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This is small conv2d 1 layer example. To run it: | ||
1. Run the run_catapult.sh script. | ||
2. Replace the leaky_relu_test.cpp in the my-Catapult-test with the leaky_relu_test.cpp a level up (if you would like the testbench to be self-checking). | ||
3. Move tb_input_features.dat and tb_output_predictions.dat to my-Catapult-test/tb_data (if you want two pre-computed examples). | ||
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Note: You can create your own image matrix or filter and get the predictions by editing then running leaky_relu.py. |
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import hls4ml | ||
# import pprint | ||
import yaml | ||
import numpy as np | ||
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print(hls4ml.__version__) | ||
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with open('config.yml', 'r') as ymlfile: | ||
config = yaml.safe_load(ymlfile) | ||
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# try tweaking the reuse_factor on one layer to get different pipelining | ||
# config['HLSConfig']['LayerName']['fc1']['ReuseFactor'] = 4 | ||
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print('NETWORK') | ||
print(config) | ||
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config['OutputDir'] = 'my-Catapult-test' | ||
config['Backend'] = 'Catapult' | ||
config['IOType'] = 'io_stream' | ||
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config['HLSConfig']['Model']['Strategy'] = 'Latency' | ||
#config['HLSConfig']['Model']['Strategy'] = 'Resource' | ||
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# 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 | ||
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print('CURRENT CONFIGURATION') | ||
print('Backend='+config['Backend']) | ||
print('IOType='+config['IOType']) | ||
print('BramFactor={bf}'.format(bf=config['HLSConfig']['Model']['BramFactor'])) | ||
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# pprint.pprint(config) | ||
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#Convert it to a hls project | ||
hls_model = hls4ml.converters.keras_to_hls(config) | ||
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hls_model.build(vsynth=False) | ||
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# URL for this info: https://fastmachinelearning.org/hls4ml/setup/QUICKSTART.html | ||
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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 | ||
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IOType : io_parallel | ||
HLSConfig: | ||
Model: | ||
Precision: ap_fixed<16, 6> | ||
ReuseFactor: 1 | ||
Strategy: Latency |
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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"} |
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import tensorflow as tf | ||
import numpy as np | ||
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# 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()) | ||
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return model | ||
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# 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 | ||
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if __name__ == '__main__': | ||
model = create_model() | ||
save_model(model, name='leaky_relu') | ||
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# 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 ] | ||
]) | ||
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# Get prediction | ||
prediction = model.predict(image_mat) | ||
print("Image Matrix\n") | ||
print(image_mat) | ||
print("Prediction\n") | ||
print(prediction) | ||
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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 ] | ||
]) | ||
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# Get prediction | ||
prediction = model.predict(image_mat2) | ||
print("Image Matrix\n") | ||
print(image_mat2) | ||
print("Prediction\n") | ||
print(prediction) | ||
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// | ||
// rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks | ||
// | ||
// Copyright (C) 2017 EJ Kreinar | ||
// | ||
// This program is free software: you can redistribute it and/or modify | ||
// it under the terms of the GNU General Public License as published by | ||
// the Free Software Foundation, either version 3 of the License, or | ||
// (at your option) any later version. | ||
// | ||
// This program is distributed in the hope that it will be useful, | ||
// but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
// GNU General Public License for more details. | ||
// | ||
// You should have received a copy of the GNU General Public License | ||
// along with this program. If not, see <http://www.gnu.org/licenses/>. | ||
// | ||
#include <fstream> | ||
#include <iostream> | ||
#include <algorithm> | ||
#include <vector> | ||
#include <map> | ||
#include <stdio.h> | ||
#include <string.h> | ||
#include <stdlib.h> | ||
#include <math.h> | ||
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#include "firmware/leaky_relu.h" | ||
#include "firmware/nnet_utils/nnet_helpers.h" | ||
// #include "firmware/parameters.h" | ||
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#include <mc_scverify.h> | ||
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//hls-fpga-machine-learning insert bram | ||
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//hls-fpga-machine-learning insert declare weights | ||
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namespace nnet { | ||
bool trace_enabled = true; | ||
std::map<std::string, void *> *trace_outputs = NULL; | ||
size_t trace_type_size = sizeof(double); | ||
} | ||
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CCS_MAIN(int argc, char *argv[]) | ||
{ | ||
//load input data from text file | ||
std::ifstream fin("tb_data/tb_input_features.dat"); | ||
//load predictions from text file | ||
std::ifstream fpr("tb_data/tb_output_predictions.dat"); | ||
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#ifdef RTL_SIM | ||
std::string RESULTS_LOG = "tb_data/rtl_cosim_results.log"; | ||
#else | ||
std::string RESULTS_LOG = "tb_data/csim_results.log"; | ||
#endif | ||
std::ofstream fout(RESULTS_LOG); | ||
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#ifndef __SYNTHESIS__ | ||
static bool loaded_weights = false; | ||
if (!loaded_weights) { | ||
//hls-fpga-machine-learning insert load weights | ||
loaded_weights = true; | ||
} | ||
#endif | ||
std::string iline; | ||
std::string pline; | ||
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if (fin.is_open() && fpr.is_open()) { | ||
while ( std::getline(fin,iline) && std::getline (fpr,pline) ) { | ||
char* cstr=const_cast<char*>(iline.c_str()); | ||
char* current; | ||
std::vector<float> in; | ||
current=strtok(cstr," "); | ||
while(current!=NULL) { | ||
in.push_back(atof(current)); | ||
current=strtok(NULL," "); | ||
} | ||
cstr=const_cast<char*>(pline.c_str()); | ||
std::vector<float> pr; | ||
current=strtok(cstr," "); | ||
while(current!=NULL) { | ||
pr.push_back(atof(current)); | ||
current=strtok(NULL," "); | ||
} | ||
// std::cout << " Input feature map size = " << in.size() << " Output predictions size = " << pr.size() << std::endl; | ||
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//hls-fpga-machine-learning insert data | ||
ac_channel<input_t> input_1/*("input_1")*/; | ||
nnet::copy_data<float, input_t, 0, N_INPUT_1_1>(in, input_1); | ||
ac_channel<result_t> layer2_out/*("layer2_out")*/; | ||
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//hls-fpga-machine-learning insert top-level-function | ||
leaky_relu(input_1,layer2_out); | ||
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result_t tmp = layer2_out[0]; | ||
for(int i = 0; i < N_INPUT_1_1; i++) | ||
{ | ||
if(fabs(pr[i]-tmp[i].to_double() >= 0.001)) | ||
{ | ||
std::cout << "FAILURE" << std::endl; | ||
std::cout << "Expected: " << pr[i] << " Actual: " << tmp[i].to_double() << std::endl; | ||
return 1; | ||
} | ||
} | ||
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//hls-fpga-machine-learning insert tb-output | ||
nnet::print_result<result_t, N_INPUT_1_1>(layer2_out, fout); | ||
} | ||
fin.close(); | ||
fpr.close(); | ||
} else { | ||
std::cout << "INFO: Unable to open input/predictions file, using default input." << std::endl; | ||
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//hls-fpga-machine-learning insert zero | ||
ac_channel<input_t> input_1/*("input_1")*/; | ||
nnet::fill_zero<input_t, N_INPUT_1_1>(input_1); | ||
ac_channel<result_t> layer2_out/*("layer2_out")*/; | ||
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//hls-fpga-machine-learning insert top-level-function | ||
leaky_relu(input_1,layer2_out); | ||
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//hls-fpga-machine-learning insert output | ||
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//hls-fpga-machine-learning insert tb-output | ||
nnet::print_result<result_t, N_INPUT_1_1>(layer2_out, fout); | ||
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} | ||
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fout.close(); | ||
std::cout << "INFO: Saved inference results to file: " << RESULTS_LOG << std::endl; | ||
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return 0; | ||
} |
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#! /bin/bash | ||
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# This script runs the Catapult flows to generate the HLS. | ||
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VENV=/wv/scratch-baimar9c/venv | ||
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MGC_HOME=/wv/hlsb/CATAPULT/TOT/CURRENT/aol/Mgc_home | ||
export MGC_HOME | ||
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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 | ||
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# needed for pytest | ||
export OSTYPE=linux-gnu | ||
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echo "Activating Virtual Environment..." | ||
# bash | ||
source $VENV/bin/activate | ||
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rm -rf ./my-Catapult-test* | ||
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# 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 | ||
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# actually run HLS4ML + Catapult (+ optional vivado RTL) | ||
python3 catapult.py | ||
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# 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 | ||
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# 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" |
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#! /bin/bash | ||
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# This script runs the Vivado flows to generate the HLS. | ||
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VENV=$HOME/venv | ||
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MGC_HOME=/wv/hlsb/CATAPULT/TOT/CURRENT/aol/Mgc_home | ||
export MGC_HOME | ||
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export PATH=/wv/hlstools/python/python37/bin:$PATH:$XILINX_VIVADO/bin:$MGC_HOME/bin:/wv/hlstools/vivado/ixl/Vivado_HLS/2017.1/bin | ||
export LD_LIBRARY_PATH=/wv/hlstools/python/python37/lib:$XILINX_VIVADO/lib/lnx64.o:$MGC_HOME/lib | ||
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python | ||
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# needed for pytest | ||
export OSTYPE=linux-gnu | ||
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echo "Activating Virtual Environment..." | ||
# bash | ||
source $VENV/bin/activate | ||
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rm -rf ./my-Vivado-test* | ||
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mkdir -p tb_data | ||
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# 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 | ||
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# actually run HLS4ML + Vivado HLS | ||
python vivado.py | ||
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# 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 | ||
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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 |
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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 |
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import hls4ml | ||
# import pprint | ||
import yaml | ||
import numpy as np | ||
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print(hls4ml.__version__) | ||
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with open('config.yml', 'r') as ymlfile: | ||
config = yaml.safe_load(ymlfile) | ||
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# try tweaking the reuse_factor on one layer to get different pipelining | ||
# config['HLSConfig']['LayerName']['fc1']['ReuseFactor'] = 4 | ||
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print('NETWORK') | ||
print(config) | ||
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config['OutputDir'] = 'my-Vivado-test' | ||
config['Backend'] = 'Vivado' | ||
config['IOType'] = 'io_stream' | ||
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config['HLSConfig']['Model']['Strategy'] = 'Latency' | ||
#config['HLSConfig']['Model']['Strategy'] = 'Resource' | ||
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# 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 | ||
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print('CURRENT CONFIGURATION') | ||
print('Backend='+config['Backend']) | ||
print('IOType='+config['IOType']) | ||
print('BramFactor={bf}'.format(bf=config['HLSConfig']['Model']['BramFactor'])) | ||
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# pprint.pprint(config) | ||
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#Convert it to a hls project | ||
hls_model = hls4ml.converters.keras_to_hls(config) | ||
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hls_model.build(vsynth=False) | ||
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# URL for this info: https://fastmachinelearning.org/hls4ml/setup/QUICKSTART.html | ||
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