diff --git a/colabs/system_metrics/WandB_Llama3_1_Training_Colab_TPU.ipynb b/colabs/system_metrics/WandB_Llama3_1_Training_Colab_TPU.ipynb new file mode 100644 index 00000000..be541fff --- /dev/null +++ b/colabs/system_metrics/WandB_Llama3_1_Training_Colab_TPU.ipynb @@ -0,0 +1,6367 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "TYiKDjBFjaX-" + }, + "source": [ + "This is a demo of Weights and Biases' support for automatic loggign of system metrics for TPUs.\n", + "\n", + "This combines the work of Felafax.ai with Weights and Biases instrumentation for logging.\n", + "\n", + "Please see the original colab and backend from Felfax [here](https://github.com/felafax/felfax), and checkout their website at [Felefax.ai](felefax.ai)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wk3kTwlqjVG5" + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Cb4zyReNm7LS", + "outputId": "596dca3f-c2a2-47e2-8630-2798adb99309" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.7/61.7 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m527.3/527.3 kB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + 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List\n", + "\n", + "# Add the parent directory of the current working directory to the Python path\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), \".\")))\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), \"..\")))\n", + "\n", + "from llama3_jax.trainer_engine import setup\n", + "\n", + "setup.setup_environment(base_dir=\"/\")\n", + "\n", + "from llama3_jax.trainer_engine import automodel_lib, jax_utils, trainer_lib\n", + "\n", + "setup.reload_modules(\"llama3_jax\")\n", + "\n", + "import jax\n", + "import jax.numpy as jnp\n", + "import chex\n", + "import optax\n", + "\n", + "import torch\n", + "from datasets import load_dataset\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dYv1s-cRk7_m" + }, + "source": [ + "# Step 0: Configure LoRA params and precision for training (jnp.bfloat16 or jnp.float32)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "D0AaGj9Y6uPQ" + }, + "outputs": [], + "source": [ + "MODEL_NAME = \"colab-llama-3.1-8B-Instruct-JAX\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 340, + "referenced_widgets": [ + "b871699796f54818984e6424dc3bd50b", + "49d5258115ea45f0ac18a6524a0b73a2", + "e4946490d75648c18c052bcb67e20e1c", + "a387f847a43244e98f4d8ba62fa28c09", + "471b0b0002cb4177b3991549e22eea98", + "6784d3e1d3c74ced841f8bf230e95cc4", + "db917bbf15154be2ab39542ece801054", + "a3b5ae7d293f41478031955cb6b84c74", + "6b167cc2902c48b5b8ff76e35849902c", + "8de6cafe4b37433d88d27f6a7081b87a", + "feee9c4613b349878e6f3c910bf9a885", + "932095e3b5574a28bbfae956e41cc2d4", + "58da8bfe4120473e8df2142969cfa67a", + "c8f373a1b9d74acfa52b0f938334f876", + "5562030a9a5c4ce483f6b4a22eded7c1", + "16b52f46435f42c0b3d9a163c9caf9be", + "565ccf250805492d86dc60092be18ce0", + "c1a849995e1b4ae3817176ade98bb378", + "9776112dffed49dfb84e3dc97b69edc4", + "682f99a146674498b05e423f0f3978c4", + 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"7d0313d8c58a40a2ae47ece7e032c358" + ] + }, + "id": "IDbUl_n86uPQ", + "outputId": "db715fb8-76bd-4b62-975f-33290cdf116a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading model colab-llama-3.1-8B-Instruct-JAX...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b871699796f54818984e6424dc3bd50b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Fetching 8 files: 0%| | 0/8 [00:00 Dict[str, jnp.ndarray]:\n", + " \"\"\"\n", + " Collates batch items and converts to JAX arrays.\n", + " \"\"\"\n", + " collated = {\"input_tokens\": [], \"target_tokens\": [], \"loss_masks\": []}\n", + " for item in batch:\n", + " for key in collated:\n", + " collated[key].append(item[key])\n", + " jax_batch = {}\n", + " for key, value in collated.items():\n", + " jax_batch[key] = jnp.array(value)\n", + " return jax_batch\n", + "\n", + " # Load and preprocess the dataset\n", + " dataset = load_dataset(\"yahma/alpaca-cleaned\", split=\"train\")\n", + " if max_examples:\n", + " dataset = dataset.select(range(max_examples))\n", + " dataset = dataset.map(_format_prompts, batched=True)\n", + "\n", + " # Create train and test dataset.\n", + " ds = dataset.train_test_split(test_size=0.15)\n", + " for split in [\"train\", \"test\"]:\n", + " ds[split] = ds[split].map(\n", + " _tokenize, batched=True, remove_columns=dataset.column_names\n", + " )\n", + "\n", + " # Create DataLoaders\n", + " dataloader_args = dict(\n", + " shuffle=True, batch_size=batch_size, collate_fn=_custom_collate_fn\n", + " )\n", + " train_dataloader = torch.utils.data.DataLoader(ds[\"train\"], **dataloader_args)\n", + " test_dataloader = torch.utils.data.DataLoader(ds[\"test\"], **dataloader_args)\n", + "\n", + " return train_dataloader, test_dataloader" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MaxHZh7zlhRH" + }, + "source": [ + "### Uncomment below code ⬇️ if you'd like to run and test your dataset pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "3nd25pbO6uPQ" + }, + "outputs": [], + "source": [ + "# def test_dataset_pipeline(tokenizer):\n", + "# \"\"\"Print shapes of first batch to verify dataset pipeline.\"\"\"\n", + "# train_loader, _ = get_dataset(tokenizer=tokenizer, batch_size=1, seq_length=32, max_examples=32)\n", + "# batch = next(iter(train_loader))\n", + "\n", + "# print(\"Input tokens shape:\", batch['input_tokens'].shape)\n", + "# print(\"Target mask shape:\", batch['target_tokens'].shape)\n", + "# test_dataset_pipeline(tokenizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vtyzzGEh6uPQ" + }, + "source": [ + "# Step 2: Configure hyperparameters below and train!" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "lX0kBNsLa7d1" + }, + "outputs": [], + "source": [ + "@chex.dataclass(frozen=True)\n", + "class TrainerConfig:\n", + " # dataset pipeline knobs\n", + " batch_size: int = 64 # 8 Is the minimum\n", + " seq_length: int = 32\n", + " dataset_size_limit: int | None = None\n", + "\n", + " # training pipeline knobs\n", + " learning_rate: float = 1e-3\n", + " num_epochs: int = 2\n", + " max_steps: int | None = 20\n", + "\n", + " print_every_n_steps: int = 5\n", + "\n", + " # eval\n", + " eval_every_n_steps: int = 10\n", + " max_eval_steps: int | None = 1\n", + "\n", + "\n", + "trainer_config = TrainerConfig()\n", + "optimizer = optax.sgd(trainer_config.learning_rate)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 209, + "referenced_widgets": [ + "3858ec6c1d7b4a6799071d17f2b6479d", + "e207daa5ecfb4b13b57613547d11f92f", + "8059e1886f7640debdb6945d63139798", + "69b955332f954e558347edf5974aca5b", + "ca65d34640f9409c8151c3feef9158c5", + "91e755a2c4f24cb1afa2802fb9e6f04c", + "b80b4f548d6d433b860891f01bebefa1", + "2a968fffe2494080851774619b8ef4f9", + "2078358c9caf4d7b9def87559a67cf28", + "f8094aa9241c43cf9b3a7e108fdd3ad5", + "e2d0425b268c4c1b9d9d303a5b31403f", + "9bbbe8b3636044babdc7ea3bc9bf1675", + "cb03b5f734524c23b0dbd8750803ffa5", + "6f0f4c2c9a7c4daa9eb32f4630c34819", + "076b4b29e72a4b77a797f8218d5f65f8", + "f5afe58be1334ab1ac56398393826452", + "619cd781bafc45459ea8d442758f0188", + "283196bb63c14e03b5107d88e7deb0fe", + "9ef59f09002e45e2935ccde02e7543f2", + "f3eded1d4f9b442c88a69d14b8c42258", + "80a12395a2234a42aa236e6c12b4b6f0", + "60faf13025884a97bc067825a0c04ede", + "a803c127c927440084e10a64d94611f3", + "ff5be0a6f6464155b6e306f80b84f4f9", + "7a377ee6162f432ba7d4743921537dc3", + "a2ac779ae25e45e8a1a0d48513cf560c", + "44237613e86348c6874783069e43be50", + "a623b4c1b5bc435a897c805cf209dc3f", + "1c67ff939df047cd910f03a24f4c8166", + "9d3898d17c7242b6a0637d5ee62bd4b5", + "408b8e88611741e2bee0d60a83fbb023", + "440d817ba8cf4da29268f530c47e365a", + "b0295ba321984ca4857a9ab3ac8b9865", + "70e50bd9f09c4b19ab7d1f003675b7ff", + "6047bc322b2e415b8f60fd101b709e1f", + "e69cbe9db6434649a59433bd0243b6c4", + "54a01d0b911e490dbba0fc31854de10d", + "de09112d7ff34cc4a254f084c185eed0", + "2530383a49744078993819acbf6d095e", + "399f22f8cd9649078ee3562a51d66c93", + "962afd4fcd954849974d2e1e54e1e654", + "7bc45733efdf4ef482f4172fe5fcae9d", + "0367d41911444fcbae164f5aeac42c36", + "943fe3aba6044ef485d44ab2feb5180b", + "a9043ab50081420692c80d87677ba6c0", + "72dedd5f94b34ddea3c47d64708caaf9", + "cc56fc6b4b734643993bfa0b65c626e4", + "0ab5937419544ddd83e85a6d9099a95c", + "6e1235b4f9194f749974f677d9715e24", + "31816489f0fb4c41a7b5673823a8e607", + "ed6a8262503c4228b5c4d9fcc7a617b9", + "8492ce8ebec743f5873b4cc45d985830", + "141da9e7194e46b9831cc17f137d5938", + "eea836ad92e944f4927a1421dad29d7e", + "c0fda5270d104731acaac1f8348c7416", + "0fca4028bc0f4b31bd4cb1b139699db3", + "5ce234b89e5a45c79ca7a6ac29b526d2", + "bcab873d0bdf46be85cebfe7ff3305c1", + "6c16802b428a43bdaaabc8fcd4e2b564", + "13f9ba6d5b624fdeafd4a664c1693532", + "ffb287ea4eef40829386ab045e181f99", + "47b27882a65e45c7b0be831eee52721e", + "87975e2807834175a5ca46eaacd1d005", + "05ef5c9f46e249f98b59fdd969af4d12", + "bfcffaa5e68d44ff867fe91c189a1114", + "84afa3108ed34ca98d00442e7e424c91" + ] + }, + "id": "WzsU9acF6uPR", + "outputId": "7121fe8b-47a2-45eb-b148-3d535e197470" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3858ec6c1d7b4a6799071d17f2b6479d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading readme: 0%| | 0.00/11.6k [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Run data is saved locally in /content/wandb/run-20241018_190904-kv0rlmxz" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Syncing run northern-wave-1 to Weights & Biases (docs)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View project at https://wandb.ai/wandb/llama-3.1-8b-fine-tune-TPU-testing" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run at https://wandb.ai/wandb/llama-3.1-8b-fine-tune-TPU-testing/runs/kv0rlmxz" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PROJECT_NAME = \"llama-3.1-8b-fine-tune-TPU\"\n", + "ENTITY = \"wandb\"\n", + "run = wandb.init(project=PROJECT_NAME, entity=ENTITY, tags=[\"Colab\"], save_code=False)\n", + "\n", + "# Organize Metrics\n", + "run.define_metric(\"train/*\", step_metric=\"train_step\")\n", + "run.define_metric(\"val/*\", step_metric=\"train_step\")\n", + "\n", + "# Log config\n", + "from dataclasses import asdict\n", + "\n", + "config_dict = asdict(trainer_config)\n", + "config_dict.update({\"model_name\": MODEL_NAME})\n", + "run.config.update(config_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "zyMTNYKJ6uPR" + }, + "outputs": [], + "source": [ + "trainer = trainer_lib.CausalLMTrainer(\n", + " model_name=MODEL_NAME,\n", + " model=model,\n", + " model_ckpt_path=model_path,\n", + " model_configurator=model_configurator,\n", + " optimizer=optimizer,\n", + " training_config=trainer_config,\n", + " mesh=jax_utils.MESH,\n", + " dtype=jnp.bfloat16, # precision to use for training\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "DATT8rQj4WE8" + }, + "outputs": [], + "source": [ + "# Redefine the CausalLMTrainer.train function to instrument with W&B and add eval\n", + "\n", + "import time\n", + "from jax.sharding import NamedSharding\n", + "from jax.sharding import PartitionSpec as PS\n", + "\n", + "\n", + "def train(trainer, train_dataloader, eval_dataloader):\n", + " total_training_time = 0\n", + " total_steps = 0\n", + "\n", + " for epoch in range(trainer.training_config.num_epochs):\n", + " print(f\"Starting epoch {epoch} of training...\")\n", + "\n", + " for step, train_batch in enumerate(train_dataloader):\n", + " trainer.current_step = epoch * len(train_dataloader) + step\n", + "\n", + " train_batch = jax.device_put(\n", + " train_batch, NamedSharding(trainer.mesh, PS(\"dp\", \"fsdp\"))\n", + " )\n", + "\n", + " sharded_rng = jax_utils.next_rng()\n", + "\n", + " # Start timing\n", + " step_start_time = time.time()\n", + "\n", + " trainer.train_state, sharded_rng, metrics = trainer.train_step(\n", + " trainer.train_state, train_batch, sharded_rng, run_jitted=False\n", + " )\n", + "\n", + " # End timing\n", + " step_end_time = time.time()\n", + "\n", + " # Calculate step duration\n", + " step_duration = step_end_time - step_start_time\n", + " total_training_time += step_duration\n", + " total_steps += 1\n", + "\n", + " # Calculate steps per second\n", + " steps_per_sec = 1 / step_duration\n", + "\n", + " to_log = {}\n", + " to_log.update(\n", + " {\n", + " \"train_step\": total_steps,\n", + " \"train/loss\": metrics[\"loss\"],\n", + " \"train/accuracy\": metrics[\"accuracy\"],\n", + " \"train/step_time\": step_duration,\n", + " \"train/step_hz\": steps_per_sec,\n", + " \"epoch\": epoch,\n", + " }\n", + " )\n", + "\n", + " if step % trainer.training_config.print_every_n_steps == 0:\n", + " print(\n", + " f\"Epoch {epoch}, Step {step}, \"\n", + " f\"Train Loss: {metrics['loss']:.4f}, \"\n", + " f\"Accuracy: {metrics['accuracy']:.4f}, \"\n", + " f\"Step Time: {step_duration:.4f}s, \"\n", + " f\"Steps/sec: {steps_per_sec:.2f}\"\n", + " )\n", + "\n", + " # Evaluate if applicable (ADDED)\n", + " # if step % trainer.training_config.eval_every_n_steps == 0:\n", + " # eval_step_start_time = time.time()\n", + " # #{'loss': avg_loss, 'accuracy': avg_accuracy}\n", + " # eval_metrics = trainer.evaluate(trainer.train_state, eval_dataloader, run_jitted=False)\n", + " # eval_time = time.time()-eval_step_start_time\n", + "\n", + " # print(f\"Epoch {epoch}, Step {step}, \"\n", + " # f\"Val Loss: {eval_metrics['avg_loss']:.4f}, \"\n", + " # f\"Val Accuracy: {eval_metrics['avg_accuracy']:.4f}, \"\n", + " # f\"Step Time: {eval_time:.4f}s\")\n", + "\n", + " # to_log.update({\n", + " # \"val/loss\": eval_metrics['avg_loss'],\n", + " # \"val/accuracy\": eval_metrics['avg_accuracy'],\n", + " # \"val/eval_time\": eval_time})\n", + "\n", + " # Log metrics to W&B\n", + " print(\"Logging:\", to_log)\n", + " run.log(to_log)\n", + "\n", + " if (\n", + " trainer.training_config.max_steps\n", + " and step >= trainer.training_config.max_steps\n", + " ):\n", + " break\n", + "\n", + " avg_steps_per_sec = total_steps / total_training_time\n", + " print(f\"Average Steps per Second: {avg_steps_per_sec:.2f}\")\n", + "\n", + " return trainer.train_state" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "PtZxDbB1vl6O", + "outputId": "60a10327-55fa-4152-cb88-706ada41110f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting epoch 0 of training...\n", + "Epoch 0, Step 0, Train Loss: 3.7602, Accuracy: 0.3438, Step Time: 71.0879s, Steps/sec: 0.01\n", + "Logging: {'train_step': 1, 'train/loss': Array(3.7602315, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 71.08793210983276, 'train/step_hz': 0.014067085232623917, 'epoch': 0}\n", + "Logging: {'train_step': 2, 'train/loss': Array(3.697931, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 16.17443823814392, 'train/step_hz': 0.061825949394750286, 'epoch': 0}\n", + "Logging: {'train_step': 3, 'train/loss': Array(3.6042004, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 16.501534700393677, 'train/step_hz': 0.060600424030629284, 'epoch': 0}\n", + "Logging: {'train_step': 4, 'train/loss': Array(3.5397573, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 16.845587253570557, 'train/step_hz': 0.05936272716096864, 'epoch': 0}\n", + "Logging: {'train_step': 5, 'train/loss': Array(3.4478605, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 17.577601194381714, 'train/step_hz': 0.056890584155455046, 'epoch': 0}\n", + "Epoch 0, Step 5, Train Loss: 3.3534, Accuracy: 0.3438, Step Time: 16.8048s, Steps/sec: 0.06\n", + "Logging: {'train_step': 6, 'train/loss': Array(3.353401, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 16.804821491241455, 'train/step_hz': 0.05950673147710569, 'epoch': 0}\n", + "Logging: {'train_step': 7, 'train/loss': Array(3.2514594, dtype=float32), 'train/accuracy': Array(0.375, dtype=float32), 'train/step_time': 17.086888551712036, 'train/step_hz': 0.05852440583161667, 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Run summary:


epoch1
train/accuracy0.96973
train/loss0.185
train/step_hz0.0574
train/step_time17.42172
train_step42

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View project at: https://wandb.ai/wandb/llama-3.1-8b-fine-tune-TPU-testing
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