examples/mnist_data_parallel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST Example with Data Parallelism\n",
    "In this notebook we provide a simple exmple of using scalax `sjit` to train models on multiple accelerators with data parallelism. We use the MNIST dataset and train a simple MLP (multi-layered percepttron) model implemented in flax. We show that scalax makes it simple to convert single GPU code to run on multiple accelerators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "h9HtQYyDAI0H"
   },
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "\n",
    "import flax\n",
    "from flax import linen as nn\n",
    "from flax.training.train_state import TrainState\n",
    "import optax\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from tqdm.notebook import tqdm, trange\n",
    "\n",
    "import torch\n",
    "import torchvision\n",
    "\n",
    "from scalax.utils import JaxRNG\n",
    "from scalax.sharding import PartitionSpec, MeshShardingHelper\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "JaxRNG.init_global_rng(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP model definition\n",
    "In this section we define a simple 4-layer MLP model with 4096 hidden units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "8aiua0iqAa5A"
   },
   "outputs": [],
   "source": [
    "class MLP(nn.Module):\n",
    "    output_dim: int\n",
    "\n",
    "    @nn.compact\n",
    "    def __call__(self, x):\n",
    "        for _ in range(4):\n",
    "            x = nn.Dense(4096)(x)\n",
    "            x = nn.relu(x)\n",
    "        output = nn.Dense(self.output_dim)(x)\n",
    "        return output\n",
    "\n",
    "\n",
    "model = MLP(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset and DataLoader\n",
    "We use the torchvision and pytorch dataloader to download and load the MNIST\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = torchvision.datasets.MNIST(\n",
    "    '/tmp/torch_data', train=True, download=True,\n",
    "    transform=torchvision.transforms.Compose([\n",
    "    torchvision.transforms.ToTensor(),\n",
    "    torchvision.transforms.Normalize(\n",
    "        (0.1307,), (0.3081,))\n",
    "    ])\n",
    ")\n",
    "dataloader = torch.utils.data.DataLoader(dataset, batch_size=1024, num_workers=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sharded Train Step\n",
    "We define the train step and shard it with the `sjit` method of `MeshShardingHelper` to implement data parallelism. In data parallelism, the model is replicated, and the data is sharded along the batch axis. We can simply specify this as arugments to `sjit`, without changing the `train_step` function. The `train_step` here is exactly the same as if we are training on a single GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "f1lJqS7afXo5"
   },
   "outputs": [],
   "source": [
    "# Create a 1D mesh with axis name `data`\n",
    "mesh = MeshShardingHelper(axis_dims=[-1], axis_names=['data'])\n",
    "\n",
    "@partial(\n",
    "    mesh.sjit,\n",
    "    in_shardings=None, # Model and data are replicated in the input\n",
    "    out_shardings=None, # Model and metrics are replicated in the output\n",
    "    # Inside the function, the data should be sharded according to the `data` axis\n",
    "    args_sharding_constraint=(None, PartitionSpec('data'), PartitionSpec('data')),\n",
    ") \n",
    "def train_step(train_state, x, y): # The train staep function is the same as for a single GPU\n",
    "    @partial(jax.value_and_grad, has_aux=True)\n",
    "    def grad_fn(params):\n",
    "        logits = model.apply(params, x)\n",
    "        one_hot = jax.nn.one_hot(y, 10, dtype=jnp.float32)\n",
    "        log_prob = jax.nn.log_softmax(logits)\n",
    "        loss = -jnp.mean(log_prob * one_hot)\n",
    "        accuracy = (jnp.argmax(logits, axis=-1) == y).astype(jnp.float32).mean()\n",
    "        return loss, accuracy\n",
    "\n",
    "    (loss, accuracy), grads = grad_fn(train_state.params)\n",
    "    train_state = train_state.apply_gradients(grads=grads)\n",
    "    return train_state, loss, accuracy\n",
    "\n",
    "\n",
    "params = model.init(JaxRNG.next_rng(), jnp.zeros((10, 28*28)))\n",
    "optimizer = optax.sgd(0.01, 0.1)\n",
    "\n",
    "train_state = TrainState.create(\n",
    "    params=params, tx=optimizer,\n",
    "    apply_fn=None\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the Sharded Train Step\n",
    "Now let's run the sharded train step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
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      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
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       "[<matplotlib.lines.Line2D at 0x7fb4d37fd210>]"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses = []\n",
    "accuracies = []\n",
    "for epoch in trange(10):\n",
    "    for x, y in dataloader:\n",
    "        x = x.reshape(-1, 28 * 28).numpy()\n",
    "        y = y.numpy()\n",
    "        train_state, loss, accuracy = train_step(train_state, x, y)\n",
    "        losses.append(loss.item())\n",
    "        accuracies.append(accuracy.item())\n",
    "\n",
    "\n",
    "plt.plot(losses)"
   ]
  }
 ],
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