examples/llama_fsdp_tp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LLaMA with FSDP and Tensor Parallelism\n",
    "In this notebook we provide a simple exmple of using scalax sjit for combining FSDP and tensor parallelism (TP). We train a simple LLaMA transformer model defined in the huggingface transformers library using huggingface datasets. We show that all of these can be done rather easily without modifying the model definition and the tran step function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "h9HtQYyDAI0H"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfs/nest/mambaforge/envs/scalax/lib/python3.10/site-packages/transformers/utils/hub.py:124: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from functools import partial\n",
    "\n",
    "import jax\n",
    "import jax.numpy as jnp\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",
    "from transformers import FlaxLlamaForCausalLM, LlamaConfig, LlamaTokenizer\n",
    "from datasets import load_dataset\n",
    "\n",
    "from scalax.utils import JaxRNG\n",
    "from scalax.sharding import PartitionSpec, MeshShardingHelper, TreePathShardingRule\n",
    "from scalax.utils import print_pytree_structure\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## C4 DataLoader\n",
    "We use the C4 dataset implemented in the Huggingface datasets library. We create a very simple data loader that tokenizes the text and apply sequnce packing to form batches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def c4_data_loader(batch_size, seq_length):\n",
    "    chunk_size = batch_size * seq_length\n",
    "    # Stream the c4 dataset without downloading\n",
    "    dataset = load_dataset('allenai/c4', 'en', split='train', streaming=True, trust_remote_code=True)\n",
    "    # Use the free OpenLLaMA tokenizer\n",
    "    tokenizer = LlamaTokenizer.from_pretrained('openlm-research/open_llama_3b_v2')\n",
    "\n",
    "    # Use a static mask and position ids here for simplicity\n",
    "    attention_mask = np.ones((batch_size, seq_length), dtype=np.int32)\n",
    "    position_ids = np.arange(seq_length, dtype=np.int32).reshape(1, -1)\n",
    "    position_ids = np.broadcast_to(position_ids, (batch_size, seq_length))\n",
    "    \n",
    "    token_buffer = []\n",
    "    for example in dataset:\n",
    "        # Sequence packing\n",
    "        token_buffer.extend(tokenizer.encode(example['text']))\n",
    "        token_buffer.append(tokenizer.eos_token_id)\n",
    "        while len(token_buffer) > chunk_size + 1:\n",
    "            batch = {\n",
    "                'input_tokens': np.array(\n",
    "                    token_buffer[:chunk_size], dtype=np.int32\n",
    "                ).reshape(batch_size, seq_length),  # Input tokens to language model\n",
    "                'target_tokens': np.array(\n",
    "                    token_buffer[1:chunk_size + 1], dtype=np.int32\n",
    "                ).reshape(batch_size, seq_length),  # Shift-by-one training target tokens\n",
    "                'attention_mask': attention_mask,\n",
    "                'position_ids': position_ids,\n",
    "            }\n",
    "            token_buffer = token_buffer[chunk_size:]\n",
    "            yield batch        \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect the Model Tree Sturcture\n",
    "To compose FSDP and tensor parallelism, we need to specify which axis should be sharded with fsdp and which axis should be sharded with tp for each tensor. To do this, we need to use `TreePathShardingRule`. `TreePathShardingRule` specifies sharding according to the path of leaf tensors in a pytree, and match it agaist user defined regular expressions. To use it, the fisrt step is to inspect the pytree structure of the model. We can do this with `scalax.utils.print_pytree_structure`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "params/lm_head/kernel: shape: (1024, 32000), dtype: float32\n",
      "params/model/embed_tokens/embedding: shape: (32000, 1024), dtype: float32\n",
      "params/model/layers/0/input_layernorm/weight: shape: (1024,), dtype: float32\n",
      "params/model/layers/0/mlp/down_proj/kernel: shape: (2048, 1024), dtype: float32\n",
      "params/model/layers/0/mlp/gate_proj/kernel: shape: (1024, 2048), dtype: float32\n",
      "params/model/layers/0/mlp/up_proj/kernel: shape: (1024, 2048), dtype: float32\n",
      "params/model/layers/0/post_attention_layernorm/weight: shape: (1024,), dtype: float32\n",
      "params/model/layers/0/self_attn/k_proj/kernel: shape: (1024, 1024), dtype: float32\n",
      "params/model/layers/0/self_attn/o_proj/kernel: shape: (1024, 1024), dtype: float32\n",
      "params/model/layers/0/self_attn/q_proj/kernel: shape: (1024, 1024), dtype: float32\n",
      "params/model/layers/0/self_attn/v_proj/kernel: shape: (1024, 1024), dtype: float32\n",
      "params/model/layers/1/input_layernorm/weight: shape: (1024,), dtype: float32\n",
      "params/model/layers/1/mlp/down_proj/kernel: shape: (2048, 1024), dtype: float32\n",
      "params/model/layers/1/mlp/gate_proj/kernel: shape: (1024, 2048), dtype: float32\n",
      "params/model/layers/1/mlp/up_proj/kernel: shape: (1024, 2048), dtype: float32\n",
      "params/model/layers/1/post_attention_layernorm/weight: shape: (1024,), dtype: float32\n",
      "params/model/layers/1/self_attn/k_proj/kernel: shape: (1024, 1024), dtype: float32\n",
      "params/model/layers/1/self_attn/o_proj/kernel: shape: (1024, 1024), dtype: float32\n",
      "params/model/layers/1/self_attn/q_proj/kernel: shape: (1024, 1024), dtype: float32\n",
      "params/model/layers/1/self_attn/v_proj/kernel: shape: (1024, 1024), dtype: float32\n",
      "params/model/layers/2/input_layernorm/weight: shape: (1024,), dtype: float32\n",
      "params/model/layers/2/mlp/down_proj/kernel: shape: (2048, 1024), dtype: float32\n",
      "params/model/layers/2/mlp/gate_proj/kernel: shape: (1024, 2048), dtype: float32\n",
      "params/model/layers/2/mlp/up_proj/kernel: shape: (1024, 2048), dtype: float32\n",
      "params/model/layers/2/post_attention_layernorm/weight: shape: (1024,), dtype: float32\n",
      "params/model/layers/2/self_attn/k_proj/kernel: shape: (1024, 1024), dtype: float32\n",
      "params/model/layers/2/self_attn/o_proj/kernel: shape: (1024, 1024), dtype: float32\n",
      "params/model/layers/2/self_attn/q_proj/kernel: shape: (1024, 1024), dtype: float32\n",
      "params/model/layers/2/self_attn/v_proj/kernel: shape: (1024, 1024), dtype: float32\n",
      "params/model/norm/weight: shape: (1024,), dtype: float32\n"
     ]
    }
   ],
   "source": [
    "# A super small llama model with only 3 layers for demo\n",
    "config = LlamaConfig(\n",
    "    num_hidden_layers=3,\n",
    "    hidden_size=1024,\n",
    "    intermediate_size=2048,\n",
    "    num_attention_heads=4,\n",
    ")\n",
    "model = FlaxLlamaForCausalLM.module_class(config=config)\n",
    "seq_length = 512\n",
    "\n",
    "def init_model_params(rng):\n",
    "    return model.init(\n",
    "        input_ids=jnp.zeros((4, seq_length), dtype=jnp.int32),\n",
    "        position_ids=jnp.zeros((4, seq_length), dtype=jnp.int32),\n",
    "        attention_mask=jnp.ones((4, seq_length), dtype=jnp.int32),\n",
    "        rngs={'params': rng},\n",
    "    )\n",
    "\n",
    "# Obtain the pytree structure without instantiating the model\n",
    "model_tree_structure = jax.eval_shape(init_model_params, jax.random.PRNGKey(42))\n",
    "\n",
    "# Print out the pytree structure to help us write sharding rule\n",
    "print_pytree_structure(model_tree_structure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define the Mesh Sharding Rule\n",
    "According to the previous pytree structure, we define the following mesh and sharding rule. We employ 2-way tensor parallelism and use the rest for FSDP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'params': {'lm_head': {'kernel': PartitionSpec('tp', 'fsdp')},\n",
       "  'model': {'embed_tokens': {'embedding': PartitionSpec('fsdp', 'tp')},\n",
       "   'layers': {'0': {'input_layernorm': {'weight': PartitionSpec()},\n",
       "     'mlp': {'down_proj': {'kernel': PartitionSpec('tp', 'fsdp')},\n",
       "      'gate_proj': {'kernel': PartitionSpec('fsdp', 'tp')},\n",
       "      'up_proj': {'kernel': PartitionSpec('fsdp', 'tp')}},\n",
       "     'post_attention_layernorm': {'weight': PartitionSpec()},\n",
       "     'self_attn': {'k_proj': {'kernel': PartitionSpec('fsdp', 'tp')},\n",
       "      'o_proj': {'kernel': PartitionSpec('tp', 'fsdp')},\n",
       "      'q_proj': {'kernel': PartitionSpec('fsdp', 'tp')},\n",
       "      'v_proj': {'kernel': PartitionSpec('fsdp', 'tp')}}},\n",
       "    '1': {'input_layernorm': {'weight': PartitionSpec()},\n",
       "     'mlp': {'down_proj': {'kernel': PartitionSpec('tp', 'fsdp')},\n",
       "      'gate_proj': {'kernel': PartitionSpec('fsdp', 'tp')},\n",
       "      'up_proj': {'kernel': PartitionSpec('fsdp', 'tp')}},\n",
       "     'post_attention_layernorm': {'weight': PartitionSpec()},\n",
       "     'self_attn': {'k_proj': {'kernel': PartitionSpec('fsdp', 'tp')},\n",
       "      'o_proj': {'kernel': PartitionSpec('tp', 'fsdp')},\n",
       "      'q_proj': {'kernel': PartitionSpec('fsdp', 'tp')},\n",
       "      'v_proj': {'kernel': PartitionSpec('fsdp', 'tp')}}},\n",
       "    '2': {'input_layernorm': {'weight': PartitionSpec()},\n",
       "     'mlp': {'down_proj': {'kernel': PartitionSpec('tp', 'fsdp')},\n",
       "      'gate_proj': {'kernel': PartitionSpec('fsdp', 'tp')},\n",
       "      'up_proj': {'kernel': PartitionSpec('fsdp', 'tp')}},\n",
       "     'post_attention_layernorm': {'weight': PartitionSpec()},\n",
       "     'self_attn': {'k_proj': {'kernel': PartitionSpec('fsdp', 'tp')},\n",
       "      'o_proj': {'kernel': PartitionSpec('tp', 'fsdp')},\n",
       "      'q_proj': {'kernel': PartitionSpec('fsdp', 'tp')},\n",
       "      'v_proj': {'kernel': PartitionSpec('fsdp', 'tp')}}}},\n",
       "   'norm': {'weight': PartitionSpec()}}}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mesh = MeshShardingHelper([-1, 2], ['fsdp', 'tp'])\n",
    "\n",
    "# Use pairs of regex, PartitionSpec to specify the sharding\n",
    "model_sharding_rule = TreePathShardingRule(\n",
    "    ('embedding', PartitionSpec('fsdp', 'tp')),\n",
    "    ('lm_head/kernel', PartitionSpec('tp', 'fsdp')),\n",
    "    # Megatron style feedfoward sharding\n",
    "    ('mlp/(up|gate)_proj/kernel', PartitionSpec('fsdp', 'tp')),\n",
    "    ('mlp/down_proj/kernel', PartitionSpec('tp', 'fsdp')),\n",
    "    # Attention should be sharded by heads\n",
    "    ('self_attn/(k|q|v)_proj/kernel', PartitionSpec('fsdp', 'tp')),\n",
    "    ('self_attn/o_proj/kernel', PartitionSpec('tp', 'fsdp')),\n",
    "    ('norm', PartitionSpec()),\n",
    ")\n",
    "\n",
    "# Inspect the sharding rule can be successfully applied\n",
    "model_sharding_rule.apply(model_tree_structure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sharded Initialization and TrainStep\n",
    "We define the initialization and train step functions and shard them with the `sjit` method of `MeshShardingHelper`. For model, we use our previously defined sharding rule, and for data, we shard the batch axis with `fsdp`. All of these can be simply specified as arugments to `sjit`, without changing the `init_fn` and `train_step` functions. The `init_fn` and `train_step` functions here are exactly the same as if we are training on a single GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "8aiua0iqAa5A"
   },
   "outputs": [],
   "source": [
    "optimizer = optax.adamw(0.001)\n",
    "\n",
    "@partial(\n",
    "    mesh.sjit,  # Shard the initialization function\n",
    "    # The initialized model and optimizer states should be sharded according\n",
    "    # to our tree path sharding rule\n",
    "    out_shardings=model_sharding_rule,\n",
    ")\n",
    "def init_fn(rng):\n",
    "    params = model.init(\n",
    "        input_ids=jnp.zeros((4, seq_length), dtype=jnp.int32),\n",
    "        position_ids=jnp.zeros((4, seq_length), dtype=jnp.int32),\n",
    "        attention_mask=jnp.ones((4, seq_length), dtype=jnp.int32),\n",
    "        rngs={'params': rng},\n",
    "    )\n",
    "    return TrainState.create(params=params, tx=optimizer, apply_fn=None)\n",
    "\n",
    "\n",
    "@partial(\n",
    "    mesh.sjit,  # Shard the train step function\n",
    "    # The input model should be sharded with model_sharding_rule, the data is loaded as replicated\n",
    "    in_shardings=(model_sharding_rule, PartitionSpec()),\n",
    "    # The output model should be sharded with model_sharding_rule, the loss metrics is replicated\n",
    "    out_shardings=(model_sharding_rule, PartitionSpec()),\n",
    "    # After the beginning of the function, the data should be sharded along the `fsdp` axis\n",
    "    args_sharding_constraint=(model_sharding_rule, PartitionSpec('fsdp')),\n",
    "    donate_argnums=(0,),  # No need to preserve the old model\n",
    ")\n",
    "def train_step(train_state, batch):\n",
    "    def loss_fn(params):\n",
    "        logits = model.apply(\n",
    "            params,\n",
    "            input_ids=batch['input_tokens'],\n",
    "            attention_mask=batch['attention_mask'],\n",
    "            position_ids=batch['position_ids'],\n",
    "        ).logits\n",
    "        loss = optax.softmax_cross_entropy_with_integer_labels(\n",
    "            logits, batch['target_tokens']\n",
    "        )\n",
    "        return jnp.mean(loss)\n",
    "    grad_fn = jax.value_and_grad(loss_fn)\n",
    "    loss, grads = grad_fn(train_state.params)\n",
    "    train_state = train_state.apply_gradients(grads=grads)\n",
    "    return train_state, loss\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the Sharded Train Step\n",
    "Now let's run the sharded train step for 100 steps and plot the loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "f1lJqS7afXo5"
   },
   "outputs": [
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n",
      "Token indices sequence length is longer than the specified maximum sequence length for this model (2637 > 2048). Running this sequence through the model will result in indexing errors\n",
      "warning: Linking two modules of different target triples: 'LLVMDialectModule' is 'nvptx64-nvidia-gpulibs' whereas '' is 'nvptx64-nvidia-cuda'\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f12246b9030>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_iterator = c4_data_loader(8, seq_length)\n",
    "train_state = init_fn(jax.random.PRNGKey(42))\n",
    "\n",
    "losses = []\n",
    "for _, batch in zip(trange(100), data_iterator):\n",
    "    train_state, loss = train_step(train_state, batch)\n",
    "    losses.append(loss)\n",
    "\n",
    "plt.plot(losses)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "15c8c58f52904c42ac3decf3f512c155": {
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