{ "cells": [ { "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, "referenced_widgets": [ "06f51749b66544818014548484143012", "180cbd8c464f4254a03caee8cbd77c86", "09edb30acdc341bb82b8187886abaae4", "9f03dc58a4e14e9b8561aaac212f2d93", "6d7805c6f8c043a6a7e40b38cf461cae", "c5592920995346e8a4e16c62fbdc98b9", "c37ec4a4c0414beeb987e93c762831f9", "b3ac6d02ce404d7d956eab9691c8e12f", "ffce1979b0e14c37bbfdcfe320292df7", "617dbf115983482da037357c9ddac393", "030472e44b0e4a50b7a3b40ffbbe8db2", "371a05fdc7a6484aa220e27c100331e0", "7d313b6f5aa2406e9951200ffed2c429", "34aa632cd1f64ed0ad0f8e6b53d963aa", "78189cd4722c497283945e720a067f2a", "828acf5c9a714256aa32341a7d1f32ed", "8e8033d919224f0c85dfa95d77803f51", "da29c0da8c884129834fa2f3718e36d8", "e5ff655732ad4955a4e2193de00e68bc", "97dd7dc5ee24448a984952baebb6a282", "bddc9c2edde84088b2e6b38fdd370028", 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"839f4b3a25094ca1b6f130f398fd6469", "514afc939ac5466c9a19ff5cac101483", "f1a249f8c8e54a9ba5ce7e9e718ad779", "c88170c5ebf2460f9ddae34f85969cbd", "68b2cf75860443d6beec062af995e000", "921336d7467c4b7794ddd92112301ae9", "277bb8e0be0e430d8e6321ba2eb058c6", "e9110a0be37546e0bb7a2641f2a30b28", "cd037d0358444f8393db75b376b82e3f", "ad51a0d67d4f4a1ea5afdc921b21358c", "e51f17aeec304b7ca5b44a2e0900bcaf", "61909f4e472841ddaa7ba523d3c2cd47", "e6a45a37ed09421baf40cb3c3c278c53", "6089f4f38a1146279edff409dd1d6225", "2a19decefd594fc388b66baf6d282d8d", "41089e1f3a3d422195c93d946959d872", "e0c5d5ae0ecc416c8a2c726880d0c05e", "a6cb141f25a049acabc5692d48b20bc9", "3e7e349d9fdc4f22b48f9c5621484efd" ] }, "outputId": "354c7390-740a-4dca-f63d-c5b7882d2dee" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/35.3k [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [10/10 00:03, Epoch 1/1]\n", "
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StepTraining Loss

" ] }, "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\u001b[0m in \u001b[0;36m\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 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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": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [1/1 00:00, Epoch 0/1]\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StepTraining Loss

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "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" }, "source": [ "* 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." ] }, { "cell_type": "code", "source": [], "metadata": { "id": "aixHzjVWAzgZ" }, "execution_count": null, "outputs": [] } ], "metadata": { "colab": { "provenance": [], "gpuType": "T4" }, "kernelspec": { "display_name": "Python(hf)", "language": "python", "name": "tocr" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": 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