{ "cells": [ { "cell_type": "markdown", "id": "a619b039", "metadata": { "colab_type": "text", "id": "view-in-github" }, "source": [ "# Recurrent Neural Networks: From Sine Waves to Shakespeare\n", "\n", "This tutorial explores Recurrent Neural Networks (RNNs) using **blox** and **Distrax**.\n", "\n", "We will cover two probabilistic modeling tasks:\n", "1. **Regression**: Modeling a noisy sine wave using a Gaussian distribution.\n", "2. **Generation**: Modeling character sequences (Shakespeare) using a Categorical distribution." ] }, { "cell_type": "code", "execution_count": 1, "id": "3dae7b96", "metadata": {}, "outputs": [], "source": [ "# Setup: ensure blox is importable.\n", "import sys\n", "\n", "sys.path.insert(0, \"../src\")\n", "\n", "# Install necessary packages.\n", "!pip install -q optax matplotlib distrax" ] }, { "cell_type": "code", "execution_count": 2, "id": "26e523d9", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/python/3.12.1/lib/python3.12/site-packages/keras/src/export/tf2onnx_lib.py:8: FutureWarning: In the future `np.object` will be defined as the corresponding NumPy scalar.\n", " if not hasattr(np, \"object\"):\n" ] } ], "source": [ "import os\n", "\n", "import blox as bx\n", "import distrax\n", "import jax\n", "import jax.numpy as jnp\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import optax\n", "import requests\n", "\n", "# Set random seed for reproducibility.\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "id": "c840ea0d", "metadata": {}, "source": [ "## Part 1: Modeling a Sine Wave (Regression)" ] }, { "cell_type": "code", "execution_count": 3, "id": "74c4bb07", "metadata": { "id": "sine-data" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def generate_sine_data(batch_size, seq_len):\n", " \"\"\"Generates sine waves with random phases.\"\"\"\n", " # Random phases for each batch element.\n", " phases = np.random.uniform(0, 2 * np.pi, size=(batch_size, 1))\n", " time = np.linspace(0, 4 * np.pi, seq_len + 1) # +1 for target\n", "\n", " # Shape: (Batch, Time)\n", " waves = np.sin(time + phases)\n", "\n", " # Add channel dimension: (Batch, Time, 1)\n", " waves = waves[..., None]\n", "\n", " # Input is t, Target is t+1\n", " inputs = waves[:, :-1]\n", " targets = waves[:, 1:]\n", " return inputs, targets\n", "\n", "\n", "x_viz, y_viz = generate_sine_data(1, 100)\n", "plt.plot(x_viz[0, :, 0], label='Input')\n", "plt.plot(y_viz[0, :, 0], label='Target (Shifted)')\n", "plt.legend()\n", "plt.title('Sample Sine Wave Data')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "id": "59009947", "metadata": { "id": "sine-model" }, "outputs": [], "source": [ "class SineRNN(bx.Module):\n", " \"\"\"RNN that outputs parameters for a Gaussian distribution.\"\"\"\n", "\n", " def __init__(self, graph: bx.Graph, hidden_dim: int, rng: bx.Rng):\n", " super().__init__(graph)\n", " self.lstm = bx.LSTM(graph.child('lstm'), hidden_size=hidden_dim, rng=rng)\n", " # Output 2 values: mean and log_std.\n", " self.head = bx.Linear(graph.child('head'), output_size=2, rng=rng)\n", "\n", " def apply(\n", " self,\n", " params: bx.Params,\n", " x: jax.Array,\n", " prev_state: bx.LSTMState | None = None,\n", " ):\n", " # x: [Batch, Time, 1]\n", " (h, final_state), params = self.lstm.apply(params, x, prev_state)\n", "\n", " # Project to Gaussian parameters.\n", " out, params = self.head(params, h)\n", " mu, log_scale = jnp.split(out, 2, axis=-1)\n", "\n", " # Constrain scale to be positive.\n", " scale = jax.nn.softplus(log_scale) + 1e-3\n", " return (mu, scale), final_state, params" ] }, { "cell_type": "code", "execution_count": null, "id": "ea351006", "metadata": { "id": "train-sine" }, "outputs": [], "source": "@jax.jit(static_argnames=['optimizer'], donate_argnames=['params', 'opt_state'])\ndef train_step_sine(params, opt_state, x, y, optimizer):\n trainable, non_trainable = params.split()\n\n def loss_fn(trainable):\n curr_params = trainable.merge(non_trainable)\n (mu, scale), _, new_params = sine_model.apply(curr_params, x)\n\n # Maximize Log Likelihood of the Gaussian.\n dist = distrax.Normal(loc=mu, scale=scale)\n loss = -dist.log_prob(y).mean()\n\n _, new_non_trainable = new_params.split()\n return loss, (loss, new_non_trainable)\n\n grads, (loss, new_non_trainable) = jax.grad(loss_fn, has_aux=True)(trainable)\n updates, new_opt_state = optimizer.update(grads, opt_state, trainable)\n new_trainable = optax.apply_updates(trainable, updates)\n return new_trainable.merge(new_non_trainable), new_opt_state, loss\n\n\n# Create model components.\ngraph = bx.Graph('net')\nrng = bx.Rng(graph.child('rng'))\nsine_model = SineRNN(graph.child('sine_rnn'), hidden_dim=32, rng=rng)\n\n# Initialize with sample data shape.\nsample_x, _ = generate_sine_data(batch_size=1, seq_len=50)\nsine_params = rng.seed(bx.Params(), seed=42)\n_, _, sine_params = sine_model.apply(sine_params, sample_x)\nsine_params = sine_params.locked()\n\n# Train.\noptimizer = optax.adam(1e-2)\nopt_state = optimizer.init(sine_params.split()[0])\n\nlosses = []\nfor i in range(1000):\n x, y = generate_sine_data(batch_size=32, seq_len=50)\n sine_params, opt_state, loss = train_step_sine(\n sine_params, opt_state, x, y, optimizer\n )\n losses.append(loss)\n if i % 100 == 0:\n print(f'Step {i}, NLL: {loss:.4f}')\n\nplt.plot(losses)\nplt.title('Sine Wave Training NLL')\nplt.show()" }, { "cell_type": "code", "execution_count": 6, "id": "e5c7a9e9", "metadata": { "id": "viz-sine" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualize predictions.\n", "x_test, y_test = generate_sine_data(1, 100)\n", "(mu, scale), _, _ = sine_model.apply(sine_params, x_test)\n", "\n", "t = np.arange(100)\n", "plt.figure(figsize=(10, 5))\n", "plt.plot(t, x_test[0, :, 0], 'k--', label='Input')\n", "plt.plot(t, mu[0, :, 0], 'b-', label='Prediction (Mean)')\n", "plt.fill_between(\n", " t,\n", " mu[0, :, 0] - 2 * scale[0, :, 0],\n", " mu[0, :, 0] + 2 * scale[0, :, 0],\n", " color='b',\n", " alpha=0.2,\n", " label='Uncertainty (2 std)',\n", ")\n", "plt.legend()\n", "plt.title('Sine Wave Prediction with Uncertainty')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "e83a1bdb", "metadata": {}, "source": [ "## Part 2: Tiny Shakespeare (Generation)\n", "\n", "Now we apply the same principles to character generation using a Categorical distribution." ] }, { "cell_type": "code", "execution_count": 7, "id": "5b4a2659", "metadata": { "id": "char-data" }, "outputs": [], "source": [ "def download_data():\n", " url = 'https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt'\n", " if not os.path.exists('input.txt'):\n", " data = requests.get(url).text\n", " with open('input.txt', 'w') as f:\n", " f.write(data)\n", " else:\n", " with open('input.txt', 'r') as f:\n", " data = f.read()\n", " return data\n", "\n", "\n", "text = download_data()\n", "chars = sorted(list(set(text)))\n", "vocab_size = len(chars)\n", "stoi = {ch: i for i, ch in enumerate(chars)}\n", "itos = {i: ch for i, ch in enumerate(chars)}\n", "\n", "\n", "def encode(s):\n", " return [stoi[c] for c in s]\n", "\n", "\n", "def decode(indices):\n", " return ''.join([itos[i] for i in indices])\n", "\n", "\n", "data = jnp.array(encode(text), dtype=jnp.uint32)\n", "train_data = data[: int(0.9 * len(data))]\n", "\n", "\n", "def get_batch(batch_size=64, block_size=128):\n", " ix = np.random.randint(0, len(train_data) - block_size, size=(batch_size,))\n", " x = jnp.stack([train_data[i : i + block_size] for i in ix])\n", " y = jnp.stack([train_data[i + 1 : i + block_size + 1] for i in ix])\n", " return x, y" ] }, { "cell_type": "code", "execution_count": 8, "id": "244d334d", "metadata": { "id": "char-model" }, "outputs": [], "source": [ "class CharRNN(bx.SequenceBase):\n", "\n", " def __init__(\n", " self,\n", " graph: bx.Graph,\n", " vocab_size: int,\n", " embed_dim: int,\n", " hidden_dim: int,\n", " rng: bx.Rng,\n", " ):\n", " super().__init__(graph)\n", " self.rng = rng\n", " self.embed = bx.Embed(\n", " graph.child('embed'),\n", " num_embeddings=vocab_size,\n", " embedding_size=embed_dim,\n", " rng=rng,\n", " )\n", " self.lstm = bx.LSTM(graph.child('lstm'), hidden_size=hidden_dim, rng=rng)\n", " self.head = bx.Linear(graph.child('head'), output_size=vocab_size, rng=rng)\n", "\n", " def __call__(\n", " self, params: bx.Params, inputs: jax.Array, prev_state: bx.LSTMState\n", " ):\n", " # Single step for generation.\n", " x_emb, params = self.embed(params, inputs)\n", " (x_hidden, new_state), params = self.lstm(params, x_emb, prev_state)\n", " logits, params = self.head(params, x_hidden)\n", " return (logits, new_state), params\n", "\n", " def apply(\n", " self,\n", " params: bx.Params,\n", " x: jax.Array,\n", " prev_state: bx.LSTMState | None = None,\n", " ):\n", " # Sequence processing for training.\n", " x_emb, params = self.embed(params, x)\n", " (x_seq, final_state), params = self.lstm.apply(params, x_emb, prev_state)\n", " logits, params = self.head(params, x_seq)\n", " return (logits, final_state), params\n", "\n", " def initial_state(self, params: bx.Params, inputs: jax.Array):\n", " return self.lstm.initial_state(params, inputs)" ] }, { "cell_type": "code", "execution_count": null, "id": "5ee1a769", "metadata": { "id": "train-char" }, "outputs": [], "source": "@jax.jit(static_argnames=['optimizer'], donate_argnames=['params', 'opt_state'])\ndef train_step_char(params, opt_state, x, y, optimizer):\n trainable, non_trainable = params.split()\n\n def loss_fn(trainable):\n curr_params = trainable.merge(non_trainable)\n (logits, _), new_params = char_model.apply(curr_params, x)\n\n # Categorical Log Likelihood.\n dist = distrax.Categorical(logits=logits)\n loss = -dist.log_prob(y).mean()\n\n _, new_non_trainable = new_params.split()\n return loss, (loss, new_non_trainable)\n\n grads, (loss, new_non_trainable) = jax.grad(loss_fn, has_aux=True)(trainable)\n updates, new_opt_state = optimizer.update(grads, opt_state, trainable)\n new_trainable = optax.apply_updates(trainable, updates)\n return new_trainable.merge(new_non_trainable), new_opt_state, loss\n\n\n# Create model components.\ngraph = bx.Graph('net')\nchar_rng = bx.Rng(graph.child('rng'))\nchar_model = CharRNN(\n graph.child('char_rnn'),\n vocab_size=vocab_size,\n embed_dim=64,\n hidden_dim=256,\n rng=char_rng,\n)\n\n# Initialize with sample data shape.\nsample_x, _ = get_batch(batch_size=1, block_size=128)\nchar_params = char_rng.seed(bx.Params(), seed=42)\n_, char_params = char_model.apply(char_params, sample_x)\nchar_params = char_params.locked()\n\n# Train.\noptimizer = optax.adamw(3e-4)\ntrainable, _ = char_params.split()\nopt_state = optimizer.init(trainable)\n\nlosses = []\nprint('Training CharRNN...')\nfor step in range(1000):\n x, y = get_batch()\n char_params, opt_state, loss = train_step_char(\n char_params, opt_state, x, y, optimizer\n )\n losses.append(loss)\n if step % 100 == 0:\n print(f'Step {step}, NLL: {loss:.4f}')\n\nplt.plot(losses)\nplt.title('CharRNN NLL')\nplt.show()" }, { "cell_type": "code", "execution_count": null, "id": "d021eaa3", "metadata": { "id": "gen-char" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated Text (temperature=1.0):\n", "ROMEO: By?,\n", "I decs is thiigd.\n", "\n", "SIMENRLETI:\n", "Tey nore gree.\n", "\n", "WPORENY:\n", "Morves nov meant aidt asaner?\n", "\n", "Lith Meray:\n", "Qurd now!\n", "The pobnet brosed; I mifmeany seel andtrat! and were gombe aftire;\n", "Vorgich stoue yous \n", "Generated Text (temperature=0.1):\n", "ROMEO: I will the mand the mand the sores the mand the mand and and the the the the the the and the the the the sorest the mand the bronger the will the sorest the hare the the beather the the hare the hare \n" ] } ], "source": [ "def generate(params, start_str, length=200, temperature=1.0):\n", " context = jnp.array([encode(start_str)], dtype=jnp.int32)\n", " state, params = char_model.initial_state(params, context)\n", "\n", " # Warmup.\n", " (logits_seq, state), params = char_model.apply(params, context, state)\n", " next_logits = logits_seq[:, -1]\n", "\n", " generated = []\n", " for _ in range(length):\n", " key, params = char_rng(params)\n", " dist = distrax.Categorical(logits=next_logits / temperature)\n", " next_token = dist.sample(seed=key)\n", " generated.append(int(next_token[0]))\n", "\n", " (next_logits, state), params = char_model(params, next_token, state)\n", "\n", " return start_str + decode(generated)\n", "\n", "\n", "print('Generated Text (temperature=1.0):')\n", "print(generate(char_params, 'ROMEO: '))\n", "print('Generated Text (temperature=0.1):')\n", "print(generate(char_params, 'ROMEO: ', temperature=0.1))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.1" } }, "nbformat": 4, "nbformat_minor": 5 }