{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Before You Start\n",
    "\n",
    "The current set of notebooks are under constant development.\n",
    "\n",
    "## Update Tutorial Repository\n",
    "\n",
    "If you have previously cloned the tutorial repository, you may need to get the latest versions of the notebooks.\n",
    "\n",
    "First check the status of your repository:\n",
    "```\n",
    "cd hls4ml-tutorial\n",
    "make clean\n",
    "git status \n",
    "```\n",
    "\n",
    "You may have some _modified_ notebooks. For example:\n",
    "\n",
    "```\n",
    "# On branch csee-e6868-spring2022\n",
    "# Changes not staged for commit:\n",
    "#   (use \"git add <file>...\" to update what will be committed)\n",
    "#   (use \"git checkout -- <file>...\" to discard changes in working directory)\n",
    "#\n",
    "#\tmodified:   part1_getting_started.ipynb\n",
    "#\tmodified:   part2_advanced_config.ipynb\n",
    "#\n",
    "no changes added to commit (use \"git add\" and/or \"git commit -a\")\n",
    "```\n",
    "\n",
    "You can make a copy of those modified notebooks if you had significat changes, otherwise the easiest thing to do is to discard those changes.\n",
    "\n",
    "**ATTENTION** You will loose your local changes!\n",
    "\n",
    "```\n",
    "git checkout *.ipynb\n",
    "```\n",
    "\n",
    "At this point, you can update you copy of the repository:\n",
    "```\n",
    "git pull\n",
    "```\n",
    "\n",
    "# Part 2: Advanced Configuration\n",
    "\n",
    "In this notebook, we will learn more about design-space exploration with hls4ml. We will focus on post-training quantization (introduced in [Part 1](part1_getting_started.ipynb)) and reuse factor.\n",
    "\n",
    "## Setup\n",
    "\n",
    "As we did in [Part 1](part1_getting_started.ipynb), let's import the libraries, call the magic functions, and setup the environment variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "os.environ['PATH'] = '/opt/xilinx/Vivado/2019.1/bin:' + os.environ['PATH']\n",
    "def is_tool(name):\n",
    "    from distutils.spawn import find_executable\n",
    "    return find_executable(name) is not None\n",
    "\n",
    "print('-----------------------------------')\n",
    "if not is_tool('vivado_hls'):\n",
    "    print('Xilinx Vivado HLS is NOT in the PATH')\n",
    "else:\n",
    "    print('Xilinx Vivado HLS is in the PATH')\n",
    "print('-----------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the dataset\n",
    "\n",
    "In [Part 1](part1_getting_started.ipynb), we saved the preprocessed dataset to files. Let's load them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_val = np.load('X_train_val.npy')\n",
    "X_test = np.load('X_test.npy')\n",
    "y_train_val = np.load('y_train_val.npy')\n",
    "y_test = np.load('y_test.npy', allow_pickle=True)\n",
    "classes = np.load('classes.npy', allow_pickle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the model\n",
    "\n",
    "In [Part 1](part1_getting_started.ipynb), we saved the trained model to file. Let's load it as well.\n",
    "\n",
    "**Make sure you've run through that walkthrough first!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "model = load_model('model_1/KERAS_check_best_model.h5')\n",
    "y_keras = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create an hls4ml configuration & model\n",
    "\n",
    "This time, we'll create an hls4ml configuration dictionary with _finer granularity_.\n",
    "\n",
    "When we used `granularity='model'`, the generated configuration was:\n",
    "```\n",
    "-----------------------------------\n",
    "Model\n",
    "  Precision:         ap_fixed<16,6>\n",
    "  ReuseFactor:       1\n",
    "  Strategy:          Latency\n",
    "-----------------------------------\n",
    "```\n",
    "\n",
    "Now instead we are using `granularity='name'` and we print the configuration, you'll notice that an entry is created for each named layer of the model. See for the first layer, for example:\n",
    "```\n",
    "-----------------------------------\n",
    "[...]\n",
    "LayerName:\n",
    "  fc1:\n",
    "      Precision:\n",
    "          weight: ap_fixed<16,6>\n",
    "          bias:   ap_fixed<16,6>\n",
    "          result: ap_fixed<16,6>\n",
    "      ReuseFactor: 1\n",
    "[...]\n",
    "-----------------------------------\n",
    "```\n",
    "Taken _out of the box_ this configuration will set all the parameters to the same settings as in [Part 1](part1_getting_started.ipynb), but we can use it as a template to start modifying things. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hls4ml\n",
    "import plotting\n",
    "config = hls4ml.utils.config_from_keras_model(model, granularity='name')\n",
    "print(\"-----------------------------------\")\n",
    "plotting.print_dict(config)\n",
    "print(\"-----------------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the remaining of the notebook we will investigate how to leverage the `Precision` and `ReuseFactor` knobs to run design-space exploration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Design Space Exploration for Post-Training Quantization\n",
    "\n",
    "### Profile\n",
    "\n",
    "With this new dictionary, we can choose the precision of _everything_ in our neural network. This is a powerful way to tune the performance, but the _fine tuning_ of all of these parameters is also complicated.\n",
    "\n",
    "The tools in `hls4ml.model.profiling` can help you choose the right precision for your model. (That said, training your model with quantization built in can get around this problem, and that is introduced in Part 4. So, don't go too far down the rabbit hole of tuning your data types without first trying out quantization aware training with [QKeras](https://github.com/google/qkeras).)\n",
    "\n",
    "The first thing to try is to numerically profile your model. This method plots the distribution of the weights (and biases) as a [box and whisker plot](https://en.wikipedia.org/wiki/Box_plot#:~:text=In%20descriptive%20statistics%2C%20a%20box,whisker%20plot%20and%20box%2Dand%2D). The grey boxes show the values which can be represented with the data types used in the `hls_model`. Generally,\n",
    "- you need the box to overlap completely with the whisker *to the right* (large values) otherwise you'll get saturation & wrap-around issues;\n",
    "- it can be okay for the box not to overlap completely *to the left* (small values), but finding how small you can go is a matter of trial-and-error.\n",
    "\n",
    "Providing data, in this case just using the first 1000 examples for speed, will show the same distributions captured at the output of each layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enable tracing for all of the layers\n",
    "for layer in config['LayerName'].keys():\n",
    "    config['LayerName'][layer]['Trace'] = True\n",
    "\n",
    "%matplotlib inline\n",
    "# Create an HLS model from the Keras model and hls4ml configuration dictionary\n",
    "hls_model = hls4ml.converters.convert_from_keras_model(model,\n",
    "                                                       hls_config=config,\n",
    "                                                       output_dir='model_1/hls4ml_prj_2',\n",
    "                                                       #fpga_part='xczu7ev-ffvc1156-2-e') # ZCU106\n",
    "                                                       part='xczu3eg-sbva484-1-e') # Ultra96\n",
    "                                                       #part='xc7z020clg400-1') # Pynq-Z1\n",
    "                                                       #part='xc7z007sclg225-1') # MiniZed\n",
    "# Run profiling\n",
    "_ = hls4ml.model.profiling.numerical(model=model, hls_model=hls_model, X=X_test[:1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customize\n",
    "\n",
    "Let's just try setting the precision of the first layer in the weights to something more narrow than 16 bits. Using fewer bits can save resources in the FPGA. After inspecting the profiling plot above, let's try 8 bits with 1 integer bit.\n",
    "\n",
    "Then create a new `hls_model`, and display the profiling with the new config. This time, just display the weight profile by not providing any data '`X`'. Then create the `HLSModel` and display the architecture. Notice the box around the weights of the first layer reflects the different precision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fine tune the precision of the weights in the first layer\n",
    "config['LayerName']['fc1']['Precision']['weight'] = 'ap_fixed<8,2>'\n",
    "\n",
    "# Create an HLS model from the Keras model and the updated hls4ml configuration dictionary\n",
    "hls_model = hls4ml.converters.convert_from_keras_model(model,\n",
    "                                                       hls_config=config,\n",
    "                                                       output_dir='model_1/hls4ml_prj_2',\n",
    "                                                       #part='xczu7ev-ffvc1156-2-e') # ZCU106\n",
    "                                                       part='xczu3eg-sbva484-1-e') # Ultra96\n",
    "                                                       #part='xc7z020clg400-1') # Pynq-Z1\n",
    "                                                       #part='xc7z007sclg225-1') # MiniZed\n",
    "# Run profiling (weights only, no X is provided)\n",
    "_ = hls4ml.model.profiling.numerical(model=model, hls_model=hls_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we did in [Part 1](part1_getting_started.ipynb), let's visualise the HLS model that we just created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hls4ml.utils.plot_model(hls_model, show_shapes=True, show_precision=True, to_file=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enable Tracing\n",
    "When we start using customised precision throughout the model, it can be useful to collect the output from each layer to find out when things have gone wrong. We enable this trace collection by setting `Trace = True` for each layer whose output we want to collect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enable tracing for all of the layers\n",
    "for layer in config['LayerName'].keys():\n",
    "    config['LayerName'][layer]['Trace'] = True\n",
    "\n",
    "# Create an HLS model from the Keras model and the updated hls4ml configuration dictionary\n",
    "hls_model = hls4ml.converters.convert_from_keras_model(model,\n",
    "                                                       hls_config=config,\n",
    "                                                       output_dir='model_1/hls4ml_prj_2',\n",
    "                                                       #part='xczu7ev-ffvc1156-2-e') # ZCU106\n",
    "                                                       part='xczu3eg-sbva484-1-e') # Ultra96\n",
    "                                                       #part='xc7z020clg400-1') # Pynq-Z1\n",
    "                                                       #part='xc7z007sclg225-1') # MiniZed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile, trace, predict\n",
    "\n",
    "Now we need to check that this model performance is still good after reducing the precision.\n",
    "\n",
    "We compile the `hls_model`, and now use the `hls_model.trace` method to collect the model output, and also the output for all the layers we enabled tracing for. The trace is a dictionary with keys corresponding to the layer names of the model; stored at that key is the array of values output by that layer, sampled from the provided data.\n",
    "\n",
    "A helper function `get_ymodel_keras` returns the same dictionary for the Keras model that is with the floating point precision.\n",
    "\n",
    "We'll just run the `trace` for the first 1000 examples, since it takes a bit longer and uses more memory than just running `predict`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# Recompile the hls model\n",
    "hls_model.compile()\n",
    "\n",
    "# Run tracing on a portion of the test set for the hls model (fixed-point precision) \n",
    "hls4ml_pred, hls4ml_trace = hls_model.trace(np.ascontiguousarray(X_test[:1000]))\n",
    "\n",
    "# Run tracing on a portion of the test set for the Keras model (floating-point precision)\n",
    "keras_trace = hls4ml.model.profiling.get_ymodel_keras(model, X_test[:1000])\n",
    "\n",
    "# Run prediction on all of the test set for the hls model (fixed-point precision)\n",
    "y_hls = hls_model.predict(np.ascontiguousarray(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manual Inspection\n",
    "Now we can print out, make plots, or do any other more detailed analysis on the output of each layer to make sure we haven't made the performance worse. And if we have, we can quickly find out where. Let's just print the output of the first layer, for the first sample, for both the Keras and hls4ml models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('-----------------------------------')\n",
    "print(\"Keras layer 'fc1', first sample:\")\n",
    "print(keras_trace['fc1'][0])\n",
    "print('-----------------------------------')\n",
    "print(\"hls4ml layer 'fc1', first sample:\")\n",
    "print(hls4ml_trace['fc1'][0])\n",
    "print('-----------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare\n",
    "Let's see if we lost performance by using 8 bits for the weights of the first layer by inspecting the accuracy and ROC curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('-----------------------------------')\n",
    "print(\"Keras  Accuracy: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_keras, axis=1))))\n",
    "print(\"hls4ml Accuracy: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_hls, axis=1))))\n",
    "print('-----------------------------------')\n",
    "\n",
    "# Enable logarithmic scale on TPR and FPR axes \n",
    "logscale_tpr = False # Y axis\n",
    "logscale_fpr = False # X axis\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 9))\n",
    "_ = plotting.plotMultiClassRoc(y_test, y_keras, classes, logscale_tpr=logscale_tpr, logscale_fpr=logscale_fpr)\n",
    "plt.gca().set_prop_cycle(None) # reset the colors\n",
    "_ = plotting.plotMultiClassRoc(y_test, y_hls, classes, logscale_tpr=logscale_tpr, logscale_fpr=logscale_fpr, linestyle='--')\n",
    "\n",
    "from matplotlib.lines import Line2D\n",
    "lines = [Line2D([0], [0], ls='-'),\n",
    "         Line2D([0], [0], ls='--')]\n",
    "from matplotlib.legend import Legend\n",
    "leg = Legend(ax, lines, labels=['keras', 'hls4ml'],\n",
    "            loc='lower right', frameon=False)\n",
    "_ = ax.add_artist(leg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The AUC results for the Keras and hls4ml implementation are again really close, but you can notice a little more difference with respect the plot in [Part 1](part1_getting_started.ipynb). Apply logaritmic scale on the FPR axis (logscale_fpr=True) to better appreciate differences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PTQ Summary\n",
    "\n",
    "We lost a small amount of accuracy compared to when we used `ap_fixed<16,6>`, but in many cases this difference will be small enough to be worth the resource saving. You can choose how aggressive to go with quantization, but it's always sensible to make the profiling plots even with the default configuration.\n",
    "\n",
    "Layer-level `trace` is very useful for finding when you reduced the bitwidth too far, or when the default configuration is no good for your model.\n",
    "\n",
    "In this model, _post training quantization_ at around 8-bits width generally seems to be the limit to how low you can go before suffering significant performance loss. In Part 4, we'll look at using _training aware quantization_ with QKeras to go much lower without losing much performance.\n",
    "\n",
    "## Design Space Exploration for Reuse Factor\n",
    "\n",
    "Now let's look at the other configuration parameter: `ReuseFactor`.\n",
    "Recall that `ReuseFactor` is our mechanism for tuning the parallelism:"
   ]
  },
  {
   "attachments": {
    "reuse.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![reuse.png](attachment:reuse.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now let's make a new configuration for this model, and set the `ReuseFactor` to `2` for every layer:\n",
    "we'll compile the model, then evaulate its performance. (Note, by creating a new config with `granularity=Model`, we're implicitly resetting the precision to `ap_fixed<16,6>` throughout.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a hls4ml configuration dictionary from the Keras model\n",
    "config = hls4ml.utils.config_from_keras_model(model, granularity='Model')\n",
    "\n",
    "# Set the ReuseFactor to 2 for all of the layers of the model\n",
    "config['Model']['ReuseFactor'] = 2\n",
    "\n",
    "print('-----------------------------------')\n",
    "# Show the generated configuration dictionary for hls4ml\n",
    "plotting.print_dict(config)\n",
    "print('-----------------------------------')\n",
    "\n",
    "# Create an HLS model from the Keras model and the updated hls4ml configuration dictionary\n",
    "hls_model = hls4ml.converters.convert_from_keras_model(model,\n",
    "                                                       hls_config=config,\n",
    "                                                       output_dir='model_1/hls4ml_prj_2',\n",
    "                                                       #part='xczu7ev-ffvc1156-2-e') # ZCU106\n",
    "                                                       part='xczu3eg-sbva484-1-e') # Ultra96\n",
    "                                                       #part='xc7z020clg400-1') # Pynq-Z1\n",
    "                                                       #part='xc7z007sclg225-1') # MiniZed\n",
    "_ = hls_model.compile()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Changing the `ReuseFactor` should not change the classification results, but let's just verify that by inspecting the accuracy and ROC curve again!\n",
    "Then we'll build the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run prediction on all of the test set for the hls model (fixed-point precision)\n",
    "y_hls = hls_model.predict(np.ascontiguousarray(X_test))\n",
    "\n",
    "print('-----------------------------------')\n",
    "print(\"Keras  Accuracy: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_keras, axis=1))))\n",
    "print(\"hls4ml Accuracy: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_hls, axis=1))))\n",
    "print('-----------------------------------')\n",
    "\n",
    "# Enable logarithmic scale on TPR and FPR axes \n",
    "logscale_tpr = False # Y axis\n",
    "logscale_fpr = False # X axis\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 9))\n",
    "_ = plotting.plotMultiClassRoc(y_test, y_keras, classes, logscale_tpr=logscale_tpr, logscale_fpr=logscale_fpr)\n",
    "plt.gca().set_prop_cycle(None) # reset the colors\n",
    "_ = plotting.plotMultiClassRoc(y_test, y_hls, classes, logscale_tpr=logscale_tpr, logscale_fpr=logscale_fpr, linestyle='--')\n",
    "\n",
    "from matplotlib.lines import Line2D\n",
    "lines = [Line2D([0], [0], ls='-'),\n",
    "         Line2D([0], [0], ls='--')]\n",
    "from matplotlib.legend import Legend\n",
    "leg = Legend(ax, lines, labels=['keras', 'hls4ml'],\n",
    "            loc='lower right', frameon=False)\n",
    "_ = ax.add_artist(leg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's run Vivado HLS to synthesize the model (_C-Synthesis_).\n",
    "\n",
    "**This takes approx. 15 minutes on Columbia servers.**\n",
    "\n",
    "While the C-Synthesis is running, we can monitor the progress looking at the log file by opening a terminal from the notebook home, and executing:\n",
    "\n",
    "`tail -f model_1/hls4ml_prj_2/vivado_hls.log`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "hls_results = hls_model.build(csim=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now print the report, compare this to the report from Exercise 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hls4ml.report.read_vivado_report('model_1/hls4ml_prj_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hls4ml.report.read_vivado_report('model_1/hls4ml_prj')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the new design, we are expecting half of the DSP usage and _Initiation Interval_ equals to 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "1. Recall the outcome of the exercise of Part 1 where we estimated how many DSPs our network should use. How does this change now we've used `ReuseFactor = 2` for the network? Does the expectation match the report this time?\n",
    "2. By leveraging `Precision` and `Reuse Factor`, try to create HLS models that fit at least the ZCU106 and Ultra96 boards."
   ]
  }
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