examples/bert.ipynb

{
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   "source": [
    "# BERT language model\n",
    "\n",
    "This is an advanced example. This example builds BERT, which is a Transformer language model.\n",
    "\n",
    "We demonstrate how to train it (to ~60% accuracy), or how to load some pretrained weights (to ~80% accuracy).\n",
    "\n",
    "This example is available as a Jupyter notebook [here](https://github.com/patrick-kidger/equinox/blob/main/examples/bert.ipynb).\n",
    "\n",
    "Authors: [Jannis Bulian](mailto:jbulian@google.com), [Jasmijn Bastings](mailto:bastings@google.com)\n",
    "\n",
    "!!! warning\n",
    "\n",
    "    This example requires a GPU to run efficiently."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hap3Kacprmdi",
    "tags": []
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EVcKtZzplkYk",
    "outputId": "ab6472f7-1861-4912-b5b4-88410438f29e",
    "tags": []
   },
   "source": [
    "For convenience we will use the HuggingFace libraries [transformers](https://github.com/huggingface/transformers) and [datasets](https://github.com/huggingface/datasets) to load the dataset and tokenise our inputs.\n",
    "\n",
    "These can be installed using `pip install transformers datasets[jax]`. (And at least at time of writing, these should be installed after JAX, as `datasets[jax]` tries to downgrade JAX.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "id": "9PkANhmalmUA",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import functools\n",
    "from typing import Dict, List, Mapping, Optional\n",
    "\n",
    "import einops  # https://github.com/arogozhnikov/einops\n",
    "import equinox as eqx\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import numpy as np\n",
    "import optax  # https://github.com/deepmind/optax\n",
    "from datasets import load_dataset  # https://github.com/huggingface/datasets\n",
    "from jaxtyping import Array, Float, Int  # https://github.com/google/jaxtyping\n",
    "from tqdm import notebook as tqdm  # https://github.com/tqdm/tqdm\n",
    "from transformers import AutoTokenizer  # https://github.com/huggingface/transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DfaUUcmTm_du",
    "tags": []
   },
   "source": [
    "## BERT implementation\n",
    "\n",
    "BERT is a Transformer model. It first embeds the input tokens (plus information about segments / positions) into vectors. This is done in the `EmbedderBlock`.\n",
    "\n",
    "Then it repeatedly applies a `TransformerLayer`, each consisting of an `AttentionBlock` and a `FeedForwardBlock`. For classification, the output of the last layer is pooled, and mapped to the number of classes to obtain the output logits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wFYuhPrH28vN",
    "tags": []
   },
   "outputs": [],
   "source": [
    "class EmbedderBlock(eqx.Module):\n",
    "    \"\"\"BERT embedder.\"\"\"\n",
    "\n",
    "    token_embedder: eqx.nn.Embedding\n",
    "    segment_embedder: eqx.nn.Embedding\n",
    "    position_embedder: eqx.nn.Embedding\n",
    "    layernorm: eqx.nn.LayerNorm\n",
    "    dropout: eqx.nn.Dropout\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        vocab_size: int,\n",
    "        max_length: int,\n",
    "        type_vocab_size: int,\n",
    "        embedding_size: int,\n",
    "        hidden_size: int,\n",
    "        dropout_rate: float,\n",
    "        key: jax.random.PRNGKey,\n",
    "    ):\n",
    "        token_key, segment_key, position_key = jax.random.split(key, 3)\n",
    "\n",
    "        self.token_embedder = eqx.nn.Embedding(\n",
    "            num_embeddings=vocab_size, embedding_size=embedding_size, key=token_key\n",
    "        )\n",
    "        self.segment_embedder = eqx.nn.Embedding(\n",
    "            num_embeddings=type_vocab_size,\n",
    "            embedding_size=embedding_size,\n",
    "            key=segment_key,\n",
    "        )\n",
    "        self.position_embedder = eqx.nn.Embedding(\n",
    "            num_embeddings=max_length, embedding_size=embedding_size, key=position_key\n",
    "        )\n",
    "        self.layernorm = eqx.nn.LayerNorm(shape=hidden_size)\n",
    "        self.dropout = eqx.nn.Dropout(dropout_rate)\n",
    "\n",
    "    def __call__(\n",
    "        self,\n",
    "        token_ids: Int[Array, \" seq_len\"],\n",
    "        position_ids: Int[Array, \" seq_len\"],\n",
    "        segment_ids: Int[Array, \" seq_len\"],\n",
    "        enable_dropout: bool = False,\n",
    "        key: Optional[jax.random.PRNGKey] = None,\n",
    "    ) -> Float[Array, \"seq_len hidden_size\"]:\n",
    "        tokens = jax.vmap(self.token_embedder)(token_ids)\n",
    "        segments = jax.vmap(self.segment_embedder)(segment_ids)\n",
    "        positions = jax.vmap(self.position_embedder)(position_ids)\n",
    "        embedded_inputs = tokens + segments + positions\n",
    "        embedded_inputs = jax.vmap(self.layernorm)(embedded_inputs)\n",
    "        embedded_inputs = self.dropout(\n",
    "            embedded_inputs, inference=not enable_dropout, key=key\n",
    "        )\n",
    "        return embedded_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rRJXC4jiSWBW",
    "tags": []
   },
   "outputs": [],
   "source": [
    "class FeedForwardBlock(eqx.Module):\n",
    "    \"\"\"A single transformer feed forward block.\"\"\"\n",
    "\n",
    "    mlp: eqx.nn.Linear\n",
    "    output: eqx.nn.Linear\n",
    "    layernorm: eqx.nn.LayerNorm\n",
    "    dropout: eqx.nn.Dropout\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        hidden_size: int,\n",
    "        intermediate_size: int,\n",
    "        dropout_rate: float,\n",
    "        key: jax.random.PRNGKey,\n",
    "    ):\n",
    "        mlp_key, output_key = jax.random.split(key)\n",
    "        self.mlp = eqx.nn.Linear(\n",
    "            in_features=hidden_size, out_features=intermediate_size, key=mlp_key\n",
    "        )\n",
    "        self.output = eqx.nn.Linear(\n",
    "            in_features=intermediate_size, out_features=hidden_size, key=output_key\n",
    "        )\n",
    "\n",
    "        self.layernorm = eqx.nn.LayerNorm(shape=hidden_size)\n",
    "        self.dropout = eqx.nn.Dropout(dropout_rate)\n",
    "\n",
    "    def __call__(\n",
    "        self,\n",
    "        inputs: Float[Array, \" hidden_size\"],\n",
    "        enable_dropout: bool = True,\n",
    "        key: Optional[jax.random.PRNGKey] = None,\n",
    "    ) -> Float[Array, \" hidden_size\"]:\n",
    "        # Feed-forward.\n",
    "        hidden = self.mlp(inputs)\n",
    "        hidden = jax.nn.gelu(hidden)\n",
    "\n",
    "        # Project back to input size.\n",
    "        output = self.output(hidden)\n",
    "        output = self.dropout(output, inference=not enable_dropout, key=key)\n",
    "\n",
    "        # Residual and layer norm.\n",
    "        output += inputs\n",
    "        output = self.layernorm(output)\n",
    "\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "z2BkNHFyZ5tx",
    "tags": []
   },
   "outputs": [],
   "source": [
    "class AttentionBlock(eqx.Module):\n",
    "    \"\"\"A single transformer attention block.\"\"\"\n",
    "\n",
    "    attention: eqx.nn.MultiheadAttention\n",
    "    layernorm: eqx.nn.LayerNorm\n",
    "    dropout: eqx.nn.Dropout\n",
    "    num_heads: int = eqx.field(static=True)\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        hidden_size: int,\n",
    "        num_heads: int,\n",
    "        dropout_rate: float,\n",
    "        attention_dropout_rate: float,\n",
    "        key: jax.random.PRNGKey,\n",
    "    ):\n",
    "        self.num_heads = num_heads\n",
    "        self.attention = eqx.nn.MultiheadAttention(\n",
    "            num_heads=num_heads,\n",
    "            query_size=hidden_size,\n",
    "            use_query_bias=True,\n",
    "            use_key_bias=True,\n",
    "            use_value_bias=True,\n",
    "            use_output_bias=True,\n",
    "            dropout_p=attention_dropout_rate,\n",
    "            key=key,\n",
    "        )\n",
    "        self.layernorm = eqx.nn.LayerNorm(shape=hidden_size)\n",
    "        self.dropout = eqx.nn.Dropout(dropout_rate)\n",
    "\n",
    "    def __call__(\n",
    "        self,\n",
    "        inputs: Float[Array, \"seq_len hidden_size\"],\n",
    "        mask: Optional[Int[Array, \" seq_len\"]],\n",
    "        enable_dropout: bool = False,\n",
    "        key: \"jax.random.PRNGKey\" = None,\n",
    "    ) -> Float[Array, \"seq_len hidden_size\"]:\n",
    "        if mask is not None:\n",
    "            mask = self.make_self_attention_mask(mask)\n",
    "        attention_key, dropout_key = (\n",
    "            (None, None) if key is None else jax.random.split(key)\n",
    "        )\n",
    "\n",
    "        attention_output = self.attention(\n",
    "            query=inputs,\n",
    "            key_=inputs,\n",
    "            value=inputs,\n",
    "            mask=mask,\n",
    "            inference=not enable_dropout,\n",
    "            key=attention_key,\n",
    "        )\n",
    "\n",
    "        result = attention_output\n",
    "        result = self.dropout(result, inference=not enable_dropout, key=dropout_key)\n",
    "        result = result + inputs\n",
    "        result = jax.vmap(self.layernorm)(result)\n",
    "        return result\n",
    "\n",
    "    def make_self_attention_mask(\n",
    "        self, mask: Int[Array, \" seq_len\"]\n",
    "    ) -> Float[Array, \"num_heads seq_len seq_len\"]:\n",
    "        \"\"\"Create self-attention mask from sequence-level mask.\"\"\"\n",
    "        mask = jnp.multiply(\n",
    "            jnp.expand_dims(mask, axis=-1), jnp.expand_dims(mask, axis=-2)\n",
    "        )\n",
    "        mask = jnp.expand_dims(mask, axis=-3)\n",
    "        mask = jnp.repeat(mask, repeats=self.num_heads, axis=-3)\n",
    "        return mask.astype(jnp.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hHS3S7NhhF11",
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   "outputs": [],
   "source": [
    "class TransformerLayer(eqx.Module):\n",
    "    \"\"\"A single transformer layer.\"\"\"\n",
    "\n",
    "    attention_block: AttentionBlock\n",
    "    ff_block: FeedForwardBlock\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        hidden_size: int,\n",
    "        intermediate_size: int,\n",
    "        num_heads: int,\n",
    "        dropout_rate: float,\n",
    "        attention_dropout_rate: float,\n",
    "        key: jax.random.PRNGKey,\n",
    "    ):\n",
    "        attention_key, ff_key = jax.random.split(key)\n",
    "\n",
    "        self.attention_block = AttentionBlock(\n",
    "            hidden_size=hidden_size,\n",
    "            num_heads=num_heads,\n",
    "            dropout_rate=dropout_rate,\n",
    "            attention_dropout_rate=attention_dropout_rate,\n",
    "            key=attention_key,\n",
    "        )\n",
    "        self.ff_block = FeedForwardBlock(\n",
    "            hidden_size=hidden_size,\n",
    "            intermediate_size=intermediate_size,\n",
    "            dropout_rate=dropout_rate,\n",
    "            key=ff_key,\n",
    "        )\n",
    "\n",
    "    def __call__(\n",
    "        self,\n",
    "        inputs: Float[Array, \"seq_len hidden_size\"],\n",
    "        mask: Optional[Int[Array, \" seq_len\"]] = None,\n",
    "        *,\n",
    "        enable_dropout: bool = False,\n",
    "        key: Optional[jax.random.PRNGKey] = None,\n",
    "    ) -> Float[Array, \"seq_len hidden_size\"]:\n",
    "        attn_key, ff_key = (None, None) if key is None else jax.random.split(key)\n",
    "        attention_output = self.attention_block(\n",
    "            inputs, mask, enable_dropout=enable_dropout, key=attn_key\n",
    "        )\n",
    "        seq_len = inputs.shape[0]\n",
    "        ff_keys = None if ff_key is None else jax.random.split(ff_key, num=seq_len)\n",
    "        output = jax.vmap(self.ff_block, in_axes=(0, None, 0))(\n",
    "            attention_output, enable_dropout, ff_keys\n",
    "        )\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "h0lJwj2HusuN",
    "tags": []
   },
   "outputs": [],
   "source": [
    "class Encoder(eqx.Module):\n",
    "    \"\"\"Full BERT encoder.\"\"\"\n",
    "\n",
    "    embedder_block: EmbedderBlock\n",
    "    layers: List[TransformerLayer]\n",
    "    pooler: eqx.nn.Linear\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        vocab_size: int,\n",
    "        max_length: int,\n",
    "        type_vocab_size: int,\n",
    "        embedding_size: int,\n",
    "        hidden_size: int,\n",
    "        intermediate_size: int,\n",
    "        num_layers: int,\n",
    "        num_heads: int,\n",
    "        dropout_rate: float,\n",
    "        attention_dropout_rate: float,\n",
    "        key: jax.random.PRNGKey,\n",
    "    ):\n",
    "        embedder_key, layer_key, pooler_key = jax.random.split(key, num=3)\n",
    "        self.embedder_block = EmbedderBlock(\n",
    "            vocab_size=vocab_size,\n",
    "            max_length=max_length,\n",
    "            type_vocab_size=type_vocab_size,\n",
    "            embedding_size=embedding_size,\n",
    "            hidden_size=hidden_size,\n",
    "            dropout_rate=dropout_rate,\n",
    "            key=embedder_key,\n",
    "        )\n",
    "\n",
    "        layer_keys = jax.random.split(layer_key, num=num_layers)\n",
    "        self.layers = []\n",
    "        for layer_key in layer_keys:\n",
    "            self.layers.append(\n",
    "                TransformerLayer(\n",
    "                    hidden_size=hidden_size,\n",
    "                    intermediate_size=intermediate_size,\n",
    "                    num_heads=num_heads,\n",
    "                    dropout_rate=dropout_rate,\n",
    "                    attention_dropout_rate=attention_dropout_rate,\n",
    "                    key=layer_key,\n",
    "                )\n",
    "            )\n",
    "\n",
    "        self.pooler = eqx.nn.Linear(\n",
    "            in_features=hidden_size, out_features=hidden_size, key=pooler_key\n",
    "        )\n",
    "\n",
    "    def __call__(\n",
    "        self,\n",
    "        token_ids: Int[Array, \" seq_len\"],\n",
    "        position_ids: Int[Array, \" seq_len\"],\n",
    "        segment_ids: Int[Array, \" seq_len\"],\n",
    "        *,\n",
    "        enable_dropout: bool = False,\n",
    "        key: Optional[jax.random.PRNGKey] = None,\n",
    "    ) -> Dict[str, Array]:\n",
    "        emb_key, l_key = (None, None) if key is None else jax.random.split(key)\n",
    "\n",
    "        embeddings = self.embedder_block(\n",
    "            token_ids=token_ids,\n",
    "            position_ids=position_ids,\n",
    "            segment_ids=segment_ids,\n",
    "            enable_dropout=enable_dropout,\n",
    "            key=emb_key,\n",
    "        )\n",
    "\n",
    "        # We assume that all 0-values should be masked out.\n",
    "        mask = jnp.asarray(token_ids != 0, dtype=jnp.int32)\n",
    "\n",
    "        x = embeddings\n",
    "        layer_outputs = []\n",
    "        for layer in self.layers:\n",
    "            cl_key, l_key = (None, None) if l_key is None else jax.random.split(l_key)\n",
    "            x = layer(x, mask, enable_dropout=enable_dropout, key=cl_key)\n",
    "            layer_outputs.append(x)\n",
    "\n",
    "        # BERT pooling.\n",
    "        # The first token in the last layer is the embedding of the \"[CLS]\" token.\n",
    "        first_token_last_layer = x[..., 0, :]\n",
    "        pooled = self.pooler(first_token_last_layer)\n",
    "        pooled = jnp.tanh(pooled)\n",
    "\n",
    "        return {\"embeddings\": embeddings, \"layers\": layer_outputs, \"pooled\": pooled}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7wHriOL6xziJ",
    "tags": []
   },
   "outputs": [],
   "source": [
    "class BertClassifier(eqx.Module):\n",
    "    \"\"\"BERT classifier.\"\"\"\n",
    "\n",
    "    encoder: Encoder\n",
    "    classifier_head: eqx.nn.Linear\n",
    "    dropout: eqx.nn.Dropout\n",
    "\n",
    "    def __init__(self, config: Mapping, num_classes: int, key: jax.random.PRNGKey):\n",
    "        encoder_key, head_key = jax.random.split(key)\n",
    "\n",
    "        self.encoder = Encoder(\n",
    "            vocab_size=config[\"vocab_size\"],\n",
    "            max_length=config[\"max_position_embeddings\"],\n",
    "            type_vocab_size=config[\"type_vocab_size\"],\n",
    "            embedding_size=config[\"hidden_size\"],\n",
    "            hidden_size=config[\"hidden_size\"],\n",
    "            intermediate_size=config[\"intermediate_size\"],\n",
    "            num_layers=config[\"num_hidden_layers\"],\n",
    "            num_heads=config[\"num_attention_heads\"],\n",
    "            dropout_rate=config[\"hidden_dropout_prob\"],\n",
    "            attention_dropout_rate=config[\"attention_probs_dropout_prob\"],\n",
    "            key=encoder_key,\n",
    "        )\n",
    "        self.classifier_head = eqx.nn.Linear(\n",
    "            in_features=config[\"hidden_size\"], out_features=num_classes, key=head_key\n",
    "        )\n",
    "        self.dropout = eqx.nn.Dropout(config[\"hidden_dropout_prob\"])\n",
    "\n",
    "    def __call__(\n",
    "        self,\n",
    "        inputs: Dict[str, Int[Array, \" seq_len\"]],\n",
    "        enable_dropout: bool = True,\n",
    "        key: jax.random.PRNGKey = None,\n",
    "    ) -> Float[Array, \" num_classes\"]:\n",
    "        seq_len = inputs[\"token_ids\"].shape[-1]\n",
    "        position_ids = jnp.arange(seq_len)\n",
    "\n",
    "        e_key, d_key = (None, None) if key is None else jax.random.split(key)\n",
    "\n",
    "        pooled_output = self.encoder(\n",
    "            token_ids=inputs[\"token_ids\"],\n",
    "            segment_ids=inputs[\"segment_ids\"],\n",
    "            position_ids=position_ids,\n",
    "            enable_dropout=enable_dropout,\n",
    "            key=e_key,\n",
    "        )[\"pooled\"]\n",
    "        pooled_output = self.dropout(\n",
    "            pooled_output, inference=not enable_dropout, key=d_key\n",
    "        )\n",
    "\n",
    "        return self.classifier_head(pooled_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cgjmQG7QrmmX"
   },
   "source": [
    "## Initialize model and load checkpoint.\n",
    "\n",
    "Here we initialize the model with the official parameters of Tiny-BERT, i.e. a 2-layer BERT model with a hidden size of 128.\n",
    "\n",
    "In this case, we assume that we've downloaded the [checkpoint](https://github.com/patrick-kidger/equinox/blob/main/examples/bert_checkpoint.eqx) provided in the examples directory. Initializing the model with pretrained weights will give you much better training performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hB8uecKzxs28",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Tiny-BERT config.\n",
    "bert_config = {\n",
    "    \"vocab_size\": 30522,\n",
    "    \"hidden_size\": 128,\n",
    "    \"num_hidden_layers\": 2,\n",
    "    \"num_attention_heads\": 2,\n",
    "    \"hidden_act\": \"gelu\",\n",
    "    \"intermediate_size\": 512,\n",
    "    \"hidden_dropout_prob\": 0.1,\n",
    "    \"attention_probs_dropout_prob\": 0.1,\n",
    "    \"max_position_embeddings\": 512,\n",
    "    \"type_vocab_size\": 2,\n",
    "    \"initializer_range\": 0.02,\n",
    "}\n",
    "\n",
    "key = jax.random.PRNGKey(5678)\n",
    "model_key, train_key = jax.random.split(key)\n",
    "classifier = BertClassifier(config=bert_config, num_classes=2, key=model_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WfeFdElT6aql",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Download the checkpoint from\n",
    "# https://github.com/patrick-kidger/equinox/blob/main/examples/bert_checkpoint.eqx\n",
    "classifier_chkpt = eqx.tree_deserialise_leaves(\"bert_checkpoint.eqx\", classifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QfkANxBFnL_H"
   },
   "source": [
    "# Training on SST2.\n",
    "\n",
    "To complete the example, we show how to fit the BERT model to a dataset. We're going to use the SST2 (Stanford Sentiment Treebank) dataset that is part of the GLUE benchmark suite."
   ]
  },
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    "id": "fiW9JNsmWvLg",
    "outputId": "0308d854-874a-4516-9ecb-77006bcc4a79",
    "tags": []
   },
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\n",
    "    \"google/bert_uncased_L-2_H-128_A-2\", model_max_length=128\n",
    ")\n",
    "\n",
    "\n",
    "def tokenize(example):\n",
    "    return tokenizer(example[\"sentence\"], padding=\"max_length\", truncation=True)\n",
    "\n",
    "\n",
    "ds = load_dataset(\"sst2\")\n",
    "ds = ds.map(tokenize, batched=True)\n",
    "ds.set_format(type=\"jax\", columns=[\"input_ids\", \"token_type_ids\", \"label\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mt2e5T5Aotby"
   },
   "source": [
    "We're using `pmap` to split the batches across all available devices for more\n",
    "efficient training. If you have e.g. just a single GPU, this makes no difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "veWsK0NtZ9BY",
    "tags": []
   },
   "outputs": [],
   "source": [
    "@eqx.filter_value_and_grad\n",
    "def compute_loss(classifier, inputs, key):\n",
    "    batch_size = inputs[\"token_ids\"].shape[0]\n",
    "    batched_keys = jax.random.split(key, num=batch_size)\n",
    "    logits = jax.vmap(classifier, in_axes=(0, None, 0))(inputs, True, batched_keys)\n",
    "    return jnp.mean(\n",
    "        optax.softmax_cross_entropy_with_integer_labels(\n",
    "            logits=logits, labels=inputs[\"label\"]\n",
    "        )\n",
    "    )\n",
    "\n",
    "\n",
    "def make_step(model, inputs, opt_state, key, tx):\n",
    "    key, new_key = jax.random.split(key)\n",
    "    loss, grads = compute_loss(model, inputs, key)\n",
    "    grads = jax.lax.pmean(grads, axis_name=\"devices\")\n",
    "\n",
    "    updates, opt_state = tx.update(grads, opt_state, model)\n",
    "    model = eqx.apply_updates(model, updates)\n",
    "    return loss, model, opt_state, new_key\n",
    "\n",
    "\n",
    "def make_eval_step(model, inputs):\n",
    "    return jax.vmap(functools.partial(model, enable_dropout=False))(inputs)\n",
    "\n",
    "\n",
    "p_make_eval_step = eqx.filter_pmap(make_eval_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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   "source": [
    "epochs = 3\n",
    "batch_size = 32\n",
    "learning_rate = 1e-5\n",
    "\n",
    "num_devices = jax.device_count()\n",
    "assert (\n",
    "    batch_size % num_devices == 0\n",
    "), \"The batch size must be a multiple of the number of devices.\"\n",
    "\n",
    "tx = optax.adam(learning_rate=learning_rate)\n",
    "tx = optax.chain(optax.clip_by_global_norm(1.0), tx)\n",
    "opt_state = tx.init(classifier_chkpt)\n",
    "\n",
    "p_make_step = eqx.filter_pmap(functools.partial(make_step, tx=tx), axis_name=\"devices\")\n",
    "\n",
    "# Replicate across devices.\n",
    "opt_state = jax.device_put_replicated(opt_state, jax.local_devices())\n",
    "model = jax.device_put_replicated(classifier_chkpt, jax.local_devices())\n",
    "train_key = jax.device_put_replicated(train_key, jax.local_devices())\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    with tqdm.tqdm(\n",
    "        ds[\"train\"].iter(batch_size=batch_size, drop_last_batch=True),\n",
    "        total=ds[\"train\"].num_rows // batch_size,\n",
    "        unit=\"steps\",\n",
    "        desc=f\"Epoch {epoch+1}/{epochs}\",\n",
    "    ) as tqdm_epoch:\n",
    "        for batch in tqdm_epoch:\n",
    "            token_ids, token_type_ids = batch[\"input_ids\"], batch[\"token_type_ids\"]\n",
    "            label = batch[\"label\"]\n",
    "\n",
    "            # Split batch across devices.\n",
    "            token_ids = einops.rearrange(\n",
    "                token_ids, \"(b1 b2) s -> b1 b2 s\", b1=num_devices\n",
    "            )\n",
    "            token_type_ids = einops.rearrange(\n",
    "                token_type_ids, \"(b1 b2) s -> b1 b2 s\", b1=num_devices\n",
    "            )\n",
    "            label = einops.rearrange(label, \"(b1 b2) -> b1 b2\", b1=num_devices)\n",
    "\n",
    "            inputs = {\n",
    "                \"token_ids\": token_ids,\n",
    "                \"segment_ids\": token_type_ids,\n",
    "                \"label\": label,\n",
    "            }\n",
    "            loss, model, opt_state, train_key = p_make_step(\n",
    "                model, inputs, opt_state, train_key\n",
    "            )\n",
    "\n",
    "            tqdm_epoch.set_postfix(loss=np.sum(loss).item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66,
     "referenced_widgets": [
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    "id": "LOBBQPetNfzz",
    "outputId": "09778aa0-8b35-4d05-aefd-84e325d5f39a",
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   "outputs": [],
   "source": [
    "outputs = []\n",
    "for batch in tqdm.tqdm(\n",
    "    ds[\"validation\"].iter(batch_size=batch_size),\n",
    "    unit=\"steps\",\n",
    "    total=np.ceil(ds[\"validation\"].num_rows / batch_size),\n",
    "    desc=\"Validation\",\n",
    "):\n",
    "    token_ids, token_type_ids = batch[\"input_ids\"], batch[\"token_type_ids\"]\n",
    "    label = batch[\"label\"]\n",
    "\n",
    "    # Split batch across devices.\n",
    "    token_ids = einops.rearrange(token_ids, \"(b1 b2) s -> b1 b2 s\", b1=num_devices)\n",
    "    token_type_ids = einops.rearrange(\n",
    "        token_type_ids, \"(b1 b2) s -> b1 b2 s\", b1=num_devices\n",
    "    )\n",
    "\n",
    "    inputs = {\"token_ids\": token_ids, \"segment_ids\": token_type_ids}\n",
    "\n",
    "    # Compare predicted class with label.\n",
    "    output = p_make_eval_step(model, inputs)\n",
    "    output = map(float, np.argmax(output.reshape(-1, 2), axis=-1) == label)\n",
    "    outputs.extend(output)\n",
    "\n",
    "print(f\"Accuracy: {100 * np.sum(outputs) / len(outputs):.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you used the pretrained checkpoint, you should be able to achieve ~80% accuracy. If you trained from randomly initialized weights instead, expect around 60% accuracy."
   ]
  }
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