notebooks-d2l/gru_jax.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/probml/probml-notebooks/blob/main/notebooks-d2l/gru_jax.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7jysFtXPFE7E"
      },
      "source": [
        "# Gated Recurrent Units\n",
        "\n",
        "We show how to implement GRUs from scratch.\n",
        "Based on sec 9.1 of http://d2l.ai/chapter_recurrent-modern/gru.html\n",
        "This uses code from the [basic RNN colab](https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/rnn_torch.ipynb).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "eemDrShfots1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d258c929-2fa4-45b8-88c3-61f21e5a7202"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l\r\u001b[K     |█▉                              | 10 kB 20.3 MB/s eta 0:00:01\r\u001b[K     |███▋                            | 20 kB 11.3 MB/s eta 0:00:01\r\u001b[K     |█████▍                          | 30 kB 9.0 MB/s eta 0:00:01\r\u001b[K     |███████▏                        | 40 kB 7.9 MB/s eta 0:00:01\r\u001b[K     |█████████                       | 51 kB 4.7 MB/s eta 0:00:01\r\u001b[K     |██████████▊                     | 61 kB 5.5 MB/s eta 0:00:01\r\u001b[K     |████████████▌                   | 71 kB 5.7 MB/s eta 0:00:01\r\u001b[K     |██████████████▎                 | 81 kB 5.6 MB/s eta 0:00:01\r\u001b[K     |████████████████                | 92 kB 6.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████▉              | 102 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████▋            | 112 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▍          | 122 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▏        | 133 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 143 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▊     | 153 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▌   | 163 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▎ | 174 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 184 kB 5.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 136 kB 39.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 72 kB 422 kB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q flax"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "o6VNlv_FYTbS"
      },
      "outputs": [],
      "source": [
        "import jax.numpy as jnp\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "from IPython import display \n",
        "\n",
        "import jax\n",
        "import flax.linen as nn\n",
        "from flax import jax_utils\n",
        "import optax\n",
        "\n",
        "import collections\n",
        "import re\n",
        "import random\n",
        "import os\n",
        "import requests\n",
        "import hashlib\n",
        "import time\n",
        "import functools\n",
        "\n",
        "random.seed(0)\n",
        "rng = jax.random.PRNGKey(0)\n",
        "\n",
        "!mkdir figures # for saving plots"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "phjRpyDNFT14"
      },
      "source": [
        "# Data\n",
        "\n",
        " As data, we use the book \"The Time Machine\" by H G Wells,\n",
        "preprocessed using the code in [this colab](https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/text_preproc_torch.ipynb)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "tLIKRJomvBV5"
      },
      "outputs": [],
      "source": [
        "class SeqDataLoader:\n",
        "    \"\"\"An iterator to load sequence data.\"\"\"\n",
        "    def __init__(self, batch_size, num_steps, use_random_iter, max_tokens):\n",
        "        if use_random_iter:\n",
        "            self.data_iter_fn = seq_data_iter_random\n",
        "        else:\n",
        "            self.data_iter_fn = seq_data_iter_sequential\n",
        "        self.corpus, self.vocab = load_corpus_time_machine(max_tokens)\n",
        "        self.batch_size, self.num_steps = batch_size, num_steps\n",
        "\n",
        "    def __iter__(self):\n",
        "        return self.data_iter_fn(self.corpus, self.batch_size, self.num_steps)\n",
        "\n",
        "class Vocab:\n",
        "    \"\"\"Vocabulary for text.\"\"\"\n",
        "    def __init__(self, tokens=None, min_freq=0, reserved_tokens=None):\n",
        "        if tokens is None:\n",
        "            tokens = []\n",
        "        if reserved_tokens is None:\n",
        "            reserved_tokens = []\n",
        "        # Sort according to frequencies\n",
        "        counter = count_corpus(tokens)\n",
        "        self.token_freqs = sorted(counter.items(), key=lambda x: x[1],\n",
        "                                  reverse=True)\n",
        "        # The index for the unknown token is 0\n",
        "        self.unk, uniq_tokens = 0, ['<unk>'] + reserved_tokens\n",
        "        uniq_tokens += [\n",
        "            token for token, freq in self.token_freqs\n",
        "            if freq >= min_freq and token not in uniq_tokens]\n",
        "        self.idx_to_token, self.token_to_idx = [], dict()\n",
        "        for token in uniq_tokens:\n",
        "            self.idx_to_token.append(token)\n",
        "            self.token_to_idx[token] = len(self.idx_to_token) - 1\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.idx_to_token)\n",
        "\n",
        "    def __getitem__(self, tokens):\n",
        "        if not isinstance(tokens, (list, tuple)):\n",
        "            return self.token_to_idx.get(tokens, self.unk)\n",
        "        return [self.__getitem__(token) for token in tokens]\n",
        "\n",
        "    def to_tokens(self, indices):\n",
        "        if not isinstance(indices, (list, tuple)):\n",
        "            return self.idx_to_token[indices]\n",
        "        return [self.idx_to_token[index] for index in indices]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "-TvZsL4gtHQ2"
      },
      "outputs": [],
      "source": [
        "def tokenize(lines, token='word'):\n",
        "    \"\"\"Split text lines into word or character tokens.\"\"\"\n",
        "    if token == 'word':\n",
        "        return [line.split() for line in lines]\n",
        "    elif token == 'char':\n",
        "        return [list(line) for line in lines]\n",
        "    else:\n",
        "        print('ERROR: unknown token type: ' + token)\n",
        "\n",
        "def count_corpus(tokens):\n",
        "    \"\"\"Count token frequencies.\"\"\"\n",
        "    # Here `tokens` is a 1D list or 2D list\n",
        "    if len(tokens) == 0 or isinstance(tokens[0], list):\n",
        "        # Flatten a list of token lists into a list of tokens\n",
        "        tokens = [token for line in tokens for token in line]\n",
        "    return collections.Counter(tokens)\n",
        "\n",
        "def seq_data_iter_random(corpus, batch_size, num_stepsz):\n",
        "    \"\"\"Generate a minibatch of subsequences using random sampling.\"\"\"\n",
        "    # Start with a random offset (inclusive of `num_steps - 1`) to partition a\n",
        "    # sequence\n",
        "    corpus = corpus[random.randint(0, num_steps - 1):]\n",
        "    # Subtract 1 since we need to account for labels\n",
        "    num_subseqs = (len(corpus) - 1) // num_steps\n",
        "    # The starting indices for subsequences of length `num_steps`\n",
        "    initial_indices = list(range(0, num_subseqs * num_steps, num_steps))\n",
        "    # In random sampling, the subsequences from two adjacent random\n",
        "    # minibatches during iteration are not necessarily adjacent on the\n",
        "    # original sequence\n",
        "    random.shuffle(initial_indices)\n",
        "\n",
        "    def data(pos):\n",
        "        # Return a sequence of length `num_steps` starting from `pos`\n",
        "        return corpus[pos:pos + num_steps]\n",
        "\n",
        "    num_batches = num_subseqs // batch_size\n",
        "    for i in range(0, batch_size * num_batches, batch_size):\n",
        "        # Here, `initial_indices` contains randomized starting indices for\n",
        "        # subsequences\n",
        "        initial_indices_per_batch = initial_indices[i:i + batch_size]\n",
        "        X = [data(j) for j in initial_indices_per_batch]\n",
        "        Y = [data(j + 1) for j in initial_indices_per_batch]\n",
        "        yield jnp.array(X), jnp.array(Y)\n",
        "\n",
        "def seq_data_iter_sequential(corpus, batch_size, num_steps):\n",
        "    \"\"\"Generate a minibatch of subsequences using sequential partitioning.\"\"\"\n",
        "    # Start with a random offset to partition a sequence\n",
        "    offset = random.randint(0, num_steps)\n",
        "    num_tokens = ((len(corpus) - offset - 1) // batch_size) * batch_size\n",
        "    Xs = jnp.array(corpus[offset:offset + num_tokens])\n",
        "    Ys = jnp.array(corpus[offset + 1:offset + 1 + num_tokens])\n",
        "    Xs, Ys = Xs.reshape(batch_size, -1), Ys.reshape(batch_size, -1)\n",
        "    num_batches = Xs.shape[1] // num_steps\n",
        "    for i in range(0, num_steps * num_batches, num_steps):\n",
        "        X = Xs[:, i:i + num_steps]\n",
        "        Y = Ys[:, i:i + num_steps]\n",
        "        yield X, Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "yRp-MH0Nv7rN"
      },
      "outputs": [],
      "source": [
        "def download(name, cache_dir=os.path.join('..', 'data')):\n",
        "    \"\"\"Download a file inserted into DATA_HUB, return the local filename.\"\"\"\n",
        "    assert name in DATA_HUB, f\"{name} does not exist in {DATA_HUB}.\"\n",
        "    url, sha1_hash = DATA_HUB[name]\n",
        "    os.makedirs(cache_dir, exist_ok=True)\n",
        "    fname = os.path.join(cache_dir, url.split('/')[-1])\n",
        "    if os.path.exists(fname):\n",
        "        sha1 = hashlib.sha1()\n",
        "        with open(fname, 'rb') as f:\n",
        "            while True:\n",
        "                data = f.read(1048576)\n",
        "                if not data:\n",
        "                    break\n",
        "                sha1.update(data)\n",
        "        if sha1.hexdigest() == sha1_hash:\n",
        "            return fname  # Hit cache\n",
        "    print(f'Downloading {fname} from {url}...')\n",
        "    r = requests.get(url, stream=True, verify=True)\n",
        "    with open(fname, 'wb') as f:\n",
        "        f.write(r.content)\n",
        "    return fname\n",
        "\n",
        "def read_time_machine():\n",
        "    \"\"\"Load the time machine dataset into a list of text lines.\"\"\"\n",
        "    with open(download('time_machine'), 'r') as f:\n",
        "        lines = f.readlines()\n",
        "    return [re.sub('[^A-Za-z]+', ' ', line).strip().lower() for line in lines]\n",
        "\n",
        "def load_corpus_time_machine(max_tokens=-1):\n",
        "    \"\"\"Return token indices and the vocabulary of the time machine dataset.\"\"\"\n",
        "    lines = read_time_machine()\n",
        "    tokens = tokenize(lines, 'char')\n",
        "    vocab = Vocab(tokens)\n",
        "    # Since each text line in the time machine dataset is not necessarily a\n",
        "    # sentence or a paragraph, flatten all the text lines into a single list\n",
        "    corpus = [vocab[token] for line in tokens for token in line]\n",
        "    if max_tokens > 0:\n",
        "        corpus = corpus[:max_tokens]\n",
        "    return corpus, vocab\n",
        "\n",
        "def load_data_time_machine(batch_size, num_steps, use_random_iter=False,\n",
        "                           max_tokens=10000):\n",
        "    \"\"\"Return the iterator and the vocabulary of the time machine dataset.\"\"\"\n",
        "    data_iter = SeqDataLoader(batch_size, num_steps, use_random_iter,\n",
        "                              max_tokens)\n",
        "    return data_iter, data_iter.vocab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "nG2T4maNYdsG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc1bd6ea-584a-439b-f0a8-19394e361b4a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading ../data/timemachine.txt from http://d2l-data.s3-accelerate.amazonaws.com/timemachine.txt...\n"
          ]
        }
      ],
      "source": [
        "DATA_HUB = dict()\n",
        "DATA_URL = 'http://d2l-data.s3-accelerate.amazonaws.com/'\n",
        "DATA_HUB['time_machine'] = (DATA_URL + 'timemachine.txt',\n",
        "                                '090b5e7e70c295757f55df93cb0a180b9691891a')\n",
        "\n",
        "batch_size, num_steps = 32, 35\n",
        "train_iter, vocab = load_data_time_machine(batch_size, num_steps)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fKa87dOCFZ8H"
      },
      "source": [
        "# Creating the model from scratch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gq3jJ8IQFjz3"
      },
      "source": [
        "Initialize the parameters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "WnwqslgzFW7E"
      },
      "outputs": [],
      "source": [
        "def get_params(vocab_size, num_hiddens, init_rng):\n",
        "    num_inputs = num_outputs = vocab_size\n",
        "\n",
        "    def normal(shape, rng):\n",
        "        return jax.random.normal(rng, shape=shape) * 0.01\n",
        "\n",
        "    def three(rng):\n",
        "        return (normal(\n",
        "            (num_inputs, num_hiddens), rng), normal((num_hiddens, num_hiddens),\n",
        "                                                   rng), jnp.zeros(num_hiddens))\n",
        "\n",
        "    update_rng, reset_rng, hidden_rng, out_rng = jax.random.split(init_rng,\n",
        "                                                                  num=4)\n",
        "    W_xz, W_hz, b_z = three(update_rng)  # Update gate parameters\n",
        "    W_xr, W_hr, b_r = three(reset_rng)  # Reset gate parameters\n",
        "    W_xh, W_hh, b_h = three(hidden_rng)  # Candidate hidden state parameters\n",
        "    # Output layer parameters\n",
        "    W_hq = normal((num_hiddens, num_outputs), out_rng)\n",
        "    b_q = jnp.zeros(num_outputs)\n",
        "    params = [W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hq, b_q]\n",
        "    return params    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xiMRAVr2Ftir"
      },
      "source": [
        "Initial state is an array of zeros of size (batch-size, num-hiddens)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "rbMB5SnMFvkZ"
      },
      "outputs": [],
      "source": [
        "def init_gru_state(batch_size, num_hiddens):\n",
        "    return (jnp.zeros((batch_size, num_hiddens)),)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9LcxOAdiF1kd"
      },
      "source": [
        "Forward function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "SfC5HhBbFv6t"
      },
      "outputs": [],
      "source": [
        "@jax.jit\n",
        "def gru(params, state, inputs):\n",
        "    W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hq, b_q = params\n",
        "    H, = state\n",
        "    outputs = []\n",
        "    for X in inputs:\n",
        "        Z = jax.nn.sigmoid((X @ W_xz) + (H @ W_hz) + b_z)\n",
        "        R = jax.nn.sigmoid((X @ W_xr) + (H @ W_hr) + b_r)\n",
        "        H_tilda = jnp.tanh((X @ W_xh) + ((R * H) @ W_hh) + b_h)\n",
        "        H = Z * H + (1 - Z) * H_tilda\n",
        "        Y = H @ W_hq + b_q\n",
        "        outputs.append(Y)\n",
        "    return jnp.concatenate(outputs, axis=0), (H,)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Qw3vhoRKdVxt"
      },
      "outputs": [],
      "source": [
        "# Make the model class \n",
        "# Input X to apply is (B,T) matrix of integers (from vocab encoding).\n",
        "# We transpose this to (T,B) then one-hot encode to (T,B,V), where V is vocab.\n",
        "# The result is passed to the forward function.\n",
        "# (We define the forward function as an argument, so we can change it later.)\n",
        "\n",
        "class RNNModelScratch: \n",
        "    \"\"\"A RNN Model implemented from scratch.\"\"\"\n",
        "    def __init__(self, vocab_size, num_hiddens, get_params, init_state,\n",
        "                 forward_fn):\n",
        "        self.vocab_size, self.num_hiddens = vocab_size, num_hiddens\n",
        "        self.init_state, self.get_params = init_state, get_params\n",
        "        self.forward_fn = forward_fn\n",
        "\n",
        "    def apply(self, params, state, X):\n",
        "        X = jax.nn.one_hot(X.T, num_classes=self.vocab_size)\n",
        "        return self.forward_fn(params, state, X)\n",
        "\n",
        "    def begin_state(self, batch_size):\n",
        "        return self.init_state(batch_size, self.num_hiddens)\n",
        "\n",
        "    def init_params(self, rng):\n",
        "        return self.get_params(self.vocab_size, self.num_hiddens, rng)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WtoCsGZ2F8G0"
      },
      "source": [
        "# Training and prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "LDuvumDhgpvh"
      },
      "outputs": [],
      "source": [
        "@jax.jit\n",
        "def grad_clipping(grads, theta):\n",
        "    \"\"\"Clip the gradient.\"\"\"\n",
        "    def grad_update(grads):\n",
        "      return jax.tree_map(lambda g: g * theta / norm, grads)\n",
        "\n",
        "    norm = jnp.sqrt(sum(jax.tree_util.tree_leaves(jax.tree_map(\n",
        "        lambda x: jnp.sum(x ** 2), grads))))\n",
        "    # Update gradient if norm > theta\n",
        "    # This is jax.jit compatible\n",
        "    grads = jax.lax.cond(norm > theta, grad_update, lambda g: g, grads)\n",
        "    return grads"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "tA_QeZH5rp1o"
      },
      "outputs": [],
      "source": [
        "class Animator:\n",
        "    \"\"\"For plotting data in animation.\"\"\"\n",
        "    def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None,\n",
        "                 ylim=None, xscale='linear', yscale='linear',\n",
        "                 fmts=('-', 'm--', 'g-.', 'r:'), nrows=1, ncols=1,\n",
        "                 figsize=(3.5, 2.5)):\n",
        "        # Incrementally plot multiple lines\n",
        "        if legend is None:\n",
        "            legend = []\n",
        "        display.set_matplotlib_formats('svg')\n",
        "        self.fig, self.axes = plt.subplots(nrows, ncols, figsize=figsize)\n",
        "        if nrows * ncols == 1:\n",
        "            self.axes = [self.axes,]\n",
        "        # Use a lambda function to capture arguments\n",
        "        self.config_axes = lambda: set_axes(self.axes[\n",
        "            0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)\n",
        "        self.X, self.Y, self.fmts = None, None, fmts\n",
        "\n",
        "    def add(self, x, y):\n",
        "        # Add multiple data points into the figure\n",
        "        if not hasattr(y, \"__len__\"):\n",
        "            y = [y]\n",
        "        n = len(y)\n",
        "        if not hasattr(x, \"__len__\"):\n",
        "            x = [x] * n\n",
        "        if not self.X:\n",
        "            self.X = [[] for _ in range(n)]\n",
        "        if not self.Y:\n",
        "            self.Y = [[] for _ in range(n)]\n",
        "        for i, (a, b) in enumerate(zip(x, y)):\n",
        "            if a is not None and b is not None:\n",
        "                self.X[i].append(a)\n",
        "                self.Y[i].append(b)\n",
        "        self.axes[0].cla()\n",
        "        for x, y, fmt in zip(self.X, self.Y, self.fmts):\n",
        "            self.axes[0].plot(x, y, fmt)\n",
        "        self.config_axes()\n",
        "        display.display(self.fig)\n",
        "        display.clear_output(wait=True)\n",
        "\n",
        "class Timer:\n",
        "    \"\"\"Record multiple running times.\"\"\"\n",
        "    def __init__(self):\n",
        "        self.times = []\n",
        "        self.start()\n",
        "\n",
        "    def start(self):\n",
        "        \"\"\"Start the timer.\"\"\"\n",
        "        self.tik = time.time()\n",
        "\n",
        "    def stop(self):\n",
        "        \"\"\"Stop the timer and record the time in a list.\"\"\"\n",
        "        self.times.append(time.time() - self.tik)\n",
        "        return self.times[-1]\n",
        "\n",
        "    def avg(self):\n",
        "        \"\"\"Return the average time.\"\"\"\n",
        "        return sum(self.times) / len(self.times)\n",
        "\n",
        "    def sum(self):\n",
        "        \"\"\"Return the sum of time.\"\"\"\n",
        "        return sum(self.times)\n",
        "\n",
        "    def cumsum(self):\n",
        "        \"\"\"Return the accumulated time.\"\"\"\n",
        "        return jnp.array(self.times).cumsum().tolist()\n",
        "\n",
        "class Accumulator:\n",
        "    \"\"\"For accumulating sums over `n` variables.\"\"\"\n",
        "    def __init__(self, n):\n",
        "        self.data = [0.0] * n\n",
        "\n",
        "    def add(self, *args):\n",
        "        self.data = [a + float(b) for a, b in zip(self.data, args)]\n",
        "\n",
        "    def reset(self):\n",
        "        self.data = [0.0] * len(self.data)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        return self.data[idx]\n",
        "\n",
        "def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):\n",
        "    \"\"\"Set the axes for matplotlib.\"\"\"\n",
        "    axes.set_xlabel(xlabel)\n",
        "    axes.set_ylabel(ylabel)\n",
        "    axes.set_xscale(xscale)\n",
        "    axes.set_yscale(yscale)\n",
        "    axes.set_xlim(xlim)\n",
        "    axes.set_ylim(ylim)\n",
        "    if legend:\n",
        "        axes.legend(legend)\n",
        "    axes.grid()\n",
        "\n",
        "@jax.jit\n",
        "def sgd(params, grads, lr, batch_size):\n",
        "    \"\"\"Minibatch stochastic gradient descent.\"\"\"\n",
        "    params = jax.tree_map(lambda p, g: p - lr * g / batch_size, params,\n",
        "                               grads)\n",
        "    return params"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "X7o1dMFttw_2"
      },
      "outputs": [],
      "source": [
        "@jax.jit\n",
        "def train_step(apply_fn, loss_fn, params, state, X, Y):\n",
        "      def loss(params, state, X, Y):\n",
        "        y = Y.T.reshape(-1) #(B,T) -> (T,B)\n",
        "        y_hat, state = apply_fn(params, state, X)\n",
        "        y_hat = y_hat.reshape(-1, y_hat.shape[-1])\n",
        "        y_one_hot = jax.nn.one_hot(y, num_classes=y_hat.shape[-1])\n",
        "        return loss_fn(y_hat, y_one_hot).mean(), state\n",
        "\n",
        "      grad_fn = jax.value_and_grad(loss, has_aux=True)\n",
        "      (l, state), grads = grad_fn(params, state, X, Y)\n",
        "      grads = grad_clipping(grads, 1)\n",
        "      return l, state, grads"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "Eq_QMLUKhw3k"
      },
      "outputs": [],
      "source": [
        "def train_epoch(net, params, train_iter, loss, updater, use_random_iter):\n",
        "    state, timer = None, Timer()\n",
        "    metric = Accumulator(2)  # Sum of training loss, no. of tokens\n",
        "    if isinstance(updater, optax.GradientTransformation):\n",
        "      updater_state = updater.init(params)\n",
        "    # Convert to jax Partial functions for jax.jit compatibility \n",
        "    apply_fn = jax.tree_util.Partial(net.apply)\n",
        "    loss_fn = jax.tree_util.Partial(loss)\n",
        "    for X, Y in train_iter:\n",
        "        if state is None or use_random_iter:\n",
        "            # Initialize `state` when either it is the first iteration or\n",
        "            # using random sampling\n",
        "            state = net.begin_state(batch_size=X.shape[0])\n",
        "        l, state, grads = train_step(apply_fn, loss_fn, params, state, X, Y)\n",
        "        if isinstance(updater, optax.GradientTransformation):\n",
        "            updates, updater_state = updater.update(grads, updater_state)\n",
        "            params = optax.apply_updates(params, updates)\n",
        "        else:\n",
        "            # batch_size=1 since the `mean` function has been invoked\n",
        "            params = updater(params, grads, batch_size=1)\n",
        "        metric.add(l * Y.size, Y.size)\n",
        "    return params, math.exp(metric[0] / metric[1]), metric[1] / timer.stop()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "0tPGxQDWiqfl"
      },
      "outputs": [],
      "source": [
        "def train(net, params, train_iter, vocab, lr, num_epochs,\n",
        "          use_random_iter=False):\n",
        "    loss = optax.softmax_cross_entropy\n",
        "    animator = Animator(xlabel='epoch', ylabel='perplexity',\n",
        "                            legend=['train'], xlim=[10, num_epochs])\n",
        "    # Initialize\n",
        "    if isinstance(net, nn.Module):\n",
        "        updater = optax.sgd(lr)\n",
        "    else:\n",
        "        updater = lambda params, grads, batch_size: sgd(params, grads, lr,\n",
        "                                                        batch_size)\n",
        "    num_preds = 50\n",
        "    predict_ = lambda prefix: predict(prefix, num_preds, net, params, vocab)\n",
        "    # Train and predict\n",
        "    for epoch in range(num_epochs):\n",
        "        params, ppl, speed = train_epoch(net, params, train_iter, loss, updater,\n",
        "                                         use_random_iter)\n",
        "        if (epoch + 1) % 10 == 0:\n",
        "            # Prediction takes time on the flax model\n",
        "            # print(predict_('time traveller'))\n",
        "            animator.add(epoch + 1, [ppl])\n",
        "    device = jax.default_backend()\n",
        "    print(f'perplexity {ppl:.1f}, {speed:.1f} tokens/sec on {device}')\n",
        "    print(predict_('time traveller'))\n",
        "    print(predict_('traveller'))\n",
        "    return params"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "MvGhZUIGd3F3"
      },
      "outputs": [],
      "source": [
        "def predict(prefix, num_preds, net, params, vocab):  \n",
        "    \"\"\"Generate new characters following the `prefix`.\"\"\"\n",
        "    state = net.begin_state(batch_size=1)\n",
        "    outputs = [vocab[prefix[0]]]\n",
        "    get_input = lambda: jnp.array([outputs[-1]]).reshape((1, 1))\n",
        "    for y in prefix[1:]:  # Warm-up period\n",
        "        _, state = net.apply(params, state, get_input())\n",
        "        outputs.append(vocab[y])\n",
        "    for _ in range(num_preds):  # Predict `num_preds` steps\n",
        "        y, state = net.apply(params, state, get_input())\n",
        "        y = y.reshape(-1, y.shape[-1])\n",
        "        outputs.append(int(y.argmax(axis=1).reshape(1)))\n",
        "    return ''.join([vocab.idx_to_token[i] for i in outputs])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "rw0uiJ4aF3jI",
        "outputId": "7be5583c-8745-4a88-cdd1-83c0779d533f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "perplexity 1.0, 41613.7 tokens/sec on gpu\n",
            "time travelleryou can show black is white by argument said filby\n",
            "travelleryou can show black is white by argument said filby\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "random.seed(0)\n",
        "vocab_size, num_hiddens = len(vocab), 256\n",
        "num_epochs, lr = 500, 1\n",
        "model = RNNModelScratch(len(vocab), num_hiddens, get_params, init_gru_state,\n",
        "                        gru)\n",
        "params = model.init_params(rng)\n",
        "params = train(model, params, train_iter, vocab, lr, num_epochs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "89IqhaoSwiRT"
      },
      "outputs": [],
      "source": [
        "class GRU(nn.Module):\n",
        "    @functools.partial(\n",
        "        nn.transforms.scan,\n",
        "        variable_broadcast='params',\n",
        "        in_axes=0, out_axes=0,\n",
        "        split_rngs={'params': False})\n",
        "    @nn.compact\n",
        "    def __call__(self, state, x):\n",
        "        return nn.GRUCell()(state, x)\n",
        "\n",
        "    @staticmethod\n",
        "    def initialize_carry(rng, batch_dims, size):\n",
        "        return nn.GRUCell.initialize_carry(rng, batch_dims, size)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class RNNModel(nn.Module):\n",
        "    \"\"\"The RNN model.\"\"\"\n",
        "    rnn: nn.Module\n",
        "    vocab_size: int\n",
        "    num_hiddens: int\n",
        "    bidirectional: bool = False\n",
        "        \n",
        "    def setup(self):\n",
        "        # If the RNN is bidirectional (to be introduced later),\n",
        "        # `num_directions` should be 2, else it should be 1.\n",
        "        if not self.bidirectional:\n",
        "            self.num_directions = 1\n",
        "        else:\n",
        "            self.num_directions = 2\n",
        "\n",
        "    @nn.compact\n",
        "    def __call__(self, state, inputs):\n",
        "        X = jax.nn.one_hot(inputs.T, num_classes=self.vocab_size)\n",
        "        state, Y = self.rnn(state, X)\n",
        "        output = nn.Dense(self.vocab_size)(Y)\n",
        "        return output, state\n",
        "\n",
        "    def begin_state(self, batch_size=1):\n",
        "        # Use fixed random key since default state init fn is just `zeros`.\n",
        "        return self.rnn.initialize_carry(jax.random.PRNGKey(0), (batch_size,),\n",
        "                                         num_hiddens)"
      ],
      "metadata": {
        "id": "wAw5qDBKQe7U"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "yDw7Ss_0F9MD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "b1773077-9c03-4b29-a462-ef3fe6bc0d46"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "perplexity 1.0, 20557.8 tokens/sec on gpu\n",
            "time travelleryou can show black is white by argument said filby\n",
            "travelleryou can show black is white by argument said filby\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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      ],
      "source": [
        "random.seed(0)\n",
        "gru_layer = GRU()\n",
        "model = RNNModel(rnn=gru_layer, vocab_size=len(vocab), num_hiddens=num_hiddens)\n",
        "initial_state = model.begin_state(batch_size)\n",
        "params = model.init(rng, initial_state, jnp.ones([batch_size, num_steps]))\n",
        "params = train(model, params, train_iter, vocab, lr, num_epochs)"
      ]
    }
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