Working Leaky Relu example

example-prjs/leaky_relu/catapult.py

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+
+import hls4ml
+# import pprint
+import yaml
+import numpy as np
+
+print(hls4ml.__version__)
+
+with open('config.yml', 'r') as ymlfile:
+  config = yaml.safe_load(ymlfile)
+
+# try tweaking the reuse_factor on one layer to get different pipelining
+# config['HLSConfig']['LayerName']['fc1']['ReuseFactor'] = 4
+
+print('NETWORK')
+print(config)
+
+config['OutputDir'] = 'my-Catapult-test'
+config['Backend'] = 'Catapult'
+config['IOType'] = 'io_stream'
+
+config['HLSConfig']['Model']['Strategy'] = 'Latency'
+#config['HLSConfig']['Model']['Strategy'] = 'Resource'
+
+# default threshold is infinity
+config['HLSConfig']['Model']['BramFactor'] = np.inf
+# set to zero to force all weights onto (external function) interface
+config['HLSConfig']['Model']['BramFactor'] = 0
+
+print('CURRENT CONFIGURATION')
+print('Backend='+config['Backend'])
+print('IOType='+config['IOType'])
+print('BramFactor={bf}'.format(bf=config['HLSConfig']['Model']['BramFactor']))
+
+# pprint.pprint(config)
+
+#Convert it to a hls project
+hls_model = hls4ml.converters.keras_to_hls(config)
+
+hls_model.build(vsynth=False)
+
+# URL for this info: https://fastmachinelearning.org/hls4ml/setup/QUICKSTART.html
+

example-prjs/leaky_relu/config.yml

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+Backend: Catapult
+KerasJson: leaky_relu.json
+KerasH5: leaky_relu_weights.h5
+OutputDir: my-Catapult-test
+ProjectName: leaky_relu
+XilinxPart: xcku115-flvb2104-2-i
+Part: xcku115-flvb2104-2-i
+ClockPeriod: 5
+
+IOType : io_parallel
+HLSConfig:
+  Model:
+    Precision: ap_fixed<16, 6>
+    ReuseFactor: 1
+    Strategy: Latency

example-prjs/leaky_relu/leaky_relu.json

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+{"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 25], "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}}, {"class_name": "LeakyReLU", "config": {"name": "leaky_re_lu", "trainable": true, "dtype": "float32", "alpha": 0.30000001192092896}}]}, "keras_version": "2.11.0", "backend": "tensorflow"}

example-prjs/leaky_relu/leaky_relu.py

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+import tensorflow as tf
+import numpy as np
+
+# Create a relu 1layer that takes in a 25 element array
+def create_model():
+	# Create a model
+	model = tf.keras.Sequential()
+	model.add(tf.keras.layers.InputLayer(input_shape=(25,)))
+	model.add(tf.keras.layers.LeakyReLU())
+
+	return model
+
+# Save model to forms for hls4ml
+def save_model(model, name=None):
+	# Save as model.h5, model_weights.h5, and model.json
+	if name is None:
+		name = model.name
+	model.save(name + '.h5')
+	model.save_weights(name + '_weights.h5')
+	with open(name + '.json', 'w') as outfile:
+		outfile.write(model.to_json())
+	return
+
+if __name__ == '__main__':
+    model = create_model()
+    save_model(model, name='leaky_relu')
+
+    # Image Matrix
+    image_mat = np.array([
+	[ 1, -2, 3, -4, 5, -5, 1, -2, 3, -4, 4, -5, 1, -2, 3, -3, 4, -5, 1, -2, 2, -3, 4, -5, 1 ]
+    ])
+
+    # Get prediction
+    prediction = model.predict(image_mat)
+    print("Image Matrix\n")
+    print(image_mat)
+    print("Prediction\n")
+    print(prediction)
+
+    image_mat2 = np.array([
+	[ -1, 2, -3, 4, -5, -6, 7, -8, 9, -10, -11, 12, -13, 14, -15, -16, 17, -18, 19, -20, -21, 22, -23, 24, -25 ]
+    ])
+
+    # Get prediction
+    prediction = model.predict(image_mat2)
+    print("Image Matrix\n")
+    print(image_mat2)
+    print("Prediction\n")
+    print(prediction)
+
+

example-prjs/leaky_relu/run_catapult.sh

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+#! /bin/bash
+
+# This script runs the Catapult flows to generate the HLS.
+
+VENV=/wv/scratch-baimar9c/venv
+
+MGC_HOME=/wv/hlsb/CATAPULT/TOT/CURRENT/aol/Mgc_home
+export MGC_HOME
+
+export PATH=/wv/hlstools/python/python37/bin:$PATH:$XILINX_VIVADO/bin:$MGC_HOME/bin
+export LD_LIBRARY_PATH=/wv/hlstools/python/python37/lib:$XILINX_VIVADO/lib/lnx64.o:$MGC_HOME/lib
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+# needed for pytest
+export OSTYPE=linux-gnu
+
+echo "Activating Virtual Environment..."
+#    bash
+source $VENV/bin/activate
+
+rm -rf ./my-Catapult-test*
+
+# to run catapult+vivado_rtl
+sed -e 's/Vivado/Catapult/g' vivado.py >catapult.py
+# to only run catapult
+# sed -e 's/Vivado/Catapult/g' vivado.py | sed -e 's/vsynth=True/vsynth=False/g' >catapult.py
+
+# actually run HLS4ML + Catapult (+ optional vivado RTL)
+python3 catapult.py
+
+# run just the C++ execution
+echo ""
+echo "====================================================="
+echo "====================================================="
+echo "C++ EXECUTION"
+pushd my-Catapult-test; rm -f a.out; $MGC_HOME/bin/g++ -std=c++17 -I. -DWEIGHTS_DIR=\"firmware/weights\" -Ifirmware -I$MGC_HOME/shared/include firmware/leaky_relu.cpp leaky_relu_test.cpp; a.out; popd
+
+# Using VSCode setup generated by Catapult
+echo ""
+echo "====================================================="
+echo "====================================================="
+echo "To launch VSCode on the C++ generated by hls4ml:"
+echo "setenv LD_LIBRARY_PATH $MGC_HOME/lib:$MGC_HOME/shared/lib"
+echo "pushd my-Catapult-test; /wv/hlstools/vscode/LATEST/code Catapult.code-workspace"

example-prjs/leaky_relu/run_vivado.sh

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+#! /bin/bash
+
+# This script runs the Vivado flows to generate the HLS.
+
+VENV=$HOME/venv
+
+MGC_HOME=/wv/hlsb/CATAPULT/TOT/CURRENT/aol/Mgc_home
+export MGC_HOME
+
+export PATH=/wv/hlstools/python/python37/bin:$PATH:$XILINX_VIVADO/bin:$MGC_HOME/bin
+export LD_LIBRARY_PATH=/wv/hlstools/python/python37/lib:$XILINX_VIVADO/lib/lnx64.o:$MGC_HOME/lib
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+# needed for pytest
+export OSTYPE=linux-gnu
+
+echo "Activating Virtual Environment..."
+#    bash
+source $VENV/bin/activate
+
+rm -rf ./my-Vivado-test*
+
+mkdir -p tb_data
+
+# to run catapult+vivado_rtl
+sed -e 's/Vivado/Catapult/g' vivado.py >catapult.py
+# to only run catapult
+# sed -e 's/Vivado/Catapult/g' vivado.py | sed -e 's/vsynth=True/vsynth=False/g' >catapult.py
+
+# actually run HLS4ML + Vivado HLS
+python3 vivado.py
+
+# run just the C++ execution
+echo ""
+echo "====================================================="
+echo "====================================================="
+echo "C++ EXECUTION"
+pushd my-Vivado-test; rm -f a.out; $MGC_HOME/bin/g++ -g -std=c++11 -I. -DWEIGHTS_DIR=\"firmware/weights\" -Ifirmware -Ifirmware/ap_types -I$MGC_HOME/shared/include firmware/leaky_relu.cpp leaky_relu_test.cpp; a.out; popd
+

example-prjs/leaky_relu/tb_input_features.dat

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+1 -2  3 -4  5 -5  1 -2  3 -4  4 -5  1 -2  3 -3  4 -5  1 -2  2 -3  4 -5 1
+-1   2  -3   4  -5  -6   7  -8   9 -10 -11  12 -13  14 -15 -16  17 -18 19 -20 -21  22 -23  24 -25

example-prjs/leaky_relu/tb_output_predictions.dat

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+1 -0.6 3 -1.2 5 -1.5 1 -0.6 3 -1.2 4 -1.5 1 -0.6 3 -0.90000004 4 -1.5 1 -0.6 2 -0.90000004 4 -1.5 1
+-0.3 2 -0.90000004 4 -1.5 -1.8000001 7 -2.4 9 -3 -3.3000002 12 -3.9 14 -4.5 -4.8 17 -5.4 19 -6 -6.3 22 -6.9 24 -7.5000005

example-prjs/leaky_relu/vivado.py

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+
+import hls4ml
+# import pprint
+import yaml
+import numpy as np
+
+print(hls4ml.__version__)
+
+with open('config.yml', 'r') as ymlfile:
+  config = yaml.safe_load(ymlfile)
+
+# try tweaking the reuse_factor on one layer to get different pipelining
+# config['HLSConfig']['LayerName']['fc1']['ReuseFactor'] = 4
+
+print('NETWORK')
+print(config)
+
+config['OutputDir'] = 'my-Vivado-test'
+config['Backend'] = 'Vivado'
+config['IOType'] = 'io_stream'
+
+config['HLSConfig']['Model']['Strategy'] = 'Latency'
+#config['HLSConfig']['Model']['Strategy'] = 'Resource'
+
+# default threshold is infinity
+config['HLSConfig']['Model']['BramFactor'] = np.inf
+# set to zero to force all weights onto (external function) interface
+config['HLSConfig']['Model']['BramFactor'] = 0
+
+print('CURRENT CONFIGURATION')
+print('Backend='+config['Backend'])
+print('IOType='+config['IOType'])
+print('BramFactor={bf}'.format(bf=config['HLSConfig']['Model']['BramFactor']))
+
+# pprint.pprint(config)
+
+#Convert it to a hls project
+hls_model = hls4ml.converters.keras_to_hls(config)
+
+hls_model.build(vsynth=False)
+
+# URL for this info: https://fastmachinelearning.org/hls4ml/setup/QUICKSTART.html
+