3-4-3-PTQ.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "3-4-3-PTQ.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tinyMLx/colabs/blob/master/3-4-3-PTQ.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8J_5h97lSPBj"
      },
      "source": [
        "# Post Training Quantization\n",
        "In this Colab we are going to explore Post Training Quantization (PTQ) in more detail. In particular we will use Python to get a sense of what is going on during quantization (effectively peeking under the hood of TensorFlow). We will also visualize the weight distributions to gain intuition for why quantization is often so successful (hint: the weights are often closely clustered around 0)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zOvpFr7OoYRF"
      },
      "source": [
        "### First import the needed packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mWLmdCq6oGmp"
      },
      "source": [
        "# For Numpy\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pprint\n",
        "import re\n",
        "import sys\n",
        "# For TensorFlow Lite (also uses some of the above)\n",
        "import logging\n",
        "logging.getLogger(\"tensorflow\").setLevel(logging.DEBUG)\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import pathlib\n",
        "import pprint\n",
        "import re\n",
        "import sys"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4R78LT-GKTI9"
      },
      "source": [
        "### Exploring Post Training Quantization Algorithms in Python\n",
        "\n",
        "Let us assume we have a weight array of size (256, 256)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0GCQFCETBncp"
      },
      "source": [
        "weights = np.random.randn(256, 256)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CHrdsPuiK3yq"
      },
      "source": [
        "In Post Training Quantization, we map the 32-bit floating point numbers to 8-bit integers. To do this, we need to find a very important value, the ***scale***. The ***scale*** value is used to convert numbers back and forth between the various representations. For example,  32-bit floating point numbers can be contructed from 8-bit Integers by the following formula:\n",
        "\n",
        "$ FP32\\_Reconstructed\\_Value = Scale \\times Int8\\_value$\n",
        "\n",
        "![Screenshot 2020-11-19 at 4.35.25 PM.png](data:image/png;base64,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)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f5evVrs7OuPk"
      },
      "source": [
        "To make sure we can cover the complete weight distribution, the scale value needs to take into account the full range of weight values which we can compute using the following formula. The denominator is 256 because that is the range of values that can be represented using 8-bits ($2^8 = 256$).\n",
        "\n",
        "$scale = \\frac{max(weights) - min(weights)}{256}$\n",
        "\n",
        "Now lets code this up!\n",
        "\n",
        "We can then use this function to quantize our weights and then reconstruct them back to floating point format. We can then see what kinds of errors are introduced by this process. Our hope is that the errors in general are small showing that this process does a good job representing our weights in a more compact format. **In general if our scale is smaller it is more likely to have smaller errors as we are not lumping as many numbers into the same bin.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2nH0t1QqK2Lc"
      },
      "source": [
        "def quantizeAndReconstruct(weights):\n",
        "    \"\"\"\n",
        "    @param W: np.ndarray\n",
        "\n",
        "    This function computes the scale value to map fp32 values to int8. The function returns a weight matrix in fp32, that is representable\n",
        "    using 8-bits.\n",
        "    \"\"\"\n",
        "    # Compute the range of the weight.\n",
        "    max_weight = np.max(weights)\n",
        "    min_weight = np.min(weights)\n",
        "    range = max_weight - min_weight\n",
        "\n",
        "    max_int8 = 2**8\n",
        "    \n",
        "    # Compute the scale\n",
        "    scale = range / max_int8\n",
        "\n",
        "    # Compute the midpoint\n",
        "    midpoint = np.mean([max_weight, min_weight])\n",
        "\n",
        "    # Next, we need to map the real fp32 values to the integers. For this, we make use of the computed scale. By diving the weight \n",
        "    # matrix with the scale, the weight matrix has a range between (-128, 127). Now, we can simply round the full precision numbers\n",
        "    # to the closest integers.\n",
        "    centered_weights = weights - midpoint\n",
        "    quantized_weights = np.rint(centered_weights / scale)\n",
        "\n",
        "    # Now, we can reconstruct the values back to fp32.\n",
        "    reconstructed_weights = scale * quantized_weights + midpoint\n",
        "    return reconstructed_weights"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "43a5rap1SXRM"
      },
      "source": [
        "reconstructed_weights = quantizeAndReconstruct(weights)\n",
        "print(\"Original weight matrix\\n\", weights)\n",
        "print(\"Weight Matrix after reconstruction\\n\", reconstructed_weights)\n",
        "errors = reconstructed_weights-weights\n",
        "max_error = np.max(errors)\n",
        "print(\"Max Error  : \", max_error)\n",
        "reconstructed_weights.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbMxeGK7sSJD"
      },
      "source": [
        "The quantized representation should not have more than 256 unique floating numbers, lets do a sanity check."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Enr4qh0FS62G"
      },
      "source": [
        "# We can use np.unique to check the number of unique floating point numbers in the weight matrix.\n",
        "np.unique(quantizeAndReconstruct(weights)).shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D9mWqsFFTvI6"
      },
      "source": [
        "### Exploring Post Training Quantization using TFLite"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L6q45fZKTl79"
      },
      "source": [
        "Now that we know how PTQ works under the hood, lets move over to seeing the actual benefits in terms of memory and speed. Since in numpy, we were representing our final weight matrix in full precision, the memory occupied was still the same. However, in TFLite, we only store the matrix in an 8-bit format. As you have seen in previous Colabs, this can lead to a decrease in size of the model by a factor of up to 4!\n",
        "\n",
        "Note: We however do not save a perfect factor of 4 in total memory usage as we now also have to store the scale (and potentially other factors needed to properly convert the numbers).\n",
        "\n",
        "Lets explore this again looking at the file sizes of the MNIST model using the [TFLite Converter](https://www.tensorflow.org/lite/convert)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQiWzhuNaR6J"
      },
      "source": [
        "# Load MNIST dataset\n",
        "mnist = keras.datasets.mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n",
        "\n",
        "# Normalize the input image so that each pixel value is between 0 to 1.\n",
        "train_images = train_images / 255.0\n",
        "test_images = test_images / 255.0\n",
        "\n",
        "# Define the model architecture\n",
        "model = keras.Sequential([\n",
        "  keras.layers.InputLayer(input_shape=(28, 28)),\n",
        "  keras.layers.Reshape(target_shape=(28, 28, 1)),\n",
        "  keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation=tf.nn.relu),\n",
        "  keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "  keras.layers.Flatten(),\n",
        "  keras.layers.Dense(10)\n",
        "])\n",
        "\n",
        "# Train the digit classification model\n",
        "model.compile(optimizer='adam',\n",
        "              loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "model.fit(\n",
        "  train_images,\n",
        "  train_labels,\n",
        "  epochs=1,\n",
        "  validation_data=(test_images, test_labels)\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhsbSzJQbPyz"
      },
      "source": [
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "tflite_models_dir = pathlib.Path(\"/content/mnist_tflite_models/\")\n",
        "tflite_models_dir.mkdir(exist_ok=True, parents=True)\n",
        "tflite_model_file = tflite_models_dir/\"mnist_model.tflite\"\n",
        "tflite_model_file.write_bytes(tflite_model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iYoCTHYBbYx2"
      },
      "source": [
        "# Convert the model using DEFAULT optimizations: https://github.com/tensorflow/tensorflow/blob/v2.4.1/tensorflow/lite/python/lite.py#L91-L130\n",
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "tflite_quant_model = converter.convert()\n",
        "tflite_model_quant_file = tflite_models_dir / \"mnist_model_quant.tflite\"\n",
        "tflite_model_quant_file.write_bytes(tflite_quant_model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhFim-h3cMpI"
      },
      "source": [
        "!ls -lh /content/mnist_tflite_models/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZzdrK-YkFrh"
      },
      "source": [
        "**Notice the size difference - the quantized model is smaller by a factor of ~4 as expected**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6u0xd_YKeney"
      },
      "source": [
        "### Software Installation to Inspect TFLite Models\n",
        "\n",
        "Before we can inspect TF Lite files in detail we need to build and install software to read the file format. First we’ll build and install the Flatbuffer compiler, which takes in a schema definition and outputs Python files to read files with that format.\n",
        "\n",
        "**Note: This will take a few minutes to run.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WToIvvhzWhh2"
      },
      "source": [
        "%%bash\n",
        "\n",
        "cd /content/\n",
        "git clone https://github.com/google/flatbuffers\n",
        "cd flatbuffers\n",
        "git checkout 0dba63909fb2959994fec11c704c5d5ea45e8d83\n",
        "cmake -G \"Unix Makefiles\" -DCMAKE_BUILD_TYPE=Release\n",
        "make\n",
        "cp flatc /usr/local/bin/\n",
        "cd /content/\n",
        "git clone --depth 1 https://github.com/tensorflow/tensorflow\n",
        "flatc --python --gen-object-api tensorflow/tensorflow/lite/schema/schema_v3.fbs\n",
        "pip install flatbuffers"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxIzqKy0WkKp"
      },
      "source": [
        "# To allow us to import the Python files we've just generated we need to update the path env variable\n",
        "sys.path.append(\"/content/tflite/\")\n",
        "import Model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-KH8TwUU-os"
      },
      "source": [
        "Then we define some utility functions that will help us convert the model into a dictionary that's easy to work with in Python."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NvEIcRfpHZai"
      },
      "source": [
        "def CamelCaseToSnakeCase(camel_case_input):\n",
        "  \"\"\"Converts an identifier in CamelCase to snake_case.\"\"\"\n",
        "  s1 = re.sub(\"(.)([A-Z][a-z]+)\", r\"\\1_\\2\", camel_case_input)\n",
        "  return re.sub(\"([a-z0-9])([A-Z])\", r\"\\1_\\2\", s1).lower()\n",
        "\n",
        "def FlatbufferToDict(fb, attribute_name=None):\n",
        "  \"\"\"Converts a hierarchy of FB objects into a nested dict.\"\"\"\n",
        "  if hasattr(fb, \"__dict__\"):\n",
        "    result = {}\n",
        "    for attribute_name in dir(fb):\n",
        "      attribute = fb.__getattribute__(attribute_name)\n",
        "      if not callable(attribute) and attribute_name[0] != \"_\":\n",
        "        snake_name = CamelCaseToSnakeCase(attribute_name)\n",
        "        result[snake_name] = FlatbufferToDict(attribute, snake_name)\n",
        "    return result\n",
        "  elif isinstance(fb, str):\n",
        "    return fb\n",
        "  elif attribute_name == \"name\" and fb is not None:\n",
        "    result = \"\"\n",
        "    for entry in fb:\n",
        "      result += chr(FlatbufferToDict(entry))\n",
        "    return result\n",
        "  elif hasattr(fb, \"__len__\"):\n",
        "    result = []\n",
        "    for entry in fb:\n",
        "      result.append(FlatbufferToDict(entry))\n",
        "    return result\n",
        "  else:\n",
        "    return fb\n",
        "\n",
        "def CreateDictFromFlatbuffer(buffer_data):\n",
        "  model_obj = Model.Model.GetRootAsModel(buffer_data, 0)\n",
        "  model = Model.ModelT.InitFromObj(model_obj)\n",
        "  return FlatbufferToDict(model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gdEvnB-7VOyh"
      },
      "source": [
        "### Visualizing TFLite model weight distributions\n",
        "\n",
        "This example uses the Inception v3 model, dating back to 2015, but you can replace it with your own file by updating the variables. To load in any TFLite model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "krpIbFGdAmbr"
      },
      "source": [
        "MODEL_ARCHIVE_NAME = 'inception_v3_2015_2017_11_10.zip'\n",
        "MODEL_ARCHIVE_URL = 'https://storage.googleapis.com/download.tensorflow.org/models/tflite/' + MODEL_ARCHIVE_NAME\n",
        "MODEL_FILE_NAME = 'inceptionv3_non_slim_2015.tflite'\n",
        "!curl -o {MODEL_ARCHIVE_NAME} {MODEL_ARCHIVE_URL}\n",
        "!unzip {MODEL_ARCHIVE_NAME}\n",
        "with open(MODEL_FILE_NAME, 'rb') as file:\n",
        " model_data = file.read()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "goVnTIi-WFfQ"
      },
      "source": [
        "Once we have the raw bytes of the file, we need to convert them into an understandable form. The utility functions and Python schema code we generated earlier will help us create a dictionary holding the file contents in a structured form. \n",
        "\n",
        "**Note: since it's a large file, this will take several minutes to run.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x4yL-jUfJlgy"
      },
      "source": [
        "model_dict = CreateDictFromFlatbuffer(model_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__1jOPQoWYwG"
      },
      "source": [
        "Now that we have the model file in a dictionary, we can examine its contents using standard Python commands. In this case we're interested in examining the tensors (arrays of values) in the first subgraph, so we're printing them out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0sFW8nwJtd-"
      },
      "source": [
        "pprint.pprint(model_dict['subgraphs'][0]['tensors'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vopHMENkWy96"
      },
      "source": [
        "Let's inspect the weight parameters of a typical convolution layer, so looking at the output above we can see that the tensor with the name 'Conv2D' has a buffer index of 212. This index points to where the raw bytes for the trained weights are stored. From the tensor properties I can see its type is '0', which [corresponds to a type of float32](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema_v3.fbs#L30).\n",
        "\n",
        "This means we have to cast the bytes into a numpy array using the frombuffer() function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tq9K8cCHJyxh"
      },
      "source": [
        "param_bytes = bytearray(model_dict['buffers'][212]['data'])\n",
        "params = np.frombuffer(param_bytes, dtype=np.float32)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "03MPfa23XpG5"
      },
      "source": [
        "With the weights loaded into a numpy array, we can now use all the standard functionality to analyze them. To start, let's print out the minimum and maximum values to understand the range."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vjmnnk2JKZ5J"
      },
      "source": [
        "params.min()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bv5ADjs1RDW_"
      },
      "source": [
        "params.max()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVexy-9aX5QY"
      },
      "source": [
        "This gives us the total range of the weight values, but how are those parameters distributed across that range?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1CAikSVREjG"
      },
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.hist(params, 100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7QFhAxNQY39S"
      },
      "source": [
        "**This shows a distribution that's heavily concentrated around zero. This explains why quantization can work quite well. With values so concentrated around zero, our scale can be quite small and therefore it is much easier to do an accurate reconstruction as we do not need to represent a large number of values!**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1DMkCS4rmehs"
      },
      "source": [
        "### More Models to Explore"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uv06PQrBmhVH"
      },
      "source": [
        "# Text Classification\n",
        "!wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/text_classification/text_classification_v2.tflite\n",
        "\n",
        "# Post Estimation\n",
        "!wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/posenet_mobilenet_v1_100_257x257_multi_kpt_stripped.tflite"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ft_02deqmkOb"
      },
      "source": [
        "TEXT_CLASSIFICATION_MODEL_FILE_NAME = \"text_classification_v2.tflite\"\n",
        "POSE_ESTIMATION_MODEL_FILE_NAME = \"posenet_mobilenet_v1_100_257x257_multi_kpt_stripped.tflite\"\n",
        "\n",
        "with open(TEXT_CLASSIFICATION_MODEL_FILE_NAME, 'rb') as file:\n",
        "  text_model_data = file.read()\n",
        "\n",
        "with open(POSE_ESTIMATION_MODEL_FILE_NAME, 'rb') as file:\n",
        "  pose_model_data = file.read()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pKxAk5m-nb4w"
      },
      "source": [
        "def aggregate_all_weights(buffers):\n",
        "    weights = []\n",
        "    for i in range(len(buffers)):\n",
        "        raw_data = buffers[i]['data']\n",
        "        if raw_data is not None:\n",
        "            param_bytes = bytearray(raw_data)\n",
        "            params = np.frombuffer(param_bytes, dtype=np.float32)\n",
        "            weights.extend(params.flatten().tolist())\n",
        "\n",
        "    weights = np.asarray(weights)\n",
        "    weights = weights[weights<50]\n",
        "    weights = weights[weights>-50]\n",
        "\n",
        "    return weights"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k7QVoa0eqiN0"
      },
      "source": [
        "Lets plot the distribution of the Text Classification Model in log scale\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NBqwfoVn8jG"
      },
      "source": [
        "model_dict_temp = CreateDictFromFlatbuffer(text_model_data)\n",
        "weights = aggregate_all_weights(model_dict_temp['buffers'])\n",
        "\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.hist(weights, 256, log=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLkcgfOZqpZT"
      },
      "source": [
        "Lets plot the distribution of the Pose Net Model in log scale\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "woRq_i7foCgn"
      },
      "source": [
        "model_dict_temp = CreateDictFromFlatbuffer(pose_model_data)\n",
        "weights = aggregate_all_weights(model_dict_temp['buffers'][:-1])\n",
        "\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.hist(weights, 256, log=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Poi4dK9yvCtZ"
      },
      "source": [
        "**Again we find that most model weights are closely packed around 0.**"
      ]
    }
  ]
}