├── README.md
├── LICENSE
└── PositionEmbedding_Discussion.ipynb
/README.md:
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1 | # Position-Embedding-Discussion
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/LICENSE:
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3101 | "cells": [
3102 | {
3103 | "cell_type": "markdown",
3104 | "source": [
3105 | "# [KerasNLP] Position Embedding Techniques in Transformers\n",
3106 | "\n",
3107 | "**Author:** [Usha Rengaraju](https://www.linkedin.com/in/usha-rengaraju-b570b7a2/)
\n",
3108 | "**Date created:** 2023/07/10
\n",
3109 | "**Last modified:** 2023/07/10
\n",
3110 | "**Description:** Position Embedding Techniques in Transformers using KerasNLP"
3111 | ],
3112 | "metadata": {
3113 | "id": "yvKc4pDsVJGN"
3114 | }
3115 | },
3116 | {
3117 | "cell_type": "markdown",
3118 | "source": [
3119 | "## Overview\n",
3120 | "\n",
3121 | "Embedding layers are the ones which convert the input data to embedding vector form with some added information like position encoding and much more. There are various embedding layers already implemented in KerasNLP which we can use on the go.\n",
3122 | "\n",
3123 | "In this guide we create a simple text classification pipeline and showcase the various embedding layers and their affects on the performance."
3124 | ],
3125 | "metadata": {
3126 | "id": "pcanbuwJ7PUX"
3127 | }
3128 | },
3129 | {
3130 | "cell_type": "markdown",
3131 | "source": [
3132 | "## Imports & setup\n",
3133 | "\n",
3134 | "This tutorial requires you to have KerasNLP installed:\n",
3135 | "\n",
3136 | "```shell\n",
3137 | "pip install keras-nlp\n",
3138 | "```\n",
3139 | "\n",
3140 | "We begin by importing all required packages:"
3141 | ],
3142 | "metadata": {
3143 | "id": "DmC_kCnI7VPq"
3144 | }
3145 | },
3146 | {
3147 | "cell_type": "code",
3148 | "execution_count": null,
3149 | "metadata": {
3150 | "id": "6_hxrvF7nKnR",
3151 | "colab": {
3152 | "base_uri": "https://localhost:8080/"
3153 | },
3154 | "outputId": "80323c7e-4991-4bf2-f741-c661508d5f93"
3155 | },
3156 | "outputs": [
3157 | {
3158 | "output_type": "stream",
3159 | "name": "stdout",
3160 | "text": [
3161 | "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/42.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
3162 | "\u001b[?25h"
3163 | ]
3164 | }
3165 | ],
3166 | "source": [
3167 | "!pip install -q keras-nlp einops"
3168 | ]
3169 | },
3170 | {
3171 | "cell_type": "code",
3172 | "source": [
3173 | "import os\n",
3174 | "import re\n",
3175 | "import json\n",
3176 | "import string\n",
3177 | "import numpy as np\n",
3178 | "import tensorflow as tf\n",
3179 | "from tensorflow import keras\n",
3180 | "from tensorflow.keras import layers\n",
3181 | "import keras_nlp"
3182 | ],
3183 | "metadata": {
3184 | "id": "TJN1sCA8nUWh"
3185 | },
3186 | "execution_count": null,
3187 | "outputs": []
3188 | },
3189 | {
3190 | "cell_type": "markdown",
3191 | "source": [
3192 | "## Data loading\n",
3193 | "\n",
3194 | "This guide uses the\n",
3195 | "[IMDB review dataset](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews)\n",
3196 | "for demonstration purposes.\n",
3197 | "\n",
3198 | "To get started, we first load the dataset:"
3199 | ],
3200 | "metadata": {
3201 | "id": "r8CwaHyl_8oX"
3202 | }
3203 | },
3204 | {
3205 | "cell_type": "code",
3206 | "source": [
3207 | "import keras_nlp\n",
3208 | "import tensorflow_datasets as tfds\n",
3209 | "\n",
3210 | "imdb_train, imdb_test = tfds.load(\n",
3211 | " \"imdb_reviews\",\n",
3212 | " split=[\"train\", \"test\"],\n",
3213 | " as_supervised=True,\n",
3214 | " batch_size=16,\n",
3215 | ")\n"
3216 | ],
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3272 | "3fb33e76f8104b04aa18dfc464b72a1b",
3273 | "d6315fd8422541a8bfa04cbabb976e94",
3274 | "58c7d4ffceb44332a3b362737e6a3554",
3275 | "5212b25a72474677b638478932d87425",
3276 | "0d891a5143fa42bfb9b106391eb91939",
3277 | "ed5f5d0280354ac9961fd05488548091",
3278 | "7cd65bceb9cb4944aeb30c186a9bc90e",
3279 | "0dc894352c154805ba2d3596f43c9e4f",
3280 | "ef3e7f7b90d24a39a535963ca42ca0a3",
3281 | "d8a83f84c41d436d9f6c6866527cf7c0",
3282 | "5e9a252ecc7041afafeb4527f214b2fd",
3283 | "a78402b082574cbbbeeeca72584496ec",
3284 | "01688391d2ea438db0388c43408de598",
3285 | "1186620e299d45819962a4aadc384935",
3286 | "31f86ffde7e84dfea6ef496a07a8bac7",
3287 | "52eb4b36d34c4cac84395dc3d7a2e531",
3288 | "a0f11fe12e104dd284054dc9e11c372d",
3289 | "c53d7572399a4e32804c6b353f055030",
3290 | "9a51db6daeb74ac590d120b6a74ce6a3",
3291 | "62afcb8f9fc242a0bf3fb455e0304f96",
3292 | "d444e4552f2c4d35a202e457bf8efd0b",
3293 | "f6ed3a1763d144fcadd5ff7c83966b80",
3294 | "f5fedede8bec441d82804ab9918f5b24",
3295 | "a183da90da714155811e0e0143e316eb",
3296 | "263db6859652481985789ad8da51447c",
3297 | "3aab6c731da44c15a9dfba98672d0735",
3298 | "e8bd7a69d9454970bc358e2994c0d1ba",
3299 | "da20567a8cc6422d8684310b92e9119e",
3300 | "cfd9342f64534a579161b6e2a94a81d8",
3301 | "75dd1253ae5940e19b5e6f745dc25bfa",
3302 | "d9641c8b5ec1466796335ab37adf92fc",
3303 | "7ad81f5aa9424c2a8987d426453c935d",
3304 | "7b7ca5db7bee4456850e3ce8babe2b99",
3305 | "c04f23fa0f0f4e7687a00fb99cc9f293",
3306 | "5afc20c36e1f4a94b82819f732a4d0f6",
3307 | "ca44537116d349ae90f23a0c012a7e00",
3308 | "2b139c7107394539bdb4891f3276301d",
3309 | "0657b8f40f98454eb2d4609bc5ccffe8",
3310 | "1ad5ada5601041f3ab7c126074927fb2",
3311 | "5a81f7fbf0a74a3e8eb09b968c8e5b9b",
3312 | "815adf56656f43b097e7228409d15494",
3313 | "19f3854e27aa4e4ea022d6e0638117f3",
3314 | "084e60db582d4e7283f0adc8e5aeb7f4",
3315 | "6948abc6e29d4b148bf04a9fc609f9e8",
3316 | "109489ef947849ef9f3a069bf2cf25d6",
3317 | "4d746014d6aa4c8d8b375dfe323c2914",
3318 | "ea4ee7e61067487eac60a152067b6485",
3319 | "44a3cc03595a4cb2a4baa16f8e4a9393",
3320 | "1fdca155f1b442099c043c320e569ffd",
3321 | "834ee15d726d4ee19f2e6b3372dead8f"
3322 | ]
3323 | },
3324 | "outputId": "ac9857a5-9eef-42f8-b85a-fb9b29a0626f"
3325 | },
3326 | "execution_count": null,
3327 | "outputs": [
3328 | {
3329 | "output_type": "stream",
3330 | "name": "stdout",
3331 | "text": [
3332 | "Downloading and preparing dataset 80.23 MiB (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\n"
3333 | ]
3334 | },
3335 | {
3336 | "output_type": "display_data",
3337 | "data": {
3338 | "text/plain": [
3339 | "Dl Completed...: 0 url [00:00, ? url/s]"
3340 | ],
3341 | "application/vnd.jupyter.widget-view+json": {
3342 | "version_major": 2,
3343 | "version_minor": 0,
3344 | "model_id": "8fc3ffd3192e410690bfb1c15cc91df8"
3345 | }
3346 | },
3347 | "metadata": {}
3348 | },
3349 | {
3350 | "output_type": "display_data",
3351 | "data": {
3352 | "text/plain": [
3353 | "Dl Size...: 0 MiB [00:00, ? MiB/s]"
3354 | ],
3355 | "application/vnd.jupyter.widget-view+json": {
3356 | "version_major": 2,
3357 | "version_minor": 0,
3358 | "model_id": "63117fcc267e4d9b9d83c81448ec891a"
3359 | }
3360 | },
3361 | "metadata": {}
3362 | },
3363 | {
3364 | "output_type": "display_data",
3365 | "data": {
3366 | "text/plain": [
3367 | "Generating splits...: 0%| | 0/3 [00:00"
3707 | ]
3708 | },
3709 | "metadata": {},
3710 | "execution_count": 42
3711 | }
3712 | ]
3713 | },
3714 | {
3715 | "cell_type": "markdown",
3716 | "source": [
3717 | "## Sine Positional Embedding\n",
3718 | "\n",
3719 | "This layer calculates the position encoding as a mix of sine and cosine functions with geometrically increasing wavelengths. Defined and formulized in Attention is All You Need.\n",
3720 | "\n",
3721 | "**max_wavelength**: The maximum angular wavelength of the sine/cosine curves, as described in Attention is All You Need. Defaults to 10000."
3722 | ],
3723 | "metadata": {
3724 | "id": "v3W3bPJPz3lx"
3725 | }
3726 | },
3727 | {
3728 | "cell_type": "code",
3729 | "source": [
3730 | "token_id_input = keras.Input(\n",
3731 | " shape=(None,),\n",
3732 | " dtype=\"int32\",\n",
3733 | " name=\"token_ids\",\n",
3734 | ")\n",
3735 | "embed = keras.layers.Embedding(\n",
3736 | " input_dim=len(vocab), output_dim=64\n",
3737 | ")(token_id_input)\n",
3738 | "outputs = keras_nlp.layers.SinePositionEncoding()(embed)\n",
3739 | "outputs = embed+outputs\n",
3740 | "outputs = keras_nlp.layers.TransformerEncoder(\n",
3741 | " num_heads=2,\n",
3742 | " intermediate_dim=128,\n",
3743 | " dropout=0.1,\n",
3744 | ")(outputs)\n",
3745 | "outputs = keras.layers.Dense(2)(outputs[:, 0, :])\n",
3746 | "model = keras.Model(\n",
3747 | " inputs=token_id_input,\n",
3748 | " outputs=outputs,\n",
3749 | ")\n",
3750 | "\n",
3751 | "model.summary()"
3752 | ],
3753 | "metadata": {
3754 | "colab": {
3755 | "base_uri": "https://localhost:8080/"
3756 | },
3757 | "id": "aVk8T47Yzzi4",
3758 | "outputId": "1bc452ab-613d-420d-97bf-3aeb595e6600"
3759 | },
3760 | "execution_count": null,
3761 | "outputs": [
3762 | {
3763 | "output_type": "stream",
3764 | "name": "stdout",
3765 | "text": [
3766 | "Model: \"model_8\"\n",
3767 | "__________________________________________________________________________________________________\n",
3768 | " Layer (type) Output Shape Param # Connected to \n",
3769 | "==================================================================================================\n",
3770 | " token_ids (InputLayer) [(None, None)] 0 [] \n",
3771 | " \n",
3772 | " embedding_3 (Embedding) (None, None, 64) 1226880 ['token_ids[0][0]'] \n",
3773 | " \n",
3774 | " sine_position_encoding_2 (Sine (None, None, 64) 0 ['embedding_3[0][0]'] \n",
3775 | " PositionEncoding) \n",
3776 | " \n",
3777 | " tf.__operators__.add_2 (TFOpLa (None, None, 64) 0 ['embedding_3[0][0]', \n",
3778 | " mbda) 'sine_position_encoding_2[0][0]'\n",
3779 | " ] \n",
3780 | " \n",
3781 | " transformer_encoder_11 (Transf (None, None, 64) 33472 ['tf.__operators__.add_2[0][0]'] \n",
3782 | " ormerEncoder) \n",
3783 | " \n",
3784 | " tf.__operators__.getitem_14 (S (None, 64) 0 ['transformer_encoder_11[0][0]'] \n",
3785 | " licingOpLambda) \n",
3786 | " \n",
3787 | " dense_8 (Dense) (None, 2) 130 ['tf.__operators__.getitem_14[0][\n",
3788 | " 0]'] \n",
3789 | " \n",
3790 | "==================================================================================================\n",
3791 | "Total params: 1,260,482\n",
3792 | "Trainable params: 1,260,482\n",
3793 | "Non-trainable params: 0\n",
3794 | "__________________________________________________________________________________________________\n"
3795 | ]
3796 | }
3797 | ]
3798 | },
3799 | {
3800 | "cell_type": "code",
3801 | "source": [
3802 | "model.compile(\n",
3803 | " loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
3804 | " optimizer=keras.optimizers.experimental.AdamW(5e-5),\n",
3805 | " metrics=keras.metrics.SparseCategoricalAccuracy(),\n",
3806 | " jit_compile=True,\n",
3807 | ")\n",
3808 | "model.fit(\n",
3809 | " imdb_preproc_train_ds,\n",
3810 | " validation_data=imdb_preproc_val_ds,\n",
3811 | " epochs=3,\n",
3812 | ")"
3813 | ],
3814 | "metadata": {
3815 | "colab": {
3816 | "base_uri": "https://localhost:8080/"
3817 | },
3818 | "id": "4Vi6tkusz8-k",
3819 | "outputId": "9a5bd483-fdf2-4086-f3e7-10a71e4cf8c9"
3820 | },
3821 | "execution_count": null,
3822 | "outputs": [
3823 | {
3824 | "output_type": "stream",
3825 | "name": "stdout",
3826 | "text": [
3827 | "Epoch 1/3\n",
3828 | "1563/1563 [==============================] - 159s 97ms/step - loss: 0.7058 - sparse_categorical_accuracy: 0.5028 - val_loss: 0.6950 - val_sparse_categorical_accuracy: 0.5000\n",
3829 | "Epoch 2/3\n",
3830 | "1563/1563 [==============================] - 159s 102ms/step - loss: 0.6889 - sparse_categorical_accuracy: 0.5364 - val_loss: 0.6695 - val_sparse_categorical_accuracy: 0.5344\n",
3831 | "Epoch 3/3\n",
3832 | "1563/1563 [==============================] - 155s 99ms/step - loss: 0.5447 - sparse_categorical_accuracy: 0.7310 - val_loss: 0.4687 - val_sparse_categorical_accuracy: 0.7697\n"
3833 | ]
3834 | },
3835 | {
3836 | "output_type": "execute_result",
3837 | "data": {
3838 | "text/plain": [
3839 | ""
3840 | ]
3841 | },
3842 | "metadata": {},
3843 | "execution_count": 49
3844 | }
3845 | ]
3846 | },
3847 | {
3848 | "cell_type": "markdown",
3849 | "source": [
3850 | "## Rotary embeddings\n",
3851 | "\n",
3852 | "Rotary position embedding is a sort of position embedding that naturally combines explicit relative position dependency in the formulation of self-attention while encoding absolute positional information with rotation matrices.\n",
3853 | "\n",
3854 | "**rotary_ndims**: The rotatory matrix dimensions\n",
3855 | "\n",
3856 | "**max_wavelength**: The maximum angular wavelength of the sine/cosine curves, as described in Attention is All You Need. Defaults to 10000."
3857 | ],
3858 | "metadata": {
3859 | "id": "S_48Odbh7CCs"
3860 | }
3861 | },
3862 | {
3863 | "cell_type": "code",
3864 | "source": [
3865 | "class RotaryEmbedding(keras.layers.Layer):\n",
3866 | " def __init__(self, rotary_ndims, max_wavelength=10000):\n",
3867 | " super().__init__()\n",
3868 | " self.rotary_ndims = int(rotary_ndims)\n",
3869 | " self.max_wavelength = max_wavelength\n",
3870 | " self.to_qk = keras.layers.Dense(units=rotary_ndims * 4, use_bias=False)\n",
3871 | "\n",
3872 | " def _apply_rotary_pos_emb(self, tensor, cos_emb, sin_emb):\n",
3873 | " cos_emb = cos_emb[: tf.shape(tensor)[0], : tf.shape(tensor)[1]]\n",
3874 | " sin_emb = sin_emb[: tf.shape(tensor)[0], : tf.shape(tensor)[1]]\n",
3875 | " x1, x2 = tf.split(tensor, 2, axis=-1)\n",
3876 | " half_rot_tensor = tf.concat((-x2, x1), axis=-1)\n",
3877 | " ret = (tensor * cos_emb) + (half_rot_tensor * sin_emb)\n",
3878 | " return ret\n",
3879 | "\n",
3880 | " def _compute_cos_sin_embedding(self, x, seq_dim=1):\n",
3881 | " seq_len = tf.shape(x)[seq_dim]\n",
3882 | " range = tf.range(\n",
3883 | " start=0, limit=self.rotary_ndims, delta=2, dtype=\"float32\"\n",
3884 | " )\n",
3885 | " inverse_freq = 1.0 / (\n",
3886 | " self.max_wavelength ** (range / self.rotary_ndims)\n",
3887 | " )\n",
3888 | " tensor = tf.range(seq_len, dtype=inverse_freq.dtype)\n",
3889 | " freqs = tf.einsum(\"i, j -> ij\", tensor, inverse_freq)\n",
3890 | " embedding = tf.concat((freqs, freqs), axis=-1)\n",
3891 | " return tf.cos(embedding), tf.sin(embedding)\n",
3892 | "\n",
3893 | " def call(self, x):\n",
3894 | " qk = self.to_qk(x)\n",
3895 | " qk = tf.split(qk, num_or_size_splits=2, axis=-1)\n",
3896 | " query, key = qk\n",
3897 | "\n",
3898 | " query_rot, query_pass = (\n",
3899 | " query[..., : self.rotary_ndims],\n",
3900 | " query[..., self.rotary_ndims :],\n",
3901 | " )\n",
3902 | " key_rot, key_pass = (\n",
3903 | " key[..., : self.rotary_ndims],\n",
3904 | " key[..., self.rotary_ndims :],\n",
3905 | " )\n",
3906 | " cos_emb, sin_emb = self._compute_cos_sin_embedding(key_rot, seq_dim=1)\n",
3907 | " query_emb = self._apply_rotary_pos_emb(query_rot, cos_emb, sin_emb)\n",
3908 | " key_emb = self._apply_rotary_pos_emb(key_rot, cos_emb, sin_emb)\n",
3909 | " query = tf.concat((query_emb, query_pass), axis=-1)\n",
3910 | "\n",
3911 | " return query\n",
3912 | "\n",
3913 | " def get_config(self):\n",
3914 | " config = super().get_config()\n",
3915 | " config.update(\n",
3916 | " {\n",
3917 | " \"rotary_ndims\": self.rotary_ndims,\n",
3918 | " \"max_wavelength\": self.max_wavelength,\n",
3919 | " }\n",
3920 | " )\n",
3921 | "\n",
3922 | " return config\n",
3923 | "\n"
3924 | ],
3925 | "metadata": {
3926 | "id": "_bqIXwR12dfi",
3927 | "colab": {
3928 | "base_uri": "https://localhost:8080/"
3929 | },
3930 | "outputId": "7986b80c-78f2-4248-8aff-82cf3a8a7f4b"
3931 | },
3932 | "execution_count": null,
3933 | "outputs": [
3934 | {
3935 | "output_type": "execute_result",
3936 | "data": {
3937 | "text/plain": [
3938 | "TensorShape([2, 128])"
3939 | ]
3940 | },
3941 | "metadata": {},
3942 | "execution_count": 46
3943 | }
3944 | ]
3945 | },
3946 | {
3947 | "cell_type": "code",
3948 | "source": [
3949 | "from einops import rearrange, repeat\n",
3950 | "\n",
3951 | "token_id_input = keras.Input(\n",
3952 | " shape=(None,),\n",
3953 | " dtype=\"int32\",\n",
3954 | " name=\"token_ids\",\n",
3955 | ")\n",
3956 | "embed = keras.layers.Embedding(\n",
3957 | " input_dim=len(vocab), output_dim=128\n",
3958 | ")(token_id_input)\n",
3959 | "outputs = RotaryEmbedding(64)(embed)\n",
3960 | "outputs=outputs+embed\n",
3961 | "outputs = keras_nlp.layers.TransformerEncoder(\n",
3962 | " num_heads=2,\n",
3963 | " intermediate_dim=128,\n",
3964 | " dropout=0.1,\n",
3965 | ")(outputs)\n",
3966 | "outputs = keras.layers.Dense(2)(outputs[:, 0, :])\n",
3967 | "model = keras.Model(\n",
3968 | " inputs=token_id_input,\n",
3969 | " outputs=outputs,\n",
3970 | ")\n",
3971 | "\n",
3972 | "model.summary()"
3973 | ],
3974 | "metadata": {
3975 | "colab": {
3976 | "base_uri": "https://localhost:8080/"
3977 | },
3978 | "id": "o4mc34DT7N6i",
3979 | "outputId": "7e2159ec-3e7a-4a96-e702-adee68c3b8c6"
3980 | },
3981 | "execution_count": null,
3982 | "outputs": [
3983 | {
3984 | "output_type": "stream",
3985 | "name": "stdout",
3986 | "text": [
3987 | "Model: \"model\"\n",
3988 | "__________________________________________________________________________________________________\n",
3989 | " Layer (type) Output Shape Param # Connected to \n",
3990 | "==================================================================================================\n",
3991 | " token_ids (InputLayer) [(None, None)] 0 [] \n",
3992 | " \n",
3993 | " embedding_7 (Embedding) (None, None, 128) 2453760 ['token_ids[0][0]'] \n",
3994 | " \n",
3995 | " rotary_embedding_37 (RotaryEmb (None, None, 128) 32768 ['embedding_7[0][0]'] \n",
3996 | " edding) \n",
3997 | " \n",
3998 | " tf.__operators__.add_3 (TFOpLa (None, None, 128) 0 ['rotary_embedding_37[0][0]', \n",
3999 | " mbda) 'embedding_7[0][0]'] \n",
4000 | " \n",
4001 | " transformer_encoder (Transform (None, None, 128) 99584 ['tf.__operators__.add_3[0][0]'] \n",
4002 | " erEncoder) \n",
4003 | " \n",
4004 | " tf.__operators__.getitem (Slic (None, 128) 0 ['transformer_encoder[0][0]'] \n",
4005 | " ingOpLambda) \n",
4006 | " \n",
4007 | " dense_37 (Dense) (None, 2) 258 ['tf.__operators__.getitem[0][0]'\n",
4008 | " ] \n",
4009 | " \n",
4010 | "==================================================================================================\n",
4011 | "Total params: 2,586,370\n",
4012 | "Trainable params: 2,586,370\n",
4013 | "Non-trainable params: 0\n",
4014 | "__________________________________________________________________________________________________\n"
4015 | ]
4016 | }
4017 | ]
4018 | },
4019 | {
4020 | "cell_type": "code",
4021 | "source": [
4022 | "model.compile(\n",
4023 | " loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
4024 | " optimizer=keras.optimizers.experimental.AdamW(5e-5),\n",
4025 | " metrics=keras.metrics.SparseCategoricalAccuracy(),\n",
4026 | " jit_compile=True,\n",
4027 | ")\n",
4028 | "model.fit(\n",
4029 | " imdb_preproc_train_ds,\n",
4030 | " validation_data=imdb_preproc_val_ds,\n",
4031 | " epochs=3,\n",
4032 | ")"
4033 | ],
4034 | "metadata": {
4035 | "colab": {
4036 | "base_uri": "https://localhost:8080/"
4037 | },
4038 | "id": "fT4eWdIR7Qcb",
4039 | "outputId": "b5a44040-ce02-48ef-d9ea-4937c5f5eb30"
4040 | },
4041 | "execution_count": null,
4042 | "outputs": [
4043 | {
4044 | "output_type": "stream",
4045 | "name": "stdout",
4046 | "text": [
4047 | "Epoch 1/3\n",
4048 | "1563/1563 [==============================] - 32s 16ms/step - loss: 0.7163 - sparse_categorical_accuracy: 0.5028 - val_loss: 0.6969 - val_sparse_categorical_accuracy: 0.5000\n",
4049 | "Epoch 2/3\n",
4050 | "1563/1563 [==============================] - 23s 15ms/step - loss: 0.7025 - sparse_categorical_accuracy: 0.5047 - val_loss: 0.6964 - val_sparse_categorical_accuracy: 0.5000\n",
4051 | "Epoch 3/3\n",
4052 | "1563/1563 [==============================] - 23s 15ms/step - loss: 0.6986 - sparse_categorical_accuracy: 0.5032 - val_loss: 0.6956 - val_sparse_categorical_accuracy: 0.5000\n"
4053 | ]
4054 | },
4055 | {
4056 | "output_type": "execute_result",
4057 | "data": {
4058 | "text/plain": [
4059 | ""
4060 | ]
4061 | },
4062 | "metadata": {},
4063 | "execution_count": 47
4064 | }
4065 | ]
4066 | },
4067 | {
4068 | "cell_type": "markdown",
4069 | "source": [
4070 | "## Alibi embeddings\n",
4071 | "\n",
4072 | "Without using actual position embeddings, ALiBi completes the positional embedding task. Instead, ALiBi penalises the attention value that a given query can give to a given key based on how far apart the key and query are from one another when calculating the attention between a given key and query. As a result, the penalty is relatively low when a key and question are close together and very high when they are far apart.\n",
4073 | "\n",
4074 | "The idea behind this strategy is the obvious one that words that are nearby matter a lot more than words that are far away. It takes in the attention head size and total heads as the input parameters.\n",
4075 | "\n",
4076 | "\n"
4077 | ],
4078 | "metadata": {
4079 | "id": "Ads3RvmfDIae"
4080 | }
4081 | },
4082 | {
4083 | "cell_type": "code",
4084 | "source": [
4085 | "!pip install einops"
4086 | ],
4087 | "metadata": {
4088 | "colab": {
4089 | "base_uri": "https://localhost:8080/"
4090 | },
4091 | "id": "_EnisJQ4GcaL",
4092 | "outputId": "5833bca3-b46a-4ce7-fea5-aaa9d3bbb1fb"
4093 | },
4094 | "execution_count": null,
4095 | "outputs": [
4096 | {
4097 | "output_type": "stream",
4098 | "name": "stdout",
4099 | "text": [
4100 | "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
4101 | "Collecting einops\n",
4102 | " Downloading einops-0.6.1-py3-none-any.whl (42 kB)\n",
4103 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
4104 | "\u001b[?25hInstalling collected packages: einops\n",
4105 | "Successfully installed einops-0.6.1\n"
4106 | ]
4107 | }
4108 | ]
4109 | },
4110 | {
4111 | "cell_type": "code",
4112 | "source": [
4113 | "import math\n",
4114 | "from einops import rearrange, repeat, reduce\n",
4115 | "\n",
4116 | "\n",
4117 | "class AlibiPositionalBias(layers.Layer):\n",
4118 | " def __init__(self, heads, total_heads):\n",
4119 | " super(AlibiPositionalBias,self).__init__()\n",
4120 | " self.heads = heads\n",
4121 | " self.total_heads = total_heads\n",
4122 | " slopes = self._get_slopes(heads)\n",
4123 | " slopes = tf.convert_to_tensor(slopes, dtype=tf.float32)\n",
4124 | " slopes = rearrange(slopes, 'h -> h 1 1')\n",
4125 | " self.slopes =slopes\n",
4126 | " self.bias=None\n",
4127 | "\n",
4128 | " def get_bias(self, i, j):\n",
4129 | " i_arange = tf.range(j - i, j)\n",
4130 | " j_arange = tf.range(j)\n",
4131 | " bias = -tf.math.abs(rearrange(j_arange, 'j -> 1 1 j') - rearrange(i_arange, 'i -> 1 i 1'))\n",
4132 | " return bias\n",
4133 | " @staticmethod\n",
4134 | " def _get_slopes(heads):\n",
4135 | " def get_slopes_power_of_2(n):\n",
4136 | " start = (2**(-2**-(math.log2(n)-3)))\n",
4137 | " ratio = start\n",
4138 | " return [start*ratio**i for i in range(n)]\n",
4139 | "\n",
4140 | " if math.log2(heads).is_integer():\n",
4141 | " return get_slopes_power_of_2(heads)\n",
4142 | "\n",
4143 | " closest_power_of_2 = 2 ** math.floor(math.log2(heads))\n",
4144 | " return get_slopes_power_of_2(closest_power_of_2) + get_slopes_power_of_2(2 * closest_power_of_2)[0::2][:heads-closest_power_of_2]\n",
4145 | "\n",
4146 | "\n",
4147 | " def call(self, i, j):\n",
4148 | " h = self.total_heads\n",
4149 | "\n",
4150 | " if self.bias and self.bias.shape[-1] >= j:\n",
4151 | " return self.bias[..., :i, :j]\n",
4152 | "\n",
4153 | " bias = self.get_bias(i, j)\n",
4154 | " bias = tf.cast(bias,dtype=tf.float32) * self.slopes\n",
4155 | " self.bias = bias\n",
4156 | "\n",
4157 | " return self.bias\n",
4158 | "\n",
4159 | "class LearnedAlibiPositionalBias(AlibiPositionalBias):\n",
4160 | " def __init__(self, heads, total_heads):\n",
4161 | " super(LearnedAlibiPositionalBias,self).__init__(heads, total_heads)\n",
4162 | " log_slopes = tf.math.log(self.slopes)\n",
4163 | " self.learned_logslopes = tf.Variable(log_slopes)\n",
4164 | "\n",
4165 | " def call(self, i, j):\n",
4166 | " h = self.heads\n",
4167 | "\n",
4168 | " def get_slopes(param):\n",
4169 | " return tf.math.exp(param)\n",
4170 | "\n",
4171 | " if self.bias and self.bias.shape[-1] >= j:\n",
4172 | " bias = self.bias[..., :i, :j]\n",
4173 | " else:\n",
4174 | " bias = self.get_bias(i, j)\n",
4175 | " self.bias=bias\n",
4176 | "\n",
4177 | " slopes = get_slopes(self.learned_logslopes)\n",
4178 | " bias = tf.cast(bias,dtype=tf.float32) * slopes\n",
4179 | "\n",
4180 | " return bias"
4181 | ],
4182 | "metadata": {
4183 | "id": "YhNrk6dYCsPI"
4184 | },
4185 | "execution_count": null,
4186 | "outputs": []
4187 | },
4188 | {
4189 | "cell_type": "code",
4190 | "source": [
4191 | "token_id_input = keras.Input(\n",
4192 | " shape=(None,),\n",
4193 | " dtype=\"int32\",\n",
4194 | " name=\"token_ids\",\n",
4195 | ")\n",
4196 | "embed = keras.layers.Embedding(\n",
4197 | " input_dim=len(vocab), output_dim=64\n",
4198 | ")(token_id_input)\n",
4199 | "outputs = embed+LearnedAlibiPositionalBias(1,32)(512,64)\n",
4200 | "outputs = keras_nlp.layers.TransformerEncoder(\n",
4201 | " num_heads=2,\n",
4202 | " intermediate_dim=128,\n",
4203 | " dropout=0.1,\n",
4204 | ")(outputs)\n",
4205 | "outputs = keras.layers.Dense(2)(outputs[:, 0, :])\n",
4206 | "model = keras.Model(\n",
4207 | " inputs=token_id_input,\n",
4208 | " outputs=outputs,\n",
4209 | ")\n",
4210 | "\n",
4211 | "model.summary()"
4212 | ],
4213 | "metadata": {
4214 | "colab": {
4215 | "base_uri": "https://localhost:8080/"
4216 | },
4217 | "outputId": "666df52c-7b73-4801-9d55-65c401886bf1",
4218 | "id": "ZrsuDAbfV-uA"
4219 | },
4220 | "execution_count": null,
4221 | "outputs": [
4222 | {
4223 | "output_type": "stream",
4224 | "name": "stdout",
4225 | "text": [
4226 | "Model: \"model_2\"\n",
4227 | "_________________________________________________________________\n",
4228 | " Layer (type) Output Shape Param # \n",
4229 | "=================================================================\n",
4230 | " token_ids (InputLayer) [(None, None)] 0 \n",
4231 | " \n",
4232 | " embedding_2 (Embedding) (None, None, 64) 1226880 \n",
4233 | " \n",
4234 | " tf.__operators__.add_2 (TFO (None, 512, 64) 0 \n",
4235 | " pLambda) \n",
4236 | " \n",
4237 | " transformer_encoder_2 (Tran (None, 512, 64) 33472 \n",
4238 | " sformerEncoder) \n",
4239 | " \n",
4240 | " tf.__operators__.getitem_2 (None, 64) 0 \n",
4241 | " (SlicingOpLambda) \n",
4242 | " \n",
4243 | " dense_2 (Dense) (None, 2) 130 \n",
4244 | " \n",
4245 | "=================================================================\n",
4246 | "Total params: 1,260,482\n",
4247 | "Trainable params: 1,260,482\n",
4248 | "Non-trainable params: 0\n",
4249 | "_________________________________________________________________\n"
4250 | ]
4251 | }
4252 | ]
4253 | },
4254 | {
4255 | "cell_type": "code",
4256 | "source": [
4257 | "model.compile(\n",
4258 | " loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
4259 | " optimizer=keras.optimizers.experimental.AdamW(5e-5),\n",
4260 | " metrics=keras.metrics.SparseCategoricalAccuracy(),\n",
4261 | " jit_compile=True,\n",
4262 | ")\n",
4263 | "model.fit(\n",
4264 | " imdb_preproc_train_ds,\n",
4265 | " validation_data=imdb_preproc_val_ds,\n",
4266 | " epochs=3,\n",
4267 | ")"
4268 | ],
4269 | "metadata": {
4270 | "id": "suG3kCuCV-uL"
4271 | },
4272 | "execution_count": null,
4273 | "outputs": []
4274 | },
4275 | {
4276 | "cell_type": "code",
4277 | "source": [],
4278 | "metadata": {
4279 | "id": "HTPukbUiM3W8"
4280 | },
4281 | "execution_count": null,
4282 | "outputs": []
4283 | }
4284 | ]
4285 | }
--------------------------------------------------------------------------------