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.gitattributes
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Exam/Deep_Learning_2025_VIII.pdf filter=lfs diff=lfs merge=lfs -text
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Assets/** filter=lfs diff=lfs merge=lfs -text
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Assets/Dataset/1-hop/dataset.csv filter=lfs diff=lfs merge=lfs -text
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.gitignore
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.LSOverride
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# Icon must end with two \r
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Icon
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Icon
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# Thumbnails
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._*
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# .nfs files are created when an open file is removed but is still being accessed
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.nfs*
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# ---> Custom
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**/Tmp/**
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**/cache/**
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!**/.gitkeep
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.vscode/extensions.json
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.vscode/extensions.json
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{
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"bierner.markdown-checkbox",
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"bierner.markdown-emoji",
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"bierner.markdown-footnotes",
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"bierner.markdown-mermaid",
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"bierner.markdown-preview-github-styles",
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"bierner.markdown-yaml-preamble",
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"davidanson.vscode-markdownlint",
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"kejun.markdown-alert",
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"yzhang.markdown-all-in-one"
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]
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}
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.vscode/launch.json
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{
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// Use IntelliSense to learn about possible attributes.
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// Hover to view descriptions of existing attributes.
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// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
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"version": "0.2.0",
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"configurations": [
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"request": "launch",
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"program": "${file}",
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"console": "integratedTerminal",
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"args": "${command:pickArgs}"
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}
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]
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}
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.vscode/settings.json
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{
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// Always treat the project root as the working dir for Jupyter
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"jupyter.notebookFileRoot": "${workspaceFolder}",
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// When you click "Run Python File in Terminal", DON'T cd into the file's folder
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"python.terminal.executeInFileDir": false,
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// Start new integrated terminals at the project root
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"terminal.integrated.cwd": "${workspaceFolder}",
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// Make pytest run from the root without needing a pytest.ini
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"python.testing.pytestEnabled": true,
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"python.testing.cwd": "${workspaceFolder}",
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"python.testing.pytestArgs": [
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"src/test"
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// Help Pylance resolve imports like `from src...` without red squiggles
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"python.analysis.extraPaths": [
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"${workspaceFolder}"
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],
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// For linux
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"terminal.integrated.env.linux": {
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"PYTHONPATH": "${workspaceFolder}"
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},
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// For OSX
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"terminal.integrated.env.osx": {
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"PYTHONPATH": "${workspaceFolder}"
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},
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// For Windows
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"terminal.integrated.env.windows": {
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"PYTHONPATH": "${workspaceFolder}"
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},
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"python.analysis.typeCheckingMode": "standard"
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}
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// {
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// // Always treat the project root as the working dir for Jupyter
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// "jupyter.notebookFileRoot": "${workspaceFolder}",
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//
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// // When you click "Run Python File in Terminal", DON'T cd into the file's folder
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// "python.terminal.executeInFileDir": false,
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//
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// // Start new integrated terminals at the project root
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// "terminal.integrated.cwd": "${workspaceFolder}",
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//
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// // Ensure Python can import from the project root no matter which file you run
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// // (so `src/` is on sys.path). Linux shown here; add osx/windows if needed.
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// "terminal.integrated.env.windows": {
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// "PYTHONPATH": "${workspaceFolder}"
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// },
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//
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// // Make pytest run from the root without needing a pytest.ini
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// "python.testing.pytestEnabled": true,
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// "python.testing.cwd": "${workspaceFolder}",
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// "python.testing.pytestArgs": ["src/test"],
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//
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// // Help Pylance resolve imports like `from src...` without red squiggles
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// "python.analysis.extraPaths": ["${workspaceFolder}"]
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// }
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "ddfb4457",
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"metadata": {},
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"outputs": [
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{
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"ename": "AssertionError",
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"evalue": "target id 3872 >= V (256). Fix TOKEN_SPACE_SIZE.",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mAssertionError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 126\u001b[39m\n\u001b[32m 124\u001b[39m \u001b[38;5;66;03m# sanity guard (helps debug vocab mismatches fast)\u001b[39;00m\n\u001b[32m 125\u001b[39m max_seen = tgt[:, :Tp].max().item()\n\u001b[32m--> \u001b[39m\u001b[32m126\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m max_seen < V \u001b[38;5;129;01mor\u001b[39;00m (tgt[:, :Tp] == PAD_TOKEN).all(), \\\n\u001b[32m 127\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mtarget id \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmax_seen\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m >= V (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mV\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m). Fix TOKEN_SPACE_SIZE.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 129\u001b[39m \u001b[38;5;66;03m# CE over all tokens produced so far (0..t). PAD is ignored by ignore_index\u001b[39;00m\n\u001b[32m 130\u001b[39m loss_t = cross_entropy(\n\u001b[32m 131\u001b[39m logits_btV.reshape(-\u001b[32m1\u001b[39m, V), \u001b[38;5;66;03m# [B*(t+1), V]\u001b[39;00m\n\u001b[32m 132\u001b[39m tgt[:, :Tp].reshape(-\u001b[32m1\u001b[39m) \u001b[38;5;66;03m# [B*(t+1)]\u001b[39;00m\n\u001b[32m 133\u001b[39m )\n",
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"\u001b[31mAssertionError\u001b[39m: target id 3872 >= V (256). Fix TOKEN_SPACE_SIZE."
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]
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}
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],
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"source": [
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"import random\n",
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"import torch\n",
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"import pandas as pd\n",
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"from pathlib import Path\n",
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"import Project_Model.Libs.Embedder as Embedder\n",
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"import Project_Model.Libs.BPE as BPE\n",
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"import Project_Model.Libs.Transformer as Transformer\n",
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"import Project_Model.Libs.TorchShims as torch_shims\n",
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"from Project_Model.Libs.Training.learning_rade_shedulers import CustomLR\n",
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"from Project_Model.Libs.Training.logistic_collector import LogitsCollector # external collector\n",
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"\n",
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"# set a fixed seed\n",
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"torch.manual_seed(0)\n",
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"random.seed(0)\n",
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"DEVICE = torch_shims.get_default_device()\n",
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"torch.set_default_device(DEVICE)\n",
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"\n",
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"# BPE Init\n",
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"VOCABULARY_PATH = Path(\"Assets/Model/toy_10/toy_dictionary.json\")\n",
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"SPECIAL_VOC = BPE.default_special_tokens()\n",
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"\n",
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"VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)\n",
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"TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)\n",
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"\n",
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"# Constants (TEMP size; will be corrected after dataset scan below)\n",
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"TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size + 1\n",
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"EMBEDDED_SIZE = 256\n",
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"FEED_FORWARD_MULTIPLIER = 4\n",
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"ATTENTION_HEADS = 4\n",
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"SENTENCE_LENGTH = 256\n",
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"NUMBER_OF_BLOCKS = 2\n",
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"MAX_EPOCHS = int(1e4)\n",
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"\n",
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"PAD_TOKEN = TOKENANO.encode(\"<PAD>\")[0]\n",
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"END_TOKEN = TOKENANO.encode(\"<END>\")[0]\n",
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"\n",
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"# Load CSV\n",
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"TOY_DATASET_PATH = Path(\"Assets/Dataset/1-hop/toy/rdf_text.csv\")\n",
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"TOY_DATASET = pd.read_csv(TOY_DATASET_PATH)\n",
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"\n",
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"TOY_BATCH_INPUT_LIST: list[list[int]] = []\n",
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"TOY_BATCH_PADDING_LIST: list[list[bool]] = []\n",
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"TOY_BATCH_TARGET_LIST: list[list[int]] = []\n",
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"TOY_BATCH_DECODER_DEFAULT: list[list[int]] = []\n",
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"\n",
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"for index, row in TOY_DATASET.iterrows():\n",
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" RDFs: str = row[\"RDFs\"]\n",
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" Abstract: str = row[\"Abstract\"]\n",
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"\n",
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" input_tokens = TOKENANO.encode(RDFs) # encoder input ids\n",
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" output_tokens = TOKENANO.encode(Abstract)[1:] # decoder target ids (shifted left)\n",
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" decoder_default_tokens = TOKENANO.encode(\"<SOS>\") # decoder input starts with <SOS>\n",
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"\n",
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" input_tokens, padding = Transformer.normalize_sequence(\n",
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" input_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
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" ) # pad/trim + end token\n",
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" output_tokens, _ = Transformer.normalize_sequence(\n",
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||||
" output_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
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||||
" ) # pad/trim + end token\n",
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" decoder_default_tokens = Transformer.pad_sequence(\n",
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||||
" decoder_default_tokens, SENTENCE_LENGTH, PAD_TOKEN\n",
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" ) # pad with PAD up to SENTENCE_LENGTH\n",
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"\n",
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" TOY_BATCH_INPUT_LIST.append(input_tokens)\n",
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" TOY_BATCH_PADDING_LIST.append(padding)\n",
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" TOY_BATCH_TARGET_LIST.append(output_tokens)\n",
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" TOY_BATCH_DECODER_DEFAULT.append(decoder_default_tokens)\n",
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"\n",
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||||
"# fix V to cover ALL ids (including specials) # <- important\n",
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"max_enc_id = max(max(row) for row in TOY_BATCH_INPUT_LIST) if TOY_BATCH_INPUT_LIST else 0\n",
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"max_tgt_id = max(max(row) for row in TOY_BATCH_TARGET_LIST) if TOY_BATCH_TARGET_LIST else 0\n",
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"TOKEN_SPACE_SIZE = max(TOKEN_SPACE_SIZE, max(PAD_TOKEN, END_TOKEN, max_enc_id, max_tgt_id) + 1)\n",
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"\n",
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"# Training loop\n",
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"LOSS_HISTORY = []\n",
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"NANOSOCRATES = Transformer.TrainingModel(\n",
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" TOKEN_SPACE_SIZE,\n",
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" EMBEDDED_SIZE,\n",
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" FEED_FORWARD_MULTIPLIER,\n",
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" ATTENTION_HEADS,\n",
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" NUMBER_OF_BLOCKS,\n",
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")\n",
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"\n",
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"collector = LogitsCollector(PAD_TOKEN, END_TOKEN, TOKENANO) # collects logits and decodes\n",
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"\n",
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"NANOSOCRATES.train()\n",
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"cross_entropy = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)\n",
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"optimizer = torch.optim.AdamW(NANOSOCRATES.parameters(), lr=1.0) # base lr works as factor\n",
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"scheduler = CustomLR(optimizer, EMBEDDED_SIZE, warmup_steps=4000, factor=1.0) # step each optimizer step\n",
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"\n",
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"current_epoch = 0\n",
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"BATCH_SIZE = min(32, len(TOY_BATCH_INPUT_LIST)) # small batch to stabilize\n",
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"\n",
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"while current_epoch < MAX_EPOCHS:\n",
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" # simple fixed mini-batch from the top; later you can shuffle/slice\n",
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" enc = torch.tensor(TOY_BATCH_INPUT_LIST[:BATCH_SIZE], dtype=torch.long) # [B,T] encoder token ids\n",
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" pad = torch.tensor(TOY_BATCH_PADDING_LIST[:BATCH_SIZE], dtype=torch.bool) # [B,T] True where encoder PAD is present\n",
|
||||
" tgt = torch.tensor(TOY_BATCH_TARGET_LIST[:BATCH_SIZE], dtype=torch.long) # [B,T] decoder targets (ground-truth)\n",
|
||||
"\n",
|
||||
" # decoder prefix buffer: <SOS> at pos 0, PAD elsewhere (no shift here) # we will fill it step by step\n",
|
||||
" dec = torch.tensor(TOY_BATCH_DECODER_DEFAULT[:BATCH_SIZE], dtype=torch.long) # [B,T]\n",
|
||||
"\n",
|
||||
" total_loss = 0.0\n",
|
||||
" collector.reset() # start fresh for this epoch\n",
|
||||
"\n",
|
||||
" T = tgt.size(1) # sequence length\n",
|
||||
" for t in range(T):\n",
|
||||
" # skip all-PAD steps to avoid CE divide-by-zero late in the sequence\n",
|
||||
" if (tgt[:, t] == PAD_TOKEN).all(): # all PAD at this timestep\n",
|
||||
" break\n",
|
||||
"\n",
|
||||
" optimizer.zero_grad(set_to_none=True) # clear grads for this token step\n",
|
||||
"\n",
|
||||
" prefix = dec[:, : t + 1] # [B, t+1] current decoder prefix\n",
|
||||
" dec_pad_mask = prefix.eq(PAD_TOKEN) # [B, t+1] True where PAD inside prefix\n",
|
||||
"\n",
|
||||
" # now decoder returns all steps up to t -> [B, t+1, V]\n",
|
||||
" logits_btV: torch.Tensor = NANOSOCRATES((enc, pad, prefix, dec_pad_mask)) # full logits for learning\n",
|
||||
" collector.add(logits_btV) # collector will take the last step\n",
|
||||
"\n",
|
||||
" Tp = logits_btV.size(1) # t+1\n",
|
||||
" V = logits_btV.size(-1) # vocab size\n",
|
||||
"\n",
|
||||
" # sanity guard (helps debug vocab mismatches fast)\n",
|
||||
" max_seen = tgt[:, :Tp].max().item()\n",
|
||||
" assert max_seen < V or (tgt[:, :Tp] == PAD_TOKEN).all(), \\\n",
|
||||
" f\"target id {max_seen} >= V ({V}). Fix TOKEN_SPACE_SIZE.\"\n",
|
||||
"\n",
|
||||
" # CE over all tokens produced so far (0..t). PAD is ignored by ignore_index\n",
|
||||
" loss_t = cross_entropy(\n",
|
||||
" logits_btV.reshape(-1, V), # [B*(t+1), V]\n",
|
||||
" tgt[:, :Tp].reshape(-1) # [B*(t+1)]\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" loss_t.backward() # backprop for this step\n",
|
||||
" optimizer.step() # update params\n",
|
||||
" scheduler.step() # Noam/warmup: step per optimizer step\n",
|
||||
"\n",
|
||||
" total_loss = float(loss_t.detach()) # keep last step loss for logging\n",
|
||||
"\n",
|
||||
" # teacher forcing: reveal the correct token for next position\n",
|
||||
" if t < T - 1:\n",
|
||||
" dec[:, t + 1] = tgt[:, t] # write ground-truth into next slot\n",
|
||||
"\n",
|
||||
" current_epoch += 1\n",
|
||||
" print(f\"EPOCH {current_epoch}\\n\\tLoss: {total_loss:.6f}\") # simple log\n",
|
||||
" collector.print_decoded() # print decoded predictions for the batch\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "deep_learning",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.13.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@ -1,308 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "7a311d4b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[7706, 290, 756, 4270, 7357, 115, 351, 1507, 1213, 410, 3382, 317, 497, 4740, 2784, 7712], [7706, 290, 756, 4270, 7357, 115, 351, 1507, 1213, 410, 3382, 317, 497, 4740, 2784, 7712], [7706, 290, 756, 4270, 7357, 115, 351, 1507, 1213, 410, 3382, 317, 497, 4740, 2784, 7712]]\n",
|
||||
"3\n",
|
||||
"Embedder Tensor: torch.Size([3, 16, 256])\n",
|
||||
"Values:\n",
|
||||
"tensor([[[-0.6981, 0.0804, -2.1672, ..., 0.3919, 0.3341, 1.0794],\n",
|
||||
" [ 2.5818, -0.2308, 0.6001, ..., -0.0500, -0.0408, -0.9852],\n",
|
||||
" [-0.6967, 0.8109, 1.3108, ..., 2.1693, 1.4143, -0.1236],\n",
|
||||
" ...,\n",
|
||||
" [ 2.1226, 2.5695, -1.6178, ..., -0.0652, -0.0802, 0.1103],\n",
|
||||
" [ 0.8770, -2.4782, 0.8536, ..., 2.0471, -1.5702, 0.7387],\n",
|
||||
" [ 1.4284, -0.4654, 0.1394, ..., 1.6520, 0.6728, 1.3851]],\n",
|
||||
"\n",
|
||||
" [[-0.6981, 0.0804, -2.1672, ..., 0.3919, 0.3341, 1.0794],\n",
|
||||
" [ 2.5818, -0.2308, 0.6001, ..., -0.0500, -0.0408, -0.9852],\n",
|
||||
" [-0.6967, 0.8109, 1.3108, ..., 2.1693, 1.4143, -0.1236],\n",
|
||||
" ...,\n",
|
||||
" [ 2.1226, 2.5695, -1.6178, ..., -0.0652, -0.0802, 0.1103],\n",
|
||||
" [ 0.8770, -2.4782, 0.8536, ..., 2.0471, -1.5702, 0.7387],\n",
|
||||
" [ 1.4284, -0.4654, 0.1394, ..., 1.6520, 0.6728, 1.3851]],\n",
|
||||
"\n",
|
||||
" [[-0.6981, 0.0804, -2.1672, ..., 0.3919, 0.3341, 1.0794],\n",
|
||||
" [ 2.5818, -0.2308, 0.6001, ..., -0.0500, -0.0408, -0.9852],\n",
|
||||
" [-0.6967, 0.8109, 1.3108, ..., 2.1693, 1.4143, -0.1236],\n",
|
||||
" ...,\n",
|
||||
" [ 2.1226, 2.5695, -1.6178, ..., -0.0652, -0.0802, 0.1103],\n",
|
||||
" [ 0.8770, -2.4782, 0.8536, ..., 2.0471, -1.5702, 0.7387],\n",
|
||||
" [ 1.4284, -0.4654, 0.1394, ..., 1.6520, 0.6728, 1.3851]]],\n",
|
||||
" grad_fn=<AddBackward0>)\n",
|
||||
"ENCODER Tensor: torch.Size([3, 1, 256])\n",
|
||||
"Values:\n",
|
||||
"tensor([[[ 8.0069e-01, 4.0532e-01, -1.8316e+00, -1.3902e+00, -1.1784e+00,\n",
|
||||
" 1.3667e+00, -9.7890e-01, 6.0696e-01, -1.4899e+00, 5.5765e-01,\n",
|
||||
" 4.5991e-02, 5.1214e-01, 3.1901e-01, 4.7577e-01, -2.9585e-01,\n",
|
||||
" -1.0811e+00, -1.5281e+00, -6.3773e-01, -9.5954e-01, 1.8497e+00,\n",
|
||||
" -1.1789e+00, -9.7387e-01, 1.1931e-01, -7.2703e-01, 5.3108e-01,\n",
|
||||
" -6.4877e-01, -4.5188e-01, 1.5185e+00, -8.3408e-01, 3.2824e-01,\n",
|
||||
" -1.8166e+00, 1.9548e+00, -5.2419e-01, -1.0693e+00, -1.8510e+00,\n",
|
||||
" 1.5440e+00, -3.2370e-01, -1.3990e+00, -4.6940e-01, 6.5840e-02,\n",
|
||||
" -9.2057e-01, 1.2513e+00, -5.9168e-01, 7.8198e-01, -1.3121e+00,\n",
|
||||
" 1.1492e+00, -2.3695e-01, -1.8935e+00, 1.1639e+00, -5.8169e-01,\n",
|
||||
" 2.5051e-01, -8.1654e-01, -1.0328e+00, 1.4285e+00, -8.1485e-01,\n",
|
||||
" 1.0614e+00, -3.3834e-01, -4.1667e-02, -1.1920e-01, 3.1383e-01,\n",
|
||||
" -5.9857e-01, 1.7327e-01, -1.6854e+00, -1.5174e+00, -2.6508e-01,\n",
|
||||
" -6.0082e-01, 5.1468e-01, 2.7909e-01, -2.5296e-01, -1.4670e+00,\n",
|
||||
" -1.3587e+00, -8.8864e-02, 3.2825e-01, 1.0950e+00, -1.0371e+00,\n",
|
||||
" 1.1744e+00, 5.2984e-01, 4.1751e-01, -9.8803e-01, 3.5631e-01,\n",
|
||||
" 4.7484e-01, 2.2435e-01, 1.4022e+00, 1.2242e+00, 1.1447e+00,\n",
|
||||
" -5.4052e-01, -9.1786e-01, -1.2299e+00, 1.1656e+00, 9.1570e-01,\n",
|
||||
" 1.8956e+00, 7.4344e-01, 4.2187e-01, -9.5426e-02, -3.2428e-01,\n",
|
||||
" 9.6364e-01, -2.3252e-01, 2.9036e-01, -2.4432e+00, 9.8019e-01,\n",
|
||||
" -4.6697e-02, 8.3910e-01, -4.3541e-01, -7.1915e-01, -7.5638e-01,\n",
|
||||
" 9.0217e-01, 2.0919e+00, -7.9533e-01, -1.5413e-01, -6.9260e-01,\n",
|
||||
" -1.3086e+00, 7.8925e-01, 1.8855e-01, 7.4043e-01, -3.8834e-01,\n",
|
||||
" 1.0272e-02, 1.0763e+00, 4.2142e-01, 6.6520e-01, 4.5996e-01,\n",
|
||||
" -8.5060e-01, -9.0101e-01, -4.2090e-01, 2.5596e-01, -1.4946e+00,\n",
|
||||
" 1.0925e-01, -7.5359e-01, -3.0447e-01, 1.0679e+00, 1.9398e+00,\n",
|
||||
" 8.1472e-01, 1.3498e+00, 1.1107e+00, 6.3288e-01, 3.1149e-01,\n",
|
||||
" -1.9333e+00, -1.5274e+00, 2.1794e-01, -3.1895e-02, 1.0756e+00,\n",
|
||||
" 1.0215e+00, 1.6938e+00, -1.0939e+00, 2.2690e+00, -7.0921e-01,\n",
|
||||
" 6.4212e-01, -6.5468e-01, 1.6839e+00, 5.7296e-01, -1.4031e+00,\n",
|
||||
" 3.9133e-01, -5.3541e-01, 4.3439e-01, -1.6785e+00, 5.2030e-03,\n",
|
||||
" 4.5155e-01, -7.0953e-01, -1.9656e-01, -3.8671e-02, -1.0927e+00,\n",
|
||||
" -3.0405e-01, -1.3818e-02, -3.7748e-01, 1.4412e+00, -1.4254e-01,\n",
|
||||
" 7.9939e-01, -8.5402e-01, -1.0330e+00, 1.7661e+00, -3.6084e-01,\n",
|
||||
" 1.5622e+00, 1.0240e+00, 1.9056e-01, -4.1480e-01, 6.9056e-01,\n",
|
||||
" 1.7204e+00, -9.9218e-01, -1.6504e-01, -1.1807e+00, 1.0827e+00,\n",
|
||||
" 1.5973e+00, 1.4849e-01, -2.2867e+00, 7.7322e-01, -6.8401e-01,\n",
|
||||
" -6.0493e-01, 1.0616e+00, -1.8034e-01, -1.8828e+00, 1.1031e-01,\n",
|
||||
" 2.5452e-01, -4.2489e-02, 8.1171e-01, 1.3429e+00, -6.5058e-01,\n",
|
||||
" -1.3531e+00, -1.2263e+00, 1.1226e+00, 1.2407e+00, -9.7453e-01,\n",
|
||||
" 9.4696e-01, 6.6186e-01, -5.0804e-01, 1.2647e-01, -1.1777e+00,\n",
|
||||
" 6.8443e-02, -1.3043e-01, 2.9595e-01, -1.5330e+00, -6.5733e-01,\n",
|
||||
" 1.1291e+00, 6.9629e-01, 4.4690e-01, 8.0151e-01, -1.2406e+00,\n",
|
||||
" 2.6085e+00, -2.0310e-01, -1.0226e+00, -6.9182e-02, 7.6600e-01,\n",
|
||||
" -9.9842e-01, 2.0896e+00, 2.6334e-01, -1.1559e-01, -6.6876e-01,\n",
|
||||
" -6.6295e-01, -1.6461e-01, 2.8270e+00, 3.2727e-01, 1.3724e+00,\n",
|
||||
" -1.0749e+00, 3.7782e-01, -1.5472e+00, 3.0822e-01, 5.7273e-02,\n",
|
||||
" 3.9136e-01, 8.2948e-01, 2.1438e-01, -9.8623e-01, 5.6053e-01,\n",
|
||||
" -1.5617e+00, -3.9595e-01, 1.0451e-02, -1.1860e+00, -1.4994e-01,\n",
|
||||
" 1.6566e+00, 2.0369e+00, -4.3995e-01, -4.4262e-01, -3.1014e-01,\n",
|
||||
" 5.9083e-01, -1.0765e+00, -5.2906e-01, 4.6039e-02, -1.0154e+00,\n",
|
||||
" 5.9942e-01]],\n",
|
||||
"\n",
|
||||
" [[ 1.2683e+00, -4.3200e-01, -1.3333e+00, -3.6705e-01, -5.8895e-01,\n",
|
||||
" 9.9266e-01, -4.2914e-01, 9.2765e-01, -1.0935e+00, 1.4975e+00,\n",
|
||||
" -5.3739e-01, -2.8332e-01, 9.1166e-01, 1.5010e+00, -2.1787e-01,\n",
|
||||
" -1.4258e+00, -2.7524e-01, -1.2602e+00, 2.0117e-01, 2.3906e+00,\n",
|
||||
" -9.6397e-01, -7.5872e-01, 3.3948e-01, -7.9353e-01, 9.1668e-01,\n",
|
||||
" 8.7734e-04, -3.0271e-01, 1.7087e+00, -1.0273e+00, 1.5174e+00,\n",
|
||||
" -2.6405e-02, 1.4236e+00, -9.9093e-01, 5.4787e-01, -1.0904e+00,\n",
|
||||
" 5.2156e-01, -6.3470e-01, -7.7688e-01, -1.2538e+00, -3.9307e-01,\n",
|
||||
" -7.6707e-01, 1.3733e+00, -7.2709e-01, 1.1185e+00, -1.5860e+00,\n",
|
||||
" -2.6148e-01, -3.7984e-01, -1.3604e+00, 9.2864e-02, -7.9642e-01,\n",
|
||||
" 1.0956e+00, 3.1202e-01, -4.1234e-01, 3.6488e-02, -1.4639e+00,\n",
|
||||
" 1.0947e+00, -7.9230e-01, 4.6913e-01, -2.3407e-01, 4.1768e-02,\n",
|
||||
" -1.5921e+00, 6.9743e-01, -7.0222e-01, -5.4705e-01, -6.5663e-01,\n",
|
||||
" -4.1810e-01, 2.7744e-01, 7.9178e-01, 7.5886e-01, -7.6302e-01,\n",
|
||||
" -1.2204e+00, -1.1103e+00, -1.3646e-01, 1.9589e+00, -1.3637e+00,\n",
|
||||
" 9.0804e-01, 2.3094e-01, -5.5953e-02, -6.7626e-01, 1.4242e+00,\n",
|
||||
" 1.0167e+00, 1.0705e+00, 2.2947e+00, 9.1274e-01, 1.2281e+00,\n",
|
||||
" -7.0638e-01, -1.2249e+00, -8.9208e-02, 1.1016e+00, 1.1940e+00,\n",
|
||||
" 3.5834e-01, 1.2961e+00, -4.6674e-01, 3.4572e-01, -4.3458e-01,\n",
|
||||
" 1.1008e+00, 3.7783e-01, -6.5841e-01, -2.3127e+00, 1.4617e+00,\n",
|
||||
" -1.2826e-01, 1.3463e-01, -8.5268e-01, -8.4144e-01, -1.8594e+00,\n",
|
||||
" 1.9260e-01, 1.6432e+00, -2.0640e-02, -5.0030e-01, -1.5334e-01,\n",
|
||||
" -6.1072e-01, -1.3694e-01, -3.7308e-01, 1.6603e+00, 1.1246e-01,\n",
|
||||
" 6.0823e-02, 7.8749e-01, -1.7002e-01, 1.2058e+00, 8.5615e-01,\n",
|
||||
" 1.2525e-01, -1.0584e+00, -4.7931e-01, 1.4088e-01, -1.8149e+00,\n",
|
||||
" 1.4654e+00, -1.0936e+00, 5.3182e-01, 9.5694e-01, 3.2472e+00,\n",
|
||||
" 3.4877e-01, 1.8491e+00, -1.5184e-01, 1.4711e+00, -7.6064e-01,\n",
|
||||
" -2.2144e+00, -1.8952e+00, -4.9502e-01, -6.6836e-01, 1.4946e+00,\n",
|
||||
" 6.7616e-01, 1.1501e+00, -9.4747e-01, 1.1009e+00, -1.4211e+00,\n",
|
||||
" 3.9528e-01, -9.5220e-01, 1.4886e+00, 7.1784e-01, -1.9941e+00,\n",
|
||||
" 6.7901e-02, -1.3109e-01, 1.1695e+00, 1.2861e-01, -2.8123e-01,\n",
|
||||
" -6.1611e-01, 1.5513e-01, -3.9289e-01, -4.5543e-02, -2.8628e-01,\n",
|
||||
" 2.6118e-01, 2.2623e-01, -6.3705e-01, 7.3591e-01, -7.8799e-01,\n",
|
||||
" 2.5053e-01, -1.5923e-01, -4.9584e-01, 1.9009e+00, -2.3263e-01,\n",
|
||||
" 1.2213e+00, 1.0313e+00, 2.0177e-02, -6.2209e-01, -3.5161e-01,\n",
|
||||
" 1.5143e+00, -7.2332e-02, 2.3909e-02, -2.1261e+00, 8.5199e-01,\n",
|
||||
" 1.9084e+00, 4.6845e-02, -2.3554e+00, 1.3735e+00, -7.3909e-01,\n",
|
||||
" -8.3949e-01, -3.9314e-01, -4.3324e-01, -9.6804e-01, -5.3124e-01,\n",
|
||||
" -6.5091e-01, -1.1738e+00, 1.3315e+00, 6.5606e-01, -1.4131e-01,\n",
|
||||
" -1.7712e+00, -1.1628e+00, 9.6813e-01, 8.7314e-01, -9.8027e-01,\n",
|
||||
" 6.9376e-01, 5.3878e-01, -1.6169e+00, 2.2860e-01, -6.2179e-01,\n",
|
||||
" -1.1043e-01, -3.9658e-01, 2.8712e-01, 8.2201e-02, 2.0888e-01,\n",
|
||||
" -5.9884e-01, 7.3092e-01, 6.9128e-01, 5.3977e-01, -1.5728e+00,\n",
|
||||
" 1.6878e+00, -8.2669e-01, -9.8076e-01, -3.4203e-01, 4.6939e-02,\n",
|
||||
" -1.3158e-01, 2.1923e+00, -6.6483e-02, -4.0687e-01, -1.2715e+00,\n",
|
||||
" -8.1549e-01, -1.2047e+00, 1.3547e+00, -4.2072e-01, 1.1674e+00,\n",
|
||||
" -5.1421e-01, 1.3055e+00, -1.1277e+00, 1.8372e+00, -1.1215e+00,\n",
|
||||
" 1.4797e+00, 2.8354e-01, -6.3974e-01, -1.2869e+00, -2.7897e-01,\n",
|
||||
" -1.0397e+00, 1.8622e-01, -5.0397e-02, -4.4865e-02, -7.6067e-01,\n",
|
||||
" 1.7715e+00, 1.5040e+00, -2.6854e-01, -5.2802e-01, -5.3407e-01,\n",
|
||||
" 2.0313e-02, -2.6276e-01, -7.0748e-01, -8.7328e-01, -3.4108e-01,\n",
|
||||
" 1.4313e+00]],\n",
|
||||
"\n",
|
||||
" [[ 7.7464e-01, -4.2187e-01, -2.0571e+00, -8.6709e-01, -1.5722e+00,\n",
|
||||
" 4.9540e-01, -1.5270e+00, 1.0499e+00, -1.9579e+00, -2.5298e-02,\n",
|
||||
" 4.3419e-01, 5.8822e-01, 1.3392e+00, 6.9604e-01, -9.7883e-01,\n",
|
||||
" -9.1354e-01, -9.1852e-01, -6.0951e-01, -6.6255e-02, 1.3907e+00,\n",
|
||||
" -6.2912e-01, -2.7524e-01, 1.9520e-02, -2.7154e-01, 1.5162e-01,\n",
|
||||
" 1.3318e-02, -8.9196e-01, 9.0976e-01, -1.3544e+00, 2.4276e-01,\n",
|
||||
" -7.4038e-01, 9.7062e-01, 3.2011e-01, 3.4486e-01, -2.3374e+00,\n",
|
||||
" 1.3311e+00, -3.1871e-02, -1.4468e+00, -1.5968e+00, 3.0418e-01,\n",
|
||||
" -7.7136e-01, 1.3427e+00, -1.2493e+00, 1.4114e+00, -1.2475e+00,\n",
|
||||
" 7.0239e-01, -9.6120e-02, -4.4365e-01, 5.3238e-01, -1.4933e+00,\n",
|
||||
" 5.4476e-01, -1.8490e-02, -5.9936e-01, 1.0878e+00, -1.8892e+00,\n",
|
||||
" 1.2810e+00, -1.0747e+00, 5.3514e-01, 1.7422e-01, 1.1354e+00,\n",
|
||||
" -7.4837e-01, 4.0327e-01, -1.8950e+00, -7.2336e-01, 2.4441e-01,\n",
|
||||
" -1.3650e-01, -4.8344e-01, 3.3921e-02, 5.0889e-01, -1.3769e+00,\n",
|
||||
" -2.5907e-01, -2.7549e-01, -1.9128e-01, 1.9751e+00, -7.1191e-01,\n",
|
||||
" 5.1910e-01, 1.0902e-01, 2.9995e-01, -3.5180e-01, -6.2139e-01,\n",
|
||||
" 7.2905e-01, -5.3177e-01, 4.3340e-01, 1.0071e+00, 1.7586e+00,\n",
|
||||
" -3.9963e-01, -2.5139e-01, -9.4213e-01, 9.2847e-01, 1.1298e+00,\n",
|
||||
" 7.8545e-01, 1.3188e+00, 3.7466e-01, 9.0773e-01, -4.0454e-02,\n",
|
||||
" 1.3444e+00, 6.0301e-01, 8.9929e-02, -2.0754e+00, 4.8614e-01,\n",
|
||||
" -9.7160e-01, 8.2446e-01, -1.1813e+00, -9.6185e-01, -9.2922e-02,\n",
|
||||
" 6.0154e-01, 1.6640e+00, -1.0461e+00, 1.5868e-01, -5.7239e-01,\n",
|
||||
" -6.2726e-01, 3.2848e-01, 5.9609e-01, 1.5563e+00, -4.0883e-01,\n",
|
||||
" 4.4902e-01, 1.4004e+00, 2.2426e-01, 3.8314e-01, -2.0641e-01,\n",
|
||||
" -1.6465e-01, -6.4645e-01, 1.5772e-01, 6.8907e-01, -1.2703e+00,\n",
|
||||
" 1.8914e-01, -6.2678e-01, 3.0179e-01, 1.2687e+00, 1.6849e+00,\n",
|
||||
" 1.5690e+00, 1.0999e+00, 1.5820e+00, -6.4808e-01, 5.1003e-01,\n",
|
||||
" -1.6674e+00, -1.2224e+00, 1.9769e-01, -1.3883e-01, 1.2179e+00,\n",
|
||||
" 1.2971e+00, 4.6259e-01, -5.8717e-01, 1.4532e+00, -1.0540e+00,\n",
|
||||
" 2.8689e-01, -1.3895e+00, 1.4014e+00, -4.0430e-01, -2.6099e+00,\n",
|
||||
" -1.0293e+00, -1.1097e+00, 8.6266e-01, -1.0535e+00, 7.1789e-01,\n",
|
||||
" 6.0642e-01, -1.2493e+00, -3.7762e-01, -4.1281e-02, -7.3049e-01,\n",
|
||||
" -7.2913e-04, -7.3122e-02, -2.3850e-01, 1.2546e+00, 1.8802e-01,\n",
|
||||
" 1.3135e+00, -5.0367e-01, 1.2456e-01, 2.7475e+00, -1.2486e+00,\n",
|
||||
" 1.4441e+00, 8.7469e-01, -5.6901e-01, -1.2145e-01, 3.1091e-01,\n",
|
||||
" 1.9406e+00, -8.1891e-01, 3.1316e-02, -1.2867e+00, 8.0780e-01,\n",
|
||||
" 7.0041e-01, 2.8903e-01, -1.6387e+00, 6.6553e-01, -1.3696e+00,\n",
|
||||
" -7.9454e-01, 3.3899e-01, -5.5822e-01, -8.1969e-01, -1.2410e-01,\n",
|
||||
" -3.7024e-01, -7.2536e-01, 7.5648e-01, 1.6899e+00, -1.7404e-01,\n",
|
||||
" -1.7191e+00, -7.2603e-01, 1.5046e+00, 8.3216e-01, -1.5304e+00,\n",
|
||||
" -1.8264e-01, 3.3451e-01, -5.6636e-02, 6.1099e-01, -9.8517e-01,\n",
|
||||
" 4.4856e-01, -8.6275e-01, 6.9264e-02, -1.1572e+00, 2.3373e-01,\n",
|
||||
" 5.9896e-01, 1.2384e-01, 1.0309e+00, 1.4273e+00, -8.4776e-01,\n",
|
||||
" 2.6236e+00, -9.0133e-01, -4.0009e-01, -4.9727e-01, 3.7945e-01,\n",
|
||||
" -9.0712e-01, 1.5725e+00, 1.6298e-01, 1.1544e-01, -4.3125e-01,\n",
|
||||
" -8.7131e-01, -2.5880e-01, 2.9032e+00, 2.7154e-01, 1.3677e+00,\n",
|
||||
" -8.8544e-01, 5.6083e-01, -1.8256e+00, 9.4832e-01, -1.0762e+00,\n",
|
||||
" 7.5421e-01, 6.5008e-01, -8.6361e-01, -1.4911e+00, -7.5930e-02,\n",
|
||||
" -1.6896e+00, 1.5223e-02, -1.5283e-01, -1.8741e+00, 1.1400e-01,\n",
|
||||
" 1.8822e+00, 2.6615e+00, 2.1607e-01, -5.6243e-01, 3.6730e-01,\n",
|
||||
" 4.0374e-01, -1.1973e+00, -5.3006e-01, -3.4750e-01, -4.4187e-01,\n",
|
||||
" 7.4358e-01]]], grad_fn=<NativeLayerNormBackward0>)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import random\n",
|
||||
"import torch\n",
|
||||
"from pathlib import Path\n",
|
||||
"import Project_Model.Libs.Embedder as Embedder\n",
|
||||
"import Project_Model.Libs.BPE as BPE\n",
|
||||
"import Project_Model.Libs.Transformer as Transformer\n",
|
||||
"\n",
|
||||
"# set a fixed seed\n",
|
||||
"torch.manual_seed(0)\n",
|
||||
"random.seed(0)\n",
|
||||
"\n",
|
||||
"TEXT = (\n",
|
||||
" \"<ABS>The Dark Knight is a 2008 superhero film directed by Christopher Nolan,<SOTL>\"\n",
|
||||
")\n",
|
||||
"OUT_TEXT = \"<START>\"\n",
|
||||
"VOCABULARY_PATH = Path(\"Assets/Model/toy_10/toy_dictionary.json\")\n",
|
||||
"SPECIAL_VOC = BPE.default_special_tokens()\n",
|
||||
"\n",
|
||||
"VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)\n",
|
||||
"TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)\n",
|
||||
"\n",
|
||||
"PAD_TOKEN = TOKENANO.encode(\"<PAD>\")[0]\n",
|
||||
"END_TOKEN = TOKENANO.encode(\"<END>\")[0]\n",
|
||||
"\n",
|
||||
"ENCODER_INPUT = TOKENANO.encode(TEXT)\n",
|
||||
"DECODER_INPUT = TOKENANO.encode(OUT_TEXT)\n",
|
||||
"MAX_LEN = len(ENCODER_INPUT) + 1\n",
|
||||
"\n",
|
||||
"EN_IN, PAD_MASK = Transformer.normalize_sequence(ENCODER_INPUT, MAX_LEN, PAD_TOKEN, END_TOKEN)\n",
|
||||
"DEC_IN, _ = Transformer.normalize_sequence(DECODER_INPUT, MAX_LEN, PAD_TOKEN, END_TOKEN)\n",
|
||||
"BATCH_LEN = 3\n",
|
||||
"\n",
|
||||
"INPUT_TOKENIZATION = [\n",
|
||||
" EN_IN\n",
|
||||
"] * BATCH_LEN\n",
|
||||
"OUTPUT_TOKENIZATION = [\n",
|
||||
" DEC_IN\n",
|
||||
"] * BATCH_LEN\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"print(INPUT_TOKENIZATION)\n",
|
||||
"\n",
|
||||
"TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size\n",
|
||||
"EMBEDDED_SIZE = 256\n",
|
||||
"FEED_FORWARD_DIM = EMBEDDED_SIZE * 4\n",
|
||||
"\n",
|
||||
"EMBEDDER = Embedder.NanoSocratesEmbedder(TOKEN_SPACE_SIZE, EMBEDDED_SIZE)\n",
|
||||
"encoder_tensor: torch.Tensor = EMBEDDER(INPUT_TOKENIZATION)\n",
|
||||
"ENCODER = torch.nn.Sequential(\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
")\n",
|
||||
"decoder_tensor: torch.Tensor = EMBEDDER(OUTPUT_TOKENIZATION)\n",
|
||||
"DECODER = torch.nn.Sequential(\n",
|
||||
" Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(len(INPUT_TOKENIZATION))\n",
|
||||
"print(f\"Embedder Tensor: {encoder_tensor.shape}\")\n",
|
||||
"print(f\"Values:\\n{encoder_tensor}\")\n",
|
||||
"\n",
|
||||
"BATCH_SIZE, TOKENS, DIMENSIONS = encoder_tensor.shape\n",
|
||||
"PAD_MASK = torch.tensor([PAD_MASK] * BATCH_LEN)\n",
|
||||
"\n",
|
||||
"encoder_out, _ = ENCODER((encoder_tensor, PAD_MASK))\n",
|
||||
"tensor: torch.Tensor\n",
|
||||
"tensor, _, _, _ = DECODER((decoder_tensor, encoder_out, encoder_out, None))\n",
|
||||
"\n",
|
||||
"print(f\"ENCODER Tensor: {tensor.shape}\")\n",
|
||||
"print(f\"Values:\\n{tensor}\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "deep_learning",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.13.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,131 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "c64b0e24",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[7706, 290, 756, 4270, 7357, 115, 351, 1507, 1213, 410, 3382, 317, 497, 4740, 2784, 7700], [7706, 290, 756, 4270, 7357, 115, 351, 1507, 1213, 410, 3382, 317, 497, 4740, 2784, 7700]]\n",
|
||||
"2\n",
|
||||
"Embedder Tensor: torch.Size([2, 16, 256])\n",
|
||||
"Values:\n",
|
||||
"tensor([[[-0.6981, 0.0804, -2.1672, ..., 0.3919, 0.3341, 1.0794],\n",
|
||||
" [ 2.5818, -0.2308, 0.6001, ..., -0.0500, -0.0408, -0.9852],\n",
|
||||
" [-0.6967, 0.8109, 1.3108, ..., 2.1693, 1.4143, -0.1236],\n",
|
||||
" ...,\n",
|
||||
" [ 2.1226, 2.5695, -1.6178, ..., -0.0652, -0.0802, 0.1103],\n",
|
||||
" [ 0.8770, -2.4782, 0.8536, ..., 2.0471, -1.5702, 0.7387],\n",
|
||||
" [-0.0495, -1.8601, 0.0405, ..., 2.3944, -0.4297, 1.1141]],\n",
|
||||
"\n",
|
||||
" [[-0.6981, 0.0804, -2.1672, ..., 0.3919, 0.3341, 1.0794],\n",
|
||||
" [ 2.5818, -0.2308, 0.6001, ..., -0.0500, -0.0408, -0.9852],\n",
|
||||
" [-0.6967, 0.8109, 1.3108, ..., 2.1693, 1.4143, -0.1236],\n",
|
||||
" ...,\n",
|
||||
" [ 2.1226, 2.5695, -1.6178, ..., -0.0652, -0.0802, 0.1103],\n",
|
||||
" [ 0.8770, -2.4782, 0.8536, ..., 2.0471, -1.5702, 0.7387],\n",
|
||||
" [-0.0495, -1.8601, 0.0405, ..., 2.3944, -0.4297, 1.1141]]],\n",
|
||||
" grad_fn=<AddBackward0>)\n",
|
||||
"ENCODER Tensor: torch.Size([2, 16, 256])\n",
|
||||
"Values:\n",
|
||||
"tensor([[[-1.6325, 0.4094, -2.1403, ..., 0.4654, 0.5993, 0.9683],\n",
|
||||
" [ 1.8236, 0.4025, -0.6972, ..., 0.2430, 0.2536, -1.0889],\n",
|
||||
" [-0.0587, 0.1618, -0.2335, ..., 1.7609, 1.2664, -0.4452],\n",
|
||||
" ...,\n",
|
||||
" [ 2.0337, 1.3184, -1.3165, ..., -0.3303, 0.6572, 0.0884],\n",
|
||||
" [ 0.5752, -2.5594, -0.2393, ..., 1.3318, -1.4236, 0.4686],\n",
|
||||
" [ 1.0075, -2.4273, -0.4593, ..., 1.6660, 0.0359, 0.2927]],\n",
|
||||
"\n",
|
||||
" [[-1.8300, -0.3079, -1.6585, ..., 0.4859, 0.5652, 0.8072],\n",
|
||||
" [ 1.5461, -0.5666, -0.0330, ..., 0.5651, 0.2974, -1.0879],\n",
|
||||
" [-0.9060, 0.2700, -0.4585, ..., 2.0363, 1.2657, -0.7060],\n",
|
||||
" ...,\n",
|
||||
" [ 1.6688, 1.7038, -1.9549, ..., -0.2052, 0.6270, 0.4598],\n",
|
||||
" [ 0.0482, -2.3951, -0.4351, ..., 1.6230, -1.3662, -0.0390],\n",
|
||||
" [ 0.8146, -2.6169, -0.6188, ..., 1.4525, 0.0507, 0.5177]]],\n",
|
||||
" grad_fn=<NativeLayerNormBackward0>)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import random\n",
|
||||
"import torch\n",
|
||||
"from pathlib import Path\n",
|
||||
"import Project_Model.Libs.Embedder as Embedder\n",
|
||||
"import Project_Model.Libs.BPE as BPE\n",
|
||||
"import Project_Model.Libs.Transformer as Transformer\n",
|
||||
"\n",
|
||||
"# set a fixed seed\n",
|
||||
"torch.manual_seed(0)\n",
|
||||
"random.seed(0)\n",
|
||||
"\n",
|
||||
"TEXT = \"<ABS>The Dark Knight is a 2008 superhero film directed by Christopher Nolan,<SOTL>\"\n",
|
||||
"\n",
|
||||
"VOCABULARY_PATH = Path(\"Assets/Model/toy_10/toy_dictionary.json\")\n",
|
||||
"SPECIAL_VOC = BPE.default_special_tokens()\n",
|
||||
"\n",
|
||||
"VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)\n",
|
||||
"TOKENANO = BPE.TokeNanoCore(\n",
|
||||
" VOCABULARY,\n",
|
||||
" SPECIAL_VOC\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"TOKENIZATION = [TOKENANO.encode(TEXT), TOKENANO.encode(TEXT)]\n",
|
||||
"print(TOKENIZATION)\n",
|
||||
"\n",
|
||||
"TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size\n",
|
||||
"EMBEDDED_SIZE = 256\n",
|
||||
"FEED_FORWARD_DIM = EMBEDDED_SIZE * 4\n",
|
||||
"\n",
|
||||
"EMBEDDER = Embedder.NanoSocratesEmbedder(TOKEN_SPACE_SIZE, EMBEDDED_SIZE)\n",
|
||||
"tensor: torch.Tensor = EMBEDDER(TOKENIZATION)\n",
|
||||
"ENCODER = torch.nn.Sequential(\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
")\n",
|
||||
"print(len(TOKENIZATION))\n",
|
||||
"print(f\"Embedder Tensor: {tensor.shape}\")\n",
|
||||
"print(f\"Values:\\n{tensor}\")\n",
|
||||
"\n",
|
||||
"BATCH_SIZE, TOKENS, DIMENSIONS = tensor.shape\n",
|
||||
"PAD_MASK = torch.tensor([[True] * TOKENS] * BATCH_SIZE, dtype=torch.bool)\n",
|
||||
"tensor, _ = ENCODER((tensor, PAD_MASK))\n",
|
||||
"print(f\"ENCODER Tensor: {tensor.shape}\")\n",
|
||||
"print(f\"Values:\\n{tensor}\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "deep_learning",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.13.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,263 +0,0 @@
|
||||
import random
|
||||
import torch
|
||||
from pathlib import Path
|
||||
import Project_Model.Libs.BPE as BPE
|
||||
import Project_Model.Libs.Transformer as Transformer
|
||||
import Project_Model.Libs.TransformerUtils as TUtils
|
||||
import Project_Model.Libs.TorchShims as torch_shims
|
||||
import Project_Model.Libs.Batch as Batch
|
||||
|
||||
# set a default device
|
||||
DEVICE = torch_shims.get_default_device()
|
||||
torch.set_default_device(DEVICE)
|
||||
|
||||
# set a fixed seed
|
||||
torch.manual_seed(0)
|
||||
random.seed(0)
|
||||
|
||||
# Get paths
|
||||
MODEL_DIR = "Assets/Model/curated"
|
||||
# MODEL_DIR= "Assets/Dataset/Tmp"
|
||||
VOCABULARY_PATH = Path("Assets/Model/small/bpe-small-16.json")
|
||||
TRAIN_DATASET_PATH = Path("Assets/Dataset/1-hop/small/holdout/train.csv")
|
||||
VALIDATION_DATASET_PATH = Path("Assets/Dataset/1-hop/small/holdout/evaluation.csv")
|
||||
TEST_DATASET_PATH = Path("Assets/Dataset/1-hop/small/holdout/test.csv")
|
||||
# TEST_DATASET_PATH = Path("Assets/Dataset/1-hop/toy/rdf_text.csv")
|
||||
MODEL_PATH = Path(f"{MODEL_DIR}/NanoSocrates.zip")
|
||||
|
||||
|
||||
# BPE Init
|
||||
SPECIAL_VOC = BPE.default_special_tokens()
|
||||
VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)
|
||||
TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)
|
||||
|
||||
|
||||
# Constants
|
||||
MASK_EXTRA_SPACE = 100
|
||||
REAL_TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size
|
||||
TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size + MASK_EXTRA_SPACE
|
||||
EMBEDDED_SIZE = 256
|
||||
FEED_FORWARD_MULTIPLIER = 4
|
||||
ATTENTION_HEADS = 4
|
||||
SENTENCE_LENGTH = 256
|
||||
NUMBER_OF_BLOCKS = 2
|
||||
|
||||
SOS_TOKEN = TOKENANO.encode("<SOS>")[0]
|
||||
PAD_TOKEN = TOKENANO.encode("<PAD>")[0]
|
||||
END_TOKEN = TOKENANO.encode("<EOS>")[0]
|
||||
SUBJ_TOKEN = TOKENANO.encode("<SUBJ>")[0]
|
||||
REL_TOKEN = TOKENANO.encode("<PRED>")[0]
|
||||
OBJ_TOKEN = TOKENANO.encode("<OBJ>")[0]
|
||||
MASK_TOKEN = TOKENANO.encode("<MASK>")[0]
|
||||
CONTINUTE_TOKEN = TOKENANO.encode("<CONTINUERDF>")[0]
|
||||
|
||||
SPECIAL_TOKENS: set[int] = set(TOKENANO.encode("".join(BPE.default_special_tokens())))
|
||||
ALLOWED_TOKENS = set([SUBJ_TOKEN, REL_TOKEN, OBJ_TOKEN])
|
||||
FORBIDDEN_TOKENS = SPECIAL_TOKENS - ALLOWED_TOKENS
|
||||
|
||||
|
||||
# Spanned_Masker
|
||||
MASKER = Transformer.SpannedMasker(REAL_TOKEN_SPACE_SIZE, FORBIDDEN_TOKENS, average_span=4)
|
||||
|
||||
TRAIN_BATCHER = Batch.Batcher(TRAIN_DATASET_PATH, SENTENCE_LENGTH, TOKENANO, MASKER)
|
||||
VALIDATION_BATCHER = Batch.Batcher(
|
||||
VALIDATION_DATASET_PATH, SENTENCE_LENGTH, TOKENANO, MASKER
|
||||
)
|
||||
TEST_BATCHER = Batch.Batcher(TEST_DATASET_PATH, SENTENCE_LENGTH, TOKENANO, MASKER, debug=True)
|
||||
|
||||
|
||||
# Model
|
||||
NANOSOCRATES_TRAIN = Transformer.TrainingModel(
|
||||
TOKEN_SPACE_SIZE,
|
||||
EMBEDDED_SIZE,
|
||||
FEED_FORWARD_MULTIPLIER,
|
||||
ATTENTION_HEADS,
|
||||
NUMBER_OF_BLOCKS,
|
||||
)
|
||||
|
||||
NANOSOCRATES = Transformer.NanoSocratesCore(
|
||||
TOKEN_SPACE_SIZE,
|
||||
SENTENCE_LENGTH,
|
||||
SOS_TOKEN,
|
||||
PAD_TOKEN,
|
||||
END_TOKEN,
|
||||
CONTINUTE_TOKEN,
|
||||
EMBEDDED_SIZE,
|
||||
FEED_FORWARD_MULTIPLIER,
|
||||
ATTENTION_HEADS,
|
||||
NUMBER_OF_BLOCKS,
|
||||
)
|
||||
|
||||
if MODEL_PATH.is_file():
|
||||
nanosocrates_dict = torch.load(MODEL_PATH, weights_only=True, map_location=DEVICE)
|
||||
NANOSOCRATES_TRAIN.load_state_dict(nanosocrates_dict)
|
||||
|
||||
_, ENCODER_ONLY, DECODER_ONLY = TUtils.decompose_nano_socrates(
|
||||
NANOSOCRATES, TOKEN_SPACE_SIZE, EMBEDDED_SIZE
|
||||
)
|
||||
|
||||
NANOSOCRATES = TUtils.train2inference(
|
||||
NANOSOCRATES_TRAIN,
|
||||
NANOSOCRATES
|
||||
)
|
||||
|
||||
NANOSOCRATES.eval()
|
||||
ENCODER_ONLY.eval()
|
||||
DECODER_ONLY.eval()
|
||||
NANOSOCRATES_TRAIN.eval()
|
||||
|
||||
task_1_metrics = []
|
||||
task_2_metrics = []
|
||||
task_3_metrics = []
|
||||
task_4_metrics = []
|
||||
|
||||
example_num = 0
|
||||
with torch.no_grad():
|
||||
for example in TEST_BATCHER.batch(1):
|
||||
|
||||
|
||||
print(f"DOING Example: {example_num}")
|
||||
|
||||
src_x, tgt_y, pad_x, pad_y, tasktype = example
|
||||
|
||||
enc_x = torch.tensor(src_x)
|
||||
|
||||
ACTUAL_BATCH_SIZE, _ = enc_x.shape
|
||||
enc_x_pad = torch.tensor(pad_x, dtype=torch.bool)
|
||||
tgt = torch.tensor(tgt_y)
|
||||
tgt_pad = torch.tensor(pad_y, dtype=torch.bool)
|
||||
|
||||
dec_x = Transformer.get_decoder_input(
|
||||
ACTUAL_BATCH_SIZE, SOS_TOKEN, PAD_TOKEN, SENTENCE_LENGTH
|
||||
)
|
||||
dec_x[:, 1:] = tgt[:, :-1]
|
||||
dec_x_pad = dec_x.eq(PAD_TOKEN)
|
||||
|
||||
out: torch.Tensor = NANOSOCRATES.inference((enc_x, enc_x_pad), tasktype)
|
||||
|
||||
tokens: list[int] = out.tolist()[0]
|
||||
tokens.append(END_TOKEN)
|
||||
tokens = list(map(lambda x: MASK_TOKEN if x > TOKENANO.vocabulary_size else x, tokens))
|
||||
out_string = TOKENANO.decode(tokens)
|
||||
|
||||
exp_tokens: list[int] = tgt_y[0]
|
||||
exp_tokens = list(map(lambda x: MASK_TOKEN if x > TOKENANO.vocabulary_size else x, exp_tokens))
|
||||
exp_string = TOKENANO.decode(exp_tokens)
|
||||
|
||||
enc_tokens: list[int] = src_x[0]
|
||||
enc_tokens = list(map(lambda x: MASK_TOKEN if x > TOKENANO.vocabulary_size else x, enc_tokens))
|
||||
enc_string = TOKENANO.decode(enc_tokens)
|
||||
|
||||
print(f"PROMPT:\n{enc_string}")
|
||||
print(f"EXPECTED:\n{exp_string}")
|
||||
print(f"ACTUAL:\n{out_string}")
|
||||
|
||||
if tasktype == Batch.TaskType.RDF2TXT:
|
||||
example_num += 1
|
||||
ref = TUtils.remove_padding(exp_tokens, PAD_TOKEN, END_TOKEN)
|
||||
pred = TUtils.remove_padding(tokens, PAD_TOKEN, END_TOKEN)
|
||||
ref_str = TOKENANO.decode(ref)
|
||||
pred_str = TOKENANO.decode(pred)
|
||||
|
||||
bleu, rouge, meteor = TUtils.rdf2txt([ref_str], [pred_str])
|
||||
|
||||
task_1_metrics.append(
|
||||
[
|
||||
bleu["bleu"], rouge["rougeL"], meteor["meteor"] # type: ignore
|
||||
]
|
||||
)
|
||||
|
||||
if tasktype == Batch.TaskType.TEXT2RDF:
|
||||
ref = TUtils.remove_padding(exp_tokens, PAD_TOKEN, END_TOKEN)
|
||||
pred = TUtils.remove_padding(tokens[1:], PAD_TOKEN, END_TOKEN)
|
||||
ref, pred = TUtils.balance_paddings(ref, pred, PAD_TOKEN)
|
||||
|
||||
precision, recall = TUtils.txt2rdf(ref, pred)
|
||||
|
||||
task_2_metrics.append(
|
||||
[
|
||||
precision["precision"], recall["recall"] # type: ignore
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
if tasktype == Batch.TaskType.MASKING:
|
||||
ref = TUtils.remove_padding(exp_tokens, PAD_TOKEN, END_TOKEN)
|
||||
pred = TUtils.remove_padding(tokens, PAD_TOKEN, END_TOKEN)
|
||||
ref, pred = TUtils.balance_paddings(ref, pred, PAD_TOKEN)
|
||||
|
||||
accuracy = TUtils.accuracy(ref, pred)
|
||||
|
||||
task_3_metrics.append(
|
||||
|
||||
accuracy["accuracy"] # type: ignore
|
||||
|
||||
)
|
||||
|
||||
if tasktype == Batch.TaskType.COMPLETATION:
|
||||
|
||||
ref = TUtils.remove_padding(exp_tokens, PAD_TOKEN, END_TOKEN)
|
||||
pred = TUtils.remove_padding(tokens, PAD_TOKEN, END_TOKEN)
|
||||
ref, pred = TUtils.balance_paddings(ref, pred, PAD_TOKEN)
|
||||
|
||||
precision, recall = TUtils.txt2rdf(ref, pred)
|
||||
|
||||
task_4_metrics.append(
|
||||
[
|
||||
precision["precision"], recall["recall"] # type: ignore
|
||||
]
|
||||
)
|
||||
|
||||
bleus = [row[0] for row in task_1_metrics]
|
||||
rouges = [row[1] for row in task_1_metrics]
|
||||
meteors = [row[2] for row in task_1_metrics]
|
||||
|
||||
prec_1 = [row[0] for row in task_2_metrics]
|
||||
rec_1 = [row[1] for row in task_2_metrics]
|
||||
|
||||
acc = task_3_metrics
|
||||
|
||||
prec_2 = [row[0] for row in task_4_metrics]
|
||||
rec_2 = [row[1] for row in task_4_metrics]
|
||||
|
||||
BLEU = TUtils.average(bleus)
|
||||
ROUGE = TUtils.average(rouges)
|
||||
METEOR = TUtils.average(meteors)
|
||||
|
||||
PREC_1 = TUtils.average(prec_1)
|
||||
REC_1 = TUtils.average(rec_1)
|
||||
F1_1 = TUtils.f1(PREC_1, REC_1)
|
||||
|
||||
ACC = TUtils.average(acc)
|
||||
|
||||
PREC_2 = TUtils.average(prec_2)
|
||||
REC_2 = TUtils.average(rec_2)
|
||||
F1_2 = TUtils.f1(PREC_2, REC_2)
|
||||
|
||||
SEPARATOR = "**************************************************************************"
|
||||
OUTPUT = "".join([
|
||||
f"{SEPARATOR}\n",
|
||||
"*\tRDF2TXT:\n",
|
||||
f"*\t\tBLEU: {BLEU} - ROUGE: {ROUGE} - METEOR: {METEOR}\n"
|
||||
f"{SEPARATOR}\n",
|
||||
"*\tTXT2RDF:\n",
|
||||
f"*\t\tPRECISION: {PREC_1} - RECALL: {REC_1} - F1: {F1_1}\n"
|
||||
f"{SEPARATOR}\n",
|
||||
"*\tRDF Completion 1:\n",
|
||||
f"*\t\tACCURACY: {ACC}\n"
|
||||
f"{SEPARATOR}\n",
|
||||
"*\tRDF Completion 2:\n",
|
||||
f"*\t\tPRECISION: {PREC_2} - RECALL: {REC_2} - F1: {F1_2}\n"
|
||||
f"{SEPARATOR}\n",
|
||||
""
|
||||
])
|
||||
|
||||
print(OUTPUT)
|
||||
|
||||
|
||||
print("\nDEBUG")
|
||||
print(task_1_metrics)
|
||||
print(task_2_metrics)
|
||||
print(task_3_metrics)
|
||||
print(task_4_metrics)
|
||||
|
||||
@ -1,157 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f5762da9",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"torch.Size([3, 17, 7714])\n",
|
||||
"torch.Size([3, 17])\n",
|
||||
"tensor([[2034, 6523, 5406, 3985, 5406, 6523, 2034, 2034, 5745, 643, 5406, 7405,\n",
|
||||
" 6523, 6230, 6419, 5745, 657],\n",
|
||||
" [2458, 830, 5745, 5745, 5406, 3741, 2034, 5745, 6302, 6419, 5406, 2411,\n",
|
||||
" 719, 830, 5745, 3189, 2775],\n",
|
||||
" [2034, 5745, 5327, 4696, 6523, 643, 6419, 1671, 6302, 4406, 5745, 643,\n",
|
||||
" 643, 1901, 1914, 1914, 719]])\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import random\n",
|
||||
"import torch\n",
|
||||
"from pathlib import Path\n",
|
||||
"import Project_Model.Libs.Embedder as Embedder\n",
|
||||
"import Project_Model.Libs.BPE as BPE\n",
|
||||
"import Project_Model.Libs.Transformer as Transformer\n",
|
||||
"\n",
|
||||
"# set a fixed seed\n",
|
||||
"torch.manual_seed(0)\n",
|
||||
"random.seed(0)\n",
|
||||
"\n",
|
||||
"# BPE Init\n",
|
||||
"VOCABULARY_PATH = Path(\"Assets/Model/toy_10/toy_dictionary.json\")\n",
|
||||
"SPECIAL_VOC = BPE.default_special_tokens()\n",
|
||||
"\n",
|
||||
"VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)\n",
|
||||
"TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Constants\n",
|
||||
"TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size + 1\n",
|
||||
"EMBEDDED_SIZE = 256\n",
|
||||
"FEED_FORWARD_DIM = EMBEDDED_SIZE * 4\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Model Init\n",
|
||||
"ENCODER_EMBEDDER = Embedder.NanoSocratesEmbedder(TOKEN_SPACE_SIZE, EMBEDDED_SIZE)\n",
|
||||
"DECODER_EMBEDDER = Embedder.NanoSocratesEmbedder(TOKEN_SPACE_SIZE, EMBEDDED_SIZE)\n",
|
||||
"\n",
|
||||
"ENCODER = torch.nn.Sequential(\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"DECODER = torch.nn.Sequential(\n",
|
||||
" Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
" Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"DETOKENER = Transformer.DeToken(\n",
|
||||
" EMBEDDED_SIZE,\n",
|
||||
" TOKEN_SPACE_SIZE\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Data\n",
|
||||
"TEXT = (\n",
|
||||
" \"<ABS>The Dark Knight is a 2008 superhero film directed by Christopher Nolan,<SOTL>\"\n",
|
||||
")\n",
|
||||
"OUT_TEXT = \"<START>\"\n",
|
||||
"\n",
|
||||
"PAD_TOKEN = TOKENANO.encode(\"<PAD>\")[0]\n",
|
||||
"END_TOKEN = TOKENANO.encode(\"<END>\")[0]\n",
|
||||
"\n",
|
||||
"ENCODER_INPUT = TOKENANO.encode(TEXT)\n",
|
||||
"DECODER_INPUT = TOKENANO.encode(OUT_TEXT)\n",
|
||||
"MAX_LEN = len(ENCODER_INPUT) + 1\n",
|
||||
"\n",
|
||||
"EN_IN, PAD_MASK = Transformer.normalize_sequence(ENCODER_INPUT, MAX_LEN, PAD_TOKEN, END_TOKEN)\n",
|
||||
"DEC_IN, _ = Transformer.normalize_sequence(DECODER_INPUT, MAX_LEN, PAD_TOKEN, END_TOKEN)\n",
|
||||
"\n",
|
||||
"BATCH_LEN = 3\n",
|
||||
"\n",
|
||||
"INPUT_TOKENIZATION = [\n",
|
||||
" EN_IN\n",
|
||||
"] * BATCH_LEN\n",
|
||||
"OUTPUT_TOKENIZATION = [\n",
|
||||
" DEC_IN\n",
|
||||
"] * BATCH_LEN\n",
|
||||
"\n",
|
||||
"encoder_tensor_input = ENCODER_EMBEDDER(INPUT_TOKENIZATION)\n",
|
||||
"encoder_padding_mask = torch.tensor([PAD_MASK] * BATCH_LEN)\n",
|
||||
"\n",
|
||||
"encoder_output, _ = ENCODER((encoder_tensor_input, encoder_padding_mask))\n",
|
||||
"\n",
|
||||
"decoder_tensor_input = DECODER_EMBEDDER(OUTPUT_TOKENIZATION)\n",
|
||||
"decoder_padding_mask = torch.tensor([[False] * MAX_LEN] * BATCH_LEN)\n",
|
||||
"\n",
|
||||
"decoder_output, _, _, _ = DECODER((decoder_tensor_input, encoder_output, encoder_output, None))\n",
|
||||
"\n",
|
||||
"logits: torch.Tensor = DETOKENER(decoder_output)\n",
|
||||
"\n",
|
||||
"print(logits.shape)\n",
|
||||
"\n",
|
||||
"# print(logits)\n",
|
||||
"\n",
|
||||
"most_probable_tokens = torch.argmax(logits, 2)\n",
|
||||
"\n",
|
||||
"print(most_probable_tokens.shape)\n",
|
||||
"print(most_probable_tokens)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "deep_learning",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.13.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,472 +0,0 @@
|
||||
import random
|
||||
import sys
|
||||
import torch
|
||||
import pandas as pd
|
||||
from pathlib import Path
|
||||
import Project_Model.Libs.Embedder as Embedder
|
||||
import Project_Model.Libs.BPE as BPE
|
||||
import Project_Model.Libs.Transformer as Transformer
|
||||
import Project_Model.Libs.TransformerUtils as TUtils
|
||||
import Project_Model.Libs.TorchShims as torch_shims
|
||||
import Project_Model.Libs.Batch as Batch
|
||||
from Project_Model.Libs.Training.loss_saver import Log
|
||||
|
||||
# set a fixed seed
|
||||
torch.manual_seed(0)
|
||||
random.seed(0)
|
||||
|
||||
|
||||
# set a default device
|
||||
DEVICE = torch_shims.get_default_device()
|
||||
torch.set_default_device(DEVICE)
|
||||
|
||||
|
||||
# Get paths
|
||||
CHECKPOINT_DIR = "Assets/Dataset/Tmp"
|
||||
VOCABULARY_PATH = Path("Assets/Model/small/bpe-small-16.json")
|
||||
TRAIN_DATASET_PATH = Path("Assets/Dataset/1-hop/toy/rdf_text.csv")
|
||||
VALIDATION_DATASET_PATH = Path("Assets/Dataset/1-hop/toy/rdf_text.csv")
|
||||
TEST_DATASET_PATH = Path("Assets/Dataset/1-hop/toy/rdf_text.csv")
|
||||
CHECKPOINT_PATH = Path(f"{CHECKPOINT_DIR}/NanoSocrates.zip")
|
||||
|
||||
NANO_OPTIM_PATH = Path(f"{CHECKPOINT_DIR}/nano_optim.zip")
|
||||
ENC_OPTIM_PATH = Path(f"{CHECKPOINT_DIR}/enc_optim.zip")
|
||||
DEC_OPTIM_PATH = Path(f"{CHECKPOINT_DIR}/dec_optim.zip")
|
||||
LAST_EPOCH_PATH = Path(f"{CHECKPOINT_DIR}/last_epoch.txt")
|
||||
|
||||
# log saver:
|
||||
loss_saver = Log(f"{CHECKPOINT_DIR}/log_loss.csv")
|
||||
|
||||
|
||||
|
||||
# BPE Init
|
||||
SPECIAL_VOC = BPE.default_special_tokens()
|
||||
VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)
|
||||
TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)
|
||||
|
||||
|
||||
# Constants
|
||||
MASK_EXTRA_SPACE = 100
|
||||
REAL_TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size
|
||||
TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size + MASK_EXTRA_SPACE
|
||||
EMBEDDED_SIZE = 256
|
||||
FEED_FORWARD_MULTIPLIER = 4
|
||||
ATTENTION_HEADS = 4
|
||||
SENTENCE_LENGTH = 256
|
||||
NUMBER_OF_BLOCKS = 2
|
||||
MAX_EPOCHS = int(300)
|
||||
PRETRAIN_EPOCHS = int(20)
|
||||
WARMUP_EPOCHS = int(30)
|
||||
MINI_BATCH_SIZE = 20
|
||||
VALIDATION_STEPS = 10
|
||||
CHECKPOINT_STEPS = VALIDATION_STEPS
|
||||
PATIENCE = 4
|
||||
CURRENT_EPOCH = -1 if not LAST_EPOCH_PATH.is_file() else int(LAST_EPOCH_PATH.read_text())
|
||||
VERBOSE = False
|
||||
LEARNING_RATE = 0.05
|
||||
LABEL_SMOOTHING = 0.01
|
||||
|
||||
SOS_TOKEN = TOKENANO.encode("<SOS>")[0]
|
||||
|
||||
PAD_TOKEN = TOKENANO.encode("<PAD>")[0]
|
||||
END_TOKEN = TOKENANO.encode("<END>")[0]
|
||||
SUBJ_TOKEN = TOKENANO.encode("<SUBJ>")[0]
|
||||
REL_TOKEN = TOKENANO.encode("<PRED>")[0]
|
||||
OBJ_TOKEN = TOKENANO.encode("<OBJ>")[0]
|
||||
MASK_TOKEN = TOKENANO.encode("<MASK>")[0]
|
||||
|
||||
SPECIAL_TOKENS: set[int] = set(TOKENANO.encode("".join(BPE.default_special_tokens())))
|
||||
ALLOWED_TOKENS = set([SUBJ_TOKEN, REL_TOKEN, OBJ_TOKEN])
|
||||
FORBIDDEN_TOKENS = SPECIAL_TOKENS - ALLOWED_TOKENS
|
||||
|
||||
|
||||
# Spanned_Masker
|
||||
MASKER = Transformer.SpannedMasker(REAL_TOKEN_SPACE_SIZE, FORBIDDEN_TOKENS, average_span=4)
|
||||
|
||||
TRAIN_BATCHER = Batch.Batcher(TRAIN_DATASET_PATH, SENTENCE_LENGTH, TOKENANO, MASKER)
|
||||
VALIDATION_BATCHER = Batch.Batcher(
|
||||
VALIDATION_DATASET_PATH, SENTENCE_LENGTH, TOKENANO, MASKER
|
||||
)
|
||||
TEST_BATCHER = Batch.Batcher(TEST_DATASET_PATH, SENTENCE_LENGTH, TOKENANO, MASKER)
|
||||
|
||||
|
||||
# Model
|
||||
NANOSOCRATES = Transformer.TrainingModel(
|
||||
TOKEN_SPACE_SIZE,
|
||||
EMBEDDED_SIZE,
|
||||
FEED_FORWARD_MULTIPLIER,
|
||||
ATTENTION_HEADS,
|
||||
NUMBER_OF_BLOCKS,
|
||||
)
|
||||
|
||||
if CHECKPOINT_PATH.is_file():
|
||||
nanosocrates_dict = torch.load(CHECKPOINT_PATH, weights_only=True)
|
||||
NANOSOCRATES.load_state_dict(nanosocrates_dict)
|
||||
|
||||
_, ENCODER_ONLY, DECODER_ONLY = TUtils.decompose_nano_socrates(
|
||||
NANOSOCRATES, TOKEN_SPACE_SIZE, EMBEDDED_SIZE
|
||||
)
|
||||
|
||||
|
||||
# Training constants
|
||||
nano_cross_entropy = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN, label_smoothing=LABEL_SMOOTHING)
|
||||
encoder_ce = torch.nn.CrossEntropyLoss( label_smoothing=LABEL_SMOOTHING)
|
||||
decoder_ce = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN, label_smoothing=LABEL_SMOOTHING)
|
||||
nano_optim = torch.optim.AdamW(NANOSOCRATES.parameters(), LEARNING_RATE)
|
||||
encoder_only_optim = torch.optim.AdamW(ENCODER_ONLY.parameters(), LEARNING_RATE)
|
||||
decoder_only_optim = torch.optim.AdamW(DECODER_ONLY.parameters(), LEARNING_RATE)
|
||||
|
||||
if NANO_OPTIM_PATH.is_file():
|
||||
optim_dict = torch.load(NANO_OPTIM_PATH)
|
||||
nano_optim.load_state_dict(optim_dict)
|
||||
|
||||
if ENC_OPTIM_PATH.is_file():
|
||||
optim_dict = torch.load(ENC_OPTIM_PATH)
|
||||
encoder_only_optim.load_state_dict(optim_dict)
|
||||
|
||||
if DEC_OPTIM_PATH.is_file():
|
||||
optim_dict = torch.load(DEC_OPTIM_PATH)
|
||||
decoder_only_optim.load_state_dict(optim_dict)
|
||||
|
||||
nano_scheduler = Transformer.WarmupLR(nano_optim, WARMUP_EPOCHS, EMBEDDED_SIZE, last_epoch=CURRENT_EPOCH)
|
||||
encoder_only_scheduler = Transformer.WarmupLR(
|
||||
encoder_only_optim, WARMUP_EPOCHS, EMBEDDED_SIZE, last_epoch=CURRENT_EPOCH
|
||||
)
|
||||
decoder_only_scheduler = Transformer.WarmupLR(
|
||||
decoder_only_optim, WARMUP_EPOCHS, EMBEDDED_SIZE, last_epoch=CURRENT_EPOCH
|
||||
)
|
||||
|
||||
current_epoch = CURRENT_EPOCH + 2
|
||||
patience = 0
|
||||
|
||||
|
||||
average_loss_validation = {
|
||||
"txt": float("inf"),
|
||||
"encoder_only": float("inf"),
|
||||
"decoder_only": float("inf"),
|
||||
}
|
||||
|
||||
|
||||
while current_epoch < MAX_EPOCHS:
|
||||
|
||||
NANOSOCRATES.train()
|
||||
ENCODER_ONLY.train()
|
||||
DECODER_ONLY.train()
|
||||
|
||||
text_batch_losses = []
|
||||
encoder_batch_losses = []
|
||||
decoder_batch_losses = []
|
||||
|
||||
batch_counter = 0
|
||||
|
||||
if VERBOSE:
|
||||
print(f"EPOCH {current_epoch} STARTING")
|
||||
|
||||
for batch in TRAIN_BATCHER.batch(MINI_BATCH_SIZE):
|
||||
|
||||
batch_counter += 1
|
||||
|
||||
src_x, tgt_y, pad_x, pad_y, tasktype = batch
|
||||
|
||||
enc_x = torch.tensor(src_x)
|
||||
|
||||
ACTUAL_BATCH_SIZE, _ = enc_x.shape
|
||||
enc_x_pad = torch.tensor(pad_x, dtype=torch.bool)
|
||||
tgt = torch.tensor(tgt_y)
|
||||
tgt_pad = torch.tensor(pad_y, dtype=torch.bool)
|
||||
|
||||
dec_x = Transformer.get_decoder_input(
|
||||
ACTUAL_BATCH_SIZE, SOS_TOKEN, PAD_TOKEN, SENTENCE_LENGTH
|
||||
)
|
||||
dec_x[:, 1:] = tgt[:, :-1]
|
||||
dec_x_pad = dec_x.eq(PAD_TOKEN)
|
||||
|
||||
if VERBOSE:
|
||||
for s in TUtils.decode_batch(enc_x, TOKENANO, MASK_TOKEN):
|
||||
print("Input")
|
||||
print(s)
|
||||
|
||||
for s in TUtils.decode_batch(enc_x_pad, TOKENANO, MASK_TOKEN):
|
||||
print("Encoder Padding mask")
|
||||
print(s)
|
||||
|
||||
for s in TUtils.decode_batch(tgt, TOKENANO, MASK_TOKEN):
|
||||
print("Desired Output")
|
||||
print(s)
|
||||
a_dx = dec_x[:,:]
|
||||
a_dx[:, -1]= END_TOKEN
|
||||
for s in TUtils.decode_batch(a_dx, TOKENANO, MASK_TOKEN):
|
||||
print("Decoder Input")
|
||||
print(s)
|
||||
|
||||
if VERBOSE:
|
||||
print(f"\tBATCH {batch_counter} Starting")
|
||||
|
||||
# Task 1 and Task 2
|
||||
if tasktype == Batch.TaskType.RDF2TXT or tasktype == Batch.TaskType.TEXT2RDF:
|
||||
|
||||
if VERBOSE:
|
||||
print(f"\tExecuting TASK 1 or 2 - BATCH {batch_counter}")
|
||||
|
||||
nano_optim.zero_grad()
|
||||
|
||||
pred_logits: torch.Tensor = NANOSOCRATES((enc_x, enc_x_pad, dec_x, dec_x_pad))
|
||||
pred_logits = pred_logits.permute(0, 2, 1)
|
||||
|
||||
loss: torch.Tensor = nano_cross_entropy(pred_logits, tgt)
|
||||
|
||||
loss.backward()
|
||||
nano_optim.step()
|
||||
|
||||
text_batch_losses.append(loss)
|
||||
continue
|
||||
|
||||
# Pretrain first
|
||||
if current_epoch < PRETRAIN_EPOCHS:
|
||||
continue
|
||||
|
||||
# Task 3
|
||||
if tasktype == Batch.TaskType.MASKING:
|
||||
|
||||
if VERBOSE:
|
||||
print(f"\tExecuting TASK 3 - BATCH {batch_counter}")
|
||||
|
||||
encoder_only_optim.zero_grad()
|
||||
|
||||
pred_logits = ENCODER_ONLY((enc_x, enc_x_pad))
|
||||
pred_logits = pred_logits.permute(0, 2, 1)
|
||||
# print(torch.max(tgt))
|
||||
|
||||
loss: torch.Tensor = encoder_ce(pred_logits, tgt)
|
||||
|
||||
loss.backward()
|
||||
encoder_only_optim.step()
|
||||
|
||||
exp_tokens: list[int] = tgt_y[0]
|
||||
exp_tokens = list(map(lambda x: MASK_TOKEN if x > TOKENANO.vocabulary_size else x, exp_tokens))
|
||||
exp_string = TOKENANO.decode(exp_tokens)
|
||||
|
||||
enc_tokens: list[int] = src_x[0]
|
||||
enc_tokens = list(map(lambda x: MASK_TOKEN if x > TOKENANO.vocabulary_size else x, enc_tokens))
|
||||
enc_string = TOKENANO.decode(enc_tokens)
|
||||
|
||||
print(f"PROMPT:\n{enc_string}")
|
||||
print(f"EXPECTED:\n{exp_string}")
|
||||
|
||||
|
||||
encoder_batch_losses.append(loss.item())
|
||||
|
||||
continue
|
||||
|
||||
# Task 4
|
||||
if tasktype == Batch.TaskType.COMPLETATION:
|
||||
|
||||
if VERBOSE:
|
||||
print(f"\tExecuting TASK 4 - BATCH {batch_counter}")
|
||||
|
||||
decoder_only_optim.zero_grad()
|
||||
|
||||
pred_logits = DECODER_ONLY((dec_x, enc_x_pad, dec_x_pad))
|
||||
pred_logits = pred_logits.permute(0, 2, 1)
|
||||
|
||||
loss: torch.Tensor = decoder_ce(pred_logits, tgt)
|
||||
|
||||
loss.backward()
|
||||
decoder_only_optim.step()
|
||||
|
||||
decoder_batch_losses.append(
|
||||
loss
|
||||
)
|
||||
|
||||
continue
|
||||
|
||||
nano_scheduler.step()
|
||||
encoder_only_scheduler.step()
|
||||
decoder_only_scheduler.step()
|
||||
|
||||
current_epoch += 1
|
||||
|
||||
if current_epoch % VALIDATION_STEPS == 0:
|
||||
|
||||
NANOSOCRATES.eval()
|
||||
ENCODER_ONLY.eval()
|
||||
DECODER_ONLY.eval()
|
||||
|
||||
with torch.no_grad():
|
||||
txt_avg_batch_losses = []
|
||||
enc_avg_batch_losses = []
|
||||
dec_avg_batch_losses = []
|
||||
|
||||
for batch in VALIDATION_BATCHER.batch(MINI_BATCH_SIZE):
|
||||
|
||||
src_x, tgt_y, pad_x, pad_y, tasktype = batch
|
||||
|
||||
enc_x = torch.tensor(src_x)
|
||||
|
||||
ACTUAL_BATCH_SIZE, _ = enc_x.shape
|
||||
enc_x_pad = torch.tensor(pad_x, dtype=torch.bool)
|
||||
tgt = torch.tensor(tgt_y)
|
||||
tgt_pad = torch.tensor(pad_y, dtype=torch.bool)
|
||||
|
||||
dec_x = Transformer.get_decoder_input(
|
||||
ACTUAL_BATCH_SIZE, SOS_TOKEN, PAD_TOKEN, SENTENCE_LENGTH
|
||||
)
|
||||
dec_x[:, 1:] = tgt[:, :-1]
|
||||
dec_x_pad = dec_x.eq(PAD_TOKEN)
|
||||
|
||||
# Task 1 and Task 2
|
||||
if (
|
||||
tasktype == Batch.TaskType.RDF2TXT
|
||||
or tasktype == Batch.TaskType.TEXT2RDF
|
||||
):
|
||||
|
||||
|
||||
pred_logits = NANOSOCRATES((enc_x, enc_x_pad, dec_x, dec_x_pad))
|
||||
|
||||
pred_logits = pred_logits.permute(0, 2, 1)
|
||||
|
||||
loss: torch.Tensor = nano_cross_entropy(
|
||||
pred_logits, tgt
|
||||
)
|
||||
|
||||
txt_avg_batch_losses.append(loss)
|
||||
|
||||
continue
|
||||
|
||||
# Pretrain first
|
||||
if current_epoch <= PRETRAIN_EPOCHS:
|
||||
continue
|
||||
|
||||
# Task 3
|
||||
if tasktype == Batch.TaskType.MASKING:
|
||||
|
||||
pred_logits = ENCODER_ONLY((enc_x, enc_x_pad))
|
||||
pred_logits = pred_logits.permute(0, 2, 1)
|
||||
|
||||
loss: torch.Tensor = encoder_ce(pred_logits, tgt)
|
||||
|
||||
enc_avg_batch_losses.append(loss.item())
|
||||
|
||||
continue
|
||||
|
||||
# Task 4
|
||||
if tasktype == Batch.TaskType.COMPLETATION:
|
||||
|
||||
|
||||
|
||||
pred_logits = DECODER_ONLY((dec_x, enc_x_pad, dec_x_pad))
|
||||
|
||||
pred_logits = pred_logits.permute(0, 2, 1)
|
||||
|
||||
loss: torch.Tensor = decoder_ce(pred_logits, tgt)
|
||||
|
||||
|
||||
dec_avg_batch_losses.append(loss)
|
||||
|
||||
continue
|
||||
|
||||
txt_avg_loss = sum(txt_avg_batch_losses) / len(txt_avg_batch_losses)
|
||||
enc_avg_loss = float("inf")
|
||||
dec_avg_loss = float("inf")
|
||||
|
||||
if current_epoch > PRETRAIN_EPOCHS:
|
||||
enc_avg_loss = sum(enc_avg_batch_losses) / len(enc_avg_batch_losses)
|
||||
dec_avg_loss = sum(dec_avg_batch_losses) / len(dec_avg_batch_losses)
|
||||
|
||||
if current_epoch < PRETRAIN_EPOCHS:
|
||||
|
||||
if txt_avg_loss < average_loss_validation["txt"]:
|
||||
average_loss_validation["txt"] = txt_avg_loss
|
||||
else:
|
||||
patience += 1
|
||||
if VERBOSE:
|
||||
print(f"losing a patience, current irritation: {patience}")
|
||||
else:
|
||||
|
||||
counter = 0
|
||||
|
||||
if txt_avg_loss > average_loss_validation["txt"]:
|
||||
|
||||
if VERBOSE:
|
||||
print("txt average is higher than lowest")
|
||||
counter += 1
|
||||
else:
|
||||
average_loss_validation["txt"] = txt_avg_loss
|
||||
|
||||
if enc_avg_loss > average_loss_validation["encoder_only"]:
|
||||
if VERBOSE:
|
||||
print("masking average is higher than lowest")
|
||||
counter += 1
|
||||
else:
|
||||
average_loss_validation["encoder_only"] = enc_avg_loss
|
||||
|
||||
if dec_avg_loss > average_loss_validation["decoder_only"]:
|
||||
if VERBOSE:
|
||||
print("decoding only average is higher than lowest")
|
||||
counter += 1
|
||||
else:
|
||||
average_loss_validation["decoder_only"] = dec_avg_loss
|
||||
|
||||
if counter > 1:
|
||||
patience += 1
|
||||
if VERBOSE:
|
||||
print(f"losing a patience, current irritation: {patience}")
|
||||
|
||||
|
||||
if counter == 0:
|
||||
patience = max(0, patience - 1)
|
||||
if VERBOSE:
|
||||
print(f"all good, gaining a patience, current irritation: {patience}")
|
||||
|
||||
|
||||
|
||||
txt_train_avg_loss = sum(text_batch_losses) / len(text_batch_losses)
|
||||
|
||||
enc_avg_train_loss = float("inf")
|
||||
dec_avg_train_loss = float("inf")
|
||||
|
||||
if current_epoch > PRETRAIN_EPOCHS:
|
||||
try:
|
||||
enc_avg_train_loss = sum(encoder_batch_losses) / len(encoder_batch_losses)
|
||||
dec_avg_train_loss = sum(decoder_batch_losses) / len(decoder_batch_losses)
|
||||
except:
|
||||
pass
|
||||
|
||||
# write on log
|
||||
loss_saver.write([current_epoch, txt_train_avg_loss,enc_avg_train_loss,dec_avg_train_loss,txt_avg_loss,enc_avg_loss,dec_avg_loss])
|
||||
SEPARATOR = "================================================================================================================"
|
||||
DEBUG_TEXT = "".join(
|
||||
[
|
||||
f"{SEPARATOR}\n",
|
||||
f"EPOCH {current_epoch}\n",
|
||||
f"{SEPARATOR}\n",
|
||||
f"Train Losses:\n",
|
||||
f"\tAvg Losses:\n",
|
||||
f"\t\tavg_txt: {txt_train_avg_loss} - avg_enc: {enc_avg_train_loss} - avg_dec: {dec_avg_train_loss}\n",
|
||||
f"{SEPARATOR}\n",
|
||||
f"Validation Losses:\n",
|
||||
f"\ttxt_loss: {txt_avg_loss} - masking_loss: {enc_avg_loss} - prediction_loss: {dec_avg_loss}\n",
|
||||
f"{SEPARATOR}\n",
|
||||
]
|
||||
)
|
||||
|
||||
print(DEBUG_TEXT)
|
||||
|
||||
# Warn about patience
|
||||
if patience == PATIENCE:
|
||||
print("Model is likely overfitting, so let's stop here")
|
||||
|
||||
# SAVE MODEL
|
||||
if current_epoch % CHECKPOINT_STEPS == 0 or patience == PATIENCE:
|
||||
print(f"Saving model at {CHECKPOINT_PATH.as_posix()}")
|
||||
torch.save(NANOSOCRATES.state_dict(), CHECKPOINT_PATH)
|
||||
torch.save(nano_optim.state_dict(), NANO_OPTIM_PATH)
|
||||
torch.save(encoder_only_optim.state_dict(), ENC_OPTIM_PATH)
|
||||
torch.save(decoder_only_optim.state_dict(), DEC_OPTIM_PATH)
|
||||
FILE = open(LAST_EPOCH_PATH, "w", encoding="utf-8")
|
||||
FILE.write(f"{current_epoch}")
|
||||
FILE.close()
|
||||
|
||||
|
||||
if patience == PATIENCE:
|
||||
exit(0)
|
||||
@ -1,224 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "adbd9598",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"c:\\Users\\Chris\\miniconda3\\envs\\deep_learning\\Lib\\site-packages\\torch\\utils\\_device.py:103: UserWarning: Aten Op fallback from XPU to CPU happends. This may have performance implications. If need debug the fallback ops please set environment variable `PYTORCH_DEBUG_XPU_FALLBACK=1` (Triggered internally at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\build\\xpu\\ATen\\RegisterXPU_0.cpp:54528.)\n",
|
||||
" return func(*args, **kwargs)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
|
||||
"\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
|
||||
"\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
|
||||
"\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import random\n",
|
||||
"import torch\n",
|
||||
"import pandas as pd\n",
|
||||
"from pathlib import Path\n",
|
||||
"import Project_Model.Libs.Embedder as Embedder\n",
|
||||
"import Project_Model.Libs.BPE as BPE\n",
|
||||
"import Project_Model.Libs.Transformer as Transformer\n",
|
||||
"import Project_Model.Libs.TorchShims as torch_shims\n",
|
||||
"\n",
|
||||
"# set a fixed seed\n",
|
||||
"torch.manual_seed(0)\n",
|
||||
"random.seed(0)\n",
|
||||
"DEVICE = torch_shims.get_default_device()\n",
|
||||
"torch.set_default_device(DEVICE)\n",
|
||||
"\n",
|
||||
"# set a default device\n",
|
||||
"\n",
|
||||
"# BPE Init\n",
|
||||
"VOCABULARY_PATH = Path(\"Assets/Model/toy_10/toy_dictionary.json\")\n",
|
||||
"SPECIAL_VOC = BPE.default_special_tokens()\n",
|
||||
"\n",
|
||||
"VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)\n",
|
||||
"TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Constants\n",
|
||||
"TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size + 1\n",
|
||||
"EMBEDDED_SIZE = 256\n",
|
||||
"FEED_FORWARD_MULTIPLIER = 4\n",
|
||||
"ATTENTION_HEADS = 8\n",
|
||||
"SENTENCE_LENGTH = 256\n",
|
||||
"NUMBER_OF_BLOCKS = 4\n",
|
||||
"MAX_EPOCHS = int(1e3)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"PAD_TOKEN = TOKENANO.encode(\"<PAD>\")[0]\n",
|
||||
"END_TOKEN = TOKENANO.encode(\"<END>\")[0]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Load CSV\n",
|
||||
"TOY_DATASET_PATH = Path(\"Assets/Dataset/1-hop/toy/rdf_text.csv\")\n",
|
||||
"\n",
|
||||
"TOY_DATASET = pd.read_csv(TOY_DATASET_PATH)\n",
|
||||
"\n",
|
||||
"TOY_BATCH_INPUT_LIST: list[list[int]] = []\n",
|
||||
"TOY_BATCH_PADDING_LIST: list[list[bool]] = []\n",
|
||||
"TOY_BATCH_TARGET_LIST: list[list[int]] = []\n",
|
||||
"TOY_BATCH_DECODER_DEFAULT: list[list[int]]= []\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"for index, row in TOY_DATASET.iterrows():\n",
|
||||
"\n",
|
||||
" RDFs: str = row[\"RDFs\"]\n",
|
||||
" Abstract: str = row[\"Abstract\"]\n",
|
||||
"\n",
|
||||
" input_tokens = TOKENANO.encode(RDFs)\n",
|
||||
" output_tokens = TOKENANO.encode(Abstract)[1:]\n",
|
||||
" decoder_default_tokens = TOKENANO.encode(\"<SOS>\")\n",
|
||||
"\n",
|
||||
" input_tokens, padding = Transformer.normalize_sequence(\n",
|
||||
" input_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
|
||||
" )\n",
|
||||
" output_tokens, _ = Transformer.normalize_sequence(\n",
|
||||
" output_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
|
||||
" )\n",
|
||||
" decoder_default_tokens, _ = Transformer.normalize_sequence(\n",
|
||||
" decoder_default_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN, False\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" TOY_BATCH_INPUT_LIST.append(input_tokens)\n",
|
||||
" TOY_BATCH_PADDING_LIST.append(padding)\n",
|
||||
" TOY_BATCH_TARGET_LIST.append(output_tokens)\n",
|
||||
" TOY_BATCH_DECODER_DEFAULT.append(decoder_default_tokens)\n",
|
||||
"\n",
|
||||
" output_tokens = TOKENANO.encode(RDFs)\n",
|
||||
" input_tokens = TOKENANO.encode(Abstract)[1:]\n",
|
||||
" decoder_default_tokens = TOKENANO.encode(\"<SOS>\")\n",
|
||||
"\n",
|
||||
" input_tokens, padding = Transformer.normalize_sequence(\n",
|
||||
" input_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
|
||||
" )\n",
|
||||
" output_tokens, _ = Transformer.normalize_sequence(\n",
|
||||
" output_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
|
||||
" )\n",
|
||||
" decoder_default_tokens, _ = Transformer.normalize_sequence(\n",
|
||||
" decoder_default_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN, False\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" TOY_BATCH_INPUT_LIST.append(input_tokens)\n",
|
||||
" TOY_BATCH_PADDING_LIST.append(padding)\n",
|
||||
" TOY_BATCH_TARGET_LIST.append(output_tokens)\n",
|
||||
" TOY_BATCH_DECODER_DEFAULT.append(decoder_default_tokens)\n",
|
||||
"\n",
|
||||
"# Training loop\n",
|
||||
"LOSS_HISTORY = []\n",
|
||||
"NANOSOCRATES = Transformer.TrainingModel(\n",
|
||||
" TOKEN_SPACE_SIZE,\n",
|
||||
" EMBEDDED_SIZE,\n",
|
||||
" FEED_FORWARD_MULTIPLIER,\n",
|
||||
" ATTENTION_HEADS,\n",
|
||||
" NUMBER_OF_BLOCKS\n",
|
||||
")\n",
|
||||
"cross_entropy = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)\n",
|
||||
"optimizer = torch.optim.AdamW(NANOSOCRATES.parameters())\n",
|
||||
"scheduler = Transformer.WarmupLR(optimizer, 4000, EMBEDDED_SIZE)\n",
|
||||
"last_loss = 0\n",
|
||||
"current_epoch = 0\n",
|
||||
"\n",
|
||||
"while current_epoch < MAX_EPOCHS:\n",
|
||||
"\n",
|
||||
" optimizer.zero_grad()\n",
|
||||
"\n",
|
||||
" encoder_list = torch.tensor(TOY_BATCH_INPUT_LIST[:])\n",
|
||||
" decoder_list = torch.tensor(TOY_BATCH_DECODER_DEFAULT[:])\n",
|
||||
" src_padding = torch.tensor(TOY_BATCH_PADDING_LIST[:], dtype=torch.bool)\n",
|
||||
"\n",
|
||||
" # Transform target into logits\n",
|
||||
" target_logits = torch.tensor(TOY_BATCH_TARGET_LIST[:])\n",
|
||||
"\n",
|
||||
" last_loss = 0\n",
|
||||
" last_prediction: torch.Tensor\n",
|
||||
"\n",
|
||||
" for i in range(0, SENTENCE_LENGTH):\n",
|
||||
"\n",
|
||||
" optimizer.zero_grad()\n",
|
||||
" tgt_padding = decoder_list.eq(PAD_TOKEN)\n",
|
||||
"\n",
|
||||
" logits: torch.Tensor = NANOSOCRATES((encoder_list, src_padding, decoder_list, tgt_padding))\n",
|
||||
" prob = torch.softmax(logits, 2)\n",
|
||||
"\n",
|
||||
" most_probable_tokens = torch.argmax(prob, 2)\n",
|
||||
" last_prediction = most_probable_tokens\n",
|
||||
"\n",
|
||||
" logits = logits[:,:i,:]\n",
|
||||
" logits = logits.permute(0, 2, 1)\n",
|
||||
"\n",
|
||||
" loss : torch.Tensor = cross_entropy(logits, target_logits[:, 0:i])\n",
|
||||
" # loss : torch.Tensor = cross_entropy(logits, target_logits)\n",
|
||||
"\n",
|
||||
" last_loss = loss\n",
|
||||
" loss.backward()\n",
|
||||
" optimizer.step()\n",
|
||||
" scheduler.step()\n",
|
||||
"\n",
|
||||
" if i < SENTENCE_LENGTH - 1:\n",
|
||||
" decoder_list[:,i+1] = target_logits[:,i]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" current_epoch += 1\n",
|
||||
"\n",
|
||||
" if current_epoch % 1 == 0:\n",
|
||||
" print(f\"EPOCH {current_epoch}\\n\\tLoss: {last_loss}\")\n",
|
||||
"\n",
|
||||
" for encoded_sentence, expected_sentence in zip(\n",
|
||||
" Transformer.tensor2token(last_prediction[:,:], END_TOKEN), # type: ignore\n",
|
||||
" Transformer.tensor2token(target_logits[:,:], END_TOKEN)\n",
|
||||
" ):\n",
|
||||
" decoded_sentence = TOKENANO.decode(encoded_sentence)\n",
|
||||
" decoded_target = TOKENANO.decode(expected_sentence)\n",
|
||||
" print(f\"\\tACTUAL:\\n\\t\\t{decoded_sentence}\\n\\tEXPECTED:\\n\\t\\t{decoded_target}\\n\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "deep_learning",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.13.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,509 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "adbef43f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"c:\\Users\\Chris\\miniconda3\\envs\\deep_learning\\Lib\\site-packages\\torch\\utils\\_device.py:103: UserWarning: Aten Op fallback from XPU to CPU happends. This may have performance implications. If need debug the fallback ops please set environment variable `PYTORCH_DEBUG_XPU_FALLBACK=1` (Triggered internally at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\build\\xpu\\ATen\\RegisterXPU_0.cpp:54528.)\n",
|
||||
" return func(*args, **kwargs)\n",
|
||||
"c:\\Users\\Chris\\miniconda3\\envs\\deep_learning\\Lib\\site-packages\\torch\\optim\\lr_scheduler.py:192: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
|
||||
" warnings.warn(\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "IndexError",
|
||||
"evalue": "list index out of range",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||||
"\u001b[31mIndexError\u001b[39m Traceback (most recent call last)",
|
||||
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 383\u001b[39m\n\u001b[32m 381\u001b[39m txt_min_train_losses = text_batch_losses[:][\u001b[32m0\u001b[39m]\n\u001b[32m 382\u001b[39m txt_avg_train_losses = text_batch_losses[:][\u001b[32m1\u001b[39m]\n\u001b[32m--> \u001b[39m\u001b[32m383\u001b[39m txt_max_train_losses = \u001b[43mtext_batch_losses\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[32m 385\u001b[39m txt_min_loss = \u001b[38;5;28mmin\u001b[39m(txt_min_train_losses)\n\u001b[32m 386\u001b[39m txt_avg_min_loss = \u001b[38;5;28msum\u001b[39m(txt_min_train_losses) / \u001b[38;5;28mlen\u001b[39m(txt_min_train_losses)\n",
|
||||
"\u001b[31mIndexError\u001b[39m: list index out of range"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import random\n",
|
||||
"import sys\n",
|
||||
"import torch\n",
|
||||
"import pandas as pd\n",
|
||||
"from pathlib import Path\n",
|
||||
"import Project_Model.Libs.Embedder as Embedder\n",
|
||||
"import Project_Model.Libs.BPE as BPE\n",
|
||||
"import Project_Model.Libs.Transformer as Transformer\n",
|
||||
"import Project_Model.Libs.TransformerUtils as TUtils\n",
|
||||
"import Project_Model.Libs.TorchShims as torch_shims\n",
|
||||
"import Project_Model.Libs.Batch as Batch\n",
|
||||
"\n",
|
||||
"# set a fixed seed\n",
|
||||
"torch.manual_seed(0)\n",
|
||||
"random.seed(0)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# set a default device\n",
|
||||
"DEVICE = torch_shims.get_default_device()\n",
|
||||
"torch.set_default_device(DEVICE)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Get paths\n",
|
||||
"VOCABULARY_PATH = Path(\"Assets/Model/small/bpe-small-16.json\")\n",
|
||||
"TRAIN_DATASET_PATH = Path(\"Assets/Dataset/1-hop/toy/rdf_text.csv\")\n",
|
||||
"VALIDATION_DATASET_PATH = Path(\"Assets/Dataset/1-hop/toy/rdf_text.csv\")\n",
|
||||
"TEST_DATASET_PATH = Path(\"Assets/Dataset/1-hop/toy/rdf_text.csv\")\n",
|
||||
"CHECKPOINT_PATH = Path(\"Assets/Dataset/Tmp/NanoSocrates.zip\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# BPE Init\n",
|
||||
"SPECIAL_VOC = BPE.default_special_tokens()\n",
|
||||
"VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)\n",
|
||||
"TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Constants\n",
|
||||
"MASK_EXTRA_SPACE = 25\n",
|
||||
"TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size + MASK_EXTRA_SPACE\n",
|
||||
"EMBEDDED_SIZE = 256\n",
|
||||
"FEED_FORWARD_MULTIPLIER = 4\n",
|
||||
"ATTENTION_HEADS = 8\n",
|
||||
"SENTENCE_LENGTH = 256\n",
|
||||
"NUMBER_OF_BLOCKS = 4\n",
|
||||
"MAX_EPOCHS = int(1e3)\n",
|
||||
"PRETRAIN_EPOCHS = int(2)\n",
|
||||
"WARMUP_EPOCHS = int(4e3)\n",
|
||||
"MINI_BATCH_SIZE = 10\n",
|
||||
"VALIDATION_STEPS = 1\n",
|
||||
"CHECKPOINT_STEPS = VALIDATION_STEPS * 4\n",
|
||||
"PATIENCE = 4\n",
|
||||
"CURRENT_EPOCH = 0\n",
|
||||
"\n",
|
||||
"SOS_TOKEN = TOKENANO.encode(\"<SOS>\")[0]\n",
|
||||
"\n",
|
||||
"PAD_TOKEN = TOKENANO.encode(\"<PAD>\")[0]\n",
|
||||
"END_TOKEN = TOKENANO.encode(\"<END>\")[0]\n",
|
||||
"SUBJ_TOKEN = TOKENANO.encode(\"<SUBJ>\")[0]\n",
|
||||
"REL_TOKEN = TOKENANO.encode(\"<PRED>\")[0]\n",
|
||||
"OBJ_TOKEN = TOKENANO.encode(\"<OBJ>\")[0]\n",
|
||||
"\n",
|
||||
"SPECIAL_TOKENS: set[int] = set(TOKENANO.encode(\"\".join(BPE.default_special_tokens())))\n",
|
||||
"ALLOWED_TOKENS = set([SUBJ_TOKEN, REL_TOKEN, OBJ_TOKEN])\n",
|
||||
"FORBIDDEN_TOKENS = SPECIAL_TOKENS - ALLOWED_TOKENS\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Spanned_Masker\n",
|
||||
"MASKER = Transformer.SpannedMasker(\n",
|
||||
" TOKEN_SPACE_SIZE,\n",
|
||||
" FORBIDDEN_TOKENS\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"TRAIN_BATCHER = Batch.Batcher(\n",
|
||||
" TRAIN_DATASET_PATH,\n",
|
||||
" SENTENCE_LENGTH,\n",
|
||||
" TOKENANO,\n",
|
||||
" MASKER\n",
|
||||
")\n",
|
||||
"VALIDATION_BATCHER = Batch.Batcher(\n",
|
||||
" VALIDATION_DATASET_PATH,\n",
|
||||
" SENTENCE_LENGTH,\n",
|
||||
" TOKENANO,\n",
|
||||
" MASKER\n",
|
||||
")\n",
|
||||
"TEST_BATCHER = Batch.Batcher(\n",
|
||||
" TEST_DATASET_PATH,\n",
|
||||
" SENTENCE_LENGTH,\n",
|
||||
" TOKENANO,\n",
|
||||
" MASKER\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Model\n",
|
||||
"NANOSOCRATES = Transformer.TrainingModel(\n",
|
||||
" TOKEN_SPACE_SIZE,\n",
|
||||
" EMBEDDED_SIZE,\n",
|
||||
" FEED_FORWARD_MULTIPLIER,\n",
|
||||
" ATTENTION_HEADS,\n",
|
||||
" NUMBER_OF_BLOCKS\n",
|
||||
")\n",
|
||||
"_, ENCODER_ONLY, DECODER_ONLY = TUtils.decompose_nano_socrates(\n",
|
||||
" NANOSOCRATES,\n",
|
||||
" TOKEN_SPACE_SIZE,\n",
|
||||
" EMBEDDED_SIZE\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Training constants\n",
|
||||
"cross_entropy = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)\n",
|
||||
"nano_optim = torch.optim.AdamW(NANOSOCRATES.parameters())\n",
|
||||
"encoder_only_optim = torch.optim.AdamW(ENCODER_ONLY.parameters())\n",
|
||||
"decoder_only_optim = torch.optim.AdamW(DECODER_ONLY.parameters())\n",
|
||||
"\n",
|
||||
"nano_scheduler = Transformer.WarmupLR(nano_optim, WARMUP_EPOCHS, EMBEDDED_SIZE)\n",
|
||||
"encoder_only_scheduler = Transformer.WarmupLR(encoder_only_optim, WARMUP_EPOCHS, EMBEDDED_SIZE)\n",
|
||||
"decoder_only_scheduler = Transformer.WarmupLR(decoder_only_optim, WARMUP_EPOCHS, EMBEDDED_SIZE)\n",
|
||||
"\n",
|
||||
"current_epoch = CURRENT_EPOCH\n",
|
||||
"patience = 0\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"average_loss_validation = {\n",
|
||||
" \"txt\": float(\"inf\"),\n",
|
||||
" \"encoder_only\": float(\"inf\"),\n",
|
||||
" \"decoder_only\": float(\"inf\")\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"while current_epoch < MAX_EPOCHS:\n",
|
||||
"\n",
|
||||
" text_batch_losses = []\n",
|
||||
" encoder_batch_losses = []\n",
|
||||
" decoder_batch_losses = []\n",
|
||||
"\n",
|
||||
" for batch in TRAIN_BATCHER.batch(MINI_BATCH_SIZE):\n",
|
||||
"\n",
|
||||
" src_x, tgt_y, pad_x, pad_y, tasktype = batch\n",
|
||||
"\n",
|
||||
" enc_x = torch.tensor(src_x)\n",
|
||||
" enc_x_pad = torch.tensor(pad_x, dtype=torch.bool)\n",
|
||||
" dec_x = Transformer.get_decoder_input(MINI_BATCH_SIZE, SOS_TOKEN, PAD_TOKEN, SENTENCE_LENGTH)\n",
|
||||
" dec_x_pad = dec_x.eq(PAD_TOKEN)\n",
|
||||
" tgt = torch.tensor(tgt_y)\n",
|
||||
" tgt_pad = torch.tensor(pad_y, dtype=torch.bool)\n",
|
||||
"\n",
|
||||
" # Task 1 and Task 2\n",
|
||||
" if tasktype == Batch.TaskType.RDF2TXT or tasktype == Batch.TaskType.TEXT2RDF:\n",
|
||||
"\n",
|
||||
" BATCH_LOSS = []\n",
|
||||
"\n",
|
||||
" for token_idx in range(0, SENTENCE_LENGTH):\n",
|
||||
"\n",
|
||||
" nano_optim.zero_grad()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" pred_logits = NANOSOCRATES((\n",
|
||||
" enc_x, enc_x_pad, dec_x, dec_x_pad\n",
|
||||
" ))\n",
|
||||
"\n",
|
||||
" pred_logits = pred_logits[:, token_idx, :]\n",
|
||||
"\n",
|
||||
" loss: torch.Tensor= cross_entropy(pred_logits, tgt[:, token_idx])\n",
|
||||
"\n",
|
||||
" loss.backward()\n",
|
||||
" nano_optim.step()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" BATCH_LOSS.append(\n",
|
||||
" loss.item()\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" if token_idx < SENTENCE_LENGTH - 1:\n",
|
||||
" dec_x[:,token_idx + 1] = tgt[:, token_idx]\n",
|
||||
"\n",
|
||||
" MIN_BATCH_LOSS = min(BATCH_LOSS)\n",
|
||||
" MAX_BATCH_LOSS = max(BATCH_LOSS)\n",
|
||||
" AVG_BATCH_LOSS = sum(BATCH_LOSS) / MINI_BATCH_SIZE\n",
|
||||
"\n",
|
||||
" text_batch_losses.append([MIN_BATCH_LOSS, AVG_BATCH_LOSS, MAX_BATCH_LOSS])\n",
|
||||
" continue\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" # Pretrain first\n",
|
||||
" if current_epoch < PRETRAIN_EPOCHS:\n",
|
||||
" continue\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" # Task 3\n",
|
||||
" if tasktype == Batch.TaskType.MASKING:\n",
|
||||
"\n",
|
||||
" encoder_only_optim.zero_grad()\n",
|
||||
"\n",
|
||||
" pred_logits = ENCODER_ONLY((\n",
|
||||
" enc_x, enc_x_pad\n",
|
||||
" ))\n",
|
||||
"\n",
|
||||
" loss: torch.Tensor= cross_entropy(pred_logits, tgt)\n",
|
||||
"\n",
|
||||
" loss.backward()\n",
|
||||
" encoder_only_optim.step()\n",
|
||||
"\n",
|
||||
" encoder_batch_losses.append(\n",
|
||||
" loss.item()\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" continue\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" # Task 4\n",
|
||||
" if tasktype == Batch.TaskType.COMPLETATION:\n",
|
||||
"\n",
|
||||
" BATCH_LOSS = []\n",
|
||||
"\n",
|
||||
" for token_idx in range(0, SENTENCE_LENGTH):\n",
|
||||
"\n",
|
||||
" decoder_only_optim.zero_grad()\n",
|
||||
"\n",
|
||||
" pred_logits = DECODER_ONLY((\n",
|
||||
" enc_x, enc_x_pad\n",
|
||||
" ))\n",
|
||||
"\n",
|
||||
" pred_logits = pred_logits[:, token_idx, :]\n",
|
||||
"\n",
|
||||
" loss: torch.Tensor= cross_entropy(pred_logits, tgt[:, token_idx])\n",
|
||||
"\n",
|
||||
" loss.backward()\n",
|
||||
" decoder_only_optim.step()\n",
|
||||
"\n",
|
||||
" BATCH_LOSS.append(\n",
|
||||
" loss.item()\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" if token_idx < SENTENCE_LENGTH - 1:\n",
|
||||
" dec_x[:,token_idx + 1] = tgt[:, token_idx]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" MIN_BATCH_LOSS = min(BATCH_LOSS)\n",
|
||||
" MAX_BATCH_LOSS = max(BATCH_LOSS)\n",
|
||||
" AVG_BATCH_LOSS = sum(BATCH_LOSS) / MINI_BATCH_SIZE\n",
|
||||
"\n",
|
||||
" decoder_batch_losses.append([MIN_BATCH_LOSS, AVG_BATCH_LOSS, MAX_BATCH_LOSS])\n",
|
||||
"\n",
|
||||
" continue\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" nano_scheduler.step()\n",
|
||||
" encoder_only_scheduler.step()\n",
|
||||
" decoder_only_scheduler.step()\n",
|
||||
"\n",
|
||||
" current_epoch += 1\n",
|
||||
"\n",
|
||||
" if current_epoch % VALIDATION_STEPS == 0:\n",
|
||||
"\n",
|
||||
" txt_avg_batch_losses = []\n",
|
||||
" enc_avg_batch_losses = []\n",
|
||||
" dec_avg_batch_losses = []\n",
|
||||
"\n",
|
||||
" for batch in VALIDATION_BATCHER.batch(MINI_BATCH_SIZE):\n",
|
||||
"\n",
|
||||
" src_x, tgt_y, pad_x, pad_y, tasktype = batch\n",
|
||||
"\n",
|
||||
" enc_x = torch.tensor(src_x)\n",
|
||||
" enc_x_pad = torch.tensor(pad_x, dtype=torch.bool)\n",
|
||||
" dec_x = Transformer.get_decoder_input(MINI_BATCH_SIZE, SOS_TOKEN, PAD_TOKEN, SENTENCE_LENGTH)\n",
|
||||
" dec_x_pad = dec_x.eq(PAD_TOKEN)\n",
|
||||
" tgt = torch.tensor(tgt_y)\n",
|
||||
" tgt_pad = torch.tensor(pad_y, dtype=torch.bool)\n",
|
||||
"\n",
|
||||
" # Task 1 and Task 2\n",
|
||||
" if tasktype == Batch.TaskType.RDF2TXT or tasktype == Batch.TaskType.TEXT2RDF:\n",
|
||||
"\n",
|
||||
" BATCH_LOSS = []\n",
|
||||
"\n",
|
||||
" for token_idx in range(0, SENTENCE_LENGTH):\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" pred_logits = NANOSOCRATES((\n",
|
||||
" enc_x, enc_x_pad, dec_x, dec_x_pad\n",
|
||||
" ))\n",
|
||||
"\n",
|
||||
" pred_logits = pred_logits[:, token_idx, :]\n",
|
||||
"\n",
|
||||
" loss: torch.Tensor= cross_entropy(pred_logits, tgt[:, token_idx])\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" BATCH_LOSS.append(\n",
|
||||
" loss.item()\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" if token_idx < SENTENCE_LENGTH - 1:\n",
|
||||
" dec_x[:,token_idx + 1] = tgt[:, token_idx]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" AVG_BATCH_LOSS = sum(BATCH_LOSS) / MINI_BATCH_SIZE\n",
|
||||
" txt_avg_batch_losses.append(AVG_BATCH_LOSS)\n",
|
||||
"\n",
|
||||
" continue\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" # Pretrain first\n",
|
||||
" if current_epoch < PRETRAIN_EPOCHS:\n",
|
||||
" continue\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" # Task 3\n",
|
||||
" if tasktype == Batch.TaskType.MASKING:\n",
|
||||
"\n",
|
||||
" pred_logits = ENCODER_ONLY((\n",
|
||||
" enc_x, enc_x_pad\n",
|
||||
" ))\n",
|
||||
"\n",
|
||||
" loss: torch.Tensor= cross_entropy(pred_logits, tgt)\n",
|
||||
"\n",
|
||||
" enc_avg_batch_losses.append(\n",
|
||||
" loss.item()\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" continue\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" # Task 4\n",
|
||||
" if tasktype == Batch.TaskType.COMPLETATION:\n",
|
||||
"\n",
|
||||
" BATCH_LOSS = []\n",
|
||||
"\n",
|
||||
" for token_idx in range(0, SENTENCE_LENGTH):\n",
|
||||
"\n",
|
||||
" pred_logits = DECODER_ONLY((\n",
|
||||
" enc_x, enc_x_pad\n",
|
||||
" ))\n",
|
||||
"\n",
|
||||
" pred_logits = pred_logits[:, token_idx, :]\n",
|
||||
"\n",
|
||||
" loss: torch.Tensor= cross_entropy(pred_logits, tgt[:, token_idx])\n",
|
||||
"\n",
|
||||
" BATCH_LOSS.append(\n",
|
||||
" loss.item()\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" if token_idx < SENTENCE_LENGTH - 1:\n",
|
||||
" dec_x[:,token_idx + 1] = tgt[:, token_idx]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" AVG_BATCH_LOSS = sum(BATCH_LOSS) / MINI_BATCH_SIZE\n",
|
||||
"\n",
|
||||
" dec_avg_batch_losses.append(AVG_BATCH_LOSS)\n",
|
||||
"\n",
|
||||
" continue\n",
|
||||
"\n",
|
||||
" txt_avg_loss = sum(txt_avg_batch_losses) / len(txt_avg_batch_losses)\n",
|
||||
" enc_avg_loss = float(\"inf\")\n",
|
||||
" dec_avg_loss = float(\"inf\")\n",
|
||||
"\n",
|
||||
" if current_epoch >= PRETRAIN_EPOCHS:\n",
|
||||
" enc_avg_loss = sum(enc_avg_batch_losses) / len(enc_avg_batch_losses)\n",
|
||||
" dec_avg_loss = sum(dec_avg_batch_losses) / len(dec_avg_batch_losses)\n",
|
||||
"\n",
|
||||
" if current_epoch < PRETRAIN_EPOCHS:\n",
|
||||
"\n",
|
||||
" if txt_avg_loss < average_loss_validation[\"txt\"]:\n",
|
||||
" average_loss_validation[\"txt\"] = txt_avg_loss\n",
|
||||
" else:\n",
|
||||
" patience += 1\n",
|
||||
" else:\n",
|
||||
"\n",
|
||||
" counter = 0\n",
|
||||
"\n",
|
||||
" if txt_avg_loss > average_loss_validation[\"txt\"]:\n",
|
||||
" counter += 1\n",
|
||||
"\n",
|
||||
" if txt_avg_loss > average_loss_validation[\"encoder_only\"]:\n",
|
||||
" counter += 1\n",
|
||||
"\n",
|
||||
" if txt_avg_loss > average_loss_validation[\"decoder_only\"]:\n",
|
||||
" counter += 1\n",
|
||||
"\n",
|
||||
" if counter > 1:\n",
|
||||
" patience += 1\n",
|
||||
"\n",
|
||||
" txt_min_train_losses = text_batch_losses[:][0]\n",
|
||||
" txt_avg_train_losses = text_batch_losses[:][1]\n",
|
||||
" txt_max_train_losses = text_batch_losses[:][2]\n",
|
||||
"\n",
|
||||
" txt_min_loss = min(txt_min_train_losses)\n",
|
||||
" txt_avg_min_loss = sum(txt_min_train_losses) / len(txt_min_train_losses)\n",
|
||||
" txt_max_loss = max(txt_max_train_losses)\n",
|
||||
" txt_avg_max_loss = sum(txt_max_train_losses) / len(txt_max_train_losses)\n",
|
||||
" txt_avg_loss = sum(txt_avg_train_losses) / len(txt_avg_train_losses)\n",
|
||||
"\n",
|
||||
" enc_avg_train_loss = float(\"inf\")\n",
|
||||
"\n",
|
||||
" dec_min_loss = float(\"inf\")\n",
|
||||
" dec_avg_min_loss = float(\"inf\")\n",
|
||||
" dec_max_loss = float(\"inf\")\n",
|
||||
" dec_avg_max_loss = float(\"inf\")\n",
|
||||
" dec_avg_loss = float(\"inf\")\n",
|
||||
"\n",
|
||||
" if current_epoch >= PRETRAIN_EPOCHS:\n",
|
||||
" enc_avg_train_loss = sum(encoder_batch_losses) / len(encoder_batch_losses)\n",
|
||||
"\n",
|
||||
" dec_min_train_losses = decoder_batch_losses[:][0]\n",
|
||||
" dec_avg_train_losses = decoder_batch_losses[:][1]\n",
|
||||
" dec_max_train_losses = decoder_batch_losses[:][2]\n",
|
||||
"\n",
|
||||
" dec_min_loss = min(dec_min_train_losses)\n",
|
||||
" dec_avg_min_loss = sum(dec_min_train_losses) / len(dec_min_train_losses)\n",
|
||||
" dec_max_loss = max(dec_max_train_losses)\n",
|
||||
" dec_avg_max_loss = sum(dec_max_train_losses) / len(dec_max_train_losses)\n",
|
||||
" dec_avg_loss = sum(dec_avg_train_losses) / len(dec_avg_train_losses)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" SEPARATOR = \"===========================================================================================\"\n",
|
||||
" DEBUG_TEXT = \"\".join([\n",
|
||||
" f\"{SEPARATOR}\\n\",\n",
|
||||
" f\"EPOCH {current_epoch}\"\n",
|
||||
" f\"{SEPARATOR}\\n\",\n",
|
||||
" f\"Train Losses:\\n\"\n",
|
||||
" f\"\\tMin Losses:\\n\"\n",
|
||||
" f\"\\t\\tmin_txt: {txt_min_loss} - avg_txt: {txt_avg_min_loss}\\n\"\n",
|
||||
" f\"\\t\\tmin_dec: {dec_min_loss} - avg_dec: {dec_avg_min_loss}\\n\"\n",
|
||||
" f\"\\tMax Losses:\\n\"\n",
|
||||
" f\"\\t\\tmax_txt: {txt_max_loss} - avg_txt: {txt_avg_max_loss}\\n\"\n",
|
||||
" f\"\\t\\tmax_dec: {dec_min_loss} - avg_dec: {dec_avg_max_loss}\\n\"\n",
|
||||
" f\"\\tAvg Losses:\\n\"\n",
|
||||
" f\"\\t\\tavg_txt: {txt_avg_loss} - avg_enc: {enc_avg_loss} - avg_dec: {dec_avg_loss}\\n\"\n",
|
||||
" f\"{SEPARATOR}\\n\",\n",
|
||||
" f\"Validation Losses:\\n\"\n",
|
||||
" f\"\\ttxt_loss: {txt_avg_loss} - masking_loss: {enc_avg_loss} - prediction: {dec_avg_loss}\"\n",
|
||||
" f\"{SEPARATOR}\\n\",\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" # Warn about patience\n",
|
||||
" if patience == PATIENCE:\n",
|
||||
" print(\n",
|
||||
" \"Model is likely overfitting, so let's stop here\"\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" # SAVE MODEL\n",
|
||||
" if current_epoch % CHECKPOINT_STEPS == 0 or patience == PATIENCE:\n",
|
||||
" print(f\"Saving model at {CHECKPOINT_PATH.as_posix()}\")\n",
|
||||
" torch.save(NANOSOCRATES.state_dict(), CHECKPOINT_PATH)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "deep_learning",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.13.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,433 +0,0 @@
|
||||
import random
|
||||
import sys
|
||||
import torch
|
||||
import pandas as pd
|
||||
from pathlib import Path
|
||||
import Project_Model.Libs.Embedder as Embedder
|
||||
import Project_Model.Libs.BPE as BPE
|
||||
import Project_Model.Libs.Transformer as Transformer
|
||||
import Project_Model.Libs.TransformerUtils as TUtils
|
||||
import Project_Model.Libs.TorchShims as torch_shims
|
||||
import Project_Model.Libs.Batch as Batch
|
||||
|
||||
# set a fixed seed
|
||||
torch.manual_seed(0)
|
||||
random.seed(0)
|
||||
|
||||
|
||||
# set a default device
|
||||
DEVICE = torch_shims.get_default_device()
|
||||
torch.set_default_device(DEVICE)
|
||||
|
||||
|
||||
# Get paths
|
||||
VOCABULARY_PATH = Path("Assets/Model/small/bpe-small-16.json")
|
||||
TRAIN_DATASET_PATH = Path("Assets/Dataset/1-hop/small/holdout/train.csv")
|
||||
VALIDATION_DATASET_PATH = Path("Assets/Dataset/1-hop/small/holdout/evaluation.csv")
|
||||
TEST_DATASET_PATH = Path("Assets/Dataset/1-hop/small/holdout/test.csv")
|
||||
CHECKPOINT_PATH = Path("Assets/Dataset/Tmp/NanoSocrates.zip")
|
||||
|
||||
|
||||
# BPE Init
|
||||
SPECIAL_VOC = BPE.default_special_tokens()
|
||||
VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)
|
||||
TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)
|
||||
|
||||
|
||||
# Constants
|
||||
MASK_EXTRA_SPACE = 100
|
||||
REAL_TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size
|
||||
TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size + MASK_EXTRA_SPACE
|
||||
EMBEDDED_SIZE = 256
|
||||
FEED_FORWARD_MULTIPLIER = 4
|
||||
ATTENTION_HEADS = 8
|
||||
SENTENCE_LENGTH = 256
|
||||
NUMBER_OF_BLOCKS = 4
|
||||
MAX_EPOCHS = int(1e3)
|
||||
PRETRAIN_EPOCHS = int(10)
|
||||
WARMUP_EPOCHS = int(4e3)
|
||||
MINI_BATCH_SIZE = 100
|
||||
VALIDATION_STEPS = 5
|
||||
CHECKPOINT_STEPS = VALIDATION_STEPS * 4
|
||||
PATIENCE = 4
|
||||
CURRENT_EPOCH = 0
|
||||
|
||||
SOS_TOKEN = TOKENANO.encode("<SOS>")[0]
|
||||
|
||||
PAD_TOKEN = TOKENANO.encode("<PAD>")[0]
|
||||
END_TOKEN = TOKENANO.encode("<END>")[0]
|
||||
SUBJ_TOKEN = TOKENANO.encode("<SUBJ>")[0]
|
||||
REL_TOKEN = TOKENANO.encode("<PRED>")[0]
|
||||
OBJ_TOKEN = TOKENANO.encode("<OBJ>")[0]
|
||||
|
||||
SPECIAL_TOKENS: set[int] = set(TOKENANO.encode("".join(BPE.default_special_tokens())))
|
||||
ALLOWED_TOKENS = set([SUBJ_TOKEN, REL_TOKEN, OBJ_TOKEN])
|
||||
FORBIDDEN_TOKENS = SPECIAL_TOKENS - ALLOWED_TOKENS
|
||||
|
||||
|
||||
# Spanned_Masker
|
||||
MASKER = Transformer.SpannedMasker(REAL_TOKEN_SPACE_SIZE, FORBIDDEN_TOKENS)
|
||||
|
||||
TRAIN_BATCHER = Batch.Batcher(TRAIN_DATASET_PATH, SENTENCE_LENGTH, TOKENANO, MASKER)
|
||||
VALIDATION_BATCHER = Batch.Batcher(
|
||||
VALIDATION_DATASET_PATH, SENTENCE_LENGTH, TOKENANO, MASKER
|
||||
)
|
||||
TEST_BATCHER = Batch.Batcher(TEST_DATASET_PATH, SENTENCE_LENGTH, TOKENANO, MASKER)
|
||||
|
||||
|
||||
# Model
|
||||
NANOSOCRATES = Transformer.TrainingModel(
|
||||
TOKEN_SPACE_SIZE,
|
||||
EMBEDDED_SIZE,
|
||||
FEED_FORWARD_MULTIPLIER,
|
||||
ATTENTION_HEADS,
|
||||
NUMBER_OF_BLOCKS,
|
||||
)
|
||||
_, ENCODER_ONLY, DECODER_ONLY = TUtils.decompose_nano_socrates(
|
||||
NANOSOCRATES, TOKEN_SPACE_SIZE, EMBEDDED_SIZE
|
||||
)
|
||||
|
||||
|
||||
# Training constants
|
||||
nano_cross_entropy = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)
|
||||
encoder_ce = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)
|
||||
decoder_ce = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)
|
||||
nano_optim = torch.optim.AdamW(NANOSOCRATES.parameters())
|
||||
encoder_only_optim = torch.optim.AdamW(ENCODER_ONLY.parameters())
|
||||
decoder_only_optim = torch.optim.AdamW(DECODER_ONLY.parameters())
|
||||
|
||||
nano_scheduler = Transformer.WarmupLR(nano_optim, WARMUP_EPOCHS, EMBEDDED_SIZE)
|
||||
encoder_only_scheduler = Transformer.WarmupLR(
|
||||
encoder_only_optim, WARMUP_EPOCHS, EMBEDDED_SIZE
|
||||
)
|
||||
decoder_only_scheduler = Transformer.WarmupLR(
|
||||
decoder_only_optim, WARMUP_EPOCHS, EMBEDDED_SIZE
|
||||
)
|
||||
|
||||
current_epoch = CURRENT_EPOCH
|
||||
patience = 0
|
||||
|
||||
|
||||
average_loss_validation = {
|
||||
"txt": float("inf"),
|
||||
"encoder_only": float("inf"),
|
||||
"decoder_only": float("inf"),
|
||||
}
|
||||
|
||||
while current_epoch < MAX_EPOCHS:
|
||||
|
||||
NANOSOCRATES.train()
|
||||
ENCODER_ONLY.train()
|
||||
DECODER_ONLY.train()
|
||||
|
||||
text_batch_losses = []
|
||||
encoder_batch_losses = []
|
||||
decoder_batch_losses = []
|
||||
|
||||
batch_counter = 0
|
||||
|
||||
print(f"EPOCH {current_epoch} STARTING")
|
||||
|
||||
for batch in TRAIN_BATCHER.batch(MINI_BATCH_SIZE):
|
||||
|
||||
batch_counter += 1
|
||||
|
||||
src_x, tgt_y, pad_x, pad_y, tasktype = batch
|
||||
|
||||
|
||||
|
||||
enc_x = torch.tensor(src_x)
|
||||
|
||||
ACTUAL_BATCH_SIZE, _ = enc_x.shape
|
||||
enc_x_pad = torch.tensor(pad_x, dtype=torch.bool)
|
||||
dec_x = Transformer.get_decoder_input(
|
||||
ACTUAL_BATCH_SIZE, SOS_TOKEN, PAD_TOKEN, SENTENCE_LENGTH
|
||||
)
|
||||
dec_x_pad = dec_x.eq(PAD_TOKEN)
|
||||
tgt = torch.tensor(tgt_y)
|
||||
tgt_pad = torch.tensor(pad_y, dtype=torch.bool)
|
||||
|
||||
print(f"\tBATCH {batch_counter} Starting")
|
||||
|
||||
# Task 1 and Task 2
|
||||
if tasktype == Batch.TaskType.RDF2TXT or tasktype == Batch.TaskType.TEXT2RDF:
|
||||
|
||||
print(f"\tExecuting TASK 1 or 2 - BATCH {batch_counter}")
|
||||
|
||||
BATCH_LOSS = []
|
||||
|
||||
|
||||
for token_idx in range(0, SENTENCE_LENGTH):
|
||||
|
||||
nano_optim.zero_grad()
|
||||
|
||||
pred_logits = NANOSOCRATES((enc_x, enc_x_pad, dec_x, dec_x_pad))
|
||||
|
||||
pred_logits = pred_logits[:, token_idx, :]
|
||||
|
||||
loss: torch.Tensor = nano_cross_entropy(pred_logits, tgt[:, token_idx])
|
||||
|
||||
loss.backward()
|
||||
nano_optim.step()
|
||||
|
||||
BATCH_LOSS.append(loss.item())
|
||||
|
||||
if token_idx < SENTENCE_LENGTH - 1:
|
||||
dec_x[:, token_idx + 1] = tgt[:, token_idx]
|
||||
|
||||
MIN_BATCH_LOSS = min(BATCH_LOSS)
|
||||
MAX_BATCH_LOSS = max(BATCH_LOSS)
|
||||
AVG_BATCH_LOSS = sum(BATCH_LOSS) / MINI_BATCH_SIZE
|
||||
|
||||
text_batch_losses.append([MIN_BATCH_LOSS, AVG_BATCH_LOSS, MAX_BATCH_LOSS])
|
||||
continue
|
||||
|
||||
# Pretrain first
|
||||
if current_epoch < PRETRAIN_EPOCHS:
|
||||
continue
|
||||
|
||||
# Task 3
|
||||
if tasktype == Batch.TaskType.MASKING:
|
||||
|
||||
print(f"\tExecuting TASK 3 - BATCH {batch_counter}")
|
||||
|
||||
encoder_only_optim.zero_grad()
|
||||
|
||||
pred_logits = ENCODER_ONLY((enc_x, enc_x_pad))
|
||||
pred_logits = pred_logits.permute(0, 2, 1)
|
||||
print(torch.max(tgt))
|
||||
loss: torch.Tensor = encoder_ce(pred_logits, tgt)
|
||||
|
||||
loss.backward()
|
||||
encoder_only_optim.step()
|
||||
|
||||
encoder_batch_losses.append(loss.item())
|
||||
|
||||
continue
|
||||
|
||||
# Task 4
|
||||
if tasktype == Batch.TaskType.COMPLETATION:
|
||||
|
||||
print(f"\tExecuting TASK 4 - BATCH {batch_counter}")
|
||||
|
||||
BATCH_LOSS = []
|
||||
|
||||
for token_idx in range(0, SENTENCE_LENGTH):
|
||||
|
||||
decoder_only_optim.zero_grad()
|
||||
|
||||
pred_logits = DECODER_ONLY((enc_x, enc_x_pad))
|
||||
|
||||
pred_logits = pred_logits[:, token_idx, :]
|
||||
|
||||
loss: torch.Tensor = decoder_ce(pred_logits, tgt[:, token_idx])
|
||||
|
||||
loss.backward()
|
||||
decoder_only_optim.step()
|
||||
|
||||
BATCH_LOSS.append(loss.item())
|
||||
|
||||
if token_idx < SENTENCE_LENGTH - 1:
|
||||
dec_x[:, token_idx + 1] = tgt[:, token_idx]
|
||||
|
||||
MIN_BATCH_LOSS = min(BATCH_LOSS)
|
||||
MAX_BATCH_LOSS = max(BATCH_LOSS)
|
||||
AVG_BATCH_LOSS = sum(BATCH_LOSS) / MINI_BATCH_SIZE
|
||||
|
||||
decoder_batch_losses.append(
|
||||
[MIN_BATCH_LOSS, AVG_BATCH_LOSS, MAX_BATCH_LOSS]
|
||||
)
|
||||
|
||||
continue
|
||||
|
||||
nano_scheduler.step()
|
||||
encoder_only_scheduler.step()
|
||||
decoder_only_scheduler.step()
|
||||
|
||||
current_epoch += 1
|
||||
|
||||
if current_epoch % VALIDATION_STEPS == 0:
|
||||
|
||||
NANOSOCRATES.eval()
|
||||
ENCODER_ONLY.eval()
|
||||
DECODER_ONLY.eval()
|
||||
|
||||
txt_avg_batch_losses = []
|
||||
enc_avg_batch_losses = []
|
||||
dec_avg_batch_losses = []
|
||||
|
||||
for batch in VALIDATION_BATCHER.batch(MINI_BATCH_SIZE):
|
||||
|
||||
src_x, tgt_y, pad_x, pad_y, tasktype = batch
|
||||
|
||||
enc_x = torch.tensor(src_x)
|
||||
|
||||
ACTUAL_BATCH_SIZE, _, _ = enc_x.shape
|
||||
enc_x_pad = torch.tensor(pad_x, dtype=torch.bool)
|
||||
dec_x = Transformer.get_decoder_input(
|
||||
ACTUAL_BATCH_SIZE, SOS_TOKEN, PAD_TOKEN, SENTENCE_LENGTH
|
||||
)
|
||||
|
||||
dec_x_pad = dec_x.eq(PAD_TOKEN)
|
||||
tgt = torch.tensor(tgt_y)
|
||||
tgt_pad = torch.tensor(pad_y, dtype=torch.bool)
|
||||
|
||||
# Task 1 and Task 2
|
||||
if (
|
||||
tasktype == Batch.TaskType.RDF2TXT
|
||||
or tasktype == Batch.TaskType.TEXT2RDF
|
||||
):
|
||||
|
||||
BATCH_LOSS = []
|
||||
|
||||
for token_idx in range(0, SENTENCE_LENGTH):
|
||||
|
||||
pred_logits = NANOSOCRATES((enc_x, enc_x_pad, dec_x, dec_x_pad))
|
||||
|
||||
pred_logits = pred_logits[:, token_idx, :]
|
||||
|
||||
loss: torch.Tensor = nano_cross_entropy(pred_logits, tgt[:, token_idx])
|
||||
|
||||
BATCH_LOSS.append(loss.item())
|
||||
|
||||
if token_idx < SENTENCE_LENGTH - 1:
|
||||
dec_x[:, token_idx + 1] = tgt[:, token_idx]
|
||||
|
||||
AVG_BATCH_LOSS = sum(BATCH_LOSS) / MINI_BATCH_SIZE
|
||||
txt_avg_batch_losses.append(AVG_BATCH_LOSS)
|
||||
|
||||
continue
|
||||
|
||||
# Pretrain first
|
||||
if current_epoch < PRETRAIN_EPOCHS:
|
||||
continue
|
||||
|
||||
# Task 3
|
||||
if tasktype == Batch.TaskType.MASKING:
|
||||
|
||||
pred_logits = ENCODER_ONLY((enc_x, enc_x_pad))
|
||||
pred_logits = pred_logits.permute(0, 2, 1)
|
||||
|
||||
loss: torch.Tensor = encoder_ce(pred_logits, tgt)
|
||||
|
||||
enc_avg_batch_losses.append(loss.item())
|
||||
|
||||
continue
|
||||
|
||||
# Task 4
|
||||
if tasktype == Batch.TaskType.COMPLETATION:
|
||||
|
||||
BATCH_LOSS = []
|
||||
|
||||
for token_idx in range(0, SENTENCE_LENGTH):
|
||||
|
||||
pred_logits = DECODER_ONLY((enc_x, enc_x_pad))
|
||||
|
||||
pred_logits = pred_logits[:, token_idx, :]
|
||||
|
||||
loss: torch.Tensor = decoder_ce(pred_logits, tgt[:, token_idx])
|
||||
|
||||
BATCH_LOSS.append(loss.item())
|
||||
|
||||
if token_idx < SENTENCE_LENGTH - 1:
|
||||
dec_x[:, token_idx + 1] = tgt[:, token_idx]
|
||||
|
||||
AVG_BATCH_LOSS = sum(BATCH_LOSS) / MINI_BATCH_SIZE
|
||||
|
||||
dec_avg_batch_losses.append(AVG_BATCH_LOSS)
|
||||
|
||||
continue
|
||||
|
||||
txt_avg_loss = sum(txt_avg_batch_losses) / len(txt_avg_batch_losses)
|
||||
enc_avg_loss = float("inf")
|
||||
dec_avg_loss = float("inf")
|
||||
|
||||
if current_epoch >= PRETRAIN_EPOCHS:
|
||||
enc_avg_loss = sum(enc_avg_batch_losses) / len(enc_avg_batch_losses)
|
||||
dec_avg_loss = sum(dec_avg_batch_losses) / len(dec_avg_batch_losses)
|
||||
|
||||
if current_epoch < PRETRAIN_EPOCHS:
|
||||
|
||||
if txt_avg_loss < average_loss_validation["txt"]:
|
||||
average_loss_validation["txt"] = txt_avg_loss
|
||||
else:
|
||||
patience += 1
|
||||
else:
|
||||
|
||||
counter = 0
|
||||
|
||||
if txt_avg_loss > average_loss_validation["txt"]:
|
||||
counter += 1
|
||||
|
||||
if txt_avg_loss > average_loss_validation["encoder_only"]:
|
||||
counter += 1
|
||||
|
||||
if txt_avg_loss > average_loss_validation["decoder_only"]:
|
||||
counter += 1
|
||||
|
||||
if counter > 1:
|
||||
patience += 1
|
||||
|
||||
txt_min_train_losses = [row[0] for row in text_batch_losses]
|
||||
txt_avg_train_losses = [row[1] for row in text_batch_losses]
|
||||
txt_max_train_losses = [row[2] for row in text_batch_losses]
|
||||
|
||||
txt_min_loss = min(txt_min_train_losses)
|
||||
txt_avg_min_loss = sum(txt_min_train_losses) / len(txt_min_train_losses)
|
||||
txt_max_loss = max(txt_max_train_losses)
|
||||
txt_avg_max_loss = sum(txt_max_train_losses) / len(txt_max_train_losses)
|
||||
txt_avg_loss = sum(txt_avg_train_losses) / len(txt_avg_train_losses)
|
||||
|
||||
enc_avg_train_loss = float("inf")
|
||||
|
||||
dec_min_loss = float("inf")
|
||||
dec_avg_min_loss = float("inf")
|
||||
dec_max_loss = float("inf")
|
||||
dec_avg_max_loss = float("inf")
|
||||
dec_avg_loss = float("inf")
|
||||
|
||||
if current_epoch >= PRETRAIN_EPOCHS:
|
||||
enc_avg_train_loss = sum(encoder_batch_losses) / len(encoder_batch_losses)
|
||||
|
||||
dec_min_train_losses = [row[0] for row in decoder_batch_losses]
|
||||
dec_avg_train_losses = [row[1] for row in decoder_batch_losses]
|
||||
dec_max_train_losses = [row[2] for row in decoder_batch_losses]
|
||||
|
||||
dec_min_loss = min(dec_min_train_losses)
|
||||
dec_avg_min_loss = sum(dec_min_train_losses) / len(dec_min_train_losses)
|
||||
dec_max_loss = max(dec_max_train_losses)
|
||||
dec_avg_max_loss = sum(dec_max_train_losses) / len(dec_max_train_losses)
|
||||
dec_avg_loss = sum(dec_avg_train_losses) / len(dec_avg_train_losses)
|
||||
|
||||
SEPARATOR = "================================================================================================================"
|
||||
DEBUG_TEXT = "".join(
|
||||
[
|
||||
f"{SEPARATOR}\n",
|
||||
f"EPOCH {current_epoch}\n",
|
||||
f"{SEPARATOR}\n",
|
||||
f"Train Losses:\n",
|
||||
f"\tMin Losses:\n",
|
||||
f"\t\tmin_txt: {txt_min_loss} - avg_txt: {txt_avg_min_loss}\n",
|
||||
f"\t\tmin_dec: {dec_min_loss} - avg_dec: {dec_avg_min_loss}\n",
|
||||
f"\tMax Losses:\n",
|
||||
f"\t\tmax_txt: {txt_max_loss} - avg_txt: {txt_avg_max_loss}\n",
|
||||
f"\t\tmax_dec: {dec_min_loss} - avg_dec: {dec_avg_max_loss}\n",
|
||||
f"\tAvg Losses:\n",
|
||||
f"\t\tavg_txt: {txt_avg_loss} - avg_enc: {enc_avg_loss} - avg_dec: {dec_avg_loss}\n",
|
||||
f"{SEPARATOR}\n",
|
||||
f"Validation Losses:\n",
|
||||
f"\ttxt_loss: {txt_avg_loss} - masking_loss: {enc_avg_loss} - prediction: {dec_avg_loss}\n",
|
||||
f"{SEPARATOR}\n",
|
||||
]
|
||||
)
|
||||
|
||||
print(DEBUG_TEXT)
|
||||
|
||||
# Warn about patience
|
||||
if patience == PATIENCE:
|
||||
print("Model is likely overfitting, so let's stop here")
|
||||
|
||||
# SAVE MODEL
|
||||
if current_epoch % CHECKPOINT_STEPS == 0 or patience == PATIENCE:
|
||||
print(f"Saving model at {CHECKPOINT_PATH.as_posix()}")
|
||||
torch.save(NANOSOCRATES.state_dict(), CHECKPOINT_PATH)
|
||||
@ -1,112 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "4ae47336",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import torch\n",
|
||||
"B, T, D = 4, 7, 32\n",
|
||||
"x = torch.randn(B, T, D)\n",
|
||||
"attn_mask = torch.triu(torch.ones(T, T, dtype=torch.bool), diagonal=1) # [T,T]\n",
|
||||
"pad_mask = torch.zeros(B, T, dtype=torch.bool) # no pads\n",
|
||||
"mha = torch.nn.MultiheadAttention(D, num_heads=4, batch_first=True)\n",
|
||||
"y, _ = mha(x, x, x, attn_mask=attn_mask, key_padding_mask=pad_mask) # should work\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "e38e3fb5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"tensor([[[0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
|
||||
" [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
|
||||
" [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]],\n",
|
||||
"\n",
|
||||
" [[0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
|
||||
" [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
|
||||
" [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"torch.nn.functional.one_hot(torch.tensor([\n",
|
||||
" [4, 1, 9],\n",
|
||||
" [2,4,5]\n",
|
||||
"]))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "7119ad53",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"device(type='cpu')"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"torch.get_default_device()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "8c95691a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"xpu\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from Project_Model.Libs.TorchShims import get_default_device\n",
|
||||
"\n",
|
||||
"print(get_default_device())"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "deep_learning",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.13.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,177 +0,0 @@
|
||||
import random
|
||||
import time
|
||||
import torch
|
||||
import pandas as pd
|
||||
from pathlib import Path
|
||||
import Project_Model.Libs.Embedder as Embedder
|
||||
import Project_Model.Libs.BPE as BPE
|
||||
import Project_Model.Libs.Transformer as Transformer
|
||||
import Project_Model.Libs.TorchShims as torch_shims
|
||||
|
||||
# set a fixed seed
|
||||
torch.manual_seed(0)
|
||||
random.seed(0)
|
||||
DEVICE = torch_shims.get_default_device()
|
||||
torch.set_default_device(DEVICE)
|
||||
|
||||
# set a default device
|
||||
|
||||
# BPE Init
|
||||
VOCABULARY_PATH = Path("Assets/Model/toy_10/toy_dictionary.json")
|
||||
SPECIAL_VOC = BPE.default_special_tokens()
|
||||
|
||||
VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)
|
||||
TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)
|
||||
|
||||
|
||||
# Constants
|
||||
TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size + 1
|
||||
EMBEDDED_SIZE = 256
|
||||
FEED_FORWARD_MULTIPLIER = 4
|
||||
ATTENTION_HEADS = 8
|
||||
SENTENCE_LENGTH = 256
|
||||
NUMBER_OF_BLOCKS = 4
|
||||
MAX_EPOCHS = int(1e3)
|
||||
|
||||
|
||||
PAD_TOKEN = TOKENANO.encode("<PAD>")[0]
|
||||
END_TOKEN = TOKENANO.encode("<END>")[0]
|
||||
|
||||
|
||||
# Load CSV
|
||||
TOY_DATASET_PATH = Path("Assets/Dataset/1-hop/toy/rdf_text.csv")
|
||||
|
||||
TOY_DATASET = pd.read_csv(TOY_DATASET_PATH)
|
||||
|
||||
TOY_BATCH_INPUT_LIST: list[list[int]] = []
|
||||
TOY_BATCH_PADDING_LIST: list[list[bool]] = []
|
||||
TOY_BATCH_TARGET_LIST: list[list[int]] = []
|
||||
TOY_BATCH_DECODER_DEFAULT: list[list[int]] = []
|
||||
|
||||
|
||||
for index, row in TOY_DATASET.iterrows():
|
||||
|
||||
RDFs: str = row["RDFs"]
|
||||
Abstract: str = row["Abstract"]
|
||||
|
||||
input_tokens = TOKENANO.encode(RDFs)
|
||||
output_tokens = TOKENANO.encode(Abstract)[1:]
|
||||
decoder_default_tokens = TOKENANO.encode("<SOS>")
|
||||
|
||||
input_tokens, padding = Transformer.normalize_sequence(
|
||||
input_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN
|
||||
)
|
||||
output_tokens, _ = Transformer.normalize_sequence(
|
||||
output_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN
|
||||
)
|
||||
decoder_default_tokens, _ = Transformer.normalize_sequence(
|
||||
decoder_default_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN, False
|
||||
)
|
||||
|
||||
TOY_BATCH_INPUT_LIST.append(input_tokens)
|
||||
TOY_BATCH_PADDING_LIST.append(padding)
|
||||
TOY_BATCH_TARGET_LIST.append(output_tokens)
|
||||
TOY_BATCH_DECODER_DEFAULT.append(decoder_default_tokens)
|
||||
|
||||
output_tokens = TOKENANO.encode(RDFs)
|
||||
input_tokens = TOKENANO.encode(Abstract)[1:]
|
||||
decoder_default_tokens = TOKENANO.encode("<SOS>")
|
||||
|
||||
input_tokens, padding = Transformer.normalize_sequence(
|
||||
input_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN
|
||||
)
|
||||
output_tokens, _ = Transformer.normalize_sequence(
|
||||
output_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN
|
||||
)
|
||||
decoder_default_tokens, _ = Transformer.normalize_sequence(
|
||||
decoder_default_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN, False
|
||||
)
|
||||
|
||||
TOY_BATCH_INPUT_LIST.append(input_tokens)
|
||||
TOY_BATCH_PADDING_LIST.append(padding)
|
||||
TOY_BATCH_TARGET_LIST.append(output_tokens)
|
||||
TOY_BATCH_DECODER_DEFAULT.append(decoder_default_tokens)
|
||||
|
||||
# Training loop
|
||||
LOSS_HISTORY = []
|
||||
NANOSOCRATES = Transformer.TrainingModel(
|
||||
TOKEN_SPACE_SIZE,
|
||||
EMBEDDED_SIZE,
|
||||
FEED_FORWARD_MULTIPLIER,
|
||||
ATTENTION_HEADS,
|
||||
NUMBER_OF_BLOCKS,
|
||||
)
|
||||
cross_entropy = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)
|
||||
optimizer = torch.optim.AdamW(NANOSOCRATES.parameters())
|
||||
scheduler = Transformer.WarmupLR(optimizer, 4000, EMBEDDED_SIZE)
|
||||
last_loss = 0
|
||||
current_epoch = 0
|
||||
|
||||
while current_epoch < MAX_EPOCHS:
|
||||
|
||||
optimizer.zero_grad()
|
||||
|
||||
encoder_list = torch.tensor(TOY_BATCH_INPUT_LIST[:])
|
||||
decoder_list = torch.tensor(TOY_BATCH_DECODER_DEFAULT[:])
|
||||
src_padding = torch.tensor(TOY_BATCH_PADDING_LIST[:], dtype=torch.bool)
|
||||
|
||||
# Transform target into logits
|
||||
target_logits = torch.tensor(TOY_BATCH_TARGET_LIST[:])
|
||||
|
||||
last_loss = 0
|
||||
last_prediction: torch.Tensor
|
||||
|
||||
LOSS_HISTORY = []
|
||||
|
||||
start = time.time_ns()
|
||||
|
||||
|
||||
for i in range(0, SENTENCE_LENGTH):
|
||||
|
||||
|
||||
optimizer.zero_grad()
|
||||
tgt_padding = decoder_list.eq(PAD_TOKEN)
|
||||
|
||||
logits: torch.Tensor = NANOSOCRATES(
|
||||
(encoder_list, src_padding, decoder_list, tgt_padding)
|
||||
)
|
||||
prob = torch.softmax(logits, 2)
|
||||
|
||||
most_probable_tokens = torch.argmax(prob, 2)
|
||||
last_prediction = most_probable_tokens
|
||||
|
||||
logits = logits[:, i, :]
|
||||
# logits = logits.permute(0, 2, 1)
|
||||
|
||||
loss: torch.Tensor = cross_entropy(logits, target_logits[:, i])
|
||||
LOSS_HISTORY.append(loss.item())
|
||||
# loss : torch.Tensor = cross_entropy(logits, target_logits[:, 0:i])
|
||||
# loss : torch.Tensor = cross_entropy(logits, target_logits)
|
||||
|
||||
last_loss = loss
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
scheduler.step()
|
||||
|
||||
if i < SENTENCE_LENGTH - 1:
|
||||
decoder_list[:, i + 1] = target_logits[:, i]
|
||||
|
||||
current_epoch += 1
|
||||
|
||||
end = time.time_ns()
|
||||
|
||||
if current_epoch % 1 == 0:
|
||||
MIN_LOSS = min(LOSS_HISTORY)
|
||||
MAX_LOSS = max(LOSS_HISTORY)
|
||||
AVERAGE_LOSS = sum(LOSS_HISTORY)/len(LOSS_HISTORY)
|
||||
print(f"EPOCH {current_epoch}\n\tTime: {(end-start)/1E9}s\n\tLoss: {last_loss}")
|
||||
print(f"\tMin Loss: {MIN_LOSS}\tAvg Loss: {AVERAGE_LOSS}\tMax Loss: {MAX_LOSS}\n")
|
||||
# for encoded_sentence, expected_sentence in zip(
|
||||
# Transformer.tensor2token(last_prediction[:, :], END_TOKEN), # type: ignore
|
||||
# Transformer.tensor2token(target_logits[:, :], END_TOKEN),
|
||||
# ):
|
||||
# decoded_sentence = TOKENANO.decode(encoded_sentence)
|
||||
# decoded_target = TOKENANO.decode(expected_sentence)
|
||||
# print(
|
||||
# f"\tACTUAL:\n\t\t{decoded_sentence}\n\tEXPECTED:\n\t\t{decoded_target}\n"
|
||||
# )
|
||||
@ -1,60 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "dd23cc94",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Current detected architecture is: xpu\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import torch\n",
|
||||
"from Project_Model.Libs.TorchShims import get_default_device\n",
|
||||
"\n",
|
||||
"DEVICE = get_default_device()\n",
|
||||
"\n",
|
||||
"print(f\"Current detected architecture is: {DEVICE.type}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6584882e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import Project_Model.Libs.Transformer as Transformer\n",
|
||||
"DECODER = Transformer.Decoder(256, 1024, 4)\n",
|
||||
"print()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "deep_learning",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.13.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,4 +0,0 @@
|
||||
from abc import ABC
|
||||
|
||||
class Encoder(ABC):
|
||||
pass
|
||||
@ -1,164 +0,0 @@
|
||||
from collections import deque
|
||||
import datetime
|
||||
from pathlib import Path
|
||||
import re
|
||||
from ..Classes import (
|
||||
NanoSocratesBPE,
|
||||
NanoSocratesChunker,
|
||||
NanoSocratesSplitter,
|
||||
NanoSocratesBatchMemoryBPE,
|
||||
)
|
||||
from ..Enums import TokenType
|
||||
from ..Utils import (
|
||||
special_regex_maker,
|
||||
iterator_with_checks,
|
||||
save_nanos_vocabulary,
|
||||
load_nanos_vocabulary,
|
||||
save_json,
|
||||
load_json,
|
||||
)
|
||||
|
||||
|
||||
class NanoSocraTraineRam:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
max_vocabulary: int,
|
||||
special_vocabulary: list[str],
|
||||
merge_treshold: int = 0,
|
||||
max_iterations: int = 0,
|
||||
print_after_iterations: int = 1,
|
||||
) -> None:
|
||||
# Bytes
|
||||
BYTE_RESERVED_TOKENS = 256
|
||||
SPECIAL_RESERVED_TOKENS = len(special_vocabulary)
|
||||
RESERVED_TOKENS = BYTE_RESERVED_TOKENS + SPECIAL_RESERVED_TOKENS
|
||||
|
||||
self.__max_vocabulary = max_vocabulary - RESERVED_TOKENS
|
||||
self.__max_iterations = max_iterations
|
||||
self.__merge_treshold = merge_treshold
|
||||
self.__special_token_regex = special_regex_maker(special_vocabulary)
|
||||
self.__print_after_iterations = print_after_iterations
|
||||
|
||||
def trainBPE(
|
||||
self,
|
||||
path: Path,
|
||||
bpe: NanoSocratesBPE | None = None,
|
||||
) -> NanoSocratesBPE:
|
||||
|
||||
if not path.is_file():
|
||||
raise FileNotFoundError()
|
||||
|
||||
if bpe is None:
|
||||
bpe = NanoSocratesBPE()
|
||||
BPE = bpe
|
||||
|
||||
if BPE.vocabulary_size > self.__max_vocabulary:
|
||||
return BPE
|
||||
|
||||
exit = False
|
||||
current_iteration = 0
|
||||
data = self.__gather_data_from_file(path)
|
||||
|
||||
while not exit:
|
||||
|
||||
current_iteration = self.__increment_counter(current_iteration)
|
||||
|
||||
LAST_VOC_SIZE = BPE.vocabulary_size
|
||||
|
||||
last_memory = None
|
||||
|
||||
_, data, last_memory = self.__round_train(BPE, data)
|
||||
|
||||
NEW_VOC_SIZE = BPE.vocabulary_size
|
||||
|
||||
if current_iteration % self.__print_after_iterations == 0:
|
||||
|
||||
DELIMITER = "==============="
|
||||
|
||||
DEBUG = "\n".join(
|
||||
[
|
||||
DELIMITER,
|
||||
f"ITERATION: {current_iteration}",
|
||||
DELIMITER,
|
||||
f"\tVocabulary size: {BPE.vocabulary_size}\n",
|
||||
f"\tFrequencies:\n{last_memory.frequencies}\n", # type: ignore (pretty sure it's not None)
|
||||
f"\tvocabulary:\n{BPE.vocabulary}",
|
||||
DELIMITER,
|
||||
"",
|
||||
]
|
||||
)
|
||||
print(DEBUG)
|
||||
|
||||
if LAST_VOC_SIZE == NEW_VOC_SIZE:
|
||||
exit = True
|
||||
continue
|
||||
|
||||
if current_iteration == self.__max_iterations:
|
||||
exit = True
|
||||
continue
|
||||
|
||||
if BPE.vocabulary_size == self.__max_vocabulary:
|
||||
exit = True
|
||||
continue
|
||||
|
||||
return BPE
|
||||
|
||||
def __round_train(self, bpe: NanoSocratesBPE, data: list[list[int]]):
|
||||
|
||||
DATA_LEN = len(data)
|
||||
NEW_DATA = []
|
||||
|
||||
counter = 0
|
||||
memory = NanoSocratesBatchMemoryBPE({}, 0)
|
||||
while len(data) > 0:
|
||||
counter += 1
|
||||
last_batch = len(data) == 1
|
||||
|
||||
piece = data.pop()
|
||||
|
||||
bpe, memory, output = bpe.fit(piece, memory, last_batch)
|
||||
|
||||
if counter % int(1E6) == 0:
|
||||
print(f"Fitted: {counter}/{DATA_LEN}")
|
||||
|
||||
if len(output) < 2:
|
||||
continue
|
||||
|
||||
NEW_DATA.append(output)
|
||||
|
||||
return (bpe, NEW_DATA, memory)
|
||||
|
||||
def __gather_data_from_file(self, path: Path) -> list[list[int]]:
|
||||
|
||||
SPLITTER = NanoSocratesSplitter(self.__special_token_regex)
|
||||
|
||||
DATA: list[list[int]] = []
|
||||
|
||||
FILE = open(path, "r", encoding="utf-8")
|
||||
file_string = FILE.read()
|
||||
FILE.close()
|
||||
|
||||
for piece, type in SPLITTER.split_text(file_string):
|
||||
|
||||
if type != TokenType.BPE:
|
||||
continue
|
||||
|
||||
int_list = self.__make_list_ids(piece)
|
||||
DATA.append(int_list)
|
||||
|
||||
return DATA
|
||||
|
||||
def __increment_counter(self, counter: int):
|
||||
|
||||
# What if overflows???
|
||||
try:
|
||||
counter += 1
|
||||
except:
|
||||
print("Integer overflow")
|
||||
counter = 1
|
||||
|
||||
return counter
|
||||
|
||||
def __make_list_ids(self, corpus: str):
|
||||
return list(corpus.encode("utf-8"))
|
||||
@ -1,248 +0,0 @@
|
||||
from collections import deque
|
||||
import datetime
|
||||
from pathlib import Path
|
||||
import re
|
||||
from ..Classes import (
|
||||
NanoSocratesBPE,
|
||||
NanoSocratesChunker,
|
||||
NanoSocratesSplitter,
|
||||
NanoSocratesBatchMemoryBPE,
|
||||
)
|
||||
from ..Enums import TokenType
|
||||
from ..Utils import (
|
||||
special_regex_maker,
|
||||
iterator_with_checks,
|
||||
save_nanos_vocabulary,
|
||||
load_nanos_vocabulary,
|
||||
save_json,
|
||||
load_json,
|
||||
)
|
||||
|
||||
|
||||
class NanoSocraTrainer:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
max_vocabulary: int,
|
||||
special_vocabulary: list[str],
|
||||
chunk_size: int,
|
||||
merge_treshold: int = 0,
|
||||
max_iterations: int = 0,
|
||||
print_after_iterations: int = 1,
|
||||
) -> None:
|
||||
# Bytes
|
||||
BYTE_RESERVED_TOKENS = 256
|
||||
SPECIAL_RESERVED_TOKENS = len(special_vocabulary)
|
||||
RESERVED_TOKENS = BYTE_RESERVED_TOKENS + SPECIAL_RESERVED_TOKENS
|
||||
|
||||
self.__max_vocabulary = max_vocabulary - RESERVED_TOKENS
|
||||
self.__max_iterations = max_iterations
|
||||
self.__chunk_size = chunk_size
|
||||
self.__merge_treshold = merge_treshold
|
||||
self.__special_token_regex = special_regex_maker(special_vocabulary)
|
||||
self.__print_after_iterations = print_after_iterations
|
||||
|
||||
def trainBPE(
|
||||
self,
|
||||
path: Path,
|
||||
cache_dir: Path,
|
||||
bpe: NanoSocratesBPE | None = None,
|
||||
resume_from_iter: int = 0,
|
||||
) -> NanoSocratesBPE:
|
||||
|
||||
if not path.is_file():
|
||||
raise FileNotFoundError()
|
||||
|
||||
if not cache_dir.is_dir():
|
||||
raise NotADirectoryError()
|
||||
|
||||
if bpe is None:
|
||||
bpe = NanoSocratesBPE()
|
||||
BPE = bpe
|
||||
|
||||
if BPE.vocabulary_size > self.__max_vocabulary:
|
||||
return BPE
|
||||
|
||||
exit = False
|
||||
cached = False
|
||||
current_iteration = 0
|
||||
input_path = path
|
||||
|
||||
NEXT_ITERATION = resume_from_iter + 1 if resume_from_iter != 0 else 0
|
||||
|
||||
PATH_GEN = self.__switch_paths(path, cache_dir, NEXT_ITERATION)
|
||||
MEMORY_PATH_GEN = self.__switch_memory(cache_dir, resume_from_iter)
|
||||
|
||||
if resume_from_iter != 0:
|
||||
cached = True
|
||||
current_iteration = resume_from_iter
|
||||
input_path = next(PATH_GEN)
|
||||
# UGLY: fixes a bug immediately, unfortunately
|
||||
_, _ = next(MEMORY_PATH_GEN)
|
||||
_, voc_cache_path = next(MEMORY_PATH_GEN)
|
||||
vocabulary = load_nanos_vocabulary(voc_cache_path)
|
||||
BPE = NanoSocratesBPE(vocabulary)
|
||||
|
||||
while not exit:
|
||||
|
||||
out_path = next(PATH_GEN)
|
||||
internal_cache_path, vocabulary_cache = next(MEMORY_PATH_GEN)
|
||||
|
||||
current_iteration = self.__increment_counter(current_iteration)
|
||||
LAST_VOC_SIZE = BPE.vocabulary_size
|
||||
|
||||
FILE = open(out_path, "w")
|
||||
|
||||
last_memory = None
|
||||
|
||||
for _, memory, output in self.__round_train(input_path, BPE, cached):
|
||||
last_memory = memory
|
||||
FILE.write(output)
|
||||
|
||||
FILE.close()
|
||||
|
||||
internal_cache = {
|
||||
"finished_iter": current_iteration,
|
||||
"read_from": f"{input_path}",
|
||||
"wrote_to": f"{out_path}",
|
||||
"at": datetime.datetime.now(datetime.timezone.utc).strftime(
|
||||
"%Y-%m-%d %H:%M:%S.%f"
|
||||
)[:-3],
|
||||
}
|
||||
|
||||
VOCABULARY = BPE.vocabulary
|
||||
|
||||
save_json(internal_cache, internal_cache_path)
|
||||
save_nanos_vocabulary(VOCABULARY, vocabulary_cache)
|
||||
|
||||
cached = True
|
||||
input_path = out_path
|
||||
|
||||
NEW_VOC_SIZE = BPE.vocabulary_size
|
||||
|
||||
if current_iteration % self.__print_after_iterations == 0:
|
||||
|
||||
DELIMITER = "==============="
|
||||
|
||||
DEBUG = "\n".join(
|
||||
[
|
||||
DELIMITER,
|
||||
f"ITERATION: {current_iteration}",
|
||||
DELIMITER,
|
||||
f"\tVocabulary size: {BPE.vocabulary_size}\n",
|
||||
f"\tFrequencies:\n{last_memory.frequencies}\n", # type: ignore (pretty sure it's not None)
|
||||
f"\tvocabulary:\n{BPE.vocabulary}",
|
||||
DELIMITER,
|
||||
"",
|
||||
]
|
||||
)
|
||||
print(DEBUG)
|
||||
|
||||
if LAST_VOC_SIZE == NEW_VOC_SIZE:
|
||||
exit = True
|
||||
continue
|
||||
|
||||
if current_iteration == self.__max_iterations:
|
||||
exit = True
|
||||
continue
|
||||
|
||||
if BPE.vocabulary_size == self.__max_vocabulary:
|
||||
exit = True
|
||||
continue
|
||||
|
||||
return BPE
|
||||
|
||||
def __round_train(self, path: Path, bpe: NanoSocratesBPE, cached: bool):
|
||||
|
||||
CHUNKER = NanoSocratesChunker(self.__chunk_size, self.__special_token_regex)
|
||||
SPLITTER = NanoSocratesSplitter(self.__special_token_regex)
|
||||
|
||||
BPE = bpe
|
||||
memory = NanoSocratesBatchMemoryBPE({}, self.__merge_treshold)
|
||||
|
||||
CHUNKER_GENERATOR = iterator_with_checks(CHUNKER.chunk(path))
|
||||
|
||||
for chunk, last_chunk in CHUNKER_GENERATOR:
|
||||
|
||||
PIECE_GENERATOR = iterator_with_checks(SPLITTER.split_text(chunk))
|
||||
|
||||
for piece, last_piece in PIECE_GENERATOR:
|
||||
|
||||
LAST_BATCH = last_chunk and last_piece
|
||||
PIECE, TOKEN_TYPE = piece
|
||||
|
||||
if TOKEN_TYPE != TokenType.BPE:
|
||||
_, _, out = BPE.fit([], memory, LAST_BATCH)
|
||||
yield (BPE, memory, PIECE)
|
||||
continue
|
||||
|
||||
PIECE_DATA = self.__make_list_ids(PIECE, cached)
|
||||
|
||||
_, _, out = BPE.fit(PIECE_DATA, memory, LAST_BATCH)
|
||||
|
||||
OUT_STRING = f"{out}"
|
||||
yield (BPE, memory, OUT_STRING)
|
||||
|
||||
def __increment_counter(self, counter: int):
|
||||
|
||||
# What if overflows???
|
||||
try:
|
||||
counter += 1
|
||||
except:
|
||||
print("Integer overflow")
|
||||
counter = 1
|
||||
|
||||
return counter
|
||||
|
||||
def __make_list_ids(self, corpus: str, cached: bool):
|
||||
|
||||
if not cached:
|
||||
return list(corpus.encode("utf-8"))
|
||||
|
||||
REDUCED_CORPUS_LEN = len(corpus) - 1
|
||||
|
||||
# Skip these cars "[" "]"
|
||||
INTS = corpus[1:REDUCED_CORPUS_LEN]
|
||||
INT_LIST = list(map(int, INTS.split(",")))
|
||||
return INT_LIST
|
||||
|
||||
def __switch_paths(self, path: Path, cache_path: Path, initial_iteration: int):
|
||||
|
||||
CORPUS_TMP_1 = cache_path / "corpus-tmp1.txt"
|
||||
CORPUS_TMP_2 = cache_path / "corpus-tmp2.txt"
|
||||
|
||||
switch = True
|
||||
|
||||
if initial_iteration % 2 == 1:
|
||||
switch = False
|
||||
|
||||
del initial_iteration
|
||||
|
||||
while True:
|
||||
if switch:
|
||||
yield CORPUS_TMP_1
|
||||
else:
|
||||
yield CORPUS_TMP_2
|
||||
switch = not switch
|
||||
|
||||
def __switch_memory(self, cache_path: Path, initial_iteration: int):
|
||||
|
||||
INTERNAL_TMP_1 = cache_path / "internal-tmp1.json"
|
||||
INTERNAL_TMP_2 = cache_path / "internal-tmp2.json"
|
||||
|
||||
VOCAB_TMP_1 = cache_path / "voc-tmp1.json"
|
||||
VOCAB_TMP_2 = cache_path / "voc-tmp2.json"
|
||||
|
||||
switch = False
|
||||
|
||||
if initial_iteration % 2 == 1:
|
||||
switch = True
|
||||
|
||||
del initial_iteration
|
||||
|
||||
while True:
|
||||
if switch:
|
||||
yield (INTERNAL_TMP_1, VOCAB_TMP_1)
|
||||
else:
|
||||
yield (INTERNAL_TMP_2, VOCAB_TMP_2)
|
||||
switch = not switch
|
||||
@ -1,280 +0,0 @@
|
||||
from collections import deque
|
||||
import datetime
|
||||
import itertools
|
||||
from multiprocessing import Pool
|
||||
import os
|
||||
from pathlib import Path
|
||||
import re
|
||||
import time
|
||||
from ..Classes import (
|
||||
NanoSocratesBPE,
|
||||
NanoSocratesChunker,
|
||||
NanoSocratesSplitter,
|
||||
NanoSocratesBatchMemoryBPE,
|
||||
)
|
||||
from ..Enums import TokenType
|
||||
from ..Utils import (
|
||||
special_regex_maker,
|
||||
iterator_with_checks,
|
||||
save_nanos_vocabulary,
|
||||
load_nanos_vocabulary,
|
||||
save_json,
|
||||
load_json,
|
||||
)
|
||||
|
||||
|
||||
def split(a, n):
|
||||
k, m = divmod(len(a), n)
|
||||
return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n))
|
||||
|
||||
|
||||
def split_fit(object: tuple[NanoSocratesBPE, list[list[int]]]):
|
||||
|
||||
bpe, data = object
|
||||
|
||||
NEW_DATA: list[list[int]] = []
|
||||
|
||||
memory = NanoSocratesBatchMemoryBPE({}, 0)
|
||||
|
||||
while len(data) > 0:
|
||||
|
||||
piece = data.pop()
|
||||
|
||||
bpe, memory, output = bpe.fit(piece, memory, False)
|
||||
|
||||
if len(output) < 2:
|
||||
continue
|
||||
|
||||
# We are sure of its type
|
||||
NEW_DATA.append(piece) # type: ignore
|
||||
|
||||
return (bpe, NEW_DATA, memory)
|
||||
|
||||
def split_encode(object: tuple[NanoSocratesBPE, list[list[int]]]):
|
||||
|
||||
bpe, data = object
|
||||
|
||||
NEW_DATA: list[list[int]] = []
|
||||
|
||||
for index, piece in zip(range(0, len(data)), data):
|
||||
output = bpe.encode_intermediate(piece)
|
||||
|
||||
if len(output) < 2:
|
||||
continue
|
||||
|
||||
# We are sure of its type
|
||||
NEW_DATA.append(data[index]) # type: ignore
|
||||
|
||||
return NEW_DATA
|
||||
|
||||
class NanoSocraTrainerPool:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
max_vocabulary: int,
|
||||
special_vocabulary: list[str],
|
||||
merge_treshold: int = 0,
|
||||
max_iterations: int = 0,
|
||||
print_after_iterations: int = 1,
|
||||
) -> None:
|
||||
# Bytes
|
||||
BYTE_RESERVED_TOKENS = 256
|
||||
SPECIAL_RESERVED_TOKENS = len(special_vocabulary)
|
||||
RESERVED_TOKENS = BYTE_RESERVED_TOKENS + SPECIAL_RESERVED_TOKENS
|
||||
|
||||
self.__max_vocabulary = max_vocabulary - RESERVED_TOKENS
|
||||
self.__max_iterations = max_iterations
|
||||
self.__merge_treshold = merge_treshold
|
||||
self.__special_token_regex = special_regex_maker(special_vocabulary)
|
||||
self.__print_after_iterations = print_after_iterations
|
||||
|
||||
# TODO: add a resume function
|
||||
def trainBPE(
|
||||
self,
|
||||
path: Path,
|
||||
cache_file: Path,
|
||||
bpe: NanoSocratesBPE | None = None,
|
||||
) -> NanoSocratesBPE:
|
||||
|
||||
if not path.is_file():
|
||||
raise FileNotFoundError()
|
||||
|
||||
if not cache_file.is_file():
|
||||
file = cache_file.open("w")
|
||||
file.close()
|
||||
|
||||
if bpe is None:
|
||||
bpe = NanoSocratesBPE()
|
||||
BPE = bpe
|
||||
|
||||
if BPE.vocabulary_size >= self.__max_vocabulary:
|
||||
return BPE
|
||||
|
||||
exit = False
|
||||
current_iteration = 0
|
||||
data = self.__gather_data_from_file(path)
|
||||
data = self.__encode_from_cache(BPE, data)
|
||||
|
||||
|
||||
while not exit:
|
||||
|
||||
current_iteration = self.__increment_counter(current_iteration)
|
||||
|
||||
LAST_VOC_SIZE = BPE.vocabulary_size
|
||||
|
||||
last_memory = None
|
||||
|
||||
start = time.time_ns()
|
||||
_, data, last_memory = self.__round_train(BPE, data)
|
||||
end = time.time_ns()
|
||||
NEW_VOC_SIZE = BPE.vocabulary_size
|
||||
|
||||
VOCABULARY = BPE.vocabulary
|
||||
|
||||
save_nanos_vocabulary(VOCABULARY, cache_file)
|
||||
|
||||
if current_iteration % self.__print_after_iterations == 0:
|
||||
|
||||
DELIMITER = "==============="
|
||||
|
||||
DEBUG = "\n".join(
|
||||
[
|
||||
DELIMITER,
|
||||
f"ITERATION: {current_iteration}",
|
||||
DELIMITER,
|
||||
f"\tVocabulary size: {BPE.vocabulary_size - 256}\n",
|
||||
f"\tTime elapsed: {(end - start)/1E9}s",
|
||||
DELIMITER,
|
||||
"",
|
||||
]
|
||||
)
|
||||
print(DEBUG)
|
||||
|
||||
if LAST_VOC_SIZE == NEW_VOC_SIZE:
|
||||
exit = True
|
||||
continue
|
||||
|
||||
if current_iteration == self.__max_iterations:
|
||||
exit = True
|
||||
continue
|
||||
|
||||
if BPE.vocabulary_size == self.__max_vocabulary:
|
||||
exit = True
|
||||
continue
|
||||
|
||||
return BPE
|
||||
|
||||
def __round_train(self, bpe: NanoSocratesBPE, data: list[list[int]]):
|
||||
|
||||
NEW_DATA: list[list[int]] = []
|
||||
|
||||
MEMORY = NanoSocratesBatchMemoryBPE({}, self.__merge_treshold)
|
||||
|
||||
fit_funct = split_fit
|
||||
CPU_COUNT = os.process_cpu_count()
|
||||
|
||||
if CPU_COUNT is None:
|
||||
raise Exception()
|
||||
|
||||
VOCABULARY = bpe.vocabulary
|
||||
|
||||
data_chunks = split(data, CPU_COUNT)
|
||||
JOBS = [(NanoSocratesBPE(VOCABULARY), chunk) for chunk in data_chunks]
|
||||
|
||||
JOB_RESULTS: list[
|
||||
tuple[NanoSocratesBPE, list[list[int]], NanoSocratesBatchMemoryBPE]
|
||||
]
|
||||
|
||||
with Pool() as pool:
|
||||
JOB_RESULTS = pool.map(fit_funct, JOBS)
|
||||
|
||||
for i, res in zip(range(0, CPU_COUNT), JOB_RESULTS):
|
||||
_, job_output, job_memory = res
|
||||
NEW_DATA.extend(job_output)
|
||||
|
||||
for key, value in job_memory.frequencies.items():
|
||||
frequency = MEMORY.frequencies.get(key)
|
||||
|
||||
if frequency is None:
|
||||
frequency = 0
|
||||
MEMORY.frequencies[key] = 0
|
||||
|
||||
frequency += value
|
||||
MEMORY.frequencies[key] = frequency
|
||||
|
||||
del job_output
|
||||
del job_memory
|
||||
|
||||
print(f"Joined {i + 1} out of {CPU_COUNT}")
|
||||
|
||||
# Get new token
|
||||
bpe.fit([], MEMORY, True)
|
||||
|
||||
print(f"Sentences from {len(data)} to {len(NEW_DATA)}")
|
||||
|
||||
return (bpe, NEW_DATA, MEMORY)
|
||||
|
||||
def __gather_data_from_file(self, path: Path) -> list[list[int]]:
|
||||
|
||||
SPLITTER = NanoSocratesSplitter(self.__special_token_regex)
|
||||
|
||||
DATA: list[list[int]] = []
|
||||
|
||||
FILE = open(path, "r", encoding="utf-8")
|
||||
file_string = FILE.read()
|
||||
FILE.close()
|
||||
|
||||
for piece, type in SPLITTER.split_text(file_string):
|
||||
|
||||
if type != TokenType.BPE:
|
||||
continue
|
||||
|
||||
int_list = self.__make_list_ids(piece)
|
||||
DATA.append(int_list)
|
||||
|
||||
return DATA
|
||||
|
||||
def __encode_from_cache(self, bpe: NanoSocratesBPE, data: list[list[int]]):
|
||||
|
||||
NEW_DATA : list[list[int]]= []
|
||||
|
||||
CPU_COUNT = os.process_cpu_count()
|
||||
|
||||
if CPU_COUNT is None:
|
||||
raise Exception()
|
||||
|
||||
VOCABULARY = bpe.vocabulary
|
||||
|
||||
data_chunks = split(data, CPU_COUNT)
|
||||
JOBS = [(NanoSocratesBPE(VOCABULARY), chunk) for chunk in data_chunks]
|
||||
|
||||
JOB_RESULTS: list[list[list[int]]]
|
||||
|
||||
with Pool() as pool:
|
||||
JOB_RESULTS = pool.map(split_encode, JOBS)
|
||||
|
||||
for i, res in zip(range(0, CPU_COUNT), JOB_RESULTS):
|
||||
job_output = res
|
||||
NEW_DATA.extend(job_output)
|
||||
|
||||
del job_output
|
||||
|
||||
print(f"Joined {i + 1} out of {CPU_COUNT}")
|
||||
|
||||
print(f"Sentences from {len(data)} to {len(NEW_DATA)}")
|
||||
|
||||
return NEW_DATA
|
||||
|
||||
def __increment_counter(self, counter: int):
|
||||
|
||||
# What if overflows???
|
||||
try:
|
||||
counter += 1
|
||||
except:
|
||||
print("Integer overflow")
|
||||
counter = 1
|
||||
|
||||
return counter
|
||||
|
||||
def __make_list_ids(self, corpus: str):
|
||||
return list(corpus.encode("utf-8"))
|
||||
@ -1,219 +0,0 @@
|
||||
from collections import deque
|
||||
from .Encoder import Encoder
|
||||
from ..Errors import OutOfDictionaryException, DuplicateWordException
|
||||
|
||||
|
||||
# ABOUT THE DICTIONARY:
|
||||
# the string is converted into utf-char bytes, that is: each char is rappresented with a set of bytes from 1 to 4.
|
||||
# each bytes get casted into an integer; such that, if an integer has its value lower then 256,
|
||||
# then it is rappresenting an utf-char-byte, otherwise it is a token-ID.
|
||||
class NanoSocratesBatchMemoryBPE:
|
||||
"""Memory to batch training. Keeps token couple frequencies, and merge_treshold"""
|
||||
|
||||
def __init__(
|
||||
self, frequencies: dict[tuple[int, int], int], merge_treshold: int
|
||||
) -> None:
|
||||
|
||||
self.frequencies = frequencies
|
||||
self.merge_treshold = merge_treshold
|
||||
|
||||
|
||||
class NanoSocratesBPE(Encoder):
|
||||
|
||||
def __init__(self, vocabulary: dict[tuple[int, int], int] | None = None) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.__vocabulary: dict[tuple[int, int], int] = {}
|
||||
self.__reverse_vocabulary: dict[int, tuple[int, int]] = {}
|
||||
|
||||
if vocabulary is None:
|
||||
return
|
||||
|
||||
for key, value in vocabulary.items():
|
||||
if value < 256:
|
||||
raise OutOfDictionaryException()
|
||||
# values under 256 are used for unpaired char
|
||||
# TODO: check if they are in order
|
||||
self.__vocabulary[key] = value
|
||||
self.__reverse_vocabulary[value] = key
|
||||
|
||||
@property
|
||||
def vocabulary_size(self):
|
||||
return len(self.__vocabulary) + 256
|
||||
|
||||
@property
|
||||
def vocabulary(self):
|
||||
return self.__vocabulary
|
||||
|
||||
@property
|
||||
def __next_id(self) -> int:
|
||||
"""
|
||||
Gets the next it
|
||||
Returns:
|
||||
int:
|
||||
"""
|
||||
return self.vocabulary_size
|
||||
|
||||
# TODO: implement fit
|
||||
def fit(
|
||||
self,
|
||||
chunk_data: list[int],
|
||||
memory: NanoSocratesBatchMemoryBPE,
|
||||
last_batch: bool,
|
||||
):
|
||||
|
||||
ENCODED_CHUNK = self.encode_intermediate(chunk_data)
|
||||
DATA_LEN_BEFORE_LAST = len(ENCODED_CHUNK) - 1
|
||||
|
||||
# update frequency of each couple of element
|
||||
for i in range(0, DATA_LEN_BEFORE_LAST):
|
||||
CANDIDATE_COUPLE = (ENCODED_CHUNK[i], ENCODED_CHUNK[i + 1])
|
||||
|
||||
frequency = memory.frequencies.get(CANDIDATE_COUPLE)
|
||||
|
||||
# Initialize frequency
|
||||
if frequency is None:
|
||||
frequency = 0
|
||||
memory.frequencies[CANDIDATE_COUPLE] = 0
|
||||
|
||||
frequency += 1
|
||||
memory.frequencies[CANDIDATE_COUPLE] = frequency
|
||||
|
||||
if not last_batch:
|
||||
return (self, memory, ENCODED_CHUNK)
|
||||
|
||||
if len(memory.frequencies) < 1:
|
||||
return (self, memory, ENCODED_CHUNK)
|
||||
|
||||
FREQUENCIES = memory.frequencies
|
||||
MAX_COUPLE = max(FREQUENCIES.items(), key=lambda item: item[1])[0]
|
||||
FREQUENCY = FREQUENCIES[MAX_COUPLE]
|
||||
|
||||
if FREQUENCY < memory.merge_treshold:
|
||||
return (self, memory, ENCODED_CHUNK)
|
||||
|
||||
self.__learn_word(MAX_COUPLE)
|
||||
|
||||
return (self, memory, ENCODED_CHUNK)
|
||||
|
||||
def encode(self, piece: str) -> list[int]:
|
||||
"""Encode a String into token IDs, it firt convert it into utf-8, then pass the list of integer to encode_intermediate()
|
||||
Args:
|
||||
piece (str):
|
||||
Returns:
|
||||
list[int]:
|
||||
"""
|
||||
converted_piece = list(piece.encode("utf-8"))
|
||||
return self.encode_intermediate(converted_piece)
|
||||
|
||||
def encode_intermediate(self, piece: list[int]) -> list[int]:
|
||||
"""Encode a piece (as list of integer) till its maximum
|
||||
Args:
|
||||
piece (list[int]): piece to encode
|
||||
Returns:
|
||||
list[int]: piece encoded
|
||||
"""
|
||||
current_piece = piece
|
||||
new_piece = self.__round_encode(current_piece)
|
||||
|
||||
# until current_piece is bigger then new_piece, keep encoding
|
||||
while len(current_piece) != len(new_piece):
|
||||
current_piece = new_piece
|
||||
new_piece = self.__round_encode(current_piece)
|
||||
|
||||
return current_piece
|
||||
|
||||
def __round_encode(self, piece: list[int]):
|
||||
"""A single round of encode that traverse all the object. Multiple round are needed for a full encode: \n
|
||||
1) "ABAB" -> "XX"
|
||||
2) "XX" -> "Y"
|
||||
Args:
|
||||
piece (list[int]): the object to encode as a list of integer
|
||||
|
||||
Returns:
|
||||
(list[int]): the one time encoded object
|
||||
"""
|
||||
|
||||
if len(piece) == 1:
|
||||
return piece
|
||||
|
||||
PIECE_LENGTH = len(piece) - 1
|
||||
NEW_PIECE: list[int] = []
|
||||
|
||||
index = 0
|
||||
while index < PIECE_LENGTH:
|
||||
|
||||
CANDIDATE_WORD = (
|
||||
piece[index],
|
||||
piece[index + 1],
|
||||
) # take a tuple of consecutive element [int]
|
||||
CANDIDATE_TOKEN = self.__vocabulary.get(CANDIDATE_WORD)
|
||||
|
||||
# if no token to substitute the tuple, append the first element
|
||||
if CANDIDATE_TOKEN is None:
|
||||
NEW_PIECE.append(piece[index])
|
||||
index += 1
|
||||
|
||||
# if the latter element of the tuple is the last element of the piece, append it
|
||||
if index == PIECE_LENGTH:
|
||||
NEW_PIECE.append(piece[index])
|
||||
|
||||
continue
|
||||
|
||||
# in this case there was a candidate token to substitute the couple of element
|
||||
NEW_PIECE.append(CANDIDATE_TOKEN)
|
||||
|
||||
index += 2
|
||||
|
||||
if index == PIECE_LENGTH:
|
||||
NEW_PIECE.append(piece[index])
|
||||
|
||||
return NEW_PIECE
|
||||
|
||||
# TODO: Remake decode to take a list of token IDs
|
||||
def decode(self, token_ids: list[int]) -> str:
|
||||
|
||||
# deque: double ended queue
|
||||
token_stack: deque[int] = deque(token_ids)
|
||||
UTF_8_STRING_ARR: bytearray = bytearray()
|
||||
|
||||
while len(token_stack) > 0:
|
||||
TOKEN_ID = token_stack.popleft()
|
||||
|
||||
if TOKEN_ID < 256:
|
||||
UTF_8_STRING_ARR.append(TOKEN_ID)
|
||||
continue
|
||||
|
||||
left_token, right_token = self.__token_decode(TOKEN_ID)
|
||||
|
||||
token_stack.appendleft(right_token)
|
||||
token_stack.appendleft(left_token)
|
||||
|
||||
return UTF_8_STRING_ARR.decode("utf-8", errors="ignore")
|
||||
|
||||
def __token_decode(self, token_id: int) -> tuple[int, int]:
|
||||
|
||||
CANDIDATE_DECODED = self.__reverse_vocabulary.get(token_id)
|
||||
|
||||
if CANDIDATE_DECODED is None:
|
||||
raise OutOfDictionaryException()
|
||||
|
||||
return CANDIDATE_DECODED
|
||||
|
||||
def __learn_word(self, words: tuple[int, int]):
|
||||
"""learn a new couple of object in the vocabulary
|
||||
Args:
|
||||
words (tuple[int, int]): the Pair of element to substitute with a new tokenID
|
||||
|
||||
Raises:
|
||||
DuplicateWordException: it launch if there is a duplicate of the new tokenID in the dictionary
|
||||
"""
|
||||
ID = self.__next_id
|
||||
|
||||
DUPLICATE = self.__vocabulary.get(words)
|
||||
|
||||
if DUPLICATE is not None:
|
||||
raise DuplicateWordException()
|
||||
|
||||
self.__vocabulary[words] = ID
|
||||
self.__reverse_vocabulary[ID] = words
|
||||
@ -1,70 +0,0 @@
|
||||
from pathlib import Path
|
||||
import re
|
||||
from ..Errors import DelimiterNotFoundException
|
||||
|
||||
|
||||
class NanoSocratesChunker:
|
||||
|
||||
def __init__(self, max_size: int, special_token_regex: re.Pattern) -> None:
|
||||
self.__max_size: int = max_size
|
||||
self.__special_token_regex: re.Pattern = special_token_regex
|
||||
self.__residual: str = ""
|
||||
|
||||
# max theorethical size of chars
|
||||
# between special tokens:
|
||||
# - min: size - len(longest_token)
|
||||
# - MAX: size - len(shortest_token)
|
||||
def chunk(self, file_path: Path):
|
||||
# read_file
|
||||
FILE = open(file_path, "r", encoding="utf-8")
|
||||
exit = False
|
||||
|
||||
while not exit:
|
||||
REMAINING_SIZE = self.__max_size - len(self.__residual)
|
||||
READ_SIZE = min(self.__max_size, REMAINING_SIZE)
|
||||
FILE_CHUNK = FILE.read(READ_SIZE)
|
||||
|
||||
if len(FILE_CHUNK) == 0:
|
||||
exit = True
|
||||
continue
|
||||
|
||||
CHUNK = self.__append_residuals(FILE_CHUNK)
|
||||
|
||||
boundaries = self.__identify_boudaries(CHUNK)
|
||||
|
||||
if boundaries is None:
|
||||
|
||||
# boundaries not found in 2 chunks,
|
||||
if len(CHUNK) > self.__max_size - 1:
|
||||
raise DelimiterNotFoundException()
|
||||
|
||||
if exit:
|
||||
yield CHUNK
|
||||
|
||||
self.__set_residual(0, CHUNK)
|
||||
continue
|
||||
|
||||
start, end = boundaries
|
||||
self.__set_residual(end, CHUNK)
|
||||
yield CHUNK[start:end]
|
||||
|
||||
def __identify_boudaries(self, corpus: str) -> tuple[int, int] | None:
|
||||
|
||||
end = 0
|
||||
|
||||
for match in self.__special_token_regex.finditer(corpus):
|
||||
# print(match)
|
||||
end = match.end()
|
||||
|
||||
if end == 0:
|
||||
return None
|
||||
|
||||
return (0, end)
|
||||
|
||||
def __append_residuals(self, corpus: str) -> str:
|
||||
RESIDUAL = self.__residual
|
||||
self.__residual = ""
|
||||
return RESIDUAL + corpus
|
||||
|
||||
def __set_residual(self, index: int, corpus: str):
|
||||
self.__residual = corpus[index:]
|
||||
@ -1,68 +0,0 @@
|
||||
from .Encoder import Encoder
|
||||
from ..Errors import OutOfDictionaryException
|
||||
|
||||
|
||||
class NanoSocratesSpecial(Encoder):
|
||||
|
||||
def __init__(
|
||||
self, bpe_vocabulary_size: int, special_tokens: list[str] = []
|
||||
) -> None:
|
||||
|
||||
super().__init__()
|
||||
|
||||
self.__bpe_offset = bpe_vocabulary_size
|
||||
self.__vocabulary: dict[str, int] = {}
|
||||
self.__reverse_vocabulary: dict[int, str] = {}
|
||||
|
||||
if len(special_tokens) == 0:
|
||||
return
|
||||
|
||||
for index, TOKEN in zip(range(0, len(special_tokens)), special_tokens):
|
||||
|
||||
CANDIDATE_ID = self.__bpe_offset + index + 1
|
||||
self.__vocabulary[TOKEN] = CANDIDATE_ID
|
||||
self.__reverse_vocabulary[CANDIDATE_ID] = TOKEN
|
||||
|
||||
@property
|
||||
def __next_id(self):
|
||||
BPE_OFFSET = self.__bpe_offset
|
||||
VOC_LENGTH = len(self.__vocabulary)
|
||||
return BPE_OFFSET + VOC_LENGTH + 1
|
||||
|
||||
@property
|
||||
def vocabulary_size(self) -> int:
|
||||
return len(self.vocabulary)
|
||||
|
||||
@property
|
||||
def vocabulary(self) -> dict[str, int]:
|
||||
return self.__vocabulary
|
||||
|
||||
@property
|
||||
def reverse_vocabulary(self) -> dict[int, str]:
|
||||
return self.__reverse_vocabulary
|
||||
|
||||
def add_special_word_to_vocabulary(self, word: str):
|
||||
CANDIDATE_INDEX = self.__next_id
|
||||
self.__vocabulary[word] = CANDIDATE_INDEX
|
||||
self.__reverse_vocabulary[CANDIDATE_INDEX] = word
|
||||
|
||||
def encode(self, word: str) -> list[int]:
|
||||
ID = self.__vocabulary.get(word)
|
||||
|
||||
if ID is None:
|
||||
raise OutOfDictionaryException()
|
||||
|
||||
return [ID]
|
||||
|
||||
def decode(self, token_id: list[int]) -> str:
|
||||
|
||||
if len(token_id) != 1:
|
||||
raise OutOfDictionaryException()
|
||||
|
||||
ID = token_id[0]
|
||||
WORD = self.__reverse_vocabulary.get(ID)
|
||||
|
||||
if WORD is None:
|
||||
raise OutOfDictionaryException()
|
||||
|
||||
return WORD
|
||||
@ -1,98 +0,0 @@
|
||||
import re
|
||||
from collections import deque
|
||||
from typing import Generator
|
||||
from ..Enums import TokenType
|
||||
|
||||
|
||||
class NanoSocratesSplitter:
|
||||
|
||||
def __init__(
|
||||
self, special_token_regex: re.Pattern, max_bpe_token_id: int = 255
|
||||
) -> None:
|
||||
# attention the regex got already compiled
|
||||
self.__special_token_regex = special_token_regex
|
||||
self.__max_bpe_token_id: int = max_bpe_token_id # used for decoding
|
||||
|
||||
def split_text(self, corpus: str) -> Generator[tuple[str, TokenType]]:
|
||||
"""Split a text using a regex given
|
||||
Args:
|
||||
corpus (str): all the corpus string to split
|
||||
Yields:
|
||||
Generator[tuple[str, TokenType]]: each time returns a piece of the splitted text: string and its TokenType. \n
|
||||
TokenType describe if the string is for the BPE or a special Token [BPE, SPECIAL]
|
||||
"""
|
||||
|
||||
bpe_start = 0
|
||||
bpe_end = len(corpus) # this can be deleted!
|
||||
|
||||
for special_token_start, special_token_end in self.__find_boundaries(corpus):
|
||||
|
||||
# FIND BPE
|
||||
bpe_end = special_token_start
|
||||
BPE_TOKEN_TEXT = corpus[bpe_start:bpe_end]
|
||||
if BPE_TOKEN_TEXT != "":
|
||||
for WORD in self.__split_words(BPE_TOKEN_TEXT):
|
||||
yield (WORD, TokenType.BPE)
|
||||
|
||||
# FIND SPECIAL TOKEN
|
||||
SPECIAL_TOKEN_TEXT = corpus[special_token_start:special_token_end]
|
||||
if SPECIAL_TOKEN_TEXT != "":
|
||||
yield (SPECIAL_TOKEN_TEXT, TokenType.SPECIAL)
|
||||
|
||||
# now save the new bpe start point
|
||||
# it will used in the next interaction
|
||||
bpe_start = special_token_end
|
||||
|
||||
def __find_boundaries(self, corpus: str) -> Generator[tuple[int, int]]:
|
||||
"""
|
||||
Find each time the start and end (not included) of the special token
|
||||
Args:
|
||||
corpus (str): the string where the special token will be searched
|
||||
Yields:
|
||||
Generator[tuple[int, int]]: Note the end is not included
|
||||
"""
|
||||
for match in self.__special_token_regex.finditer(corpus):
|
||||
start = match.start()
|
||||
end = match.end()
|
||||
|
||||
yield (start, end)
|
||||
|
||||
# make the last boundary be the end of corpus
|
||||
# eof = len(corpus)
|
||||
# yield(eof,eof)
|
||||
|
||||
def __split_words(self, bpe_piece: str) -> Generator[str]:
|
||||
|
||||
END_OF_STRING = len(bpe_piece)
|
||||
bound_start = 0
|
||||
bound_end = END_OF_STRING + 1
|
||||
for i in range(0, END_OF_STRING):
|
||||
|
||||
CANDIDATE_CHAR = bpe_piece[i]
|
||||
|
||||
if CANDIDATE_CHAR != " ":
|
||||
continue
|
||||
|
||||
bound_end = i
|
||||
|
||||
yield bpe_piece[bound_start:bound_end]
|
||||
|
||||
bound_start = bound_end
|
||||
bound_end = END_OF_STRING + 1
|
||||
|
||||
yield bpe_piece[bound_start:bound_end]
|
||||
|
||||
def split_tokens(self, corpus: list[int]) -> Generator[tuple[list[int], TokenType]]:
|
||||
|
||||
not_special_token_list: list[int] = []
|
||||
for token in corpus:
|
||||
if token > self.__max_bpe_token_id:
|
||||
|
||||
if len(not_special_token_list) > 0:
|
||||
yield (not_special_token_list, TokenType.BPE)
|
||||
not_special_token_list = []
|
||||
|
||||
yield ([token], TokenType.SPECIAL)
|
||||
continue
|
||||
|
||||
not_special_token_list.append(token)
|
||||
@ -1,8 +0,0 @@
|
||||
|
||||
from Project_Model.Libs.BPE.Classes.TokeNanoCore import TokeNanoCore
|
||||
|
||||
class TokeNano:
|
||||
|
||||
def __init__(self):
|
||||
|
||||
pass
|
||||
@ -1,84 +0,0 @@
|
||||
from pathlib import Path
|
||||
|
||||
from ..Classes import NanoSocratesSplitter
|
||||
from ..Classes import NanoSocratesBPE
|
||||
from ..Classes import NanoSocratesSpecial
|
||||
|
||||
from ..Utils import special_regex_maker
|
||||
from ..Enums import TokenType
|
||||
from ..Enums import SpecialToken
|
||||
|
||||
|
||||
class TokeNanoCore:
|
||||
def __init__(
|
||||
self,
|
||||
bpe_vocabulary: dict[tuple[int, int], int],
|
||||
special_token_list: list[str],
|
||||
# special_vocabulary: dict[str, int]
|
||||
):
|
||||
|
||||
self.__bpe_encoder = NanoSocratesBPE(bpe_vocabulary)
|
||||
|
||||
SPECIAL_REGEX = special_regex_maker(special_token_list)
|
||||
BPE_VOCABULARY_SIZE = self.__bpe_encoder.vocabulary_size
|
||||
|
||||
self.__splitter = NanoSocratesSplitter(SPECIAL_REGEX, BPE_VOCABULARY_SIZE)
|
||||
self.__special_encoder = NanoSocratesSpecial(
|
||||
BPE_VOCABULARY_SIZE, special_token_list
|
||||
)
|
||||
|
||||
@property
|
||||
def vocabulary_size(self):
|
||||
BPE_VOC_SIZE = self.__bpe_encoder.vocabulary_size
|
||||
SPECIAL_VOC_SIZE = self.__special_encoder.vocabulary_size
|
||||
return BPE_VOC_SIZE + SPECIAL_VOC_SIZE + 1
|
||||
|
||||
def encode(self, corpus: str) -> list[int]:
|
||||
output: list[int] = []
|
||||
for piece, token_type in self.__splitter.split_text(corpus):
|
||||
|
||||
if token_type == TokenType.SPECIAL:
|
||||
output.extend(self.__special_encoder.encode(piece))
|
||||
|
||||
# slow but clear
|
||||
if token_type == TokenType.BPE:
|
||||
output.extend(self.__bpe_encoder.encode(piece))
|
||||
|
||||
return output
|
||||
|
||||
|
||||
|
||||
def encode_incomplete_string(self, corpus: str) -> list[int]:
|
||||
"""
|
||||
Encode string which don't end with a special token
|
||||
"""
|
||||
corpus = corpus + SpecialToken.CORPUS_END.value
|
||||
output: list[int] = []
|
||||
for piece, token_type in self.__splitter.split_text(corpus):
|
||||
|
||||
if token_type == TokenType.SPECIAL:
|
||||
output.extend(self.__special_encoder.encode(piece))
|
||||
|
||||
# slow but clear
|
||||
if token_type == TokenType.BPE:
|
||||
output.extend(self.__bpe_encoder.encode(piece))
|
||||
|
||||
return output[:-1]
|
||||
|
||||
|
||||
|
||||
def decode(self, corpus: list[int]) -> str:
|
||||
output_str = ""
|
||||
for token, token_type in self.__splitter.split_tokens(corpus):
|
||||
# token is an integer if special, a list of integer otherwise
|
||||
if token_type == TokenType.SPECIAL:
|
||||
output_str += self.__special_encoder.decode(
|
||||
token
|
||||
) # it accept an integer
|
||||
|
||||
# slow but clear
|
||||
if token_type == TokenType.BPE:
|
||||
output_str += self.__bpe_encoder.decode(
|
||||
token
|
||||
) # it accept a list of integer
|
||||
return output_str
|
||||
@ -1,20 +0,0 @@
|
||||
from .NanoSocratesChunker import NanoSocratesChunker
|
||||
from .NanoSocratesSplitter import NanoSocratesSplitter
|
||||
from .NanoSocratesBPE import NanoSocratesBPE, NanoSocratesBatchMemoryBPE
|
||||
from .NanoSocraTrainer import NanoSocraTrainer
|
||||
from .NanoSocraTraineRam import NanoSocraTraineRam
|
||||
from .NanoSocraTrainerPool import NanoSocraTrainerPool
|
||||
from .NanoSocratesSpecial import NanoSocratesSpecial
|
||||
from .TokeNanoCore import TokeNanoCore
|
||||
from .TokeNano import TokeNano
|
||||
|
||||
__all__ = [
|
||||
"NanoSocratesChunker",
|
||||
"NanoSocratesSplitter",
|
||||
"NanoSocratesBPE",
|
||||
"NanoSocraTrainer",
|
||||
"NanoSocraTraineRam",
|
||||
"NanoSocraTrainerPool",
|
||||
"TokeNanoCore",
|
||||
"TokeNano"
|
||||
]
|
||||
@ -1,27 +0,0 @@
|
||||
from enum import Enum
|
||||
|
||||
|
||||
class SpecialToken(Enum):
|
||||
# (Enum, str) -> throws an error
|
||||
START_TRIPLE_LIST = "<SOTL>"
|
||||
START_TRIPLE = "<SOT>"
|
||||
END_TRIPLE = "<EOT>"
|
||||
SUBJECT = "<SUBJ>"
|
||||
RELATIONSHIP = "<PRED>"
|
||||
OBJECT = "<OBJ>"
|
||||
ABSTRACT = "<ABS>"
|
||||
|
||||
## Tasks' Token
|
||||
RDF_TO_TEXT = "<RDF2TXT>"
|
||||
TEXT_TO_RDF = "<TEXT2RDF>"
|
||||
CONTINUE_RDF = "<CONTINUERDF>"
|
||||
MASK = "<MASK>"
|
||||
|
||||
# BPE Training:
|
||||
# NanoSocrates
|
||||
START = "<START>"
|
||||
CORPUS_END = "<END>"
|
||||
START_OF_SEQUENCE = "<SOS>"
|
||||
END_OF_SEQUENCE = "<EOS>"
|
||||
PAD = "<PAD>"
|
||||
|
||||
@ -1,6 +0,0 @@
|
||||
from enum import Enum, auto
|
||||
|
||||
class TokenType(Enum):
|
||||
|
||||
SPECIAL = auto()
|
||||
BPE = auto()
|
||||
@ -1,6 +0,0 @@
|
||||
from .TokenType import TokenType
|
||||
from .SpecialToken import SpecialToken
|
||||
|
||||
__all__ = [
|
||||
"SpecialToken"
|
||||
]
|
||||
@ -1,4 +0,0 @@
|
||||
class DelimiterNotFoundException(Exception):
|
||||
|
||||
def __init__(self, *args: object) -> None:
|
||||
super().__init__(*args)
|
||||
@ -1,4 +0,0 @@
|
||||
class DuplicateWordException(Exception):
|
||||
|
||||
def __init__(self, *args: object) -> None:
|
||||
super().__init__(*args)
|
||||
@ -1,4 +0,0 @@
|
||||
class OutOfDictionaryException(Exception):
|
||||
|
||||
def __init__(self, *args: object) -> None:
|
||||
super().__init__(*args)
|
||||
@ -1,4 +0,0 @@
|
||||
class SentenceTooLongException(Exception):
|
||||
|
||||
def __init__(self, *args: object) -> None:
|
||||
super().__init__(*args)
|
||||
@ -1,11 +0,0 @@
|
||||
from .DelimiterNotFoundException import DelimiterNotFoundException
|
||||
from .OutOfDictionaryException import OutOfDictionaryException
|
||||
from .DuplicateWordException import DuplicateWordException
|
||||
from .SentenceTooLongException import SentenceTooLongException
|
||||
|
||||
__all__ = [
|
||||
"DelimiterNotFoundException",
|
||||
"OutOfDictionaryException",
|
||||
"DuplicateWordException",
|
||||
"SentenceTooLongException"
|
||||
]
|
||||
@ -1,15 +0,0 @@
|
||||
from .special_regex_maker import special_regex_maker
|
||||
from .lag_checker_iterator import iterator_with_checks
|
||||
from .vocabulary import save_nanos_vocabulary, load_nanos_vocabulary
|
||||
from .json_utils import save_json, load_json
|
||||
from .special_regex_maker import special_regex_maker
|
||||
from .default_special_tokens import default_special_tokens
|
||||
|
||||
__all__ = [
|
||||
"special_regex_maker",
|
||||
"iterator_with_checks",
|
||||
"save_nanos_vocabulary",
|
||||
"load_nanos_vocabulary",
|
||||
"save_json", "load_json",
|
||||
"default_special_tokens"
|
||||
]
|
||||
@ -1,4 +0,0 @@
|
||||
from ..Enums import SpecialToken
|
||||
|
||||
def default_special_tokens() -> list[str]:
|
||||
return [token.value for token in SpecialToken]
|
||||
@ -1,18 +0,0 @@
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def save_json(dictionary: dict, path: Path):
|
||||
|
||||
json_string = json.dumps(dictionary)
|
||||
FILE = open(path, "w")
|
||||
FILE.write(json_string)
|
||||
FILE.close()
|
||||
|
||||
|
||||
def load_json(path: Path) -> dict:
|
||||
FILE = open(path, "r")
|
||||
json_string = FILE.read()
|
||||
FILE.close()
|
||||
|
||||
return json.loads(json_string)
|
||||
@ -1,27 +0,0 @@
|
||||
from collections import deque
|
||||
from typing import Generator, TypeVar
|
||||
|
||||
T1 = TypeVar("T1")
|
||||
T2 = TypeVar("T2")
|
||||
T3 = TypeVar("T3")
|
||||
|
||||
|
||||
def iterator_with_checks(
|
||||
generator: Generator[T1, T2, T3],
|
||||
) -> Generator[tuple[T1, bool], T2, T3]:
|
||||
|
||||
# Here we can ignore to catch stop iteration
|
||||
# we will propagate it
|
||||
last_element = next(generator)
|
||||
|
||||
while True:
|
||||
|
||||
RETURN_ELEMENT = last_element
|
||||
try:
|
||||
element = next(generator)
|
||||
last_element = element
|
||||
yield (RETURN_ELEMENT, False)
|
||||
|
||||
except StopIteration:
|
||||
yield (RETURN_ELEMENT, True)
|
||||
break
|
||||
@ -1,15 +0,0 @@
|
||||
import re
|
||||
|
||||
|
||||
def special_regex_maker(special_tokens: list[str]) -> re.Pattern:
|
||||
"""compile a regex for the special token
|
||||
Args:
|
||||
special_tokens (list[str]): the list of special token
|
||||
|
||||
Returns:
|
||||
re.Pattern:
|
||||
"""
|
||||
|
||||
REGEX_STR = "|".join(special_tokens)
|
||||
|
||||
return re.compile(REGEX_STR)
|
||||
@ -1,49 +0,0 @@
|
||||
import json
|
||||
from pathlib import Path
|
||||
from ..Errors import OutOfDictionaryException
|
||||
|
||||
|
||||
def nanos_vocabulary2json_str(vocabulary: dict[tuple[int, int], int]) -> str:
|
||||
|
||||
JSON: dict[str, int] = {}
|
||||
|
||||
for key, item in vocabulary.items():
|
||||
TUPLE_STR = f"{key}"
|
||||
JSON[TUPLE_STR] = item
|
||||
|
||||
return json.dumps(JSON)
|
||||
|
||||
|
||||
def nanos_json_str2vocabulary(json_string: str) -> dict[tuple[int, int], int]:
|
||||
|
||||
JSON: dict[str, int] = json.loads(json_string)
|
||||
VOCABULARY: dict[tuple[int, int], int] = {}
|
||||
|
||||
for key, item in JSON.items():
|
||||
REDUCED_KEY = len(key) - 1
|
||||
KEY_STR = key[1:REDUCED_KEY]
|
||||
VOC_KEY = tuple(map(int, KEY_STR.split(",")))
|
||||
|
||||
if len(VOC_KEY) != 2:
|
||||
raise OutOfDictionaryException()
|
||||
|
||||
# Checked for weird things above
|
||||
VOCABULARY[VOC_KEY] = item # type: ignore
|
||||
|
||||
return VOCABULARY
|
||||
|
||||
|
||||
def save_nanos_vocabulary(vocabulary: dict[tuple[int, int], int], path: Path):
|
||||
|
||||
json_string = nanos_vocabulary2json_str(vocabulary)
|
||||
FILE = open(path, "w")
|
||||
FILE.write(json_string)
|
||||
FILE.close()
|
||||
|
||||
|
||||
def load_nanos_vocabulary(path: Path) -> dict[tuple[int, int], int]:
|
||||
FILE = open(path, "r")
|
||||
json_string = FILE.read()
|
||||
FILE.close()
|
||||
|
||||
return nanos_json_str2vocabulary(json_string)
|
||||
@ -1,9 +0,0 @@
|
||||
from .Classes import *
|
||||
from .Enums import *
|
||||
from .Errors import *
|
||||
from .Utils import *
|
||||
|
||||
from . import Classes
|
||||
from . import Enums
|
||||
from . import Errors
|
||||
from . import Utils
|
||||
@ -1,243 +0,0 @@
|
||||
import random
|
||||
import sys
|
||||
from typing import Any, Generator
|
||||
import pandas as pd
|
||||
from pathlib import Path
|
||||
from ..Enums import TaskType
|
||||
import Project_Model.Libs.BPE as BPE
|
||||
|
||||
# from Scripts.Libs.CleaningPipeline.special_token import SpecialToken
|
||||
from Project_Model.Libs.Transformer import (
|
||||
SpannedMasker,
|
||||
truncate_rdf_list,
|
||||
normalize_sequence,
|
||||
)
|
||||
|
||||
from Project_Model.Libs.BPE import SpecialToken
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
class Batcher:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dataset_path: Path,
|
||||
max_length: int,
|
||||
tokenizer: BPE.TokeNanoCore,
|
||||
masker: SpannedMasker,
|
||||
seed: int = 0,
|
||||
debug = False
|
||||
) -> None:
|
||||
# ABSTRACT, TRIPLE
|
||||
# tasks:
|
||||
# rdf2text: X: TRIPLE, Y: ABSTRACT
|
||||
# text2rdf: X: ABSTRACT, X:TRIPLE
|
||||
# masking ( call masker): X: incomplete_triple Y: complete_triple (as exam)
|
||||
# completation: X: TRIPLE SUBSET, Y: related TRIPLE SUBSET
|
||||
# it will truncate
|
||||
# it will instantiate spanmaskter and truncator
|
||||
self._dataset_path = dataset_path
|
||||
self._tokenizer = tokenizer
|
||||
self._masker = masker
|
||||
self.__max_length = max_length
|
||||
self._seed = seed
|
||||
# self._token_completation = TokenCompletationTransformer(sotl,eos)
|
||||
self._completation_task_token_truncator = truncate_rdf_list
|
||||
self.__debug = debug
|
||||
|
||||
def batch(self, batch_size) -> Generator[
|
||||
tuple[
|
||||
list[list[int]],
|
||||
list[list[int]],
|
||||
list[list[int]],
|
||||
list[list[int]],
|
||||
TaskType
|
||||
],
|
||||
Any,
|
||||
Any,
|
||||
]:
|
||||
"""
|
||||
Yields: X,Y,padding_X
|
||||
"""
|
||||
RNG = random.Random(self._seed)
|
||||
self._masker.reseed(self._seed)
|
||||
|
||||
for batch in pd.read_csv(self._dataset_path, chunksize=batch_size):
|
||||
|
||||
tokenized_batch = pd.DataFrame()
|
||||
# encode
|
||||
tokenized_batch[["Abstract", "RDFs"]] = batch[["Abstract", "RDFs"]].map(
|
||||
lambda t: self._tokenizer.encode(t)
|
||||
)
|
||||
|
||||
X, Y, padding_X, padding_Y = self.__rdf2txt_transformation(tokenized_batch)
|
||||
yield X, Y, padding_X, padding_Y, TaskType.RDF2TXT
|
||||
(
|
||||
X,
|
||||
Y,
|
||||
padding_X,
|
||||
padding_Y,
|
||||
) = self.__txt2rdf_transformation(tokenized_batch)
|
||||
yield X, Y, padding_X, padding_Y, TaskType.TEXT2RDF
|
||||
(
|
||||
X,
|
||||
Y,
|
||||
padding_X,
|
||||
padding_Y,
|
||||
) = self.__masking_trasformation(tokenized_batch)
|
||||
yield X, Y, padding_X, padding_Y, TaskType.MASKING
|
||||
(
|
||||
X,
|
||||
Y,
|
||||
padding_X,
|
||||
padding_Y,
|
||||
) = self.__token_completation_task(
|
||||
tokenized_batch, RNG.randint(0, sys.maxsize)
|
||||
)
|
||||
yield X, Y, padding_X, padding_Y, TaskType.COMPLETATION
|
||||
|
||||
# output = pd.concat([rdf2txt_batch,txt2rdf_batch,completation_batch],ignore_index=True)
|
||||
# output = output.sample(frac=1).reset_index(drop=True)
|
||||
# self.decode_debug(output)
|
||||
# yield output
|
||||
|
||||
def __random_subset_rdfs(self, batch: pd.DataFrame, seed=0):
|
||||
# WIP
|
||||
rng = random.Random(seed)
|
||||
|
||||
def to_list(x):
|
||||
return x.split(SpecialToken.START_TRIPLE.value)[1:]
|
||||
|
||||
batch["RDFs"] = batch["RDFs"].map(to_list)
|
||||
|
||||
def decode_debug(self, batch: pd.DataFrame):
|
||||
decoded = pd.DataFrame()
|
||||
decoded[["X", "Y"]] = batch[["X", "Y"]].map(lambda t: self._tokenizer.decode(t))
|
||||
print(decoded)
|
||||
|
||||
def __normalization(
|
||||
self, X: list[list[int]], Y: list[list[int]]
|
||||
) -> tuple[list[list[int]], list[list[int]], list[list[int]], list[list[int]]]:
|
||||
pad_token = self._tokenizer.encode(SpecialToken.PAD.value)[0]
|
||||
end_token = self._tokenizer.encode(SpecialToken.END_OF_SEQUENCE.value)[0]
|
||||
out_X = []
|
||||
padding_X = []
|
||||
out_Y = []
|
||||
padding_Y = []
|
||||
|
||||
for x in X:
|
||||
out_x, padding_x = normalize_sequence(
|
||||
x, self.__max_length, pad_token, end_token, True
|
||||
)
|
||||
out_X.append(out_x)
|
||||
padding_X.append(padding_x)
|
||||
|
||||
for y in Y:
|
||||
out_y, padding_y = normalize_sequence(
|
||||
y, self.__max_length, pad_token, end_token, True
|
||||
)
|
||||
out_Y.append(out_y)
|
||||
padding_Y.append(padding_y)
|
||||
|
||||
return out_X, out_Y, padding_X, padding_Y
|
||||
|
||||
def __rdf2txt_transformation(self, batch: pd.DataFrame):
|
||||
X: list[list[int]]
|
||||
task_token = self._tokenizer.encode(SpecialToken.RDF_TO_TEXT.value)
|
||||
out = batch.rename(columns={"RDFs": "X", "Abstract": "Y"})[["X", "Y"]]
|
||||
out["X"] = [task_token + x for x in out["X"]]
|
||||
return self.__normalization(out["X"].to_list(), out["Y"].to_list())
|
||||
|
||||
def __txt2rdf_transformation(self, batch: pd.DataFrame):
|
||||
task_token = self._tokenizer.encode(SpecialToken.TEXT_TO_RDF.value)
|
||||
out = batch.rename(columns={"Abstract": "X", "RDFs": "Y"})[["X", "Y"]]
|
||||
out["X"] = [task_token + x for x in out["X"]]
|
||||
return self.__normalization(out["X"].to_list(), out["Y"].to_list())
|
||||
|
||||
def __masking_trasformation(self, batch: pd.DataFrame):
|
||||
X = []
|
||||
Y = []
|
||||
for rdf in batch["RDFs"]:
|
||||
x, y = self._masker.mask_sequence(rdf[:self.__max_length])
|
||||
X.append(x)
|
||||
Y.append(y)
|
||||
return self.__normalization(X, Y)
|
||||
|
||||
def __token_completation_task(self, batch: pd.DataFrame, minibatch_seed: int):
|
||||
continue_triple_token = self._tokenizer.encode(SpecialToken.CONTINUE_RDF.value)[
|
||||
0
|
||||
]
|
||||
eot = self._tokenizer.encode(SpecialToken.END_TRIPLE.value)[0]
|
||||
X = []
|
||||
Y = []
|
||||
for rdf in batch["RDFs"]:
|
||||
# here first truncate to max_lenght
|
||||
rdf = rdf[: self.__max_length] # truncator that uses "eot" so no problem
|
||||
x, y = self._completation_task_token_truncator(
|
||||
rdf, 0.5, continue_triple_token, eot, minibatch_seed
|
||||
)
|
||||
X.append(x)
|
||||
Y.append(y)
|
||||
return self.__token_cmpletation_task_special_normalization(X, Y)
|
||||
|
||||
def __token_cmpletation_task_special_normalization(self, X: list[list[int]], Y: list[list[int]]
|
||||
) -> tuple[list[list[int]], list[list[int]], list[list[int]], list[list[int]]]:
|
||||
|
||||
def continue_rdf_padding(sequence: list[int], pad_token: int):
|
||||
for i, x in enumerate(sequence):
|
||||
if x == pad_token:
|
||||
i = i+1 # continueRDF will be excluded by the mask
|
||||
# fill the tail with True and stop
|
||||
return [False] * i + [True] * (len(sequence) - i)
|
||||
return [False] * len(sequence) # no pad token found
|
||||
|
||||
pad_token = self._tokenizer.encode(SpecialToken.PAD.value)[0]
|
||||
end_token = self._tokenizer.encode(SpecialToken.END_OF_SEQUENCE.value)[0]
|
||||
continue_rdf = self._tokenizer.encode(SpecialToken.CONTINUE_RDF.value)[0]
|
||||
out_X = []
|
||||
padding_X = []
|
||||
out_Y = []
|
||||
padding_Y = []
|
||||
|
||||
for x in X:
|
||||
out_x, _ = normalize_sequence(
|
||||
x, self.__max_length, pad_token, end_token, True
|
||||
)
|
||||
out_X.append(out_x)
|
||||
# padding_X.append(padding_x)
|
||||
special_padding = continue_rdf_padding(out_x,continue_rdf)
|
||||
padding_X.append(special_padding)
|
||||
|
||||
for y in Y:
|
||||
out_y, padding_y = normalize_sequence(
|
||||
y, self.__max_length, pad_token, end_token, True
|
||||
)
|
||||
out_Y.append(out_y)
|
||||
# special padding
|
||||
# special_padding = continue_rdf_padding(out_y,continue_rdf)
|
||||
# padding_Y.append(special_padding)
|
||||
padding_Y.append(padding_y)
|
||||
|
||||
return out_X, out_Y, padding_X, padding_Y
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
DATASET_PATH = Path("Assets/Dataset/Tmp/rdf_text.csv")
|
||||
VOCABULARY_path = "Assets/Dataset/Tmp/trimmed.json"
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
VOCABULARY = BPE.load_nanos_vocabulary(Path(VOCABULARY_path))
|
||||
SPECIAL_LIST = BPE.default_special_tokens()
|
||||
TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_LIST)
|
||||
SPECIAL_TOKENS: set[int] = set(TOKENANO.encode("".join(SPECIAL_LIST)))
|
||||
|
||||
MASKER = SpannedMasker(TOKENANO.vocabulary_size, SPECIAL_TOKENS)
|
||||
|
||||
prova = "<ABS>Cactus Flower is a 1969 American screwball comedy film directed by Gene Saks, and starring Walter Matthau, Ingrid Bergman and Goldie Hawn, who won an Academy Award for her performance.The screenplay was adapted by I. A. L. Diamond from the 1965 Broadway play of the same title written by Abe Burrows, which, in turn, is based on the French play Fleur de cactus by Pierre Barillet and Jean-Pierre Gredy. Cactus Flower was the ninth highest-grossing film of 1969."
|
||||
print(TOKENANO.encode(prova))
|
||||
batcher = Batcher(DATASET_PATH,256, TOKENANO, MASKER)
|
||||
for batch in batcher.batch(8):
|
||||
print(batch)
|
||||
@ -1,33 +0,0 @@
|
||||
|
||||
class TokenCompletationTransformer:
|
||||
|
||||
def __init__(self,SOTL_token,EOS_token, input_percent:float = 0.5) -> None:
|
||||
self.__SOTL_token = SOTL_token
|
||||
self.__EOS_token = EOS_token
|
||||
self.__input_percent = input_percent
|
||||
pass
|
||||
|
||||
|
||||
def get_completation_tuple(
|
||||
self,
|
||||
token_sequence: list[int],
|
||||
)-> tuple[list[int], list[int]]:
|
||||
|
||||
# split the sequence by encoded(<SOTL>), dont take the first, firts pertenge in as X the other as Y
|
||||
sotl_count =int( token_sequence.count(self.__SOTL_token) * self.__input_percent)
|
||||
|
||||
sotl_index = 0
|
||||
percent_index = 0
|
||||
while sotl_index < sotl_count:
|
||||
token = token_sequence[percent_index]
|
||||
if token == self.__SOTL_token:
|
||||
sotl_index += 1
|
||||
|
||||
percent_index+=1
|
||||
|
||||
percent_index = percent_index -1
|
||||
x_list = token_sequence[:percent_index]
|
||||
x_list.append(self.__EOS_token)
|
||||
y_list = token_sequence[percent_index:]
|
||||
return (x_list,y_list)
|
||||
|
||||
@ -1,2 +0,0 @@
|
||||
from .Batcher import Batcher
|
||||
from .TokenCompletation import TokenCompletationTransformer
|
||||
@ -1,8 +0,0 @@
|
||||
from enum import Enum, auto
|
||||
|
||||
class TaskType(Enum):
|
||||
|
||||
RDF2TXT = auto()
|
||||
TEXT2RDF = auto()
|
||||
MASKING = auto()
|
||||
COMPLETATION = auto()
|
||||
@ -1,5 +0,0 @@
|
||||
from .TaskType import TaskType
|
||||
|
||||
__all__ = [
|
||||
"TaskType"
|
||||
]
|
||||
@ -1,5 +0,0 @@
|
||||
from .Classes import *
|
||||
from .Enums import *
|
||||
|
||||
from . import Classes
|
||||
from . import Enums
|
||||
@ -1,23 +0,0 @@
|
||||
import torch
|
||||
from ..Utils import fixed_positional_encoding
|
||||
|
||||
|
||||
# WIP FOR BATCHING
|
||||
class NanoSocratesEmbedder(torch.nn.Module):
|
||||
|
||||
def __init__(self, vocabulary_size: int, embedding_size: int) -> None:
|
||||
super().__init__()
|
||||
self.__embedder = torch.nn.Embedding(vocabulary_size, embedding_size)
|
||||
|
||||
def forward(self, tokenized_sentence: torch.Tensor) -> torch.Tensor:
|
||||
|
||||
computed_embeddings: torch.Tensor = self.__embedder(tokenized_sentence)
|
||||
|
||||
_, SENTENCE_LENGHT, EMBEDDING_SIZE = computed_embeddings.shape # for batching
|
||||
|
||||
POSITIONAL_ENCODINGS = fixed_positional_encoding(
|
||||
SENTENCE_LENGHT, EMBEDDING_SIZE
|
||||
)
|
||||
|
||||
computed_embeddings = computed_embeddings + POSITIONAL_ENCODINGS # for batching
|
||||
return computed_embeddings
|
||||
@ -1,5 +0,0 @@
|
||||
from .NanoSocratesEmbedder import NanoSocratesEmbedder
|
||||
|
||||
__all__ = [
|
||||
"NanoSocratesEmbedder"
|
||||
]
|
||||
@ -1,5 +0,0 @@
|
||||
from .fixed_positional_encoding import fixed_positional_encoding
|
||||
|
||||
__all__ = [
|
||||
"fixed_positional_encoding"
|
||||
]
|
||||
@ -1,28 +0,0 @@
|
||||
import torch
|
||||
|
||||
|
||||
def fixed_positional_encoding(
|
||||
sentence_dimension: int,
|
||||
embedding_dimension: int,
|
||||
) -> torch.Tensor:
|
||||
|
||||
BIG_CONST = int(1e4)
|
||||
INITIAL_ENCODING = torch.tensor([i for i in range(0, sentence_dimension)])
|
||||
|
||||
ENCODINGS: list[torch.Tensor] = []
|
||||
|
||||
for i in range(0, embedding_dimension):
|
||||
EMBEDDING_POSITION = i
|
||||
|
||||
# Note: The original paper did not specify
|
||||
# to compute: pos mod 2!!
|
||||
DIVISOR = BIG_CONST ** ((2 * (EMBEDDING_POSITION // 2)) / embedding_dimension)
|
||||
INTERMEDIATE_ENCODING = INITIAL_ENCODING / DIVISOR
|
||||
|
||||
if EMBEDDING_POSITION % 2 == 0:
|
||||
ENCODINGS.append(torch.sin(INTERMEDIATE_ENCODING))
|
||||
continue
|
||||
|
||||
ENCODINGS.append(torch.cos(INTERMEDIATE_ENCODING))
|
||||
|
||||
return torch.stack(ENCODINGS).transpose(0, 1)
|
||||
@ -1,7 +0,0 @@
|
||||
from .Utils import *
|
||||
from .Classes import *
|
||||
|
||||
from . import Utils
|
||||
from . import Classes
|
||||
|
||||
|
||||
@ -1,70 +0,0 @@
|
||||
import evaluate
|
||||
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
|
||||
class Evaluator():
|
||||
def __init__(self) -> None:
|
||||
# txt based evaluator
|
||||
self.__rouge = evaluate.load("rouge")
|
||||
self.__rouge_types = ["rougeLsum", "rouge1", "rouge2"] #rougeLsum will work bad because it expect that each sentence are divided with /n
|
||||
self._bleu = evaluate.load("bleu")
|
||||
self._meteor = evaluate.load("meteor")
|
||||
# token based evaluator
|
||||
self.__acc_m = evaluate.load("accuracy")
|
||||
self.__prec_m = evaluate.load("precision")
|
||||
self.__rec_m = evaluate.load("recall")
|
||||
self.__f1_m = evaluate.load("f1")
|
||||
|
||||
def rdf2txt_rouge_evaluation(self, preds: list[str], refs: list[str]):
|
||||
|
||||
results = self.__rouge.compute(
|
||||
predictions=preds, references=refs,
|
||||
rouge_types=self.__rouge_types,
|
||||
use_stemmer=True,
|
||||
use_aggregator=True #F1
|
||||
)
|
||||
|
||||
return {k: float(results[k]) for k in self.__rouge_types}
|
||||
|
||||
def rdf2txt_bleu_evaluation(self, preds: list[str], refs: list[str]) -> float:
|
||||
# sacreBLEU via evaluate; expects references as list-of-lists
|
||||
# each prediction can be evaluated against a list of references, hence [[ref]]
|
||||
results = self._bleu.compute(predictions=preds, references=[[r] for r in refs])
|
||||
return float(results["bleu"]) # (native sacreBLEU scale)
|
||||
|
||||
def rdf2txt_meteor_evaluation(self, preds: list[str], refs: list[str]) -> float:
|
||||
# as bleu
|
||||
res = self._meteor.compute(predictions=preds, references=[[r] for r in refs])
|
||||
return float(res["meteor"])
|
||||
|
||||
|
||||
def __my_accuracy(self,preds: list[list[int]], refs: list[list[int]]):
|
||||
# it is done on token sequence not single token
|
||||
total = len(preds)
|
||||
correct = 0
|
||||
for p, r in zip(preds, refs):
|
||||
correct += int(p == r)
|
||||
return correct / total
|
||||
|
||||
def __accuracy(self, preds, refs):
|
||||
return accuracy_score(preds,refs)
|
||||
|
||||
def __clean_batch_by_pad(self, preds: list[list[int]], refs: list[list[int]]):
|
||||
output_preds = []
|
||||
output_refs = []
|
||||
#TODO
|
||||
pad_token: int = 7000 # percolate
|
||||
for pred, ref in zip(preds,refs):
|
||||
try:
|
||||
i = ref.index(pad_token) # first time pad token appears
|
||||
except ValueError:
|
||||
i = len(ref)
|
||||
output_preds.append(pred[:i])
|
||||
output_refs.append(ref[:i])
|
||||
|
||||
return output_preds,output_refs
|
||||
|
||||
def __precision_recall(self, preds: list[list[int]], refs: list[list[int]]):
|
||||
#TODO
|
||||
p, r, f1, _ = precision_recall_fscore_support(
|
||||
preds, refs, average="binary", zero_division=0
|
||||
) #### watch later
|
||||
return {"precision": float(p), "recall": float(r), "f1": float(f1)}
|
||||
@ -1,5 +0,0 @@
|
||||
from .get_default_device import get_default_device
|
||||
|
||||
__all__ = [
|
||||
"get_default_device"
|
||||
]
|
||||
@ -1,17 +0,0 @@
|
||||
import torch
|
||||
|
||||
def get_default_device() -> torch.device:
|
||||
|
||||
# Cuda or ROCm
|
||||
if torch.cuda.is_available():
|
||||
return torch.device("cuda")
|
||||
|
||||
# Intel GPUs
|
||||
if torch.xpu.is_available():
|
||||
return torch.device("xpu")
|
||||
|
||||
# Apple GPUs
|
||||
if torch.backends.mps.is_available():
|
||||
return torch.device("mps")
|
||||
|
||||
return torch.device("cpu")
|
||||
@ -1,7 +0,0 @@
|
||||
from .Utils import *
|
||||
|
||||
from .Utils import get_default_device
|
||||
|
||||
__all__ = [
|
||||
"get_default_device"
|
||||
]
|
||||
@ -1,43 +0,0 @@
|
||||
import torch
|
||||
|
||||
class LogitsCollector:
|
||||
def __init__(self, pad_token: int, end_token: int, tokenizer) -> None:
|
||||
self.__pad_token = pad_token # used to skip PAD
|
||||
self.__end_token = end_token # used to stop at END
|
||||
self.__tokenizer = tokenizer # exposes .decode(list[int]) -> str
|
||||
self.__steps: list[torch.Tensor] = [] # list of per-step logits [B,V]
|
||||
|
||||
def reset(self) -> None:
|
||||
self.__steps.clear() # clear history
|
||||
|
||||
def add(self, logits_step: torch.Tensor) -> None:
|
||||
if logits_step.dim() == 3: # handle [B,1,V]
|
||||
logits_step = logits_step[:, -1, :] # -> [B,V]
|
||||
self.__steps.append(logits_step.detach()) # store raw logits (detached)
|
||||
|
||||
def tokens(self) -> list[list[int]]:
|
||||
if not self.__steps:
|
||||
return []
|
||||
stack = torch.stack(self.__steps, dim=0) # [T,B,V]
|
||||
probs = torch.softmax(stack, dim=-1) # softmax over vocab -> [T,B,V]
|
||||
ids = probs.argmax(dim=-1).transpose(0, 1) # greedy ids -> [B,T]
|
||||
out: list[list[int]] = []
|
||||
for row in ids.tolist():
|
||||
seq: list[int] = []
|
||||
for tok in row:
|
||||
# if tok == self.__end_token: # stop on END
|
||||
# break
|
||||
if tok == self.__pad_token: # skip PAD
|
||||
continue
|
||||
seq.append(tok)
|
||||
out.append(seq)
|
||||
return out
|
||||
|
||||
def print_decoded(self) -> None:
|
||||
for i, seq in enumerate(self.tokens()):
|
||||
try:
|
||||
# text = text + self.__end_token
|
||||
text = self.__tokenizer.decode(seq) # decode tokens to string
|
||||
except Exception:
|
||||
text = str(seq) # fallback to ids
|
||||
print(f"[{i}] {text}") # simple print
|
||||
@ -1,20 +0,0 @@
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
class Log:
|
||||
def __init__(self, path):
|
||||
self.path = path
|
||||
header = ["epoch","avg_txt","avg_enc","avg_dec","txt_loss","masking_loss","prediction_loss"]
|
||||
|
||||
if Path(path).is_file():
|
||||
return
|
||||
|
||||
with open(self.path, "w", encoding="utf-8", newline="") as f:
|
||||
f.write(",".join(header) + "\n")
|
||||
|
||||
def write(self, loss: list[float]):
|
||||
line = ",".join(str(float(x)) for x in loss) + "\n"
|
||||
with open(self.path, "a", encoding="utf-8", newline="") as f:
|
||||
f.write(line)
|
||||
f.flush()
|
||||
os.fsync(f.fileno()) # extra durability per write # suggested against sudden crashes since it will be done
|
||||
@ -1,19 +0,0 @@
|
||||
import torch
|
||||
|
||||
|
||||
class DeToken(torch.nn.Module):
|
||||
|
||||
def __init__(self, embedding_size: int, vocabulary_size: int) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.__linear = torch.nn.Linear(embedding_size, vocabulary_size)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
|
||||
# 1) Go from latent space to vocabularu space
|
||||
x = self.__linear(x)
|
||||
|
||||
# 2) Go to logits
|
||||
# x = torch.softmax(x, 2)
|
||||
|
||||
return x
|
||||
@ -1,115 +0,0 @@
|
||||
from typing import Optional
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from .FeedForwardNetwork import FeedForwardNetwork
|
||||
from .TorchMultiHeadAttention import TorchMultiHeadAttention as MultiHeadAttention
|
||||
from ..Utils.attention_mask import get_causal_attention_mask, get_prefix_causal_mask_from_padding_mask
|
||||
|
||||
# B, L(T), E_D
|
||||
|
||||
|
||||
class Decoder(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
embedding_dimension: int,
|
||||
feed_forward_hidden_layer_dimension: int,
|
||||
number_of_attention_heads: int,
|
||||
) -> None:
|
||||
self.__attention_heads = number_of_attention_heads
|
||||
super().__init__()
|
||||
|
||||
|
||||
self.__masked_attention = MultiHeadAttention(
|
||||
embedding_dimension, number_of_attention_heads, dropout=0.1
|
||||
)
|
||||
|
||||
self.__layer_norm_1 = nn.LayerNorm(embedding_dimension)
|
||||
|
||||
self.__cross_attention = MultiHeadAttention(
|
||||
embedding_dimension, number_of_attention_heads, dropout=0.1
|
||||
)
|
||||
self.__layer_norm_2 = nn.LayerNorm(embedding_dimension)
|
||||
|
||||
self.__dropout = nn.Dropout(0.1)
|
||||
|
||||
self.__feed_forward_network = FeedForwardNetwork(
|
||||
embedding_dimension, feed_forward_hidden_layer_dimension
|
||||
)
|
||||
self.__layer_norm_3 = nn.LayerNorm(embedding_dimension)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
args: tuple[
|
||||
torch.Tensor,
|
||||
torch.Tensor,
|
||||
torch.Tensor,
|
||||
torch.Tensor,
|
||||
torch.Tensor,
|
||||
Optional[bool]
|
||||
]
|
||||
): # -> list[torch.Tensor]: # k_x = v_x . While x_q = x
|
||||
# WARNING: args is needed to have sequential
|
||||
if len(args) < 6:
|
||||
args = args + (False)
|
||||
x, k_x, v_x, src_padding_mask, tgt_padding_mask, decoder_only = args
|
||||
|
||||
# build of attention mask
|
||||
# TODO: create a prefix causal mask if needed
|
||||
if decoder_only:
|
||||
attention_mask = get_prefix_causal_mask_from_padding_mask(x.size(1),src_padding_mask,self.__attention_heads) # the correct is tgt however ...
|
||||
else:
|
||||
attention_mask = get_causal_attention_mask(x.size(1))
|
||||
|
||||
# 1) Masked Attention
|
||||
MASKED_ATTENTION = self.__masked_attention(
|
||||
x, x, x, key_padding_mask=tgt_padding_mask, attention_mask=attention_mask
|
||||
)
|
||||
|
||||
# 2) Dropout
|
||||
DROPPED_MASKED_ATTENTION = self.__dropout(MASKED_ATTENTION)
|
||||
del MASKED_ATTENTION
|
||||
|
||||
# 3) Residual Connection
|
||||
x = x + DROPPED_MASKED_ATTENTION
|
||||
del DROPPED_MASKED_ATTENTION
|
||||
|
||||
# 4) Layer Normalization
|
||||
x = self.__layer_norm_1(x)
|
||||
|
||||
|
||||
if not decoder_only:
|
||||
# 5) Encoder–decoder (cross) attention
|
||||
CROSS_ATTENTION = self.__cross_attention(
|
||||
x, k_x, v_x, key_padding_mask=src_padding_mask
|
||||
)
|
||||
|
||||
# 6) Dropout
|
||||
DROPPED_CROSS_ATTENTION = self.__dropout(CROSS_ATTENTION)
|
||||
del CROSS_ATTENTION
|
||||
|
||||
# 7) Residual Connection
|
||||
x = x + DROPPED_CROSS_ATTENTION
|
||||
del DROPPED_CROSS_ATTENTION
|
||||
|
||||
# 8) Layer Normalization
|
||||
x = self.__layer_norm_2(x)
|
||||
|
||||
# 9) Position-wise feed-forward
|
||||
FEED_FORWARD = self.__feed_forward_network(x)
|
||||
|
||||
# 10) Dropout
|
||||
DROPPED_FEED_FORWARD = self.__dropout(FEED_FORWARD)
|
||||
del FEED_FORWARD
|
||||
|
||||
# 11) Residual Connection
|
||||
x = x + DROPPED_FEED_FORWARD
|
||||
del DROPPED_FEED_FORWARD
|
||||
|
||||
# 12) Layer Normalization
|
||||
x = self.__layer_norm_3(x)
|
||||
|
||||
return (x, k_x, v_x, src_padding_mask, tgt_padding_mask, decoder_only)
|
||||
|
||||
|
||||
# use eval to disable dropout ecc
|
||||
@ -1,73 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from Project_Model.Libs.Transformer.Classes.FeedForwardNetwork import FeedForwardNetwork
|
||||
from Project_Model.Libs.Transformer.Classes.TorchMultiHeadAttention import (
|
||||
TorchMultiHeadAttention as MultiHeadAttention,
|
||||
)
|
||||
|
||||
|
||||
class Encoder(
|
||||
nn.Module
|
||||
): # in this way we expose the primitive of nn.Module for training purpose
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
embedding_dimension: int,
|
||||
feed_forward_hidden_layer_dimension: int,
|
||||
number_of_attention_heads: int,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.__attention = MultiHeadAttention(
|
||||
embedding_dimension, number_of_attention_heads, dropout=0.1
|
||||
)
|
||||
self.__layer_norm_1 = nn.LayerNorm(
|
||||
embedding_dimension
|
||||
) # norm of first "Add and Normalize"
|
||||
self.__feed_forward = FeedForwardNetwork(
|
||||
embedding_dimension, feed_forward_hidden_layer_dimension
|
||||
)
|
||||
self.__layer_norm_2 = nn.LayerNorm(
|
||||
embedding_dimension
|
||||
) # norm of second "Add and Normalize"
|
||||
self.__dropout = nn.Dropout(0.1) # ...
|
||||
|
||||
|
||||
def forward(self, args: tuple[torch.Tensor, torch.Tensor]):
|
||||
# WARNING: args is needed to have sequential
|
||||
x, padding_mask = args
|
||||
|
||||
# -> ATTENTION -> dropout -> add and normalize -> FF -> dropout -> add and normalize ->
|
||||
# Attention with Residual Connection [ input + self-attention]
|
||||
|
||||
# 1) Multi Head Attention
|
||||
ATTENTION = self.__attention(x, x, x, key_padding_mask=padding_mask)
|
||||
|
||||
# 2) Dropout
|
||||
DROPPED_ATTENTION = self.__dropout(ATTENTION)
|
||||
del ATTENTION
|
||||
|
||||
# 3) Residual Connection
|
||||
x = x + DROPPED_ATTENTION
|
||||
del DROPPED_ATTENTION
|
||||
|
||||
# 4) Layer Normalization
|
||||
x = self.__layer_norm_1(x)
|
||||
|
||||
# 5) Feed Forward
|
||||
FEED_FORWARD = self.__feed_forward(x)
|
||||
|
||||
# 6) Dropout
|
||||
DROPPED_FEED_FORWARD = self.__dropout(FEED_FORWARD)
|
||||
del FEED_FORWARD
|
||||
|
||||
# 7) Residual Connection
|
||||
x = x + DROPPED_FEED_FORWARD
|
||||
del DROPPED_FEED_FORWARD
|
||||
|
||||
# 8) Layer Normalization
|
||||
x = self.__layer_norm_2(x)
|
||||
|
||||
return (x, padding_mask)
|
||||
|
||||
|
||||
# use eval to disable dropout ecc
|
||||
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Reference in New Issue
Block a user