From 13869a4d2d400dc29b72e8fe1e06f63cb059354c Mon Sep 17 00:00:00 2001 From: Jakub Pokrywka Date: Sun, 5 Jun 2022 22:40:36 +0200 Subject: [PATCH] 12 --- cw/12_Model_transformer_autoregresywny.ipynb | 4664 ++++++++++++++++++ 1 file changed, 4664 insertions(+) create mode 100644 cw/12_Model_transformer_autoregresywny.ipynb diff --git a/cw/12_Model_transformer_autoregresywny.ipynb b/cw/12_Model_transformer_autoregresywny.ipynb new file mode 100644 index 0000000..5d519c5 --- /dev/null +++ b/cw/12_Model_transformer_autoregresywny.ipynb @@ -0,0 +1,4664 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n", + "
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Modelowanie Języka

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12. Model rekurencyjny z atencją [ćwiczenia]

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Jakub Pokrywka (2022)

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\n", + "\n", + "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://huggingface.co/gpt2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: transformers in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (4.19.2)\n", + "Requirement already satisfied: tqdm>=4.27 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (4.64.0)\n", + "Requirement already satisfied: numpy>=1.17 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (1.22.3)\n", + "Requirement already satisfied: requests in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (2.27.1)\n", + "Requirement already satisfied: packaging>=20.0 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (21.3)\n", + "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (0.12.1)\n", + "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (0.6.0)\n", + "Requirement already satisfied: filelock in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (3.7.0)\n", + "Requirement already satisfied: regex!=2019.12.17 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (2022.4.24)\n", + "Requirement already satisfied: pyyaml>=5.1 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from transformers) (6.0)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.1.1)\n", + "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from packaging>=20.0->transformers) (3.0.8)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from requests->transformers) (2020.6.20)\n", + "Requirement already satisfied: idna<4,>=2.5 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from requests->transformers) (3.3)\n", + "Requirement already satisfied: charset-normalizer~=2.0.0 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from requests->transformers) (2.0.4)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages (from requests->transformers) (1.26.9)\n" + ] + } + ], + "source": [ + "!pip install transformers" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import pipeline, set_seed, AutoTokenizer, AutoModel, AutoModelForCausalLM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### przykładowy tekst" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "TEXT = 'Today, on my way to the university,'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## użycie modelu w bibliotece transormers" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "model_name = \"gpt2\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "w przypadku długiego czasu inferencji lub za małą ilością RAMu użyj mniejszego modelu:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# model_name = 'distilgpt2'" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer = AutoTokenizer.from_pretrained(model_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "encoding = tokenizer(TEXT)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'input_ids': [8888, 11, 319, 616, 835, 284, 262, 6403, 11], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8888 \t Today\n", + "11 \t ,\n", + "319 \t on\n", + "616 \t my\n", + "835 \t way\n", + "284 \t to\n", + "262 \t the\n", + "6403 \t university\n", + "11 \t ,\n" + ] + } + ], + "source": [ + "for token in encoding['input_ids']:\n", + " print(token, '\\t', tokenizer.decode(token))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "pt_model = AutoModel.from_pretrained(model_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'input_ids': [8888, 11, 319, 616, 835, 284, 262, 6403, 11], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoding" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "poniżej pojawi się błąd, ponieważ na wejściu modelu muszą być tensory" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'list' object has no attribute 'size'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [15]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpt_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/zajeciaei/lib/python3.10/site-packages/torch/nn/modules/module.py:1110\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1106\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1107\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1109\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1110\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1111\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1112\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/anaconda3/envs/zajeciaei/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py:769\u001b[0m, in \u001b[0;36mGPT2Model.forward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 767\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou cannot specify both input_ids and inputs_embeds at the same time\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 768\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m input_ids \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 769\u001b[0m input_shape \u001b[38;5;241m=\u001b[39m \u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m()\n\u001b[1;32m 770\u001b[0m input_ids \u001b[38;5;241m=\u001b[39m input_ids\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, input_shape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m 771\u001b[0m batch_size \u001b[38;5;241m=\u001b[39m input_ids\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n", + "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'size'" + ] + } + ], + "source": [ + "pt_model(**encoding)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Today, on my way to the university,'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TEXT" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "encoding = tokenizer(TEXT, return_tensors='pt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "?pt_model.forward" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "output = pt_model(**encoding, output_hidden_states= True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=tensor([[[ 0.0502, 0.0018, -0.1750, ..., -0.1020, -0.0257, -0.1292],\n", + " [ 0.1300, 0.1757, 0.2934, ..., 0.0794, 0.1164, -0.3280],\n", + " [ 0.0021, -0.2481, 0.2638, ..., 0.1507, 0.4056, 0.2376],\n", + " ...,\n", + " [ 0.1611, -0.4680, 0.7029, ..., 0.1209, 0.3803, 0.2864],\n", + " [ 0.1791, -0.3507, -1.2709, ..., -0.1535, -0.7109, -0.2459],\n", + " 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+ "output_type": "execute_result" + } + ], + "source": [ + "output" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 9, 768])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output.hidden_states[0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 9, 768])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output.hidden_states[1].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 9, 768])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output.hidden_states[2].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(output.hidden_states)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 9, 768])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output.last_hidden_state.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "pt_model = AutoModelForCausalLM.from_pretrained(model_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "output = pt_model(**encoding)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + 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5.7653e-01, -5.7979e-01],\n", + " [-4.1779e-01, -4.9530e-01, -6.0749e-01, ..., 5.8660e-01,\n", + " 9.1405e-01, -3.4966e-02]]]], grad_fn=), tensor([[[[-1.5059e-02, -2.1934e-02, -1.3257e-01, ..., -3.3233e-03,\n", + " 5.6872e-03, -5.5921e-01],\n", + " [-4.4076e-01, 4.7031e-01, -2.1116e-01, ..., 5.7315e-01,\n", + " -3.8024e-01, 2.5338e-01],\n", + " [ 2.7640e-01, 1.0290e-01, -1.5030e-01, ..., 8.0443e-02,\n", + " -1.0340e-02, 6.5651e-01],\n", + " ...,\n", + " [ 7.7904e-01, 1.2082e+00, 3.0358e-01, ..., 4.4578e-01,\n", + " -4.0582e-02, 8.5044e-01],\n", + " [-2.0731e-01, -5.8119e-01, 4.1100e-01, ..., -1.7157e-01,\n", + " 2.8487e-01, 6.4911e-01],\n", + " [-8.6411e-01, 5.4967e-01, -4.1298e-01, ..., 9.2813e-01,\n", + " -4.2606e-01, -3.4161e-01]],\n", + "\n", + " [[ 3.8557e-02, 3.3662e-03, 5.4482e-02, ..., -5.7578e-02,\n", + " -7.4123e-02, 2.2392e-02],\n", + " [ 1.9386e-01, 1.8534e-01, 3.0680e-01, ..., -1.2764e-03,\n", + " -2.5348e-01, 8.6118e-02],\n", + " [-1.4242e-01, 3.2992e-01, 7.6395e-02, ..., 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-8.1313e-01, 3.6240e-01, ..., 6.4624e-01,\n", + " -7.6324e-01, 1.4873e+00],\n", + " ...,\n", + " [ 1.0556e-01, -3.8366e-01, 1.2748e+00, ..., -3.6558e-01,\n", + " 4.0858e-01, 2.4199e-01],\n", + " [-2.5444e-01, 1.1958e+00, -1.7147e-01, ..., 6.1984e-01,\n", + " -2.2845e-01, -1.8110e+00],\n", + " [ 3.2427e-01, 8.9915e-01, 1.1141e+00, ..., 6.8071e-01,\n", + " -3.4533e-01, -1.7910e-01]],\n", + "\n", + " [[-1.8907e-01, -6.5480e-02, 7.6243e-02, ..., -5.9887e-02,\n", + " 5.6530e-02, -7.3080e-02],\n", + " [-8.2506e-01, -3.6656e-02, 4.9222e-01, ..., 2.5220e-01,\n", + " 3.1897e-01, 1.9113e-01],\n", + " [-4.6517e-01, -2.1911e-01, -6.4030e-01, ..., 7.2280e-01,\n", + " 7.5668e-01, 5.6131e-01],\n", + " ...,\n", + " [-1.0660e+00, -4.2479e-01, -5.0573e-01, ..., -5.8658e-02,\n", + " -6.6094e-02, -4.4752e-01],\n", + " [-8.6907e-02, 1.2486e-04, -5.2314e-01, ..., 1.1544e-01,\n", + " 4.3831e-01, -1.0179e-02],\n", + " [-1.0669e+00, -7.1475e-01, 8.0158e-01, ..., -1.1919e-01,\n", + " -2.0185e-01, 3.2946e-01]],\n", + "\n", + " [[ 1.2763e-01, -1.2701e-01, 1.6529e-01, ..., -1.4527e-01,\n", + " -8.5370e-03, -1.7278e-01],\n", + " [-6.5069e-02, 3.5000e-01, 5.6586e-01, ..., -3.5917e-01,\n", + " -4.1324e-01, 2.9987e-01],\n", + " [ 2.5123e-01, 5.5106e-01, 4.2795e-01, ..., -1.0718e+00,\n", + " -6.8236e-01, -4.2256e-01],\n", + " ...,\n", + " [-6.0648e-01, -5.4619e-01, 1.4942e-02, ..., -7.6836e-01,\n", + " -5.9767e-01, -1.3891e-02],\n", + " [-3.4398e-01, -8.0992e-01, 7.4776e-01, ..., -1.8947e+00,\n", + " -2.7473e-01, 4.0089e-01],\n", + " [ 8.6354e-02, -1.2515e-02, -2.7977e-01, ..., -4.1148e-01,\n", + " -5.5178e-01, 7.0079e-02]]]], grad_fn=))), hidden_states=None, attentions=None, cross_attentions=None)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[[ -36.3292, -36.3402, -40.4228, ..., -46.0234, -44.5284,\n", + " -37.1276],\n", + " [-114.9346, -116.5035, -117.9236, ..., -117.8857, -119.3379,\n", + " -112.9298],\n", + " [-123.5036, -123.0548, -127.3876, ..., -130.5238, -130.5279,\n", + " -123.2711],\n", + " ...,\n", + " [-101.3852, -101.2506, -103.6583, ..., -103.3747, -107.7192,\n", + " -99.4521],\n", + " [ -83.0701, -84.3884, -91.9513, ..., -91.7482, -93.3971,\n", + " -85.1204],\n", + " [ -91.2749, -93.1332, -93.6408, ..., -94.3482, -93.4517,\n", + " -90.1472]]], grad_fn=)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 9, 50257])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output[0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.return_types.topk(\n", + "values=tensor([[ -32.8755, -33.1021, -33.9975, -34.4861, -34.5463],\n", + " [-105.5972, -106.3818, -106.3978, -106.9693, -107.0778],\n", + " [-113.2521, -114.7346, -114.8781, -114.9605, -115.0834],\n", + " [-118.2435, -119.2980, -119.5907, -119.6229, -119.7969],\n", + " [ -83.6241, -84.6822, -84.8526, -85.4978, -86.6938],\n", + " [ -79.9051, -80.3284, -81.6157, -81.8538, -82.9018],\n", + " [ -90.4443, -90.7053, -91.9059, -92.0003, -92.1531],\n", + " [ -75.2650, -76.9698, -77.5753, -77.6700, -77.8095],\n", + " [ -78.7985, -81.5545, -81.6846, -81.8984, -82.5938]],\n", + " grad_fn=),\n", + "indices=tensor([[ 11, 13, 198, 290, 286],\n", + " [ 262, 356, 314, 340, 257],\n", + " [ 262, 257, 1737, 2901, 2805],\n", + " [ 835, 717, 938, 10955, 1218],\n", + " [ 284, 736, 1363, 503, 422],\n", + " [ 670, 262, 616, 257, 1524],\n", + " [ 9003, 2607, 11550, 4436, 4495],\n", + " [ 11, 314, 338, 284, 287],\n", + " [ 314, 616, 257, 262, 612]]))" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.topk(output[0][0],5)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([8888, 11, 319, 616, 835, 284, 262, 6403, 11])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoding.input_ids[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Today, \t→ the\n", + "Today, on \t→ the\n", + "Today, on my \t→ way\n", + "Today, on my way \t→ to\n", + "Today, on my way to \t→ work\n", + "Today, on my way to the \t→ airport\n", + "Today, on my way to the university \t→ ,\n", + "Today, on my way to the university, \t→ I\n" + ] + } + ], + "source": [ + "for i in range(1,len(encoding.input_ids[0])):\n", + " print(tokenizer.decode(encoding.input_ids[0][:i+1]), '\\t→', tokenizer.decode(torch.topk(output[0][0],1).indices[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### generowanie tekstu" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'input_ids': tensor([[8888, 11, 319, 616, 835, 284, 262, 6403, 11]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1]])}" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "text = TEXT" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Today, on my way to the university,'" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "text" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'input_ids': tensor([[8888, 11, 319, 616, 835, 284, 262, 6403, 11]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1]])}" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "encoding = tokenizer(text, return_tensors='pt')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(10):\n", + " output =pt_model(**encoding)\n", + " text += tokenizer.decode(torch.topk(output[0][0][-1],1).indices)\n", + " encoding = tokenizer(text, return_tensors='pt')" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Today, on my way to the university, I was approached by a man who was a student'" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "text" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://towardsdatascience.com/decoding-strategies-that-you-need-to-know-for-response-generation-ba95ee0faadc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Co można zrobić, żeby poprawić wynik? Strategie dekodowania:\n", + "\n", + "- greedy search\n", + "- random sampling\n", + "- random sampling with temperature\n", + "- top-k sampling lub top-k sampling with temperature\n", + "- top-p sampling (inna nazwa: nucleus sampling) lub top-p sampling with temperature\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### https://huggingface.co/tasks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "generator = pipeline('text-generation', model=model_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Today, on my way to the university,'" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TEXT" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'generated_text': 'Today, on my way to the university, some of them would have been very pleased, and I'},\n", + " {'generated_text': 'Today, on my way to the university, and he made me dinner, and he called me back'},\n", + " {'generated_text': 'Today, on my way to the university, I saw three white girls who seemed a bit different—'},\n", + " {'generated_text': 'Today, on my way to the university, I drove through the town, past trees and bushes,'},\n", + " {'generated_text': 'Today, on my way to the university, I saw an elderly lady come up behind me.\"\\n'}]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "generator(TEXT, max_length=20, num_return_sequences=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://huggingface.co/docs/transformers/main_classes/text_generation" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'generated_text': 'Today, on my way to the university, I was approached by a man who was a student at'}]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "generator(TEXT, max_length=20, num_beams=1, do_sample=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'generated_text': 'Today, on my way to the university, I was approached by a man who was very nice and'}]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "generator(TEXT, max_length=20, num_beams=10, top_p = 0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'generated_text': 'Today, on my way to the university, I was approached by a group of students who asked me'}]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "generator(TEXT, max_length=20, num_beams=10, temperature = 1.0 )" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'generated_text': 'Today, on my way to the university, I noticed some young boys who was very active on campus'}]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "generator(TEXT, max_length=20, num_beams=10, temperature = 10.0 )" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'generated_text': 'Today, on my way to the university, the trainees have noticed how a car could become an'}]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "generator(TEXT, max_length=20, num_beams=10, temperature = 100.0 )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "inne możliwość:\n", + "\n", + "\n", + "- repetition_penalty\n", + "- length_penalty\n", + "- no_repeat_ngram_size\n", + "- bad_words_ids\n", + "- force_words_ids" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## huggingface API" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://huggingface.co/gpt2?text=Today%2C+on+my+way+to+the+university" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import CTRLTokenizer, CTRLModel" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer = CTRLTokenizer.from_pretrained(\"ctrl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CTRL" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "inputs = tokenizer(\"Opinion My dog is cute\", return_tensors=\"pt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'input_ids': tensor([[43213, 586, 3153, 8, 83781]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1]])}" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Pregnancy': 168629,\n", + " 'Christianity': 7675,\n", + " 'Explain': 106423,\n", + " 'Fitness': 63440,\n", + " 'Saving': 63163,\n", + " 'Ask': 27171,\n", + " 'Ass': 95985,\n", + " 'Joke': 163509,\n", + " 'Questions': 45622,\n", + " 'Thoughts': 49605,\n", + " 'Retail': 52342,\n", + " 'Feminism': 164338,\n", + " 'Writing': 11992,\n", + " 'Atheism': 192263,\n", + " 'Netflix': 48616,\n", + " 'Computing': 39639,\n", + " 'Opinion': 43213,\n", + " 'Alone': 44967,\n", + " 'Funny': 58917,\n", + " 'Gaming': 40358,\n", + " 'Human': 4088,\n", + " 'India': 1331,\n", + " 'Joker': 77138,\n", + " 'Diet': 36206,\n", + " 'Legal': 11859,\n", + " 'Norman': 4939,\n", + " 'Tip': 72689,\n", + " 'Weight': 52343,\n", + " 'Movies': 46273,\n", + " 'Running': 23425,\n", + " 'Science': 2090,\n", + " 'Horror': 37793,\n", + " 'Confession': 60572,\n", + " 'Finance': 12250,\n", + " 'Politics': 16360,\n", + " 'Scary': 191985,\n", + " 'Support': 12654,\n", + " 'Technologies': 32516,\n", + " 'Teenage': 66160,\n", + " 'Event': 32769,\n", + " 'Learned': 67460,\n", + " 'Notion': 182770,\n", + " 'Wikipedia': 37583,\n", + " 'Books': 6665,\n", + " 'Extract': 76050,\n", + " 'Confessions': 102701,\n", + " 'Conspiracy': 75932,\n", + " 'Links': 63674,\n", + " 'Narcissus': 150425,\n", + " 'Relationship': 54766,\n", + " 'Relationships': 134796,\n", + " 'Reviews': 41671,\n", + " 'News': 4256,\n", + " 'Translation': 26820,\n", + " 'multilingual': 128406}" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokenizer.control_codes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kuba/anaconda3/envs/zajeciaei/lib/python3.10/site-packages/transformers/models/ctrl/modeling_ctrl.py:43: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " angle_rates = 1 / torch.pow(10000, (2 * (i // 2)) / d_model_size)\n" + ] + } + ], + "source": [ + "generator = pipeline('text-generation', model=\"ctrl\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TEXT = \"Today\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "generator(\"Opinion \" + TEXT, max_length = 50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[{'generated_text': 'Opinion Today I learned that the US government has been spying on the citizens of other countries for years. \\n Score: 6 \\n \\n Title: CMV: I think that the US should not be involved in the Middle East \\n Text: I think that the US'}]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "generator(\"Technologies \" + TEXT, max_length = 50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[{'generated_text': 'Technologies Today \\n Score: 6 \\n \\n Title: The Internet is a great tool for the average person to get information and to share it with others. But it is also a great tool for the government to spy on us. \\n Score: 6 \\n \\n Title: The'}]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "generator(\"Gaming \" + TEXT, max_length = 50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[{'generated_text': 'Gaming Today \\n Score: 6 \\n \\n Title: I just got a new gaming pc and I have a question \\n Text: I just got a new gaming pc and I have a question \\n \\n I have a monitor that I bought a while back'}]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Zadanie \n", + "\n", + "Za pomocą GPT2 lub distillGPT wygenerować odpowiedzi dla wyzwania challanging america. Nie trzeba douczać modelu." + ] + } + ], + "metadata": { + "author": "Jakub Pokrywka", + "email": "kubapok@wmi.amu.edu.pl", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "lang": "pl", + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + }, + "subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]", + "title": "Ekstrakcja informacji", + "year": "2021" + }, + "nbformat": 4, + "nbformat_minor": 4 +}