Merge pull request #9 from dgburnette/prelu

Prelu

example-prjs/prelu/README.md

@@ -0,0 +1,6 @@
+This is small conv2d 1 layer example. To run it:
+1. Run the run_catapult.sh script.
+2. Replace the prelu_test.cpp in the my-Catapult-test with the prelu_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).
+
+Note: You can create your own image matrix or filter and get the predictions by editing then running prelu.py. 

example-prjs/prelu/catapult.py

@@ -0,0 +1,43 @@
+
+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/prelu/config.yml

@@ -0,0 +1,15 @@
+Backend: Catapult
+KerasJson: prelu.json
+KerasH5: prelu_weights.h5
+OutputDir: my-Catapult-test
+ProjectName: prelu
+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/prelu/prelu.json

@@ -0,0 +1 @@
+{"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": "PReLU", "config": {"name": "p_re_lu", "trainable": true, "dtype": "float32", "alpha_initializer": {"class_name": "Constant", "config": {"value": 0.25}}, "alpha_regularizer": null, "alpha_constraint": null, "shared_axes": null}}]}, "keras_version": "2.11.0", "backend": "tensorflow"}

example-prjs/prelu/prelu.py

@@ -0,0 +1,51 @@
+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.PReLU(alpha_initializer=tf.initializers.constant(0.25)))
+
+	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='prelu')
+
+    # 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/prelu/prelu_test.cpp

@@ -0,0 +1,137 @@
+//
+//    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>
+
+#include "firmware/prelu.h"
+#include "firmware/nnet_utils/nnet_helpers.h"
+// #include "firmware/parameters.h"
+
+#include <mc_scverify.h>
+
+//hls-fpga-machine-learning insert bram
+#include "firmware/weights/a2.h"
+
+//hls-fpga-machine-learning insert declare weights
+model_default_t a2[25];
+
+namespace nnet {
+    bool trace_enabled = true;
+    std::map<std::string, void *> *trace_outputs = NULL;
+    size_t trace_type_size = sizeof(double);
+}
+
+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");
+
+#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);
+
+#ifndef __SYNTHESIS__
+    static bool loaded_weights = false;
+    if (!loaded_weights) {
+        //hls-fpga-machine-learning insert load weights
+            nnet::load_weights_from_txt<model_default_t, 25>(a2, "a2.txt");
+        loaded_weights = true;
+    }
+#endif
+  std::string iline;
+  std::string pline;
+
+  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;
+
+      //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")*/;
+
+      //hls-fpga-machine-learning insert top-level-function
+      prelu(input_1,layer2_out,a2);
+
+      result_t tmp = layer2_out[0];
+      for(int i = 0; i < N_INPUT_1_1; i++)
+      {
+	if(pr[i] != tmp[i].to_double())
+	{
+		std::cout << "FAILURE" << std::endl;
+		std::cout << "Expected: " << pr[i] << " Actual: " << tmp[i].to_double() << std::endl; 
+		return 1;
+	}	
+      }
+
+      //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;
+
+    //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")*/;
+
+    //hls-fpga-machine-learning insert top-level-function
+    prelu(input_1,layer2_out,a2);
+
+    //hls-fpga-machine-learning insert output
+
+    //hls-fpga-machine-learning insert tb-output
+    nnet::print_result<result_t, N_INPUT_1_1>(layer2_out, fout);
+
+  }
+
+  fout.close();
+  std::cout << "INFO: Saved inference results to file: " << RESULTS_LOG << std::endl;
+
+  return 0;
+}

example-prjs/prelu/run_catapult.sh

@@ -0,0 +1,44 @@
+#! /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/prelu.cpp prelu_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/prelu/run_vivado.sh

@@ -0,0 +1,39 @@
+#! /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/prelu.cpp prelu_test.cpp; a.out; popd
+

example-prjs/prelu/tb_input_features.dat

@@ -0,0 +1,2 @@
+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/prelu/tb_output_predictions.dat

@@ -0,0 +1,2 @@
+1 -0.5 3 -1 5 -1.25 1 -0.5 3 -1 4 -1.25 1 -0.5 3 -0.75 4 -1.25 1 -0.5 2 -0.75 4 -1.25 1
+-0.25 2 -0.75 4 -1.25 -1.5 7 -2 9 -2.5 -2.75 12 -3.25 14 -3.75 -4 17 -4.5 19 -5 -5.25 22 -5.75 24 -6.25

example-prjs/prelu/vivado.py

@@ -0,0 +1,43 @@
+
+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
+