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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/Arunprakash-A/c27ebe06e6c8fbd21263fc54013bbf49/gradientaccumulation-for-continual-pretraining.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "QhnatX1UOheR" | |
| }, | |
| "source": [ | |
| "# Objective\n", | |
| "\n", | |
| " * We retain everything from the previous notebook with the following changes\n", | |
| " * Load the BERT large (330 million parameters) model in FP32.\n", | |
| " * Therefore, the memory requirement to load the model alone is : $330 \\times 4= 1.2GB$\n", | |
| " * While we push the model to CUDA for training, it requires about 1 to 2GB of additional memory for loading kernels.\n", | |
| " * Setting batch size to 8 will throw the cuda:OOM (Out of Memory) error.\n", | |
| " * However, increasing the batch size often helps in faster convergence and better test performance.\n", | |
| " * How do we accomplish this?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "1pHgUNZhIc_U" | |
| }, | |
| "source": [ | |
| "* In this notebook, we are going to use a technique to increase the batch size without rising OOM error\n", | |
| "* Please read the previous notebook [here](https://github.com/Arunprakash-A/DL-Pytorch-Workshop) before proceeding further." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "fo0kVJaJrO-t" | |
| }, | |
| "source": [ | |
| "# Imports" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "ap2bloKRLsrI" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "%%capture\n", | |
| "!pip install datasets\n", | |
| "!pip install transformers[torch]==4.38.2\n", | |
| "!pip install nvidia-ml-py3" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import transformers\n", | |
| "transformers.__version__" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 36 | |
| }, | |
| "id": "4U2LQg-SwWv9", | |
| "outputId": "67479c5c-63e3-4d91-8c53-176e0442afa1" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "'4.38.2'" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 3 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "5kcHUQ0OrO-y" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import warnings\n", | |
| "warnings.filterwarnings(\"ignore\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "RhXvPmpzYCiw" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from pprint import pprint\n", | |
| "import torch\n", | |
| "from transformers import AutoTokenizer,pipeline" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "FMYKbZ3ILqP6" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import datasets\n", | |
| "from datasets import load_dataset, get_dataset_split_names, get_dataset_config_names, get_dataset_config_info\n", | |
| "from transformers import AutoTokenizer" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "j3fdaNJUrO-3" | |
| }, | |
| "source": [ | |
| "# Loading dataset\n", | |
| "\n", | |
| " * We are going to do Masked Language Modelling (MLM) with continual pre-training, however, with the same \"MRPC\" dataset used in the previous notebook\n", | |
| " * We will just drop the label column and randomly mask the words in the sentences for training the model with MLM\n", | |
| " * Though the dataset is small, we refrain from doing full pre-training (you can do that if yoy wish to)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "8L2moPAsrO-4", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 369, | |
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| "metadata": {} | |
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| "source": [ | |
| "raw_dataset = load_dataset(path=\"glue\", name=\"mrpc\") # name=config_name\n", | |
| "train_split = raw_dataset[\"train\"]\n", | |
| "checkpoint = \"bert-large-uncased\"\n", | |
| "tokenizer = AutoTokenizer.from_pretrained(checkpoint)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "udqvwWZQdnWV" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def tokenize_function(example):\n", | |
| " return tokenizer(text=example[\"sentence1\"], text_pair=example[\"sentence2\"], return_special_tokens_mask=True,\n", | |
| " padding='max_length',truncation=True)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
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| "d272ea192d944c30b1d89420a8747215", | |
| "92c8b7898e4245e0a6a4ccc150c5e349", | |
| "ef0df09c436f429aa6e89941ba28cf9d", | |
| "5981efea067a4dabbda0b852d2cd2f2c", | |
| "936747008f7a41678fea7f3d44335558", | |
| "ae4e0f835d3246fe8e30b08bd763cf6e", | |
| "e1566a57d929460b8dedafbcc651bc2d", | |
| "ad1348b4275044e3b2af74b203834412", | |
| "3fdaaae75c6448eeae9d8a363bf77664", | |
| "dc4781f8ea234007829d6a910bafd96d", | |
| "6140ebb1f3fc43fe9a27a78b824d98e9", | |
| "1e3e773e004a42fa9582142dc5d7b537", | |
| "594aabb7d4e94c3c88ee72f7f779a21e", | |
| "766b4c6831554f9aa9b3a7fd0955e510", | |
| "977684939f314e6cb0564e099ecb4d74", | |
| "b6af413c4a9b41c1aa901f65a0435ba4" | |
| ] | |
| }, | |
| "outputId": "b46b6525-2083-4dfb-b84b-579b9b369940" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Map: 0%| | 0/3668 [00:00<?, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "0cf9382b665d40f7a4c477f7a0ffebcd" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Map: 0%| | 0/408 [00:00<?, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "2565fe0aba7b47c4a2b7a5ed22f42561" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Map: 0%| | 0/1725 [00:00<?, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "ae4e0f835d3246fe8e30b08bd763cf6e" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "tokenized_datasets = raw_dataset.map(tokenize_function, batched=True)\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Di94AwyTrO-8", | |
| "outputId": "93db7f48-cc8c-4157-f9c7-9665950e3ba7" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "DatasetDict({\n", | |
| " train: Dataset({\n", | |
| " features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask', 'special_tokens_mask'],\n", | |
| " num_rows: 3668\n", | |
| " })\n", | |
| " validation: Dataset({\n", | |
| " features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask', 'special_tokens_mask'],\n", | |
| " num_rows: 408\n", | |
| " })\n", | |
| " test: Dataset({\n", | |
| " features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask', 'special_tokens_mask'],\n", | |
| " num_rows: 1725\n", | |
| " })\n", | |
| "})" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 9 | |
| } | |
| ], | |
| "source": [ | |
| "tokenized_datasets" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "HrS0HwkurO--" | |
| }, | |
| "source": [ | |
| "* Remove all unused columns" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "dqWf6GwCrO-_" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "tokenized_datasets = tokenized_datasets.remove_columns([\"sentence1\",\"sentence2\",'label'])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "ZBUsfUPIrO_A", | |
| "outputId": "319d2fb4-b554-4702-a223-3b060f46867b" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "DatasetDict({\n", | |
| " train: Dataset({\n", | |
| " features: ['idx', 'input_ids', 'token_type_ids', 'attention_mask', 'special_tokens_mask'],\n", | |
| " num_rows: 3668\n", | |
| " })\n", | |
| " validation: Dataset({\n", | |
| " features: ['idx', 'input_ids', 'token_type_ids', 'attention_mask', 'special_tokens_mask'],\n", | |
| " num_rows: 408\n", | |
| " })\n", | |
| " test: Dataset({\n", | |
| " features: ['idx', 'input_ids', 'token_type_ids', 'attention_mask', 'special_tokens_mask'],\n", | |
| " num_rows: 1725\n", | |
| " })\n", | |
| "})" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 11 | |
| } | |
| ], | |
| "source": [ | |
| "tokenized_datasets" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "KaixNmDdrO_A" | |
| }, | |
| "source": [ | |
| "* Apply masking with masking probability 0.3." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "nYzy9PpxaObP" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from transformers import DataCollatorForLanguageModeling\n", | |
| "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer,mlm_probability=0.3)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "g3ov7sOvW8Yh" | |
| }, | |
| "source": [ | |
| "# The Model" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 124, | |
| "referenced_widgets": [ | |
| "f498f063941c4058906b700055060333", | |
| "77cba4ce2d8a4383a40fd72c4dfcb97d", | |
| "ba4be9d0bd734bdf9d7c594bb27f0889", | |
| "75c17b8ff4cd421e86b4551b4eb71bba", | |
| "148ff8adf5584841b0588f96e02fa9ba", | |
| "1dd362a936c649518a46d8b3809fe8df", | |
| "bdc3b2bad164485fa8881da75973508b", | |
| "27a12c6788464b50847e16d0ea87e81e", | |
| "6321ac14dd264141b818fe4d09185f7b", | |
| "91a7359f3efc42a9a2f9b3de03d2b62e", | |
| "eb8415aee74a4c33913e281fee04ca7a" | |
| ] | |
| }, | |
| "id": "AKk__zsGW_X1", | |
| "outputId": "23dd6941-5933-4198-99ba-696578ed7e47" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "model.safetensors: 0%| | 0.00/1.34G [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "f498f063941c4058906b700055060333" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Some weights of the model checkpoint at bert-large-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n", | |
| "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", | |
| "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from transformers import AutoModelForMaskedLM\n", | |
| "model = AutoModelForMaskedLM.from_pretrained(checkpoint)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "4QxeKnEXBTtE", | |
| "outputId": "781f8cdd-aff1-4630-dd57-9edc56ad04c9" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Number of parametes 335.174458 million\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(f'Number of parametes {model.num_parameters()/(10**6)} million')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "8svAYorIrO_D" | |
| }, | |
| "source": [ | |
| "* Let's just quickly check we are actually fine-tuning (continual pretraining) all the parameters in the model" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "28gDcCSgrO_D" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# for parameter in model.parameters():\n", | |
| "# if parameter.requires_grad:\n", | |
| "# print(parameter.shape)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "zNpnSq9LR6Bd" | |
| }, | |
| "source": [ | |
| "# Training using Trainer API" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "CnT9AfeWrO_E" | |
| }, | |
| "source": [ | |
| "* Till now we have neither loaded the data nor the model into the GPU.\n", | |
| "* Still a small portion (260 MB) of the GPU memory is already occupied." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Hd39OexuOW8F", | |
| "outputId": "5ae37ffd-9192-4045-d1b4-54f32873b64f" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "GPU memory occupied: 258 MB.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from pynvml import *\n", | |
| "\n", | |
| "\n", | |
| "def print_gpu_utilization():\n", | |
| " nvmlInit()\n", | |
| " handle = nvmlDeviceGetHandleByIndex(0)\n", | |
| " info = nvmlDeviceGetMemoryInfo(handle)\n", | |
| " print(f\"GPU memory occupied: {info.used//1024**2} MB.\")\n", | |
| "\n", | |
| "\n", | |
| "def print_summary(result):\n", | |
| " print(f\"Time: {result.metrics['train_runtime']:.2f}\")\n", | |
| " print(f\"Samples/second: {result.metrics['train_samples_per_second']:.2f}\")\n", | |
| "\n", | |
| "print_gpu_utilization()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "PwmzXzvKrO_E" | |
| }, | |
| "source": [ | |
| "* A callback for tracing the memory usage before and after the parameter update." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "fu4eBBXDrO_F" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from transformers import TrainerCallback\n", | |
| "\n", | |
| "class TraceMemory(TrainerCallback):\n", | |
| "\n", | |
| " def on_step_begin(self, args, state, control, **kwargs):\n", | |
| " nvmlInit()\n", | |
| " handle = nvmlDeviceGetHandleByIndex(0)\n", | |
| " info = nvmlDeviceGetMemoryInfo(handle)\n", | |
| " print(f'GPU memory step begin: {info.used//1024**2}MB')\n", | |
| "\n", | |
| " def on_step_end(self, args, state, control, **kwargs):\n", | |
| " nvmlInit()\n", | |
| " handle = nvmlDeviceGetHandleByIndex(0)\n", | |
| " info = nvmlDeviceGetMemoryInfo(handle)\n", | |
| " print(f'GPU memory step end: {info.used//1024**2}MB')\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "NfG1h8Y4rO_F" | |
| }, | |
| "source": [ | |
| " ## Batch size-1 : SGD" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "uy15qEazrO_F" | |
| }, | |
| "source": [ | |
| "* Let's first run the model with a batch size of 1 (later we need to change it to 8) and see how much memory is occupied\n", | |
| "* Internally, the model uses AdamW optimizer for updating weights\n", | |
| "* We just train using 10 samples from the dataset." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "pJ7W_WH4c-vv" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from transformers import TrainingArguments, Trainer\n", | |
| "training_args = TrainingArguments(\"test-trainer\",num_train_epochs=1, per_device_train_batch_size=1,\n", | |
| " disable_tqdm=None,gradient_accumulation_steps=1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "5XBEeF89rO_G" | |
| }, | |
| "source": [ | |
| "* Visit this [page](https://huggingface.co/docs/transformers/v4.38.2/en/main_classes/callback#transformers.TrainerCallback) to see all the available callbacks\n", | |
| "* Note, now transformer version 4.42.0 has a few more callbacks like \"on_optimizer_step\"" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "9irO-ltmW1jc" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "trainer = Trainer(\n", | |
| " model,\n", | |
| " training_args,\n", | |
| " train_dataset=tokenized_datasets[\"train\"].select(range(10)),\n", | |
| " eval_dataset=tokenized_datasets[\"validation\"].select(range(10)),\n", | |
| " data_collator=data_collator,\n", | |
| " tokenizer=tokenizer,\n", | |
| " callbacks=[TraceMemory],\n", | |
| "\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "euF2TXWpDC5n" | |
| }, | |
| "source": [ | |
| "* How much memmory do we really need to run the model with the batch size of 8.\n", | |
| "\n", | |
| "* The model parameter itself takes 1.2 GB + 1 GB (for kernel) (**it might differ based the GPU (T4, L4, V100, A100..)**)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "diRdvz6FKWWc", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "803f82ca-5a30-495d-91c9-75eaa8096062" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "GPU memory occupied: 1650 MB.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print_gpu_utilization()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "jQNFNEh5rO_J" | |
| }, | |
| "source": [ | |
| "* For each sample we need 1.2 GB for gradients and 2.4 GB for optimizer states (assuming Adam like optimizers)\n", | |
| "\n", | |
| "* Therefore, we need additional 1.2 GB per sample **for training**\n", | |
| "\n", | |
| "* So, with batch size 1, it requires in total **at least** (1.2+1.2+2.4+1.3 = 6.1)(param+grad+states+kernel) GB of memory\n", | |
| "* Note: We have ignored memory for activation\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 460 | |
| }, | |
| "id": "gJI2_r0_eNSZ", | |
| "outputId": "003590cb-2723-4325-bfe7-a78f4fd67c70" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "GPU memory step begin: 1650MB\n", | |
| "GPU memory step end: 6846MB\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div>\n", | |
| " \n", | |
| " <progress value='10' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", | |
| " [10/10 00:03, Epoch 1/1]\n", | |
| " </div>\n", | |
| " <table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: left;\">\n", | |
| " <th>Step</th>\n", | |
| " <th>Training Loss</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " </tbody>\n", | |
| "</table><p>" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "GPU memory step begin: 6846MB\n", | |
| "GPU memory step end: 6970MB\n", | |
| "GPU memory step begin: 6970MB\n", | |
| "GPU memory step end: 6970MB\n", | |
| "GPU memory step begin: 6970MB\n", | |
| "GPU memory step end: 6970MB\n", | |
| "GPU memory step begin: 6970MB\n", | |
| "GPU memory step end: 6970MB\n", | |
| "GPU memory step begin: 6970MB\n", | |
| "GPU memory step end: 6970MB\n", | |
| "GPU memory step begin: 6970MB\n", | |
| "GPU memory step end: 6970MB\n", | |
| "GPU memory step begin: 6970MB\n", | |
| "GPU memory step end: 6970MB\n", | |
| "GPU memory step begin: 6970MB\n", | |
| "GPU memory step end: 6970MB\n", | |
| "GPU memory step begin: 6970MB\n", | |
| "GPU memory step end: 6970MB\n", | |
| "TrainOutput(global_step=10, training_loss=2.392757225036621, metrics={'train_runtime': 5.6187, 'train_samples_per_second': 1.78, 'train_steps_per_second': 1.78, 'total_flos': 9320251207680.0, 'train_loss': 2.392757225036621, 'epoch': 1.0})\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "result = trainer.train()\n", | |
| "print(result)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "dk4ztV0FrO_J" | |
| }, | |
| "source": [ | |
| "* At the end of the training we have used about 7 GB of memory for batch of size 1\n", | |
| "* Note, there are 10 update steps." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "MziCbEtorO_K" | |
| }, | |
| "source": [ | |
| "* Therefore, if we use 8 samples in a batch, we need **at least** (1.2+8*1.2+2.4+1.3=14.4 GB) (Ignored memory for activation)\n", | |
| "\n", | |
| "* The colab GPU has only 16 GB of memory and **hence it will raise OOM error if batch size goes beyond 7**\n", | |
| "\n", | |
| "* Now restart the session and execute the section below (It will throw the OOM error in google colab)\n", | |
| "* I am running this notebook with A100 (so it won't be a problem)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "MqlpgTM2rO_K" | |
| }, | |
| "source": [ | |
| "## Batchsize 8: Mini-batch GD" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "mghIWdzSrO_S" | |
| }, | |
| "source": [ | |
| "* Ensure that you restarted the run-time (session) and executed all the cells above the \"Batchsize 1: SGD\" section" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "uR_KAjgPrO_S" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from transformers import TrainingArguments, Trainer\n", | |
| "training_args = TrainingArguments(\"test-trainer\",num_train_epochs=1,per_device_train_batch_size=8,\n", | |
| " disable_tqdm=None,gradient_accumulation_steps=1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "bbDAcPpTrO_S" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "trainer = Trainer(\n", | |
| " model,\n", | |
| " training_args,\n", | |
| " train_dataset=tokenized_datasets[\"train\"].select(range(10)),\n", | |
| " eval_dataset=tokenized_datasets[\"validation\"].select(range(10)),\n", | |
| " data_collator=data_collator,\n", | |
| " tokenizer=tokenizer,\n", | |
| " callbacks=[TraceMemory],\n", | |
| "\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "z0k0xJV3rO_T" | |
| }, | |
| "source": [ | |
| "* Note that there won't be any change in memory when we load the model parameters into to the GPU" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "cdJ3qj1UrO_T", | |
| "outputId": "febdb8a3-e1bc-4ad1-9040-acb518b96542" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "GPU memory occupied: 1650 MB.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print_gpu_utilization()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 335 | |
| }, | |
| "id": "_Jcv5HSDrO_U", | |
| "outputId": "366400d0-8e38-4818-c60e-fbcb6fe939ac" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "GPU memory step begin: 1650MB\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "error", | |
| "ename": "OutOfMemoryError", | |
| "evalue": "CUDA out of memory. Tried to allocate 478.00 MiB. GPU ", | |
| "traceback": [ | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[0;31mOutOfMemoryError\u001b[0m Traceback (most recent call last)", | |
| "\u001b[0;32m<ipython-input-20-a407cf59c0b0>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1622\u001b[0m \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1623\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1624\u001b[0;31m return inner_training_loop(\n\u001b[0m\u001b[1;32m 1625\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1626\u001b[0m \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 1959\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1960\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccumulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1961\u001b[0;31m \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1963\u001b[0m if (\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m 2900\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2901\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss_context_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2902\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2903\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2904\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_gpu\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mcompute_loss\u001b[0;34m(self, model, inputs, return_outputs)\u001b[0m\n\u001b[1;32m 2923\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2924\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2925\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2926\u001b[0m \u001b[0;31m# Save past state if it exists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2927\u001b[0m \u001b[0;31m# TODO: this needs to be fixed and made cleaner later.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1531\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1532\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1534\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1539\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1540\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1543\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, labels, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 1373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1374\u001b[0m \u001b[0msequence_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1375\u001b[0;31m \u001b[0mprediction_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msequence_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1376\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1377\u001b[0m \u001b[0mmasked_lm_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1531\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1532\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1534\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
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| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, sequence_output)\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 706\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msequence_output\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 707\u001b[0;31m \u001b[0mprediction_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msequence_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 708\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mprediction_scores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
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| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1539\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1540\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1543\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
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| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1539\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1540\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1543\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 478.00 MiB. GPU " | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "result = trainer.train()\n", | |
| "print(result)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "VPtV9O2ArO_U" | |
| }, | |
| "source": [ | |
| "* It occupied about 17 GB of memory for training (in A100)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "K8015u8vrO_U" | |
| }, | |
| "source": [ | |
| "* Note also that there are only two updates (make sense right?)\n", | |
| "* If you are using the colab, you might have encountered **OOM** error.\n", | |
| "* Now, restart the session and execute all the cells above the section \"Batch size 1: SGD\" and then execute the cells below" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "6QoMY6HqEnBD" | |
| }, | |
| "source": [ | |
| "# Gradient Accumulation\n", | |
| "\n", | |
| "* We know that larger batch size gives a faster convergence and also a better test performance.\n", | |
| "\n", | |
| "* How do we increase the batch size then?\n", | |
| "\n", | |
| "* That's where gradient accumulation (a simple idea) helps.\n", | |
| "\n", | |
| "* The idea is, instead of storing the gradients separately for each sample, just accumulate them (sum or mean) (like we accumulate the adam optimizer states).\n", | |
| "\n", | |
| "* Update the weights after **accumulation_steps** (instead of after passing each batch)\n", | |
| "\n", | |
| "* In the *TrainingArguments*, change the gradient accumulation steps to 8 and see the amount of memory occupied!\n", | |
| "* Carefully note that we have done only one update (bcz accumulation step is 8, but there are 10 samples in the datasets, the last 2 samples will be ignored)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "2RxsDD9vrO_V" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from transformers import TrainingArguments, Trainer\n", | |
| "training_args = TrainingArguments(\"test-trainer\",num_train_epochs=1,per_device_train_batch_size=1,\n", | |
| " disable_tqdm=None,gradient_accumulation_steps=8)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "kzpwIprXrO_V" | |
| }, | |
| "source": [ | |
| "* For each time step, we are passing a batch of samples (1 in this case) that can fit into the memory\n", | |
| "* The gradient is accumulated for each step till accumulation step\n", | |
| "* When the step reaches the accumulation_steps (8 in this case), it will update the weights\n", | |
| "* In effect, we have done mini-batch GD with batch size 8 (at the cost of training time)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "J07Hb-chrO_V" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "trainer = Trainer(\n", | |
| " model,\n", | |
| " training_args,\n", | |
| " train_dataset=tokenized_datasets[\"train\"].select(range(10)),\n", | |
| " eval_dataset=tokenized_datasets[\"validation\"].select(range(10)),\n", | |
| " data_collator=data_collator,\n", | |
| " tokenizer=tokenizer,\n", | |
| " callbacks=[TraceMemory],\n", | |
| "\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "0nRRsS--rO_W", | |
| "outputId": "55c17f59-8239-4242-d3f1-75b7b4d062b6" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "GPU memory occupied: 1650 MB.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print_gpu_utilization()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 147 | |
| }, | |
| "id": "uJLT0SSNrO_W", | |
| "outputId": "4bf9f4d1-c063-4287-cdb3-0294191fab02" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "GPU memory step begin: 1650MB\n", | |
| "GPU memory step end: 6848MB\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div>\n", | |
| " \n", | |
| " <progress value='1' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", | |
| " [1/1 00:00, Epoch 0/1]\n", | |
| " </div>\n", | |
| " <table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: left;\">\n", | |
| " <th>Step</th>\n", | |
| " <th>Training Loss</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " </tbody>\n", | |
| "</table><p>" | |
| ] | |
| }, | |
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| "output_type": "stream", | |
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| "text": [ | |
| "TrainOutput(global_step=1, training_loss=2.60465931892395, metrics={'train_runtime': 4.1165, 'train_samples_per_second': 2.429, 'train_steps_per_second': 0.243, 'total_flos': 7456200966144.0, 'train_loss': 2.60465931892395, 'epoch': 0.8})\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "result = trainer.train()\n", | |
| "print(result)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "0I134F53rO_X" | |
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| "* Note that the GPU memory required to train the model is 7 GB (as if we used SGD).\n", | |
| "* This approach gives us a better test performance.\n", | |
| "* BS:1, GAS:10 then in 100 iterations, # of weight updates will be 10 \n", | |
| "* BS:2, GAS:10 then in 50 iterations, # of weight updates will be 5 \n", | |
| "* BS:10, GAS:10 then in 1 iterations, # of weight updates will be 1 \n" | |
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Author
Arunprakash-A
commented
Jul 17, 2024
•
- Gradient accumulation is not supported for optimizers like GaLore
- Inspired from the doc : https://huggingface.co/docs/transformers/v4.18.0/en/performance
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