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minigpt_tpu_opt.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 🚀 Mini-GPT Training on Google Cloud TPUs (Optimized)\n",
"\n",
"A production-ready, optimized implementation of a small Transformer model trained on TPUs using JAX/Flax.\n",
"\n",
"**Key Features:**\n",
"- ✅ Proper `pmap` usage with replicated parameters\n",
"- ✅ Gradient synchronization across TPU cores\n",
"- ✅ Mixed precision training (bfloat16)\n",
"- ✅ Precomputed causal masks\n",
"- ✅ KV cache for efficient generation\n",
"- ✅ Vectorized top-k sampling\n",
"- ✅ Multi-batch validation\n",
"- ✅ Learning rate scheduling with warmup\n",
"- ✅ Gradient checkpointing support"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 📦 Installation\n",
"\n",
"Run this on your TPU VM to install dependencies:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install --upgrade \"jax[tpu]>=0.4.32\" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html\n",
"!pip install flax optax datasets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 📚 Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import jax\n",
"import jax.numpy as jnp\n",
"import numpy as np\n",
"import flax.linen as nn\n",
"import optax\n",
"from flax.training import train_state, checkpoints, dynamic_scale as dynamic_scale_lib\n",
"from datasets import load_dataset\n",
"from typing import Any, Tuple, Optional\n",
"from functools import partial\n",
"\n",
"print(f\"JAX version: {jax.__version__}\")\n",
"print(f\"JAX devices: {jax.devices()}\")\n",
"print(f\"Number of devices: {jax.device_count()}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ⚙️ Configuration\n",
"\n",
"Adjust these hyperparameters based on your needs:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class Config:\n",
" # Model Architecture\n",
" vocab_size: int = None # Will be set after tokenization\n",
" emb_dim: int = 256\n",
" n_layers: int = 6\n",
" n_heads: int = 4\n",
" mlp_dim: int = 1024\n",
" dropout: float = 0.1\n",
" max_seq_len: int = 512\n",
" block_size: int = 128 # Context window for training\n",
" \n",
" # Training Hyperparameters\n",
" batch_size: int = 64 # Must be divisible by number of TPU cores\n",
" learning_rate: float = 3e-4\n",
" warmup_steps: int = 100\n",
" max_steps: int = 10000\n",
" eval_interval: int = 100\n",
" log_interval: int = 50\n",
" num_eval_batches: int = 10 # Number of batches for validation\n",
" \n",
" # Optimization\n",
" use_mixed_precision: bool = True # Use bfloat16 for faster TPU training\n",
" gradient_checkpointing: bool = False # Enable for very large models\n",
" \n",
" # Generation Parameters\n",
" temperature: float = 0.8\n",
" top_k: int = 40\n",
"\n",
"config = Config()\n",
"print(\"Configuration loaded ✓\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 📖 Dataset & Tokenization\n",
"\n",
"We'll use the Tiny Shakespeare dataset with character-level tokenization:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load dataset\n",
"print(\"Loading dataset...\")\n",
"ds = load_dataset(\"tiny_shakespeare\")\n",
"text = ds[\"train\"][0][\"text\"]\n",
"\n",
"# Character-level tokenizer\n",
"chars = sorted(list(set(text)))\n",
"stoi = {c: i for i, c in enumerate(chars)}\n",
"itos = {i: c for i, c in enumerate(chars)}\n",
"config.vocab_size = len(chars)\n",
"\n",
"def encode(s):\n",
" return [stoi[c] for c in s]\n",
"\n",
"def decode(xs):\n",
" return ''.join([itos[x] for x in xs])\n",
"\n",
"# Encode all data\n",
"data = np.array(encode(text), dtype=np.int32)\n",
"\n",
"print(f\"Dataset: {len(data):,} tokens\")\n",
"print(f\"Vocab size: {config.vocab_size}\")\n",
"print(f\"\\nSample text: {text[:200]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train/Val Split"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"n = int(0.9 * len(data))\n",
"train_data = data[:n]\n",
"val_data = data[n:]\n",
"\n",
"print(f\"Train tokens: {len(train_data):,}\")\n",
"print(f\"Val tokens: {len(val_data):,}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Batch Loader"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_batch(split='train', rng=None):\n",
" \"\"\"Get a batch of data for training or validation\"\"\"\n",
" source = train_data if split == 'train' else val_data\n",
" \n",
" if rng is not None:\n",
" ix = jax.random.randint(rng, (config.batch_size,), 0, len(source) - config.block_size - 1)\n",
" else:\n",
" ix = np.random.randint(0, len(source) - config.block_size - 1, (config.batch_size,))\n",
" \n",
" x = np.stack([source[i:i + config.block_size] for i in ix])\n",
" y = np.stack([source[i + 1:i + 1 + config.block_size] for i in ix])\n",
" \n",
" return jnp.array(x), jnp.array(y)\n",
"\n",
"# Test the batch loader\n",
"x_test, y_test = get_batch('train')\n",
"print(f\"Batch shape: {x_test.shape}\")\n",
"print(f\"First sequence: {decode(list(x_test[0][:50]))}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🎭 Precompute Causal Mask\n",
"\n",
"**Optimization**: Create mask once instead of every forward pass"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Precompute causal mask for efficiency\n",
"CAUSAL_MASK = jnp.tril(jnp.ones((config.block_size, config.block_size), dtype=jnp.bool_))\n",
"print(f\"Precomputed causal mask: {CAUSAL_MASK.shape}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🏗️ Model Architecture\n",
"\n",
"### Self-Attention Module with KV Cache Support"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class SelfAttention(nn.Module):\n",
" n_heads: int\n",
" head_dim: int\n",
" dropout: float = 0.1\n",
" \n",
" @nn.compact\n",
" def __call__(self, x, train: bool = True, cache: Optional[Tuple] = None):\n",
" \"\"\"\n",
" Self-attention with optional KV caching for efficient generation.\n",
" \n",
" Args:\n",
" x: Input tensor (B, T, C)\n",
" train: Whether in training mode\n",
" cache: Optional (k_cache, v_cache) for generation\n",
" \n",
" Returns:\n",
" out: Output tensor (B, T, C)\n",
" new_cache: Updated (k_cache, v_cache) if cache was provided\n",
" \"\"\"\n",
" B, T, C = x.shape\n",
" \n",
" # QKV projection\n",
" qkv = nn.Dense(3 * C, use_bias=False)(x)\n",
" q, k, v = jnp.split(qkv, 3, axis=-1)\n",
" \n",
" # Reshape to (B, n_heads, T, head_dim)\n",
" def split_heads(t):\n",
" return t.reshape(B, T, self.n_heads, self.head_dim).transpose(0, 2, 1, 3)\n",
" \n",
" q = split_heads(q)\n",
" k = split_heads(k)\n",
" v = split_heads(v)\n",
" \n",
" # Handle KV cache for generation\n",
" if cache is not None:\n",
" k_cache, v_cache = cache\n",
" k = jnp.concatenate([k_cache, k], axis=2)\n",
" v = jnp.concatenate([v_cache, v], axis=2)\n",
" new_cache = (k, v)\n",
" else:\n",
" new_cache = None\n",
" \n",
" # Scaled dot-product attention\n",
" scale = jnp.sqrt(self.head_dim).astype(x.dtype)\n",
" attn_weights = jnp.einsum('bhqd,bhkd->bhqk', q, k) / scale\n",
" \n",
" # Apply causal mask (use precomputed global mask)\n",
" T_k = k.shape[2] # Key sequence length (may be longer with cache)\n",
" mask = CAUSAL_MASK[:T, :T_k] if T_k <= config.block_size else jnp.tril(jnp.ones((T, T_k), dtype=jnp.bool_))\n",
" attn_weights = jnp.where(mask[None, None, :, :], attn_weights, -1e10)\n",
" \n",
" attn_weights = nn.softmax(attn_weights, axis=-1)\n",
" attn_weights = nn.Dropout(rate=self.dropout, deterministic=not train)(attn_weights)\n",
" \n",
" # Apply attention to values\n",
" out = jnp.einsum('bhqk,bhkd->bhqd', attn_weights, v)\n",
" out = out.transpose(0, 2, 1, 3).reshape(B, T, C)\n",
" \n",
" # Output projection\n",
" out = nn.Dense(C)(out)\n",
" out = nn.Dropout(rate=self.dropout, deterministic=not train)(out)\n",
" \n",
" if cache is not None:\n",
" return out, new_cache\n",
" return out"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transformer Block"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class TransformerBlock(nn.Module):\n",
" n_heads: int\n",
" head_dim: int\n",
" mlp_dim: int\n",
" dropout: float = 0.1\n",
" \n",
" @nn.compact\n",
" def __call__(self, x, train: bool = True, cache: Optional[Tuple] = None):\n",
" # Self-attention with residual connection\n",
" h = nn.LayerNorm()(x)\n",
" if cache is not None:\n",
" h, new_cache = SelfAttention(self.n_heads, self.head_dim, self.dropout)(h, train=train, cache=cache)\n",
" else:\n",
" h = SelfAttention(self.n_heads, self.head_dim, self.dropout)(h, train=train, cache=None)\n",
" new_cache = None\n",
" x = x + h\n",
" \n",
" # MLP with residual connection\n",
" h = nn.LayerNorm()(x)\n",
" h = nn.Dense(self.mlp_dim)(h)\n",
" h = nn.gelu(h)\n",
" h = nn.Dropout(rate=self.dropout, deterministic=not train)(h)\n",
" h = nn.Dense(x.shape[-1])(h)\n",
" h = nn.Dropout(rate=self.dropout, deterministic=not train)(h)\n",
" x = x + h\n",
" \n",
" if cache is not None:\n",
" return x, new_cache\n",
" return x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Complete Mini-GPT Model with Mixed Precision Support"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class MiniGPT(nn.Module):\n",
" config: Config\n",
" \n",
" @nn.compact\n",
" def __call__(self, x, train: bool = True, cache_list: Optional[list] = None):\n",
" \"\"\"\n",
" Args:\n",
" x: Input tokens (B, T)\n",
" train: Training mode flag\n",
" cache_list: Optional list of (k, v) caches for each layer\n",
" \n",
" Returns:\n",
" logits: Output logits (B, T, vocab_size)\n",
" new_cache_list: Updated caches if cache_list was provided\n",
" \"\"\"\n",
" B, T = x.shape\n",
" \n",
" # Mixed precision: compute in bfloat16, but embeddings in float32\n",
" dtype = jnp.bfloat16 if self.config.use_mixed_precision and train else jnp.float32\n",
" \n",
" # Token embeddings\n",
" tok_emb = nn.Embed(\n",
" num_embeddings=self.config.vocab_size,\n",
" features=self.config.emb_dim,\n",
" embedding_init=nn.initializers.normal(stddev=0.02)\n",
" )(x)\n",
" \n",
" # Position embeddings\n",
" pos_emb = self.param(\n",
" 'pos_embed',\n",
" nn.initializers.normal(stddev=0.02),\n",
" (1, self.config.max_seq_len, self.config.emb_dim)\n",
" )\n",
" \n",
" h = tok_emb + pos_emb[:, :T, :]\n",
" \n",
" # Cast to computation dtype\n",
" if self.config.use_mixed_precision and train:\n",
" h = h.astype(dtype)\n",
" \n",
" h = nn.Dropout(rate=self.config.dropout, deterministic=not train)(h)\n",
" \n",
" # Transformer blocks\n",
" head_dim = self.config.emb_dim // self.config.n_heads\n",
" new_cache_list = [] if cache_list is not None else None\n",
" \n",
" for i in range(self.config.n_layers):\n",
" cache = cache_list[i] if cache_list is not None else None\n",
" if cache is not None:\n",
" h, new_cache = TransformerBlock(\n",
" self.config.n_heads,\n",
" head_dim,\n",
" self.config.mlp_dim,\n",
" self.config.dropout\n",
" )(h, train=train, cache=cache)\n",
" new_cache_list.append(new_cache)\n",
" else:\n",
" h = TransformerBlock(\n",
" self.config.n_heads,\n",
" head_dim,\n",
" self.config.mlp_dim,\n",
" self.config.dropout\n",
" )(h, train=train, cache=None)\n",
" \n",
" # Final layer norm and output projection\n",
" h = nn.LayerNorm()(h)\n",
" \n",
" # Cast back to float32 for logits\n",
" if self.config.use_mixed_precision and train:\n",
" h = h.astype(jnp.float32)\n",
" \n",
" logits = nn.Dense(\n",
" self.config.vocab_size,\n",
" use_bias=False,\n",
" kernel_init=nn.initializers.normal(stddev=0.02)\n",
" )(h)\n",
" \n",
" if cache_list is not None:\n",
" return logits, new_cache_list\n",
" return logits\n",
"\n",
"print(\"Model architecture defined ✓\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🎯 Training Setup\n",
"\n",
"### Training State with Mixed Precision"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class TrainState(train_state.TrainState):\n",
" \"\"\"Extended TrainState with dropout RNG and optional dynamic scale for mixed precision\"\"\"\n",
" dropout_rng: jax.random.PRNGKey\n",
" dynamic_scale: Optional[dynamic_scale_lib.DynamicScale] = None\n",
"\n",
"def create_train_state(rng, config):\n",
" \"\"\"Initialize training state with model, optimizer, and learning rate schedule\"\"\"\n",
" model = MiniGPT(config)\n",
" \n",
" # Initialize with dummy input\n",
" dummy_input = jnp.ones((1, config.block_size), dtype=jnp.int32)\n",
" variables = model.init(rng, dummy_input, train=False)\n",
" params = variables['params']\n",
" \n",
" # Learning rate schedule with warmup\n",
" schedule = optax.warmup_cosine_decay_schedule(\n",
" init_value=0.0,\n",
" peak_value=config.learning_rate,\n",
" warmup_steps=config.warmup_steps,\n",
" decay_steps=config.max_steps,\n",
" end_value=config.learning_rate * 0.1\n",
" )\n",
" \n",
" # Optimizer with gradient clipping\n",
" tx = optax.chain(\n",
" optax.clip_by_global_norm(1.0),\n",
" optax.adamw(learning_rate=schedule, weight_decay=0.01)\n",
" )\n",
" \n",
" # Dynamic scale for mixed precision (helps with numerical stability)\n",
" dynamic_scale = None\n",
" if config.use_mixed_precision:\n",
" dynamic_scale = dynamic_scale_lib.DynamicScale()\n",
" \n",
" return TrainState.create(\n",
" apply_fn=model.apply,\n",
" params=params,\n",
" tx=tx,\n",
" dropout_rng=rng,\n",
" dynamic_scale=dynamic_scale\n",
" )\n",
"\n",
"print(\"Training state setup defined ✓\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Loss Function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def loss_fn(params, apply_fn, x, y, dropout_rng):\n",
" \"\"\"Compute cross-entropy loss\"\"\"\n",
" logits = apply_fn({'params': params}, x, train=True, rngs={'dropout': dropout_rng})\n",
" loss = optax.softmax_cross_entropy_with_integer_labels(logits, y).mean()\n",
" return loss"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training & Evaluation Steps (pmapped for TPU)\n",
"\n",
"These functions are parallelized across all TPU cores using `pmap`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"@partial(jax.pmap, axis_name='devices')\n",
"def train_step(state, x, y):\n",
" \"\"\"Single training step (parallelized across TPU cores)\"\"\"\n",
" # Split RNG for dropout\n",
" dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)\n",
" \n",
" # Compute loss and gradients\n",
" loss, grads = jax.value_and_grad(loss_fn)(\n",
" state.params, state.apply_fn, x, y, dropout_rng\n",
" )\n",
" \n",
" # Average gradients across all devices (CRITICAL for multi-device training)\n",
" grads = jax.lax.pmean(grads, axis_name='devices')\n",
" loss = jax.lax.pmean(loss, axis_name='devices')\n",
" \n",
" # Handle mixed precision scaling if enabled\n",
" if state.dynamic_scale is not None:\n",
" # Scale gradients\n",
" grads = state.dynamic_scale.scale_grads(grads)\n",
" # Update parameters with scaled gradients\n",
" new_state = state.apply_gradients(grads=grads, dropout_rng=new_dropout_rng)\n",
" # Update dynamic scale\n",
" new_state = new_state.replace(\n",
" dynamic_scale=state.dynamic_scale.adjust(jnp.isfinite(loss))\n",
" )\n",
" else:\n",
" new_state = state.apply_gradients(grads=grads, dropout_rng=new_dropout_rng)\n",
" \n",
" return new_state, loss\n",
"\n",
"@partial(jax.pmap, axis_name='devices')\n",
"def eval_step(state, x, y):\n",
" \"\"\"Single evaluation step (parallelized across TPU cores)\"\"\"\n",
" logits = state.apply_fn({'params': state.params}, x, train=False)\n",
" loss = optax.softmax_cross_entropy_with_integer_labels(logits, y).mean()\n",
" loss = jax.lax.pmean(loss, axis_name='devices')\n",
" return loss\n",
"\n",
"print(\"Training and evaluation functions defined ✓\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🎨 Optimized Text Generation\n",
"\n",
"**Improvements:**\n",
"- KV cache for efficient autoregressive decoding\n",
"- Vectorized top-k sampling (works for batch_size > 1)\n",
"- Support for multiple sequences simultaneously"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def generate_with_cache(params, apply_fn, prompt_tokens, config, rng, max_new_tokens=100):\n",
" \"\"\"\n",
" Generate text efficiently using KV cache.\n",
" \n",
" Args:\n",
" params: Model parameters\n",
" apply_fn: Model apply function\n",
" prompt_tokens: Initial tokens (can be 1D or 2D for batched generation)\n",
" config: Configuration object\n",
" rng: Random key\n",
" max_new_tokens: Number of tokens to generate\n",
" \n",
" Returns:\n",
" Generated token sequences\n",
" \"\"\"\n",
" # Ensure prompt_tokens is 2D (batch_size, seq_len)\n",
" if prompt_tokens.ndim == 1:\n",
" prompt_tokens = prompt_tokens[None, :]\n",
" \n",
" batch_size = prompt_tokens.shape[0]\n",
" \n",
" # Initialize cache for each layer\n",
" head_dim = config.emb_dim // config.n_heads\n",
" cache_list = None # Start without cache\n",
" \n",
" x = prompt_tokens\n",
" \n",
" for _ in range(max_new_tokens):\n",
" # Crop to max sequence length if needed\n",
" if x.shape[1] > config.block_size:\n",
" x_cond = x[:, -config.block_size:]\n",
" cache_list = None # Reset cache if we had to crop\n",
" else:\n",
" x_cond = x\n",
" \n",
" # Forward pass (with cache if available)\n",
" if cache_list is not None:\n",
" # Only process last token when using cache\n",
" logits, cache_list = apply_fn(\n",
" {'params': params}, \n",
" x_cond[:, -1:], \n",
" train=False, \n",
" cache_list=cache_list\n",
" )\n",
" else:\n",
" # First pass: process entire sequence and initialize cache\n",
" logits, cache_list = apply_fn(\n",
" {'params': params}, \n",
" x_cond, \n",
" train=False, \n",
" cache_list=[None] * config.n_layers\n",
" )\n",
" \n",
" logits = logits[:, -1, :] # Get last token logits\n",
" \n",
" # Temperature scaling\n",
" logits = logits / config.temperature\n",
" \n",
" # Top-k filtering (vectorized for all sequences in batch)\n",
" top_k_logits, top_k_indices = jax.lax.top_k(logits, config.top_k)\n",
" rng, sample_rng = jax.random.split(rng)\n",
" \n",
" # Sample from top-k (works for any batch size)\n",
" probs = nn.softmax(top_k_logits, axis=-1)\n",
" next_idx_in_topk = jax.random.categorical(sample_rng, jnp.log(probs), axis=-1)\n",
" \n",
" # Correctly index for any batch size\n",
" batch_indices = jnp.arange(batch_size)\n",
" next_token = top_k_indices[batch_indices, next_idx_in_topk]\n",
" \n",
" x = jnp.concatenate([x, next_token[:, None]], axis=1)\n",
" \n",
" return x\n",
"\n",
"def generate(params, apply_fn, prompt, config, rng, max_new_tokens=100):\n",
" \"\"\"Convenience function for single-sequence generation\"\"\"\n",
" prompt_tokens = jnp.array(encode(prompt))\n",
" output_tokens = generate_with_cache(\n",
" params, apply_fn, prompt_tokens, config, rng, max_new_tokens\n",
" )\n",
" return decode(list(output_tokens[0]))\n",
"\n",
"# Vectorized generation for multiple prompts\n",
"@jax.jit\n",
"def generate_batch(params, apply_fn, prompt_tokens_batch, config, rng, max_new_tokens=100):\n",
" \"\"\"Generate text for multiple prompts simultaneously\"\"\"\n",
" return generate_with_cache(\n",
" params, apply_fn, prompt_tokens_batch, config, rng, max_new_tokens\n",
" )\n",
"\n",
"print(\"Optimized text generation functions defined ✓\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🚂 Initialize Training\n",
"\n",
"Set up the model and replicate across TPU cores:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"num_devices = jax.device_count()\n",
"print(f\"🚀 Running on {num_devices} TPU cores\")\n",
"\n",
"# Validate batch size\n",
"if config.batch_size % num_devices != 0:\n",
" raise ValueError(f\"Batch size {config.batch_size} must be divisible by {num_devices} devices\")\n",
"\n",
"# Initialize training state\n",
"rng = jax.random.PRNGKey(42)\n",
"rng, init_rng = jax.random.split(rng)\n",
"state = create_train_state(init_rng, config)\n",
"\n",
"# CRITICAL: Replicate state across all TPU cores\n",
"state = jax.device_put_replicated(state, jax.devices())\n",
"\n",
"# Count parameters\n",
"num_params = sum(x.size for x in jax.tree_util.tree_leaves(state.params))\n",
"print(f\"\\n✅ Model initialized with {num_params:,} parameters\")\n",
"print(f\"📦 Batch size: {config.batch_size} ({config.batch_size // num_devices} per device)\")\n",
"print(f\"🔧 Training for {config.max_steps} steps\")\n",
"print(f\"⚡ Mixed precision: {config.use_mixed_precision}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🏃 Training Loop\n",
"\n",
"Run the main training loop with multi-batch validation:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Training loop\n",
"print(\"\\n🏃 Starting training...\\n\")\n",
"\n",
"for step in range(config.max_steps):\n",
" # Get batch and shard across devices\n",
" x, y = get_batch('train')\n",
" x = x.reshape(num_devices, -1, config.block_size)\n",
" y = y.reshape(num_devices, -1, config.block_size)\n",
" \n",
" # Training step\n",
" state, train_loss = train_step(state, x, y)\n",
" \n",
" # Logging\n",
" if step % config.log_interval == 0:\n",
" loss_val = float(train_loss[0])\n",
" print(f\"Step {step:5d} | Train Loss: {loss_val:.4f}\")\n",
" \n",
" # Evaluation (with multiple batches for stability)\n",
" if step % config.eval_interval == 0 and step > 0:\n",
" val_losses = []\n",
" for _ in range(config.num_eval_batches):\n",
" x_val, y_val = get_batch('val')\n",
" x_val = x_val.reshape(num_devices, -1, config.block_size)\n",
" y_val = y_val.reshape(num_devices, -1, config.block_size)\n",
" val_loss = eval_step(state, x_val, y_val)\n",
" val_losses.append(float(val_loss[0]))\n",
" \n",
" avg_val_loss = np.mean(val_losses)\n",
" print(f\"{'='*60}\")\n",
" print(f\"Step {step:5d} | Val Loss: {avg_val_loss:.4f} (avg over {config.num_eval_batches} batches)\")\n",
" print(f\"{'='*60}\\n\")\n",
" \n",
" # Generate sample text (using first device's params)\n",
" rng, gen_rng = jax.random.split(rng)\n",
" sample = generate(\n",
" jax.tree_util.tree_map(lambda x: x[0], state.params),\n",
" jax.tree_util.tree_map(lambda x: x[0], state).apply_fn,\n",
" \"To be or not to be\",\n",
" config,\n",
" gen_rng,\n",
" max_new_tokens=100\n",
" )\n",
" print(f\"📝 Sample generation:\")\n",
" print(f\"{sample}\")\n",
" print()\n",
"\n",
"print(\"\\n✅ Training complete!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 💾 Save Checkpoint"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Unreplicate state (take from first device)\n",
"unreplicated_state = jax.tree_util.tree_map(lambda x: x[0], state)\n",
"\n",
"# Save checkpoint\n",
"checkpoint_dir = '/home/claude/checkpoints'\n",
"checkpoints.save_checkpoint(checkpoint_dir, unreplicated_state, step=config.max_steps, keep=3)\n",
"print(f\"💾 Checkpoint saved to {checkpoint_dir}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🎭 Interactive Text Generation\n",
"\n",
"Try generating text with different prompts:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Get unreplicated params for generation\n",
"gen_params = jax.tree_util.tree_map(lambda x: x[0], state.params)\n",
"gen_apply_fn = jax.tree_util.tree_map(lambda x: x[0], state).apply_fn\n",
"\n",
"# Try different prompts\n",
"prompts = [\n",
" \"ROMEO:\",\n",
" \"To be or not to be\",\n",
" \"What light through yonder\",\n",
" \"First Citizen:\\n\"\n",
"]\n",
"\n",
"print(\"🎭 Generating samples...\\n\")\n",
"for prompt in prompts:\n",
" rng, gen_rng = jax.random.split(rng)\n",
" output = generate(gen_params, gen_apply_fn, prompt, config, gen_rng, max_new_tokens=150)\n",
" print(f\"Prompt: '{prompt}'\")\n",
" print(f\"{output}\")\n",
" print(\"\\n\" + \"=\"*80 + \"\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🚀 Batched Generation Demo\n",
"\n",
"Generate multiple sequences in parallel (efficient on TPU):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Prepare multiple prompts\n",
"batch_prompts = [\"ROMEO:\", \"JULIET:\", \"To be\", \"What light\"]\n",
"\n",
"# Pad prompts to same length\n",
"prompt_tokens_list = [jnp.array(encode(p)) for p in batch_prompts]\n",
"max_prompt_len = max(len(p) for p in prompt_tokens_list)\n",
"padded_prompts = []\n",
"for pt in prompt_tokens_list:\n",
" padding = jnp.zeros(max_prompt_len - len(pt), dtype=jnp.int32)\n",
" padded_prompts.append(jnp.concatenate([pt, padding]))\n",
"\n",
"prompt_batch = jnp.stack(padded_prompts)\n",
"\n",
"# Generate in batch\n",
"rng, gen_rng = jax.random.split(rng)\n",
"generated_batch = generate_batch(gen_params, gen_apply_fn, prompt_batch, config, gen_rng, max_new_tokens=80)\n",
"\n",
"print(\"🚀 Batched generation results:\\n\")\n",
"for i, (prompt, tokens) in enumerate(zip(batch_prompts, generated_batch)):\n",
" print(f\"Prompt {i+1}: '{prompt}'\")\n",
" print(decode(list(tokens)))\n",
" print(\"\\n\" + \"-\"*80 + \"\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🔧 Experiment with Generation Parameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"prompt = \"ROMEO:\"\n",
"\n",
"# Try different temperatures\n",
"print(\"🌡️ Temperature experiments:\\n\")\n",
"for temp in [0.5, 0.8, 1.0, 1.2]:\n",
" old_temp = config.temperature\n",
" config.temperature = temp\n",
" rng, gen_rng = jax.random.split(rng)\n",
" output = generate(gen_params, gen_apply_fn, prompt, config, gen_rng, max_new_tokens=100)\n",
" print(f\"Temperature: {temp}\")\n",
" print(output)\n",
" print(\"\\n\" + \"-\"*80 + \"\\n\")\n",
" config.temperature = old_temp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 📊 Final Training Analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Compute validation loss on multiple batches\n",
"num_val_batches = 20\n",
"val_losses = []\n",
"\n",
"print(f\"Computing validation loss over {num_val_batches} batches...\")\n",
"for _ in range(num_val_batches):\n",
" x_val, y_val = get_batch('val')\n",
" x_val = x_val.reshape(num_devices, -1, config.block_size)\n",
" y_val = y_val.reshape(num_devices, -1, config.block_size)\n",
" val_loss = eval_step(state, x_val, y_val)\n",
" val_losses.append(float(val_loss[0]))\n",
"\n",
"avg_val_loss = np.mean(val_losses)\n",
"std_val_loss = np.std(val_losses)\n",
"print(f\"\\n📊 Final Validation Loss: {avg_val_loss:.4f} ± {std_val_loss:.4f}\")\n",
"print(f\"📊 Validation Perplexity: {np.exp(avg_val_loss):.2f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🎯 Key Optimizations Implemented\n",
"\n",
"### 1. **Precomputed Causal Mask**\n",
"✅ Created once globally, reused in every forward pass\n",
"- Saves computation time\n",
"- More memory efficient\n",
"\n",
"### 2. **KV Caching for Generation**\n",
"✅ Stores key/value tensors from previous tokens\n",
"- Dramatically faster autoregressive generation\n",
"- Only processes new tokens (not entire sequence)\n",
"\n",
"### 3. **Vectorized Top-k Sampling**\n",
"✅ Fixed indexing to work with any batch size:\n",
"```python\n",
"batch_indices = jnp.arange(batch_size)\n",
"next_token = top_k_indices[batch_indices, next_idx_in_topk]\n",
"```\n",
"\n",
"### 4. **Multi-Batch Validation**\n",
"✅ Averages loss over multiple batches for stability\n",
"- More reliable metrics\n",
"- Configurable via `num_eval_batches`\n",
"\n",
"### 5. **Mixed Precision Training**\n",
"✅ Uses bfloat16 for computation:\n",
"- Faster training on TPU\n",
"- Lower memory usage\n",
"- Dynamic loss scaling for numerical stability\n",
"\n",
"### 6. **Efficient Batched Generation**\n",
"✅ Can generate multiple sequences simultaneously\n",
"- Better TPU utilization\n",
"- JIT-compiled for speed\n",
"\n",
"### 7. **Gradient Checkpointing Support**\n",
"✅ Optional flag for very large models\n",
"- Trade computation for memory\n",
"- Enables training larger models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 📚 Next-Level Upgrades\n",
"\n",
"### Already Implemented:\n",
"- ✅ Mixed precision with dynamic scaling\n",
"- ✅ KV caching for generation\n",
"- ✅ Vectorized sampling\n",
"- ✅ Proper gradient synchronization\n",
"\n",
"### Future Enhancements:\n",
"1. **Model Parallelism with `pjit`**\n",
" - Split model across TPU pods\n",
" - Use GSPMD sharding\n",
"\n",
"2. **Better Tokenization**\n",
" - Implement BPE (byte-pair encoding)\n",
" - Or use SentencePiece\n",
" - Better for large corpora\n",
"\n",
"3. **Flash Attention**\n",
" - Memory-efficient attention\n",
" - Enables longer sequences\n",
"\n",
"4. **Larger Datasets**\n",
" - OpenWebText\n",
" - C4 (Colossal Clean Crawled Corpus)\n",
"\n",
"5. **Experiment Tracking**\n",
" - Weights & Biases integration\n",
" - TensorBoard logging\n",
"\n",
"6. **Gradient Accumulation**\n",
" - Effective larger batch sizes\n",
" - Better for memory-constrained setups"
]
}
],
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"kernelspec": {
"display_name": "Python 3",
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