mirror of
https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
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🧪 colab experiment
This commit is contained in:
Varuna Jayasiri
@ -155,7 +155,7 @@
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<url>
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<loc>https://nn.labml.ai/index.html</loc>
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<lastmod>2021-02-05T16:30:00+00:00</lastmod>
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<lastmod>2021-02-07T16:30:00+00:00</lastmod>
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<priority>1.00</priority>
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</url>
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@ -428,21 +428,21 @@
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<url>
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<loc>https://nn.labml.ai/transformers/xl/index.html</loc>
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<lastmod>2021-02-05T16:30:00+00:00</lastmod>
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<lastmod>2021-02-07T16:30:00+00:00</lastmod>
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<priority>1.00</priority>
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</url>
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<url>
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<loc>https://nn.labml.ai/transformers/xl/experiment.html</loc>
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<lastmod>2021-02-05T16:30:00+00:00</lastmod>
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<lastmod>2021-02-07T16:30:00+00:00</lastmod>
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<priority>1.00</priority>
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</url>
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<url>
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<loc>https://nn.labml.ai/transformers/xl/relative_mha.html</loc>
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<lastmod>2021-02-05T16:30:00+00:00</lastmod>
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<lastmod>2021-02-07T16:30:00+00:00</lastmod>
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<priority>1.00</priority>
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</url>
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@ -90,7 +90,7 @@ are introduced at the attention calculation.</p>
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<p>Annotated implementation of relative multi-headed attention is in <a href="relative_mha.html"><code>relative_mha.py</code></a>.</p>
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<p>Here’s <a href="experiment.html">the training code</a> and a notebook for training a transformer XL model on Tiny Shakespeare dataset.</p>
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<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/transformers/xl/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
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<a href="https://web.lab-ml.com/run?uuid=c4656c605b9311eba13d0242ac1c0002"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
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<a href="https://web.lab-ml.com/run?uuid=d3b6760c692e11ebb6a70242ac1c0002"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">37</span><span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span>
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@ -29,7 +29,7 @@ Annotated implementation of relative multi-headed attention is in [`relative_mha
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Here's [the training code](experiment.html) and a notebook for training a transformer XL model on Tiny Shakespeare dataset.
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[](https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/transformers/xl/experiment.ipynb)
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[](https://web.lab-ml.com/run?uuid=c4656c605b9311eba13d0242ac1c0002)
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[](https://web.lab-ml.com/run?uuid=d3b6760c692e11ebb6a70242ac1c0002)
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"""
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347
labml_nn/transformers/xl/experiment.ipynb
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347
labml_nn/transformers/xl/experiment.ipynb
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@ -0,0 +1,347 @@
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "Switch Transformer",
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"provenance": [],
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"collapsed_sections": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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"accelerator": "GPU"
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"cells": [
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"cell_type": "markdown",
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"metadata": {
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"id": "AYV_dMVDxyc2"
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},
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"source": [
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"[](https://github.com/lab-ml/nn)\n",
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"[](https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/transformers/xl/experiment.ipynb) \n",
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"\n",
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"## Transformer XL\n",
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"\n",
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"This is an experiment training Shakespeare dataset with a Transformer XL model."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "AahG_i2y5tY9"
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},
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"source": [
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"Install the `labml-nn` package"
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]
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},
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{
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"cell_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "1a4a59ce-b300-4d9f-baee-15720d696773"
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},
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"source": [
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"!pip install labml-nn"
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],
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Collecting labml-nn\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/97/eb/122e36372ca246eced93fbe380730c660cde63ac26dcb6440610bc8709dc/labml_nn-0.4.86-py3-none-any.whl (145kB)\n",
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"\u001b[K |████████████████████████████████| 102kB 12.9MB/s \n",
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"\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from labml-nn) (1.19.5)\n",
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"Collecting labml-helpers>=0.4.74\n",
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" Downloading https://files.pythonhosted.org/packages/71/40/c0b73ed57edbb05d98b5abbdc82d852e5efe5739898215274b423b97bddc/labml_helpers-0.4.74-py3-none-any.whl\n",
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"Collecting einops\n",
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" Downloading https://files.pythonhosted.org/packages/5d/a0/9935e030634bf60ecd572c775f64ace82ceddf2f504a5fd3902438f07090/einops-0.3.0-py2.py3-none-any.whl\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/d7/cb/ec98155c501b68dcb11314c7992cd3df6dce193fd763084338a117967d53/GitPython-3.1.12-py3-none-any.whl (159kB)\n",
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"\u001b[K |████████████████████████████████| 163kB 58.7MB/s \n",
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"\u001b[?25hRequirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch->labml-nn) (0.16.0)\n",
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"Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/dist-packages (from torch->labml-nn) (0.8)\n",
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"Collecting gitdb<5,>=4.0.1\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/48/11/d1800bca0a3bae820b84b7d813ad1eff15a48a64caea9c823fc8c1b119e8/gitdb-4.0.5-py3-none-any.whl (63kB)\n",
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"\u001b[K |████████████████████████████████| 71kB 8.2MB/s \n",
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" Downloading https://files.pythonhosted.org/packages/d5/1e/6130925131f639b2acde0f7f18b73e33ce082ff2d90783c436b52040af5a/smmap-3.0.5-py2.py3-none-any.whl\n",
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"Installing collected packages: smmap, gitdb, gitpython, labml, labml-helpers, einops, labml-nn\n",
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"Successfully installed einops-0.3.0 gitdb-4.0.5 gitpython-3.1.12 labml-0.4.99 labml-helpers-0.4.74 labml-nn-0.4.86 smmap-3.0.5\n"
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"id": "SE2VUQ6L5zxI"
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"source": [
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"Imports"
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]
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{
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"cell_type": "code",
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"metadata": {
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"id": "0hJXx_g0wS2C"
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},
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"\n",
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"from labml import experiment\n",
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"from labml.configs import option\n",
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"from labml_helpers.module import Module\n",
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"from labml_nn.transformers.xl.experiment import Configs"
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],
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"execution_count": 2,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Lpggo0wM6qb-"
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},
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"source": [
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"Create an experiment"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "bFcr9k-l4cAg"
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},
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"source": [
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"experiment.create(name=\"transformer_xl\")"
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],
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"execution_count": 3,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "-OnHLi626tJt"
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},
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"source": [
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"Initialize configurations"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Piz0c5f44hRo"
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},
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"source": [
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"conf = Configs()"
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],
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"execution_count": 4,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "wwMzCqpD6vkL"
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},
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"source": [
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"Set experiment configurations and assign a configurations dictionary to override configurations"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 17
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},
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"id": "e6hmQhTw4nks",
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"outputId": "839820be-f5a9-476d-d458-7d2ff3278e89"
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},
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"source": [
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"experiment.configs(conf,\n",
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" # A dictionary of configurations to override\n",
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" {'tokenizer': 'character',\n",
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" 'text': 'tiny_shakespeare',\n",
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" 'optimizer.learning_rate': 1.,\n",
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" 'optimizer.optimizer': 'Noam',\n",
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" 'prompt': 'It is',\n",
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" 'prompt_separator': '',\n",
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"\n",
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" 'train_loader': 'sequential_train_loader',\n",
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" 'valid_loader': 'sequential_valid_loader',\n",
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"\n",
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" 'seq_len': 2,\n",
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" 'mem_len': 32,\n",
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" 'epochs': 128,\n",
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" 'batch_size': 32,\n",
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" 'inner_iterations': 25,\n",
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" })"
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],
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"execution_count": 5,
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/html": [
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"<pre style=\"overflow-x: scroll;\"></pre>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {
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"tags": []
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}
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "EvI7MtgJ61w5"
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},
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"source": [
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"Set PyTorch models for loading and saving"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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||||
"colab": {
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||||
"base_uri": "https://localhost:8080/",
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"height": 255
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},
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"id": "GDlt7dp-5ALt",
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"outputId": "0543a726-fcf4-4493-dbe9-ab62c5bea94f"
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},
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"source": [
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"experiment.add_pytorch_models({'model': conf.model})"
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],
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"execution_count": 6,
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/html": [
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"<pre style=\"overflow-x: scroll;\">Prepare model...\n",
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" Prepare n_tokens...\n",
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" Prepare text...\n",
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" Prepare tokenizer<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t3.81ms</span>\n",
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" Download<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t133.10ms</span>\n",
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" Load data<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t7.14ms</span>\n",
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" Tokenize<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t28.06ms</span>\n",
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" Build vocabulary<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t139.53ms</span>\n",
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" Prepare text<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t327.93ms</span>\n",
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" Prepare n_tokens<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t338.87ms</span>\n",
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" Prepare device...\n",
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" Prepare device_info<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t61.05ms</span>\n",
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" Prepare device<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t68.43ms</span>\n",
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"Prepare model<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t10,682.78ms</span>\n",
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||||
"</pre>"
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||||
],
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"text/plain": [
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||||
"<IPython.core.display.HTML object>"
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]
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},
|
||||
"metadata": {
|
||||
"tags": []
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}
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||||
}
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "markdown",
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||||
"metadata": {
|
||||
"id": "KJZRf8527GxL"
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},
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"source": [
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"Start the experiment and run the training loop."
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]
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||||
},
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{
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"cell_type": "code",
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||||
"metadata": {
|
||||
"colab": {
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||||
"base_uri": "https://localhost:8080/",
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||||
"height": 493
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||||
},
|
||||
"id": "aIAWo7Fw5DR8",
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||||
"outputId": "64764a3f-e8d7-4c06-b803-0da6ff756163"
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},
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"source": [
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"# Start the experiment\n",
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"with experiment.start():\n",
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" conf.run()"
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],
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"execution_count": null,
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"outputs": [
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{
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||||
"output_type": "display_data",
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||||
"data": {
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"text/html": [
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||||
"<pre style=\"overflow-x: scroll;\">\n",
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||||
"<strong><span style=\"text-decoration: underline\">transformer_xl</span></strong>: <span style=\"color: #208FFB\">d3b6760c692e11ebb6a70242ac1c0002</span>\n",
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"\t[dirty]: <strong><span style=\"color: #DDB62B\">\"\"</span></strong>\n",
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||||
"Initialize...\n",
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||||
" Prepare mode<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t6.08ms</span>\n",
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"Initialize<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t91.71ms</span>\n",
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"Prepare validator...\n",
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||||
" Prepare valid_loader<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t72.62ms</span>\n",
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||||
"<span style=\"color: #C5C1B4\"></span>\n",
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||||
"<span style=\"color: #C5C1B4\">--------------------------------------------------</span><span style=\"color: #DDB62B\"><strong><span style=\"text-decoration: underline\"></span></strong></span>\n",
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||||
"<span style=\"color: #DDB62B\"><strong><span style=\"text-decoration: underline\">LABML WARNING</span></strong></span>\n",
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||||
"<span style=\"color: #DDB62B\"><strong><span style=\"text-decoration: underline\"></span></strong></span>LabML App Warning: <span style=\"color: #60C6C8\">empty_token: </span><strong>Please create a valid token at https://web.lab-ml.com.</strong>\n",
|
||||
"<strong>Click on the experiment link to monitor the experiment and add it to your experiments list.</strong><span style=\"color: #C5C1B4\"></span>\n",
|
||||
"<span style=\"color: #C5C1B4\">--------------------------------------------------</span>\n",
|
||||
"<span style=\"color: #208FFB\">Monitor experiment at </span><a href='https://web.lab-ml.com/run?uuid=d3b6760c692e11ebb6a70242ac1c0002' target='blank'>https://web.lab-ml.com/run?uuid=d3b6760c692e11ebb6a70242ac1c0002</a>\n",
|
||||
"Prepare validator<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t159.97ms</span>\n",
|
||||
"Prepare trainer...\n",
|
||||
" Prepare train_loader<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t105.71ms</span>\n",
|
||||
"Prepare trainer<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t138.48ms</span>\n",
|
||||
"Prepare training_loop...\n",
|
||||
" Prepare loop_count<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t34.64ms</span>\n",
|
||||
"Prepare training_loop<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t276.11ms</span>\n",
|
||||
"<span style=\"color: #C5C1B4\">It is</span><strong>m</strong><strong>D</strong><strong>$</strong><strong>I</strong><strong>m</strong><strong>?</strong><strong>P</strong><strong>h</strong><strong>g</strong><strong>p</strong><strong>Q</strong><strong>x</strong><strong>P</strong><strong>P</strong><strong>P</strong><strong>P</strong><strong>,</strong><strong>o</strong><strong>F</strong><strong>r</strong><strong>F</strong><strong>r</strong><strong>F</strong><strong>r</strong><strong>F</strong>\n",
|
||||
"<span style=\"color: #C5C1B4\">It is</span><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong>\n",
|
||||
"<span style=\"color: #C5C1B4\">It is</span><strong>e</strong><strong>e</strong><strong> </strong><strong>a</strong><strong>n</strong><strong>d</strong><strong> </strong><strong>a</strong><strong>n</strong><strong>d</strong><strong> </strong><strong>a</strong><strong>n</strong><strong>d</strong><strong> </strong><strong>a</strong><strong>n</strong><strong>d</strong><strong> </strong><strong>a</strong><strong>n</strong><strong>d</strong><strong> </strong><strong>a</strong><strong>t</strong>\n",
|
||||
"<span style=\"color: #C5C1B4\">It is</span><strong>e</strong><strong>d</strong><strong>,</strong><strong></strong>\n",
|
||||
"<strong></strong><strong>T</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>w</strong><strong>h</strong><strong>i</strong><strong>s</strong><strong> </strong><strong>w</strong><strong>i</strong><strong>t</strong><strong>h</strong><strong> </strong><strong>w</strong><strong>e</strong><strong> </strong><strong>w</strong><strong>i</strong><strong>t</strong><strong>h</strong>\n",
|
||||
"<span style=\"color: #C5C1B4\">It is</span><strong>e</strong><strong>n</strong><strong>t</strong><strong>e</strong><strong>r</strong><strong> </strong><strong>h</strong><strong>e</strong><strong>a</strong><strong>v</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>m</strong><strong>e</strong><strong> </strong><strong>a</strong><strong>n</strong><strong>d</strong><strong> </strong><strong>a</strong><strong>n</strong>\n",
|
||||
"<strong><span style=\"color: #DDB62B\"> 200,768: </span></strong>Sample:<span style=\"color: #C5C1B4\"> 100%</span><span style=\"color: #208FFB\"> 393ms </span>Train:<span style=\"color: #C5C1B4\"> 20%</span><span style=\"color: #208FFB\"> 1,161,309ms </span>Valid:<span style=\"color: #C5C1B4\"> 16%</span><span style=\"color: #208FFB\"> 32,270ms </span> loss.train: <span style=\"color: #C5C1B4\"> 2.12580</span> accuracy.train: <span style=\"color: #C5C1B4\">0.335542</span> loss.valid: <strong> 2.23436</strong> accuracy.valid: <strong>0.326474</strong> <span style=\"color: #208FFB\">1,193,972ms</span><span style=\"color: #D160C4\"> 0:04m/ 42:23m </span></pre>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "oBXXlP2b7XZO"
|
||||
},
|
||||
"source": [
|
||||
""
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
}
|
||||
]
|
||||
}
|
||||
Reference in New Issue
Block a user