forked from filipg/aitech-eks-pub
271 lines
6.7 KiB
Plaintext
271 lines
6.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"<div class=\"alert alert-block alert-info\">\n",
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"<h1> Ekstrakcja informacji </h1>\n",
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"<h2> 15. <i>Sieci Transformer i ich zastosowanie w ekstrakcji informacji</i> [wykład]</h2> \n",
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"<h3> Filip Graliński (2021)</h3>\n",
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"</div>\n",
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"\n",
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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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"source": [
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"## Modele Transformer\n",
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"\n"
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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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"source": [
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"### Atencja\n",
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"\n"
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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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"source": [
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"Atencję w modelach Transformer można interpretować jako rodzaj\n",
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"„miękkiego” odpytywania swego rodzaju bazy danych, w której\n",
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"przechowywane są pary klucz-wartość. Mamy trzy rodzaje wektorów (a\n",
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"właściwie macierzy, bo wektory są od razu upakowane w macierze):\n",
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"\n",
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"- $Q$ - macierz zapytań,\n",
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"- $K$ - macierz kluczy,\n",
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"- $V$ - macierz wartości odpowiadających kluczom $K$.\n",
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"\n",
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"W atencji modeli Transformer patrzymy jak bardzo zapytania $Q$ pasują\n",
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"do kluczy $K$ i na tej podstawie zwracamy wartości $V$ (im bardziej\n",
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"**klucz** pasuje do **zapytania**, tym większy wkład wnosi odpowiednia **wartość**).\n",
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"Ten rodzaj odpytywania można zrealizować z pomocą mnożenia macierzy i funkcji softmax:\n",
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"\n",
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"$$\\operatorname{Atention}(Q,K,V) = \\operatorname{softmax}(QK^T)V$$\n",
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"\n"
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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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"source": [
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"#### Uproszczony przykład\n",
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"\n"
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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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"source": [
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"Załóżmy, że rozmiar embeddingu wynosi 4, w macierzach rozpatrywać\n",
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"będziemy po 3 wektory naraz (możemy sobie wyobrazić, że zdanie zawiera 3 wyrazy).\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[20.5700, 36.2400, 31.1000],\n",
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" [15.1100, 13.9100, 7.9500],\n",
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" [ 2.2100, 7.1800, 7.4000]])"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import torch\n",
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"\n",
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"Q = torch.tensor([\n",
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" [0.3, -2.0, 0.4, 6.0],\n",
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" [-1.0, 1.5, 0.2, 3.0],\n",
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" [0.3, -1.0, 0.2, 1.0]])\n",
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"\n",
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"K = torch.tensor([\n",
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" [-0.5, 1.7, 0.3, 4.0],\n",
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" [0.4, -1.5, 0.3, 5.5],\n",
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" [-1.0, -3.5, 1.0, 4.0]])\n",
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"\n",
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"M = Q @ torch.transpose(K, 0, 1)\n",
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"M"
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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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"source": [
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"Jak widać, najbardziej pierwszy wektor $Q$ pasuje do drugiego wektora $K$.\n",
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"Znormalizujmy te wartości używać funkcji softmax.\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[1.5562e-07, 9.9418e-01, 5.8236e-03],\n",
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" [7.6807e-01, 2.3134e-01, 5.9683e-04],\n",
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" [3.0817e-03, 4.4385e-01, 5.5307e-01]])"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import torch\n",
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"\n",
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"Mn = torch.softmax(M, 1)\n",
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"Mn"
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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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"source": [
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"Drugi wektor zapytania najbardziej pasuje do pierwszego klucza, trochę\n",
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"mniej do drugiego klucza, o wiele mniej do trzeciego klucza. Te\n",
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"wektory to oczywiście wektory atencji (drugie słowo najbardziej\n",
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"„patrzy” na pierwsze słowo).\n",
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"\n",
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"Teraz będziemy przemnażać przez wektory wartości:\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[ 3.9750e+00, 9.9419e-02, 1.0116e-01, 1.5765e-01, 5.8255e-04],\n",
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" [ 9.2517e-01, 6.9357e+00, 2.3313e-02, -3.8112e+00, 9.2174e-01],\n",
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" [ 1.6095e+00, 7.2120e-02, 2.1031e-01, 5.5597e+00, 5.9005e-02]])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import torch\n",
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"\n",
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"V = torch.tensor([\n",
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" [0.0, 9.0, 0.0, -5.0, 1.2],\n",
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" [4.0, 0.1, 0.1, 0.1, 0.0],\n",
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" [-0.3, 0.0, 0.3, 10.0, 0.1]])\n",
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"\n",
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"Mn @ V"
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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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"source": [
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"#### Dodatkowa normalizacja\n",
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"\n"
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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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"source": [
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"W praktyce dobrze jest znormalizować pierwszy iloczyn przez\n",
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"$\\sqrt{d_k}$, gdzie $d_k$ to rozmiar wektora klucza.\n",
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"\n",
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"$$\\operatorname{Atention}(Q,K,V) = \\operatorname{softmax}(\\frac{QK^T}{\\sqrt{d^k}})V$$\n",
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"\n"
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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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"source": [
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"#### Skąd się biorą Q, K i V?\n",
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"\n"
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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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"source": [
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"Wektory (macierze) $Q$, $K$ i $V$ w pierwszej warstwie pochodzą z\n",
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"embeddingów tokenów $E$ (właściwie jednostek BPE).\n",
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"\n",
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"- $Q$ = $EW^Q$\n",
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"- $K$ = $EW^K$\n",
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"- $V$ = $EW^V$\n",
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"\n",
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"W kolejnych warstwach zamiast $E$ wykorzystywane jest wyjście z poprzedniej warstwy.\n",
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"\n"
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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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"source": [
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"### Literatura\n",
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"\n"
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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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"source": [
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"[https://arxiv.org/pdf/1706.03762.pdf](https://arxiv.org/pdf/1706.03762.pdf)\n",
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"\n"
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]
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}
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],
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"metadata": {
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"author": "Filip Graliński",
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"email": "filipg@amu.edu.pl",
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"lang": "pl",
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.6"
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},
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"org": null,
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"subtitle": "15.Sieci Transformer i ich zastosowanie w ekstrakcji informacji[wykład]",
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"title": "Ekstrakcja informacji",
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"year": "2021"
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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