From 83bfea7146c4df61ad594cde5de30ebd291661a3 Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Tue, 5 May 2020 11:03:46 +0300 Subject: [PATCH 01/23] Updated README.md --- README.md | 324 ++++++++++++----------------------------------------- report.pdf | Bin 0 -> 123602 bytes 2 files changed, 70 insertions(+), 254 deletions(-) create mode 100644 report.pdf diff --git a/README.md b/README.md index 2fa3115..552bc63 100644 --- a/README.md +++ b/README.md @@ -1,254 +1,70 @@ -# Modern Application Production - -Language: Python - -Version Control: GitHub - -Tests: Python unittest package - -Tasks: [Trello](https://https://trello.com/invite/b/6k7nigbp/8d26ddad39c33393cf6053e7ac0ac4ac/pmldl) - -User Stories: [GitHub issues](https://github.com/FrogTravel/PMLDL/issues) - -We are using feature-oriented branches - -Team: Irina Podlipnova, Boris Guryev, Ekaterina Levchenko (Scram Master), Vladislav Kuleykin - -## Project Proposal - -### General - -These days jokes and memes become part of modern people's life. Each day everybody spends time consuming content. Communities like *Reddit* or *9GAG* periodically release meme calendars. - -We propose a question-answer joke generator telegram bot with the aim to distinguish if artificially generated content can entertain users at the same level as human-created jokes. - -### Training -For training, we will apply transfer learning. As a starting model we will try different state-of-the-art language models, such as *BERT* [2], *GPT-2* [3]. As a training dataset will use set from *Kaggle* [1], which we will extend with other 2-liner jokes from the internet. - -### Evaluation -Joking is a subjective topic, so we are planning to ask people to evaluate samples of generated jokes. And afterward, we'll compare the results for different models. - -### Expected Final Result -We are planning to deploy this project into telegram infrastructure for simple interaction with the user. The flexibility of this platform covers all requirements described above such as: deliver content, collecting evaluation from users and easy deployment. - -1) [ Kaggle dataset](https://www.kaggle.com/jiriroz/qa-jokes ) -2) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. -Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova -3) Language Models are Unsupervised Multitask Learners. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever - -## Sprint 1 -### Burndown chart -![](https://i.imgur.com/FZn2vI5.png) -storypoints on vertical x days on horizontal axis -Blue - ideal burndown, Green - our result - -## Sprint 2 & 3 -### Burndown charts -#### Sprint 2 -![](https://i.imgur.com/nYpBchu.png) -storypoints on vertical x days on horizontal axis -Blue - ideal burndown, Green - our result -#### Sprint 3 -![](https://i.imgur.com/mVXGmoX.png) -storypoints on vertical x days on horizontal axis -Blue - ideal burndown, Green - our result - -Below are reports that we made for our course assignments. They might be different from the real tasks in trello in some way - -### Dataset -The dataset we going to use is taken from Kaggle competition [4]. It contains more than 38000 samples of so-called question-answer jokes. All jokes are parsed from Reddit. - - -Jokes examples: - - Q: Why is Yoda the worst copilot? - A: "Yoda, are we still going the right way?" - "Off course we are" - - Q: What do you call a blonde who dyes her hair brown? - A: AI (Artificial Intelligence) - - - Q: Why do programmers confuse Christmas and Halloween? - A: Because Dec 25 is Oct 31 - -Also, we'd like to extend Kaggle's dataset with some more examples because we still think that the number of jokes in the dataset is not enough to train a good model. Moreover, some jokes are duplicated, so it makes the model to "overfit" and generate a lot of similar jokes. There can be at least 3 possible ways to solve this problem: add more examples, so to neglect the influence of duplicates on the model; exclude duplicates from the dataset using some measurement of similarity or just ignore it assuming that this will not lead to overfitting. - -Finally, we need to clear the dataset and make some pre-processing because jokes are just parsed from Reddit and not cleaned up. So there can be some notes inside jokes, explanations, editions marks, some credits to authors of jokes, jokes can be in the wrong format and etc. Below some examples of such cases: - -Initial version: - - Q: How do Muslims laugh? - A: Muahahahamed Note: I don't have any prejudices against Islamic - -Pre-processed version: - - Q: How do Muslims laugh? - A: Muahahahamed - -Initial version: - - Q: Why did Eric Clapton make the switch from PC to Apple? - A: Well because he had a horrible experience with windows. (credit to Neil Hamburger for this amazing joke) - -Pre-processed version: - - Q: Why did Eric Clapton make the switch from PC to Apple? - A: Well because he had a horrible experience with windows. - -Initial version: - - Q: What do you have left after you burn a French alphabet? - A: H Edit: I don't like explaining jokes but since the first guy didn't get I might as well: When pronounced in a French accent it sounds like ash. - -Pre-processed version: - - Q: What do you have left after you burn a French alphabet? - A: H -Initial version: - - Q: Why does Santa have three gardens? - A: Q: Why does Santa have three gardens? A: So he can ""hoe, hoe, hoe."" - -Pre-processed version: - - Q: Why does Santa have three gardens? - A: So he can ""hoe, hoe, hoe."" - - -### Telegram bot -#### Reasons -We decided to use a Telegram bot because it is a very lightweight solution to communicate with a user and retrieve feedback from them. The flexibility of the bot API allows us to implement all planned features and possible increments in the future. - -#### Functional description -Our primary feature is the question-answer jokes generation when the joke sends by a user and funny candidate answer returned by bot. Each generated joke will include two inline buttons: "Thumb-up" and "Thumb-down". Then we can collect accepted by humans jokes to pool and use them in future: a) for fine-tuning; b) for sharing funny joke responses. There are two ways of doing so - directly in a bot or in a separate channel. - - - -### Language models review -#### BERT -Still a famous and widely used model. However, initially this model natural to use for tasks like classification, named entity detection or casual embedding [1]. However, there are several improvements on BERT adopted to model language [2] or trained/fine-tuned for modeling language - -For now, we took the pre-trained model from [2] and produced several samples for text generation with non-empty (provided by us) seed sentences: - - Q: who touched my pasta ? - A: bad people , like me . like pretty girl . - but so far i was a little hungry . - - Q: who touched my spaghetti ? - A: nobody had never touched my hand , - pleasant bo ##le , who had not played any such game yet ? - -So, even pre-trained for sentence generation, this model still can return some meaningful in context respond as well as fluctuated sequence. Still, we will try to fine-tune the model with our data to evaluate final behavior. - -There are many BERT modifications and one of them could be applicable for our task. We found the Question-Answer dataset [3] with links to implementation papers. Probably one of their models or training approaches will be significant for our work. - - - -### GPT-2 -#### Model selection -GPT-2 is the current state-of-the-art generative transformer model. It's a direct scale-up of their previous GPT model, the only change is the number of parameters ($>$10x more), and new, cleaner dataset (also $>$10x bigger). GPT-2 achieves state-of-the-art scores on a variety of domain-specific language modeling tasks without any fine-tuning. - -Also, GPT-2 has models of different sizes\footnote{We use the names from the https://huggingface.co/transformers/v2.3.0/pretrained_models.html transformers framework documentation, and as we have multiple limitations: - -- *processing power* - current consumer GPUs can't even fit \texttt{gpt2-large} and \texttt{gpt2-xl} models for training, so our focus will be on a smaller versions; -- *small dataset* - as our dataset is quite small (3.6 MB) compared to original (40 GB), we'll not be able to fine-tune big models, but for the small models, this is a right amount of data, as they fine-tune fast; -- *inference time* - and as we plan to deploy the final model, we need to think about inference time, as it needs to be fast, for the bot to provide in-time responses. - - -So, for now we'll consider gpt-2, gpt-2-medium and DistilGPT2 models. - -### Fine-tuning results -For now we tried to fine-tune the gpt-2 and gpt-2-medium models, and their results were not distinct from each other, so I'll cover them jointly. - -Firstly, disclaimer: there's a lot of filthy words, as the dataset is full of them. - -The model generates a lot of classic cross the road, change the bulb jokes, which again can come from a bad jokes balance in the dataset: - - Q: Why did the monkey cross the road? - A: For his brother's sake. - - Q: How many babies does it takes to screw in a lightbulb? - A: About 4,000. - - Q: How many feminists does it take to change a lightbulb? - A: None. They can't change anything. - -Most of the other jokes make no sense: - - Q: What is Gordon Ramsay's favorite beverage? - A: Mountain Bison - - Q: Why is it so windy in Russia? - A: Because everything is worth it. - - Q: What do Jesus and the world have in common? - A: They are all tied up by a knot of knotches. - - Q: Why do Chinese men laugh when circumcised? - A: This is how they greet the penis, not theirs. - - Q: Who is Mario? - A: More of a dictator who can watch video games while hiding in secrets. - -And sometimes... - - - Q: What does the man and an egg sandwich have in common? - A: They both have eggs. - - Q: What do you call a person that has a fetish for cheese? - A: A cheetah. - - Q: What's the most popular food at a gay barbecue? - A: KFC. - - Q: Where do poor people live? - A: In India. - -### References -1) https://github.com/huggingface/transformers/issues/401 - Transformers package "How can I generate new text after having fine-tuned BERT on a custom dataset?" issue discussion. -2) https://arxiv.org/pdf/1902.04094.pdf - BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model -3) https://rajpurkar.github.io/SQuAD-explorer/ - SQuAD - The Stanford Question Answering Dataset -4) https://www.kaggle.com/jiriroz/qa-jokes - Kaggle. Question-Answer Jokes -5) https://github.com/FrogTravel/PMLDL - Our Github repository - - -## Sprint 4 -### Burndown chart -![](https://i.imgur.com/TAce2qR.png) -storypoints on vertical x days on horizontal axis -Blue - ideal burndown, Green - our result -### Overview -This week we focused on the prototype development. We contributed in the Telegram bot, deployment part and improvement of the fine-tuning of the GPT-2. - -### Telegram bot -During this sprint, we implemented the last part for our bot interface. Our bot has two use cases. The first one is the /joke command, which generates the joke as a pair "Joke-Answer." Another case only the "answer" generation. To use this function, send a question to our bot, it will concern any text without backslash as a question for a generation. Though we will change this in the next spring to reduce the load on the server. -After generation, the bot will display two buttons - thumb up and thumb down. The user might grade the joke. After that, the "grading" message will be changed to "Thank you for your feedback." - -### Deployment -First of all, we reviewed open source solutions for wrapping models into web API. - -*Cortex* [2] - an open-source platform for deploying machine learning models as production web services. Provides logging, auto-scaling, forwarding GPU into the container, etc. However, this framework is designed to be self-hosted on AWS infrastructure. During this project, we aimed to run anything locally, because of the hardware requirements of the selected language model. - -*Model Asset Exchange (MAX)* [3] - this is a project template wrapped around flask python backend framework. We found it over architected because the main goal of this project is to force developers to provide API documentation to the endpoints. - -Finally, we decided to rewrite the CLI of the transformers repository [1] in simple format convenient for our task. In our case, it is enough to keep the model in memory - usual python class instance - and run forward whenever bot receives a message from the user. But, as requests can be processed in an asynchronous manner (especially in time of presentation), we found an issue of GPU memory leak when more than 1 requests processed. - -### Training -For the training part, this week we worked on improving the fine-tuning of the GPT-2 model: - -- *Fixed preprocessing* - we had a few errors as we forgot to put special tokens for the start and end of the documents, so now they are fixed. -- *Experimented with different frameworks* - we experimented with a couple of TensorFlow/Pytorch frameworks for fine-tuning the GPT-2, transformers [1] and gpt-2-simple [5]. And now are settled at the transformers, with the planning of some adoption of functionality from the other one. -- *Collected new dataset for fine-tuning* - as our previous results were repetitive and not diverse, as we think it is because of the small size of the dataset (3 MB), so we proposed an additional dataset of stand up transcripts (13 MB), which we gathered ourselves. We haven't yet tested our proposal. - -### References -1) https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py - Transformers. Run language model example -2) https://github.com/cortexlabs/cortex/ Cortex. Cloud native model serving infrastructure -3) https://developer.ibm.com/tutorials/getting-started-with-the-ibm-code-model-asset-exchange/ IBM Model Asset Exchange -4) https://github.com/FrogTravel/PMLDL}{Our Github repository -5) https://github.com/minimaxir/gpt-2-simple}{\texttt{gpt-2-simple GitHub page - -## Sprint 5 - -We are now at Sprint 5. We are connecting our bot interface to a backend and making tests for a bot. We fine-tune the final model at the backend. +# Joke Generator Bot +A Question-Answer joke generator bot for Telegram. As a joke generator we used a fine-tuned GPT-2 model. + +**Authors**: +* Vlad Kuleykin +* Boris Guryev +* Ekaterina Levchenko +* Irina Podlipnova + +## Purposes +This project was done during the *Practical Machine Learning and Deep Learning* course at Spring 2020 semester at *Innopolis University* (Innopolis, Russia). You can find our technical report in `report.pdf` file. + +## How to Use +Put in the bot token and path to the model in `bot.cfg`, after which you can run the next command: +```cmd +python3 bot\main_bot.py +``` +And you have a working bot! + +## How to train a model +To train the model, we have three datasets: +* **QA Jokes** (3.29 MB) - the original dataset we've found on Kaggle [1]. It contains ~38k question-answer jokes +* **Short Jokes** (22.8 MB) - the biggest dataset in our collection, it was also found on Kaggle [2] and consists of ~231k short length jokes from Twitter and Reddit. But it also contains a lot of noise and misspellings +* **Stand up transcripts** (13.5 MB) - the manually scraped dataset of stand up transcripts from one site [3] + +But you're free to use others! (and please, write me if you found good one) + +For the training, the `GPT-2 train helper.ipynb` in train folder can come in handy. As it can convert the datasets to appropriate GPT-2 input files and extract the QA jokes from the *Short Jokes* dataset. + +And as for the actual training use the `run_language_modelling.py` script taken from the Transformers library [4]: +```cmd +python3 run_language_modeling.py \ + --model_type=gpt2 \ + --model_name_or_path=**INPUT_MODEL_PATH** \ + --output_dir=**OUTPUT_MODEL_PATH** \ + --learning_rate=1e-05 \ + --num_train_epochs=10 \ + --per_gpu_train_batch_size=2 \ + --gradient_accumulation_steps=8 \ + --save_steps=1000 \ + --train_data_file=**TRAIN_DATA_PATH** \ + --do_train \ +``` +If the model doesn't fit in your GPU, try changing the `block_size` or `per_gpu_train_batch_size`. + +To test the current model run the `run_generation.py` script: +```cmd +python3 run_generation.py \ + --model_type=gpt2 \ + --model_name_or_path=**TRAINED_MODEL_PATH** \ + --prompt="[QUESTION]" \ + --length=60 \ + --stop_token="<|endoftext|>" \ + --temperature=0.9 \ + --repetition_penalty=1.05 \ + --k=50 \ + --p=0.95 \ + --num_return_sequences=40 +``` +Feel free to experiment with the `temperature`, `k`, `p` and `repetition_penalty`, to get better insights as to what do this arguments do, visit this [link][5]. + + + +[1]: https://www.kaggle.com/jiriroz/qa-jokes "QA Jokes dataset" +[2]: https://www.kaggle.com/abhinavmoudgil95/short-jokes "Short Jokes dataset" +[3]: https://render.githubusercontent.com/view/scrapsfromtheloft.com "Stand Up transcripts site" + +[4]: https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py "Transformers. Run language model example" + +[5]: https://huggingface.co/blog/how-to-generate "Hugging face. 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It contains ~38k question-answer jokes -* **Short Jokes** (22.8 MB) - the biggest dataset in our collection, it was also found on Kaggle [2] and consists of ~231k short length jokes from Twitter and Reddit. But it also contains a lot of noise and misspellings -* **Stand up transcripts** (13.5 MB) - the manually scraped dataset of stand up transcripts from one site [3] +* **QA Jokes** (3.29 MB) - the original dataset we've found on [Kaggle][1]. It contains ~38k question-answer jokes +* **Short Jokes** (22.8 MB) - the biggest dataset in our collection, it was also found on [Kaggle][2] and consists of ~231k short length jokes from Twitter and Reddit. But it also contains a lot of noise and misspellings +* **Stand up transcripts** (13.5 MB) - the manually scraped dataset of stand up transcripts from one [site][3] But you're free to use others! (and please, write me if you found good one) For the training, the `GPT-2 train helper.ipynb` in train folder can come in handy. As it can convert the datasets to appropriate GPT-2 input files and extract the QA jokes from the *Short Jokes* dataset. -And as for the actual training use the `run_language_modelling.py` script taken from the Transformers library [4]: +And as for the actual training use the `run_language_modelling.py` script taken from the [Transformers library][4]: ```cmd python3 run_language_modeling.py \ --model_type=gpt2 \ @@ -67,4 +67,4 @@ Feel free to experiment with the `temperature`, `k`, `p` and `repetition_penalty [4]: https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py "Transformers. Run language model example" -[5]: https://huggingface.co/blog/how-to-generate "Hugging face. How to generate" \ No newline at end of file +[5]: https://huggingface.co/blog/how-to-generate "Hugging face. How to generate" From ed73205acb35c42977e806ef63d7338e3f278fec Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Tue, 5 May 2020 11:15:40 +0300 Subject: [PATCH 03/23] Fixed error in dataset --- data/short_jokes.csv | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data/short_jokes.csv b/data/short_jokes.csv index 19ecf03..b18d1c2 100644 --- a/data/short_jokes.csv +++ b/data/short_jokes.csv @@ -152006,7 +152006,7 @@ 152476,"What do you call 4 mexicans in quick sand? Cuatro sinko" 152477,"What did the pirate say when he saw the dank meme? arrr lmao" 152478,"What did Pitbull ask for Christmas? Dolly." -152479,"How many Karma whores does it take to change a lightbulb? I don't care. +152479,"How many Karma whores does it take to change a lightbulb? I don't care." 152480,"A man walks into a bar and says... ""Argh, fuck!""" 152481,"I know a great knock knock joke But you have to start it." 152482,"What do you call a group of transsexual surfers? The radical left." From cd9126b5e1cfb0232424ece4c67ff2be2ff741c1 Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Tue, 5 May 2020 12:02:20 +0300 Subject: [PATCH 04/23] Updated start message. --- bot/main_bot.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/bot/main_bot.py b/bot/main_bot.py index 979fc30..49a9117 100644 --- a/bot/main_bot.py +++ b/bot/main_bot.py @@ -89,7 +89,13 @@ def button_handler(update, context): def start(update, context): - update.message.reply_text("Use /joke to generate a joke") + update.message.reply_text("Welcome to the *Joke Generator Bot*." + + "\n\nUse `/joke` to generate a joke. " + + "Or, if you want a joke on some specific topic from me, " + + "just write me a question and I'll answer it in a playful form." + + "\nTo help me learn, please sent feedback on jokes through the 👍/👎 buttons. " + + "\n\n**DISCLAIMER**: This bot is still very dumb and produces a lot of dark and racist humor. " + + "Don't judge him, he learned them from the people", parse_mode=telegram.ParseMode.MARKDOWN) def error(update, context): From 47e66944a244e4ea89020a52d552fe3976559aee Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Tue, 5 May 2020 12:03:33 +0300 Subject: [PATCH 05/23] Supressed info from the `transformers` logger. --- bot/inference.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/bot/inference.py b/bot/inference.py index 67b5150..5c435a2 100644 --- a/bot/inference.py +++ b/bot/inference.py @@ -1,4 +1,5 @@ import torch +import logging from transformers import ( CTRLLMHeadModel, @@ -24,6 +25,10 @@ "xlm": (XLMWithLMHeadModel, XLMTokenizer), } +# Don't show warnings. +logging.getLogger("transformers.tokenization_utils").setLevel(logging.ERROR) +logging.getLogger("transformers.modeling_utils").setLevel(logging.ERROR) +logging.getLogger("transformers.configuration_utils").setLevel(logging.ERROR) class ModelWrapper: def __init__(self, model_path, model_name, From 838d3eddfd4491b521c218a97942d1183949c4f8 Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Tue, 5 May 2020 12:37:59 +0300 Subject: [PATCH 06/23] Added message for the `\help` command. --- bot/main_bot.py | 26 +++++++++++++++++++------- 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/bot/main_bot.py b/bot/main_bot.py index 49a9117..a7f9e6d 100644 --- a/bot/main_bot.py +++ b/bot/main_bot.py @@ -39,6 +39,17 @@ pos = "1" neg = "2" +GREETING_MESSAGE = "Welcome to the *Joke Generator Bot*." + +HELP_MESSAGE = "Use `/joke` to generate a joke. " + \ + "Or, if you want a joke on some specific topic from me, " + \ + "just write me a question and I'll answer it in a playful form." + \ + "\n\nTo help me learn, please sent feedback on jokes through the 👍/👎 buttons." + +DISCLAIMER_MESSAGE = "**DISCLAIMER**: This bot is still very dumb and " + \ + "produces a lot of dark and racist humor. " + \ + "Don't judge him, he learned them from the people" + def send_typing_action(func): """Sends typing action while processing func command.""" @@ -89,14 +100,14 @@ def button_handler(update, context): def start(update, context): - update.message.reply_text("Welcome to the *Joke Generator Bot*." + - "\n\nUse `/joke` to generate a joke. " + - "Or, if you want a joke on some specific topic from me, " + - "just write me a question and I'll answer it in a playful form." + - "\nTo help me learn, please sent feedback on jokes through the 👍/👎 buttons. " + - "\n\n**DISCLAIMER**: This bot is still very dumb and produces a lot of dark and racist humor. " + - "Don't judge him, he learned them from the people", parse_mode=telegram.ParseMode.MARKDOWN) + update.message.reply_text(GREETING_MESSAGE + '\n\n' + + HELP_MESSAGE + '\n\n' + + DISCLAIMER_MESSAGE, parse_mode=telegram.ParseMode.MARKDOWN) + +def help(update, context): + update.message.reply_text(HELP_MESSAGE + '\n\n' + DISCLAIMER_MESSAGE, + parse_mode=telegram.ParseMode.MARKDOWN) def error(update, context): """Log Errors caused by Updates.""" @@ -112,6 +123,7 @@ def main(): updater.dispatcher.add_handler(CommandHandler('joke', joke_command_handler)) updater.dispatcher.add_handler(CallbackQueryHandler(button_handler)) updater.dispatcher.add_handler(CommandHandler('start', start)) + updater.dispatcher.add_handler(CommandHandler('help', help)) updater.dispatcher.add_handler(CallbackQueryHandler(text_handler)) updater.dispatcher.add_error_handler(error) updater.dispatcher.add_handler(MessageHandler(Filters.text, text_handler)) From 7edde123d22c32170e723c8642f3c4caa8a1ce98 Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Tue, 5 May 2020 12:44:23 +0300 Subject: [PATCH 07/23] Small refactoring. Moved `Dataset` and `Joke` into `data.py`. --- bot/data.py | 33 +++++++++++++++++++++++++++++++++ bot/joke.py | 4 ---- bot/joke_generator.py | 32 +++----------------------------- 3 files changed, 36 insertions(+), 33 deletions(-) create mode 100644 bot/data.py delete mode 100644 bot/joke.py diff --git a/bot/data.py b/bot/data.py new file mode 100644 index 0000000..ebc06b7 --- /dev/null +++ b/bot/data.py @@ -0,0 +1,33 @@ +import os +import pandas as pd +from joke_generator import JokeGenerator + +class Dataset: + """Wrapper for the DataFrame to return values similar + to `AbstractJokeGenerator` output. + """ + + def __init__(self, dataset_path): + self.name = os.path.split(dataset_path)[1] + self.data = pd.read_csv(dataset_path) + + def __getitem__(self, idx): + question = self.data['Question'].iloc[idx].strip() + answer = self.data['Answer'].iloc[idx].strip() + text = (JokeGenerator.default_promt_token + + question + '\n' + + JokeGenerator.answer_token + ' ' + + answer) + return { + 'text': text, + 'generated_by': self.name, + } + + def __len__(self): + return len(self.data) + +class Joke: + """An interface class for a Joke.""" + def __init__(self, text, id): + self.id = id + self.text = text diff --git a/bot/joke.py b/bot/joke.py deleted file mode 100644 index 91eee0d..0000000 --- a/bot/joke.py +++ /dev/null @@ -1,4 +0,0 @@ -class Joke: - def __init__(self, text, id): - self.id = id - self.text = text diff --git a/bot/joke_generator.py b/bot/joke_generator.py index d51347f..dffbec2 100644 --- a/bot/joke_generator.py +++ b/bot/joke_generator.py @@ -4,15 +4,15 @@ import re from itertools import cycle -import pandas as pd import storage from inference import ModelWrapper -from joke import Joke +from data import Dataset, Joke from abc import ABC, abstractmethod def synchronized(func): + """Decorator for the syncronized usage of the function.""" func.__lock__ = threading.Lock() def synced_func(*args, **kws): @@ -154,8 +154,7 @@ def __init__(self, dataset_paths, model_paths, max_joke_len=40, jokes_buffer_siz """ super().__init__(jokes_buffer_size) - self.models = list() - self.key2pool = dict() + self.models, self.key2pool = list(), dict() for model_path in model_paths: model_name = os.path.split(model_path)[1] self.models.append(ModelWrapper(model_path, model_name, max_length=max_joke_len)) @@ -189,31 +188,6 @@ def _get_joke_from_buffer(self): return self.key2pool[key].pop() -class Dataset: - """Wrapper for the DataFrame to return values similar - to `AbstractJokeGenerator` output. - """ - - def __init__(self, dataset_path): - self.name = os.path.split(dataset_path)[1] - self.data = pd.read_csv(dataset_path) - - def __getitem__(self, idx): - question = self.data['Question'].iloc[idx].strip() - answer = self.data['Answer'].iloc[idx].strip() - text = (JokeGenerator.default_promt_token - + question + '\n' - + JokeGenerator.answer_token + ' ' - + answer) - return { - 'text': text, - 'generated_by': self.name, - } - - def __len__(self): - return len(self.data) - - if __name__ == '__main__': datasets = ["data/qa_jokes.csv"] From b9e071d052752f46248d925609f54b2949667e4c Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Tue, 5 May 2020 13:02:39 +0300 Subject: [PATCH 08/23] More refactoring and small bug fix. --- bot/data.py | 11 +++++------ bot/joke_generator.py | 22 ++++++++++++++-------- 2 files changed, 19 insertions(+), 14 deletions(-) diff --git a/bot/data.py b/bot/data.py index ebc06b7..9bf1f33 100644 --- a/bot/data.py +++ b/bot/data.py @@ -1,23 +1,22 @@ import os import pandas as pd -from joke_generator import JokeGenerator class Dataset: """Wrapper for the DataFrame to return values similar to `AbstractJokeGenerator` output. """ - def __init__(self, dataset_path): + def __init__(self, dataset_path, default_promt_token, answer_token): + self.default_promt_token = default_promt_token + self.answer_token = answer_token self.name = os.path.split(dataset_path)[1] self.data = pd.read_csv(dataset_path) def __getitem__(self, idx): question = self.data['Question'].iloc[idx].strip() answer = self.data['Answer'].iloc[idx].strip() - text = (JokeGenerator.default_promt_token - + question + '\n' - + JokeGenerator.answer_token + ' ' - + answer) + text = (self.default_promt_token + question + '\n' + + self.answer_token + ' ' + answer) return { 'text': text, 'generated_by': self.name, diff --git a/bot/joke_generator.py b/bot/joke_generator.py index dffbec2..959a549 100644 --- a/bot/joke_generator.py +++ b/bot/joke_generator.py @@ -3,14 +3,12 @@ import threading import re from itertools import cycle +from abc import ABC, abstractmethod import storage from inference import ModelWrapper from data import Dataset, Joke -from abc import ABC, abstractmethod - - def synchronized(func): """Decorator for the syncronized usage of the function.""" func.__lock__ = threading.Lock() @@ -155,12 +153,20 @@ def __init__(self, dataset_paths, model_paths, max_joke_len=40, jokes_buffer_siz super().__init__(jokes_buffer_size) self.models, self.key2pool = list(), dict() - for model_path in model_paths: - model_name = os.path.split(model_path)[1] - self.models.append(ModelWrapper(model_path, model_name, max_length=max_joke_len)) + print('[INFO] Loading models...') + for m_path in model_paths: + m_name = os.path.split(m_path)[1] + self.models.append(ModelWrapper(m_path, m_name, + max_length=max_joke_len)) self._fill_jokes_buffer(self.models[-1]) - - self.datasets = [Dataset(path) for path in dataset_paths] + print(f'[INFO] Loaded {self.models[-1].name}') + + print('[INFO] Loading datasets...') + self.datasets = list() + for d_path in dataset_paths: + self.datasets.append(Dataset(d_path, self.default_promt_token, + self.answer_token)) + print('[INFO] Ready to work!') self.num_of_pools = len(self.models) + len(self.datasets) @synchronized From c89c95debbb756d8c19f7238d4ccc8495b61e115 Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Tue, 5 May 2020 14:40:31 +0300 Subject: [PATCH 09/23] Rescructured the code and improved logging. --- .gitignore | 8 ++-- bot/data.py | 3 ++ bot/inference.py | 1 + bot/joke_generator.py | 96 ++++++++++++++++++++++++++----------------- bot/main_bot.py | 8 +++- 5 files changed, 74 insertions(+), 42 deletions(-) diff --git a/.gitignore b/.gitignore index ef7be74..4ec288d 100644 --- a/.gitignore +++ b/.gitignore @@ -211,7 +211,7 @@ dmypy.json ### VisualStudioCode ### .vscode/* -!.vscode/settings.json +.vscode/settings.json !.vscode/tasks.json !.vscode/launch.json !.vscode/extensions.json @@ -229,8 +229,10 @@ model train/runs train/output* train/models - # prep files data/prep # jokes database -jokes.db \ No newline at end of file +!jokes.db +*.db +# log files +*.log \ No newline at end of file diff --git a/bot/data.py b/bot/data.py index 9bf1f33..34d69f4 100644 --- a/bot/data.py +++ b/bot/data.py @@ -1,4 +1,5 @@ import os +import logging import pandas as pd class Dataset: @@ -11,12 +12,14 @@ def __init__(self, dataset_path, default_promt_token, answer_token): self.answer_token = answer_token self.name = os.path.split(dataset_path)[1] self.data = pd.read_csv(dataset_path) + self.logger = logging.getLogger("DS: " + self.name) def __getitem__(self, idx): question = self.data['Question'].iloc[idx].strip() answer = self.data['Answer'].iloc[idx].strip() text = (self.default_promt_token + question + '\n' + self.answer_token + ' ' + answer) + self.logger.info('Got joke from dataset') return { 'text': text, 'generated_by': self.name, diff --git a/bot/inference.py b/bot/inference.py index 5c435a2..db616fa 100644 --- a/bot/inference.py +++ b/bot/inference.py @@ -51,6 +51,7 @@ def __init__(self, model_path, model_name, self.tokenizer = tokenizer_class.from_pretrained(model_path) self.model = model_class.from_pretrained(model_path) self.name = model_name + self.logger = logging.getLogger('ML: ' + self.name) self.model.to(device) def __encode(self, text): diff --git a/bot/joke_generator.py b/bot/joke_generator.py index 959a549..08331c8 100644 --- a/bot/joke_generator.py +++ b/bot/joke_generator.py @@ -1,7 +1,8 @@ -import random import os -import threading import re +import random +import threading +import logging from itertools import cycle from abc import ABC, abstractmethod @@ -68,10 +69,9 @@ def generate_joke(self, model, promt=""): :return: `Joke` object """ if promt: - print(f'[INFO] continue - Model: {model.name}') res = self._continue_joke(model, promt) else: - res = self._get_joke_from_buffer() + res = self._get_new_joke() res['text'] = self._prettify_result(res['text']) joke_id = self.store.add_joke(**res) return Joke(id=joke_id, text=res['text']) @@ -92,27 +92,46 @@ def __call_model(self, model, prompt, num_return_sequences): } for seq in model.generate(prompt, num_return_sequences)] @synchronized - def _fill_jokes_buffer(self, model): - """Fill the jokes buffer associated with given model. - WARNING: Default implementation just returns the result. + def _get_from_buffer(self, model, buffer): + """Get a joke from the buffer. + + :param buffer: buffer to get the joke from + :param model: model to use to fill the buffer + :return: joke from the buffer + """ + model.logger.info('Got joke from the buffer') + if len(buffer) == 0: + self._fill_buffer(model) + return buffer.pop() + + @synchronized + @abstractmethod + def _fill_buffer(self, model): + """Fill the buffer associated with this model.""" + pass + + @synchronized + def _generate_for_buffer(self, model): + """Generates jokes to fill the buffer. :param model: model to use :return: see `__call_model` function """ + model.logger.info('Filling the buffer') return self.__call_model(model, self.default_promt_token, num_return_sequences=self.jokes_buffer_size) @synchronized @abstractmethod - def _get_joke_from_buffer(self, model): - """Get the new joke from the buffer for given model. - If needed update the buffer. + def _get_new_joke(self, model): + """Get the new joke from the given model. """ pass @synchronized def _continue_joke(self, model, promt): """Continue the joke given in promt.""" + model.logger.info('Continue joke') model_promt = self.custom_promt.format(' ' + promt.strip()) return self.__call_model(model, model_promt, num_return_sequences=1)[0] @@ -122,22 +141,24 @@ class JokeGenerator(AbstractJokeGenerator): def __init__(self, model_path, max_joke_len=40, jokes_buffer_size=16, model_device='cpu'): super().__init__(jokes_buffer_size) model_name = os.path.split(model_path)[1] + self.logger = logging.getLogger(type(self).__name__) + self.logger.info('Loading models...') self.model = ModelWrapper(model_path, model_name, max_length=max_joke_len) - self._fill_jokes_buffer() + self.logger.info(f'Loaded {self.models[-1].name}') + self._fill_buffer() + self.logger.info('Ready to work!') @synchronized - def _fill_jokes_buffer(self): - self.jokes_buffer = super()._fill_jokes_buffer(self.model) - + def generate_joke(self, promt=""): + return super().generate_joke(self.model, promt) + @synchronized - def _get_joke_from_buffer(self): - if len(self.jokes_buffer) == 0: - self._fill_jokes_buffer() - return self.jokes_buffer.pop() + def _fill_buffer(self, model): + self.jokes_buffer = super()._generate_for_buffer(model) @synchronized - def generate_joke(self, promt=""): - return super().generate_joke(self.model, promt) + def _get_new_joke(self): + return self._get_from_buffer(self.model, self.jokes_buffer) class TestABGenerator(AbstractJokeGenerator): @@ -153,20 +174,22 @@ def __init__(self, dataset_paths, model_paths, max_joke_len=40, jokes_buffer_siz super().__init__(jokes_buffer_size) self.models, self.key2pool = list(), dict() - print('[INFO] Loading models...') + self.logger = logging.getLogger(type(self).__name__) + self.logger.info('Loading models...') for m_path in model_paths: m_name = os.path.split(m_path)[1] self.models.append(ModelWrapper(m_path, m_name, max_length=max_joke_len)) - self._fill_jokes_buffer(self.models[-1]) - print(f'[INFO] Loaded {self.models[-1].name}') + self.logger.info(f'Loaded {self.models[-1].name}') + self._fill_buffer(self.models[-1]) - print('[INFO] Loading datasets...') + self.logger.info('Loading datasets...') self.datasets = list() for d_path in dataset_paths: self.datasets.append(Dataset(d_path, self.default_promt_token, self.answer_token)) - print('[INFO] Ready to work!') + self.logger.info(f'Loaded {self.datasets[-1].name}') + self.logger.info('Ready to work!') self.num_of_pools = len(self.models) + len(self.datasets) @synchronized @@ -174,30 +197,29 @@ def generate_joke(self, promt=""): idx = random.randint(0, len(self.models) - 1) model = self.models[idx] return super().generate_joke(model, promt) - + @synchronized - def _fill_jokes_buffer(self, model): - self.key2pool[model.name] = super()._fill_jokes_buffer(model) + def _fill_buffer(self, model): + self.key2pool[model.name] = super()._generate_for_buffer(model) @synchronized - def _get_joke_from_buffer(self): + def _get_new_joke(self): + """Get the joke either from the model buffer, or dataset.""" idx = random.randint(0, self.num_of_pools - 1) - res = {} if idx < len(self.datasets): - print(f'[INFO] generate - Dataset: {self.datasets[idx].name}') return random.choice(self.datasets[idx]) idx = idx - len(self.datasets) - 1 key = self.models[idx].name - print(f'[INFO] generate - Model: {key}') - if len(self.key2pool[key]) == 0: - self._fill_jokes_buffer(self.models[idx]) - return self.key2pool[key].pop() + return self._get_from_buffer(self.models[idx], + self.key2pool[key]) if __name__ == '__main__': + logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', + level=logging.INFO) - datasets = ["data/qa_jokes.csv"] - models = ["train/models/output_6"] + datasets = ["../data/qa_jokes.csv"] + models = ["../train/models/output_8"] gen = TestABGenerator(datasets, models) diff --git a/bot/main_bot.py b/bot/main_bot.py index a7f9e6d..9469037 100644 --- a/bot/main_bot.py +++ b/bot/main_bot.py @@ -1,5 +1,6 @@ import logging import os +import sys from functools import wraps from configparser import ConfigParser @@ -11,9 +12,12 @@ """ Basic example for a bot that uses inline keyboards. """ - logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', - level=logging.INFO) + level=logging.INFO, + handlers=[ + logging.FileHandler("run.log"), + logging.StreamHandler(sys.stdout), + ]) logger = logging.getLogger(__name__) cfg = ConfigParser() From a6547343d7ceb57f3b07cca8d64d8f9ab38587c9 Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Tue, 5 May 2020 14:45:18 +0300 Subject: [PATCH 10/23] Code style fixed. --- bot/data.py | 3 +++ bot/inference.py | 1 + bot/joke_generator.py | 32 +++++++++++++++++++------------- bot/main_bot.py | 27 ++++++++++++++------------- bot/storage.py | 4 +++- 5 files changed, 40 insertions(+), 27 deletions(-) diff --git a/bot/data.py b/bot/data.py index 34d69f4..de1ca5d 100644 --- a/bot/data.py +++ b/bot/data.py @@ -2,6 +2,7 @@ import logging import pandas as pd + class Dataset: """Wrapper for the DataFrame to return values similar to `AbstractJokeGenerator` output. @@ -28,8 +29,10 @@ def __getitem__(self, idx): def __len__(self): return len(self.data) + class Joke: """An interface class for a Joke.""" + def __init__(self, text, id): self.id = id self.text = text diff --git a/bot/inference.py b/bot/inference.py index db616fa..ba5e45d 100644 --- a/bot/inference.py +++ b/bot/inference.py @@ -30,6 +30,7 @@ logging.getLogger("transformers.modeling_utils").setLevel(logging.ERROR) logging.getLogger("transformers.configuration_utils").setLevel(logging.ERROR) + class ModelWrapper: def __init__(self, model_path, model_name, device='cpu', diff --git a/bot/joke_generator.py b/bot/joke_generator.py index 08331c8..54e8632 100644 --- a/bot/joke_generator.py +++ b/bot/joke_generator.py @@ -10,6 +10,7 @@ from inference import ModelWrapper from data import Dataset, Joke + def synchronized(func): """Decorator for the syncronized usage of the function.""" func.__lock__ = threading.Lock() @@ -20,6 +21,7 @@ def synced_func(*args, **kws): return synced_func + class AbstractJokeGenerator(ABC): """Abstract class for joke generation using `ModelWrapper`.""" @@ -41,7 +43,7 @@ def positive_grade(self, user_id, joke_id): def negative_grade(self, user_id, joke_id): self.store.add_or_update_vote( joke_id=joke_id, user_id=user_id, rating=self.NEG_GRADE) - + def _prettify_result(self, model_output): def pp_answer(text): """Pretty-print the answer.""" @@ -62,7 +64,7 @@ def pp_answer(text): @synchronized def generate_joke(self, model, promt=""): """Generate the joke from given promt. - + :param model: model to use to generate joke. :param promt: (optional) promt for a joke, if not given generates the whole joke @@ -75,7 +77,7 @@ def generate_joke(self, model, promt=""): res['text'] = self._prettify_result(res['text']) joke_id = self.store.add_joke(**res) return Joke(id=joke_id, text=res['text']) - + @synchronized def __call_model(self, model, prompt, num_return_sequences): """Call the model to generate the joke. @@ -90,11 +92,11 @@ def __call_model(self, model, prompt, num_return_sequences): 'generated_by': model.name, 'text': seq } for seq in model.generate(prompt, num_return_sequences)] - + @synchronized def _get_from_buffer(self, model, buffer): """Get a joke from the buffer. - + :param buffer: buffer to get the joke from :param model: model to use to fill the buffer :return: joke from the buffer @@ -109,7 +111,7 @@ def _get_from_buffer(self, model, buffer): def _fill_buffer(self, model): """Fill the buffer associated with this model.""" pass - + @synchronized def _generate_for_buffer(self, model): """Generates jokes to fill the buffer. @@ -119,15 +121,15 @@ def _generate_for_buffer(self, model): """ model.logger.info('Filling the buffer') return self.__call_model(model, self.default_promt_token, - num_return_sequences=self.jokes_buffer_size) - + num_return_sequences=self.jokes_buffer_size) + @synchronized @abstractmethod def _get_new_joke(self, model): """Get the new joke from the given model. """ pass - + @synchronized def _continue_joke(self, model, promt): """Continue the joke given in promt.""" @@ -135,6 +137,7 @@ def _continue_joke(self, model, promt): model_promt = self.custom_promt.format(' ' + promt.strip()) return self.__call_model(model, model_promt, num_return_sequences=1)[0] + class JokeGenerator(AbstractJokeGenerator): """Simple Joke generator using one model.""" @@ -151,7 +154,7 @@ def __init__(self, model_path, max_joke_len=40, jokes_buffer_size=16, model_devi @synchronized def generate_joke(self, promt=""): return super().generate_joke(self.model, promt) - + @synchronized def _fill_buffer(self, model): self.jokes_buffer = super()._generate_for_buffer(model) @@ -164,8 +167,11 @@ def _get_new_joke(self): class TestABGenerator(AbstractJokeGenerator): """Joke generator for a/b testing. Outputs the joke from either of models/datasets. - Chooses the source randomly.""" - def __init__(self, dataset_paths, model_paths, max_joke_len=40, jokes_buffer_size=16, model_device='cpu'): + Chooses the source randomly. + """ + + def __init__(self, dataset_paths, model_paths, max_joke_len=40, + jokes_buffer_size=16, model_device='cpu'): """ Loads datasets and models. Initiates pools and orders of passing :param dataset_paths: paths to the dataset @@ -197,7 +203,7 @@ def generate_joke(self, promt=""): idx = random.randint(0, len(self.models) - 1) model = self.models[idx] return super().generate_joke(model, promt) - + @synchronized def _fill_buffer(self, model): self.key2pool[model.name] = super()._generate_for_buffer(model) diff --git a/bot/main_bot.py b/bot/main_bot.py index 9469037..450c274 100644 --- a/bot/main_bot.py +++ b/bot/main_bot.py @@ -15,8 +15,8 @@ logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO, handlers=[ - logging.FileHandler("run.log"), - logging.StreamHandler(sys.stdout), + logging.FileHandler("run.log"), + logging.StreamHandler(sys.stdout), ]) logger = logging.getLogger(__name__) @@ -33,9 +33,9 @@ } if cfg['bot']['ab_test'].lower() == 'true': joke_generator = TestABGenerator(dataset_paths=dataset_paths, - model_paths=model_paths, - **model_args - ) + model_paths=model_paths, + **model_args + ) else: joke_generator = JokeGenerator(model_path=model_paths[0], **model_args) @@ -48,7 +48,7 @@ HELP_MESSAGE = "Use `/joke` to generate a joke. " + \ "Or, if you want a joke on some specific topic from me, " + \ "just write me a question and I'll answer it in a playful form." + \ - "\n\nTo help me learn, please sent feedback on jokes through the 👍/👎 buttons." + "\n\nTo help me learn, please sent feedback on jokes through the 👍/👎 buttons." DISCLAIMER_MESSAGE = "**DISCLAIMER**: This bot is still very dumb and " + \ "produces a lot of dark and racist humor. " + \ @@ -103,16 +103,17 @@ def button_handler(update, context): context.bot.answer_callback_query(query.id, "Thank you for your feedback") -def start(update, context): - update.message.reply_text(GREETING_MESSAGE + '\n\n' - + HELP_MESSAGE + '\n\n' + - DISCLAIMER_MESSAGE, parse_mode=telegram.ParseMode.MARKDOWN) +def start_handler(update, context): + update.message.reply_text(GREETING_MESSAGE + '\n\n' + + HELP_MESSAGE + '\n\n' + + DISCLAIMER_MESSAGE, parse_mode=telegram.ParseMode.MARKDOWN) -def help(update, context): +def help_handler(update, context): update.message.reply_text(HELP_MESSAGE + '\n\n' + DISCLAIMER_MESSAGE, parse_mode=telegram.ParseMode.MARKDOWN) + def error(update, context): """Log Errors caused by Updates.""" logger.warning('Update "%s" caused error "%s"', update, context.error) @@ -126,8 +127,8 @@ def main(): updater.dispatcher.add_handler(CommandHandler('joke', joke_command_handler)) updater.dispatcher.add_handler(CallbackQueryHandler(button_handler)) - updater.dispatcher.add_handler(CommandHandler('start', start)) - updater.dispatcher.add_handler(CommandHandler('help', help)) + updater.dispatcher.add_handler(CommandHandler('start', start_handler)) + updater.dispatcher.add_handler(CommandHandler('help', help_handler)) updater.dispatcher.add_handler(CallbackQueryHandler(text_handler)) updater.dispatcher.add_error_handler(error) updater.dispatcher.add_handler(MessageHandler(Filters.text, text_handler)) diff --git a/bot/storage.py b/bot/storage.py index 4e18e73..780754e 100644 --- a/bot/storage.py +++ b/bot/storage.py @@ -5,6 +5,7 @@ db = SqliteDatabase('jokes.db') default_generated = "unknown" + class BaseModel(Model): class Meta: database = db @@ -38,7 +39,8 @@ def add_joke(text, generated_by=default_generated): def add_or_update_vote(joke_id, user_id, rating): # https://stackoverflow.com/questions/33485312/insert-or-update-a-peewee-record-in-python - Vote.insert(joke_id=joke_id, user_id=user_id, rate=rating).on_conflict('replace').execute() + Vote.insert(joke_id=joke_id, user_id=user_id, + rate=rating).on_conflict('replace').execute() db.connect() From 3085eabb4d021eedfff74a62a6f8e1c2b9df4cb6 Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Tue, 5 May 2020 14:55:11 +0300 Subject: [PATCH 11/23] Updated README.md --- README.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/README.md b/README.md index 3ad097d..4fbc91b 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,17 @@ python3 bot\main_bot.py ``` And you have a working bot! +All dispatched jokes will be stored in `jokes.db` with the feedback in a `vote` table. + +Also, you can see the run log in stdout and `run.log` file. + +### A/B Testing +For the purpose of testing the quality of our bot we added the ability to test the models against the jokes from datasets. + +To do this, change the flag `ab_test` in configuration file to `true` and add the wanted model paths and dataset paths to corresponding fields with the `,` as delimeter. + +The bot will randomly choose the sourse for the next joke and will put the model/dataset path in `generated_by` column in database. + ## How to train a model To train the model, we have three datasets: * **QA Jokes** (3.29 MB) - the original dataset we've found on [Kaggle][1]. It contains ~38k question-answer jokes @@ -62,7 +73,9 @@ Feel free to experiment with the `temperature`, `k`, `p` and `repetition_penalty [1]: https://www.kaggle.com/jiriroz/qa-jokes "QA Jokes dataset" + [2]: https://www.kaggle.com/abhinavmoudgil95/short-jokes "Short Jokes dataset" + [3]: https://render.githubusercontent.com/view/scrapsfromtheloft.com "Stand Up transcripts site" [4]: https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py "Transformers. Run language model example" From b4ea43f38f427897e3ea09f0460c898995940970 Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Fri, 8 May 2020 23:01:19 +0300 Subject: [PATCH 12/23] Removed `@syncronized` for methods which don't call the models. --- bot/joke_generator.py | 7 ------- 1 file changed, 7 deletions(-) diff --git a/bot/joke_generator.py b/bot/joke_generator.py index 54e8632..bcdb9f2 100644 --- a/bot/joke_generator.py +++ b/bot/joke_generator.py @@ -3,7 +3,6 @@ import random import threading import logging -from itertools import cycle from abc import ABC, abstractmethod import storage @@ -61,7 +60,6 @@ def pp_answer(text): else: return [pp_answer(ans) for ans in model_output] - @synchronized def generate_joke(self, model, promt=""): """Generate the joke from given promt. @@ -123,7 +121,6 @@ def _generate_for_buffer(self, model): return self.__call_model(model, self.default_promt_token, num_return_sequences=self.jokes_buffer_size) - @synchronized @abstractmethod def _get_new_joke(self, model): """Get the new joke from the given model. @@ -151,7 +148,6 @@ def __init__(self, model_path, max_joke_len=40, jokes_buffer_size=16, model_devi self._fill_buffer() self.logger.info('Ready to work!') - @synchronized def generate_joke(self, promt=""): return super().generate_joke(self.model, promt) @@ -159,7 +155,6 @@ def generate_joke(self, promt=""): def _fill_buffer(self, model): self.jokes_buffer = super()._generate_for_buffer(model) - @synchronized def _get_new_joke(self): return self._get_from_buffer(self.model, self.jokes_buffer) @@ -198,7 +193,6 @@ def __init__(self, dataset_paths, model_paths, max_joke_len=40, self.logger.info('Ready to work!') self.num_of_pools = len(self.models) + len(self.datasets) - @synchronized def generate_joke(self, promt=""): idx = random.randint(0, len(self.models) - 1) model = self.models[idx] @@ -208,7 +202,6 @@ def generate_joke(self, promt=""): def _fill_buffer(self, model): self.key2pool[model.name] = super()._generate_for_buffer(model) - @synchronized def _get_new_joke(self): """Get the joke either from the model buffer, or dataset.""" idx = random.randint(0, self.num_of_pools - 1) From 427413097f1bf5adf51c14e7b5d957c083b7c054 Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Sat, 9 May 2020 11:53:39 +0300 Subject: [PATCH 13/23] Removed redundant word joke in arguments. --- bot/inference.py | 4 ++-- bot/joke_generator.py | 21 ++++++++++----------- bot/main_bot.py | 4 ++-- 3 files changed, 14 insertions(+), 15 deletions(-) diff --git a/bot/inference.py b/bot/inference.py index ba5e45d..6d352d5 100644 --- a/bot/inference.py +++ b/bot/inference.py @@ -35,7 +35,7 @@ class ModelWrapper: def __init__(self, model_path, model_name, device='cpu', model_type='gpt2', - max_length=40, + max_len=40, temperature=0.9, num_return_sequences=1, repetition_penalty=1.0, @@ -47,7 +47,7 @@ def __init__(self, model_path, model_name, self.p = p self.temperature = temperature self.device = device = torch.device(device) - self.max_length = max_length + self.max_length = max_len model_class, tokenizer_class = MODEL_CLASSES[model_type] self.tokenizer = tokenizer_class.from_pretrained(model_path) self.model = model_class.from_pretrained(model_path) diff --git a/bot/joke_generator.py b/bot/joke_generator.py index bcdb9f2..b631eb7 100644 --- a/bot/joke_generator.py +++ b/bot/joke_generator.py @@ -31,9 +31,9 @@ class AbstractJokeGenerator(ABC): POS_GRADE = 1 NEG_GRADE = -1 - def __init__(self, jokes_buffer_size): + def __init__(self, buffer_size): self.store = storage - self.jokes_buffer_size = jokes_buffer_size + self.buffer_size = buffer_size def positive_grade(self, user_id, joke_id): self.store.add_or_update_vote( @@ -119,7 +119,7 @@ def _generate_for_buffer(self, model): """ model.logger.info('Filling the buffer') return self.__call_model(model, self.default_promt_token, - num_return_sequences=self.jokes_buffer_size) + num_return_sequences=self.buffer_size) @abstractmethod def _get_new_joke(self, model): @@ -138,12 +138,12 @@ def _continue_joke(self, model, promt): class JokeGenerator(AbstractJokeGenerator): """Simple Joke generator using one model.""" - def __init__(self, model_path, max_joke_len=40, jokes_buffer_size=16, model_device='cpu'): - super().__init__(jokes_buffer_size) + def __init__(self, model_path, max_len=40, buffer_size=16, model_device='cpu'): + super().__init__(buffer_size) model_name = os.path.split(model_path)[1] self.logger = logging.getLogger(type(self).__name__) self.logger.info('Loading models...') - self.model = ModelWrapper(model_path, model_name, max_length=max_joke_len) + self.model = ModelWrapper(model_path, model_name, max_length=max_len) self.logger.info(f'Loaded {self.models[-1].name}') self._fill_buffer() self.logger.info('Ready to work!') @@ -165,22 +165,21 @@ class TestABGenerator(AbstractJokeGenerator): Chooses the source randomly. """ - def __init__(self, dataset_paths, model_paths, max_joke_len=40, - jokes_buffer_size=16, model_device='cpu'): + def __init__(self, dataset_paths, model_paths, max_len=40, + buffer_size=16, model_device='cpu'): """ Loads datasets and models. Initiates pools and orders of passing :param dataset_paths: paths to the dataset :param model_paths: paths to the model """ - super().__init__(jokes_buffer_size) + super().__init__(buffer_size) self.models, self.key2pool = list(), dict() self.logger = logging.getLogger(type(self).__name__) self.logger.info('Loading models...') for m_path in model_paths: m_name = os.path.split(m_path)[1] - self.models.append(ModelWrapper(m_path, m_name, - max_length=max_joke_len)) + self.models.append(ModelWrapper(m_path, m_name, max_length=max_len)) self.logger.info(f'Loaded {self.models[-1].name}') self._fill_buffer(self.models[-1]) diff --git a/bot/main_bot.py b/bot/main_bot.py index 450c274..984a143 100644 --- a/bot/main_bot.py +++ b/bot/main_bot.py @@ -27,8 +27,8 @@ model_paths = model_cfg['model_paths'].split(',') dataset_paths = model_cfg['dataset_paths'].split(',') model_args = { - 'max_joke_len': int(model_cfg['max_joke_len']), - 'jokes_buffer_size': int(model_cfg['buffer_size']), + 'max_len': int(model_cfg['max_joke_len']), + 'buffer_size': int(model_cfg['buffer_size']), 'model_device': model_cfg['device'] } if cfg['bot']['ab_test'].lower() == 'true': From f684de440ba271d628dfd3161eb7d0234ea3c60a Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Sat, 9 May 2020 14:47:16 +0300 Subject: [PATCH 14/23] Moved genetator arguments to config field and fixed bugs. --- bot/data.py | 6 ++-- bot/inference.py | 26 +++++++------- bot/joke_generator.py | 83 +++++++++++++++++++++++++------------------ bot/main_bot.py | 10 +++--- 4 files changed, 70 insertions(+), 55 deletions(-) diff --git a/bot/data.py b/bot/data.py index de1ca5d..99317e2 100644 --- a/bot/data.py +++ b/bot/data.py @@ -8,8 +8,8 @@ class Dataset: to `AbstractJokeGenerator` output. """ - def __init__(self, dataset_path, default_promt_token, answer_token): - self.default_promt_token = default_promt_token + def __init__(self, dataset_path, promt_token, answer_token): + self.promt_token = promt_token self.answer_token = answer_token self.name = os.path.split(dataset_path)[1] self.data = pd.read_csv(dataset_path) @@ -18,7 +18,7 @@ def __init__(self, dataset_path, default_promt_token, answer_token): def __getitem__(self, idx): question = self.data['Question'].iloc[idx].strip() answer = self.data['Answer'].iloc[idx].strip() - text = (self.default_promt_token + question + '\n' + text = (self.promt_token + question + '\n' + self.answer_token + ' ' + answer) self.logger.info('Got joke from dataset') return { diff --git a/bot/inference.py b/bot/inference.py index 6d352d5..df72ae8 100644 --- a/bot/inference.py +++ b/bot/inference.py @@ -1,4 +1,5 @@ import torch +from torch import LongTensor import logging from transformers import ( @@ -37,32 +38,33 @@ def __init__(self, model_path, model_name, model_type='gpt2', max_len=40, temperature=0.9, - num_return_sequences=1, + n_return_sequences=1, repetition_penalty=1.0, k=50, - p=0.95,): - self.num_return_sequences = num_return_sequences + p=0.95, + **kwargs): + self.n_return_sequences = n_return_sequences self.repetition_penalty = repetition_penalty self.k = k self.p = p self.temperature = temperature - self.device = device = torch.device(device) + self.device = torch.device(device) self.max_length = max_len model_class, tokenizer_class = MODEL_CLASSES[model_type] self.tokenizer = tokenizer_class.from_pretrained(model_path) self.model = model_class.from_pretrained(model_path) self.name = model_name self.logger = logging.getLogger('ML: ' + self.name) - self.model.to(device) + self.model.to(self.device) def __encode(self, text): - encoded_prompt = self.tokenizer.encode( - text, add_special_tokens=False, return_tensors="pt") + encoded_prompt = self.tokenizer.encode(text, add_special_tokens=False) + encoded_prompt = LongTensor(encoded_prompt).unsqueeze(0) return encoded_prompt.to(self.device) - def generate(self, beginning, num_return_sequences=None): - if num_return_sequences is None: - num_return_sequences = self.num_return_sequences + def generate(self, beginning, n_return_sequences=None): + if n_return_sequences is None: + n_return_sequences = self.n_return_sequences encoded_prompt = self.__encode(beginning) output_sequences = self.model.generate( input_ids=encoded_prompt, @@ -72,7 +74,7 @@ def generate(self, beginning, num_return_sequences=None): top_p=self.p, repetition_penalty=self.repetition_penalty, do_sample=True, - num_return_sequences=num_return_sequences, + num_return_sequences=n_return_sequences, ) # Remove the batch dimension when returning multiple sequences @@ -93,7 +95,7 @@ def generate(self, beginning, num_return_sequences=None): # dt = datetime.datetime.now() - start # print("\t", dt) # Batch processing test - res = m.generate("[QUESTION] ", num_return_sequences=4) + res = m.generate("[QUESTION] ", n_return_sequences=4) for j in res: print(j) diff --git a/bot/joke_generator.py b/bot/joke_generator.py index b631eb7..bb0724e 100644 --- a/bot/joke_generator.py +++ b/bot/joke_generator.py @@ -24,16 +24,24 @@ def synced_func(*args, **kws): class AbstractJokeGenerator(ABC): """Abstract class for joke generation using `ModelWrapper`.""" - default_promt_token = '[QUESTION]' - answer_token = '[ANSWER]' - custom_promt = f'{default_promt_token}{{}}\n{answer_token}' - stop_token = '<|endoftext|>' POS_GRADE = 1 NEG_GRADE = -1 - def __init__(self, buffer_size): + def_config = { + 'buffer_size': 16, + 'max_len': 50, + 'device': 'cpu', + 'promt_token': '[QUESTION]', + 'answer_token': '[ANSWER]', + 'custom_promt': '[QUESTION]{}\n[ANSWER]', + 'stop_token': '<|endoftext|>', + } + + def __init__(self, config=None): self.store = storage - self.buffer_size = buffer_size + self.config = AbstractJokeGenerator.def_config.copy() + if config: + self.config.update(config) def positive_grade(self, user_id, joke_id): self.store.add_or_update_vote( @@ -43,16 +51,23 @@ def negative_grade(self, user_id, joke_id): self.store.add_or_update_vote( joke_id=joke_id, user_id=user_id, rating=self.NEG_GRADE) + def _escape_markdown(self, text): + """Escape the possible markdown tokens in the text generated by model.""" + return re.sub(r'((([_*]).+?\3[^_*]*)*)([_*])', "\g<1>\\\\\g<4>", text) + def _prettify_result(self, model_output): def pp_answer(text): """Pretty-print the answer.""" - # Remove all text after the stop token - text = text[: text.find(self.stop_token) if self.stop_token else None] - # Remove multiple answers - text = self.answer_token.join(text.split(self.answer_token, 2)[:2]) + # Remove all text after the stop token. + text = text[: text.find(self.config['stop_token']) + if self.config['stop_token'] else None] + # Remove multiple answers. + text = self.config['answer_token'].join(text.split(self.config['answer_token'], 2)[:2]) + # Escape markdown tokens. + text = self._escape_markdown(text) # Replace model tokens with html formatted ones. - text = re.sub(f'\{self.default_promt_token} *', 'Question: ', text) - text = re.sub(f'\{self.answer_token} *', '\nAnswer: ', text) + text = re.sub(f'\{self.config["promt_token"]} *', '**Question:** ', text) + text = re.sub(f'\{self.config["answer_token"]} *', '\n**Answer:** ', text) return text if isinstance(model_output, str): @@ -77,19 +92,19 @@ def generate_joke(self, model, promt=""): return Joke(id=joke_id, text=res['text']) @synchronized - def __call_model(self, model, prompt, num_return_sequences): + def __call_model(self, model, prompt, n_return_sequences): """Call the model to generate the joke. :param model: model to use :param promt: prompt for the model - :param num_return_sequences: number of sequences to generate - :return: list of (num_return_sequences) dicts + :param n_return_sequences: number of sequences to generate + :return: list of (n_return_sequences) dicts with 'text' and 'generated_by' fields """ return [{ 'generated_by': model.name, 'text': seq - } for seq in model.generate(prompt, num_return_sequences)] + } for seq in model.generate(prompt, n_return_sequences)] @synchronized def _get_from_buffer(self, model, buffer): @@ -118,8 +133,8 @@ def _generate_for_buffer(self, model): :return: see `__call_model` function """ model.logger.info('Filling the buffer') - return self.__call_model(model, self.default_promt_token, - num_return_sequences=self.buffer_size) + return self.__call_model(model, self.config['promt_token'], + n_return_sequences=self.config['buffer_size']) @abstractmethod def _get_new_joke(self, model): @@ -131,21 +146,21 @@ def _get_new_joke(self, model): def _continue_joke(self, model, promt): """Continue the joke given in promt.""" model.logger.info('Continue joke') - model_promt = self.custom_promt.format(' ' + promt.strip()) - return self.__call_model(model, model_promt, num_return_sequences=1)[0] + model_promt = self.config['custom_promt'].format(' ' + promt.strip()) + return self.__call_model(model, model_promt, n_return_sequences=1)[0] class JokeGenerator(AbstractJokeGenerator): """Simple Joke generator using one model.""" - def __init__(self, model_path, max_len=40, buffer_size=16, model_device='cpu'): - super().__init__(buffer_size) + def __init__(self, model_path, config): + super().__init__(config) model_name = os.path.split(model_path)[1] self.logger = logging.getLogger(type(self).__name__) - self.logger.info('Loading models...') - self.model = ModelWrapper(model_path, model_name, max_length=max_len) - self.logger.info(f'Loaded {self.models[-1].name}') - self._fill_buffer() + self.logger.info('Loading model...') + self.model = ModelWrapper(model_path, model_name, **config) + self.logger.info(f'Loaded {self.model.name}') + self._fill_buffer(self.model) self.logger.info('Ready to work!') def generate_joke(self, promt=""): @@ -165,32 +180,30 @@ class TestABGenerator(AbstractJokeGenerator): Chooses the source randomly. """ - def __init__(self, dataset_paths, model_paths, max_len=40, - buffer_size=16, model_device='cpu'): + def __init__(self, dataset_paths, model_paths, config): """ Loads datasets and models. Initiates pools and orders of passing :param dataset_paths: paths to the dataset :param model_paths: paths to the model """ - super().__init__(buffer_size) - + super().__init__(config) self.models, self.key2pool = list(), dict() self.logger = logging.getLogger(type(self).__name__) self.logger.info('Loading models...') for m_path in model_paths: m_name = os.path.split(m_path)[1] - self.models.append(ModelWrapper(m_path, m_name, max_length=max_len)) + self.models.append(ModelWrapper(m_path, m_name, **config)) self.logger.info(f'Loaded {self.models[-1].name}') self._fill_buffer(self.models[-1]) self.logger.info('Loading datasets...') self.datasets = list() for d_path in dataset_paths: - self.datasets.append(Dataset(d_path, self.default_promt_token, - self.answer_token)) + self.datasets.append(Dataset(d_path, self.config['promt_token'], + self.config['answer_token'])) self.logger.info(f'Loaded {self.datasets[-1].name}') self.logger.info('Ready to work!') - self.num_of_pools = len(self.models) + len(self.datasets) + self.n_pools = len(self.models) + len(self.datasets) def generate_joke(self, promt=""): idx = random.randint(0, len(self.models) - 1) @@ -203,7 +216,7 @@ def _fill_buffer(self, model): def _get_new_joke(self): """Get the joke either from the model buffer, or dataset.""" - idx = random.randint(0, self.num_of_pools - 1) + idx = random.randint(0, self.n_pools - 1) if idx < len(self.datasets): return random.choice(self.datasets[idx]) idx = idx - len(self.datasets) - 1 diff --git a/bot/main_bot.py b/bot/main_bot.py index 984a143..509d58d 100644 --- a/bot/main_bot.py +++ b/bot/main_bot.py @@ -29,15 +29,15 @@ model_args = { 'max_len': int(model_cfg['max_joke_len']), 'buffer_size': int(model_cfg['buffer_size']), - 'model_device': model_cfg['device'] + 'device': model_cfg['device'] } if cfg['bot']['ab_test'].lower() == 'true': joke_generator = TestABGenerator(dataset_paths=dataset_paths, model_paths=model_paths, - **model_args + config=model_args ) else: - joke_generator = JokeGenerator(model_path=model_paths[0], **model_args) + joke_generator = JokeGenerator(model_path=model_paths[0], config=model_args) splitter = "::" pos = "1" @@ -87,8 +87,8 @@ def general_joke_handler(update, context, promt_text=""): reply_markup = InlineKeyboardMarkup(keyboard) - update.message.reply_text( - joke.text, reply_markup=reply_markup, parse_mode=telegram.ParseMode.HTML) + update.message.reply_text(joke.text, reply_markup=reply_markup, + parse_mode=telegram.ParseMode.MARKDOWN) def button_handler(update, context): From 14a5fd233adeca5418a3fe3c14e526a5d23c7954 Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Sat, 9 May 2020 14:53:16 +0300 Subject: [PATCH 15/23] Added Russian model training & usage. --- .gitignore | 1 + bot/bot.cfg | 1 + bot/inference.py | 2 + bot/joke_generator.py | 36 + bot/main_bot.py | 8 +- bot/yt_encoder.py | 57 + train/GPT-2 train helper.ipynb | 571 ++- train/Web scraper.ipynb | 3276 ++++++++++++++++- train/ru_transformers-master/LICENSE | 201 + train/ru_transformers-master/environment.yml | 36 + train/ru_transformers-master/fit.sh | 22 + .../ru_transformers-master/run_generation.py | 206 ++ .../run_lm_finetuning.py | 661 ++++ train/ru_transformers-master/yt_encoder.py | 57 + 14 files changed, 4949 insertions(+), 186 deletions(-) create mode 100644 bot/yt_encoder.py create mode 100644 train/ru_transformers-master/LICENSE create mode 100644 train/ru_transformers-master/environment.yml create mode 100644 train/ru_transformers-master/fit.sh create mode 100644 train/ru_transformers-master/run_generation.py create mode 100644 train/ru_transformers-master/run_lm_finetuning.py create mode 100644 train/ru_transformers-master/yt_encoder.py diff --git a/.gitignore b/.gitignore index 4ec288d..d5ae3a7 100644 --- a/.gitignore +++ b/.gitignore @@ -229,6 +229,7 @@ model train/runs train/output* train/models +train/ru_gpt2 # prep files data/prep # jokes database diff --git a/bot/bot.cfg b/bot/bot.cfg index c6e15fd..ccb88e3 100644 --- a/bot/bot.cfg +++ b/bot/bot.cfg @@ -3,6 +3,7 @@ token = ... ab_test = false [model] model_paths = model +rus_model_path = rus_model dataset_paths = data/qa_jokes.csv max_joke_len = 40 buffer_size = 16 diff --git a/bot/inference.py b/bot/inference.py index df72ae8..6044982 100644 --- a/bot/inference.py +++ b/bot/inference.py @@ -16,9 +16,11 @@ XLNetLMHeadModel, XLNetTokenizer, ) +from yt_encoder import YTEncoder MODEL_CLASSES = { "gpt2": (GPT2LMHeadModel, GPT2Tokenizer), + "gpt2-yttm": (GPT2LMHeadModel, YTEncoder), "ctrl": (CTRLLMHeadModel, CTRLTokenizer), "openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer), "xlnet": (XLNetLMHeadModel, XLNetTokenizer), diff --git a/bot/joke_generator.py b/bot/joke_generator.py index bb0724e..51fec25 100644 --- a/bot/joke_generator.py +++ b/bot/joke_generator.py @@ -225,6 +225,42 @@ def _get_new_joke(self): self.key2pool[key]) +class RussianModelWrapper(AbstractJokeGenerator): + + def __init__(self, eng_model, rus_model_path, config): + super().__init__(config) + self.eng_model = eng_model + config.update({ # Spaces are because of the tokenizer + 'promt_token': '[ ВОПРОС]', + 'answer_token': '[ ОТВЕТ]', + 'custom_promt': '[ ВОПРОС]{}\n[ ОТВЕТ]', + 'stop_token': '<| endoftext|>', + 'model_type': 'gpt2-yttm', + }) + self.rus_model = JokeGenerator(rus_model_path, config) + + @staticmethod + def cyrillic_ratio(text): + return sum(map(len, re.findall('[\u0400-\u04FF]*', text))) / len(text) + + def generate_joke(self, promt=""): + """If the ratio of cyrillic symbols if high enough, + generate using russian model.""" + if promt.strip() == '/шутка': + return self.rus_model.generate_joke('') + if promt and self.cyrillic_ratio(promt) > 0.4: + return self.rus_model.generate_joke(promt) + return self.eng_model.generate_joke(promt) + + @synchronized + def _fill_buffer(self, model): + assert False, "Unreachable code. _fill_buffer function in RussianModelWrapper." + + def _get_new_joke(self): + """Get the joke from the english model.""" + return self.eng_model._get_new_joke() + + if __name__ == '__main__': logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO) diff --git a/bot/main_bot.py b/bot/main_bot.py index 509d58d..85916b8 100644 --- a/bot/main_bot.py +++ b/bot/main_bot.py @@ -5,7 +5,7 @@ from configparser import ConfigParser import telegram -from joke_generator import JokeGenerator, TestABGenerator +from joke_generator import JokeGenerator, TestABGenerator, RussianModelWrapper from telegram import InlineKeyboardButton, InlineKeyboardMarkup, ChatAction from telegram.ext import Updater, CommandHandler, CallbackQueryHandler, MessageHandler, Filters @@ -39,6 +39,12 @@ else: joke_generator = JokeGenerator(model_path=model_paths[0], config=model_args) +if model_cfg.get('rus_model_path'): + joke_generator = RussianModelWrapper(eng_model=joke_generator, + rus_model_path=model_cfg.get('rus_model_path'), + config=model_args + ) + splitter = "::" pos = "1" neg = "2" diff --git a/bot/yt_encoder.py b/bot/yt_encoder.py new file mode 100644 index 0000000..73b0fba --- /dev/null +++ b/bot/yt_encoder.py @@ -0,0 +1,57 @@ +"""Byte pair encoding utilities""" +import os +import youtokentome as yttm +import hashlib +from transformers.tokenization_utils import PreTrainedTokenizer +import shutil +import regex as re +from os.path import samefile + +NEW_LINE = '<|n|>' + +class YTEncoder(PreTrainedTokenizer): + def_name = 'encoder.model' + def __init__(self, filename, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + self.max_len_single_sentence = 1024 # no default special tokens - you can update this value if you add special tokens + self.max_len_sentences_pair = 1024 # no default special tokens - you can update this value if you add special tokens + + if os.path.isdir(filename): filename = os.path.join(filename, self.def_name) + + self.bpe = yttm.BPE(filename) + self.hash = hashlib.sha512(open(filename, 'rb').read()).hexdigest()[:10] + self.filename = filename + + def encode(self, text, **kwargs): + if text and text[0] != ' ': text = ' ' + text + text = re.sub(r'(?=[^ ])([\W])([\w])',r'\g<1> \g<2>',text) + text = text.replace('\n', f' {NEW_LINE} ') + + return self.bpe.encode([text], output_type=yttm.OutputType.ID)[0] + + + def decode(self, tokens, **kwargs): # I hate regexps + if not isinstance(tokens, list): + tokens = tokens.tolist() + result = self.bpe.decode(tokens)[0] + result = re.sub(r'( )?(<\|n\|>)( )?', r'\n', result) + result = re.sub(r'([\n(]) (\w)', r'\g<1>\g<2>', result) + result = re.sub(r'(\W)([«"''\n(]|^) (\w)', r'\g<1>\g<2>\g<3>', result) + result = re.sub(r'(\w)- (\w)', r'\g<1>-\g<2>', result) + return result + + def tokenize(self, text, **kwargs): + return self.encode(text) + + @classmethod + def from_pretrained(cls, *inputs, **kwargs): + return cls(*inputs, **kwargs) + + def add_special_tokens_single_sentence(self, token_ids): + return token_ids + + def save_pretrained(self, save_directory): + src = self.filename + dst = os.path.join(save_directory, self.def_name) + if src != dst: + shutil.copyfile(src, dst) diff --git a/train/GPT-2 train helper.ipynb b/train/GPT-2 train helper.ipynb index bebe7b5..76511b1 100644 --- a/train/GPT-2 train helper.ipynb +++ b/train/GPT-2 train helper.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -11,17 +11,31 @@ "import random\n", "from glob import glob\n", "import re\n", + "from tqdm.notebook import tqdm\n", "import numpy as np\n", "import pandas as pd\n", "import transformers\n", "\n", + "# Eng\n", + "\n", "qa_jokes_filepath = os.path.join('..', 'data', 'qa_jokes.csv')\n", "short_jokes_filepath = os.path.join('..', 'data', 'short_jokes.csv')\n", "transcripts_path = os.path.join('..', 'data', 'transcripts')\n", "\n", "qa_jokes_prep_outpath = os.path.join('..', 'data', 'prep', 'qa_jokes_gpt2.txt')\n", "short_jokes_prep_outpath = os.path.join('..', 'data', 'prep', 'short_jokes_gpt2.txt')\n", - "transcripts_prep_outpath = os.path.join('..', 'data', 'prep', 'transcripts_gpt2.txt')" + "transcripts_prep_outpath = os.path.join('..', 'data', 'prep', 'transcripts_gpt2.txt')\n", + "\n", + "# Rus\n", + "\n", + "rus_qa_jokes_filepath = os.path.join('..', 'data', 'rus_qa_jokes.csv')\n", + "rus_jokes_filepath = os.path.join('..', 'data', 'rus_jokes.csv')\n", + "rus_stories_filepath = os.path.join('..', 'data', 'anekdot_stories.csv')\n", + "\n", + "\n", + "rus_qa_jokes_prep_outpath = os.path.join('..', 'data', 'prep', 'rus_qa_jokes_gpt2.txt')\n", + "rus_jokes_prep_outpath = os.path.join('..', 'data', 'prep', 'rus_jokes_gpt2.txt')\n", + "rus_stories_prep_outpath = os.path.join('..', 'data', 'prep', 'rus_stories_gpt2.txt')" ] }, { @@ -33,17 +47,29 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 154, "metadata": {}, "outputs": [], "source": [ "def fix_encoding(s):\n", " \"\"\"Skip characters that can't be encoded by standard encoder.\"\"\"\n", - " return s.encode('latin1', 'ignore').decode('utf8', 'ignore')\n", + " return s.encode('utf-8', 'ignore').decode('utf8', 'ignore')\n", + "\n", + "# TODO: Add &nbsp; > <\n", + "regexps = [ # Regexp for the special chars\n", + " (re.compile('♦'), '*'),\n", + " (re.compile('\\n *\\n'), '\\n'), # Replace multiple newlines with one\n", + " (re.compile(r' {2,}'), ' '), # Replace multiple spaces with one\n", + "]\n", "\n", - "def write_to_file(file_path, text):\n", + "def fix_text(s):\n", + " for regexp in regexps:\n", + " s = regexp[0].sub(regexp[1], s)\n", + " return fix_encoding(s.strip())\n", + "\n", + "def write_to_file(file_path, text, encoding=None):\n", " os.makedirs(os.path.dirname(file_path), exist_ok=True)\n", - " with open(file_path, 'w') as out_file:\n", + " with open(file_path, 'w', encoding=encoding) as out_file:\n", " out_file.write(text)\n", "\n", "START_DOC_TOKEN = ''\n", @@ -62,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -70,13 +96,13 @@ "output_type": "stream", "text": [ "\n", - "RangeIndex: 38233 entries, 0 to 38232\n", + "RangeIndex: 38232 entries, 0 to 38231\n", "Data columns (total 3 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 ID 38233 non-null int64 \n", - " 1 Question 38233 non-null object\n", - " 2 Answer 38233 non-null object\n", + " 0 ID 38232 non-null int64 \n", + " 1 Question 38232 non-null object\n", + " 2 Answer 38232 non-null object\n", "dtypes: int64(1), object(2)\n", "memory usage: 896.2+ KB\n" ] @@ -89,15 +115,17 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ - "qa_corpus = ''\n", + "qa_corpus = []\n", "for _, question, answer in qa_jokes.values:\n", - " qa_corpus += fix_encoding(f'{START_DOC_TOKEN}[QUESTION] {question}\\n[ANSWER] {answer}\\n{END_DOC_TOKEN}\\n')\n", + " qa_corpus.append(f'{START_DOC_TOKEN}[QUESTION] {question}\\n[ANSWER] {answer}\\n{END_DOC_TOKEN}')\n", "\n", - "write_to_file(qa_jokes_prep_outpath, qa_corpus)" + "qa_corpus = '\\n'.join(map(lambda s: fix_text(s), qa_corpus))\n", + "\n", + "write_to_file(qa_jokes_prep_outpath, qa_corpus, encoding='utf-8')" ] }, { @@ -110,40 +138,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Additional cleaning\n", - "from glob import glob\n", - "import re\n", - "for f_path in glob(os.path.join(transcripts_path,'*')):\n", - " with open(f_path, encoding='utf8') as f:\n", - " text = ' '.join(f.readlines())\n", - " text = re.sub('\\n *\\n', '\\n', text)\n", - " text = re.sub(r' {2,}', ' ', text)\n", - " if len(re.findall(r'html|http|jpe?g|png|mp4', text)) > 0:\n", - " print('Has http|html in it:', f_path)\n", - " with open(f_path, 'w', encoding='utf8') as f:\n", - " f.write(text)" - ] - }, - { - "cell_type": "code", - "execution_count": 115, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ - "transcript_corpus = ''\n", + "transcript_corpus = []\n", "# Load transcripts.\n", "for file_path in glob(os.path.join(transcripts_path, '*')):\n", " with open(file_path, 'r', encoding='utf8') as in_file:\n", - " transcript_corpus += START_DOC_TOKEN + ''.join(in_file.read()) + END_DOC_TOKEN + '\\n'\n", + " if len(re.findall(r'html|http|jpe?g|png|mp4', text)) > 0:\n", + " print('Has http|html in it:', f_path)\n", + " transcript_corpus.append(START_DOC_TOKEN + ''.join(in_file.read()) + END_DOC_TOKEN)\n", "\n", - "transcript_corpus = fix_encoding(fix_encoding(transcript_corpus))\n", + "transcript_corpus = '\\n'.join(map(lambda s: fix_text(s), transcript_corpus))\n", "\n", "# Save all them as dataset.\n", - "write_to_file(transcripts_prep_outpath, transcript_corpus)" + "write_to_file(transcripts_prep_outpath, transcript_corpus, encoding='utf-8')" ] }, { @@ -158,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -166,12 +176,12 @@ "output_type": "stream", "text": [ "\n", - "RangeIndex: 230975 entries, 0 to 230974\n", + "RangeIndex: 230974 entries, 0 to 230973\n", "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 ID 230975 non-null int64 \n", - " 1 Joke 230975 non-null object\n", + " 0 ID 230974 non-null int64 \n", + " 1 Joke 230974 non-null object\n", "dtypes: int64(1), object(1)\n", "memory usage: 3.5+ MB\n" ] @@ -184,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -199,16 +209,16 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "\"What's the top song by the Vietnamese Beatles? Rice Fields Forever.\"" + "'What do you call a Muslim organization that rejects Muhammed? A non-prophet'" ] }, - "execution_count": 61, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -221,7 +231,20 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "jokes = {'Question': [], 'Answer': []}\n", + "for i, joke in short_jokes.iloc[qa_jokes_in_short_jokes[ind]].values:\n", + " sentences = sent_tokenize(joke.strip())\n", + " question, answer = sentences[0], ' '.join(sentences[1:])\n", + " jokes['']" + ] + }, + { + "cell_type": "code", + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -236,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -246,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -258,6 +281,249 @@ "write_to_file(short_jokes_prep_outpath, short_jokes_corpus)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Russian Stories" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 110529 entries, 0 to 110528\n", + "Data columns (total 1 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Text 110529 non-null object\n", + "dtypes: object(1)\n", + "memory usage: 1.7+ MB\n" + ] + } + ], + "source": [ + "rus_stories = pd.read_csv(rus_stories_filepath, index_col=0)\n", + "rus_stories.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [], + "source": [ + "rus_stories_corpus = '\\n'.join(map(lambda s: START_DOC_TOKEN + fix_text(s[0]) + END_DOC_TOKEN, rus_stories.values))\n", + "\n", + "# Save all them as dataset.\n", + "write_to_file(rus_stories_prep_outpath, rus_stories_corpus, encoding='utf-8')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Russian Jokes" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 439057 entries, 0 to 439056\n", + "Data columns (total 1 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Text 439057 non-null object\n", + "dtypes: object(1)\n", + "memory usage: 6.7+ MB\n" + ] + } + ], + "source": [ + "rus_jokes = pd.read_csv(rus_jokes_filepath, index_col=0)\n", + "rus_jokes.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Text
0Один хороший анекдот — это дополнительные 15 м...
1Старик и старуха в суде. Судья: — Почему разво...
2Х♦♦ — железо, пока горячо.
3— Сколько нужно вагонов, чтобы вывезти всех де...
4Женщина: в 20 лет — лепестки розы в 30 лет — с...
......
439052Вот было бы так: поел, завалился на диван, сп...
439053Мишустин доездился без пропуска - вот и заболе...
439054Напоследок Мишустин из больницы пообещал сдела...
439055План следующего выступления: 1.Ситуация с коро...
439056- Видел обращение Путина? - В кого?!
\n", + "

439057 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " Text\n", + "0 Один хороший анекдот — это дополнительные 15 м...\n", + "1 Старик и старуха в суде. Судья: — Почему разво...\n", + "2 Х♦♦ — железо, пока горячо.\n", + "3 — Сколько нужно вагонов, чтобы вывезти всех де...\n", + "4 Женщина: в 20 лет — лепестки розы в 30 лет — с...\n", + "... ...\n", + "439052 Вот было бы так: поел, завалился на диван, сп...\n", + "439053 Мишустин доездился без пропуска - вот и заболе...\n", + "439054 Напоследок Мишустин из больницы пообещал сдела...\n", + "439055 План следующего выступления: 1.Ситуация с коро...\n", + "439056 - Видел обращение Путина? - В кого?!\n", + "\n", + "[439057 rows x 1 columns]" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rus_jokes" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [], + "source": [ + "rus_jokes_corpus = '\\n'.join(map(lambda s: START_DOC_TOKEN + fix_text(s[0]) + END_DOC_TOKEN, rus_jokes.values))\n", + "\n", + "\n", + "# # Save all them as dataset.\n", + "write_to_file(rus_jokes_prep_outpath, rus_jokes_corpus, encoding='utf-8')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Russian QA Jokes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 60414 entries, 0 to 60413\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Unnamed: 0 60414 non-null int64 \n", + " 1 Question 60414 non-null object\n", + " 2 Answer 60414 non-null object\n", + "dtypes: int64(1), object(2)\n", + "memory usage: 1.4+ MB\n" + ] + } + ], + "source": [ + "rus_qa_jokes = pd.read_csv(rus_qa_jokes_filepath)\n", + "rus_qa_jokes.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [], + "source": [ + "rus_qa_corpus = [] \n", + "for _, question, answer in rus_qa_jokes.values:\n", + " rus_qa_corpus.append(fix_text(f'{START_DOC_TOKEN}[QUESTION] {question}\\n[ANSWER] {answer}\\n{END_DOC_TOKEN}'))\n", + "\n", + "rus_qa_corpus = '\\n'.join(rus_qa_corpus)\n", + "\n", + "write_to_file(rus_qa_jokes_prep_outpath, rus_qa_corpus, encoding='utf-8')" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -267,15 +533,22 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def create_cmd_command(python_path, script, kwargs, flags):\n", " args = ' '.join(f'--{k}={v}' for k, v in kwargs.items())\n", " args += ' ' + ' '.join(f'--{f}' for f in flags)\n", - " return f'{python_path} {script} {args}'\n", - "\n", + " return f'{python_path} {script} {args}'" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ "python_path = r'C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\python.exe'\n", "script = r'run_language_modeling.py'\n", "train_kwargs = {\n", @@ -284,9 +557,9 @@ " 'output_dir':'output',\n", " 'block_size': 512,\n", " 'learning_rate': 1e-6,\n", - " 'num_train_epochs': 10,\n", + " 'num_train_epochs': 3,\n", " 'per_gpu_train_batch_size': 2,\n", - " 'gradient_accumulation_steps': 4,\n", + " 'gradient_accumulation_steps': 8,\n", " 'save_steps': 1000,\n", "# 'max_steps': 20000,\n", "}\n", @@ -316,39 +589,39 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "# train_kwargs['train_data_file'] = transcripts_prep_outpath\n", "# train_kwargs['train_data_file'] = short_jokes_prep_outpath\n", - "train_kwargs['train_data_file'] = qa_jokes_prep_outpath" + "# train_kwargs['train_data_file'] = qa_jokes_prep_outpath" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "From: models\\output_7 \n", - "To: models\\output_8\n" + "From: ru_gpt2\\m_checkpoint-3364613 \n", + "To: models\\rus_1\n" ] } ], "source": [ "# train_kwargs['model_name_or_path'] = train_outputs[0]\n", - "train_kwargs['model_name_or_path'] = os.path.join('models', train_outputs[8])\n", - "train_kwargs['output_dir'] = os.path.join('models', train_outputs[9])\n", + "train_kwargs['model_name_or_path'] = os.path.join('models', train_outputs[9])\n", + "train_kwargs['output_dir'] = os.path.join('models', train_outputs[10])\n", "print('From:', train_kwargs['model_name_or_path'], '\\nTo:', train_kwargs['output_dir'])" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 52, "metadata": { "scrolled": true }, @@ -357,7 +630,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\python.exe run_language_modeling.py --model_type=gpt2 --model_name_or_path=models\\output_7 --output_dir=models\\output_8 --block_size=512 --learning_rate=1e-06 --num_train_epochs=10 --per_gpu_train_batch_size=2 --gradient_accumulation_steps=4 --save_steps=1000 --train_data_file=..\\data\\prep\\qa_jokes_gpt2.txt --do_train\n" + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\python.exe run_language_modeling.py --model_type=gpt2 --model_name_or_path=ru_gpt2\\m_checkpoint-3364613 --output_dir=models\\rus_1 --block_size=512 --learning_rate=1e-06 --num_train_epochs=3 --per_gpu_train_batch_size=2 --gradient_accumulation_steps=8 --save_steps=1000 --train_data_file=..\\data\\prep\\rus_qa_jokes_gpt2.txt --do_train --overwrite_output_dir\n" ] } ], @@ -375,14 +648,14 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 132, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\python.exe run_generation.py --model_type=gpt2 --model_name_or_path=models\\output_8 --prompt=\"[QUESTION]\" --length=100 --stop_token=\"<|endoftext|>\" --temperature=0.9 --repetition_penalty=1.05 --k=50 --p=0.95 --num_return_sequences=40 \n" + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\python.exe run_generation.py --model_type=gpt2 --model_name_or_path=models\\rus_2 --prompt=\"[QUESTION]\" --length=100 --stop_token=\"<|endoftext|>\" --temperature=0.9 --repetition_penalty=1.05 --k=50 --p=0.95 --num_return_sequences=40 \n" ] } ], @@ -408,6 +681,170 @@ "print(cmd_command)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ru_transformers\n", + "https://github.com/mgrankin/ru_transformers" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "python_path = r'C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\python.exe'\n", + "script = r'ru_transformers-master\\run_lm_finetuning.py'\n", + "train_kwargs = {\n", + " 'model_type': 'gpt2', # gpt2, ctrl, openai-gpt, xlnet, transfo-xl, xlm\n", + " 'model_name_or_path':'gpt2',\n", + " 'output_dir':'output',\n", + " 'block_size': 512,\n", + " 'learning_rate': 1e-4,\n", + " 'num_train_epochs': 3,\n", + " 'per_gpu_train_batch_size': 2,\n", + " 'gradient_accumulation_steps': 8,\n", + " 'save_steps': 1000,\n", + " 'save_total_limit': 3,\n", + " 'logging_steps':10,\n", + " 'warmup_samples':2000,\n", + " 'tokenizer_class': 'YTEncoder',\n", + " 'tokenizer_name': '{}/encoder.model',\n", + " 'unfreeze_level': 0,\n", + "# 'max_steps': 20000,\n", + "}\n", + "\n", + "train_outputs = [\n", + " r'ru_gpt2/s_checkpoint-1900000', # 'ru_gpt2\\m_checkpoint-3364613'\n", + " 'rus_test_1', # rus_stories_prep_outpath 5e-5 16\n", + " 'rus_test_2', # rus_jokes 9 hours 1e-6 32\n", + " 'rus_test_3', #\n", + " 'rus_test_4',\n", + "]\n", + "\n", + "\n", + "train_flags = [\n", + " 'do_train',\n", + " 'overwrite_output_dir',\n", + " 'lr_decay',\n", + "# 'fp16',\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..\\data\\prep\\rus_jokes_gpt2.txt\n" + ] + } + ], + "source": [ + "# train_kwargs['train_data_file'] = rus_stories_prep_outpath\n", + "train_kwargs['train_data_file'] = rus_jokes_prep_outpath\n", + "# train_kwargs['train_data_file'] = rus_qa_jokes_prep_outpath\n", + "print(train_kwargs['train_data_file'])" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "From: ru_gpt2/s_checkpoint-1900000 \n", + "To: models\\rus_test_1\n" + ] + } + ], + "source": [ + "train_kwargs['model_name_or_path'] = train_outputs[0]\n", + "# train_kwargs['model_name_or_path'] = os.path.join('models', train_outputs[1])\n", + "\n", + "train_kwargs['tokenizer_name'] = train_kwargs['tokenizer_name'].format(train_kwargs['model_name_or_path'])\n", + "train_kwargs['output_dir'] = os.path.join('models', train_outputs[1])\n", + "print('From:', train_kwargs['model_name_or_path'], '\\nTo:', train_kwargs['output_dir'])" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\python.exe ru_transformers-master\\run_lm_finetuning.py --model_type=gpt2 --model_name_or_path=ru_gpt2/s_checkpoint-1900000 --output_dir=models\\rus_test_1 --block_size=512 --learning_rate=0.0001 --num_train_epochs=3 --per_gpu_train_batch_size=2 --gradient_accumulation_steps=8 --save_steps=1000 --save_total_limit=3 --logging_steps=10 --warmup_samples=2000 --tokenizer_class=YTEncoder --tokenizer_name=ru_gpt2/s_checkpoint-1900000/encoder.model --unfreeze_level=0 --train_data_file=..\\data\\prep\\rus_jokes_gpt2.txt --do_train --overwrite_output_dir --lr_decay\n" + ] + } + ], + "source": [ + "cmd_command = create_cmd_command(python_path, script, train_kwargs, train_flags)\n", + "print(cmd_command)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# MovingLoss=0.47, Perplexity=1.61\n", + " --do_eval \\\n", + " --evaluate_during_training \\\n", + " --eval_steps 1000 \\\n", + " --eval_data_file=./data/classic/valid \\" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Generate" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\python.exe ru_transformers-master\\run_generation.py --model_type=gpt2-yttm --model_name_or_path=models\\rus_4 --length=200 --temperature=0.9 --top_k=50 --top_p=0.95 \n" + ] + } + ], + "source": [ + "gen_script = r'ru_transformers-master\\run_generation.py'\n", + "generate_kwargs = {\n", + " 'model_type': 'gpt2-yttm',\n", + " 'model_name_or_path': train_kwargs['output_dir'],\n", + "# 'prompt': rf'\"{START_DOC_TOKEN}[QUESTION]\"',\n", + " 'length': 200,\n", + " 'temperature': 0.9, # temperature of 1.0 has no effect, lower tend toward greedy sampling\n", + " 'top_k': 50,\n", + " 'top_p': 0.95,\n", + "# 'padding_text': '', # Padding text for Transfo-XL and XLNet.\n", + "}\n", + "gen_flags = []\n", + "\n", + "cmd_command = create_cmd_command(python_path, gen_script, generate_kwargs, gen_flags)\n", + "print(cmd_command)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/train/Web scraper.ipynb b/train/Web scraper.ipynb index 6f61154..44411f0 100644 --- a/train/Web scraper.ipynb +++ b/train/Web scraper.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 111, "metadata": {}, "outputs": [], "source": [ @@ -13,6 +13,7 @@ "from bs4.element import Tag\n", "import re\n", "import os\n", + "import pandas as pd\n", "from tqdm.notebook import tqdm\n", "import socket\n", "\n", @@ -21,19 +22,66 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ + "headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}\n", + "\n", "regexps = [\n", " (re.compile('♪[^♪]*♪|\\[[^\\]]*\\]|\\([^\\)]*\\)'), ' '),\n", + " (re.compile('<\\/br>'), '\\n'),\n", " (re.compile('<\\/?[\\w ]*>'), ' '), # for <\\br> and similar tags\n", "]" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def save_file(path, txt, encoding=None):\n", + " # Create the corresponding folder (if needed)\n", + " os.makedirs(os.path.dirname(path), exist_ok=True)\n", + " with open(path, 'w', encoding=encoding) as out_file:\n", + " out_file.writelines(txt)\n", + "\n", + "\n", + "def process_block(block):\n", + " result = []\n", + " # If not text, skip\n", + " try:\n", + " if block.name in ['img'] or (block.name == 'div' and block.get('class', [''])[0] == 'yarpp-related'):\n", + " return result\n", + " except AttributeError:\n", + " print('[ERROR] AttributeError!')\n", + " print(type(block))\n", + " print(block)\n", + " print('---------------------------------')\n", + " # If is a tag, process it's content\n", + " if isinstance(block, Tag):\n", + " for sub_block in block.contents:\n", + " result.extend(process_block(sub_block))\n", + " return result\n", + " for regexp, sub_str in regexps:\n", + " block = regexp.sub(sub_str, block)\n", + " block = block.strip()\n", + " if block:\n", + " result.append(block)\n", + " return result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## scrapsfromtheloft.com" + ] + }, + { + "cell_type": "code", + "execution_count": 174, "metadata": {}, "outputs": [], "source": [ @@ -79,13 +127,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 179, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "673ecaa8ed484a79b1d0962f361a5b95", + "model_id": "defc52907dd54ba3866d3ff288e2f44b", "version_major": 2, "version_minor": 0 }, @@ 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60\u001b[1;33m \u001b[0mscrap_page\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscrap_transcript\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl_transcripts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_batches\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m40\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msave_to_files\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m\u001b[0m in \u001b[0;36mscrap_page\u001b[1;34m(func, urls_gen, n_batches, save_to_files)\u001b[0m\n\u001b[0;32m 44\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 45\u001b[0m \u001b[0mpbar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_description\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Loading {} 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in ['img'] or (block.name == 'div' and block.get('class', [''])[0] == 'yarpp-related'):\n", - " return result\n", - " except AttributeError:\n", - " print('[ERROR] AttributeError!')\n", - " print(type(block))\n", - " print(block)\n", - " print('---------------------------------')\n", - " # If is a tag, process it's content\n", - " if isinstance(block, Tag):\n", - " for sub_block in block.contents:\n", - " result.extend(process_block(sub_block))\n", - " return result\n", - " for regexp, sub_str in regexps:\n", - " block = regexp.sub(sub_str, block)\n", - " block = block.strip()\n", - " if block:\n", - " result.append(block)\n", - " return result\n", - "\n", - "\n", - "def scrap_transcript(url, file_path):\n", - " try:\n", - " transcript_page = requests.get(url)\n", - " except requests.exceptions.ConnectionError:\n", - " print('[ERROR] Connection error to', url)\n", - " return\n", - " transcript_soup = BeautifulSoup(transcript_page.content, 'html.parser')\n", - " content_blocks = transcript_soup.findAll('div', 'post-content')\n", + "def scrap_transcript(soup, transcript_url):\n", + " file_name = transcript_url[:-1].rsplit('/', 1)[-1]\n", + " file_path = os.path.join('test', file_name + '.txt')\n", + " if not ('transcript' in file_path.lower()):\n", + " print('[WARN] Possibly page without transcript!', transcript_url)\n", + " content_blocks = soup.findAll('div', 'post-content')\n", " if len(content_blocks) != 1:\n", " print('[WARN] strange content in', url)\n", " return\n", " content = process_block(content_blocks[0])\n", + " # Merge smal paragraphs.\n", " stripped_content = ['']\n", " for line in content:\n", " if len(stripped_content[-1]) < 200:\n", " stripped_content[-1] += ' ' + line\n", " else:\n", " stripped_content.append(line)\n", - " save_file(file_path, '\\n'.join(stripped_content), encoding='utf8')\n", + " stripped_content = '\\n'.join(stripped_content)\n", + " if file_path:\n", + " save_file(file_path, stripped_content, encoding='utf8')\n", + " return [stripped_content]\n", + "\n", + "def url_transcripts(n_batches, pbar):\n", + " URL = 'https://scrapsfromtheloft.com/comedy/page/{}/'\n", + " for i in range(n_batches):\n", + " # Load block of pages\n", + " pbar.set_description('Loading {} block...'.format(i+1))\n", + " page = requests.get(URL.format(i))\n", + " soup = BeautifulSoup(page.content, 'html.parser')\n", + " blocks = soup.body.findAll('div', 'fusion-post-content post-content')\n", + " # Extract link to the page\n", + " for j, block in enumerate(blocks):\n", + " block_title = block.find('h2', 'entry-title fusion-post-title').a\n", + " yield block_title['href']\n", + " \n", + "def scrap_page(func, urls_gen, n_batches, save_to_files=False):\n", + " n_processed = 0\n", + " pbar = tqdm(total=n_batches)\n", + " accumulator = []\n", + " for i, url in enumerate(urls_gen(n_batches, pbar)):\n", + " # Skip \"bad\" pages\n", + " if url in pages_to_skip:\n", + " continue\n", + " try:\n", + " pbar.set_description('Loading {} page...'.format(i+1))\n", + " page = requests.get(url)\n", + " pbar.set_description('Processing {} page...'.format(i+1))\n", + " except requests.exceptions.ConnectionError:\n", + " print('[ERROR] Connection error to', url)\n", + " pbar.update(1)\n", + " continue\n", + " soup = BeautifulSoup(page.content, 'html.parser')\n", + " new_items = func(soup, url)\n", + " accumulator.extend(new_items)\n", + " n_processed += len(new_items)\n", + " print('[INFO] Processed another ', len(new_items), 'items, for a total of', n_processed)\n", + " pbar.update(1)\n", + " return accumulator\n", + "\n", + "scrap_page(scrap_transcript, url_transcripts, n_batches=40, save_to_files=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## http://anecdotica.ru/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def scrap_anecdotica(soup, _):\n", + " jokes = soup.findAll('div', 'item_text')\n", + " return [' '.join(process_block(joke)) for joke in jokes]\n", "\n", + "def urls_anecdotica(n_batches, _):\n", + " URL = 'http://anecdotica.ru/all/{}'\n", + " for i in range(1, n_batches + 1):\n", + " yield URL.format(i)\n", "\n", - "n_batches = 40\n", - "skip_downloaded = False\n", + "anecdotica = scrap_page(scrap_anecdotica, urls_anecdotica, n_batches=1000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## https://www.anekdot.ru/" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ef39f6ea68d74f629bed08b248728d22", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=936000.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] No more jokes at: https://www.anekdot.ru/an/an9501/s950133;0,100.html\n", + "[INFO] No more jokes at: https://www.anekdot.ru/an/an9502/s950233;0,100.html\n", + "[INFO] No more jokes at: https://www.anekdot.ru/an/an9503/s950333;0,100.html\n", + "[INFO] No more jokes at: https://www.anekdot.ru/an/an9504/s950433;0,100.html\n", + "[INFO] No more jokes at: https://www.anekdot.ru/an/an9505/s950533;0,100.html\n", + "[INFO] No more jokes at: https://www.anekdot.ru/an/an9506/s950633;0,100.html\n", + "[INFO] No more jokes at: https://www.anekdot.ru/an/an9507/s950733;0,100.html\n", + "[INFO] No more jokes at: https://www.anekdot.ru/an/an9508/s950833;0,100.html\n", + "[INFO] No more jokes at: https://www.anekdot.ru/an/an9509/s950933;0,100.html\n", + "[INFO] No more jokes at: https://www.anekdot.ru/an/an9510/s951033;0,100.html\n", + "[INFO] No more jokes at: https://www.anekdot.ru/an/an9511/s951133;0,100.html\n", + "[INFO] No more jokes at: https://www.anekdot.ru/an/an9512/s951233;0,100.html\n", + "[INFO] No more 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"[INFO] Processed another 101 items, for a total of 215989\n", + "[INFO] Processed another 40 items, for a total of 216029\n", + "[INFO] No more jokes at: https://www.anekdot.ru/an/an2003/s200333;600,100.html\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Processed another 101 items, for a total of 216130\n", + "[INFO] Processed another 101 items, for a total of 216231\n", + "[INFO] Processed another 101 items, for a total of 216332\n", + "[INFO] Processed another 101 items, for a total of 216433\n", + "[INFO] Processed another 50 items, for a total of 216483\n", + "[INFO] No more jokes at: https://www.anekdot.ru/an/an2004/s200433;500,100.html\n", + "[INFO] Processed another 62 items, for a total of 216545\n", + "[INFO] No more jokes at: https://www.anekdot.ru/an/an2005/s200533;100,100.html\n", + "[INFO] No more jokes at: https://www.anekdot.ru/an/an2006/s200633;0,100.html\n", + "[INFO] No more jokes at: 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year;month;year;month;start_from\n", + " # All years from 1995 to 2020\n", + " years = list(map(lambda x: f'{x:0>2}', list(range(95, 100)) + list(range(0, 21))))\n", + " months = list(map(lambda x: f'{x:0>2}', list(range(1, 13))))\n", + " for year in years:\n", + " for month in months:\n", + " for start_ind in range(0, max_num_jokes, 100):\n", + " yield URL.format(year, month, year, month, start_ind)\n", + "\n", + "years = list(map(lambda x: f'{x:0>2}', list(range(95, 100)) + list(range(0, 21)))) # All years from 1995 to 2020\n", + "months = list(map(lambda x: f'{x:0>2}', list(range(1, 13))))\n", + "max_num = 3000\n", + "anekdot_jokes = []\n", "n_processed = 0\n", - "pbar = tqdm(total=n_batches)\n", - "for i in range(n_batches):\n", - " pbar.set_description('Loading {} batch...'.format(i+1))\n", - "# time.sleep(1)\n", - " page = requests.get(URL.format(i))\n", - " pbar.set_description('Processing {} batch...'.format(i+1))\n", - " soup = BeautifulSoup(page.content, 'html.parser')\n", - " blocks = soup.body.findAll('div', 'fusion-post-content post-content')\n", - " for j, block in enumerate(blocks):\n", - " pbar.set_description('Processing {} block...'.format(j+1))\n", - " block_title = block.find('h2', 'entry-title fusion-post-title').a\n", - " transcript_url = block_title['href']\n", - " file_name = transcript_url[:-1].rsplit('/', 1)[-1]\n", - " file_path = os.path.join(output_path, file_name + '.txt')\n", - " # Skip `bad` pages\n", - " if transcript_url in pages_to_skip:\n", - " if os.path.exists(file_path):\n", - " os.remove(file_path)\n", - " continue\n", - " if not ('transcript' in block_title.contents[0].lower() or transcript_url in ok_pages):\n", - " print('[WARN] Possibly page without transcript!', transcript_url)\n", - " if not (os.path.exists(file_path) and skip_downloaded):\n", - " try:\n", - " scrap_transcript(transcript_url, file_path)\n", - " except Exception:\n", - " print('[ERROR] Some error on:', transcript_url)\n", - " n_processed += len(blocks)\n", - " print('[INFO] Processed another ', len(blocks), 'blocks, for a total of', n_processed)\n", - " pbar.update(1)" + "pbar = tqdm(total=max_num * len(years) * len(months))\n", + "for year in years:\n", + " for month in months:\n", + " for start_ind in range(0, max_num, 100):\n", + " page_url = URL.format(year, month, year, month, start_ind)\n", + " page = requests.get(page_url, headers=headers)\n", + " pbar.set_description(f'Processing {year} {month} {start_ind} page...')\n", + " soup = BeautifulSoup(page.content, 'html.parser')\n", + " new_items = [' '.join(process_block(joke)) for joke in soup.findAll('div', 'text')]\n", + " if len(new_items) == 0:\n", + " print('[INFO] No more jokes at:', page_url)\n", + " pbar.update(max_num - start_ind)\n", + " break\n", + " anekdot_jokes.extend(new_items)\n", + " n_processed += len(new_items)\n", + " print('[INFO] Processed another ', len(new_items), 'items, for a total of', n_processed)\n", + " pbar.update(100)" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [], + "source": [ + "# pd.DataFrame(anekdot_jokes, columns=['Text']).to_csv('../data/anekdot_repetitive.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Telegram channel\n", + "https://t.me/ligaplohihshutok" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "with open('../data/liga-plohih-shutok.json', encoding='utf-8') as in_file:\n", + " liga_jokes = json.loads(in_file.read())" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame(liga_jokes, columns=['Text']).to_csv('../data/ru_lpsh_jokes.json')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extract QA jokes\n", + "We can extract the QA jokes from the datasets with the general jokes." + ] + }, + { + "cell_type": "code", + "execution_count": 297, + "metadata": {}, + "outputs": [], + "source": [ + "import nltk\n", + "\n", + "regexps = [ # Regexp for the special chars\n", + " (re.compile('♦'), '*'),\n", + " (re.compile('\\n *\\n'), '\\n'), # Replace multiple newlines with one\n", + " (re.compile(r' {2,}'), ' '), # Replace multiple spaces with one\n", + "]\n", + "\n", + "def fix_text(s):\n", + " for regexp in regexps:\n", + " s = regexp[0].sub(regexp[1], s)\n", + " s = s.strip(' -—')\n", + " s = re.sub('^(?:вопрос|ответ):?', '', s, flags=re.IGNORECASE)\n", + " return s.strip()\n", + "\n", + "\n", + "def extract_qa_jokes(iterator, max_num_sents=2):\n", + " res = []\n", + " pbar = tqdm(total=len(iterator))\n", + " for i, joke in enumerate(iterator):\n", + " joke = re.sub(r'^[^\\n\\:\\?\\.]*(?:Армянское|Армянскому?|Армянского) *радио[^\\n:?]*\\:', '', joke, flags=re.IGNORECASE)\n", + " sentences = list(map(lambda s: fix_text(s), nltk.sent_tokenize(joke, language=\"russian\")))\n", + " if sentences[0][-1] == '?' and 1 < len(sentences) <= max_num_sents:\n", + " res.append({\n", + " 'Question': sentences[0],\n", + " 'Answer': ' '.join(sentences[1:])\n", + " })\n", + " if i % 500 == 0:\n", + " pbar.set_description(f'Extracted: {len(res)} jokes')\n", + " pbar.update(1)\n", + " pbar.set_description(f'Extracted: {len(res)} jokes')\n", + " pbar.close()\n", + " return res" + ] + }, + { + "cell_type": "code", + "execution_count": 298, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d32ffaae0eea431e944a0c61603b1fb2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=20025.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "542ecf204dd141b7ad969fc0f5c0bcfc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=200621.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b3fb2b5c62914927b9760c0ef5774b3f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=218411.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b0f553f1848b403e85e3cef5bcd284b3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=1806.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "47983" + ] + }, + "execution_count": 298, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "files = [\n", + " '../data/anecdotika.csv',\n", + " '../data/anekdot_fresh.csv',\n", + " '../data/anekdot_others.csv',\n", + " '../data/ru_lpsh_jokes.json',\n", + "]\n", + "\n", + "qa_jokes = []\n", + "\n", + "for file in files:\n", + " jokes = pd.read_csv(file)\n", + " qa_anekdot = extract_qa_jokes(jokes['Text'].values, max_num_sents=3)\n", + " qa_jokes.extend(qa_anekdot)\n", + "len(qa_jokes)" + ] + }, + { + "cell_type": "code", + "execution_count": 299, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame.from_dict(qa_jokes).to_csv('../data/rus_qa_jokes.csv')" ] } ], diff --git a/train/ru_transformers-master/LICENSE b/train/ru_transformers-master/LICENSE new file mode 100644 index 0000000..261eeb9 --- /dev/null +++ b/train/ru_transformers-master/LICENSE @@ -0,0 +1,201 @@ + Apache 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/train/ru_transformers-master/environment.yml b/train/ru_transformers-master/environment.yml new file mode 100644 index 0000000..bf3df4a --- /dev/null +++ b/train/ru_transformers-master/environment.yml @@ -0,0 +1,36 @@ +name: gpt +channels: + - fastai + - pytorch + - conda-forge + - defaults +dependencies: + - ipykernel + - python + - pip + - fastai + - pytorch>=1.3 + - cuda100 + - regex + - langdetect + - tensorboardX + - cudatoolkit + - tensorboard>=1.14 + - tensorflow>=2 + - uvicorn + - terraform + - google-cloud-sdk + - tar + - zstd + - transformers + - pip: + - pyTelegramBotAPI + - pysocks + - tendo + - schedule + - fastapi>=0.41 + - youtokentome + - requests + - rupo + - transitions + - mezmorize \ No newline at end of file diff --git a/train/ru_transformers-master/fit.sh b/train/ru_transformers-master/fit.sh new file mode 100644 index 0000000..b7569b6 --- /dev/null +++ b/train/ru_transformers-master/fit.sh @@ -0,0 +1,22 @@ +python tpu_lm_finetuning.py \ + --seed=$RANDOM \ + --output_dir=$OUTPUT \ + --model_type=gpt2 \ + --model_name_or_path=$MODEL_SIZE \ + --do_train \ + --train_data_file=$TRAIN_FILE \ + --reload_data_file 1 \ + --per_gpu_train_batch_size $BS \ + --save_steps=10000 \ + --logging_steps=100 \ + --warmup_samples ${WARMUP:-128000} \ + --learning_rate $LR \ + --overwrite_output_dir \ + --tokenizer_class YTEncoder \ + --tokenizer_name bpe/yt.model \ + --evaluate_during_training \ + --eval_data_file=${VALID:-./data/classic/valid} \ + --per_gpu_eval_batch_size $BS \ + --save_total_limit 30 \ + --num_train_epochs $NUM_EPOCH \ + --unfreeze_level $UNFREEZE diff --git a/train/ru_transformers-master/run_generation.py b/train/ru_transformers-master/run_generation.py new file mode 100644 index 0000000..9a63921 --- /dev/null +++ b/train/ru_transformers-master/run_generation.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +# coding=utf-8 +# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/Transformer-XL/XLNet) +""" +from __future__ import absolute_import, division, print_function, unicode_literals + +import argparse +import logging +from tqdm import trange + +import torch +import torch.nn.functional as F +import numpy as np + +from transformers import (GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig, + GPT2LMHeadModel, GPT2Tokenizer, + OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, + XLNetLMHeadModel, XLNetTokenizer, + TransfoXLLMHeadModel, TransfoXLTokenizer, ) +from yt_encoder import YTEncoder + + +logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', + datefmt = '%m/%d/%Y %H:%M:%S', + level = logging.INFO) +logger = logging.getLogger(__name__) + +MAX_LENGTH = int(10000) # Hardcoded max length to avoid infinite loop + +ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig)), ()) + +MODEL_CLASSES = { + 'gpt2': (GPT2LMHeadModel, GPT2Tokenizer), + 'gpt2-yttm': (GPT2LMHeadModel, YTEncoder), + 'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer), + 'xlnet': (XLNetLMHeadModel, XLNetTokenizer), + 'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer), +} + +# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia +# in https://github.com/rusiaaman/XLNet-gen#methodology +# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e +PADDING_TEXT = """ In 1991, the remains of Russian Tsar Nicholas II and his family +(except for Alexei and Maria) are discovered. +The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the +remainder of the story. 1883 Western Siberia, +a young Grigori Rasputin is asked by his father and a group of men to perform magic. +Rasputin has a vision and denounces one of the men as a horse thief. Although his +father initially slaps him for making such an accusation, Rasputin watches as the +man is chased outside and beaten. Twenty years later, Rasputin sees a vision of +the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, +with people, even a bishop, begging for his blessing. """ + +FILTER_VALUE=-float('Inf') + +def set_seed(args): + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if args.n_gpu > 0: + torch.cuda.manual_seed_all(args.seed) + + +def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')): + """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering + Args: + logits: logits distribution shape (vocabulary size) + top_k > 0: keep only top k tokens with highest probability (top-k filtering). + top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering). + Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751) + From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317 + """ + assert logits.dim() == 1 # batch size 1 for now - could be updated for more but the code would be less clear + top_k = min(top_k, logits.size(-1)) # Safety check + if top_k > 0: + # Remove all tokens with a probability less than the last token of the top-k + indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] + logits[indices_to_remove] = filter_value + + if top_p > 0.0: + sorted_logits, sorted_indices = torch.sort(logits, descending=True) + cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) + + # Remove tokens with cumulative probability above the threshold + sorted_indices_to_remove = cumulative_probs > top_p + # Shift the indices to the right to keep also the first token above the threshold + sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() + sorted_indices_to_remove[..., 0] = 0 + + indices_to_remove = sorted_indices[sorted_indices_to_remove] + logits[indices_to_remove] = filter_value + return logits + +def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=0, top_p=0.0, + is_xlnet=False, device='cpu', max_input=1023, filter_single=[], filter_double=[]): + context = torch.tensor(context, dtype=torch.long, device=device) + context = context.unsqueeze(0).repeat(num_samples, 1) + generated = context + with torch.no_grad(): + for _ in trange(length): + + inputs = {'input_ids': generated[:,-max_input:]} + if is_xlnet: + # XLNet is a direct (predict same token, not next token) and bi-directional model by default + # => need one additional dummy token in the input (will be masked), attention mask and target mapping (see model docstring) + input_ids = torch.cat((generated, torch.zeros((1, 1), dtype=torch.long, device=device)), dim=1) + perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float, device=device) + perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token + target_mapping = torch.zeros((1, 1, input_ids.shape[1]), dtype=torch.float, device=device) + target_mapping[0, 0, -1] = 1.0 # predict last token + inputs = {'input_ids': input_ids, 'perm_mask': perm_mask, 'target_mapping': target_mapping} + + outputs = model(**inputs) # Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet (cached hidden-states) + next_tokens = torch.zeros(num_samples, dtype=torch.long).to(device) + for isample in range(num_samples): + next_token_logits = outputs[0][isample, -1, :] / temperature + + next_token_logits[filter_single] = FILTER_VALUE + # filter blank line = double \n + if generated[isample, -1] in filter_double: + next_token_logits[generated[isample, -1]] = FILTER_VALUE + + filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p) + next_tokens[isample] = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1) + + generated = torch.cat((generated, next_tokens.unsqueeze(-1)), dim=1) + return generated + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--model_type", default=None, type=str, required=True, + help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys())) + parser.add_argument("--model_name_or_path", default=None, type=str, required=True, + help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS)) + parser.add_argument("--prompt", type=str, default="") + parser.add_argument("--padding_text", type=str, default="") + parser.add_argument("--length", type=int, default=20) + parser.add_argument("--temperature", type=float, default=1.0) + parser.add_argument("--top_k", type=int, default=0) + parser.add_argument("--top_p", type=float, default=0.9) + parser.add_argument("--no_cuda", action='store_true', + help="Avoid using CUDA when available") + parser.add_argument('--seed', type=int, default=42, + help="random seed for initialization") + args = parser.parse_args() + + args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") + args.n_gpu = torch.cuda.device_count() + + set_seed(args) + + args.model_type = args.model_type.lower() + model_class, tokenizer_class = MODEL_CLASSES[args.model_type] + tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path) + model = model_class.from_pretrained(args.model_name_or_path) + model.to(args.device) + model.eval() + + if args.length < 0 and model.config.max_position_embeddings > 0: + args.length = model.config.max_position_embeddings + elif 0 < model.config.max_position_embeddings < args.length: + args.length = model.config.max_position_embeddings # No generation bigger than model size + elif args.length < 0: + args.length = MAX_LENGTH # avoid infinite loop + + print(args) + while True: + raw_text = args.prompt if args.prompt else input("Model prompt >>> ") + if args.model_type in ["transfo-xl", "xlnet"]: + # Models with memory likes to have a long prompt for short inputs. + raw_text = (args.padding_text if args.padding_text else PADDING_TEXT) + raw_text + context_tokens = tokenizer.encode(raw_text) + out = sample_sequence( + model=model, + context=context_tokens, + length=args.length, + temperature=args.temperature, + top_k=args.top_k, + top_p=args.top_p, + device=args.device, + is_xlnet=bool(args.model_type == "xlnet"), + ) + out = out[0, len(context_tokens):].tolist() + text = tokenizer.decode(out) # , clean_up_tokenization_spaces=True + print(text) + if args.prompt: + break + return text + + +if __name__ == '__main__': + main() diff --git a/train/ru_transformers-master/run_lm_finetuning.py b/train/ru_transformers-master/run_lm_finetuning.py new file mode 100644 index 0000000..3d21c58 --- /dev/null +++ b/train/ru_transformers-master/run_lm_finetuning.py @@ -0,0 +1,661 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa). +GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned +using a masked language modeling (MLM) loss. +""" + +from __future__ import absolute_import, division, print_function + +import argparse +import glob +import logging +import os +import pickle +import random +import regex as re +import shutil + +import numpy as np +import torch +from torch.utils.data import DataLoader, Dataset, SequentialSampler, RandomSampler +from torch.utils.data.distributed import DistributedSampler + +try: + from torch.utils.tensorboard import SummaryWriter +except: + from tensorboardX import SummaryWriter + +from tqdm import tqdm, trange +from dataclasses import dataclass +from fastprogress import progress_bar +from fastai.basics import * + +from run_generation import sample_sequence + +from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, + BertConfig, BertForMaskedLM, BertTokenizer, + GPT2Config, GPT2LMHeadModel, GPT2Tokenizer, + OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, + RobertaConfig, RobertaForMaskedLM, RobertaTokenizer, + DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer) + +from yt_encoder import YTEncoder + +logger = logging.getLogger(__name__) + + +MODEL_CLASSES = { + 'gpt2': (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer), + 'openai-gpt': (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer), + 'bert': (BertConfig, BertForMaskedLM, BertTokenizer), + 'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer), + 'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer) +} + +@dataclass +class MovingLoss(): + steps:int=1000 + avg_loss = (0.0, 0.0) + def add(self, batch_loss:float): + k_s = 1 - 1/self.steps + avg_loss = self.avg_loss + self.avg_loss = (self.avg_loss[0] * k_s + batch_loss * (1-k_s), + self.avg_loss[1] * k_s + 1.0 * (1-k_s)) + @property + def loss(self): + if self.avg_loss[1]: + return self.avg_loss[0] / self.avg_loss[1] + +def print_sample(model, tokenizer, device, args): + model.eval() + raw_text = """ На словах ты Лев Толстой,\n А на деле -""" + context_tokens = tokenizer.encode(raw_text) + out = sample_sequence( + model=model, + context=context_tokens, + length=500, + temperature=1, + top_k=0, + top_p=0.9, + device=device, + #is_xlnet=bool(args.model_type == "xlnet"), + ) + out = out[0, len(context_tokens):].tolist() + text = raw_text + tokenizer.decode(out) + print(text) + + with open(os.path.join(args.output_dir, 'sample.txt'), 'w') as f: + f.write(text) + + model.train() + +class TextDataset(Dataset): + @staticmethod + def process_file(file_path, tokenizer, block_size, shuffle): + directory, filename = os.path.split(file_path) + directory = os.path.join(directory, 'cached') + os.makedirs(directory, exist_ok=True) + cached_features_file = os.path.join(directory, f'cached_lm_{block_size}_{tokenizer.hash}_{filename}') + + if os.path.exists(cached_features_file): + with open(cached_features_file, 'rb') as handle: + tokenized_text = pickle.load(handle) + else: + with open(file_path, encoding="utf-8") as f: + text = f.read() + if hasattr(tokenizer, 'encode'): + tokenized_text = tokenizer.encode(text) + else: + tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text)) + with open(cached_features_file, 'wb') as handle: + pickle.dump(tokenized_text, handle, protocol=pickle.HIGHEST_PROTOCOL) + + examples = [] + # add random shift + max_shift = max(min(block_size, len(tokenized_text) - block_size), 0) + rnd_shift = random.randrange(max_shift) if max_shift and shuffle else 0 + + for i in range(rnd_shift, len(tokenized_text)-block_size+1, block_size): + examples.append(tokenizer.add_special_tokens_single_sentence(tokenized_text[i:i+block_size])) + # Note that we are loosing the last truncated example here for the sake of simplicity (no padding) + # If your dataset is small, first you should loook for a bigger one :-) and second you + # can change this behavior by adding (model specific) padding. + return examples + + def __init__(self, tokenizer, file_path='train', args=None, shuffle=True): + if not hasattr(tokenizer, 'hash'): tokenizer.hash = '' + + logger.info(f"Loading features from {file_path}") + if os.path.isfile(file_path): + files = [file_path] + else: + assert os.path.isdir(file_path) + files = glob.glob(os.path.join(file_path, '*.txt')) + + files = sorted(files) + if shuffle: random.shuffle(files) + + files = files[:10000] + + self.examples = [] + for fn in progress_bar(files): + self.examples.extend(self.process_file(fn, tokenizer, args.block_size, shuffle)) + + def __len__(self): + return len(self.examples) + + def __getitem__(self, item): + return torch.tensor(self.examples[item]) + + +def load_and_cache_examples(args, tokenizer, evaluate=False): + dataset = TextDataset(tokenizer, file_path=args.eval_data_file if evaluate else args.train_data_file, args=args, shuffle=not evaluate) + return dataset + + +def set_seed(args): + return # no + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if args.n_gpu > 0: + torch.cuda.manual_seed_all(args.seed) + +def _rotate_checkpoints(args, checkpoint_prefix, use_mtime=False): + if not args.save_total_limit: + return + if args.save_total_limit <= 0: + return + + # Check if we should delete older checkpoint(s) + glob_checkpoints = glob.glob(os.path.join(args.output_dir, '{}-*'.format(checkpoint_prefix))) + if len(glob_checkpoints) <= args.save_total_limit: + return + + ordering_and_checkpoint_path = [] + for path in glob_checkpoints: + if use_mtime: + ordering_and_checkpoint_path.append((os.path.getmtime(path), path)) + else: + regex_match = re.match('.*{}-([0-9]+)'.format(checkpoint_prefix), path) + if regex_match and regex_match.groups(): + ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path)) + + checkpoints_sorted = sorted(ordering_and_checkpoint_path) + checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted] + number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit) + checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete] + for checkpoint in checkpoints_to_be_deleted: + logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint)) + shutil.rmtree(checkpoint) + + +def mask_tokens(inputs, tokenizer, args): + """ Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """ + labels = inputs.clone() + # We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa) + probability_matrix = torch.full(labels.shape, args.mlm_probability) + special_tokens_mask = [tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()] + probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0) + masked_indices = torch.bernoulli(probability_matrix).bool() + labels[~masked_indices] = -1 # We only compute loss on masked tokens + + # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) + indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices + inputs[indices_replaced] = tokenizer.convert_tokens_to_ids(tokenizer.mask_token) + + # 10% of the time, we replace masked input tokens with random word + indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced + random_words = torch.randint(len(tokenizer), labels.shape, dtype=torch.long) + inputs[indices_random] = random_words[indices_random] + + # The rest of the time (10% of the time) we keep the masked input tokens unchanged + return inputs, labels + +def save_state(args, model, tokenizer, global_step): + def save_dir(output_dir): + # Create output directory if needed + if not os.path.exists(output_dir) and args.local_rank in [-1, 0]: + os.makedirs(output_dir) + logger.info(f"Saving model checkpoint to {output_dir}") + # Save a trained model, configuration and tokenizer using `save_pretrained()`. + # They can then be reloaded using `from_pretrained()` + model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training + model_to_save.save_pretrained(output_dir) + tokenizer.save_pretrained(output_dir) + + # Good practice: save your training arguments together with the trained model + torch.save(args, os.path.join(output_dir, 'training_args.bin')) + with open(os.path.join(output_dir, 'step.txt'), 'w') as c: c.write(str(global_step)) + + save_dir(args.output_dir) + checkpoint_prefix = 'checkpoint' + output_dir = os.path.join(args.output_dir, f'{checkpoint_prefix}-{global_step}') + save_dir(output_dir) + _rotate_checkpoints(args, checkpoint_prefix) + +class SummaryWriterP(SummaryWriter): + def __init__(self, prefix=None, logdir=None, comment='', *args, **kwargs): + if prefix: + import socket + from datetime import datetime + current_time = datetime.now().strftime('%b%d_%H-%M-%S') + logdir = os.path.join(prefix, + 'runs', current_time + '_' + socket.gethostname() + comment) + super().__init__(logdir, comment, *args, **kwargs) + +def train(args, train_dataset, model, tokenizer): + """ Train the model """ + if args.local_rank in [-1, 0]: + tb_writer = SummaryWriterP(args.output_dir) + + args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) + train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) + train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) + + if args.max_steps > 0: + t_total = args.max_steps + args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 + else: + t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs + + # Prepare optimizer and schedule (linear warmup and decay) + no_decay = ['bias', 'LayerNorm.weight'] + optimizer_grouped_parameters = [ + {'params': [p for n, p in model.named_parameters() if p.requires_grad and not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay}, + {'params': [p for n, p in model.named_parameters() if p.requires_grad and any(nd in n for nd in no_decay)], 'weight_decay': 0.0} + ] + optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) + warmup_steps = args.warmup_samples // args.train_batch_size + if args.lr_decay: + scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total) + else: + scheduler = get_constant_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps) + + if args.fp16: + try: + from apex import amp + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") + model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) + + # multi-gpu training (should be after apex fp16 initialization) + if args.n_gpu > 1: + model = torch.nn.DataParallel(model) + + # Distributed training (should be after apex fp16 initialization) + if args.local_rank != -1: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], + output_device=args.local_rank, + find_unused_parameters=True) + + # Train! + logger.info("***** Running training *****") + logger.info(" Num examples = %d", len(train_dataset)) + logger.info(" Num Epochs = %d", args.num_train_epochs) + logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) + logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d", + args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1)) + logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) + logger.info(" Total optimization steps = %d", t_total) + + try: + with open(os.path.join(args.model_name_or_path, 'step.txt'), 'r') as c: + global_step = int(c.readline()) + except OSError as e: + global_step = 0 + + tr_loss, logging_loss = 0.0, 0.0 + moving_loss = MovingLoss(10000//args.logging_steps) + model.zero_grad() + + train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]) + set_seed(args) # Added here for reproducibility (even between python 2 and 3) + try: + for _ in train_iterator: + epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) + for step, batch in enumerate(epoch_iterator): + inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch) + inputs = inputs.to(args.device) + labels = labels.to(args.device) + model.train() + outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels) + loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc) + + if args.n_gpu > 1: + loss = loss.mean() # mean() to average on multi-gpu parallel training + if args.gradient_accumulation_steps > 1: + loss = loss / args.gradient_accumulation_steps + + if args.fp16: + with amp.scale_loss(loss, optimizer) as scaled_loss: + scaled_loss.backward() + else: + loss.backward() + + tr_loss += loss.item() + moving_loss.add(loss.item()) + if (step + 1) % args.gradient_accumulation_steps == 0: + if args.fp16: + torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) + else: + torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) + optimizer.step() + scheduler.step() # Update learning rate schedule + model.zero_grad() + global_step += 1 + + # Log metrics + if args.local_rank == -1 and args.evaluate_during_training and global_step % args.eval_steps == 0: # Only evaluate when single GPU otherwise metrics may not average well + results = evaluate(args, model, tokenizer, f"checkpoint-{global_step}") + for key, value in results.items(): + tb_writer.add_scalar('eval_{}'.format(key), value, global_step) + + if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: + tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step) + tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step) + logging_loss = tr_loss + epoch_iterator.set_postfix(MovingLoss=f'{moving_loss.loss:.2f}', Perplexity=f'{torch.exp(torch.tensor(moving_loss.loss)):.2f}') + + if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: + # Save model checkpoint + save_state(args, model, tokenizer, global_step) + + if args.max_steps > 0 and global_step > args.max_steps: + epoch_iterator.close() + break + print_sample(model, tokenizer, args.device, args) + if args.max_steps > 0 and global_step > args.max_steps: + train_iterator.close() + break + except (KeyboardInterrupt, SystemExit): + save_state(args, model, tokenizer, global_step) + raise + + if args.local_rank in [-1, 0]: + tb_writer.close() + + return global_step, tr_loss / global_step + + +def evaluate(args, model, tokenizer, prefix=""): + # Loop to handle MNLI double evaluation (matched, mis-matched) + eval_output_dir = args.output_dir + + eval_dataset = load_and_cache_examples(args, tokenizer, evaluate=True) + + if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]: + os.makedirs(eval_output_dir) + + args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) + # Note that DistributedSampler samples randomly + eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) + eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) + + # Eval! + logger.info("***** Running evaluation {} *****".format(prefix)) + logger.info(" Num examples = %d", len(eval_dataset)) + logger.info(" Batch size = %d", args.eval_batch_size) + eval_loss = 0.0 + nb_eval_steps = 0 + model.eval() + + for batch in tqdm(eval_dataloader, desc="Evaluating"): + batch = batch.to(args.device) + + with torch.no_grad(): + outputs = model(batch, masked_lm_labels=batch) if args.mlm else model(batch, labels=batch) + lm_loss = outputs[0] + eval_loss += lm_loss.item() #lm_loss.mean().item() + nb_eval_steps += 1 + + eval_loss = eval_loss / nb_eval_steps + perplexity = torch.exp(torch.tensor(eval_loss)) + + result = { + "perplexity": perplexity + } + + output_eval_file = os.path.join(eval_output_dir, "eval_results.txt") + with open(output_eval_file, "w") as writer: + logger.info("***** Eval results {} *****".format(prefix)) + for key in sorted(result.keys()): + logger.info(" %s = %s", key, str(result[key])) + writer.write("%s = %s\n" % (key, str(result[key]))) + + return result + + +def main(): + parser = argparse.ArgumentParser() + + ## Required parameters + parser.add_argument("--train_data_file", default=None, type=str, required=True, + help="The input training data file (a text file).") + parser.add_argument("--output_dir", default=None, type=str, required=True, + help="The output directory where the model predictions and checkpoints will be written.") + + ## Other parameters + parser.add_argument("--eval_data_file", default=None, type=str, + help="An optional input evaluation data file to evaluate the perplexity on (a text file).") + + parser.add_argument("--model_type", default="bert", type=str, + help="The model architecture to be fine-tuned.") + parser.add_argument("--model_name_or_path", default="bert-base-cased", type=str, + help="The model checkpoint for weights initialization.") + + parser.add_argument("--mlm", action='store_true', + help="Train with masked-language modeling loss instead of language modeling.") + parser.add_argument("--mlm_probability", type=float, default=0.15, + help="Ratio of tokens to mask for masked language modeling loss") + + parser.add_argument("--config_name", default="", type=str, + help="Optional pretrained config name or path if not the same as model_name_or_path") + parser.add_argument("--tokenizer_name", default="", type=str, + help="Optional pretrained tokenizer name or path if not the same as model_name_or_path") + parser.add_argument("--tokenizer_class", default="", type=str, + help="Optional pretrained tokenizer clas") + parser.add_argument("--cache_dir", default="", type=str, + help="Optional directory to store the pre-trained models downloaded from s3 (instread of the default one)") + parser.add_argument("--block_size", default=-1, type=int, + help="Optional input sequence length after tokenization." + "The training dataset will be truncated in block of this size for training." + "Default to the model max input length for single sentence inputs (take into account special tokens).") + parser.add_argument("--do_train", action='store_true', + help="Whether to run training.") + parser.add_argument("--do_eval", action='store_true', + help="Whether to run eval on the dev set.") + parser.add_argument("--evaluate_during_training", action='store_true', + help="Run evaluation during training at each logging step.") + parser.add_argument('--eval_steps', type=int, default=100, + help="Evaluate every X updates steps.") + parser.add_argument("--do_lower_case", action='store_true', + help="Set this flag if you are using an uncased model.") + + parser.add_argument("--per_gpu_train_batch_size", default=4, type=int, + help="Batch size per GPU/CPU for training.") + parser.add_argument("--per_gpu_eval_batch_size", default=4, type=int, + help="Batch size per GPU/CPU for evaluation.") + parser.add_argument('--gradient_accumulation_steps', type=int, default=1, + help="Number of updates steps to accumulate before performing a backward/update pass.") + parser.add_argument("--learning_rate", default=5e-5, type=float, + help="The initial learning rate for Adam.") + parser.add_argument("--weight_decay", default=0.0, type=float, + help="Weight deay if we apply some.") + parser.add_argument("--adam_epsilon", default=1e-6, type=float, + help="Epsilon for Adam optimizer.") + parser.add_argument("--max_grad_norm", default=1.0, type=float, + help="Max gradient norm.") + parser.add_argument("--num_train_epochs", default=1.0, type=float, + help="Total number of training epochs to perform.") + parser.add_argument("--max_steps", default=-1, type=int, + help="If > 0: set total number of training steps to perform. Override num_train_epochs.") + parser.add_argument("--warmup_samples", default=0, type=int, + help="Linear warmup over warmup_samples.") + parser.add_argument("--lr_decay", action='store_true', + help="Decay LR using get_linear_schedule_with_warmup.") + + parser.add_argument("--unfreeze_level", default=-1, type=int, + help="If > 0: freeze all layers except few first and last.") + + parser.add_argument('--logging_steps', type=int, default=50, + help="Log every X updates steps.") + parser.add_argument('--save_steps', type=int, default=50, + help="Save checkpoint every X updates steps.") + parser.add_argument('--save_total_limit', type=int, default=None, + help='Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default') + parser.add_argument("--eval_all_checkpoints", action='store_true', + help="Evaluate all checkpoints starting with the same prefix as model_name_or_path ending and ending with step number") + parser.add_argument("--no_cuda", action='store_true', + help="Avoid using CUDA when available") + parser.add_argument('--overwrite_output_dir', action='store_true', + help="Overwrite the content of the output directory") + parser.add_argument('--overwrite_cache', action='store_true', + help="Overwrite the cached training and evaluation sets") + parser.add_argument('--seed', type=int, default=42, + help="random seed for initialization") + + parser.add_argument('--fp16', action='store_true', + help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit") + parser.add_argument('--fp16_opt_level', type=str, default='O1', + help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." + "See details at https://nvidia.github.io/apex/amp.html") + parser.add_argument("--local_rank", type=int, default=-1, + help="For distributed training: local_rank") + parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.") + parser.add_argument('--server_port', type=str, default='', help="For distant debugging.") + args = parser.parse_args() + + if args.model_type in ["bert", "roberta", "distilbert"] and not args.mlm: + raise ValueError("BERT and RoBERTa do not have LM heads but masked LM heads. They must be run using the --mlm " + "flag (masked language modeling).") + if args.eval_data_file is None and args.do_eval: + raise ValueError("Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file " + "or remove the --do_eval argument.") + + if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir: + raise ValueError(f"Output directory ({args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome.") + + # Setup distant debugging if needed + if args.server_ip and args.server_port: + # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script + import ptvsd + print("Waiting for debugger attach") + ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) + ptvsd.wait_for_attach() + + # Setup CUDA, GPU & distributed training + if args.local_rank == -1 or args.no_cuda: + device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") + args.n_gpu = torch.cuda.device_count() + else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs + torch.cuda.set_device(args.local_rank) + device = torch.device("cuda", args.local_rank) + torch.distributed.init_process_group(backend='nccl') + args.n_gpu = 1 + args.device = device + + # Setup logging + logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', + datefmt = '%m/%d/%Y %H:%M:%S', + level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN) + logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", + args.local_rank, args.device, args.n_gpu, bool(args.local_rank != -1), args.fp16) + + # Set seed + set_seed(args) + + # Load pretrained model and tokenizer + if args.local_rank not in [-1, 0]: + torch.distributed.barrier() # Barrier to make sure only the first process in distributed training download model & vocab + + config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] + config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path) + if args.tokenizer_class: tokenizer_class = globals()[args.tokenizer_class] + tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case) + if args.block_size <= 0: + args.block_size = tokenizer.max_len_single_sentence # Our input block size will be the max possible for the model + args.block_size = min(args.block_size, tokenizer.max_len_single_sentence) + model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config) + model.to(args.device) + + print(200*'/') + print(len([param for item in flatten_model(model) + for param in item.parameters() + if param.requires_grad])) # freeze all layers but few first and last + if args.unfreeze_level >= 0: + flat = flatten_model(model) + flat = [item for item in flat if list(item.parameters())] + i_start = 3 + i_end = 1 + need_grads = set(flat[:i_start+args.unfreeze_level*3]) | set(flat[-(i_end+args.unfreeze_level*3):]) + for item in flat: + requires_grad(item, item in need_grads) + print(200*'/') + print(len([param for item in flatten_model(model) + for param in item.parameters() + if param.requires_grad])) + + if args.local_rank == 0: + torch.distributed.barrier() # End of barrier to make sure only the first process in distributed training download model & vocab + + logger.info("Training/evaluation parameters %s", args) + + # Training + if args.do_train: + if args.local_rank not in [-1, 0]: + torch.distributed.barrier() # Barrier to make sure only the first process in distributed training process the dataset, and the others will use the cache + + train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False) + + if args.local_rank == 0: + torch.distributed.barrier() + + global_step, tr_loss = train(args, train_dataset, model, tokenizer) + logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) + + # Saving best-practices: if you use save_pretrained for the model and tokenizer, you can reload them using from_pretrained() + if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): + save_state(args, model, tokenizer, global_step) + + # Load a trained model and vocabulary that you have fine-tuned + model = model_class.from_pretrained(args.output_dir) + tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) + model.to(args.device) + + # Evaluation + results = {} + if args.do_eval and args.local_rank in [-1, 0]: + checkpoints = [args.output_dir] + if args.eval_all_checkpoints: + checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True))) + logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging + logger.info("Evaluate the following checkpoints: %s", checkpoints) + for checkpoint in checkpoints: + global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else "" + model = model_class.from_pretrained(checkpoint) + model.to(args.device) + result = evaluate(args, model, tokenizer, prefix=global_step) + result = dict((k + '_{}'.format(global_step), v) for k, v in result.items()) + results.update(result) + + return results + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/train/ru_transformers-master/yt_encoder.py b/train/ru_transformers-master/yt_encoder.py new file mode 100644 index 0000000..a4dcf00 --- /dev/null +++ b/train/ru_transformers-master/yt_encoder.py @@ -0,0 +1,57 @@ +"""Byte pair encoding utilities""" +import os +import youtokentome as yttm +import hashlib +from transformers.tokenization_utils import PreTrainedTokenizer +import shutil +import regex as re +from os.path import samefile + +NEW_LINE = '<|n|>' + +class YTEncoder(PreTrainedTokenizer): + def_name = 'encoder.model' + def __init__(self, filename, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + self.max_len_single_sentence = 1024 # no default special tokens - you can update this value if you add special tokens + self.max_len_sentences_pair = 1024 # no default special tokens - you can update this value if you add special tokens + + if os.path.isdir(filename): filename = os.path.join(filename, self.def_name) + + self.bpe = yttm.BPE(filename) + self.hash = hashlib.sha512(open(filename, 'rb').read()).hexdigest()[:10] + self.filename = filename + + def encode(self, text): + if text and text[0] != ' ': text = ' ' + text + text = re.sub(r'(?=[^ ])([\W])([\w])',r'\g<1> \g<2>',text) + text = text.replace('\n', f' {NEW_LINE} ') + + return self.bpe.encode([text], output_type=yttm.OutputType.ID)[0] + + + def decode(self, tokens): # I hate regexps + if not isinstance(tokens,list): + tokens = tokens.tolist() + result = self.bpe.decode(tokens)[0] + result = re.sub(r'( )?(<\|n\|>)( )?', r'\n', result) + result = re.sub(r'([\n(]) (\w)',r'\g<1>\g<2>', result) + result = re.sub(r'(\W)([«"''\n(]|^) (\w)',r'\g<1>\g<2>\g<3>', result) + result = re.sub(r'(\w)- (\w)',r'\g<1>-\g<2>', result) + return result + + def tokenize(self, text, **kwargs): + return self.encode(text) + + @classmethod + def from_pretrained(cls, *inputs, **kwargs): + return cls(*inputs, **kwargs) + + def add_special_tokens_single_sentence(self, token_ids): + return token_ids + + def save_pretrained(self, save_directory): + src = self.filename + dst = os.path.join(save_directory, self.def_name) + if src != dst: + shutil.copyfile(src, dst) From 7205173151dc7bf90b0219f6fc92ca4c4ffe2f03 Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Sat, 9 May 2020 16:48:16 +0300 Subject: [PATCH 16/23] Fixed bold formatting. --- bot/joke_generator.py | 4 ++-- bot/main_bot.py | 5 ++--- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/bot/joke_generator.py b/bot/joke_generator.py index 51fec25..13c7bd2 100644 --- a/bot/joke_generator.py +++ b/bot/joke_generator.py @@ -66,8 +66,8 @@ def pp_answer(text): # Escape markdown tokens. text = self._escape_markdown(text) # Replace model tokens with html formatted ones. - text = re.sub(f'\{self.config["promt_token"]} *', '**Question:** ', text) - text = re.sub(f'\{self.config["answer_token"]} *', '\n**Answer:** ', text) + text = re.sub(f'\{self.config["promt_token"]} *', '*Question*: ', text) + text = re.sub(f'\{self.config["answer_token"]} *', '\n*Answer*: ', text) return text if isinstance(model_output, str): diff --git a/bot/main_bot.py b/bot/main_bot.py index 85916b8..a03c5ef 100644 --- a/bot/main_bot.py +++ b/bot/main_bot.py @@ -49,14 +49,14 @@ pos = "1" neg = "2" -GREETING_MESSAGE = "Welcome to the *Joke Generator Bot*." +GREETING_MESSAGE = "Welcome to the _Joke Generator Bot_." HELP_MESSAGE = "Use `/joke` to generate a joke. " + \ "Or, if you want a joke on some specific topic from me, " + \ "just write me a question and I'll answer it in a playful form." + \ "\n\nTo help me learn, please sent feedback on jokes through the 👍/👎 buttons." -DISCLAIMER_MESSAGE = "**DISCLAIMER**: This bot is still very dumb and " + \ +DISCLAIMER_MESSAGE = "*DISCLAIMER*: This bot is still very dumb and " + \ "produces a lot of dark and racist humor. " + \ "Don't judge him, he learned them from the people" @@ -92,7 +92,6 @@ def general_joke_handler(update, context, promt_text=""): InlineKeyboardButton("👎", callback_data=f'{joke_id}{splitter}{neg}')]] reply_markup = InlineKeyboardMarkup(keyboard) - update.message.reply_text(joke.text, reply_markup=reply_markup, parse_mode=telegram.ParseMode.MARKDOWN) From 53a42ba5838b60ee9c9aedc7489955fa3a6713ab Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Mon, 11 May 2020 14:18:05 +0300 Subject: [PATCH 17/23] Finalized training code. Merged transformers and ru_transformers scripts. --- bot/yt_encoder.py | 4 +- train/GPT-2 train helper.ipynb | 155 +- train/Web scraper.ipynb | 6503 ++++++++++------- train/ru_transformers-master/LICENSE | 201 - train/ru_transformers-master/environment.yml | 36 - train/ru_transformers-master/fit.sh | 22 - .../ru_transformers-master/run_generation.py | 206 - .../run_lm_finetuning.py | 23 +- train/run_generation.py | 355 +- train/run_lm_finetuning.py | 662 ++ .../yt_encoder.py | 5 +- 11 files changed, 4768 insertions(+), 3404 deletions(-) delete mode 100644 train/ru_transformers-master/LICENSE delete mode 100644 train/ru_transformers-master/environment.yml delete mode 100644 train/ru_transformers-master/fit.sh delete mode 100644 train/ru_transformers-master/run_generation.py create mode 100644 train/run_lm_finetuning.py rename train/{ru_transformers-master => }/yt_encoder.py (87%) diff --git a/bot/yt_encoder.py b/bot/yt_encoder.py index 73b0fba..c24286b 100644 --- a/bot/yt_encoder.py +++ b/bot/yt_encoder.py @@ -13,9 +13,7 @@ class YTEncoder(PreTrainedTokenizer): def_name = 'encoder.model' def __init__(self, filename, *inputs, **kwargs): super().__init__(*inputs, **kwargs) - self.max_len_single_sentence = 1024 # no default special tokens - you can update this value if you add special tokens - self.max_len_sentences_pair = 1024 # no default special tokens - you can update this value if you add special tokens - + if os.path.isdir(filename): filename = os.path.join(filename, self.def_name) self.bpe = yttm.BPE(filename) diff --git a/train/GPT-2 train helper.ipynb b/train/GPT-2 train helper.ipynb index 76511b1..e0d3890 100644 --- a/train/GPT-2 train helper.ipynb +++ b/train/GPT-2 train helper.ipynb @@ -2,9 +2,40 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" + ] + } + ], "source": [ "import os\n", "import json\n", @@ -47,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -484,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 135, "metadata": {}, "outputs": [ { @@ -492,15 +523,15 @@ "output_type": "stream", "text": [ "\n", - "RangeIndex: 60414 entries, 0 to 60413\n", + "RangeIndex: 67563 entries, 0 to 67562\n", "Data columns (total 3 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 Unnamed: 0 60414 non-null int64 \n", - " 1 Question 60414 non-null object\n", - " 2 Answer 60414 non-null object\n", + " 0 Unnamed: 0 67563 non-null int64 \n", + " 1 Question 67563 non-null object\n", + " 2 Answer 67563 non-null object\n", "dtypes: int64(1), object(2)\n", - "memory usage: 1.4+ MB\n" + "memory usage: 1.5+ MB\n" ] } ], @@ -511,13 +542,13 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 136, "metadata": {}, "outputs": [], "source": [ - "rus_qa_corpus = [] \n", + "rus_qa_corpus = []\n", "for _, question, answer in rus_qa_jokes.values:\n", - " rus_qa_corpus.append(fix_text(f'{START_DOC_TOKEN}[QUESTION] {question}\\n[ANSWER] {answer}\\n{END_DOC_TOKEN}'))\n", + " rus_qa_corpus.append(fix_text(f'{START_DOC_TOKEN}[ ВОПРОС] {question}\\n[ ОТВЕТ] {answer}\\n{END_DOC_TOKEN}'))\n", "\n", "rus_qa_corpus = '\\n'.join(rus_qa_corpus)\n", "\n", @@ -533,7 +564,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -545,12 +576,12 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "python_path = r'C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\python.exe'\n", - "script = r'run_language_modeling.py'\n", + "script = r'run_lm_finetuning.py'\n", "train_kwargs = {\n", " 'model_type': 'gpt2', # gpt2, ctrl, openai-gpt, xlnet, transfo-xl, xlm\n", " 'model_name_or_path':'gpt2',\n", @@ -575,53 +606,53 @@ " 'output_4', # short_jokes 1e-7, 2 grad_acc 2\n", " 'output_5', # qa_jokes 1e-5, 3 grad_acc 8 - most funny yet\n", " 'output_6', # qa_jokes 1e-5, 3 grad_acc 4\n", - " 'output_7', # qa_jokes 1e-6, 2 grad_acc 2 - ?\n", - " 'output_8', # qa_jokes 1e-6, 10 grad_acc 8 ###\n", + " 'output_7', # qa_jokes 1e-6, 2 grad_acc 2\n", + " 'output_8', # qa_jokes 1e-6, 10 grad_acc 8\n", " \n", "]\n", "\n", "train_flags = [\n", " 'do_train',\n", - "# 'overwrite_output_dir',\n", + " 'overwrite_output_dir',\n", "# 'fp16',\n", "]" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# train_kwargs['train_data_file'] = transcripts_prep_outpath\n", "# train_kwargs['train_data_file'] = short_jokes_prep_outpath\n", - "# train_kwargs['train_data_file'] = qa_jokes_prep_outpath" + "train_kwargs['train_data_file'] = qa_jokes_prep_outpath" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "From: ru_gpt2\\m_checkpoint-3364613 \n", - "To: models\\rus_1\n" + "From: gpt2 \n", + "To: models\\output\n" ] } ], "source": [ - "# train_kwargs['model_name_or_path'] = train_outputs[0]\n", - "train_kwargs['model_name_or_path'] = os.path.join('models', train_outputs[9])\n", - "train_kwargs['output_dir'] = os.path.join('models', train_outputs[10])\n", + "train_kwargs['model_name_or_path'] = train_outputs[0]\n", + "# train_kwargs['model_name_or_path'] = os.path.join('models', train_outputs[0])\n", + "train_kwargs['output_dir'] = os.path.join('models', train_outputs[1])\n", "print('From:', train_kwargs['model_name_or_path'], '\\nTo:', train_kwargs['output_dir'])" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 20, "metadata": { "scrolled": true }, @@ -630,7 +661,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\python.exe run_language_modeling.py --model_type=gpt2 --model_name_or_path=ru_gpt2\\m_checkpoint-3364613 --output_dir=models\\rus_1 --block_size=512 --learning_rate=1e-06 --num_train_epochs=3 --per_gpu_train_batch_size=2 --gradient_accumulation_steps=8 --save_steps=1000 --train_data_file=..\\data\\prep\\rus_qa_jokes_gpt2.txt --do_train --overwrite_output_dir\n" + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\python.exe ru_transformers-master\\run_lm_finetuning.py --model_type=gpt2 --model_name_or_path=gpt2 --output_dir=models\\output --block_size=512 --learning_rate=1e-06 --num_train_epochs=3 --per_gpu_train_batch_size=2 --gradient_accumulation_steps=8 --save_steps=1000 --train_data_file=..\\data\\prep\\qa_jokes_gpt2.txt --do_train --overwrite_output_dir\n" ] } ], @@ -691,37 +722,38 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 177, "metadata": {}, "outputs": [], "source": [ "python_path = r'C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\python.exe'\n", - "script = r'ru_transformers-master\\run_lm_finetuning.py'\n", + "script = r'run_lm_finetuning.py'\n", "train_kwargs = {\n", - " 'model_type': 'gpt2', # gpt2, ctrl, openai-gpt, xlnet, transfo-xl, xlm\n", + " 'model_type': 'gpt2-yttm', # gpt2, ctrl, openai-gpt, xlnet, transfo-xl, xlm\n", " 'model_name_or_path':'gpt2',\n", " 'output_dir':'output',\n", " 'block_size': 512,\n", - " 'learning_rate': 1e-4,\n", - " 'num_train_epochs': 3,\n", + " 'learning_rate': 5e-7,\n", + " 'num_train_epochs': 5,\n", " 'per_gpu_train_batch_size': 2,\n", - " 'gradient_accumulation_steps': 8,\n", + " 'gradient_accumulation_steps': 16,\n", " 'save_steps': 1000,\n", " 'save_total_limit': 3,\n", - " 'logging_steps':10,\n", - " 'warmup_samples':2000,\n", - " 'tokenizer_class': 'YTEncoder',\n", - " 'tokenizer_name': '{}/encoder.model',\n", - " 'unfreeze_level': 0,\n", + " 'logging_steps': 10,\n", + " 'warmup_samples': 500,\n", + " 'unfreeze_level': -1,\n", "# 'max_steps': 20000,\n", "}\n", "\n", "train_outputs = [\n", " r'ru_gpt2/s_checkpoint-1900000', # 'ru_gpt2\\m_checkpoint-3364613'\n", - " 'rus_test_1', # rus_stories_prep_outpath 5e-5 16\n", - " 'rus_test_2', # rus_jokes 9 hours 1e-6 32\n", - " 'rus_test_3', #\n", - " 'rus_test_4',\n", + " 'rus_test_1', # rus_jokes 1 1e-4 16 unfreeze 0\n", + " 'rus_test_2', # rus_jokes 1 5e-5 16 unfreeze 1\n", + " 'rus_test_3', # rus_jokes 1 5e-5 16 unfreeze 2\n", + " 'rus_test_4', # rus_jokes 1 1e-4 16 unfreeze 7\n", + " 'rus_test_5', # rus_jokes 2 5e-6 16 unfreeze -1\n", + " 'rus_test_6', # rus_qa_jokes \n", + " 'rus_test_7', # rus_qa_jokes\n", "]\n", "\n", "\n", @@ -735,57 +767,57 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 178, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "..\\data\\prep\\rus_jokes_gpt2.txt\n" + "..\\data\\prep\\rus_qa_jokes_gpt2.txt\n" ] } ], "source": [ "# train_kwargs['train_data_file'] = rus_stories_prep_outpath\n", - "train_kwargs['train_data_file'] = rus_jokes_prep_outpath\n", - "# train_kwargs['train_data_file'] = rus_qa_jokes_prep_outpath\n", + "# train_kwargs['train_data_file'] = rus_jokes_prep_outpath\n", + "train_kwargs['train_data_file'] = rus_qa_jokes_prep_outpath\n", "print(train_kwargs['train_data_file'])" ] }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 179, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "From: ru_gpt2/s_checkpoint-1900000 \n", - "To: models\\rus_test_1\n" + "From: models\\rus_test_6 \n", + "To: models\\rus_test_7\n" ] } ], "source": [ - "train_kwargs['model_name_or_path'] = train_outputs[0]\n", - "# train_kwargs['model_name_or_path'] = os.path.join('models', train_outputs[1])\n", + "# train_kwargs['model_name_or_path'] = train_outputs[0]\n", + "train_kwargs['model_name_or_path'] = os.path.join('models', train_outputs[6])\n", "\n", - "train_kwargs['tokenizer_name'] = train_kwargs['tokenizer_name'].format(train_kwargs['model_name_or_path'])\n", - "train_kwargs['output_dir'] = os.path.join('models', train_outputs[1])\n", + "# train_kwargs['tokenizer_name'] = train_kwargs['tokenizer_name'].format(train_kwargs['model_name_or_path'])\n", + "train_kwargs['output_dir'] = os.path.join('models', train_outputs[7])\n", "print('From:', train_kwargs['model_name_or_path'], '\\nTo:', train_kwargs['output_dir'])" ] }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 180, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\python.exe ru_transformers-master\\run_lm_finetuning.py --model_type=gpt2 --model_name_or_path=ru_gpt2/s_checkpoint-1900000 --output_dir=models\\rus_test_1 --block_size=512 --learning_rate=0.0001 --num_train_epochs=3 --per_gpu_train_batch_size=2 --gradient_accumulation_steps=8 --save_steps=1000 --save_total_limit=3 --logging_steps=10 --warmup_samples=2000 --tokenizer_class=YTEncoder --tokenizer_name=ru_gpt2/s_checkpoint-1900000/encoder.model --unfreeze_level=0 --train_data_file=..\\data\\prep\\rus_jokes_gpt2.txt --do_train --overwrite_output_dir --lr_decay\n" + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\python.exe ru_transformers-master\\run_lm_finetuning.py --model_type=gpt2-yttm --model_name_or_path=models\\rus_test_6 --output_dir=models\\rus_test_7 --block_size=512 --learning_rate=5e-07 --num_train_epochs=5 --per_gpu_train_batch_size=2 --gradient_accumulation_steps=16 --save_steps=1000 --save_total_limit=3 --logging_steps=10 --warmup_samples=500 --unfreeze_level=-1 --train_data_file=..\\data\\prep\\rus_qa_jokes_gpt2.txt --do_train --overwrite_output_dir --lr_decay\n" ] } ], @@ -800,7 +832,6 @@ "metadata": {}, "outputs": [], "source": [ - "# MovingLoss=0.47, Perplexity=1.61\n", " --do_eval \\\n", " --evaluate_during_training \\\n", " --eval_steps 1000 \\\n", @@ -816,28 +847,28 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\python.exe ru_transformers-master\\run_generation.py --model_type=gpt2-yttm --model_name_or_path=models\\rus_4 --length=200 --temperature=0.9 --top_k=50 --top_p=0.95 \n" + "C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\python.exe ru_transformers-master\\run_generation.py --model_type=gpt2-yttm --model_name_or_path=models\\output --length=200 --temperature=0.9 --stop_token=\"<|endoftext|>\" --top_k=50 --top_p=0.95 --num_return_sequences=20 \n" ] } ], "source": [ - "gen_script = r'ru_transformers-master\\run_generation.py'\n", + "gen_script = r'run_generation.py'\n", "generate_kwargs = {\n", " 'model_type': 'gpt2-yttm',\n", " 'model_name_or_path': train_kwargs['output_dir'],\n", - "# 'prompt': rf'\"{START_DOC_TOKEN}[QUESTION]\"',\n", " 'length': 200,\n", " 'temperature': 0.9, # temperature of 1.0 has no effect, lower tend toward greedy sampling\n", + " 'stop_token': f'\"{END_DOC_TOKEN}\"',\n", " 'top_k': 50,\n", " 'top_p': 0.95,\n", - "# 'padding_text': '', # Padding text for Transfo-XL and XLNet.\n", + " 'num_return_sequences':20,\n", "}\n", "gen_flags = []\n", "\n", diff --git a/train/Web scraper.ipynb b/train/Web scraper.ipynb index 44411f0..8c13d98 100644 --- a/train/Web scraper.ipynb +++ b/train/Web scraper.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 111, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,33 @@ " block = block.strip()\n", " if block:\n", " result.append(block)\n", - " return result" + " return result\n", + "\n", + "pages_to_skip = []\n", + "\n", + "def scrap_page(func, urls_gen, n_batches, save_to_files=False):\n", + " n_processed = 0\n", + " pbar = tqdm(total=n_batches)\n", + " accumulator = []\n", + " for i, url in enumerate(urls_gen(n_batches, pbar)):\n", + " # Skip \"bad\" pages\n", + " if url in pages_to_skip:\n", + " continue\n", + " try:\n", + " pbar.set_description('Loading {} page...'.format(i+1))\n", + " page = requests.get(url)\n", + " pbar.set_description('Processing {} page...'.format(i+1))\n", + " except requests.exceptions.ConnectionError:\n", + " print('[ERROR] Connection error to', url)\n", + " pbar.update(1)\n", + " continue\n", + " soup = BeautifulSoup(page.content, 'html.parser')\n", + " new_items = func(soup, url)\n", + " accumulator.extend(new_items)\n", + " n_processed += len(new_items)\n", + " print('[INFO] Processed another ', len(new_items), 'items, for a total of', n_processed)\n", + " pbar.update(1)\n", + " return accumulator" ] }, { @@ -225,30 +251,6 @@ " for j, block in enumerate(blocks):\n", " block_title = block.find('h2', 'entry-title fusion-post-title').a\n", " yield block_title['href']\n", - " \n", - "def scrap_page(func, urls_gen, n_batches, save_to_files=False):\n", - " n_processed = 0\n", - " pbar = tqdm(total=n_batches)\n", - " accumulator = []\n", - " for i, url in enumerate(urls_gen(n_batches, pbar)):\n", - " # Skip \"bad\" pages\n", - " if url in pages_to_skip:\n", - " continue\n", - " try:\n", - " pbar.set_description('Loading {} page...'.format(i+1))\n", - " page = requests.get(url)\n", - " pbar.set_description('Processing {} page...'.format(i+1))\n", - " except requests.exceptions.ConnectionError:\n", - " print('[ERROR] Connection error to', url)\n", - " pbar.update(1)\n", - " continue\n", - " soup = BeautifulSoup(page.content, 'html.parser')\n", - " new_items = func(soup, url)\n", - " accumulator.extend(new_items)\n", - " n_processed += len(new_items)\n", - " print('[INFO] Processed another ', len(new_items), 'items, for a total of', n_processed)\n", - " pbar.update(1)\n", - " return accumulator\n", "\n", "scrap_page(scrap_transcript, url_transcripts, n_batches=40, save_to_files=True)" ] @@ -257,14 +259,1073 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## http://anecdotica.ru/" + "## Russian\n", + "\n", + "### http://anecdotica.ru/" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b4905dc2a6224f09ac023874ef2c70a3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Processed another 25 items, for a total of 25\n", 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+ ] + } + ], "source": [ "def scrap_anecdotica(soup, _):\n", " jokes = soup.findAll('div', 'item_text')\n", @@ -278,27 +1339,38 @@ "anecdotica = scrap_page(scrap_anecdotica, urls_anecdotica, n_batches=1000)" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# pd.DataFrame(anecdotica, columns=['Text']).to_csv('../data/anecdotika.csv')" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## https://www.anekdot.ru/" + "### https://www.anekdot.ru/" ] }, { "cell_type": "code", - "execution_count": 227, - "metadata": {}, + "execution_count": 44, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ef39f6ea68d74f629bed08b248728d22", + "model_id": "958487d9ce9b49d6ac5718380b711826", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, max=936000.0), HTML(value='')))" + "HBox(children=(FloatProgress(value=0.0, max=1248000.0), HTML(value='')))" ] }, "metadata": {}, @@ -308,1028 +1380,1150 @@ "name": "stdout", "output_type": "stream", "text": [ - "[INFO] No more jokes at: https://www.anekdot.ru/an/an9501/s950133;0,100.html\n", - "[INFO] No more jokes at: https://www.anekdot.ru/an/an9502/s950233;0,100.html\n", - "[INFO] No more jokes at: https://www.anekdot.ru/an/an9503/s950333;0,100.html\n", - "[INFO] No more jokes at: https://www.anekdot.ru/an/an9504/s950433;0,100.html\n", - "[INFO] No more jokes at: https://www.anekdot.ru/an/an9505/s950533;0,100.html\n", - "[INFO] No more jokes at: https://www.anekdot.ru/an/an9506/s950633;0,100.html\n", - "[INFO] No more jokes at: https://www.anekdot.ru/an/an9507/s950733;0,100.html\n", - "[INFO] No more jokes at: https://www.anekdot.ru/an/an9508/s950833;0,100.html\n", - "[INFO] No more jokes at: https://www.anekdot.ru/an/an9509/s950933;0,100.html\n", - "[INFO] No more jokes at: 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https://www.anekdot.ru/an/an2011/x201133;0,100.html\n", + "[INFO] No more jokes at: https://www.anekdot.ru/an/an2012/x201233;0,100.html\n" ] } ], "source": [ - "URL = 'https://www.anekdot.ru/an/an{}{}/j{}{}33;{},100.html' # year;month;year;month;start_from\n", - "headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}\n", - "\n", - "\n", - "def urls_anekdot(max_num_jokes):\n", + "def urls_anekdot(j_type, step, max_num_jokes, pbar):\n", " # Шутки: j - свежие; s - повторные; x - остальные\n", " # Истории: o - свежие;\n", - " URL = 'https://www.anekdot.ru/an/an{}{}/j{}{}33;{},100.html' # year;month;year;month;start_from\n", + " URL = f'https://www.anekdot.ru/an/an{{}}{{}}/{j_type}{{}}{{}}33;{{}},100.html' # year;month;year;month;start_from\n", " # All years from 1995 to 2020\n", " years = list(map(lambda x: f'{x:0>2}', list(range(95, 100)) + list(range(0, 21))))\n", " months = list(map(lambda x: f'{x:0>2}', list(range(1, 13))))\n", " for year in years:\n", " for month in months:\n", - " for start_ind in range(0, max_num_jokes, 100):\n", - " yield URL.format(year, month, year, month, start_ind)\n", + " pbar.set_description(f'Processing {year} {month} page...')\n", + " for start_ind in range(0, max_num_jokes, step):\n", + " yield (URL.format(year, month, year, month, start_ind),\n", + " ((max_num - start_ind) // step) - 1)\n", "\n", - "years = list(map(lambda x: f'{x:0>2}', list(range(95, 100)) + list(range(0, 21)))) # All years from 1995 to 2020\n", - "months = list(map(lambda x: f'{x:0>2}', list(range(1, 13))))\n", - "max_num = 3000\n", - "anekdot_jokes = []\n", - "n_processed = 0\n", + "anekdot_jokes, n_processed = [], 0\n", "pbar = tqdm(total=max_num * len(years) * len(months))\n", - "for year in years:\n", - " for month in months:\n", - " for start_ind in range(0, max_num, 100):\n", - " page_url = URL.format(year, month, year, month, start_ind)\n", - " page = requests.get(page_url, headers=headers)\n", - " pbar.set_description(f'Processing {year} {month} {start_ind} page...')\n", - " soup = BeautifulSoup(page.content, 'html.parser')\n", - " new_items = [' '.join(process_block(joke)) for joke in soup.findAll('div', 'text')]\n", - " if len(new_items) == 0:\n", - " print('[INFO] No more jokes at:', page_url)\n", - " pbar.update(max_num - start_ind)\n", - " break\n", - " anekdot_jokes.extend(new_items)\n", - " n_processed += len(new_items)\n", - " print('[INFO] Processed another ', len(new_items), 'items, for a total of', n_processed)\n", - " pbar.update(100)" + "max_num, step = 4000, 100\n", + "iterator = urls_anekdot('x', step, max_num, pbar)\n", + "for page_url, n_skip in iterator:\n", + " page = requests.get(page_url, headers=headers)\n", + " soup = BeautifulSoup(page.content, 'html.parser')\n", + " new_items = [' '.join(process_block(joke)) for joke in soup.findAll('div', 'text')]\n", + " if len(new_items) == 0:\n", + " print('[INFO] No more jokes at:', page_url)\n", + " for _ in range(n_skip):\n", + " next(iterator)\n", + " pbar.update(100)\n", + " continue\n", + " anekdot_jokes.extend(new_items)\n", + " n_processed += len(new_items)\n", + " print('[INFO] Processed another ', len(new_items), 'items, for a total of', n_processed)\n", + " pbar.update(100)" ] }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "218544" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(anekdot_jokes)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ - "# pd.DataFrame(anekdot_jokes, columns=['Text']).to_csv('../data/anekdot_repetitive.csv')" + "# pd.DataFrame(anekdot_jokes, columns=['Text']).to_csv('../data/anekdot_others.csv')" ] }, { @@ -3079,7 +4159,7 @@ }, { "cell_type": "code", - "execution_count": 235, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -3090,11 +4170,11 @@ }, { "cell_type": "code", - "execution_count": 239, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ - "pd.DataFrame(liga_jokes, columns=['Text']).to_csv('../data/ru_lpsh_jokes.json')" + "pd.DataFrame(liga_jokes, columns=['Text']).to_csv('../data/ru_lpsh_jokes.csv')" ] }, { @@ -3107,11 +4187,12 @@ }, { "cell_type": "code", - "execution_count": 297, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "import nltk\n", + "import traceback\n", "\n", "regexps = [ # Regexp for the special chars\n", " (re.compile('♦'), '*'),\n", @@ -3131,13 +4212,18 @@ " res = []\n", " pbar = tqdm(total=len(iterator))\n", " for i, joke in enumerate(iterator):\n", - " joke = re.sub(r'^[^\\n\\:\\?\\.]*(?:Армянское|Армянскому?|Армянского) *радио[^\\n:?]*\\:', '', joke, flags=re.IGNORECASE)\n", - " sentences = list(map(lambda s: fix_text(s), nltk.sent_tokenize(joke, language=\"russian\")))\n", - " if sentences[0][-1] == '?' and 1 < len(sentences) <= max_num_sents:\n", - " res.append({\n", - " 'Question': sentences[0],\n", - " 'Answer': ' '.join(sentences[1:])\n", - " })\n", + " try:\n", + " joke = re.sub(r'^[^\\n\\:\\?\\.]*(?:Армянское|Армянскому?|Армянского) *радио[^\\n:?]*\\:', '', joke, flags=re.IGNORECASE)\n", + " sentences = [fix_text(s) for s in nltk.sent_tokenize(joke, language=\"russian\")]\n", + " sentences = [s for s in sentences if s]\n", + " if sentences and sentences[0][-1] == '?' and 1 < len(sentences) <= max_num_sents:\n", + " res.append({\n", + " 'Question': sentences[0],\n", + " 'Answer': ' '.join(sentences[1:])\n", + " })\n", + " except:\n", + " print(f'Error at {i}')\n", + " traceback.print_exc()\n", " if i % 500 == 0:\n", " pbar.set_description(f'Extracted: {len(res)} jokes')\n", " pbar.update(1)\n", @@ -3148,13 +4234,13 @@ }, { "cell_type": "code", - "execution_count": 298, + "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d32ffaae0eea431e944a0c61603b1fb2", + "model_id": "c2e9eff8880041ac849c0a346c42cf4e", "version_major": 2, "version_minor": 0 }, @@ -3175,17 +4261,97 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "542ecf204dd141b7ad969fc0f5c0bcfc", + "model_id": "0559cb4a15674b7485ee7c0b14bbe7cb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=200476.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d8fb891271da4190bee04addb25e8c51", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, max=200621.0), HTML(value='')))" + "HBox(children=(FloatProgress(value=0.0, max=216603.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error at 49407\n", + "Error at 64195\n", + "Error at 69074\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"\", line 23, in extract_qa_jokes\n", + " joke = re.sub(r'^[^\\n\\:\\?\\.]*(?:Армянское|Армянскому?|Армянского) *радио[^\\n:?]*\\:', '', joke, flags=re.IGNORECASE)\n", + " File \"C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\re.py\", line 192, in sub\n", + " return _compile(pattern, flags).sub(repl, string, count)\n", + "TypeError: expected string or bytes-like object\n", + "Traceback (most recent call last):\n", + " File \"\", line 23, in extract_qa_jokes\n", + " joke = re.sub(r'^[^\\n\\:\\?\\.]*(?:Армянское|Армянскому?|Армянского) *радио[^\\n:?]*\\:', '', joke, flags=re.IGNORECASE)\n", + " File \"C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\re.py\", line 192, in sub\n", + " return _compile(pattern, flags).sub(repl, string, count)\n", + "TypeError: expected string or bytes-like object\n", + "Traceback (most recent call last):\n", + " File \"\", line 23, in extract_qa_jokes\n", + " joke = re.sub(r'^[^\\n\\:\\?\\.]*(?:Армянское|Армянскому?|Армянского) *радио[^\\n:?]*\\:', '', joke, flags=re.IGNORECASE)\n", + " File \"C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\re.py\", line 192, in sub\n", + " return _compile(pattern, flags).sub(repl, string, count)\n", + "TypeError: expected string or bytes-like object\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error at 132540\n", + "Error at 134146\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"\", line 23, in extract_qa_jokes\n", + " joke = re.sub(r'^[^\\n\\:\\?\\.]*(?:Армянское|Армянскому?|Армянского) *радио[^\\n:?]*\\:', '', joke, flags=re.IGNORECASE)\n", + " File \"C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\re.py\", line 192, in sub\n", + " return _compile(pattern, flags).sub(repl, string, count)\n", + "TypeError: expected string or bytes-like object\n", + "Traceback (most recent call last):\n", + " File \"\", line 23, in extract_qa_jokes\n", + " joke = re.sub(r'^[^\\n\\:\\?\\.]*(?:Армянское|Армянскому?|Армянского) *радио[^\\n:?]*\\:', '', joke, flags=re.IGNORECASE)\n", + " File \"C:\\Users\\Alex\\Anaconda3\\envs\\pytorch\\lib\\re.py\", line 192, in sub\n", + " return _compile(pattern, flags).sub(repl, string, count)\n", + "TypeError: expected string or bytes-like object\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -3196,12 +4362,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b3fb2b5c62914927b9760c0ef5774b3f", + "model_id": "2c1cc2d3e29f4cdb99eba3da3a0fd0e8", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, max=218411.0), HTML(value='')))" + "HBox(children=(FloatProgress(value=0.0, max=218544.0), HTML(value='')))" ] }, "metadata": {}, @@ -3217,7 +4383,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b0f553f1848b403e85e3cef5bcd284b3", + "model_id": "7e65de2cc55143ec9cc09615e1df4e88", "version_major": 2, "version_minor": 0 }, @@ -3238,10 +4404,10 @@ { "data": { "text/plain": [ - "47983" + "67563" ] }, - "execution_count": 298, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -3250,8 +4416,9 @@ "files = [\n", " '../data/anecdotika.csv',\n", " '../data/anekdot_fresh.csv',\n", + " '../data/anekdot_repetative.csv',\n", " '../data/anekdot_others.csv',\n", - " '../data/ru_lpsh_jokes.json',\n", + " '../data/ru_lpsh_jokes.csv',\n", "]\n", "\n", "qa_jokes = []\n", @@ -3265,11 +4432,11 @@ }, { "cell_type": "code", - "execution_count": 299, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ - "pd.DataFrame.from_dict(qa_jokes).to_csv('../data/rus_qa_jokes.csv')" + "# pd.DataFrame.from_dict(qa_jokes).to_csv('../data/rus_qa_jokes.csv')" ] } ], diff --git a/train/ru_transformers-master/LICENSE b/train/ru_transformers-master/LICENSE deleted file mode 100644 index 261eeb9..0000000 --- a/train/ru_transformers-master/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/Transformer-XL/XLNet) -""" -from __future__ import absolute_import, division, print_function, unicode_literals - -import argparse -import logging -from tqdm import trange - -import torch -import torch.nn.functional as F -import numpy as np - -from transformers import (GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig, - GPT2LMHeadModel, GPT2Tokenizer, - OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, - XLNetLMHeadModel, XLNetTokenizer, - TransfoXLLMHeadModel, TransfoXLTokenizer, ) -from yt_encoder import YTEncoder - - -logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', - datefmt = '%m/%d/%Y %H:%M:%S', - level = logging.INFO) -logger = logging.getLogger(__name__) - -MAX_LENGTH = int(10000) # Hardcoded max length to avoid infinite loop - -ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig)), ()) - -MODEL_CLASSES = { - 'gpt2': (GPT2LMHeadModel, GPT2Tokenizer), - 'gpt2-yttm': (GPT2LMHeadModel, YTEncoder), - 'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer), - 'xlnet': (XLNetLMHeadModel, XLNetTokenizer), - 'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer), -} - -# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia -# in https://github.com/rusiaaman/XLNet-gen#methodology -# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e -PADDING_TEXT = """ In 1991, the remains of Russian Tsar Nicholas II and his family -(except for Alexei and Maria) are discovered. -The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the -remainder of the story. 1883 Western Siberia, -a young Grigori Rasputin is asked by his father and a group of men to perform magic. -Rasputin has a vision and denounces one of the men as a horse thief. Although his -father initially slaps him for making such an accusation, Rasputin watches as the -man is chased outside and beaten. Twenty years later, Rasputin sees a vision of -the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, -with people, even a bishop, begging for his blessing. """ - -FILTER_VALUE=-float('Inf') - -def set_seed(args): - np.random.seed(args.seed) - torch.manual_seed(args.seed) - if args.n_gpu > 0: - torch.cuda.manual_seed_all(args.seed) - - -def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')): - """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering - Args: - logits: logits distribution shape (vocabulary size) - top_k > 0: keep only top k tokens with highest probability (top-k filtering). - top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering). - Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751) - From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317 - """ - assert logits.dim() == 1 # batch size 1 for now - could be updated for more but the code would be less clear - top_k = min(top_k, logits.size(-1)) # Safety check - if top_k > 0: - # Remove all tokens with a probability less than the last token of the top-k - indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] - logits[indices_to_remove] = filter_value - - if top_p > 0.0: - sorted_logits, sorted_indices = torch.sort(logits, descending=True) - cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) - - # Remove tokens with cumulative probability above the threshold - sorted_indices_to_remove = cumulative_probs > top_p - # Shift the indices to the right to keep also the first token above the threshold - sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() - sorted_indices_to_remove[..., 0] = 0 - - indices_to_remove = sorted_indices[sorted_indices_to_remove] - logits[indices_to_remove] = filter_value - return logits - -def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=0, top_p=0.0, - is_xlnet=False, device='cpu', max_input=1023, filter_single=[], filter_double=[]): - context = torch.tensor(context, dtype=torch.long, device=device) - context = context.unsqueeze(0).repeat(num_samples, 1) - generated = context - with torch.no_grad(): - for _ in trange(length): - - inputs = {'input_ids': generated[:,-max_input:]} - if is_xlnet: - # XLNet is a direct (predict same token, not next token) and bi-directional model by default - # => need one additional dummy token in the input (will be masked), attention mask and target mapping (see model docstring) - input_ids = torch.cat((generated, torch.zeros((1, 1), dtype=torch.long, device=device)), dim=1) - perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float, device=device) - perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token - target_mapping = torch.zeros((1, 1, input_ids.shape[1]), dtype=torch.float, device=device) - target_mapping[0, 0, -1] = 1.0 # predict last token - inputs = {'input_ids': input_ids, 'perm_mask': perm_mask, 'target_mapping': target_mapping} - - outputs = model(**inputs) # Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet (cached hidden-states) - next_tokens = torch.zeros(num_samples, dtype=torch.long).to(device) - for isample in range(num_samples): - next_token_logits = outputs[0][isample, -1, :] / temperature - - next_token_logits[filter_single] = FILTER_VALUE - # filter blank line = double \n - if generated[isample, -1] in filter_double: - next_token_logits[generated[isample, -1]] = FILTER_VALUE - - filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p) - next_tokens[isample] = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1) - - generated = torch.cat((generated, next_tokens.unsqueeze(-1)), dim=1) - return generated - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--model_type", default=None, type=str, required=True, - help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys())) - parser.add_argument("--model_name_or_path", default=None, type=str, required=True, - help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS)) - parser.add_argument("--prompt", type=str, default="") - parser.add_argument("--padding_text", type=str, default="") - parser.add_argument("--length", type=int, default=20) - parser.add_argument("--temperature", type=float, default=1.0) - parser.add_argument("--top_k", type=int, default=0) - parser.add_argument("--top_p", type=float, default=0.9) - parser.add_argument("--no_cuda", action='store_true', - help="Avoid using CUDA when available") - parser.add_argument('--seed', type=int, default=42, - help="random seed for initialization") - args = parser.parse_args() - - args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") - args.n_gpu = torch.cuda.device_count() - - set_seed(args) - - args.model_type = args.model_type.lower() - model_class, tokenizer_class = MODEL_CLASSES[args.model_type] - tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path) - model = model_class.from_pretrained(args.model_name_or_path) - model.to(args.device) - model.eval() - - if args.length < 0 and model.config.max_position_embeddings > 0: - args.length = model.config.max_position_embeddings - elif 0 < model.config.max_position_embeddings < args.length: - args.length = model.config.max_position_embeddings # No generation bigger than model size - elif args.length < 0: - args.length = MAX_LENGTH # avoid infinite loop - - print(args) - while True: - raw_text = args.prompt if args.prompt else input("Model prompt >>> ") - if args.model_type in ["transfo-xl", "xlnet"]: - # Models with memory likes to have a long prompt for short inputs. - raw_text = (args.padding_text if args.padding_text else PADDING_TEXT) + raw_text - context_tokens = tokenizer.encode(raw_text) - out = sample_sequence( - model=model, - context=context_tokens, - length=args.length, - temperature=args.temperature, - top_k=args.top_k, - top_p=args.top_p, - device=args.device, - is_xlnet=bool(args.model_type == "xlnet"), - ) - out = out[0, len(context_tokens):].tolist() - text = tokenizer.decode(out) # , clean_up_tokenization_spaces=True - print(text) - if args.prompt: - break - return text - - -if __name__ == '__main__': - main() diff --git a/train/ru_transformers-master/run_lm_finetuning.py b/train/ru_transformers-master/run_lm_finetuning.py index 3d21c58..d55b619 100644 --- a/train/ru_transformers-master/run_lm_finetuning.py +++ b/train/ru_transformers-master/run_lm_finetuning.py @@ -32,7 +32,7 @@ import numpy as np import torch -from torch.utils.data import DataLoader, Dataset, SequentialSampler, RandomSampler +from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler from torch.utils.data.distributed import DistributedSampler try: @@ -61,6 +61,7 @@ MODEL_CLASSES = { 'gpt2': (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer), + 'gpt2-yttm': (GPT2Config, GPT2LMHeadModel, YTEncoder), 'openai-gpt': (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer), 'bert': (BertConfig, BertForMaskedLM, BertTokenizer), 'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer), @@ -69,10 +70,10 @@ @dataclass class MovingLoss(): - steps:int=1000 + steps:int = 1000 avg_loss = (0.0, 0.0) def add(self, batch_loss:float): - k_s = 1 - 1/self.steps + k_s = 1 - 1 / self.steps avg_loss = self.avg_loss self.avg_loss = (self.avg_loss[0] * k_s + batch_loss * (1-k_s), self.avg_loss[1] * k_s + 1.0 * (1-k_s)) @@ -130,8 +131,8 @@ def process_file(file_path, tokenizer, block_size, shuffle): max_shift = max(min(block_size, len(tokenized_text) - block_size), 0) rnd_shift = random.randrange(max_shift) if max_shift and shuffle else 0 - for i in range(rnd_shift, len(tokenized_text)-block_size+1, block_size): - examples.append(tokenizer.add_special_tokens_single_sentence(tokenized_text[i:i+block_size])) + for i in range(rnd_shift, len(tokenized_text) - block_size + 1, block_size): + examples.append(tokenizer.build_inputs_with_special_tokens(tokenized_text[i:i+block_size])) # Note that we are loosing the last truncated example here for the sake of simplicity (no padding) # If your dataset is small, first you should loook for a bigger one :-) and second you # can change this behavior by adding (model specific) padding. @@ -164,12 +165,12 @@ def __getitem__(self, item): def load_and_cache_examples(args, tokenizer, evaluate=False): - dataset = TextDataset(tokenizer, file_path=args.eval_data_file if evaluate else args.train_data_file, args=args, shuffle=not evaluate) + file_path = args.eval_data_file if evaluate else args.train_data_file + dataset = TextDataset(tokenizer, file_path=file_path, args=args, shuffle=not evaluate) return dataset def set_seed(args): - return # no random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) @@ -264,6 +265,7 @@ def train(args, train_dataset, model, tokenizer): if args.local_rank in [-1, 0]: tb_writer = SummaryWriterP(args.output_dir) + # XXX: Collate function args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) @@ -287,6 +289,8 @@ def train(args, train_dataset, model, tokenizer): else: scheduler = get_constant_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps) + # XXX: No check if saved optimizer or scheduler states exist + if args.fp16: try: from apex import amp @@ -321,7 +325,7 @@ def train(args, train_dataset, model, tokenizer): global_step = 0 tr_loss, logging_loss = 0.0, 0.0 - moving_loss = MovingLoss(10000//args.logging_steps) + moving_loss = MovingLoss(10000 // args.logging_steps) model.zero_grad() train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]) @@ -468,8 +472,6 @@ def main(): help="Optional pretrained config name or path if not the same as model_name_or_path") parser.add_argument("--tokenizer_name", default="", type=str, help="Optional pretrained tokenizer name or path if not the same as model_name_or_path") - parser.add_argument("--tokenizer_class", default="", type=str, - help="Optional pretrained tokenizer clas") parser.add_argument("--cache_dir", default="", type=str, help="Optional directory to store the pre-trained models downloaded from s3 (instread of the default one)") parser.add_argument("--block_size", default=-1, type=int, @@ -586,7 +588,6 @@ def main(): config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path) - if args.tokenizer_class: tokenizer_class = globals()[args.tokenizer_class] tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case) if args.block_size <= 0: args.block_size = tokenizer.max_len_single_sentence # Our input block size will be the max possible for the model diff --git a/train/run_generation.py b/train/run_generation.py index 3f90ee5..4fd429a 100644 --- a/train/run_generation.py +++ b/train/run_generation.py @@ -14,52 +14,47 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -""" Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/CTRL/Transformer-XL/XLNet) +""" Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/Transformer-XL/XLNet) """ - +from __future__ import absolute_import, division, print_function, unicode_literals import argparse import logging +from tqdm import trange -import numpy as np import torch +import torch.nn.functional as F +import numpy as np + +from transformers import (GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig, + GPT2LMHeadModel, GPT2Tokenizer, + OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, + XLNetLMHeadModel, XLNetTokenizer, + TransfoXLLMHeadModel, TransfoXLTokenizer, ) +from yt_encoder import YTEncoder + -from transformers import ( - CTRLLMHeadModel, - CTRLTokenizer, - GPT2LMHeadModel, - GPT2Tokenizer, - OpenAIGPTLMHeadModel, - OpenAIGPTTokenizer, - TransfoXLLMHeadModel, - TransfoXLTokenizer, - XLMTokenizer, - XLMWithLMHeadModel, - XLNetLMHeadModel, - XLNetTokenizer, -) - - -logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, -) +logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', + datefmt = '%m/%d/%Y %H:%M:%S', + level = logging.INFO) logger = logging.getLogger(__name__) MAX_LENGTH = int(10000) # Hardcoded max length to avoid infinite loop +ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig)), ()) + MODEL_CLASSES = { - "gpt2": (GPT2LMHeadModel, GPT2Tokenizer), - "ctrl": (CTRLLMHeadModel, CTRLTokenizer), - "openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer), - "xlnet": (XLNetLMHeadModel, XLNetTokenizer), - "transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer), - "xlm": (XLMWithLMHeadModel, XLMTokenizer), + 'gpt2': (GPT2LMHeadModel, GPT2Tokenizer), + 'gpt2-yttm': (GPT2LMHeadModel, YTEncoder), + 'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer), + 'xlnet': (XLNetLMHeadModel, XLNetTokenizer), + 'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer), } # Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia # in https://github.com/rusiaaman/XLNet-gen#methodology # and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e -PADDING_TEXT = """In 1991, the remains of Russian Tsar Nicholas II and his family +PADDING_TEXT = """ In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western Siberia, @@ -70,6 +65,7 @@ the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop, begging for his blessing. """ +FILTER_VALUE=-float('Inf') def set_seed(args): np.random.seed(args.seed) @@ -78,186 +74,159 @@ def set_seed(args): torch.cuda.manual_seed_all(args.seed) -# -# Functions to prepare models' input -# - - -def prepare_ctrl_input(args, _, tokenizer, prompt_text): - if args.temperature > 0.7: - logger.info("CTRL typically works better with lower temperatures (and lower top_k).") - - encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False) - if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()): - logger.info("WARNING! You are not starting your generation from a control code so you won't get good results") - return prompt_text - - -def prepare_xlm_input(args, model, tokenizer, prompt_text): - # kwargs = {"language": None, "mask_token_id": None} - - # Set the language - use_lang_emb = hasattr(model.config, "use_lang_emb") and model.config.use_lang_emb - if hasattr(model.config, "lang2id") and use_lang_emb: - available_languages = model.config.lang2id.keys() - if args.xlm_language in available_languages: - language = args.xlm_language - else: - language = None - while language not in available_languages: - language = input("Using XLM. Select language in " + str(list(available_languages)) + " >>> ") - - model.config.lang_id = model.config.lang2id[language] - # kwargs["language"] = tokenizer.lang2id[language] - - # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers - # XLM masked-language modeling (MLM) models need masked token - # is_xlm_mlm = "mlm" in args.model_name_or_path - # if is_xlm_mlm: - # kwargs["mask_token_id"] = tokenizer.mask_token_id - - return prompt_text - - -def prepare_xlnet_input(args, _, tokenizer, prompt_text): - prompt_text = (args.padding_text if args.padding_text else PADDING_TEXT) + prompt_text - return prompt_text - - -def prepare_transfoxl_input(args, _, tokenizer, prompt_text): - prompt_text = (args.padding_text if args.padding_text else PADDING_TEXT) + prompt_text - return prompt_text - - -PREPROCESSING_FUNCTIONS = { - "ctrl": prepare_ctrl_input, - "xlm": prepare_xlm_input, - "xlnet": prepare_xlnet_input, - "transfo-xl": prepare_transfoxl_input, -} - - -def adjust_length_to_model(length, max_sequence_length): - if length < 0 and max_sequence_length > 0: - length = max_sequence_length - elif 0 < max_sequence_length < length: - length = max_sequence_length # No generation bigger than model size - elif length < 0: - length = MAX_LENGTH # avoid infinite loop - return length +def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')): + """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering + Args: + logits: logits distribution shape (vocabulary size) + top_k > 0: keep only top k tokens with highest probability (top-k filtering). + top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering). + Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751) + From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317 + """ + assert logits.dim() == 1 # batch size 1 for now - could be updated for more but the code would be less clear + top_k = min(top_k, logits.size(-1)) # Safety check + if top_k > 0: + # Remove all tokens with a probability less than the last token of the top-k + indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] + logits[indices_to_remove] = filter_value + + if top_p > 0.0: + sorted_logits, sorted_indices = torch.sort(logits, descending=True) + cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) + + # Remove tokens with cumulative probability above the threshold + sorted_indices_to_remove = cumulative_probs > top_p + # Shift the indices to the right to keep also the first token above the threshold + sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() + sorted_indices_to_remove[..., 0] = 0 + + indices_to_remove = sorted_indices[sorted_indices_to_remove] + logits[indices_to_remove] = filter_value + return logits + +def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=0, top_p=0.0, + is_xlnet=False, device='cpu', max_input=1023, filter_single=[], filter_double=[]): + context = torch.tensor(context, dtype=torch.long, device=device) + context = context.unsqueeze(0).repeat(num_samples, 1) + generated = context + with torch.no_grad(): + for _ in trange(length): + + inputs = {'input_ids': generated[:,-max_input:]} + if is_xlnet: + # XLNet is a direct (predict same token, not next token) and bi-directional model by default + # => need one additional dummy token in the input (will be masked), attention mask and target mapping (see model docstring) + input_ids = torch.cat((generated, torch.zeros((1, 1), dtype=torch.long, device=device)), dim=1) + perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float, device=device) + perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token + target_mapping = torch.zeros((1, 1, input_ids.shape[1]), dtype=torch.float, device=device) + target_mapping[0, 0, -1] = 1.0 # predict last token + inputs = {'input_ids': input_ids, 'perm_mask': perm_mask, 'target_mapping': target_mapping} + + outputs = model(**inputs) # Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet (cached hidden-states) + next_tokens = torch.zeros(num_samples, dtype=torch.long).to(device) + for isample in range(num_samples): + next_token_logits = outputs[0][isample, -1, :] / temperature + + next_token_logits[filter_single] = FILTER_VALUE + # filter blank line = double \n + if generated[isample, -1] in filter_double: + next_token_logits[generated[isample, -1]] = FILTER_VALUE + + filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p) + next_tokens[isample] = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1) + + generated = torch.cat((generated, next_tokens.unsqueeze(-1)), dim=1) + return generated def main(): parser = argparse.ArgumentParser() - parser.add_argument( - "--model_type", - default=None, - type=str, - required=True, - help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), - ) - parser.add_argument( - "--model_name_or_path", - default=None, - type=str, - required=True, - help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(MODEL_CLASSES.keys()), - ) - + parser.add_argument("--model_type", default=None, type=str, required=True, + help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys())) + parser.add_argument("--model_name_or_path", default=None, type=str, required=True, + help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS)) parser.add_argument("--prompt", type=str, default="") + parser.add_argument("--padding_text", type=str, default="") parser.add_argument("--length", type=int, default=20) parser.add_argument("--stop_token", type=str, default=None, help="Token at which text generation is stopped") - - parser.add_argument( - "--temperature", - type=float, - default=1.0, - help="temperature of 1.0 has no effect, lower tend toward greedy sampling", - ) - parser.add_argument( - "--repetition_penalty", type=float, default=1.0, help="primarily useful for CTRL model; in that case, use 1.2" - ) - parser.add_argument("--k", type=int, default=0) - parser.add_argument("--p", type=float, default=0.9) - - parser.add_argument("--padding_text", type=str, default="", help="Padding text for Transfo-XL and XLNet.") - parser.add_argument("--xlm_language", type=str, default="", help="Optional language when used with the XLM model.") - - parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") - parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available") + parser.add_argument("--temperature", type=float, default=1.0) + parser.add_argument("--top_k", type=int, default=0) + parser.add_argument("--top_p", type=float, default=0.9) + parser.add_argument("--no_cuda", action='store_true', + help="Avoid using CUDA when available") + parser.add_argument('--seed', type=int, default=42, + help="random seed for initialization") parser.add_argument("--num_return_sequences", type=int, default=1, help="The number of samples to generate.") args = parser.parse_args() args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") - args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count() + args.n_gpu = torch.cuda.device_count() set_seed(args) - # Initialize the model and tokenizer - try: - args.model_type = args.model_type.lower() - model_class, tokenizer_class = MODEL_CLASSES[args.model_type] - except KeyError: - raise KeyError("the model {} you specified is not supported. You are welcome to add it and open a PR :)") - + args.model_type = args.model_type.lower() + model_class, tokenizer_class = MODEL_CLASSES[args.model_type] tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path) model = model_class.from_pretrained(args.model_name_or_path) model.to(args.device) - - args.length = adjust_length_to_model(args.length, max_sequence_length=model.config.max_position_embeddings) - logger.info(args) - - prompt_text = args.prompt if args.prompt else input("Model prompt >>> ") - - # Different models need different input formatting and/or extra arguments - requires_preprocessing = args.model_type in PREPROCESSING_FUNCTIONS.keys() - if requires_preprocessing: - prepare_input = PREPROCESSING_FUNCTIONS.get(args.model_type) - preprocessed_prompt_text = prepare_input(args, model, tokenizer, prompt_text) - encoded_prompt = tokenizer.encode( - preprocessed_prompt_text, add_special_tokens=False, return_tensors="pt", add_space_before_punct_symbol=True - ) - else: - encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False, return_tensors="pt") - encoded_prompt = encoded_prompt.to(args.device) - - output_sequences = model.generate( - input_ids=encoded_prompt, - max_length=args.length + len(encoded_prompt[0]), - temperature=args.temperature, - top_k=args.k, - top_p=args.p, - repetition_penalty=args.repetition_penalty, - do_sample=True, - num_return_sequences=args.num_return_sequences, - ) - - # Remove the batch dimension when returning multiple sequences - if len(output_sequences.shape) > 2: - output_sequences.squeeze_() - - generated_sequences = [] - - for generated_sequence_idx, generated_sequence in enumerate(output_sequences): - print("=== GENERATED SEQUENCE {} ===".format(generated_sequence_idx + 1)) - generated_sequence = generated_sequence.tolist() - - # Decode text - text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True) - - # Remove all text after the stop token - text = text[: text.find(args.stop_token) if args.stop_token else None] - - # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing - total_sequence = ( - prompt_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :] + model.eval() + + if args.length < 0 and model.config.max_position_embeddings > 0: + args.length = model.config.max_position_embeddings + elif 0 < model.config.max_position_embeddings < args.length: + args.length = model.config.max_position_embeddings # No generation bigger than model size + elif args.length < 0: + args.length = MAX_LENGTH # avoid infinite loop + + print(args) + while True: + raw_text = args.prompt if args.prompt else input("Model prompt >>> ") + encoded_prompt = tokenizer.encode(raw_text) + if args.model_type in ["transfo-xl", "xlnet"]: + # Models with memory likes to have a long prompt for short inputs. + raw_text = (args.padding_text if args.padding_text else PADDING_TEXT) + raw_text + context_tokens = tokenizer.encode(raw_text) + out = sample_sequence( + model=model, + context=context_tokens, + length=args.length, + temperature=args.temperature, + top_k=args.top_k, + top_p=args.top_p, + device=args.device, + is_xlnet=bool(args.model_type == "xlnet"), + num_samples=args.num_return_sequences, ) - - generated_sequences.append(total_sequence) - print(total_sequence) - - return generated_sequences - - -if __name__ == "__main__": + # Remove the batch dimension when returning multiple sequences + print(out.shape) + if len(out.shape) > 2: + out.squeeze_() + + generated_sequences = [] + for generated_sequence_idx, generated_sequence in enumerate(out): + print("=== GENERATED SEQUENCE {} ===".format(generated_sequence_idx + 1)) + generated_sequence = generated_sequence.tolist() + + # Decode text + text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True) + + # Remove all text after the stop token + text = text[: text.find(args.stop_token) if args.stop_token else None] + + # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing + total_sequence = ( + args.prompt + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :] + ) + + generated_sequences.append(total_sequence) + print(total_sequence) + # text = tokenizer.decode(out) # , clean_up_tokenization_spaces=True + # print(text) + if args.prompt: + break + return text + + +if __name__ == '__main__': main() diff --git a/train/run_lm_finetuning.py b/train/run_lm_finetuning.py new file mode 100644 index 0000000..1f6ce7e --- /dev/null +++ b/train/run_lm_finetuning.py @@ -0,0 +1,662 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa). +GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned +using a masked language modeling (MLM) loss. +""" + +from __future__ import absolute_import, division, print_function + +import argparse +import glob +import logging +import os +import pickle +import random +import regex as re +import shutil + +import numpy as np +import torch +from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler +from torch.utils.data.distributed import DistributedSampler + +try: + from torch.utils.tensorboard import SummaryWriter +except: + from tensorboardX import SummaryWriter + +from tqdm import tqdm, trange +from dataclasses import dataclass +from fastprogress import progress_bar +from fastai.basics import * + +from run_generation import sample_sequence + +from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, + BertConfig, BertForMaskedLM, BertTokenizer, + GPT2Config, GPT2LMHeadModel, GPT2Tokenizer, + OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, + RobertaConfig, RobertaForMaskedLM, RobertaTokenizer, + DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer) + +from yt_encoder import YTEncoder + +logger = logging.getLogger(__name__) + + +MODEL_CLASSES = { + 'gpt2': (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer), + 'gpt2-yttm': (GPT2Config, GPT2LMHeadModel, YTEncoder), + 'openai-gpt': (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer), + 'bert': (BertConfig, BertForMaskedLM, BertTokenizer), + 'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer), + 'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer) +} + +@dataclass +class MovingLoss(): + steps:int = 1000 + avg_loss = (0.0, 0.0) + def add(self, batch_loss:float): + k_s = 1 - 1 / self.steps + avg_loss = self.avg_loss + self.avg_loss = (self.avg_loss[0] * k_s + batch_loss * (1-k_s), + self.avg_loss[1] * k_s + 1.0 * (1-k_s)) + @property + def loss(self): + if self.avg_loss[1]: + return self.avg_loss[0] / self.avg_loss[1] + +def print_sample(model, tokenizer, device, args): + model.eval() + raw_text = """ На словах ты Лев Толстой,\n А на деле -""" + context_tokens = tokenizer.encode(raw_text) + out = sample_sequence( + model=model, + context=context_tokens, + length=500, + temperature=1, + top_k=0, + top_p=0.9, + device=device, + #is_xlnet=bool(args.model_type == "xlnet"), + ) + out = out[0, len(context_tokens):].tolist() + text = raw_text + tokenizer.decode(out) + print(text) + + with open(os.path.join(args.output_dir, 'sample.txt'), 'w') as f: + f.write(text) + + model.train() + +class TextDataset(Dataset): + @staticmethod + def process_file(file_path, tokenizer, block_size, shuffle): + directory, filename = os.path.split(file_path) + directory = os.path.join(directory, 'cached') + os.makedirs(directory, exist_ok=True) + cached_features_file = os.path.join(directory, f'cached_lm_{block_size}_{tokenizer.hash}_{filename}') + + if os.path.exists(cached_features_file): + with open(cached_features_file, 'rb') as handle: + tokenized_text = pickle.load(handle) + else: + with open(file_path, encoding="utf-8") as f: + text = f.read() + if hasattr(tokenizer, 'encode'): + tokenized_text = tokenizer.encode(text) + else: + tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text)) + with open(cached_features_file, 'wb') as handle: + pickle.dump(tokenized_text, handle, protocol=pickle.HIGHEST_PROTOCOL) + + examples = [] + # add random shift + max_shift = max(min(block_size, len(tokenized_text) - block_size), 0) + rnd_shift = random.randrange(max_shift) if max_shift and shuffle else 0 + + for i in range(rnd_shift, len(tokenized_text)-block_size+1, block_size): + examples.append(tokenizer.build_inputs_with_special_tokens(tokenized_text[i:i+block_size])) + # Note that we are loosing the last truncated example here for the sake of simplicity (no padding) + # If your dataset is small, first you should loook for a bigger one :-) and second you + # can change this behavior by adding (model specific) padding. + return examples + + def __init__(self, tokenizer, file_path='train', args=None, shuffle=True): + if not hasattr(tokenizer, 'hash'): tokenizer.hash = '' + + logger.info(f"Loading features from {file_path}") + if os.path.isfile(file_path): + files = [file_path] + else: + assert os.path.isdir(file_path) + files = glob.glob(os.path.join(file_path, '*.txt')) + + files = sorted(files) + if shuffle: random.shuffle(files) + + files = files[:10000] + + self.examples = [] + for fn in progress_bar(files): + self.examples.extend(self.process_file(fn, tokenizer, args.block_size, shuffle)) + + def __len__(self): + return len(self.examples) + + def __getitem__(self, item): + return torch.tensor(self.examples[item]) + + +def load_and_cache_examples(args, tokenizer, evaluate=False): + file_path = args.eval_data_file if evaluate else args.train_data_file + dataset = TextDataset(tokenizer, file_path=file_path, args=args, shuffle=not evaluate) + return dataset + + +def set_seed(args): + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if args.n_gpu > 0: + torch.cuda.manual_seed_all(args.seed) + +def _rotate_checkpoints(args, checkpoint_prefix, use_mtime=False): + if not args.save_total_limit: + return + if args.save_total_limit <= 0: + return + + # Check if we should delete older checkpoint(s) + glob_checkpoints = glob.glob(os.path.join(args.output_dir, '{}-*'.format(checkpoint_prefix))) + if len(glob_checkpoints) <= args.save_total_limit: + return + + ordering_and_checkpoint_path = [] + for path in glob_checkpoints: + if use_mtime: + ordering_and_checkpoint_path.append((os.path.getmtime(path), path)) + else: + regex_match = re.match('.*{}-([0-9]+)'.format(checkpoint_prefix), path) + if regex_match and regex_match.groups(): + ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path)) + + checkpoints_sorted = sorted(ordering_and_checkpoint_path) + checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted] + number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit) + checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete] + for checkpoint in checkpoints_to_be_deleted: + logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint)) + shutil.rmtree(checkpoint) + + +def mask_tokens(inputs, tokenizer, args): + """ Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """ + labels = inputs.clone() + # We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa) + probability_matrix = torch.full(labels.shape, args.mlm_probability) + special_tokens_mask = [tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()] + probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0) + masked_indices = torch.bernoulli(probability_matrix).bool() + labels[~masked_indices] = -1 # We only compute loss on masked tokens + + # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) + indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices + inputs[indices_replaced] = tokenizer.convert_tokens_to_ids(tokenizer.mask_token) + + # 10% of the time, we replace masked input tokens with random word + indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced + random_words = torch.randint(len(tokenizer), labels.shape, dtype=torch.long) + inputs[indices_random] = random_words[indices_random] + + # The rest of the time (10% of the time) we keep the masked input tokens unchanged + return inputs, labels + +def save_state(args, model, tokenizer, global_step): + def save_dir(output_dir): + # Create output directory if needed + if not os.path.exists(output_dir) and args.local_rank in [-1, 0]: + os.makedirs(output_dir) + logger.info(f"Saving model checkpoint to {output_dir}") + # Save a trained model, configuration and tokenizer using `save_pretrained()`. + # They can then be reloaded using `from_pretrained()` + model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training + model_to_save.save_pretrained(output_dir) + tokenizer.save_pretrained(output_dir) + + # Good practice: save your training arguments together with the trained model + torch.save(args, os.path.join(output_dir, 'training_args.bin')) + with open(os.path.join(output_dir, 'step.txt'), 'w') as c: c.write(str(global_step)) + + save_dir(args.output_dir) + checkpoint_prefix = 'checkpoint' + output_dir = os.path.join(args.output_dir, f'{checkpoint_prefix}-{global_step}') + save_dir(output_dir) + _rotate_checkpoints(args, checkpoint_prefix) + +class SummaryWriterP(SummaryWriter): + def __init__(self, prefix=None, logdir=None, comment='', *args, **kwargs): + if prefix: + import socket + from datetime import datetime + current_time = datetime.now().strftime('%b%d_%H-%M-%S') + logdir = os.path.join(prefix, + 'runs', current_time + '_' + socket.gethostname() + comment) + super().__init__(logdir, comment, *args, **kwargs) + +def train(args, train_dataset, model, tokenizer): + """ Train the model """ + if args.local_rank in [-1, 0]: + tb_writer = SummaryWriterP(args.output_dir) + + # XXX: Collate function + args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) + train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) + train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) + + if args.max_steps > 0: + t_total = args.max_steps + args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 + else: + t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs + + # Prepare optimizer and schedule (linear warmup and decay) + no_decay = ['bias', 'LayerNorm.weight'] + optimizer_grouped_parameters = [ + {'params': [p for n, p in model.named_parameters() if p.requires_grad and not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay}, + {'params': [p for n, p in model.named_parameters() if p.requires_grad and any(nd in n for nd in no_decay)], 'weight_decay': 0.0} + ] + optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) + warmup_steps = args.warmup_samples // args.train_batch_size + if args.lr_decay: + scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total) + else: + scheduler = get_constant_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps) + + # XXX: No check if saved optimizer or scheduler states exist + + if args.fp16: + try: + from apex import amp + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") + model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) + + # multi-gpu training (should be after apex fp16 initialization) + if args.n_gpu > 1: + model = torch.nn.DataParallel(model) + + # Distributed training (should be after apex fp16 initialization) + if args.local_rank != -1: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], + output_device=args.local_rank, + find_unused_parameters=True) + + # Train! + logger.info("***** Running training *****") + logger.info(" Num examples = %d", len(train_dataset)) + logger.info(" Num Epochs = %d", args.num_train_epochs) + logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) + logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d", + args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1)) + logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) + logger.info(" Total optimization steps = %d", t_total) + + try: + with open(os.path.join(args.model_name_or_path, 'step.txt'), 'r') as c: + global_step = int(c.readline()) + except OSError as e: + global_step = 0 + + tr_loss, logging_loss = 0.0, 0.0 + moving_loss = MovingLoss(10000 // args.logging_steps) + model.zero_grad() + + train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]) + set_seed(args) # Added here for reproducibility (even between python 2 and 3) + try: + for _ in train_iterator: + epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) + for step, batch in enumerate(epoch_iterator): + inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch) + inputs = inputs.to(args.device) + labels = labels.to(args.device) + model.train() + outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels) + loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc) + + if args.n_gpu > 1: + loss = loss.mean() # mean() to average on multi-gpu parallel training + if args.gradient_accumulation_steps > 1: + loss = loss / args.gradient_accumulation_steps + + if args.fp16: + with amp.scale_loss(loss, optimizer) as scaled_loss: + scaled_loss.backward() + else: + loss.backward() + + tr_loss += loss.item() + moving_loss.add(loss.item()) + if (step + 1) % args.gradient_accumulation_steps == 0: + if args.fp16: + torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) + else: + torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) + optimizer.step() + scheduler.step() # Update learning rate schedule + model.zero_grad() + global_step += 1 + + # Log metrics + if args.local_rank == -1 and args.evaluate_during_training and global_step % args.eval_steps == 0: # Only evaluate when single GPU otherwise metrics may not average well + results = evaluate(args, model, tokenizer, f"checkpoint-{global_step}") + for key, value in results.items(): + tb_writer.add_scalar('eval_{}'.format(key), value, global_step) + + if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: + tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step) + tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step) + logging_loss = tr_loss + epoch_iterator.set_postfix(MovingLoss=f'{moving_loss.loss:.2f}', Perplexity=f'{torch.exp(torch.tensor(moving_loss.loss)):.2f}') + + if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: + # Save model checkpoint + save_state(args, model, tokenizer, global_step) + + if args.max_steps > 0 and global_step > args.max_steps: + epoch_iterator.close() + break + print_sample(model, tokenizer, args.device, args) + if args.max_steps > 0 and global_step > args.max_steps: + train_iterator.close() + break + except (KeyboardInterrupt, SystemExit): + save_state(args, model, tokenizer, global_step) + raise + + if args.local_rank in [-1, 0]: + tb_writer.close() + + return global_step, tr_loss / global_step + + +def evaluate(args, model, tokenizer, prefix=""): + # Loop to handle MNLI double evaluation (matched, mis-matched) + eval_output_dir = args.output_dir + + eval_dataset = load_and_cache_examples(args, tokenizer, evaluate=True) + + if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]: + os.makedirs(eval_output_dir) + + args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) + # Note that DistributedSampler samples randomly + eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) + eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) + + # Eval! + logger.info("***** Running evaluation {} *****".format(prefix)) + logger.info(" Num examples = %d", len(eval_dataset)) + logger.info(" Batch size = %d", args.eval_batch_size) + eval_loss = 0.0 + nb_eval_steps = 0 + model.eval() + + for batch in tqdm(eval_dataloader, desc="Evaluating"): + batch = batch.to(args.device) + + with torch.no_grad(): + outputs = model(batch, masked_lm_labels=batch) if args.mlm else model(batch, labels=batch) + lm_loss = outputs[0] + eval_loss += lm_loss.item() #lm_loss.mean().item() + nb_eval_steps += 1 + + eval_loss = eval_loss / nb_eval_steps + perplexity = torch.exp(torch.tensor(eval_loss)) + + result = { + "perplexity": perplexity + } + + output_eval_file = os.path.join(eval_output_dir, "eval_results.txt") + with open(output_eval_file, "w") as writer: + logger.info("***** Eval results {} *****".format(prefix)) + for key in sorted(result.keys()): + logger.info(" %s = %s", key, str(result[key])) + writer.write("%s = %s\n" % (key, str(result[key]))) + + return result + + +def main(): + parser = argparse.ArgumentParser() + + ## Required parameters + parser.add_argument("--train_data_file", default=None, type=str, required=True, + help="The input training data file (a text file).") + parser.add_argument("--output_dir", default=None, type=str, required=True, + help="The output directory where the model predictions and checkpoints will be written.") + + ## Other parameters + parser.add_argument("--eval_data_file", default=None, type=str, + help="An optional input evaluation data file to evaluate the perplexity on (a text file).") + + parser.add_argument("--model_type", default="bert", type=str, + help="The model architecture to be fine-tuned.") + parser.add_argument("--model_name_or_path", default="bert-base-cased", type=str, + help="The model checkpoint for weights initialization.") + + parser.add_argument("--mlm", action='store_true', + help="Train with masked-language modeling loss instead of language modeling.") + parser.add_argument("--mlm_probability", type=float, default=0.15, + help="Ratio of tokens to mask for masked language modeling loss") + + parser.add_argument("--config_name", default="", type=str, + help="Optional pretrained config name or path if not the same as model_name_or_path") + parser.add_argument("--tokenizer_name", default="", type=str, + help="Optional pretrained tokenizer name or path if not the same as model_name_or_path") + parser.add_argument("--cache_dir", default="", type=str, + help="Optional directory to store the pre-trained models downloaded from s3 (instread of the default one)") + parser.add_argument("--block_size", default=-1, type=int, + help="Optional input sequence length after tokenization." + "The training dataset will be truncated in block of this size for training." + "Default to the model max input length for single sentence inputs (take into account special tokens).") + parser.add_argument("--do_train", action='store_true', + help="Whether to run training.") + parser.add_argument("--do_eval", action='store_true', + help="Whether to run eval on the dev set.") + parser.add_argument("--evaluate_during_training", action='store_true', + help="Run evaluation during training at each logging step.") + parser.add_argument('--eval_steps', type=int, default=100, + help="Evaluate every X updates steps.") + parser.add_argument("--do_lower_case", action='store_true', + help="Set this flag if you are using an uncased model.") + + parser.add_argument("--per_gpu_train_batch_size", default=4, type=int, + help="Batch size per GPU/CPU for training.") + parser.add_argument("--per_gpu_eval_batch_size", default=4, type=int, + help="Batch size per GPU/CPU for evaluation.") + parser.add_argument('--gradient_accumulation_steps', type=int, default=1, + help="Number of updates steps to accumulate before performing a backward/update pass.") + parser.add_argument("--learning_rate", default=5e-5, type=float, + help="The initial learning rate for Adam.") + parser.add_argument("--weight_decay", default=0.0, type=float, + help="Weight deay if we apply some.") + parser.add_argument("--adam_epsilon", default=1e-6, type=float, + help="Epsilon for Adam optimizer.") + parser.add_argument("--max_grad_norm", default=1.0, type=float, + help="Max gradient norm.") + parser.add_argument("--num_train_epochs", default=1.0, type=float, + help="Total number of training epochs to perform.") + parser.add_argument("--max_steps", default=-1, type=int, + help="If > 0: set total number of training steps to perform. Override num_train_epochs.") + parser.add_argument("--warmup_samples", default=0, type=int, + help="Linear warmup over warmup_samples.") + parser.add_argument("--lr_decay", action='store_true', + help="Decay LR using get_linear_schedule_with_warmup.") + + parser.add_argument("--unfreeze_level", default=-1, type=int, + help="If > 0: freeze all layers except few first and last.") + + parser.add_argument('--logging_steps', type=int, default=50, + help="Log every X updates steps.") + parser.add_argument('--save_steps', type=int, default=50, + help="Save checkpoint every X updates steps.") + parser.add_argument('--save_total_limit', type=int, default=None, + help='Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default') + parser.add_argument("--eval_all_checkpoints", action='store_true', + help="Evaluate all checkpoints starting with the same prefix as model_name_or_path ending and ending with step number") + parser.add_argument("--no_cuda", action='store_true', + help="Avoid using CUDA when available") + parser.add_argument('--overwrite_output_dir', action='store_true', + help="Overwrite the content of the output directory") + parser.add_argument('--overwrite_cache', action='store_true', + help="Overwrite the cached training and evaluation sets") + parser.add_argument('--seed', type=int, default=42, + help="random seed for initialization") + + parser.add_argument('--fp16', action='store_true', + help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit") + parser.add_argument('--fp16_opt_level', type=str, default='O1', + help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." + "See details at https://nvidia.github.io/apex/amp.html") + parser.add_argument("--local_rank", type=int, default=-1, + help="For distributed training: local_rank") + parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.") + parser.add_argument('--server_port', type=str, default='', help="For distant debugging.") + args = parser.parse_args() + + if args.model_type in ["bert", "roberta", "distilbert"] and not args.mlm: + raise ValueError("BERT and RoBERTa do not have LM heads but masked LM heads. They must be run using the --mlm " + "flag (masked language modeling).") + if args.eval_data_file is None and args.do_eval: + raise ValueError("Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file " + "or remove the --do_eval argument.") + + if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir: + raise ValueError(f"Output directory ({args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome.") + + # Setup distant debugging if needed + if args.server_ip and args.server_port: + # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script + import ptvsd + print("Waiting for debugger attach") + ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) + ptvsd.wait_for_attach() + + # Setup CUDA, GPU & distributed training + if args.local_rank == -1 or args.no_cuda: + device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") + args.n_gpu = torch.cuda.device_count() + else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs + torch.cuda.set_device(args.local_rank) + device = torch.device("cuda", args.local_rank) + torch.distributed.init_process_group(backend='nccl') + args.n_gpu = 1 + args.device = device + + # Setup logging + logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', + datefmt = '%m/%d/%Y %H:%M:%S', + level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN) + logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", + args.local_rank, args.device, args.n_gpu, bool(args.local_rank != -1), args.fp16) + + # Set seed + set_seed(args) + + # Load pretrained model and tokenizer + if args.local_rank not in [-1, 0]: + torch.distributed.barrier() # Barrier to make sure only the first process in distributed training download model & vocab + + config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] + config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path) + tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case) + if args.block_size <= 0: + args.block_size = tokenizer.max_len_single_sentence # Our input block size will be the max possible for the model + args.block_size = min(args.block_size, tokenizer.max_len_single_sentence) + model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config) + model.to(args.device) + + print(200*'/') + print(len([param for item in flatten_model(model) + for param in item.parameters() + if param.requires_grad])) # freeze all layers but few first and last + if args.unfreeze_level >= 0: + flat = flatten_model(model) + flat = [item for item in flat if list(item.parameters())] + i_start = 3 + i_end = 1 + need_grads = set(flat[:i_start+args.unfreeze_level*3]) | set(flat[-(i_end+args.unfreeze_level*3):]) + for item in flat: + requires_grad(item, item in need_grads) + print(200*'/') + print(len([param for item in flatten_model(model) + for param in item.parameters() + if param.requires_grad])) + + if args.local_rank == 0: + torch.distributed.barrier() # End of barrier to make sure only the first process in distributed training download model & vocab + + logger.info("Training/evaluation parameters %s", args) + + # Training + if args.do_train: + if args.local_rank not in [-1, 0]: + torch.distributed.barrier() # Barrier to make sure only the first process in distributed training process the dataset, and the others will use the cache + + train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False) + + if args.local_rank == 0: + torch.distributed.barrier() + + global_step, tr_loss = train(args, train_dataset, model, tokenizer) + logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) + + # Saving best-practices: if you use save_pretrained for the model and tokenizer, you can reload them using from_pretrained() + if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): + save_state(args, model, tokenizer, global_step) + + # Load a trained model and vocabulary that you have fine-tuned + model = model_class.from_pretrained(args.output_dir) + tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) + model.to(args.device) + + # Evaluation + results = {} + if args.do_eval and args.local_rank in [-1, 0]: + checkpoints = [args.output_dir] + if args.eval_all_checkpoints: + checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True))) + logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging + logger.info("Evaluate the following checkpoints: %s", checkpoints) + for checkpoint in checkpoints: + global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else "" + model = model_class.from_pretrained(checkpoint) + model.to(args.device) + result = evaluate(args, model, tokenizer, prefix=global_step) + result = dict((k + '_{}'.format(global_step), v) for k, v in result.items()) + results.update(result) + + return results + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/train/ru_transformers-master/yt_encoder.py b/train/yt_encoder.py similarity index 87% rename from train/ru_transformers-master/yt_encoder.py rename to train/yt_encoder.py index a4dcf00..d9bd797 100644 --- a/train/ru_transformers-master/yt_encoder.py +++ b/train/yt_encoder.py @@ -13,8 +13,6 @@ class YTEncoder(PreTrainedTokenizer): def_name = 'encoder.model' def __init__(self, filename, *inputs, **kwargs): super().__init__(*inputs, **kwargs) - self.max_len_single_sentence = 1024 # no default special tokens - you can update this value if you add special tokens - self.max_len_sentences_pair = 1024 # no default special tokens - you can update this value if you add special tokens if os.path.isdir(filename): filename = os.path.join(filename, self.def_name) @@ -47,6 +45,9 @@ def tokenize(self, text, **kwargs): def from_pretrained(cls, *inputs, **kwargs): return cls(*inputs, **kwargs) + def build_inputs_with_special_tokens(self, token_ids): + return token_ids + def add_special_tokens_single_sentence(self, token_ids): return token_ids From 4f54c500dd2248900fb7116e3360baf823bfe51b Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Mon, 11 May 2020 14:31:38 +0300 Subject: [PATCH 18/23] Removed old train code. --- .../run_lm_finetuning.py | 662 --------------- train/run_language_modeling.py | 787 ------------------ 2 files changed, 1449 deletions(-) delete mode 100644 train/ru_transformers-master/run_lm_finetuning.py delete mode 100644 train/run_language_modeling.py diff --git a/train/ru_transformers-master/run_lm_finetuning.py b/train/ru_transformers-master/run_lm_finetuning.py deleted file mode 100644 index d55b619..0000000 --- a/train/ru_transformers-master/run_lm_finetuning.py +++ /dev/null @@ -1,662 +0,0 @@ -# coding=utf-8 -# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. -# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa). -GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned -using a masked language modeling (MLM) loss. -""" - -from __future__ import absolute_import, division, print_function - -import argparse -import glob -import logging -import os -import pickle -import random -import regex as re -import shutil - -import numpy as np -import torch -from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler -from torch.utils.data.distributed import DistributedSampler - -try: - from torch.utils.tensorboard import SummaryWriter -except: - from tensorboardX import SummaryWriter - -from tqdm import tqdm, trange -from dataclasses import dataclass -from fastprogress import progress_bar -from fastai.basics import * - -from run_generation import sample_sequence - -from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, - BertConfig, BertForMaskedLM, BertTokenizer, - GPT2Config, GPT2LMHeadModel, GPT2Tokenizer, - OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, - RobertaConfig, RobertaForMaskedLM, RobertaTokenizer, - DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer) - -from yt_encoder import YTEncoder - -logger = logging.getLogger(__name__) - - -MODEL_CLASSES = { - 'gpt2': (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer), - 'gpt2-yttm': (GPT2Config, GPT2LMHeadModel, YTEncoder), - 'openai-gpt': (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer), - 'bert': (BertConfig, BertForMaskedLM, BertTokenizer), - 'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer), - 'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer) -} - -@dataclass -class MovingLoss(): - steps:int = 1000 - avg_loss = (0.0, 0.0) - def add(self, batch_loss:float): - k_s = 1 - 1 / self.steps - avg_loss = self.avg_loss - self.avg_loss = (self.avg_loss[0] * k_s + batch_loss * (1-k_s), - self.avg_loss[1] * k_s + 1.0 * (1-k_s)) - @property - def loss(self): - if self.avg_loss[1]: - return self.avg_loss[0] / self.avg_loss[1] - -def print_sample(model, tokenizer, device, args): - model.eval() - raw_text = """ На словах ты Лев Толстой,\n А на деле -""" - context_tokens = tokenizer.encode(raw_text) - out = sample_sequence( - model=model, - context=context_tokens, - length=500, - temperature=1, - top_k=0, - top_p=0.9, - device=device, - #is_xlnet=bool(args.model_type == "xlnet"), - ) - out = out[0, len(context_tokens):].tolist() - text = raw_text + tokenizer.decode(out) - print(text) - - with open(os.path.join(args.output_dir, 'sample.txt'), 'w') as f: - f.write(text) - - model.train() - -class TextDataset(Dataset): - @staticmethod - def process_file(file_path, tokenizer, block_size, shuffle): - directory, filename = os.path.split(file_path) - directory = os.path.join(directory, 'cached') - os.makedirs(directory, exist_ok=True) - cached_features_file = os.path.join(directory, f'cached_lm_{block_size}_{tokenizer.hash}_{filename}') - - if os.path.exists(cached_features_file): - with open(cached_features_file, 'rb') as handle: - tokenized_text = pickle.load(handle) - else: - with open(file_path, encoding="utf-8") as f: - text = f.read() - if hasattr(tokenizer, 'encode'): - tokenized_text = tokenizer.encode(text) - else: - tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text)) - with open(cached_features_file, 'wb') as handle: - pickle.dump(tokenized_text, handle, protocol=pickle.HIGHEST_PROTOCOL) - - examples = [] - # add random shift - max_shift = max(min(block_size, len(tokenized_text) - block_size), 0) - rnd_shift = random.randrange(max_shift) if max_shift and shuffle else 0 - - for i in range(rnd_shift, len(tokenized_text) - block_size + 1, block_size): - examples.append(tokenizer.build_inputs_with_special_tokens(tokenized_text[i:i+block_size])) - # Note that we are loosing the last truncated example here for the sake of simplicity (no padding) - # If your dataset is small, first you should loook for a bigger one :-) and second you - # can change this behavior by adding (model specific) padding. - return examples - - def __init__(self, tokenizer, file_path='train', args=None, shuffle=True): - if not hasattr(tokenizer, 'hash'): tokenizer.hash = '' - - logger.info(f"Loading features from {file_path}") - if os.path.isfile(file_path): - files = [file_path] - else: - assert os.path.isdir(file_path) - files = glob.glob(os.path.join(file_path, '*.txt')) - - files = sorted(files) - if shuffle: random.shuffle(files) - - files = files[:10000] - - self.examples = [] - for fn in progress_bar(files): - self.examples.extend(self.process_file(fn, tokenizer, args.block_size, shuffle)) - - def __len__(self): - return len(self.examples) - - def __getitem__(self, item): - return torch.tensor(self.examples[item]) - - -def load_and_cache_examples(args, tokenizer, evaluate=False): - file_path = args.eval_data_file if evaluate else args.train_data_file - dataset = TextDataset(tokenizer, file_path=file_path, args=args, shuffle=not evaluate) - return dataset - - -def set_seed(args): - random.seed(args.seed) - np.random.seed(args.seed) - torch.manual_seed(args.seed) - if args.n_gpu > 0: - torch.cuda.manual_seed_all(args.seed) - -def _rotate_checkpoints(args, checkpoint_prefix, use_mtime=False): - if not args.save_total_limit: - return - if args.save_total_limit <= 0: - return - - # Check if we should delete older checkpoint(s) - glob_checkpoints = glob.glob(os.path.join(args.output_dir, '{}-*'.format(checkpoint_prefix))) - if len(glob_checkpoints) <= args.save_total_limit: - return - - ordering_and_checkpoint_path = [] - for path in glob_checkpoints: - if use_mtime: - ordering_and_checkpoint_path.append((os.path.getmtime(path), path)) - else: - regex_match = re.match('.*{}-([0-9]+)'.format(checkpoint_prefix), path) - if regex_match and regex_match.groups(): - ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path)) - - checkpoints_sorted = sorted(ordering_and_checkpoint_path) - checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted] - number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit) - checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete] - for checkpoint in checkpoints_to_be_deleted: - logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint)) - shutil.rmtree(checkpoint) - - -def mask_tokens(inputs, tokenizer, args): - """ Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """ - labels = inputs.clone() - # We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa) - probability_matrix = torch.full(labels.shape, args.mlm_probability) - special_tokens_mask = [tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()] - probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0) - masked_indices = torch.bernoulli(probability_matrix).bool() - labels[~masked_indices] = -1 # We only compute loss on masked tokens - - # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) - indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices - inputs[indices_replaced] = tokenizer.convert_tokens_to_ids(tokenizer.mask_token) - - # 10% of the time, we replace masked input tokens with random word - indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced - random_words = torch.randint(len(tokenizer), labels.shape, dtype=torch.long) - inputs[indices_random] = random_words[indices_random] - - # The rest of the time (10% of the time) we keep the masked input tokens unchanged - return inputs, labels - -def save_state(args, model, tokenizer, global_step): - def save_dir(output_dir): - # Create output directory if needed - if not os.path.exists(output_dir) and args.local_rank in [-1, 0]: - os.makedirs(output_dir) - logger.info(f"Saving model checkpoint to {output_dir}") - # Save a trained model, configuration and tokenizer using `save_pretrained()`. - # They can then be reloaded using `from_pretrained()` - model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training - model_to_save.save_pretrained(output_dir) - tokenizer.save_pretrained(output_dir) - - # Good practice: save your training arguments together with the trained model - torch.save(args, os.path.join(output_dir, 'training_args.bin')) - with open(os.path.join(output_dir, 'step.txt'), 'w') as c: c.write(str(global_step)) - - save_dir(args.output_dir) - checkpoint_prefix = 'checkpoint' - output_dir = os.path.join(args.output_dir, f'{checkpoint_prefix}-{global_step}') - save_dir(output_dir) - _rotate_checkpoints(args, checkpoint_prefix) - -class SummaryWriterP(SummaryWriter): - def __init__(self, prefix=None, logdir=None, comment='', *args, **kwargs): - if prefix: - import socket - from datetime import datetime - current_time = datetime.now().strftime('%b%d_%H-%M-%S') - logdir = os.path.join(prefix, - 'runs', current_time + '_' + socket.gethostname() + comment) - super().__init__(logdir, comment, *args, **kwargs) - -def train(args, train_dataset, model, tokenizer): - """ Train the model """ - if args.local_rank in [-1, 0]: - tb_writer = SummaryWriterP(args.output_dir) - - # XXX: Collate function - args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) - train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) - train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) - - if args.max_steps > 0: - t_total = args.max_steps - args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 - else: - t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs - - # Prepare optimizer and schedule (linear warmup and decay) - no_decay = ['bias', 'LayerNorm.weight'] - optimizer_grouped_parameters = [ - {'params': [p for n, p in model.named_parameters() if p.requires_grad and not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay}, - {'params': [p for n, p in model.named_parameters() if p.requires_grad and any(nd in n for nd in no_decay)], 'weight_decay': 0.0} - ] - optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) - warmup_steps = args.warmup_samples // args.train_batch_size - if args.lr_decay: - scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total) - else: - scheduler = get_constant_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps) - - # XXX: No check if saved optimizer or scheduler states exist - - if args.fp16: - try: - from apex import amp - except ImportError: - raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") - model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) - - # multi-gpu training (should be after apex fp16 initialization) - if args.n_gpu > 1: - model = torch.nn.DataParallel(model) - - # Distributed training (should be after apex fp16 initialization) - if args.local_rank != -1: - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], - output_device=args.local_rank, - find_unused_parameters=True) - - # Train! - logger.info("***** Running training *****") - logger.info(" Num examples = %d", len(train_dataset)) - logger.info(" Num Epochs = %d", args.num_train_epochs) - logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) - logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d", - args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1)) - logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) - logger.info(" Total optimization steps = %d", t_total) - - try: - with open(os.path.join(args.model_name_or_path, 'step.txt'), 'r') as c: - global_step = int(c.readline()) - except OSError as e: - global_step = 0 - - tr_loss, logging_loss = 0.0, 0.0 - moving_loss = MovingLoss(10000 // args.logging_steps) - model.zero_grad() - - train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]) - set_seed(args) # Added here for reproducibility (even between python 2 and 3) - try: - for _ in train_iterator: - epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) - for step, batch in enumerate(epoch_iterator): - inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch) - inputs = inputs.to(args.device) - labels = labels.to(args.device) - model.train() - outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels) - loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc) - - if args.n_gpu > 1: - loss = loss.mean() # mean() to average on multi-gpu parallel training - if args.gradient_accumulation_steps > 1: - loss = loss / args.gradient_accumulation_steps - - if args.fp16: - with amp.scale_loss(loss, optimizer) as scaled_loss: - scaled_loss.backward() - else: - loss.backward() - - tr_loss += loss.item() - moving_loss.add(loss.item()) - if (step + 1) % args.gradient_accumulation_steps == 0: - if args.fp16: - torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) - else: - torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) - optimizer.step() - scheduler.step() # Update learning rate schedule - model.zero_grad() - global_step += 1 - - # Log metrics - if args.local_rank == -1 and args.evaluate_during_training and global_step % args.eval_steps == 0: # Only evaluate when single GPU otherwise metrics may not average well - results = evaluate(args, model, tokenizer, f"checkpoint-{global_step}") - for key, value in results.items(): - tb_writer.add_scalar('eval_{}'.format(key), value, global_step) - - if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: - tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step) - tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step) - logging_loss = tr_loss - epoch_iterator.set_postfix(MovingLoss=f'{moving_loss.loss:.2f}', Perplexity=f'{torch.exp(torch.tensor(moving_loss.loss)):.2f}') - - if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: - # Save model checkpoint - save_state(args, model, tokenizer, global_step) - - if args.max_steps > 0 and global_step > args.max_steps: - epoch_iterator.close() - break - print_sample(model, tokenizer, args.device, args) - if args.max_steps > 0 and global_step > args.max_steps: - train_iterator.close() - break - except (KeyboardInterrupt, SystemExit): - save_state(args, model, tokenizer, global_step) - raise - - if args.local_rank in [-1, 0]: - tb_writer.close() - - return global_step, tr_loss / global_step - - -def evaluate(args, model, tokenizer, prefix=""): - # Loop to handle MNLI double evaluation (matched, mis-matched) - eval_output_dir = args.output_dir - - eval_dataset = load_and_cache_examples(args, tokenizer, evaluate=True) - - if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]: - os.makedirs(eval_output_dir) - - args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) - # Note that DistributedSampler samples randomly - eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) - eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) - - # Eval! - logger.info("***** Running evaluation {} *****".format(prefix)) - logger.info(" Num examples = %d", len(eval_dataset)) - logger.info(" Batch size = %d", args.eval_batch_size) - eval_loss = 0.0 - nb_eval_steps = 0 - model.eval() - - for batch in tqdm(eval_dataloader, desc="Evaluating"): - batch = batch.to(args.device) - - with torch.no_grad(): - outputs = model(batch, masked_lm_labels=batch) if args.mlm else model(batch, labels=batch) - lm_loss = outputs[0] - eval_loss += lm_loss.item() #lm_loss.mean().item() - nb_eval_steps += 1 - - eval_loss = eval_loss / nb_eval_steps - perplexity = torch.exp(torch.tensor(eval_loss)) - - result = { - "perplexity": perplexity - } - - output_eval_file = os.path.join(eval_output_dir, "eval_results.txt") - with open(output_eval_file, "w") as writer: - logger.info("***** Eval results {} *****".format(prefix)) - for key in sorted(result.keys()): - logger.info(" %s = %s", key, str(result[key])) - writer.write("%s = %s\n" % (key, str(result[key]))) - - return result - - -def main(): - parser = argparse.ArgumentParser() - - ## Required parameters - parser.add_argument("--train_data_file", default=None, type=str, required=True, - help="The input training data file (a text file).") - parser.add_argument("--output_dir", default=None, type=str, required=True, - help="The output directory where the model predictions and checkpoints will be written.") - - ## Other parameters - parser.add_argument("--eval_data_file", default=None, type=str, - help="An optional input evaluation data file to evaluate the perplexity on (a text file).") - - parser.add_argument("--model_type", default="bert", type=str, - help="The model architecture to be fine-tuned.") - parser.add_argument("--model_name_or_path", default="bert-base-cased", type=str, - help="The model checkpoint for weights initialization.") - - parser.add_argument("--mlm", action='store_true', - help="Train with masked-language modeling loss instead of language modeling.") - parser.add_argument("--mlm_probability", type=float, default=0.15, - help="Ratio of tokens to mask for masked language modeling loss") - - parser.add_argument("--config_name", default="", type=str, - help="Optional pretrained config name or path if not the same as model_name_or_path") - parser.add_argument("--tokenizer_name", default="", type=str, - help="Optional pretrained tokenizer name or path if not the same as model_name_or_path") - parser.add_argument("--cache_dir", default="", type=str, - help="Optional directory to store the pre-trained models downloaded from s3 (instread of the default one)") - parser.add_argument("--block_size", default=-1, type=int, - help="Optional input sequence length after tokenization." - "The training dataset will be truncated in block of this size for training." - "Default to the model max input length for single sentence inputs (take into account special tokens).") - parser.add_argument("--do_train", action='store_true', - help="Whether to run training.") - parser.add_argument("--do_eval", action='store_true', - help="Whether to run eval on the dev set.") - parser.add_argument("--evaluate_during_training", action='store_true', - help="Run evaluation during training at each logging step.") - parser.add_argument('--eval_steps', type=int, default=100, - help="Evaluate every X updates steps.") - parser.add_argument("--do_lower_case", action='store_true', - help="Set this flag if you are using an uncased model.") - - parser.add_argument("--per_gpu_train_batch_size", default=4, type=int, - help="Batch size per GPU/CPU for training.") - parser.add_argument("--per_gpu_eval_batch_size", default=4, type=int, - help="Batch size per GPU/CPU for evaluation.") - parser.add_argument('--gradient_accumulation_steps', type=int, default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.") - parser.add_argument("--learning_rate", default=5e-5, type=float, - help="The initial learning rate for Adam.") - parser.add_argument("--weight_decay", default=0.0, type=float, - help="Weight deay if we apply some.") - parser.add_argument("--adam_epsilon", default=1e-6, type=float, - help="Epsilon for Adam optimizer.") - parser.add_argument("--max_grad_norm", default=1.0, type=float, - help="Max gradient norm.") - parser.add_argument("--num_train_epochs", default=1.0, type=float, - help="Total number of training epochs to perform.") - parser.add_argument("--max_steps", default=-1, type=int, - help="If > 0: set total number of training steps to perform. Override num_train_epochs.") - parser.add_argument("--warmup_samples", default=0, type=int, - help="Linear warmup over warmup_samples.") - parser.add_argument("--lr_decay", action='store_true', - help="Decay LR using get_linear_schedule_with_warmup.") - - parser.add_argument("--unfreeze_level", default=-1, type=int, - help="If > 0: freeze all layers except few first and last.") - - parser.add_argument('--logging_steps', type=int, default=50, - help="Log every X updates steps.") - parser.add_argument('--save_steps', type=int, default=50, - help="Save checkpoint every X updates steps.") - parser.add_argument('--save_total_limit', type=int, default=None, - help='Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default') - parser.add_argument("--eval_all_checkpoints", action='store_true', - help="Evaluate all checkpoints starting with the same prefix as model_name_or_path ending and ending with step number") - parser.add_argument("--no_cuda", action='store_true', - help="Avoid using CUDA when available") - parser.add_argument('--overwrite_output_dir', action='store_true', - help="Overwrite the content of the output directory") - parser.add_argument('--overwrite_cache', action='store_true', - help="Overwrite the cached training and evaluation sets") - parser.add_argument('--seed', type=int, default=42, - help="random seed for initialization") - - parser.add_argument('--fp16', action='store_true', - help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit") - parser.add_argument('--fp16_opt_level', type=str, default='O1', - help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." - "See details at https://nvidia.github.io/apex/amp.html") - parser.add_argument("--local_rank", type=int, default=-1, - help="For distributed training: local_rank") - parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.") - parser.add_argument('--server_port', type=str, default='', help="For distant debugging.") - args = parser.parse_args() - - if args.model_type in ["bert", "roberta", "distilbert"] and not args.mlm: - raise ValueError("BERT and RoBERTa do not have LM heads but masked LM heads. They must be run using the --mlm " - "flag (masked language modeling).") - if args.eval_data_file is None and args.do_eval: - raise ValueError("Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file " - "or remove the --do_eval argument.") - - if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir: - raise ValueError(f"Output directory ({args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome.") - - # Setup distant debugging if needed - if args.server_ip and args.server_port: - # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script - import ptvsd - print("Waiting for debugger attach") - ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) - ptvsd.wait_for_attach() - - # Setup CUDA, GPU & distributed training - if args.local_rank == -1 or args.no_cuda: - device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") - args.n_gpu = torch.cuda.device_count() - else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs - torch.cuda.set_device(args.local_rank) - device = torch.device("cuda", args.local_rank) - torch.distributed.init_process_group(backend='nccl') - args.n_gpu = 1 - args.device = device - - # Setup logging - logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', - datefmt = '%m/%d/%Y %H:%M:%S', - level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN) - logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", - args.local_rank, args.device, args.n_gpu, bool(args.local_rank != -1), args.fp16) - - # Set seed - set_seed(args) - - # Load pretrained model and tokenizer - if args.local_rank not in [-1, 0]: - torch.distributed.barrier() # Barrier to make sure only the first process in distributed training download model & vocab - - config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] - config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path) - tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case) - if args.block_size <= 0: - args.block_size = tokenizer.max_len_single_sentence # Our input block size will be the max possible for the model - args.block_size = min(args.block_size, tokenizer.max_len_single_sentence) - model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config) - model.to(args.device) - - print(200*'/') - print(len([param for item in flatten_model(model) - for param in item.parameters() - if param.requires_grad])) # freeze all layers but few first and last - if args.unfreeze_level >= 0: - flat = flatten_model(model) - flat = [item for item in flat if list(item.parameters())] - i_start = 3 - i_end = 1 - need_grads = set(flat[:i_start+args.unfreeze_level*3]) | set(flat[-(i_end+args.unfreeze_level*3):]) - for item in flat: - requires_grad(item, item in need_grads) - print(200*'/') - print(len([param for item in flatten_model(model) - for param in item.parameters() - if param.requires_grad])) - - if args.local_rank == 0: - torch.distributed.barrier() # End of barrier to make sure only the first process in distributed training download model & vocab - - logger.info("Training/evaluation parameters %s", args) - - # Training - if args.do_train: - if args.local_rank not in [-1, 0]: - torch.distributed.barrier() # Barrier to make sure only the first process in distributed training process the dataset, and the others will use the cache - - train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False) - - if args.local_rank == 0: - torch.distributed.barrier() - - global_step, tr_loss = train(args, train_dataset, model, tokenizer) - logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) - - # Saving best-practices: if you use save_pretrained for the model and tokenizer, you can reload them using from_pretrained() - if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): - save_state(args, model, tokenizer, global_step) - - # Load a trained model and vocabulary that you have fine-tuned - model = model_class.from_pretrained(args.output_dir) - tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) - model.to(args.device) - - # Evaluation - results = {} - if args.do_eval and args.local_rank in [-1, 0]: - checkpoints = [args.output_dir] - if args.eval_all_checkpoints: - checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True))) - logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging - logger.info("Evaluate the following checkpoints: %s", checkpoints) - for checkpoint in checkpoints: - global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else "" - model = model_class.from_pretrained(checkpoint) - model.to(args.device) - result = evaluate(args, model, tokenizer, prefix=global_step) - result = dict((k + '_{}'.format(global_step), v) for k, v in result.items()) - results.update(result) - - return results - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/train/run_language_modeling.py b/train/run_language_modeling.py deleted file mode 100644 index 5d451e7..0000000 --- a/train/run_language_modeling.py +++ /dev/null @@ -1,787 +0,0 @@ -# coding=utf-8 -# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. -# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa). -GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned -using a masked language modeling (MLM) loss. -""" - - -import argparse -import glob -import logging -import os -import pickle -import random -import re -import shutil -from typing import Dict, List, Tuple - -import numpy as np -import torch -from torch.nn.utils.rnn import pad_sequence -from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler -from torch.utils.data.distributed import DistributedSampler -from tqdm import tqdm, trange - -from transformers import ( - MODEL_WITH_LM_HEAD_MAPPING, - WEIGHTS_NAME, - AdamW, - AutoConfig, - AutoModelWithLMHead, - AutoTokenizer, - PreTrainedModel, - PreTrainedTokenizer, - get_linear_schedule_with_warmup, -) - - -try: - from torch.utils.tensorboard import SummaryWriter -except ImportError: - from tensorboardX import SummaryWriter - - -logger = logging.getLogger(__name__) - - -MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys()) -MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) - - -class TextDataset(Dataset): - def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512): - assert os.path.isfile(file_path) - - block_size = block_size - (tokenizer.max_len - tokenizer.max_len_single_sentence) - - directory, filename = os.path.split(file_path) - cached_features_file = os.path.join( - directory, args.model_type + "_cached_lm_" + str(block_size) + "_" + filename - ) - - if os.path.exists(cached_features_file) and not args.overwrite_cache: - logger.info("Loading features from cached file %s", cached_features_file) - with open(cached_features_file, "rb") as handle: - self.examples = pickle.load(handle) - else: - logger.info("Creating features from dataset file at %s", directory) - - self.examples = [] - with open(file_path, encoding="utf-8") as f: - text = f.read() - - tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text)) - - for i in range(0, len(tokenized_text) - block_size + 1, block_size): # Truncate in block of block_size - self.examples.append(tokenizer.build_inputs_with_special_tokens(tokenized_text[i : i + block_size])) - # Note that we are loosing the last truncated example here for the sake of simplicity (no padding) - # If your dataset is small, first you should loook for a bigger one :-) and second you - # can change this behavior by adding (model specific) padding. - - logger.info("Saving features into cached file %s", cached_features_file) - with open(cached_features_file, "wb") as handle: - pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL) - - def __len__(self): - return len(self.examples) - - def __getitem__(self, item): - return torch.tensor(self.examples[item], dtype=torch.long) - - -class LineByLineTextDataset(Dataset): - def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512): - assert os.path.isfile(file_path) - # Here, we do not cache the features, operating under the assumption - # that we will soon use fast multithreaded tokenizers from the - # `tokenizers` repo everywhere =) - logger.info("Creating features from dataset file at %s", file_path) - - with open(file_path, encoding="utf-8") as f: - lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())] - - self.examples = tokenizer.batch_encode_plus(lines, add_special_tokens=True, max_length=block_size)["input_ids"] - - def __len__(self): - return len(self.examples) - - def __getitem__(self, i): - return torch.tensor(self.examples[i], dtype=torch.long) - - -def load_and_cache_examples(args, tokenizer, evaluate=False): - file_path = args.eval_data_file if evaluate else args.train_data_file - if args.line_by_line: - return LineByLineTextDataset(tokenizer, args, file_path=file_path, block_size=args.block_size) - else: - return TextDataset(tokenizer, args, file_path=file_path, block_size=args.block_size) - - -def set_seed(args): - random.seed(args.seed) - np.random.seed(args.seed) - torch.manual_seed(args.seed) - if args.n_gpu > 0: - torch.cuda.manual_seed_all(args.seed) - - -def _sorted_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -> List[str]: - ordering_and_checkpoint_path = [] - - glob_checkpoints = glob.glob(os.path.join(args.output_dir, "{}-*".format(checkpoint_prefix))) - - for path in glob_checkpoints: - if use_mtime: - ordering_and_checkpoint_path.append((os.path.getmtime(path), path)) - else: - regex_match = re.match(".*{}-([0-9]+)".format(checkpoint_prefix), path) - if regex_match and regex_match.groups(): - ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path)) - - checkpoints_sorted = sorted(ordering_and_checkpoint_path) - checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted] - return checkpoints_sorted - - -def _rotate_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -> None: - if not args.save_total_limit: - return - if args.save_total_limit <= 0: - return - - # Check if we should delete older checkpoint(s) - checkpoints_sorted = _sorted_checkpoints(args, checkpoint_prefix, use_mtime) - if len(checkpoints_sorted) <= args.save_total_limit: - return - - number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit) - checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete] - for checkpoint in checkpoints_to_be_deleted: - logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint)) - shutil.rmtree(checkpoint) - - -def mask_tokens(inputs: torch.Tensor, tokenizer: PreTrainedTokenizer, args) -> Tuple[torch.Tensor, torch.Tensor]: - """ Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """ - - if tokenizer.mask_token is None: - raise ValueError( - "This tokenizer does not have a mask token which is necessary for masked language modeling. Remove the --mlm flag if you want to use this tokenizer." - ) - - labels = inputs.clone() - # We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa) - probability_matrix = torch.full(labels.shape, args.mlm_probability) - special_tokens_mask = [ - tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist() - ] - probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0) - if tokenizer._pad_token is not None: - padding_mask = labels.eq(tokenizer.pad_token_id) - probability_matrix.masked_fill_(padding_mask, value=0.0) - masked_indices = torch.bernoulli(probability_matrix).bool() - labels[~masked_indices] = -100 # We only compute loss on masked tokens - - # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) - indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices - inputs[indices_replaced] = tokenizer.convert_tokens_to_ids(tokenizer.mask_token) - - # 10% of the time, we replace masked input tokens with random word - indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced - random_words = torch.randint(len(tokenizer), labels.shape, dtype=torch.long) - inputs[indices_random] = random_words[indices_random] - - # The rest of the time (10% of the time) we keep the masked input tokens unchanged - return inputs, labels - - -def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedTokenizer) -> Tuple[int, float]: - """ Train the model """ - if args.local_rank in [-1, 0]: - tb_writer = SummaryWriter() - - args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) - - def collate(examples: List[torch.Tensor]): - if tokenizer._pad_token is None: - return pad_sequence(examples, batch_first=True) - return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id) - - train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) - train_dataloader = DataLoader( - train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=collate - ) - - if args.max_steps > 0: - t_total = args.max_steps - args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 - else: - t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs - - model = model.module if hasattr(model, "module") else model # Take care of distributed/parallel training - model.resize_token_embeddings(len(tokenizer)) - - # Prepare optimizer and schedule (linear warmup and decay) - no_decay = ["bias", "LayerNorm.weight"] - optimizer_grouped_parameters = [ - { - "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], - "weight_decay": args.weight_decay, - }, - {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0}, - ] - optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) - scheduler = get_linear_schedule_with_warmup( - optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total - ) - - # Check if saved optimizer or scheduler states exist - if ( - args.model_name_or_path - and os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) - and os.path.isfile(os.path.join(args.model_name_or_path, "scheduler.pt")) - ): - # Load in optimizer and scheduler states - optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt"))) - scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt"))) - - if args.fp16: - try: - from apex import amp - except ImportError: - raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") - model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) - - # multi-gpu training (should be after apex fp16 initialization) - if args.n_gpu > 1: - model = torch.nn.DataParallel(model) - - # Distributed training (should be after apex fp16 initialization) - if args.local_rank != -1: - model = torch.nn.parallel.DistributedDataParallel( - model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True - ) - - # Train! - logger.info("***** Running training *****") - logger.info(" Num examples = %d", len(train_dataset)) - logger.info(" Num Epochs = %d", args.num_train_epochs) - logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) - logger.info( - " Total train batch size (w. parallel, distributed & accumulation) = %d", - args.train_batch_size - * args.gradient_accumulation_steps - * (torch.distributed.get_world_size() if args.local_rank != -1 else 1), - ) - logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) - logger.info(" Total optimization steps = %d", t_total) - - global_step = 0 - epochs_trained = 0 - steps_trained_in_current_epoch = 0 - # Check if continuing training from a checkpoint - if args.model_name_or_path and os.path.exists(args.model_name_or_path): - try: - # set global_step to gobal_step of last saved checkpoint from model path - checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0] - global_step = int(checkpoint_suffix) - epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps) - steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps) - - logger.info(" Continuing training from checkpoint, will skip to saved global_step") - logger.info(" Continuing training from epoch %d", epochs_trained) - logger.info(" Continuing training from global step %d", global_step) - logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch) - except ValueError: - logger.info(" Starting fine-tuning.") - - tr_loss, logging_loss = 0.0, 0.0 - - model.zero_grad() - train_iterator = trange( - epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0] - ) - set_seed(args) # Added here for reproducibility - for epoch in train_iterator: - epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) - - if args.local_rank != -1: - train_sampler.set_epoch(epoch) - - for step, batch in enumerate(epoch_iterator): - - # Skip past any already trained steps if resuming training - if steps_trained_in_current_epoch > 0: - steps_trained_in_current_epoch -= 1 - continue - - inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch) - inputs = inputs.to(args.device) - labels = labels.to(args.device) - model.train() - outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels) - loss = outputs[0] # model outputs are always tuple in transformers (see doc) - - if args.n_gpu > 1: - loss = loss.mean() # mean() to average on multi-gpu parallel training - if args.gradient_accumulation_steps > 1: - loss = loss / args.gradient_accumulation_steps - - if args.fp16: - with amp.scale_loss(loss, optimizer) as scaled_loss: - scaled_loss.backward() - else: - loss.backward() - - tr_loss += loss.item() - if (step + 1) % args.gradient_accumulation_steps == 0: - if args.fp16: - torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) - else: - torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) - optimizer.step() - scheduler.step() # Update learning rate schedule - model.zero_grad() - global_step += 1 - - if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: - # Log metrics - if ( - args.local_rank == -1 and args.evaluate_during_training - ): # Only evaluate when single GPU otherwise metrics may not average well - results = evaluate(args, model, tokenizer) - for key, value in results.items(): - tb_writer.add_scalar("eval_{}".format(key), value, global_step) - tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step) - tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step) - logging_loss = tr_loss - - if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: - checkpoint_prefix = "checkpoint" - # Save model checkpoint - output_dir = os.path.join(args.output_dir, "{}-{}".format(checkpoint_prefix, global_step)) - os.makedirs(output_dir, exist_ok=True) - model_to_save = ( - model.module if hasattr(model, "module") else model - ) # Take care of distributed/parallel training - model_to_save.save_pretrained(output_dir) - tokenizer.save_pretrained(output_dir) - - torch.save(args, os.path.join(output_dir, "training_args.bin")) - logger.info("Saving model checkpoint to %s", output_dir) - - _rotate_checkpoints(args, checkpoint_prefix) - - torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) - torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")) - logger.info("Saving optimizer and scheduler states to %s", output_dir) - - if args.max_steps > 0 and global_step > args.max_steps: - epoch_iterator.close() - break - if args.max_steps > 0 and global_step > args.max_steps: - train_iterator.close() - break - - if args.local_rank in [-1, 0]: - tb_writer.close() - - return global_step, tr_loss / global_step - - -def evaluate(args, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, prefix="") -> Dict: - # Loop to handle MNLI double evaluation (matched, mis-matched) - eval_output_dir = args.output_dir - - eval_dataset = load_and_cache_examples(args, tokenizer, evaluate=True) - - if args.local_rank in [-1, 0]: - os.makedirs(eval_output_dir, exist_ok=True) - - args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) - # Note that DistributedSampler samples randomly - - def collate(examples: List[torch.Tensor]): - if tokenizer._pad_token is None: - return pad_sequence(examples, batch_first=True) - return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id) - - eval_sampler = SequentialSampler(eval_dataset) - eval_dataloader = DataLoader( - eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate - ) - - # multi-gpu evaluate - if args.n_gpu > 1: - model = torch.nn.DataParallel(model) - - # Eval! - logger.info("***** Running evaluation {} *****".format(prefix)) - logger.info(" Num examples = %d", len(eval_dataset)) - logger.info(" Batch size = %d", args.eval_batch_size) - eval_loss = 0.0 - nb_eval_steps = 0 - model.eval() - - for batch in tqdm(eval_dataloader, desc="Evaluating"): - inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch) - inputs = inputs.to(args.device) - labels = labels.to(args.device) - - with torch.no_grad(): - outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels) - lm_loss = outputs[0] - eval_loss += lm_loss.mean().item() - nb_eval_steps += 1 - - eval_loss = eval_loss / nb_eval_steps - perplexity = torch.exp(torch.tensor(eval_loss)) - - result = {"perplexity": perplexity} - - output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt") - with open(output_eval_file, "w") as writer: - logger.info("***** Eval results {} *****".format(prefix)) - for key in sorted(result.keys()): - logger.info(" %s = %s", key, str(result[key])) - writer.write("%s = %s\n" % (key, str(result[key]))) - - return result - - -def main(): - parser = argparse.ArgumentParser() - - # Required parameters - parser.add_argument( - "--train_data_file", default=None, type=str, required=True, help="The input training data file (a text file)." - ) - parser.add_argument( - "--output_dir", - type=str, - required=True, - help="The output directory where the model predictions and checkpoints will be written.", - ) - parser.add_argument( - "--model_type", type=str, required=True, help="The model architecture to be trained or fine-tuned.", - ) - - # Other parameters - parser.add_argument( - "--eval_data_file", - default=None, - type=str, - help="An optional input evaluation data file to evaluate the perplexity on (a text file).", - ) - parser.add_argument( - "--line_by_line", - action="store_true", - help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.", - ) - parser.add_argument( - "--should_continue", action="store_true", help="Whether to continue from latest checkpoint in output_dir" - ) - parser.add_argument( - "--model_name_or_path", - default=None, - type=str, - help="The model checkpoint for weights initialization. Leave None if you want to train a model from scratch.", - ) - - parser.add_argument( - "--mlm", action="store_true", help="Train with masked-language modeling loss instead of language modeling." - ) - parser.add_argument( - "--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss" - ) - - parser.add_argument( - "--config_name", - default=None, - type=str, - help="Optional pretrained config name or path if not the same as model_name_or_path. If both are None, initialize a new config.", - ) - parser.add_argument( - "--tokenizer_name", - default=None, - type=str, - help="Optional pretrained tokenizer name or path if not the same as model_name_or_path. If both are None, initialize a new tokenizer.", - ) - parser.add_argument( - "--cache_dir", - default=None, - type=str, - help="Optional directory to store the pre-trained models downloaded from s3 (instead of the default one)", - ) - parser.add_argument( - "--block_size", - default=-1, - type=int, - help="Optional input sequence length after tokenization." - "The training dataset will be truncated in block of this size for training." - "Default to the model max input length for single sentence inputs (take into account special tokens).", - ) - parser.add_argument("--do_train", action="store_true", help="Whether to run training.") - parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") - parser.add_argument( - "--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step." - ) - - parser.add_argument("--per_gpu_train_batch_size", default=4, type=int, help="Batch size per GPU/CPU for training.") - parser.add_argument( - "--per_gpu_eval_batch_size", default=4, type=int, help="Batch size per GPU/CPU for evaluation." - ) - parser.add_argument( - "--gradient_accumulation_steps", - type=int, - default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.", - ) - parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") - parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") - parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") - parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") - parser.add_argument( - "--num_train_epochs", default=1.0, type=float, help="Total number of training epochs to perform." - ) - parser.add_argument( - "--max_steps", - default=-1, - type=int, - help="If > 0: set total number of training steps to perform. Override num_train_epochs.", - ) - parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") - - parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.") - parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.") - parser.add_argument( - "--save_total_limit", - type=int, - default=None, - help="Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default", - ) - parser.add_argument( - "--eval_all_checkpoints", - action="store_true", - help="Evaluate all checkpoints starting with the same prefix as model_name_or_path ending and ending with step number", - ) - parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available") - parser.add_argument( - "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory" - ) - parser.add_argument( - "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" - ) - parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") - - parser.add_argument( - "--fp16", - action="store_true", - help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", - ) - parser.add_argument( - "--fp16_opt_level", - type=str, - default="O1", - help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." - "See details at https://nvidia.github.io/apex/amp.html", - ) - parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") - parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.") - parser.add_argument("--server_port", type=str, default="", help="For distant debugging.") - args = parser.parse_args() - - if args.model_type in ["bert", "roberta", "distilbert", "camembert"] and not args.mlm: - raise ValueError( - "BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the --mlm " - "flag (masked language modeling)." - ) - if args.eval_data_file is None and args.do_eval: - raise ValueError( - "Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file " - "or remove the --do_eval argument." - ) - if args.should_continue: - sorted_checkpoints = _sorted_checkpoints(args) - if len(sorted_checkpoints) == 0: - raise ValueError("Used --should_continue but no checkpoint was found in --output_dir.") - else: - args.model_name_or_path = sorted_checkpoints[-1] - - if ( - os.path.exists(args.output_dir) - and os.listdir(args.output_dir) - and args.do_train - and not args.overwrite_output_dir - and not args.should_continue - ): - raise ValueError( - "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format( - args.output_dir - ) - ) - - # Setup distant debugging if needed - if args.server_ip and args.server_port: - # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script - import ptvsd - - print("Waiting for debugger attach") - ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) - ptvsd.wait_for_attach() - - # Setup CUDA, GPU & distributed training - if args.local_rank == -1 or args.no_cuda: - device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") - args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count() - else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs - torch.cuda.set_device(args.local_rank) - device = torch.device("cuda", args.local_rank) - torch.distributed.init_process_group(backend="nccl") - args.n_gpu = 1 - args.device = device - - # Setup logging - logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%m/%d/%Y %H:%M:%S", - level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN, - ) - logger.warning( - "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", - args.local_rank, - device, - args.n_gpu, - bool(args.local_rank != -1), - args.fp16, - ) - - # Set seed - set_seed(args) - - # Load pretrained model and tokenizer - if args.local_rank not in [-1, 0]: - torch.distributed.barrier() # Barrier to make sure only the first process in distributed training download model & vocab - - if args.config_name: - config = AutoConfig.from_pretrained(args.config_name, cache_dir=args.cache_dir) - elif args.model_name_or_path: - config = AutoConfig.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir) - else: - # When we release a pip version exposing CONFIG_MAPPING, - # we can do `config = CONFIG_MAPPING[args.model_type]()`. - raise ValueError( - "You are instantiating a new config instance from scratch. This is not supported, but you can do it from another script, save it," - "and load it from here, using --config_name" - ) - - if args.tokenizer_name: - tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, cache_dir=args.cache_dir) - elif args.model_name_or_path: - tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir) - else: - raise ValueError( - "You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another script, save it," - "and load it from here, using --tokenizer_name" - ) - - if args.block_size <= 0: - args.block_size = tokenizer.max_len - # Our input block size will be the max possible for the model - else: - args.block_size = min(args.block_size, tokenizer.max_len) - - if args.model_name_or_path: - model = AutoModelWithLMHead.from_pretrained( - args.model_name_or_path, - from_tf=bool(".ckpt" in args.model_name_or_path), - config=config, - cache_dir=args.cache_dir, - ) - else: - logger.info("Training new model from scratch") - model = AutoModelWithLMHead.from_config(config) - - model.to(args.device) - - if args.local_rank == 0: - torch.distributed.barrier() # End of barrier to make sure only the first process in distributed training download model & vocab - - logger.info("Training/evaluation parameters %s", args) - - # Training - if args.do_train: - if args.local_rank not in [-1, 0]: - torch.distributed.barrier() # Barrier to make sure only the first process in distributed training process the dataset, and the others will use the cache - - train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False) - - if args.local_rank == 0: - torch.distributed.barrier() - - global_step, tr_loss = train(args, train_dataset, model, tokenizer) - logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) - - # Saving best-practices: if you use save_pretrained for the model and tokenizer, you can reload them using from_pretrained() - if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): - # Create output directory if needed - if args.local_rank in [-1, 0]: - os.makedirs(args.output_dir, exist_ok=True) - - logger.info("Saving model checkpoint to %s", args.output_dir) - # Save a trained model, configuration and tokenizer using `save_pretrained()`. - # They can then be reloaded using `from_pretrained()` - model_to_save = ( - model.module if hasattr(model, "module") else model - ) # Take care of distributed/parallel training - model_to_save.save_pretrained(args.output_dir) - tokenizer.save_pretrained(args.output_dir) - - # Good practice: save your training arguments together with the trained model - torch.save(args, os.path.join(args.output_dir, "training_args.bin")) - - # Load a trained model and vocabulary that you have fine-tuned - model = AutoModelWithLMHead.from_pretrained(args.output_dir) - tokenizer = AutoTokenizer.from_pretrained(args.output_dir) - model.to(args.device) - - # Evaluation - results = {} - if args.do_eval and args.local_rank in [-1, 0]: - checkpoints = [args.output_dir] - if args.eval_all_checkpoints: - checkpoints = list( - os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)) - ) - logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging - logger.info("Evaluate the following checkpoints: %s", checkpoints) - for checkpoint in checkpoints: - global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else "" - prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else "" - - model = AutoModelWithLMHead.from_pretrained(checkpoint) - model.to(args.device) - result = evaluate(args, model, tokenizer, prefix=prefix) - result = dict((k + "_{}".format(global_step), v) for k, v in result.items()) - results.update(result) - - return results - - -if __name__ == "__main__": - main() From 3acd171ed2e2709511c24a09d4254c8f8acf72ac Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Mon, 11 May 2020 14:45:17 +0300 Subject: [PATCH 19/23] Fixed the error with the `_fill_buffer` function. Fix #4 --- bot/joke_generator.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/bot/joke_generator.py b/bot/joke_generator.py index 13c7bd2..89e0b16 100644 --- a/bot/joke_generator.py +++ b/bot/joke_generator.py @@ -114,15 +114,16 @@ def _get_from_buffer(self, model, buffer): :param model: model to use to fill the buffer :return: joke from the buffer """ - model.logger.info('Got joke from the buffer') if len(buffer) == 0: - self._fill_buffer(model) + buffer = self._fill_buffer(model) + model.logger.info('Got joke from the buffer') return buffer.pop() @synchronized @abstractmethod def _fill_buffer(self, model): - """Fill the buffer associated with this model.""" + """Fill and return the buffer associated with given model. + """ pass @synchronized @@ -169,6 +170,7 @@ def generate_joke(self, promt=""): @synchronized def _fill_buffer(self, model): self.jokes_buffer = super()._generate_for_buffer(model) + return self.jokes_buffer def _get_new_joke(self): return self._get_from_buffer(self.model, self.jokes_buffer) @@ -213,6 +215,7 @@ def generate_joke(self, promt=""): @synchronized def _fill_buffer(self, model): self.key2pool[model.name] = super()._generate_for_buffer(model) + return self.key2pool[model.name] def _get_new_joke(self): """Get the joke either from the model buffer, or dataset.""" From e6ed8a6fbe4d36d2fd67961f8bbb7b523da920a6 Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Mon, 11 May 2020 15:05:10 +0300 Subject: [PATCH 20/23] Fixed error in `_prettify_result`. Fix #2 --- bot/joke_generator.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/bot/joke_generator.py b/bot/joke_generator.py index 89e0b16..7825bb2 100644 --- a/bot/joke_generator.py +++ b/bot/joke_generator.py @@ -59,10 +59,12 @@ def _prettify_result(self, model_output): def pp_answer(text): """Pretty-print the answer.""" # Remove all text after the stop token. - text = text[: text.find(self.config['stop_token']) - if self.config['stop_token'] else None] + stop_token_ind = text.find(self.config.get('stop_token', None)) + if stop_token_ind > -1: + text = text[:stop_token_ind] # Remove multiple answers. - text = self.config['answer_token'].join(text.split(self.config['answer_token'], 2)[:2]) + text = self.config['answer_token'].join( + text.split(self.config['answer_token'], 2)[:2]) # Escape markdown tokens. text = self._escape_markdown(text) # Replace model tokens with html formatted ones. From 9f196640259d57e64c78ed07220f1284f18b9a48 Mon Sep 17 00:00:00 2001 From: Vladislav Kuleykin Date: Mon, 11 May 2020 15:23:51 +0300 Subject: [PATCH 21/23] Update README.md --- README.md | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 4fbc91b..770f2af 100644 --- a/README.md +++ b/README.md @@ -28,6 +28,10 @@ To do this, change the flag `ab_test` in configuration file to `true` and add th The bot will randomly choose the sourse for the next joke and will put the model/dataset path in `generated_by` column in database. +### Russian model (can be any other language) +I added the feature to wrap the original models with the model for other language. +So, currently, if you provide `rus_model_path` field in the configuration file, bot will create a wrapping around the original model with the provided one. And if it will find cirrilic text in the promt, or command `\шутка` it will use the Russian model to generate the answer. + ## How to train a model To train the model, we have three datasets: * **QA Jokes** (3.29 MB) - the original dataset we've found on [Kaggle][1]. It contains ~38k question-answer jokes @@ -38,9 +42,9 @@ But you're free to use others! (and please, write me if you found good one) For the training, the `GPT-2 train helper.ipynb` in train folder can come in handy. As it can convert the datasets to appropriate GPT-2 input files and extract the QA jokes from the *Short Jokes* dataset. -And as for the actual training use the `run_language_modelling.py` script taken from the [Transformers library][4]: +And as for the actual training use the `run_lm_finetuning.py` script merged from two other scripts from [Transformers library][4] and [ru_transformers repo][6]: ```cmd -python3 run_language_modeling.py \ +python3 run_lm_finetuning.py \ --model_type=gpt2 \ --model_name_or_path=**INPUT_MODEL_PATH** \ --output_dir=**OUTPUT_MODEL_PATH** \ @@ -48,11 +52,14 @@ python3 run_language_modeling.py \ --num_train_epochs=10 \ --per_gpu_train_batch_size=2 \ --gradient_accumulation_steps=8 \ + --logging_steps=100 \ --save_steps=1000 \ + --unfreeze_level 0 \ --train_data_file=**TRAIN_DATA_PATH** \ --do_train \ ``` If the model doesn't fit in your GPU, try changing the `block_size` or `per_gpu_train_batch_size`. +As for the tips how to train the model, visit the [ru_transformers repo][6]. To test the current model run the `run_generation.py` script: ```cmd @@ -70,6 +77,8 @@ python3 run_generation.py \ ``` Feel free to experiment with the `temperature`, `k`, `p` and `repetition_penalty`, to get better insights as to what do this arguments do, visit this [link][5]. +### Russian model +To train the russian model, please refer to the [ru_transformers repo][6] instructions. I only fixed some errors in the scripts provided there. So, if you would want to train using my script, please instead of `--tokenizer_class YTEncoder` flag provide `--model_type=gpt2-yttm`. Everything else should be the same. [1]: https://www.kaggle.com/jiriroz/qa-jokes "QA Jokes dataset" @@ -81,3 +90,5 @@ Feel free to experiment with the `temperature`, `k`, `p` and `repetition_penalty [4]: https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py "Transformers. Run language model example" [5]: https://huggingface.co/blog/how-to-generate "Hugging face. How to generate" + +[6]: https://github.com/mgrankin/ru_transformers "Russian GPT-2" From 22b2b2a5dfbde2efd33c299cd628025851f17b40 Mon Sep 17 00:00:00 2001 From: Vladislav Kuleykin Date: Mon, 11 May 2020 15:25:56 +0300 Subject: [PATCH 22/23] Update requirements.txt --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index f4060ed..60c1e16 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,4 +2,5 @@ numpy pandas python-telegram-bot peewee -transformers \ No newline at end of file +youtokentome +transformers From 3616dec21be5012b627cacc9389f6978a262ca1d Mon Sep 17 00:00:00 2001 From: Vlad Kuleykin Date: Mon, 11 May 2020 15:40:51 +0300 Subject: [PATCH 23/23] Added images and log example to README. --- README.md | 17 ++++++++++++++++- images/answers.png | Bin 0 -> 775209 bytes images/greeting.png | Bin 0 -> 364418 bytes 3 files changed, 16 insertions(+), 1 deletion(-) create mode 100644 images/answers.png create mode 100644 images/greeting.png diff --git a/README.md b/README.md index 770f2af..ad34c10 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,11 @@ # Joke Generator Bot A Question-Answer joke generator bot for Telegram. As a joke generator we used a fine-tuned GPT-2 model. +

+ Bot greeting message + Jokes example +

+ **Authors**: * Vlad Kuleykin * Boris Guryev @@ -19,7 +24,17 @@ And you have a working bot! All dispatched jokes will be stored in `jokes.db` with the feedback in a `vote` table. -Also, you can see the run log in stdout and `run.log` file. +Also, you can see the run log in stdout and `run.log` file: + +```log +2020-05-11 14:40:23,546 - DS: qa_jokes.csv - INFO - Got joke from dataset +2020-05-11 14:40:25,613 - ML: output_one - INFO - Got joke from the buffer +2020-05-11 14:40:27,483 - DS: shqa_jokes.csv - INFO - Got joke from dataset +2020-05-11 14:40:29,205 - ML: output_8 - INFO - Filling the buffer +2020-05-11 14:40:29,205 - ML: output_8 - INFO - Got joke from the buffer +``` +Where `DS` stands for the dataset and `ML` stands for the model. + ### A/B Testing For the purpose of testing the quality of our bot we added the ability to test the models against the jokes from datasets. diff --git a/images/answers.png b/images/answers.png new file mode 100644 index 0000000000000000000000000000000000000000..1d950671d2c246e2f8ac5f9593ced92fa7d6d565 GIT binary patch literal 775209 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