- x = np.zeros((1, 784), 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_tfc_w2a2_ipstitch_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_tfc_w2a2_ipstitch_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_tfc_w2a2_ipstitch_whole_rtlsim.onnx")
- 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_tfc_w2a2_verify_all():
- # use the streamlined model as the "golden" model for right answers
- golden = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w2a2_streamlined.onnx"
- )
- iname = golden.graph.input[0].name
- oname = golden.graph.output[0].name
- 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)
- x = nph.to_array(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_tfc_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_tfc_w2a2_ipstitch_cppsim.onnx")
- sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w2a2_ipstitch_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_tfc_w2a2_ipstitch_nodebynode_rtlsim.onnx"
- )
- sdp_node.set_nodeattr(
- "model", build_dir + "/end2end_tfc_w2a2_ipstitch_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_tfc_w2a2_ipstitch_whole_rtlsim.onnx"
- )
- sdp_node.set_nodeattr(
- "model", build_dir + "/end2end_tfc_w2a2_ipstitch_whole_rtlsim.onnx"
- )
- 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()
-
-
-@pytest.mark.vivado
-def test_end2end_tfc_w2a2_make_pynq_proj():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w2a2_ipstitch.onnx")
- model = model.transform(MakePYNQProject(test_pynq_board))
- model.save(build_dir + "/end2end_tfc_w2a2_pynq_project.onnx")
-
-
-@pytest.mark.slow
-@pytest.mark.vivado
-def test_end2end_tfc_w2a2_synth_pynq_project():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w2a2_pynq_project.onnx"
- )
- model = model.transform(SynthPYNQProject())
- model = model.transform(AnnotateResources("synth"))
- warnings.warn(
- "Post-synthesis resources (excluding shell): "
- + model.get_metadata_prop("res_total_synth")
- )
- model.save(build_dir + "/end2end_tfc_w2a2_synth.onnx")
-
-
-def test_end2end_tfc_w2a2_make_driver():
- model = load_test_checkpoint_or_skip(build_dir + "/end2end_tfc_w2a2_synth.onnx")
- model = model.transform(MakePYNQDriver(platform="zynq"))
- model.save(build_dir + "/end2end_tfc_w2a2_pynq_driver.onnx")
-
-
-def test_end2end_tfc_w2a2_deploy_on_pynq():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_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_tfc_w2a2_pynq_deploy.onnx")
- except KeyError:
- pytest.skip("PYNQ board IP address not specified")
-
-
-def test_end2end_tfc_w2a2_run_on_pynq():
- # use the streamlined model as the "golden" model for right answers
- golden = load_test_checkpoint_or_skip(
- build_dir + "/end2end_tfc_w2a2_streamlined.onnx"
- )
- iname = golden.graph.input[0].name
- oname = golden.graph.output[0].name
- 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)
- x = nph.to_array(input_tensor)
- # x = np.zeros(ishape, dtype=np.float32)
- # 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_tfc_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_tfc_w2a2_pynq_deploy.onnx")
- sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w2a2_pynq_deploy.onnx")
- ret = execute_onnx(parent_model, {iname: x}, True)
- y = ret[oname]
- assert np.isclose(y, y_golden).all()
-
- except KeyError:
- pytest.skip("PYNQ board IP address not specified")
diff --git a/tests/end2end/test_vitis_end2end_cnv_w1a1.py b/tests/end2end/test_vitis_end2end_cnv_w1a1.py
deleted file mode 100644
index 106591aca21363f1246c43b9b24461d838293993..0000000000000000000000000000000000000000
--- a/tests/end2end/test_vitis_end2end_cnv_w1a1.py
+++ /dev/null
@@ -1,269 +0,0 @@
-# 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 pytest
-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 finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
-import finn.transformation.streamline.absorb as absorb
-from finn.core.onnx_exec import execute_onnx
-from finn.custom_op.registry import getCustomOp
-from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
-from finn.transformation.fold_constants import FoldConstants
-
-from finn.transformation.fpgadataflow.create_dataflow_partition import (
- CreateDataflowPartition,
-)
-from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
-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.basic import alveo_part_map, alveo_default_platform
-from finn.util.test import get_test_model_trained, load_test_checkpoint_or_skip
-from finn.transformation.fpgadataflow.vitis_build import VitisBuild, VitisOptStrategy
-import pkg_resources as pk
-from finn.transformation.double_to_single_float import DoubleToSingleFloat
-from finn.transformation.move_reshape import RemoveCNVtoFCFlatten
-from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
-from finn.transformation.streamline.reorder import MakeMaxPoolNHWC
-from finn.transformation.infer_data_layouts import InferDataLayouts
-from finn.transformation.fpgadataflow.annotate_cycles import AnnotateCycles
-from finn.transformation.fpgadataflow.annotate_resources import AnnotateResources
-import warnings
-
-build_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
-test_alveo_board = os.getenv("ALVEO_BOARD", default="U250")
-test_fpga_part = alveo_part_map[test_alveo_board]
-test_platform = alveo_default_platform[test_alveo_board]
-target_clk_ns = 10
-mem_mode = "decoupled"
-
-
-def test_end2end_vitis_cnv_w1a1_export():
- import brevitas.onnx as bo
-
- tfc = get_test_model_trained("CNV", 1, 1)
- bo.export_finn_onnx(
- tfc, (1, 3, 32, 32), build_dir + "/end2end_vitis_cnv_w1a1_export.onnx"
- )
-
-
-def test_end2end_vitis_cnv_w1a1_import_and_tidy():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_cnv_w1a1_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_vitis_cnv_w1a1_tidy.onnx")
-
-
-def test_end2end_vitis_cnv_w1a1_streamline():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_cnv_w1a1_tidy.onnx"
- )
- model = model.transform(Streamline())
- model = model.transform(LowerConvsToMatMul())
- model = model.transform(MakeMaxPoolNHWC())
- model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
- model = model.transform(ConvertBipolarMatMulToXnorPopcount())
- model = model.transform(Streamline())
- model = model.transform(RemoveUnusedTensors())
- model.save(build_dir + "/end2end_vitis_cnv_w1a1_streamlined.onnx")
-
-
-def test_end2end_vitis_cnv_w1a1_convert_to_hls_layers():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_cnv_w1a1_streamlined.onnx"
- )
- model = model.transform(to_hls.InferBinaryStreamingFCLayer(mem_mode))
- model = model.transform(to_hls.InferQuantizedStreamingFCLayer(mem_mode))
- model = model.transform(to_hls.InferConvInpGen())
- model = model.transform(to_hls.InferStreamingMaxPool())
- model = model.transform(GiveUniqueNodeNames())
- model = model.transform(RemoveCNVtoFCFlatten())
- model = model.transform(InferDataLayouts())
- model.save(build_dir + "/end2end_vitis_cnv_w1a1_hls_layers.onnx")
-
-
-def test_end2end_vitis_cnv_w1a1_create_dataflow_partition():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_cnv_w1a1_hls_layers.onnx"
- )
- parent_model = model.transform(CreateDataflowPartition())
- parent_model.save(build_dir + "/end2end_vitis_cnv_w1a1_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_vitis_cnv_w1a1_dataflow_model.onnx")
-
-
-def test_end2end_vitis_cnv_w1a1_fold():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_cnv_w1a1_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 = [
- (16, 3, 256),
- (32, 32, 256),
- (16, 32, 256),
- (16, 32, 256),
- (4, 32, 214),
- (1, 32, 2),
- (1, 4, 126),
- (1, 8, 62),
- (5, 1, 6),
- ]
- for fcl, (pe, simd, ififodepth) 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)
-
- 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(AnnotateResources("estimate"))
- model = model.transform(AnnotateCycles())
- model.save(build_dir + "/end2end_vitis_cnv_w1a1_folded.onnx")
-
-
-@pytest.mark.slow
-@pytest.mark.vivado
-def test_end2end_vitis_cnv_w1a1_build():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_cnv_w1a1_folded.onnx"
- )
- model = model.transform(
- VitisBuild(
- test_fpga_part,
- target_clk_ns,
- test_platform,
- strategy=VitisOptStrategy.BUILD_SPEED,
- )
- )
- model.save(build_dir + "/end2end_vitis_cnv_w1a1_build.onnx")
-
-
-def test_end2end_vitis_cnv_w1a1_annotate_resources():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_cnv_w1a1_build.onnx"
- )
- model = model.transform(AnnotateResources("synth"))
- warnings.warn(
- "Post-synthesis resources (excluding shell): "
- + model.get_metadata_prop("res_total_synth")
- )
- warnings.warn(
- "Post-synthesis resources (all inclusive): "
- + model.get_metadata_prop("res_total_top_synth")
- )
- model.save(build_dir + "/end2end_vitis_cnv_w1a1_annotate_resources.onnx")
-
-
-def test_end2end_vitis_cnv_w1a1_deploy_on_pynq():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_cnv_w1a1_build.onnx"
- )
- try:
- ip = os.environ["ALVEO_IP"] # no fault for this one; skip if not defined
- if ip == "":
- pytest.skip("Alveo host IP address not specified")
- username = os.getenv("ALVEO_USERNAME", "xilinx")
- password = os.getenv("ALVEO_PASSWORD", "xilinx")
- port = os.getenv("ALVEO_PORT", 22)
- target_dir = os.getenv("ALVEO_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_vitis_cnv_w1a1_pynq_deploy.onnx")
- except KeyError:
- pytest.skip("Alveo host IP address not specified")
-
-
-def test_end2end_vitis_cnv_w1a1_run_on_pynq():
- # use the streamlined model as the "golden" model for right answers
- golden = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_cnv_w1a1_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)
- # 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_vitis_cnv_w1a1_dataflow_parent.onnx"
- )
- iname = parent_model.graph.input[0].name
- oname = parent_model.graph.output[0].name
- try:
- ip = os.environ["ALVEO_IP"] # NOQA
- if ip == "":
- pytest.skip("Alveo host 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_vitis_cnv_w1a1_pynq_deploy.onnx"
- )
- sdp_node.set_nodeattr(
- "model", build_dir + "/end2end_vitis_cnv_w1a1_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("Alveo host IP address not specified")
diff --git a/tests/end2end/test_vitis_end2end_tfc_w1a1.py b/tests/end2end/test_vitis_end2end_tfc_w1a1.py
deleted file mode 100644
index 1eef964d4de81ce490809808fcdb9687a0f69b2f..0000000000000000000000000000000000000000
--- a/tests/end2end/test_vitis_end2end_tfc_w1a1.py
+++ /dev/null
@@ -1,252 +0,0 @@
-# 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 pytest
-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 onnx.numpy_helper as nph
-
-import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
-import finn.transformation.streamline.absorb as absorb
-
-from finn.custom_op.registry import getCustomOp
-from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
-from finn.transformation.fold_constants import FoldConstants
-
-from finn.transformation.fpgadataflow.create_dataflow_partition import (
- CreateDataflowPartition,
-)
-
-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
-from finn.transformation.streamline.round_thresholds import RoundAndClipThresholds
-from finn.util.basic import alveo_part_map, alveo_default_platform
-from finn.util.test import get_test_model_trained, load_test_checkpoint_or_skip
-from finn.transformation.fpgadataflow.vitis_build import VitisBuild, VitisOptStrategy
-from finn.transformation.infer_data_layouts import InferDataLayouts
-from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
-from pkgutil import get_data
-from finn.transformation.fpgadataflow.annotate_resources import AnnotateResources
-from finn.core.onnx_exec import execute_onnx
-import warnings
-
-build_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
-test_alveo_board = os.getenv("ALVEO_BOARD", default="U250")
-test_fpga_part = alveo_part_map[test_alveo_board]
-test_platform = alveo_default_platform[test_alveo_board]
-target_clk_ns = 10
-mem_mode = "decoupled"
-
-
-def test_end2end_vitis_tfc_w1a1_export():
- import brevitas.onnx as bo
-
- tfc = get_test_model_trained("TFC", 1, 1)
- bo.export_finn_onnx(
- tfc, (1, 1, 28, 28), build_dir + "/end2end_vitis_tfc_w1a1_export.onnx"
- )
-
-
-def test_end2end_vitis_tfc_w1a1_import_and_tidy():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_tfc_w1a1_export.onnx"
- )
- model = model.transform(InferShapes())
- model = model.transform(FoldConstants())
- model = model.transform(GiveUniqueNodeNames())
- model = model.transform(GiveReadableTensorNames())
- model = model.transform(InferDataTypes())
- model = model.transform(RemoveStaticGraphInputs())
- model.save(build_dir + "/end2end_vitis_tfc_w1a1_tidy.onnx")
-
-
-def test_end2end_vitis_tfc_w1a1_streamline():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_tfc_w1a1_tidy.onnx"
- )
- model = model.transform(Streamline())
- model = model.transform(RemoveUnusedTensors())
- model.save(build_dir + "/end2end_vitis_tfc_w1a1_streamlined.onnx")
-
-
-def test_end2end_vitis_tfc_w1a1_convert_to_hls_layers():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_tfc_w1a1_streamlined.onnx"
- )
- model = model.transform(ConvertBipolarMatMulToXnorPopcount())
- model = model.transform(absorb.AbsorbAddIntoMultiThreshold())
- model = model.transform(absorb.AbsorbMulIntoMultiThreshold())
- model = model.transform(RoundAndClipThresholds())
- model = model.transform(to_hls.InferBinaryStreamingFCLayer(mem_mode))
- model = model.transform(InferDataLayouts())
- model.save(build_dir + "/end2end_vitis_tfc_w1a1_hls_layers.onnx")
-
-
-def test_end2end_vitis_tfc_w1a1_create_dataflow_partition():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_tfc_w1a1_hls_layers.onnx"
- )
- parent_model = model.transform(CreateDataflowPartition())
- parent_model.save(build_dir + "/end2end_vitis_tfc_w1a1_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_vitis_tfc_w1a1_dataflow_model.onnx")
-
-
-def test_end2end_vitis_tfc_w1a1_fold():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_tfc_w1a1_dataflow_model.onnx"
- )
- fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
- # (PE, SIMD, in_fifo_depth, out_fifo_depth, ramstyle) for each layer
- config = [
- (16, 49, 16, 64, "block"),
- (8, 8, 64, 64, "auto"),
- (8, 8, 64, 64, "auto"),
- (10, 8, 64, 10, "distributed"),
- ]
- for fcl, (pe, simd, ififo, ofifo, ramstyle) in zip(fc_layers, config):
- fcl_inst = getCustomOp(fcl)
- fcl_inst.set_nodeattr("PE", pe)
- fcl_inst.set_nodeattr("SIMD", simd)
- fcl_inst.set_nodeattr("inFIFODepth", ififo)
- fcl_inst.set_nodeattr("outFIFODepth", ofifo)
- fcl_inst.set_nodeattr("ram_style", ramstyle)
-
- model.save(build_dir + "/end2end_vitis_tfc_w1a1_folded.onnx")
-
-
-@pytest.mark.slow
-@pytest.mark.vitis
-def test_end2end_vitis_tfc_w1a1_build():
- if "VITIS_PATH" not in os.environ:
- pytest.skip("VITIS_PATH not set")
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_tfc_w1a1_folded.onnx"
- )
- model = model.transform(
- VitisBuild(
- test_fpga_part,
- target_clk_ns,
- test_platform,
- strategy=VitisOptStrategy.BUILD_SPEED,
- )
- )
- model.save(build_dir + "/end2end_vitis_tfc_w1a1_build.onnx")
-
-
-def test_end2end_vitis_tfc_w1a1_annotate_resources():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_tfc_w1a1_build.onnx"
- )
- model = model.transform(AnnotateResources("synth"))
- warnings.warn(
- "Post-synthesis resources (excluding shell): "
- + model.get_metadata_prop("res_total_synth")
- )
- warnings.warn(
- "Post-synthesis resources (all inclusive): "
- + model.get_metadata_prop("res_total_top_synth")
- )
- model.save(build_dir + "/end2end_vitis_tfc_w1a1_annotate_resources.onnx")
-
-
-def test_end2end_vitis_tfc_w1a1_deploy_on_pynq():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_tfc_w1a1_build.onnx"
- )
- try:
- ip = os.environ["ALVEO_IP"] # no fault for this one; skip if not defined
- if ip == "":
- pytest.skip("PYNQ board IP address not specified")
- username = os.getenv("ALVEO_USERNAME", "xilinx")
- password = os.getenv("ALVEO_PASSWORD", "xilinx")
- port = os.getenv("ALVEO_PORT", 22)
- target_dir = os.getenv("ALVEO_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_vitis_tfc_w1a1_pynq_deploy.onnx")
- except KeyError:
- pytest.skip("PYNQ board IP address not specified")
-
-
-def test_end2end_vitis_tfc_w1a1_run_on_pynq():
- # use the streamlined model as the "golden" model for right answers
- golden = load_test_checkpoint_or_skip(
- build_dir + "/end2end_vitis_tfc_w1a1_streamlined.onnx"
- )
- iname = golden.graph.input[0].name
- oname = golden.graph.output[0].name
- 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)
- x = nph.to_array(input_tensor)
- # x = np.zeros(ishape, dtype=np.float32)
- # 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_vitis_tfc_w1a1_dataflow_parent.onnx"
- )
- iname = parent_model.graph.input[0].name
- oname = parent_model.graph.output[0].name
- try:
- ip = os.environ["ALVEO_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_vitis_tfc_w1a1_pynq_deploy.onnx"
- )
- sdp_node.set_nodeattr(
- "model", build_dir + "/end2end_vitis_tfc_w1a1_pynq_deploy.onnx"
- )
- ret = execute_onnx(parent_model, {iname: x}, True)
- y = ret[oname]
- assert np.isclose(y, y_golden).all()
-
- except KeyError:
- pytest.skip("PYNQ board IP address not specified")
diff --git a/tests/end2end/test_zynqbuild_end2end_cnv_w1a1.py b/tests/end2end/test_zynqbuild_end2end_cnv_w1a1.py
deleted file mode 100644
index 84394eff86c35d7132991173a77c65cfceac37c4..0000000000000000000000000000000000000000
--- a/tests/end2end/test_zynqbuild_end2end_cnv_w1a1.py
+++ /dev/null
@@ -1,255 +0,0 @@
-# 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 pytest
-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 finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
-import finn.transformation.streamline.absorb as absorb
-from finn.core.onnx_exec import execute_onnx
-from finn.custom_op.registry import getCustomOp
-from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
-from finn.transformation.fold_constants import FoldConstants
-
-from finn.transformation.fpgadataflow.create_dataflow_partition import (
- CreateDataflowPartition,
-)
-from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
-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.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.make_zynq_proj import ZynqBuild
-import pkg_resources as pk
-from finn.transformation.double_to_single_float import DoubleToSingleFloat
-from finn.transformation.move_reshape import RemoveCNVtoFCFlatten
-from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
-from finn.transformation.streamline.reorder import MakeMaxPoolNHWC
-from finn.transformation.infer_data_layouts import InferDataLayouts
-from finn.transformation.fpgadataflow.annotate_cycles import AnnotateCycles
-import warnings
-
-
-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_zynqbuild_cnv_w1a1_export():
- import brevitas.onnx as bo
-
- tfc = get_test_model_trained("CNV", 1, 1)
- bo.export_finn_onnx(
- tfc, (1, 3, 32, 32), build_dir + "/end2end_zynqbuild_cnv_w1a1_export.onnx"
- )
-
-
-def test_end2end_zynqbuild_cnv_w1a1_import_and_tidy():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_cnv_w1a1_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_zynqbuild_cnv_w1a1_tidy.onnx")
-
-
-def test_end2end_zynqbuild_cnv_w1a1_streamline():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_cnv_w1a1_tidy.onnx"
- )
- model = model.transform(Streamline())
- model = model.transform(LowerConvsToMatMul())
- model = model.transform(MakeMaxPoolNHWC())
- model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
- model = model.transform(ConvertBipolarMatMulToXnorPopcount())
- model = model.transform(Streamline())
- model = model.transform(RemoveUnusedTensors())
- model.save(build_dir + "/end2end_zynqbuild_cnv_w1a1_streamlined.onnx")
-
-
-def test_end2end_zynqbuild_cnv_w1a1_convert_to_hls_layers():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_cnv_w1a1_streamlined.onnx"
- )
- model = model.transform(to_hls.InferBinaryStreamingFCLayer(mem_mode))
- model = model.transform(to_hls.InferQuantizedStreamingFCLayer(mem_mode))
- model = model.transform(to_hls.InferConvInpGen())
- model = model.transform(to_hls.InferStreamingMaxPool())
- model = model.transform(GiveUniqueNodeNames())
- model = model.transform(RemoveCNVtoFCFlatten())
- model = model.transform(InferDataLayouts())
- model.save(build_dir + "/end2end_zynqbuild_cnv_w1a1_hls_layers.onnx")
-
-
-def test_end2end_zynqbuild_cnv_w1a1_create_dataflow_partition():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_cnv_w1a1_hls_layers.onnx"
- )
- parent_model = model.transform(CreateDataflowPartition())
- parent_model.save(build_dir + "/end2end_zynqbuild_cnv_w1a1_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_zynqbuild_cnv_w1a1_dataflow_model.onnx")
-
-
-def test_end2end_zynqbuild_cnv_w1a1_fold():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_cnv_w1a1_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 = [
- (16, 3, 256),
- (32, 32, 256),
- (16, 32, 256),
- (16, 32, 256),
- (4, 32, 214),
- (1, 32, 2),
- (1, 4, 126),
- (1, 8, 62),
- (5, 1, 6),
- ]
- for fcl, (pe, simd, ififodepth) 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)
-
- 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(AnnotateResources("estimate"))
- model = model.transform(AnnotateCycles())
- model.save(build_dir + "/end2end_zynqbuild_cnv_w1a1_folded.onnx")
-
-
-@pytest.mark.slow
-@pytest.mark.vivado
-def test_end2end_zynqbuild_cnv_w1a1_build():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_cnv_w1a1_folded.onnx"
- )
- model = model.transform(ZynqBuild(test_pynq_board, target_clk_ns))
- model = model.transform(AnnotateResources("synth"))
- warnings.warn(
- "Post-synthesis resources (excluding shell): "
- + model.get_metadata_prop("res_total_synth")
- )
- warnings.warn(
- "Post-synthesis resources (all inclusive): "
- + model.get_metadata_prop("res_total_top_synth")
- )
- model.save(build_dir + "/end2end_zynqbuild_cnv_w1a1_build.onnx")
-
-
-def test_end2end_zynqbuild_cnv_w1a1_deploy_on_pynq():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_cnv_w1a1_build.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_zynqbuild_cnv_w1a1_pynq_deploy.onnx")
- except KeyError:
- pytest.skip("PYNQ board IP address not specified")
-
-
-def test_end2end_zynqbuild_cnv_w1a1_run_on_pynq():
- # use the streamlined model as the "golden" model for right answers
- golden = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_cnv_w1a1_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)
- # 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_zynqbuild_cnv_w1a1_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_zynqbuild_cnv_w1a1_pynq_deploy.onnx"
- )
- sdp_node.set_nodeattr(
- "model", build_dir + "/end2end_zynqbuild_cnv_w1a1_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_zynqbuild_end2end_tfc_w1a1.py b/tests/end2end/test_zynqbuild_end2end_tfc_w1a1.py
deleted file mode 100644
index e73393c71e0b0e168ad51c27dc415ef2c755bcab..0000000000000000000000000000000000000000
--- a/tests/end2end/test_zynqbuild_end2end_tfc_w1a1.py
+++ /dev/null
@@ -1,233 +0,0 @@
-# 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 pkgutil import get_data
-
-import pytest
-
-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 onnx.numpy_helper as nph
-
-import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
-import finn.transformation.streamline.absorb as absorb
-from finn.core.onnx_exec import execute_onnx
-from finn.custom_op.registry import getCustomOp
-from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
-from finn.transformation.fold_constants import FoldConstants
-
-from finn.transformation.fpgadataflow.create_dataflow_partition import (
- CreateDataflowPartition,
-)
-from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
-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
-from finn.transformation.streamline.round_thresholds import RoundAndClipThresholds
-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.infer_data_layouts import InferDataLayouts
-from finn.transformation.fpgadataflow.make_zynq_proj import ZynqBuild
-import warnings
-
-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_zynqbuild_tfc_w1a1_export():
- import brevitas.onnx as bo
-
- tfc = get_test_model_trained("TFC", 1, 1)
- bo.export_finn_onnx(
- tfc, (1, 1, 28, 28), build_dir + "/end2end_zynqbuild_tfc_w1a1_export.onnx"
- )
-
-
-def test_end2end_zynqbuild_tfc_w1a1_import_and_tidy():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_tfc_w1a1_export.onnx"
- )
- model = model.transform(InferShapes())
- model = model.transform(FoldConstants())
- model = model.transform(GiveUniqueNodeNames())
- model = model.transform(GiveReadableTensorNames())
- model = model.transform(InferDataTypes())
- model = model.transform(RemoveStaticGraphInputs())
- model.save(build_dir + "/end2end_zynqbuild_tfc_w1a1_tidy.onnx")
-
-
-def test_end2end_zynqbuild_tfc_w1a1_streamline():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_tfc_w1a1_tidy.onnx"
- )
- model = model.transform(Streamline())
- model = model.transform(RemoveUnusedTensors())
- model.save(build_dir + "/end2end_zynqbuild_tfc_w1a1_streamlined.onnx")
-
-
-def test_end2end_zynqbuild_tfc_w1a1_convert_to_hls_layers():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_tfc_w1a1_streamlined.onnx"
- )
- model = model.transform(ConvertBipolarMatMulToXnorPopcount())
- model = model.transform(absorb.AbsorbAddIntoMultiThreshold())
- model = model.transform(absorb.AbsorbMulIntoMultiThreshold())
- model = model.transform(RoundAndClipThresholds())
- model = model.transform(to_hls.InferBinaryStreamingFCLayer(mem_mode))
- model = model.transform(InferDataLayouts())
- model.save(build_dir + "/end2end_zynqbuild_tfc_w1a1_hls_layers.onnx")
-
-
-def test_end2end_zynqbuild_tfc_w1a1_create_dataflow_partition():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_tfc_w1a1_hls_layers.onnx"
- )
- parent_model = model.transform(CreateDataflowPartition())
- parent_model.save(build_dir + "/end2end_zynqbuild_tfc_w1a1_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_zynqbuild_tfc_w1a1_dataflow_model.onnx")
-
-
-def test_end2end_zynqbuild_tfc_w1a1_fold():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_tfc_w1a1_dataflow_model.onnx"
- )
- fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
- # (PE, SIMD, in_fifo_depth, out_fifo_depth, ramstyle) for each layer
- config = [
- (16, 49, 16, 64, "block"),
- (8, 8, 64, 64, "auto"),
- (8, 8, 64, 64, "auto"),
- (10, 8, 64, 10, "distributed"),
- ]
- for fcl, (pe, simd, ififo, ofifo, ramstyle) in zip(fc_layers, config):
- fcl_inst = getCustomOp(fcl)
- fcl_inst.set_nodeattr("PE", pe)
- fcl_inst.set_nodeattr("SIMD", simd)
- fcl_inst.set_nodeattr("inFIFODepth", ififo)
- fcl_inst.set_nodeattr("outFIFODepth", ofifo)
- fcl_inst.set_nodeattr("ram_style", ramstyle)
-
- model.save(build_dir + "/end2end_zynqbuild_tfc_w1a1_folded.onnx")
-
-
-@pytest.mark.slow
-@pytest.mark.vivado
-def test_end2end_zynqbuild_tfc_w1a1_build():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_tfc_w1a1_folded.onnx"
- )
- model = model.transform(ZynqBuild(test_pynq_board, target_clk_ns))
- model = model.transform(AnnotateResources("synth"))
- warnings.warn(
- "Post-synthesis resources (excluding shell): "
- + model.get_metadata_prop("res_total_synth")
- )
- warnings.warn(
- "Post-synthesis resources (all inclusive): "
- + model.get_metadata_prop("res_total_top_synth")
- )
- model.save(build_dir + "/end2end_zynqbuild_tfc_w1a1_build.onnx")
-
-
-def test_end2end_zynqbuild_tfc_w1a1_deploy_on_pynq():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_tfc_w1a1_build.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_zynqbuild_tfc_w1a1_pynq_deploy.onnx")
- except KeyError:
- pytest.skip("PYNQ board IP address not specified")
-
-
-def test_end2end_zynqbuild_tfc_w1a1_run_on_pynq():
- # use the streamlined model as the "golden" model for right answers
- golden = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_tfc_w1a1_streamlined.onnx"
- )
- iname = golden.graph.input[0].name
- oname = golden.graph.output[0].name
- 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)
- x = nph.to_array(input_tensor)
- # x = np.zeros(ishape, dtype=np.float32)
- # 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_zynqbuild_tfc_w1a1_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_zynqbuild_tfc_w1a1_pynq_deploy.onnx"
- )
- sdp_node.set_nodeattr(
- "model", build_dir + "/end2end_zynqbuild_tfc_w1a1_pynq_deploy.onnx"
- )
- ret = execute_onnx(parent_model, {iname: x}, True)
- y = ret[oname]
- assert np.isclose(y, y_golden).all()
-
- except KeyError:
- pytest.skip("PYNQ board IP address not specified")
diff --git a/tests/end2end/test_zynqbuild_end2end_tfc_w2a2.py b/tests/end2end/test_zynqbuild_end2end_tfc_w2a2.py
deleted file mode 100644
index 294235719d66e1b3d15e6e72504209736f278457..0000000000000000000000000000000000000000
--- a/tests/end2end/test_zynqbuild_end2end_tfc_w2a2.py
+++ /dev/null
@@ -1,222 +0,0 @@
-# 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 pkgutil import get_data
-
-import pytest
-
-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 onnx.numpy_helper as nph
-import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
-from finn.core.onnx_exec import execute_onnx
-from finn.custom_op.registry import getCustomOp
-from finn.transformation.fold_constants import FoldConstants
-from finn.transformation.fpgadataflow.create_dataflow_partition import (
- CreateDataflowPartition,
-)
-from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
-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
-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.make_zynq_proj import ZynqBuild
-import warnings
-
-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_zynqbuild_tfc_w2a2_export():
- import brevitas.onnx as bo
-
- tfc = get_test_model_trained("TFC", 2, 2)
- bo.export_finn_onnx(
- tfc, (1, 1, 28, 28), build_dir + "/end2end_zynqbuild_tfc_w2a2_export.onnx"
- )
-
-
-def test_end2end_zynqbuild_tfc_w2a2_import_and_tidy():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_tfc_w2a2_export.onnx"
- )
- model = model.transform(InferShapes())
- model = model.transform(FoldConstants())
- model = model.transform(GiveUniqueNodeNames())
- model = model.transform(GiveReadableTensorNames())
- model = model.transform(InferDataTypes())
- model = model.transform(RemoveStaticGraphInputs())
- model.save(build_dir + "/end2end_zynqbuild_tfc_w2a2_tidy.onnx")
-
-
-def test_end2end_zynqbuild_tfc_w2a2_streamline():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_tfc_w2a2_tidy.onnx"
- )
- model = model.transform(Streamline())
- model = model.transform(RemoveUnusedTensors())
- model.save(build_dir + "/end2end_zynqbuild_tfc_w2a2_streamlined.onnx")
-
-
-def test_end2end_zynqbuild_tfc_w2a2_convert_to_hls_layers():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_tfc_w2a2_streamlined.onnx"
- )
- model = model.transform(to_hls.InferQuantizedStreamingFCLayer(mem_mode))
- model.save(build_dir + "/end2end_zynqbuild_tfc_w2a2_hls_layers.onnx")
-
-
-def test_end2end_zynqbuild_tfc_w2a2_create_dataflow_partition():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_tfc_w2a2_hls_layers.onnx"
- )
- parent_model = model.transform(CreateDataflowPartition())
- parent_model.save(build_dir + "/end2end_zynqbuild_tfc_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_zynqbuild_tfc_w2a2_dataflow_model.onnx")
-
-
-def test_end2end_zynqbuild_tfc_w2a2_fold():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_tfc_w2a2_dataflow_model.onnx"
- )
- fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
- # (PE, SIMD, in_fifo_depth, out_fifo_depth, ramstyle) for each layer
- config = [
- (16, 49, 16, 64, "block"),
- (8, 8, 64, 64, "auto"),
- (8, 8, 64, 64, "auto"),
- (10, 8, 64, 10, "distributed"),
- ]
- for fcl, (pe, simd, ififo, ofifo, ramstyle) in zip(fc_layers, config):
- fcl_inst = getCustomOp(fcl)
- fcl_inst.set_nodeattr("PE", pe)
- fcl_inst.set_nodeattr("SIMD", simd)
- fcl_inst.set_nodeattr("inFIFODepth", ififo)
- fcl_inst.set_nodeattr("outFIFODepth", ofifo)
- fcl_inst.set_nodeattr("ram_style", ramstyle)
-
- model.save(build_dir + "/end2end_zynqbuild_tfc_w2a2_folded.onnx")
-
-
-@pytest.mark.slow
-@pytest.mark.vivado
-def test_end2end_zynqbuild_tfc_w2a2_build():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_tfc_w2a2_folded.onnx"
- )
- model = model.transform(ZynqBuild(test_pynq_board, target_clk_ns))
- model = model.transform(AnnotateResources("synth"))
- warnings.warn(
- "Post-synthesis resources (excluding shell): "
- + model.get_metadata_prop("res_total_synth")
- )
- warnings.warn(
- "Post-synthesis resources (all inclusive): "
- + model.get_metadata_prop("res_total_top_synth")
- )
- model.save(build_dir + "/end2end_zynqbuild_tfc_w2a2_build.onnx")
-
-
-def test_end2end_zynqbuild_tfc_w2a2_deploy_on_pynq():
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_tfc_w2a2_build.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_zynqbuild_tfc_w2a2_pynq_deploy.onnx")
- except KeyError:
- pytest.skip("PYNQ board IP address not specified")
-
-
-def test_end2end_zynqbuild_tfc_w2a2_run_on_pynq():
- # use the streamlined model as the "golden" model for right answers
- golden = load_test_checkpoint_or_skip(
- build_dir + "/end2end_zynqbuild_tfc_w2a2_streamlined.onnx"
- )
- iname = golden.graph.input[0].name
- oname = golden.graph.output[0].name
- 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)
- x = nph.to_array(input_tensor)
- # x = np.zeros(ishape, dtype=np.float32)
- # 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_zynqbuild_tfc_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_zynqbuild_tfc_w2a2_pynq_deploy.onnx"
- )
- sdp_node.set_nodeattr(
- "model", build_dir + "/end2end_zynqbuild_tfc_w2a2_pynq_deploy.onnx"
- )
- ret = execute_onnx(parent_model, {iname: x}, True)
- y = ret[oname]
- assert np.isclose(y, y_golden).all()
-
- except KeyError:
- pytest.skip("PYNQ board IP address not specified")
diff --git a/tests/fpgadataflow/test_convert_to_hls_channelwise_layer.py b/tests/fpgadataflow/test_convert_to_hls_channelwise_layer.py
index d09c64a1250f78604c1a0a362cf234712de2cf57..9ff2bedb96613b94310fc743d39fc2f4d9618677 100644
--- a/tests/fpgadataflow/test_convert_to_hls_channelwise_layer.py
+++ b/tests/fpgadataflow/test_convert_to_hls_channelwise_layer.py
@@ -10,9 +10,6 @@ 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
@@ -101,7 +98,6 @@ def test_convert_to_hls_channelwise_layer(
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")
diff --git a/tests/fpgadataflow/test_convert_to_hls_conv_layer.py b/tests/fpgadataflow/test_convert_to_hls_conv_layer.py
index 9be9c904b0be0a8c1ab2421590922ae6cf2e1295..f4817d70ce3080738e7b7321bfc686b73ad55fe1 100644
--- a/tests/fpgadataflow/test_convert_to_hls_conv_layer.py
+++ b/tests/fpgadataflow/test_convert_to_hls_conv_layer.py
@@ -11,9 +11,6 @@ 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
@@ -121,7 +118,6 @@ def test_convert_to_hls_conv_layer(conv_config, depthwise, exec_mode):
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")
diff --git a/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py b/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
index 20e3ee08d7ffdd013a89d26bb71d86ccc554a5b4..e8b50efef0723c1394c2bdd438a87e090071507d 100644
--- a/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
+++ b/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
@@ -42,7 +42,6 @@ from finn.transformation.infer_shapes import InferShapes
from finn.transformation.infer_data_layouts import InferDataLayouts
from finn.transformation.streamline import Streamline
from finn.util.test import get_test_model_trained
-from finn.transformation.double_to_single_float import DoubleToSingleFloat
from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
@@ -61,7 +60,6 @@ def test_convert_to_hls_layers_cnv_w1a1(fused_activation):
cnv = get_test_model_trained("CNV", 1, 1)
bo.export_finn_onnx(cnv, (1, 3, 32, 32), export_onnx_path_cnv)
model = ModelWrapper(export_onnx_path_cnv)
- model = model.transform(DoubleToSingleFloat())
model = model.transform(InferShapes())
model = model.transform(FoldConstants())
model = model.transform(GiveUniqueNodeNames())
diff --git a/tests/fpgadataflow/test_convert_to_hls_layers_synthetic.py b/tests/fpgadataflow/test_convert_to_hls_layers_synthetic.py
index 9d861929f3d421c431a27ccac5d513938aa7d726..3c8da5de1d8629b3692646e1aa18120ffcc30b99 100644
--- a/tests/fpgadataflow/test_convert_to_hls_layers_synthetic.py
+++ b/tests/fpgadataflow/test_convert_to_hls_layers_synthetic.py
@@ -46,7 +46,6 @@ from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.infer_data_layouts import InferDataLayouts
from finn.util.basic import gen_finn_dt_tensor
from finn.util.test import soft_verify_topk
-from finn.transformation.double_to_single_float import DoubleToSingleFloat
from finn.transformation.insert_topk import InsertTopK
import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
@@ -149,7 +148,6 @@ def test_convert_to_hls_layers_synthetic(ch, ifmdim, idt):
model = make_model(ch, ifmdim)
model.save(export_onnx_path)
model = ModelWrapper(export_onnx_path)
- model = model.transform(DoubleToSingleFloat())
model = model.transform(InferShapes())
model = model.transform(FoldConstants())
model = model.transform(GiveUniqueNodeNames())
diff --git a/tests/fpgadataflow/test_depthwise_convolution.py b/tests/fpgadataflow/test_depthwise_convolution.py
index f530926e46ac5c116c3f15688c7f2face7954a30..f269a1ed7247503f561425b97115694503522171 100644
--- a/tests/fpgadataflow/test_depthwise_convolution.py
+++ b/tests/fpgadataflow/test_depthwise_convolution.py
@@ -51,9 +51,6 @@ from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
from finn.transformation.general import GiveUniqueNodeNames
from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
-from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
- ReplaceVerilogRelPaths,
-)
def set_up_reference_model(act, idt, wdt, k, ifm_dim, ifm_ch, stride, padding):
@@ -243,7 +240,6 @@ def test_depthwise_conv_hls_rtlsim(act, pe, k, stride, padding):
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())
assert oxe.compare_execution(model, new_model, input_dict)
diff --git a/tests/fpgadataflow/test_fpgadataflow_addstreams.py b/tests/fpgadataflow/test_fpgadataflow_addstreams.py
index 81456796a75c6bf6a01c0a1f83c38b0b39bf4c81..e4191c75d6249d22b36e41fed50c5f7896f13c22 100644
--- a/tests/fpgadataflow/test_fpgadataflow_addstreams.py
+++ b/tests/fpgadataflow/test_fpgadataflow_addstreams.py
@@ -42,9 +42,6 @@ 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,
-)
from finn.custom_op.registry import getCustomOp
from finn.analysis.fpgadataflow.exp_cycles_per_layer import exp_cycles_per_layer
@@ -112,7 +109,6 @@ def test_fpgadataflow_addstreams(idt, ch, fold, exec_mode):
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")
diff --git a/tests/fpgadataflow/test_fpgadataflow_channelwise_ops.py b/tests/fpgadataflow/test_fpgadataflow_channelwise_ops.py
index 23ce8314e9c45196d7311ac58cb6bb5ef5267220..0e0ce7d542f605cc6af5df13b45d670cfcafa5a9 100644
--- a/tests/fpgadataflow/test_fpgadataflow_channelwise_ops.py
+++ b/tests/fpgadataflow/test_fpgadataflow_channelwise_ops.py
@@ -43,9 +43,6 @@ 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,
-)
from finn.custom_op.registry import getCustomOp
from finn.analysis.fpgadataflow.exp_cycles_per_layer import exp_cycles_per_layer
@@ -129,7 +126,6 @@ def test_fpgadataflow_channelwise_ops(idt, act, pdt, nf, ich, func, vecs, exec_m
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")
diff --git a/tests/fpgadataflow/test_fpgadataflow_duplicatestreams.py b/tests/fpgadataflow/test_fpgadataflow_duplicatestreams.py
index 5066b9709cac922f6bd3670ec7199f3e0f8fd9a2..47401ddb9546d5b32a5d36c6731981aabe0ca7cd 100644
--- a/tests/fpgadataflow/test_fpgadataflow_duplicatestreams.py
+++ b/tests/fpgadataflow/test_fpgadataflow_duplicatestreams.py
@@ -44,9 +44,6 @@ 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,
-)
from finn.custom_op.registry import getCustomOp
from finn.analysis.fpgadataflow.exp_cycles_per_layer import exp_cycles_per_layer
@@ -119,7 +116,6 @@ def test_fpgadataflow_duplicatestreams(idt, ch, fold, imdim, exec_mode):
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")
diff --git a/tests/fpgadataflow/test_fpgadataflow_fclayer.py b/tests/fpgadataflow/test_fpgadataflow_fclayer.py
index 37a1cc81ebd0824cdd8ac2c073298ad39424f57f..feff580002c317a3a8754dba2b6a9f291e408ac5 100644
--- a/tests/fpgadataflow/test_fpgadataflow_fclayer.py
+++ b/tests/fpgadataflow/test_fpgadataflow_fclayer.py
@@ -46,9 +46,6 @@ 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 calculate_signed_dot_prod_range, gen_finn_dt_tensor
-from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
- ReplaceVerilogRelPaths,
-)
from finn.analysis.fpgadataflow.exp_cycles_per_layer import exp_cycles_per_layer
@@ -304,7 +301,6 @@ def test_fpgadataflow_fclayer_rtlsim(mem_mode, idt, wdt, act, nf, sf, mw, mh):
model = model.transform(GiveUniqueNodeNames())
model = model.transform(PrepareIP("xc7z020clg400-1", 5))
model = model.transform(HLSSynthIP())
- model = model.transform(ReplaceVerilogRelPaths())
model = model.transform(PrepareRTLSim())
y_produced = oxe.execute_onnx(model, input_dict)["outp"]
assert (y_produced.reshape(y_expected.shape) == y_expected).all(), "rtlsim failed"
@@ -405,7 +401,6 @@ def test_fpgadataflow_fclayer_large_depth_decoupled_mode_rtlsim(
model = model.transform(GiveUniqueNodeNames())
model = model.transform(PrepareIP("xc7z020clg400-1", 5))
model = model.transform(HLSSynthIP())
- model = model.transform(ReplaceVerilogRelPaths())
model = model.transform(PrepareRTLSim())
y_produced = oxe.execute_onnx(model, input_dict)["outp"]
assert (y_produced.reshape(y_expected.shape) == y_expected).all(), "rtlsim failed"
diff --git a/tests/fpgadataflow/test_fpgadataflow_fifo.py b/tests/fpgadataflow/test_fpgadataflow_fifo.py
index 8f6f5e2e5fc380d8dc4e5d4413d1503687ca67ae..53de417eac175d8b700e84aecb304895a5942c16 100644
--- a/tests/fpgadataflow/test_fpgadataflow_fifo.py
+++ b/tests/fpgadataflow/test_fpgadataflow_fifo.py
@@ -2,36 +2,19 @@ import pytest
import os
from onnx import TensorProto, helper
-
from finn.core.datatype import DataType
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
-from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP
-
+from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
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.util.basic import gen_finn_dt_tensor
-
import finn.core.onnx_exec as oxe
-from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker
-from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
-from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
-from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver
-from finn.transformation.fpgadataflow.make_pynq_proj import MakePYNQProject
-from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
- ReplaceVerilogRelPaths,
-)
-from finn.transformation.fpgadataflow.synth_pynq_proj import SynthPYNQProject
-from finn.util.basic import pynq_part_map
-from finn.core.throughput_test import throughput_test_remote
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]
+test_fpga_part = "xc7z020clg400-1"
target_clk_ns = 10
@@ -87,7 +70,6 @@ def test_fpgadataflow_fifo_rtlsim(Shape, folded_shape, depth, finn_dtype):
model = make_single_fifo_modelwrapper(Shape, depth, folded_shape, finn_dtype)
model = model.transform(SetExecMode("rtlsim"))
- model = model.transform(InsertTLastMarker())
model = model.transform(GiveUniqueNodeNames())
model = model.transform(PrepareIP(test_fpga_part, target_clk_ns))
model = model.transform(HLSSynthIP())
@@ -98,33 +80,3 @@ def test_fpgadataflow_fifo_rtlsim(Shape, folded_shape, depth, finn_dtype):
).all(), """The output values are not the same as the
input values anymore."""
assert y.shape == tuple(Shape), """The output shape is incorrect."""
-
- try:
- ip = os.environ["PYNQ_IP"] # NOQA
- if ip == "":
- pytest.skip("PYNQ board IP address not specified")
- model = model.transform(ReplaceVerilogRelPaths())
- model = model.transform(CreateStitchedIP(test_fpga_part, target_clk_ns))
- model = model.transform(MakePYNQProject(test_pynq_board))
- model = model.transform(SynthPYNQProject())
- model = model.transform(MakePYNQDriver(platform="zynq"))
- 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))
- res = throughput_test_remote(model)
- expected_dict = {}
- expected_dict["runtime[ms]"] = []
- expected_dict["throughput[images/s]"] = []
- expected_dict["DRAM_in_bandwidth[Mb/s]"] = []
- expected_dict["DRAM_out_bandwidth[Mb/s]"] = []
- for key in expected_dict:
- assert (
- key in res
- ), """Throughput test not successful, no value for {}
- in result dictionary""".format(
- key
- )
- except KeyError:
- pytest.skip("PYNQ board IP address not specified")
diff --git a/tests/fpgadataflow/test_fpgadataflow_globalaccpool.py b/tests/fpgadataflow/test_fpgadataflow_globalaccpool.py
index 27f1a32a481f006818fbdd7e879bd9dd92242c80..191e00022a0b0ab11fcf4d1a476442cbd824408d 100644
--- a/tests/fpgadataflow/test_fpgadataflow_globalaccpool.py
+++ b/tests/fpgadataflow/test_fpgadataflow_globalaccpool.py
@@ -42,9 +42,6 @@ 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,
-)
from finn.custom_op.registry import getCustomOp
from finn.analysis.fpgadataflow.exp_cycles_per_layer import exp_cycles_per_layer
@@ -112,7 +109,6 @@ def test_fpgadataflow_globalaccpool(idt, ch, fold, imdim, exec_mode):
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")
diff --git a/tests/fpgadataflow/test_fpgadataflow_ipstitch.py b/tests/fpgadataflow/test_fpgadataflow_ipstitch.py
index 75a67f3508f1de69cebf466589d195a99f08dd94..2e9d25778147b1aa774c56f877c35d094c62e2bc 100644
--- a/tests/fpgadataflow/test_fpgadataflow_ipstitch.py
+++ b/tests/fpgadataflow/test_fpgadataflow_ipstitch.py
@@ -45,10 +45,6 @@ from finn.transformation.fpgadataflow.create_dataflow_partition import (
from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker
from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
-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
-import finn.transformation.fpgadataflow.replace_verilog_relpaths as rvp
from finn.transformation.general import GiveUniqueNodeNames
from finn.util.basic import (
gen_finn_dt_tensor,
@@ -72,7 +68,7 @@ test_fpga_part = pynq_part_map[test_pynq_board]
ip_stitch_model_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
-def create_one_fc_model():
+def create_one_fc_model(mem_mode="const"):
# create a model with a StreamingFCLayer instance with no activation
# the wider range of the full accumulator makes debugging a bit easier
wdt = DataType.INT2
@@ -105,6 +101,7 @@ def create_one_fc_model():
ActVal=actval,
binaryXnorMode=binary_xnor_mode,
noActivation=no_act,
+ mem_mode=mem_mode,
)
graph = helper.make_graph(
@@ -209,11 +206,10 @@ def create_two_fc_model(mem_mode="decoupled"):
return model
-# exec_mode of StreamingDataflowPartition
-# @pytest.mark.parametrize("exec_mode", ["remote_pynq"]) #, "rtlsim"])
+@pytest.mark.parametrize("mem_mode", ["const", "decoupled"])
@pytest.mark.vivado
-def test_fpgadataflow_ipstitch_gen_model(): # exec_mode):
- model = create_one_fc_model()
+def test_fpgadataflow_ipstitch_gen_model(mem_mode):
+ model = create_one_fc_model(mem_mode)
if model.graph.node[0].op_type == "StreamingDataflowPartition":
sdp_node = getCustomOp(model.graph.node[0])
assert sdp_node.__class__.__name__ == "StreamingDataflowPartition"
@@ -226,15 +222,17 @@ def test_fpgadataflow_ipstitch_gen_model(): # exec_mode):
model = model.transform(HLSSynthIP())
assert model.graph.node[0].op_type == "StreamingFCLayer_Batch"
assert model.graph.node[-1].op_type == "TLastMarker"
- model.save(ip_stitch_model_dir + "/test_fpgadataflow_ipstitch_gen_model.onnx")
+ model.save(
+ ip_stitch_model_dir + "/test_fpgadataflow_ipstitch_gen_model_%s.onnx" % mem_mode
+ )
+@pytest.mark.parametrize("mem_mode", ["const", "decoupled"])
@pytest.mark.vivado
-def test_fpgadataflow_ipstitch_do_stitch():
+def test_fpgadataflow_ipstitch_do_stitch(mem_mode):
model = load_test_checkpoint_or_skip(
- ip_stitch_model_dir + "/test_fpgadataflow_ipstitch_gen_model.onnx"
+ ip_stitch_model_dir + "/test_fpgadataflow_ipstitch_gen_model_%s.onnx" % mem_mode
)
- model = model.transform(rvp.ReplaceVerilogRelPaths())
model = model.transform(CreateStitchedIP(test_fpga_part, 5))
vivado_stitch_proj_dir = model.get_metadata_prop("vivado_stitch_proj")
assert vivado_stitch_proj_dir is not None
@@ -243,13 +241,14 @@ def test_fpgadataflow_ipstitch_do_stitch():
vivado_stitch_vlnv = model.get_metadata_prop("vivado_stitch_vlnv")
assert vivado_stitch_vlnv is not None
assert vivado_stitch_vlnv == "xilinx_finn:finn:finn_design:1.0"
- model.save(ip_stitch_model_dir + "/test_fpgadataflow_ip_stitch.onnx")
+ model.save(ip_stitch_model_dir + "/test_fpgadataflow_ip_stitch_%s.onnx" % mem_mode)
+@pytest.mark.parametrize("mem_mode", ["const", "decoupled"])
@pytest.mark.vivado
-def test_fpgadataflow_ipstitch_rtlsim():
+def test_fpgadataflow_ipstitch_rtlsim(mem_mode):
model = load_test_checkpoint_or_skip(
- ip_stitch_model_dir + "/test_fpgadataflow_ip_stitch.onnx"
+ ip_stitch_model_dir + "/test_fpgadataflow_ip_stitch_%s.onnx" % mem_mode
)
model.set_metadata_prop("rtlsim_trace", "whole_trace.vcd")
sim = pyverilate_stitched_ip(model)
@@ -293,11 +292,12 @@ def test_fpgadataflow_ipstitch_rtlsim():
assert (rtlsim_res == x).all()
+@pytest.mark.parametrize("mem_mode", ["const", "decoupled"])
@pytest.mark.vivado
@pytest.mark.slow
-def test_fpgadataflow_ipstitch_synth_ooc():
+def test_fpgadataflow_ipstitch_synth_ooc(mem_mode):
model = load_test_checkpoint_or_skip(
- ip_stitch_model_dir + "/test_fpgadataflow_ip_stitch.onnx"
+ ip_stitch_model_dir + "/test_fpgadataflow_ip_stitch_%s.onnx" % mem_mode
)
model = model.transform(SynthOutOfContext(test_fpga_part, 5))
ret = model.get_metadata_prop("res_total_ooc_synth")
@@ -314,95 +314,6 @@ def test_fpgadataflow_ipstitch_synth_ooc():
assert ret["fmax_mhz"] > 100
-@pytest.mark.vivado
-def test_fpgadataflow_ipstitch_pynq_projgen():
- model = load_test_checkpoint_or_skip(
- ip_stitch_model_dir + "/test_fpgadataflow_ip_stitch.onnx"
- )
- model = model.transform(MakePYNQProject(test_pynq_board))
- vivado_pynq_proj_dir = model.get_metadata_prop("vivado_pynq_proj")
- assert vivado_pynq_proj_dir is not None
- assert os.path.isdir(vivado_pynq_proj_dir)
- model.save(ip_stitch_model_dir + "/test_fpgadataflow_pynq_projgen.onnx")
-
-
-@pytest.mark.slow
-@pytest.mark.vivado
-def test_fpgadataflow_ipstitch_pynq_synth():
- model = load_test_checkpoint_or_skip(
- ip_stitch_model_dir + "/test_fpgadataflow_pynq_projgen.onnx"
- )
- model = model.transform(SynthPYNQProject())
- bitfile = model.get_metadata_prop("bitfile")
- assert bitfile is not None
- assert os.path.isfile(bitfile)
- model.save(ip_stitch_model_dir + "/test_fpgadataflow_ipstitch_pynq_synth.onnx")
-
-
-def test_fpgadataflow_ipstitch_pynq_driver():
- model = load_test_checkpoint_or_skip(
- ip_stitch_model_dir + "/test_fpgadataflow_ipstitch_pynq_synth.onnx"
- )
- model = model.transform(MakePYNQDriver(platform="zynq"))
- driver_dir = model.get_metadata_prop("pynq_driver_dir")
- assert driver_dir is not None
- assert os.path.isdir(driver_dir)
- model.save(ip_stitch_model_dir + "/test_fpgadataflow_ipstitch_pynq_driver.onnx")
-
-
-def test_fpgadataflow_ipstitch_pynq_deployment_folder():
- model = load_test_checkpoint_or_skip(
- ip_stitch_model_dir + "/test_fpgadataflow_ipstitch_pynq_driver.onnx"
- )
- try:
- ip = os.environ["PYNQ_IP"] # no default 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))
- pynq_ip = model.get_metadata_prop("pynq_ip")
- pynq_username = model.get_metadata_prop("pynq_username")
- pynq_password = model.get_metadata_prop("pynq_password")
- pynq_target_dir = model.get_metadata_prop("pynq_target_dir")
-
- assert pynq_ip == ip
- assert pynq_username == username
- assert pynq_password == password
- assert pynq_target_dir == target_dir
-
- deployment_dir = model.get_metadata_prop("pynq_deploy_dir")
- assert deployment_dir is not None
- assert os.path.isdir(deployment_dir)
-
- model.save(
- ip_stitch_model_dir + "/test_fpgadataflow_ipstitch_pynq_deployment.onnx"
- )
- except KeyError:
- pytest.skip("PYNQ board IP address not specified")
-
-
-def test_fpgadataflow_ipstitch_remote_execution():
- try:
- ip = os.environ["PYNQ_IP"] # NOQA
- if ip == "":
- pytest.skip("PYNQ board IP address not specified")
- model = load_test_checkpoint_or_skip(
- ip_stitch_model_dir + "/test_fpgadataflow_ipstitch_pynq_deployment.onnx"
- )
- iname = "inp"
- idt = model.get_tensor_datatype(iname)
- ishape = model.get_tensor_shape(iname)
- x = gen_finn_dt_tensor(idt, ishape)
- input_dict = {"inp": x}
- outp = execute_onnx(model, input_dict)
- 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":
@@ -441,6 +352,9 @@ def test_fpgadataflow_ipstitch_vitis(board, period_ns, extw):
model = load_test_checkpoint_or_skip(sdp_node.get_nodeattr("model"))
model = model.transform(VitisBuild(fpga_part, period_ns, platform))
model.save(ip_stitch_model_dir + "/test_fpgadataflow_ipstitch_vitis.onnx")
+ assert model.get_metadata_prop("platform") == "alveo"
+ assert os.path.isdir(model.get_metadata_prop("vitis_link_proj"))
+ assert os.path.isfile(model.get_metadata_prop("bitfile"))
# board
diff --git a/tests/fpgadataflow/test_fpgadataflow_labelselect.py b/tests/fpgadataflow/test_fpgadataflow_labelselect.py
index 9bc77cd47fd6115823f9a35d98e8874ee3f98b2d..dae91b94120e94eb86bbc4b958decd581f36e671 100644
--- a/tests/fpgadataflow/test_fpgadataflow_labelselect.py
+++ b/tests/fpgadataflow/test_fpgadataflow_labelselect.py
@@ -43,9 +43,6 @@ 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.util.test import soft_verify_topk
-from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
- ReplaceVerilogRelPaths,
-)
def make_labelselect_modelwrapper(labels, pe, k, idt):
@@ -116,7 +113,6 @@ def test_fpgadataflow_labelselect(idt, labels, fold, k, exec_mode):
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")
diff --git a/tests/fpgadataflow/test_fpgadataflow_thresholding.py b/tests/fpgadataflow/test_fpgadataflow_thresholding.py
index 1715bcad0dd29799cdc99497179ce8635058f3be..ad69b89d8a159f6eb423c0739bdb4c0dd5103792 100644
--- a/tests/fpgadataflow/test_fpgadataflow_thresholding.py
+++ b/tests/fpgadataflow/test_fpgadataflow_thresholding.py
@@ -44,9 +44,6 @@ 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,
-)
from finn.custom_op.registry import getCustomOp
from finn.analysis.fpgadataflow.exp_cycles_per_layer import exp_cycles_per_layer
@@ -126,7 +123,6 @@ def test_fpgadataflow_thresholding(idt, act, nf, ich, exec_mode):
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")
diff --git a/tests/pynq/test_pynq_performance_end2end.py b/tests/pynq/test_pynq_performance_end2end.py
deleted file mode 100644
index ea04ed9c402944730bf35374f46636ec433fe4c2..0000000000000000000000000000000000000000
--- a/tests/pynq/test_pynq_performance_end2end.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import os
-
-import pytest
-import numpy as np
-from scipy.stats import linregress
-import warnings
-from finn.util.test import load_test_checkpoint_or_skip
-from finn.core.throughput_test import throughput_test_remote
-
-build_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
-
-
-@pytest.mark.parametrize(
- "end2end_example",
- [
- "zynqbuild_tfc_w1a1",
- "zynqbuild_cnv_w1a1",
- "vitis_tfc_w1a1",
- "tfc_w1a1",
- "cnv_w1a1",
- "cnv_w2a2",
- ],
-)
-@pytest.mark.slow
-def test_pynq_performance_end2end(end2end_example):
- model = load_test_checkpoint_or_skip(
- build_dir + "/end2end_%s_pynq_deploy.onnx" % end2end_example
- )
- try:
- ip = os.environ["PYNQ_IP"] # NOQA
- board = os.environ["PYNQ_BOARD"] # NOQA
- if ip == "" or board == "":
- pytest.skip("PYNQ board or IP address not specified")
- ret = dict()
- # try a range of batch sizes, some may fail due to insufficient DMA
- # buffers
- bsize_range_in = [2 ** i for i in range(16)]
- bsize_range = []
- for bsize in bsize_range_in:
- res = throughput_test_remote(model, bsize)
- if res is not None:
- ret[bsize] = res
- bsize_range.append(bsize)
- else:
- # assume we reached largest possible N
- break
-
- y = [ret[key]["runtime[ms]"] for key in bsize_range]
- lrret = linregress(bsize_range, y)
- ret_str = ""
- ret_str += "\n" + "%s Throughput Test Results" % end2end_example
- ret_str += "\n" + "-----------------------------"
- ret_str += "\n" + "From linear regression:"
- ret_str += "\n" + "Invocation overhead: %f ms" % lrret.intercept
- ret_str += "\n" + "Time per sample: %f ms" % lrret.slope
- ret_str += "\n" + "Raw data:"
-
- ret_str += "\n" + "{:<8} {:<16} {:<16} {:<16} {:<16} {:<16}".format(
- "N", "runtime[ms]", "fclk[mhz]", "fps", "DRAM rd[Mb/s]", "DRAM wr[Mb/s]"
- )
- for k in bsize_range:
- v = ret[k]
- ret_str += "\n" + "{:<8} {:<16} {:<16} {:<16} {:<16} {:<16}".format(
- k,
- np.round(v["runtime[ms]"], 4),
- v["fclk[mhz]"],
- np.round(v["throughput[images/s]"], 2),
- np.round(v["DRAM_in_bandwidth[Mb/s]"], 2),
- np.round(v["DRAM_out_bandwidth[Mb/s]"], 2),
- )
- ret_str += "\n" + "-----------------------------"
- warnings.warn(ret_str)
-
- except KeyError:
- pytest.skip("PYNQ board or IP address not specified")
diff --git a/tests/pynq/test_pynq_performance_fifo.py b/tests/pynq/test_pynq_performance_fifo.py
deleted file mode 100644
index b1f69527d98694b325f910cdffd21c3914aeff7e..0000000000000000000000000000000000000000
--- a/tests/pynq/test_pynq_performance_fifo.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import os
-
-import pytest
-
-import numpy as np
-from onnx import TensorProto, helper
-
-from finn.core.datatype import DataType
-from finn.core.modelwrapper import ModelWrapper
-from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
-from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP
-from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
-from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker
-from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
-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
-import finn.transformation.fpgadataflow.replace_verilog_relpaths as rvp
-from finn.transformation.general import GiveUniqueNodeNames
-from finn.util.basic import pynq_part_map, pynq_native_port_width
-from finn.core.throughput_test import throughput_test_remote
-from scipy.stats import linregress
-import warnings
-
-
-def make_single_fifo_modelwrapper(Shape, Depth, fld_shape, finn_dtype):
-
- inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, Shape)
- outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, Shape)
-
- FIFO_node = helper.make_node(
- "StreamingFIFO",
- ["inp"],
- ["outp"],
- domain="finn",
- backend="fpgadataflow",
- depth=Depth,
- folded_shape=fld_shape,
- dataType=str(finn_dtype.name),
- )
-
- graph = helper.make_graph(
- nodes=[FIFO_node], name="fifo_graph", inputs=[inp], outputs=[outp]
- )
-
- model = helper.make_model(graph, producer_name="fifo-model")
- model = ModelWrapper(model)
-
- model.set_tensor_datatype("inp", finn_dtype)
- model.set_tensor_datatype("outp", finn_dtype)
-
- return model
-
-
-@pytest.mark.vivado
-@pytest.mark.slow
-def test_pynq_performance_fifo():
- try:
- ip = os.environ["PYNQ_IP"] # NOQA
- board = os.environ["PYNQ_BOARD"] # NOQA
- if ip == "" or board == "":
- pytest.skip("PYNQ board or IP address not specified")
- fifo_width = pynq_native_port_width[board]
- shape = (1, fifo_width)
- folded_shape = (1, 1, fifo_width)
- depth = 16
- clk_ns = 10
- dtype = DataType.BIPOLAR
- fpga_part = pynq_part_map[board]
- 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 = make_single_fifo_modelwrapper(shape, depth, folded_shape, dtype)
- model = model.transform(InsertTLastMarker())
- model = model.transform(GiveUniqueNodeNames())
- model = model.transform(PrepareIP(fpga_part, clk_ns))
- model = model.transform(HLSSynthIP())
- model = model.transform(rvp.ReplaceVerilogRelPaths())
- model = model.transform(CreateStitchedIP(fpga_part, clk_ns))
- model = model.transform(MakePYNQProject(board))
- model = model.transform(SynthPYNQProject())
- model = model.transform(MakePYNQDriver(platform="zynq"))
- model = model.transform(DeployToPYNQ(ip, port, username, password, target_dir))
-
- ret = dict()
- # try a range of batch sizes, some may fail due to insufficient DMA
- # buffers
- bsize_range_in = [2 ** i for i in range(20)]
- bsize_range = []
- for bsize in bsize_range_in:
- res = throughput_test_remote(model, bsize)
- if res is not None:
- ret[bsize] = res
- bsize_range.append(bsize)
- else:
- # assume we reached largest possible N
- break
-
- y = [ret[key]["runtime[ms]"] for key in bsize_range]
- lrret = linregress(bsize_range, y)
- ret_str = ""
- ret_str += "\n" + "FIFO Throughput Test Results"
- ret_str += "\n" + "-----------------------------"
- ret_str += "\n" + "From linear regression:"
- ret_str += "\n" + "Invocation overhead: %f ms" % lrret.intercept
- ret_str += "\n" + "Time per sample: %f ms" % lrret.slope
- ret_str += "\n" + "Raw data:"
-
- ret_str += "\n" + "{:<8} {:<16} {:<16} {:<16} {:<16} {:<16}".format(
- "N", "runtime[ms]", "fclk[mhz]", "fps", "DRAM rd[Mb/s]", "DRAM wr[Mb/s]"
- )
- for k in bsize_range:
- v = ret[k]
- ret_str += "\n" + "{:<8} {:<16} {:<16} {:<16} {:<16} {:<16}".format(
- k,
- np.round(v["runtime[ms]"], 4),
- v["fclk[mhz]"],
- np.round(v["throughput[images/s]"], 2),
- np.round(v["DRAM_in_bandwidth[Mb/s]"], 2),
- np.round(v["DRAM_out_bandwidth[Mb/s]"], 2),
- )
- ret_str += "\n" + "-----------------------------"
- warnings.warn(ret_str)
-
- except KeyError:
- pytest.skip("PYNQ board or IP address not specified")
diff --git a/tests/transformation/streamline/test_streamline_cnv.py b/tests/transformation/streamline/test_streamline_cnv.py
index bcb66a2c22eb4d6a998580129881793bbc86b250..82a38636e3927e17e5e2a3e8714f46082bba10e4 100644
--- a/tests/transformation/streamline/test_streamline_cnv.py
+++ b/tests/transformation/streamline/test_streamline_cnv.py
@@ -44,7 +44,6 @@ from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline import Streamline
from finn.util.test import get_test_model_trained
from finn.util.basic import make_build_dir
-from finn.transformation.double_to_single_float import DoubleToSingleFloat
export_onnx_path = make_build_dir("test_streamline_cnv_")
@@ -62,7 +61,6 @@ def test_streamline_cnv(size, wbits, abits):
fc = get_test_model_trained(size, wbits, abits)
bo.export_finn_onnx(fc, (1, 3, 32, 32), finn_onnx)
model = ModelWrapper(finn_onnx)
- model = model.transform(DoubleToSingleFloat())
model = model.transform(InferShapes())
model = model.transform(FoldConstants())
model = model.transform(GiveUniqueNodeNames())
diff --git a/tests/transformation/streamline/test_streamline_fc.py b/tests/transformation/streamline/test_streamline_fc.py
index dd7e756b4021af26c228804d4b509ecff032347e..9ce98066cfbf9d1c64514b957d8a260705fd0d7c 100644
--- a/tests/transformation/streamline/test_streamline_fc.py
+++ b/tests/transformation/streamline/test_streamline_fc.py
@@ -82,7 +82,7 @@ def test_streamline_fc(size, wbits, abits):
model = model.transform(RemoveUnusedTensors())
assert len(model.graph.initializer) == 11
assert len(model.graph.value_info) == 21
- assert len(model.graph.quantization_annotation) == 18
+ assert len(model.graph.quantization_annotation) == 20
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_batchnorm_to_affine.py b/tests/transformation/test_batchnorm_to_affine.py
index 43110c6bf9e5469b2ca21ac667d7f92808017fb8..a3df5ae9bbd3f99bc29bc088a5f461122af06d81 100644
--- a/tests/transformation/test_batchnorm_to_affine.py
+++ b/tests/transformation/test_batchnorm_to_affine.py
@@ -41,7 +41,6 @@ from finn.transformation.batchnorm_to_affine import BatchNormToAffine
from finn.transformation.fold_constants import FoldConstants
from finn.transformation.infer_shapes import InferShapes
from finn.util.test import get_test_model_trained
-from finn.transformation.double_to_single_float import DoubleToSingleFloat
export_onnx_path = "test_output_bn2affine.onnx"
@@ -50,7 +49,6 @@ def test_batchnorm_to_affine_cnv_w1a1():
lfc = get_test_model_trained("CNV", 1, 1)
bo.export_finn_onnx(lfc, (1, 3, 32, 32), export_onnx_path)
model = ModelWrapper(export_onnx_path)
- model = model.transform(DoubleToSingleFloat())
model = model.transform(InferShapes())
model = model.transform(FoldConstants())
fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
diff --git a/tests/transformation/test_conv_lowering.py b/tests/transformation/test_conv_lowering.py
index ab545d483321f8c52625b5401828277987bba3a9..b6ab634b374dea3ba309bbf12654c73c0a90e36c 100644
--- a/tests/transformation/test_conv_lowering.py
+++ b/tests/transformation/test_conv_lowering.py
@@ -40,7 +40,6 @@ from finn.transformation.fold_constants import FoldConstants
from finn.transformation.infer_shapes import InferShapes
from finn.util.test import get_test_model_trained
from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
-from finn.transformation.double_to_single_float import DoubleToSingleFloat
import finn.core.onnx_exec as oxe
from finn.custom_op.im2col import compute_conv_output_dim
from finn.util.basic import gen_finn_dt_tensor
@@ -53,7 +52,6 @@ def test_conv_lowering_cnv_w1a1():
cnv = get_test_model_trained("CNV", 1, 1)
bo.export_finn_onnx(cnv, (1, 3, 32, 32), export_onnx_path)
model = ModelWrapper(export_onnx_path)
- model = model.transform(DoubleToSingleFloat())
model = model.transform(InferShapes())
model = model.transform(FoldConstants())
fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
diff --git a/tests/transformation/test_infer_data_layouts.py b/tests/transformation/test_infer_data_layouts.py
index d6d9920043114c78e970842aee5955e3150cf526..0bc30ea0eb48087606545c86e705328217b004ca 100644
--- a/tests/transformation/test_infer_data_layouts.py
+++ b/tests/transformation/test_infer_data_layouts.py
@@ -37,7 +37,6 @@ from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeN
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline import Streamline
from finn.util.test import get_test_model_trained
-from finn.transformation.double_to_single_float import DoubleToSingleFloat
from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
@@ -51,7 +50,6 @@ def test_infer_data_layouts():
cnv = get_test_model_trained("CNV", 1, 1)
bo.export_finn_onnx(cnv, (1, 3, 32, 32), export_onnx_path_cnv)
model = ModelWrapper(export_onnx_path_cnv)
- model = model.transform(DoubleToSingleFloat())
model = model.transform(InferShapes())
model = model.transform(FoldConstants())
model = model.transform(GiveUniqueNodeNames())
diff --git a/tests/transformation/test_linear_past_eltwise.py b/tests/transformation/test_linear_past_eltwise.py
index 4cff5e5e1d40986a006cc02186fce21a907c2ef1..f5af2307fb042879a837a26c50715c8ec1b96963 100644
--- a/tests/transformation/test_linear_past_eltwise.py
+++ b/tests/transformation/test_linear_past_eltwise.py
@@ -37,7 +37,6 @@ from finn.transformation.fold_constants import FoldConstants
from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
from finn.transformation.streamline.reorder import MoveLinearPastEltwiseAdd
from finn.transformation.infer_shapes import InferShapes
-from finn.transformation.double_to_single_float import DoubleToSingleFloat
import pytest
@@ -105,7 +104,6 @@ def test_linear_past_eltwise_add(ch, ifmdim):
model = make_model(input_tensor_shape)
model.save(export_onnx_path)
model = ModelWrapper(export_onnx_path)
- model = model.transform(DoubleToSingleFloat())
model = model.transform(InferShapes())
model = model.transform(FoldConstants())
model = model.transform(GiveUniqueNodeNames())
diff --git a/tests/transformation/test_topk_insert.py b/tests/transformation/test_topk_insert.py
index a18e63384150f140cb63ec7b438283eb4797266c..a9faac4df0caf973d9aae6430e007eac349a7c43 100644
--- a/tests/transformation/test_topk_insert.py
+++ b/tests/transformation/test_topk_insert.py
@@ -21,7 +21,7 @@ import pytest
export_onnx_path = "test_topk_insert.onnx"
-@pytest.mark.parametrize("k", [1, 5, 10])
+@pytest.mark.parametrize("k", [1, 2])
def test_topk_insert(k):
tfc = get_test_model_trained("TFC", 1, 1)
bo.export_finn_onnx(tfc, (1, 1, 28, 28), export_onnx_path)