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Add generation utils with greedy search and tests
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‎notebooks/hf_vs_tt_t5.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Ensuring the TorchText T5 implementation matches other OSS implementations\n",
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"\n",
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"> In order to run this notebook, you will need to install the huggingface library with the following command: `pip install transformers`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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"from transformers import T5Model\n",
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"from torchtext.prototype.models import T5_BASE\n",
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"\n",
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"import torch"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {},
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"outputs": [],
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"source": [
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"input_sentence = [\"translate to Spanish: My name is Joe\"]\n",
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"output_sentence = [\"Me llamo Joe\"]\n",
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"\n",
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"transform = T5_BASE.transform()\n",
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"tt_t5_model = T5_BASE.get_model()\n",
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"\n",
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"hf_t5_model = T5Model.from_pretrained(\"t5-base\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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"tokenized_sentence = transform(input_sentence)\n",
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"tokenized_output = transform(output_sentence)\n",
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"\n",
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"tt_output = tt_t5_model(encoder_tokens=tokenized_sentence, decoder_tokens=tokenized_output)\n",
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"hf_output = hf_t5_model(input_ids=tokenized_sentence, decoder_input_ids=tokenized_output, return_dict=True)\n",
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"\n",
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"assert torch.all(tt_output[\"encoder_output\"].eq(hf_output[\"encoder_last_hidden_state\"]))\n",
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"assert torch.all(tt_output[\"decoder_output\"].eq(hf_output[\"last_hidden_state\"]))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.13 ('torchtext39')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "63c8862cb56f124e3ee7674b73de745eeb216416a9b24f78d1fcb7c775bff1b7"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎notebooks/hf_with_torchtext_gen.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In order to run this notebook, you will need to install the huggingface library with the following command: `pip install transformers`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/opt/homebrew/Caskroom/miniforge/base/envs/torchtext39/lib/python3.9/site-packages/tqdm-4.64.0-py3.9.egg/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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"source": [
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"from transformers import T5ForConditionalGeneration, T5Tokenizer, BartForConditionalGeneration, BartTokenizer, GPT2LMHeadModel, GPT2Tokenizer\n",
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"from torchtext.prototype.generate import GenerationUtil"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"t5 = T5ForConditionalGeneration.from_pretrained(\"t5-base\")\n",
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"bart = BartForConditionalGeneration.from_pretrained(\"facebook/bart-large-cnn\")\n",
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"gpt2 = GPT2LMHeadModel.from_pretrained(\"gpt2\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/opt/homebrew/Caskroom/miniforge/base/envs/torchtext39/lib/python3.9/site-packages/transformers/models/t5/tokenization_t5.py:164: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
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"For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
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"- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
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"- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
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"- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
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" warnings.warn(\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['owning a dog is good for you, according to studies. a dog is']\n"
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]
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}
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],
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"source": [
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"# Testing Huggingface's T5\n",
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"test_sequence = [\"summarize: studies have shown that owning a dog is good for you\"]\n",
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"generative_hf_t5 = GenerationUtil(t5, is_encoder_decoder=True, is_huggingface_model=True)\n",
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"t5_tokenizer = T5Tokenizer.from_pretrained(\"t5-base\")\n",
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"test_sequence_tk = t5_tokenizer(test_sequence, return_tensors=\"pt\").input_ids\n",
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"tokens = generative_hf_t5.generate(test_sequence_tk, max_len=20, pad_idx=t5.config.pad_token_id)\n",
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"print(t5_tokenizer.batch_decode(tokens, skip_special_tokens=True))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['PG. PG&E said it scheduled the blackouts in response to forecasts for high winds.']\n"
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]
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}
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],
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"source": [
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"# Testing Huggingface's BART\n",
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"test_sequence = [\"PG&E stated it scheduled the blackouts in response to forecasts for high winds \"\n",
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" \"amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were \"\n",
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" \"scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.\"]\n",
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"generative_hf_bart = GenerationUtil(bart, is_encoder_decoder=True, is_huggingface_model=True)\n",
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"bart_tokenizer = BartTokenizer.from_pretrained(\"facebook/bart-large-cnn\")\n",
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"test_sequence_tk = bart_tokenizer(test_sequence, return_tensors=\"pt\").input_ids\n",
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"tokens = generative_hf_bart.generate(test_sequence_tk, max_len=20, pad_idx=bart.config.pad_token_id)\n",
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"print(bart_tokenizer.batch_decode(tokens, skip_special_tokens=True))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[\"I enjoy walking with my cute dog, but I'm not sure if I'll ever be able to\"]\n"
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]
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}
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],
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"source": [
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"# Testing Huggingface's GPT2\n",
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"test_sequence = [\"I enjoy walking with my cute dog\"]\n",
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"generative_hf_gpt2 = GenerationUtil(gpt2, is_encoder_decoder=False, is_huggingface_model=True)\n",
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"gpt2_tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n",
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"test_sequence_tk = gpt2_tokenizer(test_sequence, return_tensors=\"pt\").input_ids\n",
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"tokens = generative_hf_gpt2.generate(test_sequence_tk, max_len=20, pad_idx=gpt2.config.pad_token_id)\n",
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"print(gpt2_tokenizer.batch_decode(tokens, skip_special_tokens=True))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.13 ('torchtext39')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "63c8862cb56f124e3ee7674b73de745eeb216416a9b24f78d1fcb7c775bff1b7"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎test/torchtext_unittest/prototype/test_generate.py‎

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from unittest.mock import patch
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from torchtext.prototype.generate import GenerationUtil
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from torchtext.prototype.models import T5_BASE_GENERATION
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from torchtext_unittest.common.torchtext_test_case import TorchtextTestCase
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import torch
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class TestGenerationUtil(TorchtextTestCase):
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def setUp(self) -> None:
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super().setUp()
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t5_base = T5_BASE_GENERATION
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self.transform = t5_base.transform()
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self.model = t5_base.get_model()
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self.model.eval()
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# Examples taken from T5 Paper and Huggingface
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self.inputs = self.transform(
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[
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"summarize: studies have shown that owning a dog is good for you",
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"translate English to German: That is good.",
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"cola sentence: The course is jumping well.",
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"stsb sentence1: The rhino grazed on the grass. sentence2: A rhino is grazing in a field.",
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"summarize: state authorities dispatched emergency crews tuesday to survey the damage after an onslaught of severe weather in mississippi...",
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]
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)
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torch.manual_seed(0)
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def test_greedy_generate_with_t5(self) -> None:
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generation_model = GenerationUtil(self.model)
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tokens = generation_model.generate(self.inputs, num_beams=1, max_len=30)
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generated_text = self.transform.decode(tokens.tolist())
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expected_generated_text = [
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"a dog is good for you, according to studies . owning a dog is good for you, according to studies .",
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"Das ist gut.",
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"acceptable",
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"4.0",
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"mississippi authorities dispatch emergency crews to survey damage . severe weather in mississippi has caused extensive damage",
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]
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self.assertEqual(generated_text, expected_generated_text)
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def test_generate_errors_with_incorrect_beams(self) -> None:
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generation_model = GenerationUtil(self.model, is_encoder_decoder=True)
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with self.assertRaises(ValueError):
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generation_model.generate(self.inputs, num_beams=0)
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@patch("logging.Logger.warning")
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def test_warns_when_no_max_len_provided(self, mock) -> None:
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generation_model = GenerationUtil(self.model)
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generation_model.generate(self.inputs)
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mock.assert_called_with("`max_len` was not specified. Defaulting to 100 tokens.")

‎torchtext/prototype/generate.py‎

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@@ -29,6 +29,7 @@ class GenerationUtil:
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More examples can be found in the `notebooks` directory of this repository.
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"""
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def __init__(self, model: nn.Module, is_encoder_decoder: bool = True, is_huggingface_model: bool = False) -> None:
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self.model = model
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self.is_encoder_decoder = is_encoder_decoder
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eos_idx (int): End of sequence index.
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pad_idx (int): Padding index.
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**model_kwargs
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Returns:
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Batch of sequences decoded by greedy search.
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"""
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encoder = self.model.get_encoder()
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model_kwargs["encoder_outputs"] = encoder(inputs)
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inputs = self._prepare_decoder_ids_for_generation(len(inputs), device=inputs.device, **model_kwargs)
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if max_len is None:
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# Too hard to try to figure out the exact max_seq_length for each model
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logger.warning("`max_len` was not specified. Defaulting to 256 tokens.")

‎torchtext/prototype/models/t5/model.py‎

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for p in self.parameters():
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p.requires_grad = False
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@torch.jit.ignore
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def prepare_inputs_for_generation(self, input_ids, encoder_outputs):
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return {"decoder_tokens": input_ids, "encoder_outputs": encoder_outputs}
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