From 0a33a3a840a3dd117e7300624e2afa1aee5abff4 Mon Sep 17 00:00:00 2001 From: Nicolas Date: Thu, 23 May 2019 11:53:09 -0700 Subject: [PATCH] Big update: Keras, Tensorflow, Horovod, CuDNN (#57) --- .gitignore | 2 +- README.md | 533 ++++--- bin/cakechat_server.py | 5 +- cakechat/api/config.py | 8 +- cakechat/api/response.py | 37 +- cakechat/api/utils.py | 8 +- cakechat/api/v1/server.py | 23 +- cakechat/config.py | 129 +- cakechat/dialog_model/abstract_callbacks.py | 137 ++ cakechat/dialog_model/abstract_model.py | 141 ++ cakechat/dialog_model/callbacks.py | 112 ++ cakechat/dialog_model/factory.py | 64 +- .../candidates/abstract_generator.py | 4 +- .../inference/candidates/beamsearch.py | 42 +- .../inference/candidates/sampling.py | 17 +- cakechat/dialog_model/inference/factory.py | 4 +- cakechat/dialog_model/inference/predict.py | 12 +- cakechat/dialog_model/inference/predictor.py | 7 +- cakechat/dialog_model/inference/reranking.py | 8 +- .../dialog_model/inference/tests/predict.py | 27 +- .../dialog_model/inference/tests/sampling.py | 14 +- cakechat/dialog_model/inference/utils.py | 23 +- cakechat/dialog_model/inference_model.py | 48 + cakechat/dialog_model/keras_model.py | 307 ++++ cakechat/dialog_model/layers.py | 99 +- cakechat/dialog_model/model.py | 1306 ++++++++--------- cakechat/dialog_model/model_utils.py | 98 +- cakechat/dialog_model/quality/__init__.py | 3 +- cakechat/dialog_model/quality/logging.py | 167 +-- .../quality/metrics/distinctness.py | 7 +- .../quality/metrics/lexical_simlarity.py | 4 +- .../dialog_model/quality/metrics/plotters.py | 60 + .../dialog_model/quality/metrics/ranking.py | 2 +- .../dialog_model/quality/metrics/utils.py | 22 + .../dialog_model/quality/tests/metrics.py | 5 + cakechat/dialog_model/train.py | 254 ---- cakechat/utils/data_types.py | 20 +- cakechat/utils/dataset_loader.py | 107 +- cakechat/utils/env.py | 92 +- cakechat/utils/files_utils.py | 104 +- cakechat/utils/logger.py | 1 + cakechat/utils/offense_detector/detector.py | 19 +- cakechat/utils/plotters.py | 25 - cakechat/utils/profile.py | 2 +- cakechat/utils/s3/__init__.py | 3 +- cakechat/utils/s3/resolver.py | 20 +- cakechat/utils/s3/utils.py | 7 - cakechat/utils/tee_file.py | 6 +- cakechat/utils/telegram_bot_client.py | 6 +- cakechat/utils/text_processing/__init__.py | 2 +- cakechat/utils/text_processing/config.py | 2 +- .../utils/text_processing/corpus_iterator.py | 8 +- cakechat/utils/text_processing/dialog.py | 1 - .../utils/text_processing/str_processor.py | 27 +- cakechat/utils/text_processing/utils.py | 9 +- cakechat/utils/w2v/__init__.py | 3 +- cakechat/utils/w2v/model.py | 70 +- cakechat/utils/w2v/utils.py | 36 +- .../c_idx_processed_dialogs.json | 1 + .../test_processed_dialogs.txt | 20 + .../train_processed_dialogs.txt | 200 +-- data/quality/context_free_questions.txt | 3 +- data/quality/context_free_validation_set.txt | 10 +- .../tokens_index/t_idx_processed_dialogs.json | 1 + dockerfiles/Dockerfile.cpu | 28 +- dockerfiles/Dockerfile.gpu | 105 +- dockerfiles/Dockerfile3.cpu | 44 - dockerfiles/Dockerfile3.gpu | 102 -- requirements-local.txt | 2 + requirements.txt | 23 +- tools/distributed_train.py | 26 + tools/download_model.py | 26 - tools/fetch.py | 46 + tools/generate_predictions.py | 77 +- tools/generate_predictions_for_condition.py | 17 +- tools/prepare_index_files.py | 26 +- tools/quality/condition_quality.py | 143 +- tools/quality/prediction_distinctness.py | 75 +- tools/quality/ranking_quality.py | 17 +- tools/telegram_bot.py | 6 +- tools/test_api.py | 4 +- tools/train.py | 142 +- tools/train_w2v.py | 29 + 83 files changed, 3090 insertions(+), 2392 deletions(-) create mode 100644 cakechat/dialog_model/abstract_callbacks.py create mode 100644 cakechat/dialog_model/abstract_model.py create mode 100644 cakechat/dialog_model/callbacks.py create mode 100644 cakechat/dialog_model/inference_model.py create mode 100644 cakechat/dialog_model/keras_model.py create mode 100644 cakechat/dialog_model/quality/metrics/plotters.py delete mode 100644 cakechat/dialog_model/train.py delete mode 100644 cakechat/utils/plotters.py delete mode 100644 cakechat/utils/s3/utils.py create mode 100644 data/conditions_index/c_idx_processed_dialogs.json create mode 100644 data/corpora_processed/test_processed_dialogs.txt create mode 100644 data/tokens_index/t_idx_processed_dialogs.json delete mode 100644 dockerfiles/Dockerfile3.cpu delete mode 100644 dockerfiles/Dockerfile3.gpu create mode 100644 requirements-local.txt create mode 100644 tools/distributed_train.py delete mode 100644 tools/download_model.py create mode 100644 tools/fetch.py create mode 100644 tools/train_w2v.py diff --git a/.gitignore b/.gitignore index 3228c3d..d0d30de 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,6 @@ dist/* build/* tags venv -data +results !data/corpora_processed !data/quality diff --git a/README.md b/README.md index c54ae60..ff0f8a0 100644 --- a/README.md +++ b/README.md @@ -1,220 +1,354 @@ # CakeChat: Emotional Generative Dialog System -CakeChat is a dialog system that is able to express emotions in a text conversation. [Try it online!](https://cakechat.replika.ai/) +CakeChat is a chatbot that is able to express emotions via conversations. +[Try it online!](https://cakechat.replika.ai/) -![Demo](https://user-images.githubusercontent.com/764902/34832660-92570bfe-f6fe-11e7-9802-db2f8730a997.png) +![Demo](https://user-images.githubusercontent.com/2272790/57650691-3a8b9280-7580-11e9-9b60-ae3b28692c05.png) -It is written in [Theano](http://deeplearning.net/software/theano/) and [Lasagne](https://github.com/Lasagne/Lasagne). It uses end-to-end trained embeddings of 5 different emotions to generate responses conditioned by a given emotion. +CakeChat is built on [Keras](https://keras.io/) and [Tensorflow](https://www.tensorflow.org). -The code is flexible and allows to condition a response by an arbitrary categorical variable defined for some samples in the training data. With CakeChat you can, for example, train your own persona-based neural conversational model[\[5\]](#f5) or create an emotional chatting machine without external memory[\[4\]](#f4). +The code is flexible and allows to condition model's responses by an arbitrary categorical variable. +For example, you can train your own persona-based neural conversational model[\[1\]](#f1) +or create an emotional chatting machine[\[2\]](#f2). ## Table of contents - * [Network architecture and features](#network-architecture-and-features) - * [Quick start](#quick-start) - * [Setup](#setup) - * [Docker](#docker) - * [CPU-only setup](#cpu-only-setup) - * [GPU-enabled setup](#gpu-enabled-setup) - * [Manual setup](#manual-setup) - * [Getting the model](#getting-the-model) - * [Using a pre-trained model](#using-a-pre-trained-model) - * [Training your own model](#training-your-own-model) - * [Existing training datasets](#existing-training-datasets) - * [Running the system](#running-the-system) - * [Local HTTP\-server](#local-http-server) - * [HTTP\-server API description](#http-server-api-description) - * [Gunicorn HTTP\-server](#gunicorn-http-server) - * [Telegram bot](#telegram-bot) - * [Repository overview](#repository-overview) - * [Important tools](#important-tools) - * [Important configuration settings](#important-configuration-settings) - * [Example use cases](#example-use-cases) - * [References](#references) - * [Credits & Support](#credits--support) - * [License](#license) + 1. [Network architecture and features](#network-architecture-and-features) + 1. [Quick start](#quick-start) + 1. [Setup for training and testing](#setup-for-training-and-testing) + 1. [Docker](#docker) + 1. [CPU-only setup](#cpu-only-setup) + 1. [GPU-enabled setup](#gpu-enabled-setup) + 1. [Manual setup](#manual-setup) + 1. [Getting the pre-trained model](#getting-the-pre-trained-model) + 1. [Training data](#training-data) + 1. [Training the model](#training-the-model) + 1. [Fine-tuning the pre-trained model on your data](#fine-tuning-the-pre-trained-model-on-your-data) + 1. [Training the model from scratch](#training-the-model-from-scratch) + 1. [Distributed train](#distributed-train) + 1. [Validation metrics calculation](#validation-metrics-calculation) + 1. [Testing the trained model](#testing-the-trained-model) + 1. [Running CakeChat server](#running-cakechat-server) + 1. [Local HTTP\-server](#local-http-server) + 1. [HTTP\-server API description](#http-server-api-description) + 1. [Gunicorn HTTP\-server](#gunicorn-http-server) + 1. [Telegram bot](#telegram-bot) + 1. [Repository overview](#repository-overview) + 1. [Important tools](#important-tools) + 1. [Important configuration settings](#important-configuration-settings) + 1. [Example use cases](#example-use-cases) + 1. [References](#references) + 1. [Credits & Support](#credits--support) + 1. [License](#license) ## Network architecture and features -![Network architecture](https://user-images.githubusercontent.com/4047271/34774960-71c7bfa0-f622-11e7-812b-cdb84472577a.png) - - -* Model: - * Hierarchical Recurrent Encoder-Decoder (HRED) architecture for handling deep dialog context[\[7\]](#f7) - * Multilayer RNN with GRU cells. First layer of the utterance-level encoder is always bidirectional. - * Thought vector is fed into decoder on each decoding step. - * Decoder can be conditioned on any string label. For example: emotion label or id of a person talking. -* Word embedding layer: - * May be initialized using w2v model trained on your own corpus. - * Embedding layer may either stay fixed of be fine-tuned along with all other weights of the network. -* Decoding - * 4 different response generation algorithms: "sampling", "beamsearch", "sampling-reranking" and "beamsearch-reranking". - Reranking of the generated candidates is performed according to the log-likelihood or MMI-criteria[\[3\]](#f3). - See [configuration settings description](#important-configuration-settings) for details. -* Metrics: - * Perplexity - * n-gram distinct metrics adjusted to the samples size[\[3\]](#f3). - * Lexical similarity between samples of the model and some fixed dataset. - Lexical similarity is a cosine distance between TF-IDF vector of responses generated by the model and tokens - in the dataset. - * Ranking metrics: mean average precision and mean recall@k.[\[8\]](#f8) +![Network architecture](https://user-images.githubusercontent.com/2272790/57819307-b7fc0200-773c-11e9-971b-4f73a72ef8ba.png) + + +Model: + + * Hierarchical Recurrent Encoder-Decoder (HRED) architecture for handling deep dialog context[\[3\]](#f3). + * Multilayer RNN with GRU cells. The first layer of the utterance-level encoder is always bidirectional. + By default, CuDNNGRU implementation is used for ~25% acceleration during inference. + * Thought vector is fed into decoder on each decoding step. + * Decoder can be conditioned on any categorical label, for example, emotion label or persona id. + +Word embedding layer: + + * May be initialized using w2v model trained on your corpus. + * Embedding layer may be either fixed or fine-tuned along with other weights of the network. + +Decoding + + * 4 different response generation algorithms: "sampling", "beamsearch", "sampling-reranking" and "beamsearch-reranking". + Reranking of the generated candidates is performed according to the log-likelihood or MMI-criteria[\[4\]](#f4). + See [configuration settings description](#important-configuration-settings) for details. + +Metrics: + + * Perplexity + * n-gram distinct metrics adjusted to the samples size[\[4\]](#f4). + * Lexical similarity between samples of the model and some fixed dataset. + Lexical similarity is a cosine distance between TF-IDF vector of responses generated by the model and tokens + in the dataset. + * Ranking metrics: mean average precision and mean recall@k.[\[5\]](#f5) ## Quick start -Run the CPU-only pre-built docker image & start the CakeChat serving the model on 8080 port: +In case you are familiar with [Docker](https://docs.docker.com) here is the easiest way to run a pre-trained CakeChat +model as a server. You may need to run the following commands with `sudo`. + +CPU version: ```(bash) -docker run --name cakechat-dev -p 127.0.0.1:8080:8080 -it lukalabs/cakechat:latest \ +docker pull lukalabs/cakechat:latest && \ + +docker run --name cakechat-server -p 127.0.0.1:8080:8080 -it lukalabs/cakechat:latest \ bash -c "python bin/cakechat_server.py" ``` -(Or) using the GPU-enabled image: +GPU version: + +```(bash) +docker pull lukalabs/cakechat-gpu:latest && \ + +nvidia-docker run --name cakechat-gpu-server -p 127.0.0.1:8080:8080 -it lukalabs/cakechat-gpu:latest \ + bash -c "CUDA_VISIBLE_DEVICES=0 python bin/cakechat_server.py" +``` +That's it! Now test your CakeChat server by running the following command on your host machine: ```(bash) -nvidia-docker run --name cakechat-gpu-dev -p 127.0.0.1:8080:8080 -it lukalabs/cakechat-gpu:latest \ - bash -c "USE_GPU=0 python bin/cakechat_server.py" +python tools/test_api.py -f localhost -p 8080 -c "hi!" -c "hi, how are you?" -c "good!" -e "joy" +``` + +The response dict may look like this: +``` +{'response': "I'm fine!"} ``` -That's it! Now you can try it by running `python tools/test_api.py -f localhost -p 8080 -c "Hi! How are you?"` from the host command line. -## Setup +## Setup for training and testing ### Docker -This is the easiest way to set up the environment and install all the dependencies. +Docker is the easiest way to set up the environment and install all the dependencies for training and testing. -#### CPU-only setup -1. Install [Docker](https://docs.docker.com/engine/installation/) +#### CPU-only setup +*Note: +We strongly recommend using GPU-enabled environment for training CakeChat model. +Inference can be made both on GPUs and CPUs.* -2. Build a docker image +1. Install [Docker](https://docs.docker.com/engine/installation/). -Build a CPU-only image: +2. Pull a CPU-only docker image from dockerhub: ```(bash) -docker build -t cakechat:latest -f dockerfiles/Dockerfile.cpu dockerfiles/ +docker pull lukalabs/cakechat:latest ``` -3. Start the container - -Run a docker container in the CPU-only environment +3. Run a docker container in the CPU-only environment: ```(bash) -docker run --name -it cakechat:latest +docker run --name -it lukalabs/cakechat:latest ``` #### GPU-enabled setup 1. Install [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) for the GPU support. -2. Build a GPU-enabled docker image: +2. Pull GPU-enabled docker image from dockerhub: ```(bash) -nvidia-docker build -t cakechat-gpu:latest -f dockerfiles/Dockerfile.gpu dockerfiles/ +docker pull lukalabs/cakechat-gpu:latest ``` -3. Start the container - -Run a docker container in the GPU-enabled environment: +3. Run a docker container in the GPU-enabled environment: ```(bash) -nvidia-docker run --name -it cakechat-gpu:latest +nvidia-docker run --name -it cakechat-gpu:latest ``` -That's it! Now you can train your model and chat with it. - +That's it! Now you can train your model and chat with it. See the corresponding section below for further instructions. ### Manual setup -If you don't want to deal with docker images and containers, you can always simply run (with `sudo`, `--user` or inside your **[virtualenv](https://virtualenv.pypa.io/en/stable/)**): +If you don't want to deal with docker, you can install all the requirements manually: ```(bash) -pip install -r requirements.txt +pip install -r requirements.txt -r requirements-local.txt ``` -Most likely this will do the job. -**NB:** This method only provides a CPU-only environment. To get a GPU support, you'll need to build and install **[libgpuarray](http://deeplearning.net/software/libgpuarray/installation.html)** by yourself (see [Dockerfile.gpu](dockerfiles/Dockerfile.gpu) for example). +**NB:** + +We recommend installing the requirements inside a [virtualenv](https://virtualenv.pypa.io/en/stable/) to prevent +messing with your system packages. + + +## Getting the pre-trained model + +You can download our pre-trained model weights by running `python tools/fetch.py`. + +The params of the pre-trained model are the following: + + * context size **3** (, , ) + * each encoded utterance contains **up to 30 tokens** + * the decoded utterance contains **up to 32 tokens** + * both encoder and decoder have **2 GRU layers** with **768 hidden units** each + * first layer of the encoder is bidirectional + + +### Training data + +The model was trained on a preprocessed Twitter corpus with ~50 million dialogs (11Gb of text data). +To clean up the corpus, we removed +* URLs, retweets and citations; +* mentions and hashtags that are not preceded by regular words or punctuation marks; +* messages that contain more than 30 tokens. + +We used our emotions classifier to label each utterance with one of the following 5 emotions: `"neutral", "joy", +"anger", "sadness", "fear"`, and used these labels during training. +To mark-up your own corpus with emotions you can use, for example, [DeepMoji tool](https://github.com/bfelbo/DeepMoji). + +Unfortunately, due to Twitter's privacy policy, we are not allowed to provide our dataset. +You can train a dialog model on any text conversational dataset available to you, a great overview of existing +conversational datasets can be found here: https://breakend.github.io/DialogDatasets/ +The training data should be a txt file, where each line is a valid json object, representing a list of dialog utterances. +Refer to our [dummy train dataset](data/corpora_processed/train_processed_dialogs.txt) to see the necessary +file structure. Replace this dummy corpus with your data before training. -## Getting the model -### Using a pre-trained model +## Training the model -Run `python tools/download_model.py` to download our pre-trained model. +There are two options: -The model is trained with **context size 3** where -the encoded sequence contains **30 tokens or less** and -the decoded sequence contains **32 tokens or less**. -Both encoder and decoder contain **2 GRU layers** with **512 hidden units** each. +1. training from scratch +1. fine-tuning the provided trained model -The model was trained on a Twitter preprocessed conversational data. -To clean up the data, we removed URLs, retweets and citations. -Also we removed mentions and hashtags that are not preceded by normal words or punctuation marks -and filtered out all messages that contains more than 30 tokens. -Then we marked out each utterance with our emotions classifier that predicts one of the -5 emotions: "neutral", "joy", "anger", "sadness" and "fear". -To mark-up your own corpus with emotions you can use, for example, [DeepMoji tool](https://github.com/bfelbo/DeepMoji) -or any other emotions classifier that you have. +The first approach is less restrictive: you can use any training data you want and set any config params of the model. +However, you should be aware that you'll need enough train data (~50Mb at least), one or more GPUs and enough +patience (days) to get good model's responses. -#### Initalizing model weights from file -For some tools (for example [`tools/train.py`](tools/train.py)) you can specify the path to model's initialization weights via `--init_weights` argument. +The second approach is limited by the choice of config params of the pre-trained model – see `cakechat/config.py` for +the complete list. If the default params are suitable for your task, fine-tuning should be a good option. -The weights may come from a trained CakeChat model or from a model with a different architecture. -In the latter case some parameters of Cakechat model may be left without initialization: -a parameter will be initialized with a saved value if the parameter's name and shape are -identical to the saved parameter, otherwise the parameter will keep its default initialization weights. -See `load_weights` function for the details. +### Fine-tuning the pre-trained model on your data -### Training your own model +1. Fetch the pre-trained model from Amazon S3 by running `python tools/fetch.py`. -1. Put your training text corpus to [`data/corpora_processed/`](data/corpora_processed/). -Each line of the corpus file should be a JSON object containing a list of dialog messages sorted in chronological order. -Code is fully language-agnostic — you can use any unicode texts in datasets. -Refer to our dummy corpus to see the input format [`data/corpora_processed/train_processed_dialogs.txt`](data/corpora_processed/train_processed_dialogs.txt). +1. Put your training text corpus to +[`data/corpora_processed/train_processed_dialogs.txt`](data/corpora_processed/train_processed_dialogs.txt). Make sure that your +dataset is large enough, otherwise your model risks to overfit the data and the results will be poor. -2. The following datasets are used for validation and early stopping: +1. Run `python tools/train.py`. + 1. The script will look for the pre-trained model weights in `results/nn_models`, the full path is inferred from the + set of config params. + 1. If you want to initialize the model weights from a custom file, you can specify the path to the file via `-i` + argument, for example, `python tools/train.py -i results/nn_models/my_saved_weights/model.current`. + 1. Don't forget to set `CUDA_VISIBLE_DEVICES=` environment variable (with as in output of + **nvidia-smi** command) if you want to use GPU. For example, `CUDA_VISIBLE_DEVICES=0 python tools/train.py` will run the + train process on the 0-th GPU. + 1. Use parameter `-s` to train the model on a subset of the first N samples of your training data to speed up + preprocessing for debugging. For example, run `python tools/train.py -s 1000` to train on the first 1000 samples. -* [`data/corpora_processed/val_processed_dialogs.txt`](data/corpora_processed/val_processed_dialogs.txt)(dummy example) - for the context sensitive dataset -* [`data/quality/context_free_validation_set.txt`](data/quality/context_free_validation_set.txt) - for the context-free validation dataset -* [`data/quality/context_free_questions.txt`](data/quality/context_free_questions.txt) - is used for generating responses for logging and computing distinct-metrics -* [`data/quality/context_free_test_set.txt`](data/quality/context_free_test_set.txt) - is used for computing metrics of the trained model, e.g. ranking metrics +Weights of the trained model are saved to `results/nn_models/`. -3. Set up training parameters in [`cakechat/config.py`](cakechat/config.py). +### Training the model from scratch + +1. Put your training text corpus to +[`data/corpora_processed/train_processed_dialogs.txt`](data/corpora_processed/train_processed_dialogs.txt). + +1. Set up training parameters in [`cakechat/config.py`](cakechat/config.py). See [configuration settings description](#important-configuration-settings) for more details. -4. Run `python tools/prepare_index_files.py` to build the index files with tokens and conditions from the training corpus. -5. Run `python tools/train.py`. Don't forget to set `USE_GPU=` environment variable (with GPU_ID as from **nvidia-smi**) if you want to use GPU. -Use `SLICE_TRAINSET=N` to train the model on a subset of the first N samples of your training data to speed up preprocessing for debugging. -6. You can also set `IS_DEV=1` to enable the "development mode". It uses a reduced number of model parameters (decreased hidden layer dimensions, input and output sizes of token sequences, etc.), performs verbose logging and disables Theano graph optimizations. Use this mode for debugging. -7. Weights of your model will be saved in `data/nn_models/`. -### Existing training datasets -You can train a dialog model on any text conversational dataset available to you. A great overview of existing conversational datasets can be found here: https://breakend.github.io/DialogDatasets/ +1. Consider running `PYTHONHASHSEED=42 python tools/prepare_index_files.py` to build the index files with tokens and +conditions from the training corpus. Make sure to set `PYTHONHASHSEED` environment variable, otherwise you may get +different index files for different launches of the script. +**Warning:** this script overwrites the original tokens index files `data/tokens_index/t_idx_processed_dialogs.json` and +`data/conditions_index/c_idx_processed_dialogs.json`. +You should only run this script in case your corpus is large enough to contain all the words that you want your model +to understand. Otherwise, consider fine-tuning the pre-trained model as described above. If you messed up with index +files and want to get the default versions, delete your copies and run `python tools/fetch.py` anew. + +1. Consider running `python tools/train_w2v.py` to build w2v embedding from the training corpus. +**Warning:** this script overwrites the original w2v weights that are stored in `data/w2v_models`. +You should only run this script in case your corpus is large enough to contain all the words that you want your model +to understand. Otherwise, consider fine-tuning the pre-trained model as described above. If you messed up with w2v +files and want to get the default version, delete your file copy and run `python tools/fetch.py` anew. + +1. Run `python tools/train.py`. + 1. Don't forget to set `CUDA_VISIBLE_DEVICES=` environment variable (with +as in output of **nvidia-smi** command) if you want to use GPU. For example `CUDA_VISIBLE_DEVICES=0 python tools/train.py` +will run the train process on the 0-th GPU. + 1. Use parameter `-s` to train the model on a subset of the first N samples of your training data to speed up +preprocessing for debugging. For example, run `python tools/train.py -s 1000` to train on the first 1000 samples. + +1. You can also set `IS_DEV=1` to enable the "development mode". It uses a reduced number of model parameters +(decreased hidden layer dimensions, input and output sizes of token sequences, etc.) and performs verbose logging. +Refer to the bottom lines of `cakechat/config.py` for the complete list of dev params. + +Weights of the trained model are saved to `results/nn_models/`. + +### Distributed train + +GPU-enabled docker container supports distributed train on multiple GPUs using [horovod](https://github.com/horovod/horovod). + +For example, run `python tools/distributed_train.py -g 0 1` to start training on 0 and 1 GPUs. -## Running the system +### Validation metrics calculation + +During training the following datasets are used for validations metrics calculation: + +* [`data/corpora_processed/val_processed_dialogs.txt`](data/corpora_processed/val_processed_dialogs.txt)(dummy example, replace with your data) – for the +context-sensitive dataset +* [`data/quality/context_free_validation_set.txt`](data/quality/context_free_validation_set.txt) – for the context-free +validation dataset +* [`data/quality/context_free_questions.txt`](data/quality/context_free_questions.txt) – is used for generating +responses for logging and computing distinct-metrics +* [`data/quality/context_free_test_set.txt`](data/quality/context_free_test_set.txt) – is used for computing metrics of +the trained model, e.g. ranking metrics + +The metrics are stored to `cakechat/results/tensorboard` and can be visualized using +[Tensorboard](https://www.tensorflow.org/guide/summaries_and_tensorboard). +If you run a docker container from the provided CPU or GPU-enabled docker image, tensorboard server should start +automatically and serve on `http://localhost:6006`. Open this link in your browser to see the training graphs. + +If you installed the requirements manually, start tensorboard server first by running the following command from your +cakechat root directory: + +``` +mkdir -p results/tensorboard && tensorboard --logdir=results/tensorboard 2>results/tensorboard/err.log & +``` + +After that proceed to `http://localhost:6006`. + + +### Testing the trained model + +You can run the following tools to evaluate your trained model on +[test data](data/corpora_processed/test_processed_dialogs.txt)(dummy example, replace with your data): + +* [`tools/quality/ranking_quality.py`](tools/quality/ranking_quality.py) – +computes ranking metrics of a dialog model +* [`tools/quality/prediction_distinctness.py`](tools/quality/prediction_distinctness.py) – +computes distinct-metrics of a dialog model +* [`tools/quality/condition_quality.py`](tools/quality/condition_quality.py) – +computes metrics on different subsets of data according to the condition value +* [`tools/generate_predictions.py`](tools/generate_predictions.py) – +evaluates the model. Generates predictions of a dialog model on the set of given dialog contexts and then computes +metrics. Note that you should have a reverse-model in the `data/nn_models` directory if you want to use "\*-reranking" +prediction modes +* [`tools/generate_predictions_for_condition.py`](tools/generate_predictions_for_condition.py) – +generates predictions for a given condition value + + +## Running CakeChat server ### Local HTTP-server -Run a server that processes HTTP-requests with given input messages (contexts) and returns response messages of the model: +Run a server that processes HTTP-requests with given input messages and returns response messages from the model: ```(bash) python bin/cakechat_server.py ``` -Specify `USE_GPU=` environment variable if you want to use a certain GPU. +Specify `CUDA_VISIBLE_DEVICES=` environment variable to run the server on a certain GPU. -Wait until the model is compiled. -**Don't forget to run [`tools/download_model.py`](tools/download_model.py) prior to running [`bin/cakechat_server.py`](bin/cakechat_server.py) if you want to start an API with our pre-trained model.** +Don't forget to run `python tools/fetch.py` prior to starting the server if you want to use our pre-trained model. -To make sure everything works fine, test the model on the following conversation: +To make sure everything works fine, test the model on the following conversation > – Hi, Eddie, what's up? > – Not much, what about you? > – Fine, thanks. Are you going to the movies tomorrow? +by running the command: ```(bash) python tools/test_api.py -f 127.0.0.1 -p 8080 \ @@ -223,6 +357,11 @@ python tools/test_api.py -f 127.0.0.1 -p 8080 \ -c "Fine, thanks. Are you going to the movies tomorrow?" ``` +You should get a meaningful answer, for example: +``` +{'response': "Of course!"} +``` + #### HTTP-server API description ##### /cakechat_api/v1/actions/get_response @@ -246,130 +385,144 @@ data: { ``` 200 OK { - 'response': 'I\'m fine!' + 'response': 'I\'m fine!' } ``` ### Gunicorn HTTP-server -We recommend to use [Gunicorn](http://gunicorn.org/) for serving the API of your model at a production scale. +We recommend using [Gunicorn](http://gunicorn.org/) for serving the API of your model at production scale. + +1. Install gunicorn: `pip install gunicorn` -Run a server that processes HTTP-queries with input messages and returns response messages of the model: +2. Run a server that processes HTTP-queries with input messages and returns response messages of the model: ```(bash) cd bin && gunicorn cakechat_server:app -w 1 -b 127.0.0.1:8080 --timeout 2000 ``` -You may need to install gunicorn from pip: `pip install gunicorn`. - ### Telegram bot -You can also test your model in a Telegram bot: -[create a telegram bot](https://core.telegram.org/bots#3-how-do-i-create-a-bot) and run +You can run your CakeChat model as a Telegram bot: -`python tools/telegram_bot.py --token ` +1. [Create a telegram bot](https://core.telegram.org/bots#3-how-do-i-create-a-bot) to get bot's token. +2. Run `python tools/telegram_bot.py --token ` and chat with it on Telegram. ## Repository overview -* `cakechat/dialog_model/` - contains computational graph, training procedure and other model utilities -* `cakechat/dialog_model/inference/` - algorithms for response generation -* `cakechat/dialog_model/quality/` - code for metrics calculation and logging -* `cakechat/utils/` - utilities for text processing, w2v training, etc. -* `cakechat/api/` - functions to run http server: API configuration, error handling -* `tools/` - scripts for training, testing and evaluating your model +* `cakechat/dialog_model/` – contains computational graph, training procedure and other model utilities +* `cakechat/dialog_model/inference/` – algorithms for response generation +* `cakechat/dialog_model/quality/` – code for metrics calculation and logging +* `cakechat/utils/` – utilities for text processing, w2v training, etc. +* `cakechat/api/` – functions to run http server: API configuration, error handling +* `tools/` – scripts for training, testing and evaluating your model ### Important tools -* [`bin/cakechat_server.py`](bin/cakechat_server.py) - -Runs an HTTP-server that returns response messages of the model given dialog contexts and an emotion. See [run section](#gunicorn-http-server) for details. -* [`tools/train.py`](tools/train.py) - -Trains the model on your data. You can specify the path to model's initialization weights via `--init_weights` argument. Also use the `--reverse` flag to train the model used in "\*-reranking" response generation algorithms for more accurate predictions. -* [`tools/prepare_index_files.py`](tools/prepare_index_files.py) - -Prepares index for the most commonly used tokens and conditions. Use this script before training the model. -* [`tools/quality/ranking_quality.py`](tools/quality/ranking_quality.py) - +* [`bin/cakechat_server.py`](bin/cakechat_server.py) – +Runs an HTTP-server that returns response messages of the model given dialog contexts and an emotion. +See [run section](#gunicorn-http-server) for details. +* [`tools/train.py`](tools/train.py) – +Trains the model on your data. You can use the `--reverse` option to train a reverse-model used in "\*-reranking" response +generation algorithms for more accurate predictions. +* [`tools/prepare_index_files.py`](tools/prepare_index_files.py) – +Prepares index for the most commonly used tokens and conditions. Use this script before training the model from scratch +on your own data. +* [`tools/quality/ranking_quality.py`](tools/quality/ranking_quality.py) – Computes ranking metrics of a dialog model. -* [`tools/quality/prediction_distinctness.py`](tools/quality/prediction_distinctness.py) - +* [`tools/quality/prediction_distinctness.py`](tools/quality/prediction_distinctness.py) – Computes distinct-metrics of a dialog model. -See the [features section](#network=architecture=and-features) for details about the metrics. -* [`tools/quality/condition_quality.py`](tools/quality/condition_quality.py) - -Computes metrics on different subsets of a data according to the condition value. -* [`tools/generate_predictions.py`](tools/generate_predictions.py) - -Evaluates the model. Generates predictions of a dialog model on the set of given dialog contexts and then computes metrics. -Note that you should have a reverse-model in the `data/nn_models` directory, if you want to use "\*-reranking" prediction modes. -* [`tools/generate_predictions_for_condition.py`](tools/generate_predictions_for_condition.py) - +* [`tools/quality/condition_quality.py`](tools/quality/condition_quality.py) – +Computes metrics on different subsets of data according to the condition value. +* [`tools/generate_predictions.py`](tools/generate_predictions.py) – +Evaluates the model. Generates predictions of a dialog model on the set of given dialog contexts and then computes +metrics. Note that you should have a reverse-model in the `results/nn_models` directory if you want to use "\*-reranking" +prediction modes. +* [`tools/generate_predictions_for_condition.py`](tools/generate_predictions_for_condition.py) – Generates predictions for a given condition value. -* [`tools/test_api.py`](tools/test_api.py) - +* [`tools/test_api.py`](tools/test_api.py) – Example code to send requests to a running HTTP-server. -* [`tools/download_model.py`](tools/download_model.py) - -Downloads the pre-trained model and index files associated with it. Also compiles the whole model once to create Theano cache. -* [`tools/telegram_bot.py`](tools/telegram_bot.py) - -Runs a Telegram bot that uses a trained model. +* [`tools/fetch.py`](tools/fetch.py) – +Downloads the pre-trained model and index files associated with it. +* [`tools/telegram_bot.py`](tools/telegram_bot.py) – +Runs Telegram bot on top of trained model. ### Important configuration settings -All the configuration parameters for the network architecture, training, predicting and logging steps are defined in [`cakechat/config.py`](cakechat/config.py). -Some inference parameters used in an HTTP-server are defined in [`cakechat/api/config.py`](cakechat/api/config.py). +All the configuration parameters for the network architecture, training, predicting and logging steps are defined in +[`cakechat/config.py`](cakechat/config.py). Some inference parameters used in an HTTP-server are defined in +[`cakechat/api/config.py`](cakechat/api/config.py). * Network architecture and size * `HIDDEN_LAYER_DIMENSION` is the main parameter that defines the number of hidden units in recurrent layers. * `WORD_EMBEDDING_DIMENSION` and `CONDITION_EMBEDDING_DIMENSION` define the number of hidden units that each token/condition are mapped into. - Together they sum up to the dimension of input vector passed to the encoder RNN. * Number of units of the output layer of the decoder is defined by the number of tokens in the dictionary in the - tokens_index directory. + `tokens_index` directory. * Decoding algorithm: * `PREDICTION_MODE_FOR_TESTS` defines how the responses of the model are generated. The options are the following: - - **sampling** – response is sampled from output distribution token-by-token. + - **sampling** – response is sampled from output distribution token-by-token. For every token the temperature transform is performed prior to sampling. You can control the temperature value by tuning `DEFAULT_TEMPERATURE` parameter. - **sampling-reranking** – multiple candidate-responses are generated using sampling procedure described above. - After that the candidates are ranked according to their MMI-score[\[3\]](#f3) + After that the candidates are ranked according to their MMI-score[\[4\]](#f4) You can tune this mode by picking `SAMPLES_NUM_FOR_RERANKING` and `MMI_REVERSE_MODEL_SCORE_WEIGHT` parameters. - - **beamsearch** – candidates are sampled using [beam search algorithm](https://en.wikipedia.org/wiki/Beam_search). + - **beamsearch** – candidates are sampled using + [beam search algorithm](https://en.wikipedia.org/wiki/Beam_search). The candidates are ordered according to their log-likelihood score computed by the beam search procedure. - - **beamsearch-reranking** – same as above, but the candidates are re-ordered after the generation in the same way as - in sampling-reranking mode. + - **beamsearch-reranking** – same as above, but the candidates are re-ordered after the generation in the same + way as in sampling-reranking mode. Note that there are other parameters that affect the response generation process. See `REPETITION_PENALIZE_COEFFICIENT`, `NON_PENALIZABLE_TOKENS`, `MAX_PREDICTIONS_LENGTH`. + ## Example use cases -By providing additional condition labels within a dataset entries, you can build the following models: -* [A Persona-Based Neural Conversation Model][5] — a model that allows to condition responses on a persona ID to make them lexically similar to the given persona's linguistic style. -* [Emotional Chatting Machine][4]-like model — a model that allows to condition responses on an emotion to provide emotional styles (anger, sadness, joy, etc). -* [Topic Aware Neural Response Generation][6]-like model — a model that allows to condition responses on a certain topic to keep the topic-aware conversation. +By providing additional condition labels within dataset entries, you can build the following models: +* [A Persona-Based Neural Conversation Model][5] — a model that allows to condition responses on a persona ID to make +them lexically similar to the given persona's linguistic style. +* [Emotional Chatting Machine][4]-like model — a model that allows conditioning responses on different emotions to provide +emotional styles (anger, sadness, joy, etc). +* [Topic Aware Neural Response Generation][6]-like model — a model that allows to condition responses on a certain +topic to keep the topic-aware conversation. + +To make use of these extra conditions, please refer to the section [Training the model](#training-the-model). +Just set the "condition" field in the [training set](data/corpora_processed/train_processed_dialogs.txt) to one of the +following: **persona ID**, **emotion** or **topic** label, update the index files and start the training. -To make use of these extra conditions, please refer to the section [Training your own model](#training-your-own-model). Just set the "condition" field in the [training set](data/corpora_processed/train_processed_dialogs.txt) to one of the following: **persona ID**, **emotion** or **topic** label, update the index files and start the training. ## References -* \[1\] [A Neural Conversational Model][1] -* \[2\] [How NOT To Evaluate Your Dialogue System][2] -* \[3\] [A Diversity-Promoting Objective Function for Neural Conversation Models][3] -* \[4\] [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory][4] -* \[5\] [A Persona-Based Neural Conversation Model][5] +* \[1\] [A Persona-Based Neural Conversation Model][1] +* \[2\] [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory][2] +* \[3\] [A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion][3] +* \[4\] [A Diversity-Promoting Objective Function for Neural Conversation Models][4] +* \[5\] [Quantitative Evaluation of User Simulation Techniques for Spoken Dialogue Systems][5] * \[6\] [Topic Aware Neural Response Generation][6] -* \[7\] [A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion][7] -* \[8\] [Quantitative Evaluation of User Simulation Techniques for Spoken Dialogue Systems][8] - -[1]: https://arxiv.org/pdf/1506.05869.pdf -[2]: https://arxiv.org/pdf/1603.08023.pdf -[3]: https://arxiv.org/pdf/1510.03055.pdf -[4]: https://arxiv.org/pdf/1704.01074.pdf -[5]: https://arxiv.org/pdf/1603.06155.pdf + +[1]: https://arxiv.org/pdf/1603.06155.pdf +[2]: https://arxiv.org/pdf/1704.01074.pdf +[3]: https://arxiv.org/pdf/1507.02221.pdf +[4]: https://arxiv.org/pdf/1510.03055.pdf +[5]: http://mi.eng.cam.ac.uk/~sjy/papers/scgy05.pdf [6]: https://arxiv.org/pdf/1606.08340v2.pdf -[7]: https://arxiv.org/pdf/1507.02221.pdf -[8]: http://mi.eng.cam.ac.uk/~sjy/papers/scgy05.pdf ## Credits & Support -**CakeChat** is developed and maintained by the [Replika team](https://replika.ai): [Michael Khalman](https://github.com/mihaha), [Nikita Smetanin](https://github.com/nsmetanin), [Artem Sobolev](https://github.com/artsobolev), [Nicolas Ivanov](https://github.com/nicolas-ivanov), [Artem Rodichev](https://github.com/rodart) and [Denis Fedorenko](https://github.com/sadreamer). Demo by [Oleg Akbarov](https://github.com/olegakbarov), [Alexander Kuznetsov](https://github.com/alexkuz) and [Vladimir Chernosvitov](http://chernosvitov.com/). +**CakeChat** is developed and maintained by the [Replika team](https://replika.ai): + +[Nicolas Ivanov](https://github.com/nicolas-ivanov), [Michael Khalman](https://github.com/mihaha), +[Nikita Smetanin](https://github.com/nsmetanin), [Artem Rodichev](https://github.com/rodart) and +[Denis Fedorenko](https://github.com/sadreamer). + +Demo by [Oleg Akbarov](https://github.com/olegakbarov), [Alexander Kuznetsov](https://github.com/alexkuz) and +[Vladimir Chernosvitov](http://chernosvitov.com/). -All issues and feature requests can be tracked here - [GitHub Issues](https://github.com/lukalabs/cakechat/issues). +All issues and feature requests can be tracked here – [GitHub Issues](https://github.com/lukalabs/cakechat/issues). ## License -© 2018 Luka, Inc. Licensed under the Apache License, Version 2.0. See LICENSE file for more details. +© 2019 Luka, Inc. Licensed under the Apache License, Version 2.0. See LICENSE file for more details. diff --git a/bin/cakechat_server.py b/bin/cakechat_server.py index 2f28e28..7953f0d 100644 --- a/bin/cakechat_server.py +++ b/bin/cakechat_server.py @@ -3,9 +3,10 @@ sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -from cakechat.utils.env import init_theano_env +from cakechat.utils.env import set_keras_tf_session -init_theano_env() +gpu_memory_fraction = os.environ.get('GPU_MEMORY_FRACTION', 0.1) +set_keras_tf_session(gpu_memory_fraction) from cakechat.api.v1.server import app diff --git a/cakechat/api/config.py b/cakechat/api/config.py index e301214..3ff6822 100644 --- a/cakechat/api/config.py +++ b/cakechat/api/config.py @@ -1,15 +1,15 @@ from cakechat.config import PREDICTION_MODES # Prediction mode used in API -PREDICTION_MODE = PREDICTION_MODES.sampling +PREDICTION_MODE = PREDICTION_MODES.sampling_reranking # In case of PREDICTION_MODES.{beamsearch, beamsearch-reranking, sampling-reranking} choose random non-offensive # response out of K best candidates proposed by the algorithm. -NUM_BEST_CANDIDATES_TO_PICK_FROM = 5 +NUM_BEST_CANDIDATES_TO_PICK_FROM = 3 # In case of PREDICTION_MODES.sampling generate samples one-by-one until a non-offensive sample occurs. This parameter # defines max number of samples will be generated until succeed. -SAMPLING_ATTEMPTS_NUM = 5 +SAMPLING_ATTEMPTS_NUM = 10 # Default response text in case we weren't able to produce appropriate response. -DEFAULT_RESPONSE = '' +DEFAULT_RESPONSE = '🙊' diff --git a/cakechat/api/response.py b/cakechat/api/response.py index 2c11ee9..3120988 100644 --- a/cakechat/api/response.py +++ b/cakechat/api/response.py @@ -1,30 +1,31 @@ import random -from six.moves import xrange, map - from cakechat.api.config import PREDICTION_MODE, NUM_BEST_CANDIDATES_TO_PICK_FROM, SAMPLING_ATTEMPTS_NUM, \ DEFAULT_RESPONSE from cakechat.config import INPUT_CONTEXT_SIZE, INPUT_SEQUENCE_LENGTH, PREDICTION_MODES -from cakechat.dialog_model.factory import get_trained_model +from cakechat.dialog_model.factory import get_trained_model, get_reverse_model from cakechat.dialog_model.inference import get_nn_responses, warmup_predictor from cakechat.dialog_model.model_utils import transform_contexts_to_token_ids, transform_conditions_to_ids -from cakechat.utils.offense_detector.config import OFFENSIVE_PHRASES_PATH from cakechat.utils.offense_detector import OffenseDetector -from cakechat.utils.text_processing import get_tokens_sequence, get_pretty_str_from_tokens_sequence +from cakechat.utils.offense_detector.config import OFFENSIVE_PHRASES_PATH +from cakechat.utils.text_processing import get_tokens_sequence, prettify_response _offense_detector = OffenseDetector(OFFENSIVE_PHRASES_PATH) -_cakechat_model = get_trained_model(fetch_from_s3=False) +_cakechat_model = get_trained_model(reverse_model=get_reverse_model(PREDICTION_MODE)) warmup_predictor(_cakechat_model, PREDICTION_MODE) +def _is_appropriate_response(response): + return response != '' and not _offense_detector.has_offensive_ngrams(response) + + def _get_non_offensive_response_using_fast_sampling(context_tokens_ids, condition_id): - for _ in xrange(SAMPLING_ATTEMPTS_NUM): + for _ in range(SAMPLING_ATTEMPTS_NUM): response = get_nn_responses( context_tokens_ids, _cakechat_model, PREDICTION_MODES.sampling, condition_ids=condition_id)[0][0] - tokenized_response = get_tokens_sequence(response) - if not _offense_detector.has_offensive_ngrams(tokenized_response): - return get_pretty_str_from_tokens_sequence(tokenized_response) + if _is_appropriate_response(response): + return prettify_response(response) return DEFAULT_RESPONSE @@ -37,14 +38,10 @@ def _get_non_offensive_response(context_tokens_ids, condition_id): output_candidates_num=NUM_BEST_CANDIDATES_TO_PICK_FROM, condition_ids=condition_id)[0] - tokenized_responses = [get_tokens_sequence(response) for response in responses] - non_offensive_tokenized_responses = [ - r for r in tokenized_responses if not _offense_detector.has_offensive_ngrams(r) - ] - - if non_offensive_tokenized_responses: - tokenized_response = random.choice(non_offensive_tokenized_responses) - return get_pretty_str_from_tokens_sequence(tokenized_response) + responses = list(filter(_is_appropriate_response, responses)) + if responses: + selected_response = random.choice(responses) + return prettify_response(selected_response) return DEFAULT_RESPONSE @@ -60,9 +57,7 @@ def get_response(dialog_context, emotion): context_tokens_ids = transform_contexts_to_token_ids(tokenized_dialog_contexts, _cakechat_model.token_to_index, INPUT_SEQUENCE_LENGTH, INPUT_CONTEXT_SIZE) - condition_ids_num = len(context_tokens_ids) - condition_ids = transform_conditions_to_ids([emotion] * condition_ids_num, _cakechat_model.condition_to_index, - condition_ids_num) + condition_ids = transform_conditions_to_ids([emotion], _cakechat_model.condition_to_index, n_dialogs=1) if PREDICTION_MODE == PREDICTION_MODES.sampling: # Different strategy here for better performance. return _get_non_offensive_response_using_fast_sampling(context_tokens_ids, condition_ids) diff --git a/cakechat/api/utils.py b/cakechat/api/utils.py index e654282..2244a17 100644 --- a/cakechat/api/utils.py +++ b/cakechat/api/utils.py @@ -1,5 +1,4 @@ from flask import jsonify -from six import text_type def get_api_error_response(message, code, logger): @@ -8,8 +7,7 @@ def get_api_error_response(message, code, logger): def _is_list_of_unicode_strings(data): - return bool(data and isinstance(data, (list, tuple)) and - all(isinstance(s, text_type) for s in data)) + return data and isinstance(data, (list, tuple)) and all(isinstance(s, str) for s in data) def parse_dataset_param(params, param_name, required=True): @@ -18,8 +16,8 @@ def parse_dataset_param(params, param_name, required=True): dataset = params[param_name] if not _is_list_of_unicode_strings(dataset): - raise ValueError('`%s` should be non-empty list of unicode strings' % param_name) + raise ValueError('`{}` should be non-empty list of unicode strings'.format(param_name)) if not all(dataset): - raise ValueError('`%s` should not contain empty strings' % param_name) + raise ValueError('`{}` should not contain empty strings'.format(param_name)) return dataset diff --git a/cakechat/api/v1/server.py b/cakechat/api/v1/server.py index 98294a4..4eb4047 100644 --- a/cakechat/api/v1/server.py +++ b/cakechat/api/v1/server.py @@ -4,7 +4,6 @@ from cakechat.api.utils import get_api_error_response, parse_dataset_param from cakechat.config import EMOTIONS_TYPES, DEFAULT_CONDITION from cakechat.utils.logger import get_logger -from cakechat.utils.profile import timer _logger = get_logger(__name__) @@ -12,29 +11,29 @@ @app.route('/cakechat_api/v1/actions/get_response', methods=['POST']) -@timer def get_model_response(): params = request.get_json() - _logger.info('request params: %s' % params) + _logger.info('request params: {}'.format(params)) try: dialog_context = parse_dataset_param(params, param_name='context') except KeyError as e: - return get_api_error_response('Malformed request, no "%s" param was found' % str(e), 400, _logger) + return get_api_error_response('Malformed request, no "{}" param was found'.format(e), 400, _logger) except ValueError as e: - return get_api_error_response('Malformed request: %s' % str(e), 400, _logger) + return get_api_error_response('Malformed request: {}'.format(e), 400, _logger) emotion = params.get('emotion', DEFAULT_CONDITION) if emotion not in EMOTIONS_TYPES: - return get_api_error_response('Malformed request, emotion param "%s" is not in emotion list %s' % - (emotion, list(EMOTIONS_TYPES)), 400, _logger) + return get_api_error_response( + 'Malformed request, emotion param "{}" is not in emotion list {}'.format(emotion, list(EMOTIONS_TYPES)), + 400, _logger) response = get_response(dialog_context, emotion) + _logger.info('Given response: "{}" for context: {}; emotion "{}"'.format(response, dialog_context, emotion)) - if not response: - _logger.error('No response for context: %s; emotion "%s"' % (dialog_context, emotion)) - return jsonify({}), 200 + return jsonify({'response': response}), 200 - _logger.info('Given response: "%s" for context: %s; emotion "%s"' % (response, dialog_context, emotion)) - return jsonify({'response': response}), 200 +@app.errorhandler(Exception) +def on_exception(exception): + return get_api_error_response('Can\'t process request: {}'.format(exception), 500, _logger) diff --git a/cakechat/config.py b/cakechat/config.py index a0410b9..811cbba 100644 --- a/cakechat/config.py +++ b/cakechat/config.py @@ -3,47 +3,52 @@ from cakechat.utils.data_structures import create_namedtuple_instance from cakechat.utils.env import is_dev_env +MODEL_NAME = 'cakechat_v2.0_keras_tf' + +INTX = 'uint16' # use unsigined 16-bits int representation for memory efficiency RANDOM_SEED = 42 # Fix the random seed to a certain value to make everything reproducible # AWS S3 params -S3_MODELS_BUCKET_NAME = 'cake-chat-data' # S3 bucket with all the data +S3_MODELS_BUCKET_NAME = 'cake-chat-data-v2' # S3 bucket with all the data S3_NN_MODEL_REMOTE_DIR = 'nn_models' # S3 remote directory with models itself S3_TOKENS_IDX_REMOTE_DIR = 'tokens_index' # S3 remote directory with tokens index S3_CONDITIONS_IDX_REMOTE_DIR = 'conditions_index' # S3 remote directory with conditions index S3_W2V_REMOTE_DIR = 'w2v_models' # S3 remote directory with pre-trained w2v models -# data params -DATA_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data') # Directory to store all the data -# e.g. datasets, models, indices -NN_MODELS_DIR = os.path.join(DATA_DIR, 'nn_models') # Path to a directory for saving and restoring dialog models -PROCESSED_CORPUS_DIR = os.path.join(DATA_DIR, 'corpora_processed') # Path to a processed corpora datasets -TOKEN_INDEX_DIR = os.path.join(DATA_DIR, 'tokens_index') # Path to a prepared tokens index file -CONDITION_IDS_INDEX_DIR = os.path.join(DATA_DIR, 'conditions_index') # Path to a prepared conditions index file +# train datasets +DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data') +PROCESSED_CORPUS_DIR = os.path.join(DATA_PATH, 'corpora_processed') +TOKEN_INDEX_DIR = os.path.join(DATA_PATH, 'tokens_index') # Path to prepared tokens index directory +CONDITION_IDS_INDEX_DIR = os.path.join(DATA_PATH, 'conditions_index') # Path to prepared conditions index directory # train & val data params BASE_CORPUS_NAME = 'processed_dialogs' # Basic corpus name prefix -TRAIN_CORPUS_NAME = 'train_' + BASE_CORPUS_NAME # Corpus name prefix for the training dataset -CONTEXT_SENSITIVE_VAL_CORPUS_NAME = 'val_' + BASE_CORPUS_NAME # Corpus name prefix for the validation dataset - +TRAIN_CORPUS_NAME = 'train_' + BASE_CORPUS_NAME # Training dataset filename prefix +CONTEXT_SENSITIVE_VAL_CORPUS_NAME = 'val_' + BASE_CORPUS_NAME # Validation dataset filename prefix for intermediate +CONTEXT_SENSITIVE_TEST_CORPUS_NAME = 'test_' + BASE_CORPUS_NAME # Testing dataset for final metrics calculation MAX_VAL_LINES_NUM = 10000 # Max lines number from validation set to be used for metrics calculation -VAL_SUBSET_SIZE = 250 # Subset from the validation dataset to be used to calculated some validation metrics -TRAIN_SUBSET_SIZE = int(os.environ['SLICE_TRAINSET']) if 'SLICE_TRAINSET' in os.environ else None # Subset from the -# training dataset to be used during the training. In case of None use all lines in the train dataset (default behavior) -# test data paths -TEST_DATA_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data', 'quality') +# test datasets +TEST_DATA_DIR = os.path.join(DATA_PATH, 'quality') CONTEXT_FREE_VAL_CORPUS_NAME = 'context_free_validation_set' # Context-free validation set path TEST_CORPUS_NAME = 'context_free_test_set' # Context-free test set path QUESTIONS_CORPUS_NAME = 'context_free_questions' # Context-free questions only path +# directory to store model wights and calcualted metrics +RESULTS_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'results') # Directory to store training results +TENSORBOARD_LOG_DIR = os.path.join(RESULTS_PATH, 'tensorboard') # Path to tensorboard logs directory + # word embeddings params USE_PRETRAINED_W2V_EMBEDDINGS_LAYER = True # Whether to use word2vec to pre-train weights for the embedding layer TRAIN_WORD_EMBEDDINGS_LAYER = True # Allow fine-tuning of the word embedding layer during the model training -W2V_MODEL_DIR = os.path.join(DATA_DIR, 'w2v_models') # Path to store & load trained word2vec models +W2V_MODEL_DIR = os.path.join(DATA_PATH, 'w2v_models') # Path to store & load trained word2vec models WORD_EMBEDDING_DIMENSION = 128 # word2vec embedding dimension W2V_WINDOW_SIZE = 10 # word2vec window size, used during the w2v pre-training USE_SKIP_GRAM = True # Use skip-gram word2vec mode. When False, CBOW is used +TOKEN_REPRESENTATION_SIZE = 256 MIN_WORD_FREQ = 1 # Minimum frequency of a word to be used in word2vec pre-calculation +VOCABULARY_MAX_SIZE = 50000 # Maximum vocabulary size in tokens +MAX_CONDITIONS_NUM = 5 # Maximum conditions num # condition inputs. We use five major emotions to condition our model's predictions EMOTIONS_TYPES = create_namedtuple_instance( @@ -52,23 +57,25 @@ CONDITION_EMBEDDING_DIMENSION = 128 # Conditions embedding layer dimension to be trained. # NN architecture params -ENCODER_DEPTH = 2 # Number of recurrent (GRU) layers for the encoder -DECODER_DEPTH = 2 # Number of recurrent (GRU) layers for the decoder -HIDDEN_LAYER_DIMENSION = 512 # Dimension for the recurrent layer +HIDDEN_LAYER_DIMENSION = 768 # Dimension for the recurrent layer DENSE_DROPOUT_RATIO = 0.2 # Use dropout with the given ratio before decoder's output +USE_CUDNN = bool(os.environ.get('CUDA_VISIBLE_DEVICES')) # True by default for GPU-enable machines (provides ~25% inference +# speed up) and False on CPU-only machines since they does not support CuDNN # training params +EPOCHS_NUM = 2 # Total epochs num +BATCH_SIZE = 196 # Number of samples to be used for gradient estimation on each train step. In case of using multiple +# GPUs for train, each worker will have this number of samples on each step. +SHUFFLE_TRAINING_BATCHES = True # Shuffle training batches in the dataset each epoch + INPUT_SEQUENCE_LENGTH = 30 # Input sequence length for the model during the training; INPUT_CONTEXT_SIZE = 3 # Maximum depth of the conversational history to be used in encoder (at least 1) OUTPUT_SEQUENCE_LENGTH = 32 # Output sequence length. Better to keep as INPUT_SEQUENCE_LENGTH+2 for start/end tokens -BATCH_SIZE = 192 # Default batch size which fits into 8GB of GPU memory -SHUFFLE_TRAINING_BATCHES = True # Shuffle training batches in the dataset each epoch -EPOCHS_NUM = 100 # Total epochs num -GRAD_CLIP = 5.0 # Gradient clipping passed into theano.gradient.grad_clip() -LEARNING_RATE = 1.0 # Learning rate for the chosen optimizer (currently using Adadelta, see model.py) -# model params -NN_MODEL_PREFIX = 'cakechat_v1.3' # Specify prefix to be prepended to model's name +GRAD_CLIP = 5.0 # Gradient clipping param passed to optimizer +LEARNING_RATE = 6.0 # Learning rate for Adadelta optimzer +LOG_RUN_METADATA = False # Set 'True' to profile memory consumption and computation time on tensorboard +AUTOENCODER_MODE = False # Set 'True' to switch seq2seq (x -> y) into autoencoder (x -> x). Used for debugging # predictions params MAX_PREDICTIONS_LENGTH = 40 # Max. number of tokens which can be generated on the prediction step @@ -90,35 +97,55 @@ DEFAULT_TEMPERATURE = 0.5 # Default softmax temperature used for sampling # Options for beamsearch and sampling-reranking: -BEAM_SIZE = 20 # Size of the beam (beamsearch only) -SAMPLES_NUM_FOR_RERANKING = 20 # Number of samples used in reranking (sampling-reranking only) +BEAM_SIZE = 10 # Size of the beam (beamsearch only) +SAMPLES_NUM_FOR_RERANKING = 10 # Number of samples used in reranking (sampling-reranking only) MMI_REVERSE_MODEL_SCORE_WEIGHT = 1.0 # Weight for MMI reranking reverse-model score, see the paper: # 0.0 - scoring is performing using completely the default model, 1.0 - using completely the reverse model # Logging params -LOG_CANDIDATES_NUM = 10 # Number of candidates to be printed to output during the logging +LOG_CANDIDATES_NUM = 3 # Number of candidates to be printed to output during the logging SCREEN_LOG_NUM_TEST_LINES = 10 # Number of first test lines to use when logging outputs on screen -SCREEN_LOG_FREQUENCY_PER_BATCHES = 500 # How many batches to train until next logging of output on screen -LOG_TO_TB_FREQUENCY_PER_BATCHES = 500 # How many batches to train until next metrics computed for TensorBoard -LOG_TO_FILE_FREQUENCY_PER_BATCHES = 2500 # How many batches to train until next logging of all the output into file -SAVE_MODEL_FREQUENCY_PER_BATCHES = 2500 # How many batches to train until next logging of all the output into file -AVG_LOSS_DECAY = 0.99 # Decay for the averaging the loss +EVAL_STATE_PER_BATCHES = 500 # How many batches to train until next metrics computed for TensorBoard -# Use reduced sizes for input/output sequences, hidden layers and datasets sizes for the 'Developer Mode' +# Use reduced params values for development if is_dev_env(): - INPUT_SEQUENCE_LENGTH = 7 - OUTPUT_SEQUENCE_LENGTH = 9 - MAX_PREDICTIONS_LENGTH = 5 - BATCH_SIZE = 128 - HIDDEN_LAYER_DIMENSION = 7 - SCREEN_LOG_FREQUENCY_PER_BATCHES = 2 - LOG_TO_TB_FREQUENCY_PER_BATCHES = 3 - LOG_TO_FILE_FREQUENCY_PER_BATCHES = 4 - SAVE_MODEL_FREQUENCY_PER_BATCHES = 4 - WORD_EMBEDDING_DIMENSION = 15 - SAMPLES_NUM_FOR_RERANKING = BEAM_SIZE = 5 + # train & val data params + MAX_VAL_LINES_NUM = 10 + + # word embeddings params + USE_PRETRAINED_W2V_EMBEDDINGS_LAYER = True + TRAIN_WORD_EMBEDDINGS_LAYER = True + WORD_EMBEDDING_DIMENSION = 64 + VOCABULARY_MAX_SIZE = 1000 + MAX_CONDITIONS_NUM = 5 + + # condition inputs + CONDITION_EMBEDDING_DIMENSION = 1 + + # NN architecture params + HIDDEN_LAYER_DIMENSION = 128 + DENSE_DROPOUT_RATIO = 0.2 + USE_CUDNN = False + + # training params + INPUT_SEQUENCE_LENGTH = 3 + INPUT_CONTEXT_SIZE = 1 + OUTPUT_SEQUENCE_LENGTH = 5 + BATCH_SIZE = 4 + SHUFFLE_TRAINING_BATCHES = False + EPOCHS_NUM = 4 + LEARNING_RATE = 1.0 + LOG_RUN_METADATA = False + AUTOENCODER_MODE = False + + # predictions params + MAX_PREDICTIONS_LENGTH = 4 + + # options for beamsearch and sampling-reranking: + SAMPLES_NUM_FOR_RERANKING = 5 + BEAM_SIZE = 5 + + # logging params LOG_CANDIDATES_NUM = 3 - USE_PRETRAINED_W2V_EMBEDDINGS_LAYER = False - VAL_SUBSET_SIZE = 100 - MAX_VAL_LINES_NUM = 100 - TRAIN_SUBSET_SIZE = 10000 + SCREEN_LOG_NUM_TEST_LINES = 4 + EVAL_STATE_PER_BATCHES = 5 diff --git a/cakechat/dialog_model/abstract_callbacks.py b/cakechat/dialog_model/abstract_callbacks.py new file mode 100644 index 0000000..50965ce --- /dev/null +++ b/cakechat/dialog_model/abstract_callbacks.py @@ -0,0 +1,137 @@ +""" +Essentials for using training callbacks together with AbstractKerasModel. + +TL;DR +1. If you are implementing your own callback, please inherit it from `AbstractKerasModelCallback`. +2. If you are using a stock (keras) callback, please wrap it with `ParametrizedCallback` +""" +import abc + +import keras + + +class _KerasCallbackAdapter(keras.callbacks.Callback): + """ + Class that adapts `AbstractKerasModelCallback`-based callback to the native keras one. Not assumed to be used + directly in the client code. + """ + + def __init__(self, callback): + """ + :param callback: instance of `AbstractKerasModelCallback` + """ + super(_KerasCallbackAdapter, self).__init__() + self.__callback = callback + + def on_epoch_begin(self, epoch, logs=None): + return self.__callback.on_epoch_begin(epoch, logs) + + def on_epoch_end(self, epoch, logs=None): + return self.__callback.on_epoch_end(epoch, logs) + + def on_batch_begin(self, batch, logs=None): + return self.__callback.on_batch_begin(batch, logs) + + def on_batch_end(self, batch, logs=None): + return self.__callback.on_batch_end(batch, logs) + + def on_train_begin(self, logs=None): + return self.__callback.on_train_begin(logs) + + def on_train_end(self, logs=None): + return self.__callback.on_train_end(logs) + + +class _AbstractCallback(object, metaclass=abc.ABCMeta): + """ + Common interface for training callbacks used with AbstractKerasModel models + """ + + @property + @abc.abstractmethod + def callback_params(self): + """ + :return dict of params that affect the resulting model + """ + pass + + @property + @abc.abstractmethod + def runs_only_on_main_worker(self): + """ + :return True, if this callback runs only on main worker (in case of distributed training), False otherwise + """ + pass + + +class AbstractKerasModelCallback(_AbstractCallback, metaclass=abc.ABCMeta): + """ + Base callback class that is compatible with `AbstractKerasModel` (so it can be used within keras via + `_KerasCallbackAdapter`). If you are implementing your own callback that utilizes `AbstractKerasModel`, please + inherit from this class and not from `keras.callbacks.Callback` in order to isolate your callback's variables and + methods from the ones that belong to `keras.callbacks.Callback`. + See `EvaluateAndSaveBestIntermediateModelCallback` as an example. + """ + + def __init__(self, model): + """ + :param model: instance of `AbstractKerasModel` + """ + super(AbstractKerasModelCallback, self).__init__() + self._model = model + + def on_epoch_begin(self, epoch, logs=None): + pass + + def on_epoch_end(self, epoch, logs=None): + pass + + def on_batch_begin(self, batch, logs=None): + pass + + def on_batch_end(self, batch, logs=None): + pass + + def on_train_begin(self, logs=None): + pass + + def on_train_end(self, logs=None): + pass + + +class ParametrizedCallback(_AbstractCallback): + """ + Provides `_AbstractCallback` interface for arbitrary callback object. + If you are going to use one of the stock keras callbacks, please choose one of the following options: + 1. Use this class to instantiate a AbstractKerasModel-friendly callback object. Specify `callback_params` if the callback + affects the resulting model by some parameters + 2. Create class (ParametrizedCallback) and pass the original callback object, as well as + `callback_params` if the callback affects the resulting model. Recommended, if you are going to publish your + callback in the repository. + See `AbstractKerasModel#_create_essential_callbacks` as an example. + """ + + def __init__(self, callback, runs_only_on_main_worker, callback_params=None): + """ + :param callback: arbitrary callback object (e.g. instance of `keras.callbacks.Callback`) + :param runs_only_on_main_worker: True, if this callback runs only on main worker (in case of distributed + training), False otherwise + :param callback_params: dict of params that affect the resulting model + :return: + """ + super(ParametrizedCallback, self).__init__() + self._callback = callback + self._callback_params = callback_params or {} + self._runs_only_on_main_worker = runs_only_on_main_worker + + @property + def callback_params(self): + return self._callback_params + + @property + def runs_only_on_main_worker(self): + return self._runs_only_on_main_worker + + @property + def callback(self): + return self._callback diff --git a/cakechat/dialog_model/abstract_model.py b/cakechat/dialog_model/abstract_model.py new file mode 100644 index 0000000..9fe0261 --- /dev/null +++ b/cakechat/dialog_model/abstract_model.py @@ -0,0 +1,141 @@ +import abc +import hashlib +import json +import os + +from cakechat.dialog_model.quality.metrics.utils import MetricsSerializer +from cakechat.utils.files_utils import DummyFileResolver +from cakechat.utils.logger import WithLogger + + +class AbstractModel(WithLogger, metaclass=abc.ABCMeta): + # Model resources default values + _MODEL_RESOURCE_NAME = 'model' + _METRICS_RESOURCE_NAME = 'metrics' + + def __init__(self, model_resolver_factory=None, metrics_serializer=None): + """ + :param model_resolver_factory: a factory of `cakechat.utils.files_utils.AbstractFileResolver` that + takes model path and returns a file resolver object + :param metrics_serializer: an instance compatible with the interface of + `cakechat.dialog_model.quality.metrics.utils.MetricsSerializer` + :return: + """ + super(AbstractModel, self).__init__() + + self._model = None + self.__model_resolver_factory = model_resolver_factory if model_resolver_factory else DummyFileResolver + + self._model_init_path = None + + self._metrics = None + self._metrics_serializer = metrics_serializer if metrics_serializer else MetricsSerializer() + + @property + def model(self): + return self._model + + @property + def metrics(self): + return self._metrics + + @property + def _model_params_str(self): + return json.dumps(self.model_params, sort_keys=True) + + @property + def model_id(self): + if not hasattr(self, '__uniq_id'): + self.__uniq_id = hashlib.md5(self._model_params_str.encode()).hexdigest() + + # it's enough to use only the first 12 characters of the hash to avoid collisions + # see: http://stackoverflow.com/a/18134919 + short_id = self.__uniq_id[:12] + return '{}_{}'.format(self.model_name, short_id) + + @property + def model_path(self): + return os.path.join(self._model_dir, self.model_id) + + @property + def _model_resource_path(self): + return os.path.join(self.model_path, self._MODEL_RESOURCE_NAME) + + @property + def _metrics_resource_path(self): + return os.path.join(self.model_path, self._METRICS_RESOURCE_NAME) + + @property + @abc.abstractmethod + def model_name(self): + """ + Returns human-readable model name + + :return: + """ + pass + + @property + @abc.abstractmethod + def model_params(self): + """ + Returns a dict with model params. Note that these params are used to compute a unique model id, so they should + reflect the full model state (including the data (or its id) on which the model is trained/validated). + + :return: + """ + pass + + @property + @abc.abstractmethod + def _model_dir(self): + pass + + @abc.abstractmethod + def train_model(self, *args, **kwargs): + """ + Trains the model. Put just for method name unification + + :param args: + :param kwargs: + :return: + """ + pass + + @abc.abstractmethod + def _load_model(self, fresh_model, model_resource_path): + """ + Fills given fresh_model by parameters stored in model_resource_path + + :param fresh_model: model object + :param model_resource_path: + :return: loaded model + """ + pass + + @abc.abstractmethod + def _save_model(self, model_resource_path): + pass + + @abc.abstractmethod + def _evaluate(self): + """ + Evaluates model on validation set/sets + + :return: { dataset_name : { metric_name : metric_value } } + """ + pass + + def resolve_model(self): + self._logger.info('Looking for the previously trained model') + self._logger.info('Model params str: {}'.format(self._model_params_str)) + model_path = self._model_init_path or self._model_resource_path + + if not self._model_init_path and not self.__model_resolver_factory(self.model_path).resolve(): + err_msg = 'Can\'t find previously trained model in {}'.format(self.model_path) + self._logger.error(err_msg) + raise ValueError(err_msg) + + self._logger.info('Loading previously calculated model') + self._model = self._load_model(self._model, model_path) + self._logger.info('Loaded model: {}'.format(model_path)) diff --git a/cakechat/dialog_model/callbacks.py b/cakechat/dialog_model/callbacks.py new file mode 100644 index 0000000..a822700 --- /dev/null +++ b/cakechat/dialog_model/callbacks.py @@ -0,0 +1,112 @@ +import time + +from cakechat.config import SCREEN_LOG_NUM_TEST_LINES, PREDICTION_MODE_FOR_TESTS, LOG_CANDIDATES_NUM, LOG_RUN_METADATA, \ + EVAL_STATE_PER_BATCHES +from cakechat.dialog_model.inference import get_nn_responses +from cakechat.dialog_model.keras_model import EvaluateAndSaveBestIntermediateModelCallback +from cakechat.dialog_model.model_utils import transform_context_token_ids_to_sentences +from cakechat.utils.dataset_loader import load_questions_set +from cakechat.utils.logger import laconic_logger + + +class CakeChatEvaluatorCallback(EvaluateAndSaveBestIntermediateModelCallback): + def __init__(self, + model, + index_to_token, + batch_size, + batches_num_per_epoch, + eval_state_per_batches=EVAL_STATE_PER_BATCHES, + prediction_mode_for_tests=PREDICTION_MODE_FOR_TESTS, + log_run_metadata=LOG_RUN_METADATA, + screen_log_num_test_lines=SCREEN_LOG_NUM_TEST_LINES, + log_candidates_num=LOG_CANDIDATES_NUM): + """ + :param model: CakeChatModel object + :param eval_state_per_batches: run model evaluation each `eval_state_per_batches` steps + """ + super(CakeChatEvaluatorCallback, self).__init__(model, eval_state_per_batches) + + self._index_to_token = index_to_token + self._token_to_index = {v: k for k, v in index_to_token.items()} + + self._val_contexts_tokens_ids = load_questions_set(self._token_to_index).x[:screen_log_num_test_lines] + self._val_contexts = \ + transform_context_token_ids_to_sentences(self._val_contexts_tokens_ids, self._index_to_token) + + self._batch_size = batch_size + self._batches_num = batches_num_per_epoch + + self._cur_batch_id = 0 + self._cur_loss = 0 + + self._batch_start_time = None + self._total_training_time = 0 + + # logging params + self._prediction_mode_for_tests = prediction_mode_for_tests + self._log_run_metadata = log_run_metadata + self._log_candidates_num = log_candidates_num + + def on_batch_begin(self, batch, logs=None): + self._batch_start_time = time.time() + + def on_batch_end(self, batch, logs=None): + self._total_training_time += time.time() - self._batch_start_time + + if batch % self._eval_state_per_batches == 0: + self._cur_loss = logs.get('loss') + self._log_train_statistics() + self._log_sample_answers() + self._eval_and_save_current_model(batch) + self._log_metrics({'train': {'loss': self._cur_loss}}) + + if self._log_run_metadata: + self._model.metrics_plotter.log_run_metadata(self._model.model_id, self._model.run_metadata) + self._logger.info('Logged run_metadata to tensorboard') + + self._cur_batch_id += 1 + + def _log_train_statistics(self): + total_time = time.time() - self._training_start_time + + progress = self._cur_batch_id / self._batches_num # may be more than 100% if epochs num is more than 1 + avr_time_per_batch = total_time / (self._cur_batch_id + 1) + expected_time_per_epoch = avr_time_per_batch * self._batches_num + total_training_time_in_percent = self._total_training_time / total_time + + batches_per_sec = self._cur_batch_id / total_time + samples_per_sec = self._cur_batch_id * self._batch_size / total_time + + self._logger.info('Train statistics:\n') + + laconic_logger.info('batch:\t{batch_id} / {batches_num} ({progress:.1%})'.format( + batch_id=self._cur_batch_id, batches_num=self._batches_num, progress=progress)) + + laconic_logger.info('loss:\t{loss:.2f}'.format(loss=self._cur_loss)) + + laconic_logger.info('time:\tepoch estimate {epoch_time:.2f} h | total {total_time:.2f} h | ' + 'train {training_time:.2f} h ({training_time_percent:.1%})'.format( + epoch_time=expected_time_per_epoch / 3600, # in hours + total_time=total_time / 3600, # in hours + training_time=self._total_training_time / 3600, # in hours + training_time_percent=total_training_time_in_percent)) + + laconic_logger.info('speed:\t{batches_per_hour:.0f} batches/h, {samples_per_sec:.0f} samples/sec\n'.format( + batches_per_hour=batches_per_sec * 3600, samples_per_sec=samples_per_sec)) + + def _log_sample_answers(self): + self._logger.info('Sample responses for {} mode:'.format(self._prediction_mode_for_tests)) + + responses = get_nn_responses( + self._val_contexts_tokens_ids, + self._model, + self._prediction_mode_for_tests, + output_candidates_num=self._log_candidates_num) + + for context, response_candidates in zip(self._val_contexts, responses): + laconic_logger.info('') # for better readability + + for i, response in enumerate(response_candidates): + laconic_logger.info('{0: <35}\t #{1: <2d} --> \t{2}'.format(context, i + 1, response)) + + laconic_logger.info('') # for better readability diff --git a/cakechat/dialog_model/factory.py b/cakechat/dialog_model/factory.py index cd2d2a1..0061dc7 100644 --- a/cakechat/dialog_model/factory.py +++ b/cakechat/dialog_model/factory.py @@ -3,23 +3,28 @@ from cachetools import cached from cakechat.config import BASE_CORPUS_NAME, S3_MODELS_BUCKET_NAME, S3_TOKENS_IDX_REMOTE_DIR, \ - S3_NN_MODEL_REMOTE_DIR, S3_CONDITIONS_IDX_REMOTE_DIR -from cakechat.dialog_model.model import get_nn_model -from cakechat.utils.s3 import S3FileResolver + S3_NN_MODEL_REMOTE_DIR, S3_CONDITIONS_IDX_REMOTE_DIR, PREDICTION_MODES, TRAIN_CORPUS_NAME, \ + USE_PRETRAINED_W2V_EMBEDDINGS_LAYER +from cakechat.dialog_model.inference_model import InferenceCakeChatModel +from cakechat.utils.data_types import ModelParam +from cakechat.utils.dataset_loader import get_validation_data_id, get_validation_sets_names from cakechat.utils.files_utils import FileNotFoundException +from cakechat.utils.s3 import S3FileResolver, get_s3_model_resolver from cakechat.utils.text_processing import get_index_to_token_path, load_index_to_item, get_index_to_condition_path +from cakechat.utils.w2v.model import get_w2v_model_id def _get_index_to_token(fetch_from_s3): index_to_token_path = get_index_to_token_path(BASE_CORPUS_NAME) + file_name = os.path.basename(index_to_token_path) if fetch_from_s3: tokens_idx_resolver = S3FileResolver(index_to_token_path, S3_MODELS_BUCKET_NAME, S3_TOKENS_IDX_REMOTE_DIR) if not tokens_idx_resolver.resolve(): - raise FileNotFoundException('Can\'t get index_to_token because file does not exist at S3') + raise FileNotFoundException('No such file on S3: {}'.format(file_name)) else: if not os.path.exists(index_to_token_path): - raise FileNotFoundException('Can\'t get index_to_token because file does not exist. ' - 'Run tools/download_model.py first to get all required files or construct it by yourself.') + raise FileNotFoundException('No such file: {}'.format(file_name) + + 'Run "python tools/fetch.py" first to get all necessary files.') return load_index_to_item(index_to_token_path) @@ -30,28 +35,49 @@ def _get_index_to_condition(fetch_from_s3): index_to_condition_resolver = S3FileResolver(index_to_condition_path, S3_MODELS_BUCKET_NAME, S3_CONDITIONS_IDX_REMOTE_DIR) if not index_to_condition_resolver.resolve(): - raise FileNotFoundException('Can\'t get index_to_condition because file does not exist at S3') + raise FileNotFoundException('Can\'t get index_to_condition because file does not exist on S3') else: if not os.path.exists(index_to_condition_path): raise FileNotFoundException('Can\'t get index_to_condition because file does not exist. ' - 'Run tools/download_model.py first to get all required files or construct it by yourself.') + 'Run tools/fetch.py first to get all required files or construct ' + 'it yourself.') return load_index_to_item(index_to_condition_path) @cached(cache={}) -def get_trained_model(reverse=False, fetch_from_s3=True): +def get_trained_model(is_reverse_model=False, reverse_model=None, fetch_from_s3=True): + """ + Get a trained model, direct or reverse. + :param is_reverse_model: boolean, if True, a reverse trained model will be returned to be used during inference + in direct model in *_reranking modes; if False, a direct trained model is returned + :param reverse_model: object of a reverse model to be used in direct model in *_reranking inference modes + :param fetch_from_s3: boolean, if True, download trained model from Amazon S3 if the the model is not found locally; + if False, the model presence will be checked only locally + :return: + """ if fetch_from_s3: - resolver_factory = S3FileResolver.init_resolver( - bucket_name=S3_MODELS_BUCKET_NAME, remote_dir=S3_NN_MODEL_REMOTE_DIR) + resolver_factory = get_s3_model_resolver(S3_MODELS_BUCKET_NAME, S3_NN_MODEL_REMOTE_DIR) else: resolver_factory = None - nn_model, model_exists = get_nn_model(index_to_token=_get_index_to_token(fetch_from_s3), - index_to_condition=_get_index_to_condition(fetch_from_s3), - resolver_factory=resolver_factory, - is_reverse_model=reverse) - if not model_exists: - raise FileNotFoundException('Can\'t get the pre-trained model. Run tools/download_model.py first ' - 'to get all required files or train it by yourself.') - return nn_model + w2v_model_id = get_w2v_model_id() if USE_PRETRAINED_W2V_EMBEDDINGS_LAYER else None + + model = InferenceCakeChatModel( + index_to_token=_get_index_to_token(fetch_from_s3), + index_to_condition=_get_index_to_condition(fetch_from_s3), + training_data_param=ModelParam(value=None, id=TRAIN_CORPUS_NAME), + validation_data_param=ModelParam(value=None, id=get_validation_data_id(get_validation_sets_names())), + w2v_model_param=ModelParam(value=None, id=w2v_model_id), + model_resolver=resolver_factory, + is_reverse_model=is_reverse_model, + reverse_model=reverse_model) + + model.init_model() + model.resolve_model() + return model + + +def get_reverse_model(prediction_mode): + reranking_modes = [PREDICTION_MODES.beamsearch_reranking, PREDICTION_MODES.sampling_reranking] + return get_trained_model(is_reverse_model=True) if prediction_mode in reranking_modes else None diff --git a/cakechat/dialog_model/inference/candidates/abstract_generator.py b/cakechat/dialog_model/inference/candidates/abstract_generator.py index 001b392..1ad485a 100644 --- a/cakechat/dialog_model/inference/candidates/abstract_generator.py +++ b/cakechat/dialog_model/inference/candidates/abstract_generator.py @@ -1,9 +1,7 @@ from abc import ABCMeta, abstractmethod -class AbstractCandidatesGenerator(object): - __metaclass__ = ABCMeta - +class AbstractCandidatesGenerator(object, metaclass=ABCMeta): @abstractmethod def generate_candidates(self, context_token_ids, condition_ids, output_seq_len): pass diff --git a/cakechat/dialog_model/inference/candidates/beamsearch.py b/cakechat/dialog_model/inference/candidates/beamsearch.py index 646841e..4c821f4 100644 --- a/cakechat/dialog_model/inference/candidates/beamsearch.py +++ b/cakechat/dialog_model/inference/candidates/beamsearch.py @@ -1,7 +1,9 @@ +from itertools import zip_longest + +import keras.backend as K import numpy as np -from six.moves import xrange, zip_longest -import theano +from cakechat.config import INTX from cakechat.dialog_model.inference.candidates.abstract_generator import AbstractCandidatesGenerator from cakechat.dialog_model.inference.service_tokens import ServiceTokensIDs from cakechat.dialog_model.inference.utils import get_next_token_log_prob_one_step, get_thought_vectors @@ -33,9 +35,9 @@ def _get_k_max_elements_indices_and_scores(vec, k, mask=None): def _init_hidden_states_and_candidates(self, output_seq_len): # This array will contain beam_size candidates, each of which output_seq_len long. - # dtype=np.int32, because this array stores ids of tokens which are integers. + # dtype=INTX, because this array stores ids of tokens which are integers. self._cur_candidates = np.full( - (self._beam_size, output_seq_len), self._service_tokens_ids.pad_token_id, dtype=np.int32) + (self._beam_size, output_seq_len), self._service_tokens_ids.pad_token_id, dtype=INTX) # First, fill in first token of each candidate self._cur_candidates[:, 0] = self._service_tokens_ids.start_token_id # and prepare an array for score of each candidate @@ -43,25 +45,25 @@ def _init_hidden_states_and_candidates(self, output_seq_len): # Finished candidates are going to be concatenated here. Each candidate will be of shape=(1, output_seq_len), # But now we have 0 candidates: that's why we need to initialize this array with shape=(0, output_seq_len) - self._finished_candidates = np.zeros((0, output_seq_len), dtype=np.int32) + self._finished_candidates = np.zeros((0, output_seq_len), dtype=INTX) # Same story here: 0 candidates so long => shape=0. - self._finished_candidates_scores = np.zeros(0, dtype=np.float32) + self._finished_candidates_scores = np.zeros(0, dtype=K.floatx()) # For each candidate in the beam, for each layer of the decoder we need hidden_states_dim numbers to store # this array self._hidden_states_batch = np.zeros( (self._beam_size, self._nn_model.decoder_depth, self._nn_model.hidden_layer_dim), - dtype=theano.config.floatX) # By default, numpy has dtype=np.float64, but this array is passed - # right into theano functions, so we need to have explicit type declaring here. + dtype=K.floatx()) # By default, numpy has dtype=np.float64, but this array is passed + # right into model's functions, so we need to have explicit type declaring here. def _compute_thought_vectors(self, context_token_ids): # thought_vector is (1 x thought_vector_dim); - # context_token_ids is (input_seq_len), but we need to make it 1 x input_seq_len, because theano functions + # context_token_ids is (input_seq_len), but we need to make it 1 x input_seq_len, because model's functions # require input_seq_len dimension to be the second one. thought_vector = get_thought_vectors(self._nn_model, context_token_ids[np.newaxis, :]) - # All theano functions process each sequence independently: every input sequence is matched to the corresponding - # output sequence. So if we want to have probability of all outputs given the save inputs, we need to repeat - # the input times. = beam_size here. + # All model's functions process each sequence independently: every input sequence is matched to the + # corresponding output sequence. So if we want to get probabilities of all outputs given the saved inputs, + # we need to repeat the input times. = beam_size here. self._thought_batch = np.repeat(thought_vector, self._beam_size, axis=0) def _update_next_candidates_and_hidden_states(self, token_idx, best_non_finished_candidates_indices, @@ -78,7 +80,7 @@ def _update_next_candidates_and_hidden_states(self, token_idx, best_non_finished # Separate arrays for the updated hidden states next_hidden_states_batch = np.zeros_like(self._hidden_states_batch) # and the candidates - next_step_candidates = np.full_like(self._cur_candidates, self._service_tokens_ids.pad_token_id, dtype=np.int32) + next_step_candidates = np.full_like(self._cur_candidates, self._service_tokens_ids.pad_token_id, dtype=INTX) for i, candidate_idx in enumerate(best_non_finished_candidates_indices): # expanded_beam_tokens contains the last token for each of the beam_size^2 candidates in the expanded beam. @@ -111,11 +113,11 @@ def _update_finished_candidates(self, token_idx, best_finished_candidates_indice # These are only finished candidates on the current step. We will further append this array to # self._finished_candidates - # dtype=np.int32, because this array stores ids of tokens which are integers. + # dtype=INTX, because this array stores ids of tokens which are integers. cur_finished_candidates = \ - np.full((n_finished_candidates, output_seq_len), self._service_tokens_ids.pad_token_id, dtype=np.int32) + np.full((n_finished_candidates, output_seq_len), self._service_tokens_ids.pad_token_id, dtype=INTX) - cur_finished_candidates_scores = np.full(n_finished_candidates, 0, dtype=np.float32) + cur_finished_candidates_scores = np.full(n_finished_candidates, 0, dtype=K.floatx()) for i, candidate_idx in enumerate(best_finished_candidates_indices): # expanded_beam_tokens contains the last token for each of the beam_size^2 candidates in the expanded beam # to get all the other tokens we need to get which original candidate this token in the expanded beam @@ -171,10 +173,10 @@ def _get_aggregated_scores_and_tokens_for_expanded_beam(self, next_token_score_b # Expanded beam is beam_size x beam_size candidates that we consider on the next step. # But we don't want to keep all the candidates themselves for better performance. So we just keep # the last token and the total score. - expanded_beam_scores = np.zeros((self._beam_size * self._beam_size), dtype=np.float32) - expanded_beam_tokens = np.zeros((self._beam_size * self._beam_size), dtype=np.int32) + expanded_beam_scores = np.zeros((self._beam_size * self._beam_size), dtype=K.floatx()) + expanded_beam_tokens = np.zeros((self._beam_size * self._beam_size), dtype=INTX) - for candidate_idx in xrange(self._beam_size): + for candidate_idx in range(self._beam_size): # Get beam_size candidates on each step next_token_candidates, next_token_scores = \ self._get_k_max_elements_indices_and_scores(next_token_score_batch[candidate_idx], self._beam_size) @@ -219,7 +221,7 @@ def _generate_candidates_for_one_context(self, condition_id, output_seq_len): self._cur_candidates[:, 1], self._cur_candidates_scores = self._get_k_max_elements_indices_and_scores( next_token_score_batch[0], self._beam_size) - for token_idx in xrange(2, output_seq_len): # Start from 2 because first token candidates are already filled. + for token_idx in range(2, output_seq_len): # Start from 2 because first token candidates are already filled. # This array has shape beam_size x vocab_size. We use this scores to select best tokens for the beam # on the next step. next_token_score_batch = self._compute_next_token_score_batch(token_idx, condition_id) diff --git a/cakechat/dialog_model/inference/candidates/sampling.py b/cakechat/dialog_model/inference/candidates/sampling.py index 01f7c22..11f343a 100644 --- a/cakechat/dialog_model/inference/candidates/sampling.py +++ b/cakechat/dialog_model/inference/candidates/sampling.py @@ -1,7 +1,7 @@ +import keras.backend as K import numpy as np -from six.moves import xrange -import theano +from cakechat.config import INTX from cakechat.dialog_model.inference.candidates.abstract_generator import AbstractCandidatesGenerator from cakechat.dialog_model.inference.service_tokens import ServiceTokensIDs from cakechat.dialog_model.inference.utils import get_next_token_prob_one_step, get_thought_vectors @@ -19,7 +19,7 @@ def __init__(self, batch_size, banned_tokens_ids, non_penalizable_tokens_ids, re self._batch_size = batch_size self._banned_tokens_ids = banned_tokens_ids self._non_penalizable_tokens_ids = non_penalizable_tokens_ids - self._used_tokens_ids = [[] for _ in xrange(batch_size)] + self._used_tokens_ids = [[] for _ in range(batch_size)] self._repetition_penalization_coefficient = repetition_penalization_coefficient def sample(self, probabilities, sample_idx, temperature=1.0): @@ -71,18 +71,17 @@ def _sample_response(self, thought_vectors, condition_ids, output_seq_len): # this array hidden_states_batch = np.zeros( (batch_size, self._nn_model.decoder_depth, self._nn_model.hidden_layer_dim), - dtype=theano.config.floatX) # By default, numpy has dtype=np.float64, but this array is passed - # right into theano functions, so we need to have explicit type declaring here. + dtype=K.floatx()) # By default, numpy has dtype=np.float64, but this array is passed + # right into model's functions, so we need to have explicit type declaring here. - response_tokens_ids = np.full( - (batch_size, output_seq_len), self._service_tokens_ids.pad_token_id, dtype=np.int32) + response_tokens_ids = np.full((batch_size, output_seq_len), self._service_tokens_ids.pad_token_id, dtype=INTX) # Track finished responses to skip prediction step for them is_response_finished = np.zeros(batch_size, dtype=np.bool) # Fill in first tokens of each response in the batch: response_tokens_ids[:, 0] = self._service_tokens_ids.start_token_id - for token_idx in xrange(1, output_seq_len): # Starting with the second token + for token_idx in range(1, output_seq_len): # Starting with the second token hidden_states_batch, next_token_probs_batch = \ get_next_token_prob_one_step(self._nn_model, thought_vectors, hidden_states_batch, response_tokens_ids[:, token_idx - 1], # previous token for each response @@ -116,7 +115,7 @@ def generate_candidates(self, context_tokens_ids, condition_ids, output_seq_len) """ thought_vectors = get_thought_vectors(self._nn_model, context_tokens_ids) sampled_candidates = [ - self._sample_response(thought_vectors, condition_ids, output_seq_len) for _ in xrange(self._samples_num) + self._sample_response(thought_vectors, condition_ids, output_seq_len) for _ in range(self._samples_num) ] # Transpose the result: candidate_id x batch_size x seq_len -> batch_size x candidate_id x seq_len diff --git a/cakechat/dialog_model/inference/factory.py b/cakechat/dialog_model/inference/factory.py index cef9526..e1b73a3 100644 --- a/cakechat/dialog_model/inference/factory.py +++ b/cakechat/dialog_model/inference/factory.py @@ -1,5 +1,4 @@ from cakechat.config import PREDICTION_MODES -from cakechat.dialog_model.factory import get_trained_model from cakechat.dialog_model.inference.candidates import BeamsearchCandidatesGenerator, SamplingCandidatesGenerator from cakechat.dialog_model.inference.predictor import Predictor from cakechat.dialog_model.inference.reranking import DummyReranker, MMIReranker @@ -28,8 +27,7 @@ def predictor_factory(nn_model, mode, config): if config['mmi_reverse_model_score_weight'] <= 0: raise ValueError('mmi_reverse_model_score_weight should be > 0 for reranking mode') - reverse_model = get_trained_model(reverse=True) - reranker = MMIReranker(nn_model, reverse_model, config['mmi_reverse_model_score_weight'], + reranker = MMIReranker(nn_model, nn_model.reverse_model, config['mmi_reverse_model_score_weight'], config['repetition_penalization_coefficient']) else: reranker = DummyReranker() diff --git a/cakechat/dialog_model/inference/predict.py b/cakechat/dialog_model/inference/predict.py index 64dcd86..3089e60 100644 --- a/cakechat/dialog_model/inference/predict.py +++ b/cakechat/dialog_model/inference/predict.py @@ -69,7 +69,7 @@ def get_nn_response_ids(context_token_ids, kwargs['samples_num'] = output_candidates_num prediction_config = PredictionConfig(mode, **kwargs) - _logger.debug('Generating predicted response for the following params: %s' % prediction_config) + _logger.debug('Generating predicted response for the following params: {}'.format(prediction_config)) predictor = predictor_factory(nn_model, mode, prediction_config.get_options_dict()) responses = predictor.predict_responses(context_token_ids, output_seq_len, condition_ids, output_candidates_num) @@ -98,12 +98,14 @@ def get_nn_responses(context_token_ids, :return: list of lists of strings, shape (contexts_num, output_candidates_num) """ - response_tokens_ids = get_nn_response_ids(context_token_ids, nn_model, mode, output_candidates_num, - output_seq_len, condition_ids, **kwargs) + response_tokens_ids = get_nn_response_ids(context_token_ids, nn_model, mode, output_candidates_num, output_seq_len, + condition_ids, **kwargs) # shape (contexts_num, output_candidates_num, output_seq_len), numpy array of integers - responses = [transform_token_ids_to_sentences(response_candidates_tokens_ids, nn_model.index_to_token) - for response_candidates_tokens_ids in response_tokens_ids] + responses = [ + transform_token_ids_to_sentences(response_candidates_tokens_ids, nn_model.index_to_token) + for response_candidates_tokens_ids in response_tokens_ids + ] # responses shape (contexts_num, output_candidates_num), list of lists of strings return responses diff --git a/cakechat/dialog_model/inference/predictor.py b/cakechat/dialog_model/inference/predictor.py index 4c054ca..c114af4 100644 --- a/cakechat/dialog_model/inference/predictor.py +++ b/cakechat/dialog_model/inference/predictor.py @@ -1,5 +1,6 @@ import numpy as np -from six.moves import xrange + +from cakechat.config import INTX class Predictor(object): @@ -18,9 +19,9 @@ def _select_best_candidates(reranked_candidates, candidates_num): # reranked_candidates is a list of lists (we need too keep it this way because we can have different number # of candidates for each context), so we can't just write reranked_candidates.shape[2] output_seq_len = reranked_candidates[0][0].size - result = np.zeros((batch_size, candidates_num, output_seq_len), dtype=np.int32) + result = np.zeros((batch_size, candidates_num, output_seq_len), dtype=INTX) # Loop here instead of slices because number of candidates for each context may vary here - for i in xrange(batch_size): + for i in range(batch_size): for j, candidate in enumerate(reranked_candidates[i]): if j >= candidates_num: break diff --git a/cakechat/dialog_model/inference/reranking.py b/cakechat/dialog_model/inference/reranking.py index ac02e6b..1b2eda4 100644 --- a/cakechat/dialog_model/inference/reranking.py +++ b/cakechat/dialog_model/inference/reranking.py @@ -1,7 +1,7 @@ from abc import ABCMeta, abstractmethod +from itertools import zip_longest import numpy as np -from six.moves import xrange, zip_longest from cakechat.dialog_model.inference.service_tokens import ServiceTokensIDs from cakechat.dialog_model.inference.utils import get_sequence_score_by_thought_vector, get_sequence_score, \ @@ -14,9 +14,7 @@ _logger = get_logger(__name__) -class AbstractCandidatesReranker(object): - __metaclass__ = ABCMeta - +class AbstractCandidatesReranker(object, metaclass=ABCMeta): @abstractmethod def rerank_candidates(self, contexts, all_candidates, condition_ids): pass @@ -104,6 +102,6 @@ def rerank_candidates(self, contexts, all_candidates, condition_ids): batch_size = len(contexts) # reranked_candidates[i][j] = j-th best response for i-th question reranked_candidates = [ - [all_candidates[i][j] for j in scores_order[i]] for i in xrange(batch_size) # yapf: disable + [all_candidates[i][j] for j in scores_order[i]] for i in range(batch_size) # yapf: disable ] return reranked_candidates diff --git a/cakechat/dialog_model/inference/tests/predict.py b/cakechat/dialog_model/inference/tests/predict.py index a6f2be1..37a6a21 100644 --- a/cakechat/dialog_model/inference/tests/predict.py +++ b/cakechat/dialog_model/inference/tests/predict.py @@ -2,20 +2,21 @@ import sys import unittest +import keras.backend as K import numpy as np -from six.moves import xrange sys.path.append( os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))) -from cakechat.utils.env import init_theano_env +from cakechat.utils.env import init_cuda_env -init_theano_env() +init_cuda_env() from cakechat.dialog_model.factory import get_trained_model from cakechat.dialog_model.inference import get_sequence_log_probs from cakechat.dialog_model.inference.utils import get_next_token_log_prob_one_step -from cakechat.config import DECODER_DEPTH, HIDDEN_LAYER_DIMENSION, RANDOM_SEED +from cakechat.config import HIDDEN_LAYER_DIMENSION, RANDOM_SEED, INPUT_CONTEXT_SIZE, \ + INPUT_SEQUENCE_LENGTH, OUTPUT_SEQUENCE_LENGTH, INTX np.random.seed(seed=RANDOM_SEED) @@ -28,10 +29,11 @@ def _predict_log_probabilities_one_step(nn_model, x_batch, y_batch): All the rest of the sequence is filled with PAD_TOKENs. """ thought_vectors_batch = nn_model.get_thought_vectors(x_batch) - hidden_states_batch = np.zeros((x_batch.shape[0], DECODER_DEPTH, HIDDEN_LAYER_DIMENSION), dtype=np.float32) + hidden_states_batch = np.zeros( + (x_batch.shape[0], nn_model.decoder_depth, HIDDEN_LAYER_DIMENSION), dtype=K.floatx()) total_log_probs = np.zeros((y_batch.shape[0], y_batch.shape[1] - 1, nn_model.vocab_size)) - for token_idx in xrange(1, y_batch.shape[1]): + for token_idx in range(1, y_batch.shape[1]): hidden_states_batch, next_token_log_probs_batch = \ get_next_token_log_prob_one_step(nn_model, thought_vectors_batch, hidden_states_batch, y_batch[:, token_idx - 1], condition_ids=None) @@ -43,14 +45,15 @@ def _predict_log_probabilities_one_step(nn_model, x_batch, y_batch): def test_one_step_decoder(self): nn_model = get_trained_model() - _EPS = 1e-6 + _EPS = 1e-5 batch_size = 1 - context_size = 3 - input_seq_len = 10 - output_seq_len = 9 + # input batches shapes should correspond to the shapes of the trained model layers + context_size = INPUT_CONTEXT_SIZE + input_seq_len = INPUT_SEQUENCE_LENGTH + output_seq_len = OUTPUT_SEQUENCE_LENGTH - x = np.random.randint(0, nn_model.vocab_size, size=(batch_size, context_size, input_seq_len), dtype=np.int32) - y = np.random.randint(0, nn_model.vocab_size, size=(batch_size, output_seq_len), dtype=np.int32) + x = np.random.randint(0, nn_model.vocab_size, size=(batch_size, context_size, input_seq_len), dtype=INTX) + y = np.random.randint(0, nn_model.vocab_size, size=(batch_size, output_seq_len), dtype=INTX) ground_truth_log_probabilities = get_sequence_log_probs(nn_model, x, y, condition_ids=None) one_step_log_probabilities = self._predict_log_probabilities_one_step(nn_model, x, y) diff --git a/cakechat/dialog_model/inference/tests/sampling.py b/cakechat/dialog_model/inference/tests/sampling.py index cd5e360..6070e70 100644 --- a/cakechat/dialog_model/inference/tests/sampling.py +++ b/cakechat/dialog_model/inference/tests/sampling.py @@ -2,9 +2,9 @@ import sys import unittest +import keras.backend as K import numpy as np from scipy.stats import binom -from six.moves import xrange sys.path.append( os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))) @@ -39,7 +39,7 @@ def test_sample_list(self): def test_sample_ndarray(self): # Error rate is p(token1) + p(token2) = conf_level / 2 + conf_level / 2 = conf_level - probs = np.array([_CONFIDENCE_LEVEL / 2, _CONFIDENCE_LEVEL / 2, 1 - _CONFIDENCE_LEVEL], dtype=np.float32) + probs = np.array([_CONFIDENCE_LEVEL / 2, _CONFIDENCE_LEVEL / 2, 1 - _CONFIDENCE_LEVEL], dtype=K.floatx()) token_sampler = TokenSampler( batch_size=1, @@ -61,7 +61,7 @@ def test_sample_probs(self): adjusted_confidence_level = _CONFIDENCE_LEVEL / len(probs) # bonferroni correction confidence_intervals = [binom.interval(1 - adjusted_confidence_level, _SAMPLES_NUM, p) for p in probs] est_probs_from, est_probs_to = zip(*confidence_intervals) - samples = np.array([token_sampler.sample(probs, 0) for _ in xrange(_SAMPLES_NUM)]) + samples = np.array([token_sampler.sample(probs, 0) for _ in range(_SAMPLES_NUM)]) counts = {val: np.sum(samples == val) for val in np.unique(samples)} for i, _ in enumerate(probs): @@ -69,7 +69,7 @@ def test_sample_probs(self): self.assertGreaterEqual(counts[i], est_probs_from[i]) def test_sample_with_zeros(self): - probs = np.array([1.0, 0, 0], dtype=np.float32) + probs = np.array([1.0, 0, 0], dtype=K.floatx()) token_sampler = TokenSampler( batch_size=1, @@ -84,7 +84,7 @@ def test_sample_banned_tokens(self): eps = _CONFIDENCE_LEVEL * 0.3 # Here we multiply the confidence level by 0.3 so that after removal of banned token and renormalization # the probability of an error remains equal to _CONFIDENCE_LEVEL value. - probs = np.array([0.7, 0.3 - eps, eps], dtype=np.float32) + probs = np.array([0.7, 0.3 - eps, eps], dtype=K.floatx()) token_sampler = TokenSampler( batch_size=1, @@ -97,7 +97,7 @@ def test_sample_banned_tokens(self): def test_sample_banned_tokens_2(self): eps = 1e-6 - probs = np.array([1.0 - eps, eps, 0], dtype=np.float32) + probs = np.array([1.0 - eps, eps, 0], dtype=K.floatx()) token_sampler = TokenSampler( batch_size=1, @@ -113,7 +113,7 @@ def test_repetition_penalization(self): probs = [0.5, 0.5] actual_num_nonequal_pairs = 0 - for _ in xrange(_SAMPLES_NUM): + for _ in range(_SAMPLES_NUM): token_sampler = TokenSampler( batch_size=1, banned_tokens_ids=[], diff --git a/cakechat/dialog_model/inference/utils.py b/cakechat/dialog_model/inference/utils.py index a4184cc..4d0e19c 100644 --- a/cakechat/dialog_model/inference/utils.py +++ b/cakechat/dialog_model/inference/utils.py @@ -1,7 +1,6 @@ import numpy as np -from six.moves import xrange -from cakechat.config import BATCH_SIZE, DEFAULT_CONDITION +from cakechat.config import BATCH_SIZE, DEFAULT_CONDITION, INTX from cakechat.dialog_model.model_utils import get_training_batch @@ -10,48 +9,48 @@ def _predict_batch_by_batch(predict_fn, batched_inputs, non_batched_inputs=None, Splits prediction for big dataset in order to save GPU memory. Equivalent to predict_fn(*batched_inputs + non_batched_inputs). - predict_fn: Compiled theano-function. + predict_fn: compiled keras predict function. batched_inputs: - Inputs that we split into batches. On each iteration, we only pass one batch of this data into theano function. + Inputs that we split into batches. On each iteration, we only pass one batch of this data into predict_fn. non_batched_inputs: Inputs that we do not split into batches. These inputs are the same for each call of predict_fn batch_size: int Size of each batch that we split our batched_inputs into num_ouputs: int, default=1 - Number of returned items of each theano function. + Number of items returned on each call of predict_fn. """ if non_batched_inputs is None: non_batched_inputs = [] - results = [[] for _ in xrange(num_outputs)] + results = [[] for _ in range(num_outputs)] for inputs_batch in get_training_batch(batched_inputs, batch_size): args = list(inputs_batch) + non_batched_inputs cur_result = predict_fn(*args) if num_outputs > 1: - for i in xrange(num_outputs): + for i in range(num_outputs): results[i].append(cur_result[i]) else: results[0].append(cur_result) if num_outputs > 1: - return tuple(np.concatenate(results[i]) for i in xrange(num_outputs)) + return tuple(np.concatenate(results[i]) for i in range(num_outputs)) else: return np.concatenate(results[0]) def _handle_condition_ids(condition_ids, condition_to_index, num_responses): """ - Returns condition_ids preprocessed to match theano shapes. + Returns condition_ids preprocessed to match the shape of responses batch. Specifically: If condition_ids is None it is replaced with default condition index repeated num_responses times. If condition_ids is an one index, it is repeated num_responses times. If condition_ids is an array, assert that the shape is right. """ if condition_ids is None: - return np.array([condition_to_index[DEFAULT_CONDITION]] * num_responses, dtype=np.int32) + return np.array([condition_to_index[DEFAULT_CONDITION]] * num_responses, dtype=INTX) - condition_ids = np.array(condition_ids, dtype=np.int32) + condition_ids = np.array(condition_ids, dtype=INTX) if len(condition_ids.shape) == 0: # If condition_ids is one number return np.repeat(condition_ids[np.newaxis], num_responses, axis=0) elif condition_ids.shape != (num_responses, ): @@ -69,7 +68,7 @@ def _predict_one_step(predict_fn, condition_ids=None, temperature=1.0): condition_ids = _handle_condition_ids(condition_ids, condition_to_index, thought_vectors.shape[0]) - # We need newaxis to match the expected shape of an argument passed to theano function + # We need newaxis to match the expected shape of an argument passed to predict_fn function prev_tokens_ids = prev_tokens_ids[:, np.newaxis] hidden_states, token_scores = \ diff --git a/cakechat/dialog_model/inference_model.py b/cakechat/dialog_model/inference_model.py new file mode 100644 index 0000000..11efb1a --- /dev/null +++ b/cakechat/dialog_model/inference_model.py @@ -0,0 +1,48 @@ +from cakechat.dialog_model.keras_model import KerasTFModelIsolator +from cakechat.dialog_model.model import CakeChatModel + + +class InferenceCakeChatModel(CakeChatModel, KerasTFModelIsolator): + """ + Inference-aimed extension of CakeChatModel, which supports isolation of underlying Keras (Tensorflow) model + to fit multi-model multi-threaded run-time environments + """ + + def __init__(self, + index_to_token, + index_to_condition, + training_data_param=None, + validation_data_param=None, + w2v_model_param=None, + model_init_path=None, + model_resolver=None, + is_reverse_model=False, + reverse_model=None): + KerasTFModelIsolator.__init__(self) + + self.init_model = self._isolate_func(self.init_model) + self.resolve_model = self._isolate_func(self.resolve_model) + self.print_weights_summary = self._isolate_func(self.print_weights_summary) + self.train_model = self._isolate_func(self.train_model) + + self.get_utterance_encoding = self._isolate_func(self.get_utterance_encoding) + self.get_thought_vectors = self._isolate_func(self.get_thought_vectors) + self.predict_prob = self._isolate_func(self.predict_prob) + self.predict_prob_by_thought_vector = self._isolate_func(self.predict_prob_by_thought_vector) + self.predict_prob_one_step = self._isolate_func(self.predict_prob_one_step) + self.predict_log_prob = self._isolate_func(self.predict_log_prob) + self.predict_log_prob_one_step = self._isolate_func(self.predict_log_prob_one_step) + self.predict_sequence_score = self._isolate_func(self.predict_sequence_score) + self.predict_sequence_score_by_thought_vector = self._isolate_func( + self.predict_sequence_score_by_thought_vector) + + super(InferenceCakeChatModel, self).__init__( + index_to_token=index_to_token, + index_to_condition=index_to_condition, + training_data_param=training_data_param, + validation_data_param=validation_data_param, + w2v_model_param=w2v_model_param, + model_init_path=model_init_path, + model_resolver=model_resolver, + is_reverse_model=is_reverse_model, + reverse_model=reverse_model) diff --git a/cakechat/dialog_model/keras_model.py b/cakechat/dialog_model/keras_model.py new file mode 100644 index 0000000..2cdf4da --- /dev/null +++ b/cakechat/dialog_model/keras_model.py @@ -0,0 +1,307 @@ +import abc +import os +import time +from datetime import timedelta + +from keras import backend as K + + +from cakechat.dialog_model.abstract_callbacks import AbstractKerasModelCallback, ParametrizedCallback, \ + _KerasCallbackAdapter +from cakechat.dialog_model.abstract_model import AbstractModel +from cakechat.dialog_model.quality.metrics.plotters import DummyMetricsPlotter +from cakechat.utils.env import is_main_horovod_worker, set_horovod_worker_random_seed +from cakechat.utils.files_utils import is_non_empty_file +from cakechat.utils.logger import WithLogger + + +class KerasTFModelIsolator(object): + def __init__(self): + # Use global keras (tensorflow) session config here + keras_session_config = K.get_session()._config + + self._keras_isolated_graph = K.tf.Graph() + self._keras_isolated_session = K.tf.Session(graph=self._keras_isolated_graph, config=keras_session_config) + + def _isolate_func(self, func): + def wrapper(*args, **kwargs): + with self._keras_isolated_graph.as_default(): + with self._keras_isolated_session.as_default(): + return func(*args, **kwargs) + + return wrapper + + +class EvaluateAndSaveBestIntermediateModelCallback(AbstractKerasModelCallback, WithLogger): + def __init__(self, model, eval_state_per_batches): + """ + :param model: AbstractKerasModel object + :param eval_state_per_batches: run model evaluation each `eval_state_per_batches` steps + """ + super(EvaluateAndSaveBestIntermediateModelCallback, self).__init__(model) + WithLogger.__init__(self) + + self._eval_state_per_batches = eval_state_per_batches + self._training_start_time = None + self._cur_epoch_start_time = None + + @property + def callback_params(self): + return {'eval_state_per_batches': self._eval_state_per_batches} + + @property + def runs_only_on_main_worker(self): + return True + + @staticmethod + def _get_formatted_time(seconds): + return str(timedelta(seconds=int(seconds))) + + def _log_metrics(self, dataset_name_to_metrics): + for dataset_name, metrics in dataset_name_to_metrics.items(): + for metric_name, metric_value in metrics.items(): + self._logger.info('{} {} = {}'.format(dataset_name, metric_name, metric_value)) + self._model.metrics_plotter.plot(self._model.model_id, '{}/{}'.format(dataset_name, metric_name), + metric_value) + + def _eval_and_save_current_model(self, batch_num=None): + total_elapsed_time = time.time() - self._training_start_time + self._logger.info('Total elapsed time: {}'.format(self._get_formatted_time(total_elapsed_time))) + + if batch_num: + elapsed_time_per_batch = (time.time() - self._cur_epoch_start_time) / batch_num + self._logger.info('Cur batch num: {}; Train time per batch: {:.2f} seconds'.format( + batch_num, elapsed_time_per_batch)) + + dataset_name_to_metrics = self._model._evaluate() + self._log_metrics(dataset_name_to_metrics) + + if not os.path.exists(self._model.model_path): + os.makedirs(self._model.model_path) + + if self._model.metrics is None or self._model._is_better_model(dataset_name_to_metrics, self._model.metrics): + self._logger.info('Obtained new best model. Saving it to {}'.format(self._model._model_resource_path)) + self._model._save_model(self._model._model_resource_path) + self._model._metrics_serializer.save_metrics(self._model._metrics_resource_path, dataset_name_to_metrics) + self._model._metrics = dataset_name_to_metrics + + self._model._save_model(self._model._model_progress_resource_path) + + def on_train_begin(self, logs=None): + self._logger.info('Start training') + self._training_start_time = time.time() + + def on_train_end(self, logs=None): + self._logger.info('Stop training and compute final model metrics') + self._eval_and_save_current_model() + + def on_batch_end(self, batch_num, logs=None): + if batch_num > 0 and batch_num % self._eval_state_per_batches == 0: + self._eval_and_save_current_model(batch_num) + + def on_epoch_begin(self, epoch_num, logs=None): + cur_epoch_num = epoch_num + 1 + self._logger.info('Starting epoch {}'.format(cur_epoch_num)) + self._cur_epoch_start_time = time.time() + + def on_epoch_end(self, epoch_num, logs=None): + cur_epoch_num = epoch_num + 1 + cur_epoch_time = time.time() - self._cur_epoch_start_time + self._logger.info('For epoch {} elapsed time: {}'.format(cur_epoch_num, + self._get_formatted_time(cur_epoch_time))) + + +class AbstractKerasModel(AbstractModel, metaclass=abc.ABCMeta): + # Model resources default values + _MODEL_PROGRESS_RESOURCE_NAME = 'model.current' + + def __init__(self, metrics_plotter=None, horovod=None, training_callbacks=None, *args, **kwargs): + """ + :param metrics_plotter: object that plots training and validation metrics (see `TensorboardMetricsPlotter`) + :param horovod: horovod module initialized for training on multiple GPUs. If None, uses single GPU, or CPU + :param training_callbacks: list of instances of `AbstractKerasModelCallback`/`ParametrizedCallback` or None. + In subclasses, please call `_create_essential_callbacks` to get essential callbacks, and/or put your own + ones in this argument. + """ + super(AbstractKerasModel, self).__init__(*args, **kwargs) + + self._metrics_plotter = metrics_plotter if metrics_plotter else DummyMetricsPlotter() + self._horovod = horovod + + self._class_weight = None + self._callbacks = training_callbacks or [] + + @staticmethod + def _create_essential_callbacks(model, horovod=None, eval_state_per_batches=None): + """ + :param model: a model object, typically `self` + :param horovod: if not None, adds callback for model params broadcasting between workers + :param eval_state_per_batches: if not None, adds callback to evaluate the model every `eval_state_per_batches` + batches + :return: a list of callbacks + """ + callbacks = [] + + if horovod: + callbacks.append( + ParametrizedCallback( + horovod.callbacks.BroadcastGlobalVariablesCallback(0), runs_only_on_main_worker=False)) + + if eval_state_per_batches: + callbacks.append(EvaluateAndSaveBestIntermediateModelCallback(model, eval_state_per_batches)) + + return callbacks + + def _get_worker_callbacks(self): + if is_main_horovod_worker(self._horovod): + # all callbacks should be run on main worker + return self._callbacks + + # but not all callbacks should be run on a not main worker + return [callback for callback in self._callbacks if not callback.runs_only_on_main_worker] + + @staticmethod + def _to_keras_callbacks(callbacks): + """ + Casts AbstractKerasModel callbacks (see `cakechat.dialog_model.callbacks`) to the keras-based ones (instances of + `keras.callbacks.Callback`) + :param callbacks: + :return: + """ + keras_callbacks = [] + for custom_callback in callbacks: + if isinstance(custom_callback, AbstractKerasModelCallback): + keras_callback = _KerasCallbackAdapter(custom_callback) + elif isinstance(custom_callback, ParametrizedCallback): + keras_callback = custom_callback.callback + else: + raise ValueError('Unsupported callback type: {}'.format(type(custom_callback))) + + keras_callbacks.append(keras_callback) + + return keras_callbacks + + def _set_class_weight(self, class_weight): + self._class_weight = class_weight + + @property + @abc.abstractmethod + def _model_params(self): + pass + + @property + def model_params(self): + params = { + 'training_callbacks': { + cb.__class__.__name__: cb.callback_params for cb in self._callbacks if cb.callback_params + } + } + params.update(self._model_params) + return params + + @property + def _model_progress_resource_path(self): + return os.path.join(self.model_path, self._MODEL_PROGRESS_RESOURCE_NAME) + + @property + def model(self): + self.init_model() + return self._model + + @property + def metrics_plotter(self): + return self._metrics_plotter + + @abc.abstractmethod + def _get_training_model(self): + pass + + @abc.abstractmethod + def _build_model(self): + pass + + @abc.abstractmethod + def _is_better_model(self, new_metrics, old_metrics): + pass + + @abc.abstractmethod + def _get_training_batch_generator(self): + """ + :return: generator with (inputs, targets) or (inputs, targets, sample_weights) tuples. + The generator is expected to loop over its data indefinitely. + An epoch finishes when epoch_batches_num batches have been seen by the training worker. + """ + pass + + @abc.abstractmethod + def _get_epoch_batches_num(self): + pass + + def _save_model(self, model_file_path): + self._model.save(model_file_path, overwrite=True) + self._logger.info('Saved model weights to {}'.format(model_file_path)) + + def _load_model(self, fresh_model, model_file_path): + fresh_model.load_weights(model_file_path, by_name=True) + self._logger.info('Restored model weights from {}'.format(model_file_path)) + return fresh_model + + def _load_model_if_exists(self): + if is_non_empty_file(self._model_progress_resource_path): + self._model = self._load_model(self._model, self._model_progress_resource_path) + self._metrics = self._metrics_serializer.load_metrics(self._metrics_resource_path) + return + + self._logger.info('Could not find saved model at {}\nModel will be trained from scratch.\n' + .format(self._model_progress_resource_path)) + + def print_weights_summary(self): + summary = '\n\nModel weights summary:' + summary += '\n\t{0:<80} {1:<20} {2:}\n'.format('layer name', 'output shape:', 'size:') + + weights_names = [weight.name for layer in self._model.layers for weight in layer.weights] + weights = self._model.get_weights() + + total_network_size = 0 + for name, weight in zip(weights_names, weights): + param_size = weight.nbytes / 1024 / 1024 + summary += '\n\t{0:<80} {1:20} {2:<.2f}Mb'.format(name, str(weight.shape), param_size) + total_network_size += param_size + + summary += '\n\nTotal network size: {0:.1f} Mb\n'.format(total_network_size) + self._logger.info(summary) + + def init_model(self): + if not self._model: + self._logger.info('Initializing NN model') + self._model = self._build_model() + self._logger.info('NN model is initialized\n') + self.print_weights_summary() + + def train_model(self): + self.init_model() + self._load_model_if_exists() + + set_horovod_worker_random_seed(self._horovod) + training_batch_generator = self._get_training_batch_generator() + + epoch_batches_num = self._get_epoch_batches_num() + workers_num = self._horovod.size() if self._horovod else 1 + + self._logger.info('Total epochs num = {}; Total batches per epochs = {}; Total workers for train = {}'.format( + self.model_params['epochs_num'], epoch_batches_num, workers_num)) + + worker_callbacks = self._get_worker_callbacks() + training_model = self._get_training_model() + training_model.fit_generator( + training_batch_generator, + steps_per_epoch=epoch_batches_num // workers_num, + callbacks=self._to_keras_callbacks(worker_callbacks), + epochs=self.model_params['epochs_num'], + class_weight=self._class_weight, + verbose=0, + workers=0) + + # reload model with the best quality + if is_main_horovod_worker(self._horovod): + self._model = self._load_model(self._model, self._model_resource_path) diff --git a/cakechat/dialog_model/layers.py b/cakechat/dialog_model/layers.py index c245ad2..7fbe7ea 100644 --- a/cakechat/dialog_model/layers.py +++ b/cakechat/dialog_model/layers.py @@ -1,64 +1,53 @@ -import theano.tensor as T -from lasagne.layers.base import MergeLayer, Layer -from six.moves import xrange +from keras.layers import K, RepeatVector, Lambda -class RepeatLayer(Layer): +def repeat_vector(inputs): """ - Layer that repeats input n times along 1 axis and reshapes. - The idea is to take some data for each object in the batch and repeat n times along the sequence axis. - For example for repeating thought vector returned by encoder to feed into decoder in SEQ2SEQ model. - - input: tensor of shape N_1 x N_2 x ... x N_D - output: tensor of shape N_1 x n x N_2 x ... x N_D - """ - - def __init__(self, incoming, n, **kwargs): - super(RepeatLayer, self).__init__(incoming, **kwargs) - self._n = n - - def get_output_shape_for(self, input_shape): - repeat_times = None if isinstance(self._n, T.TensorVariable) else self._n - return tuple([input_shape[0], repeat_times] + list(input_shape[1:])) - - def get_output_for(self, input, **kwargs): - new_shape = [input.shape[0], 1] + [input.shape[k] for k in xrange(1, input.ndim)] - - output = T.reshape(input, new_shape, ndim=input.ndim + 1) # see the details in pydoc - output = T.repeat(output, self._n, axis=1) - return output - - -class NotEqualMaskLayer(Layer): - """ - Layer that outputs binary matrix according to elementwise non-equality to a specific element + Temporary solution: + Use this function within a Lambda layer to get a repeated layer with a variable 1-st dimension (seq_len). + May be useful to further feed it to a Concatenate layer. + + inputs == (layer_for_repeat, layer_for_getting_rep_num): + layer_for_repeat: shape == (batch_size, vector_dim) + layer_for_getting_rep_num: shape == (batch_size, seq_len, ...) + :return: + repeated layer_for_repeat, shape == (batch_size, seq_len, vector_dim) """ + layer_for_repeat, layer_for_getting_rep_num = inputs + repeated_vector = RepeatVector( + n=K.shape(layer_for_getting_rep_num)[1], name='custom_repeat_vector')(layer_for_repeat) + # shape == (batch_size, seq_len, vector_dim) + return repeated_vector - def __init__(self, incoming, x, **kwargs): - super(NotEqualMaskLayer, self).__init__(incoming, **kwargs) - self._x = x - - def get_output_shape_for(self, input_shape): - return input_shape - - def get_output_for(self, input, **kwargs): - return T.neq(input, self._x) - -class SwitchLayer(MergeLayer): +def softmax_with_temperature(logits, temperature): """ - Layer that performs switching from one input to another according to the condition which is theano.iscalar that contains 0 or 1. - If condition contains 1 then the output will be the output of the first layer in incomings. - If condition contains 0 then the output will be the output of the second layer in incomings. + :param logits: shape == (batch_size, seq_len, vocab_size), float32 + :param temperature: shape == (batch_size, 1), float32 + :return: + transformed tokens probs, shape == (batch_size, seq_len, vocab_size), float32 """ - def __init__(self, incomings, condition, **kwargs): - super(SwitchLayer, self).__init__(incomings, **kwargs) - self._condition = condition - - def get_output_shape_for(self, input_shapes): - return input_shapes[1] - - def get_output_for(self, inputs, **kwargs): - output = T.switch(self._condition, inputs[0], inputs[1]) - return output + def softmax_with_temp(args): + logits, temperature = args + repeat_num = K.shape(logits)[1] + temperature_repeated = RepeatVector(repeat_num)(temperature) + # shape == (batch_size, seq_len) + scaled_logits = logits / temperature_repeated + # shape == (batch_size, seq_len, vocab_size) + + # for numerical stability (e.g. for low temperatures): + scaled_logits = scaled_logits - K.max(scaled_logits, axis=2, keepdims=True) + # shape == (batch_size, seq_len, vocab_size) + transformed_probs = K.softmax(scaled_logits) + # shape == (batch_size, seq_len, vocab_size) + return transformed_probs + + # wrap transformation in Lambda to turn the result to Keras layer + transformed_probs = Lambda( + function=softmax_with_temp, + mask=lambda inputs, inputs_masks: inputs_masks[0], # function to get mask of the first input + name='softmax_with_temperature')([logits, temperature]) + # output shape == (batch_size, seq_len, vocab_size) + + return transformed_probs diff --git a/cakechat/dialog_model/model.py b/cakechat/dialog_model/model.py index 5f456c6..47bbd52 100644 --- a/cakechat/dialog_model/model.py +++ b/cakechat/dialog_model/model.py @@ -1,732 +1,732 @@ +import math import os -from collections import OrderedDict +from functools import partial -import lasagne import numpy as np -import theano -import theano.tensor as T -from lasagne.init import Normal -from lasagne.layers import InputLayer, DenseLayer, GRULayer, reshape, EmbeddingLayer, SliceLayer, ConcatLayer, \ - DropoutLayer, get_output, get_all_params, get_all_param_values, get_all_layers, \ - get_output_shape -from lasagne.objectives import categorical_crossentropy -from six.moves import xrange -from six import iteritems - -from cakechat.config import HIDDEN_LAYER_DIMENSION, GRAD_CLIP, LEARNING_RATE, \ - TRAIN_WORD_EMBEDDINGS_LAYER, WORD_EMBEDDING_DIMENSION, ENCODER_DEPTH, DECODER_DEPTH, DENSE_DROPOUT_RATIO, \ - CONDITION_EMBEDDING_DIMENSION, NN_MODEL_PREFIX, BASE_CORPUS_NAME, INPUT_CONTEXT_SIZE, INPUT_SEQUENCE_LENGTH, \ - OUTPUT_SEQUENCE_LENGTH, NN_MODELS_DIR -from cakechat.dialog_model.layers import RepeatLayer, NotEqualMaskLayer, SwitchLayer -from cakechat.utils.files_utils import DummyFileResolver, FileNotFoundException, ensure_dir -from cakechat.utils.logger import get_logger, laconic_logger +import tensorflow as tf +from keras import Input, Model, optimizers +from keras.layers import K, Bidirectional, Embedding, Concatenate, Dense, Dropout, TimeDistributed, \ + Reshape, Lambda, CuDNNGRU, GRU + +from cakechat.config import HIDDEN_LAYER_DIMENSION, GRAD_CLIP, LEARNING_RATE, TRAIN_WORD_EMBEDDINGS_LAYER, \ + WORD_EMBEDDING_DIMENSION, DENSE_DROPOUT_RATIO, CONDITION_EMBEDDING_DIMENSION, MODEL_NAME, BASE_CORPUS_NAME, \ + INPUT_CONTEXT_SIZE, INPUT_SEQUENCE_LENGTH, OUTPUT_SEQUENCE_LENGTH, BATCH_SIZE, LOG_RUN_METADATA, \ + TENSORBOARD_LOG_DIR, EPOCHS_NUM, SHUFFLE_TRAINING_BATCHES, RANDOM_SEED, RESULTS_PATH, USE_CUDNN +from cakechat.dialog_model.callbacks import CakeChatEvaluatorCallback +from cakechat.dialog_model.keras_model import AbstractKerasModel +from cakechat.dialog_model.layers import repeat_vector, softmax_with_temperature +from cakechat.dialog_model.model_utils import get_training_batch +from cakechat.dialog_model.quality.metrics.perplexity import calculate_model_mean_perplexity +from cakechat.dialog_model.quality.metrics.plotters import TensorboardMetricsPlotter +from cakechat.utils.data_structures import create_namedtuple_instance +from cakechat.utils.logger import WithLogger from cakechat.utils.text_processing import SPECIAL_TOKENS +from cakechat.utils.w2v.utils import get_token_vector -_logger = get_logger(__name__) - -class CakeChatModel(object): +class CakeChatModel(AbstractKerasModel, WithLogger): def __init__(self, index_to_token, index_to_condition, + training_data_param, + validation_data_param, + w2v_model_param, model_init_path=None, - nn_models_dir=NN_MODELS_DIR, - model_prefix=NN_MODEL_PREFIX, + model_resolver=None, + model_name=MODEL_NAME, corpus_name=BASE_CORPUS_NAME, skip_token=SPECIAL_TOKENS.PAD_TOKEN, - learning_rate=LEARNING_RATE, - grad_clip=GRAD_CLIP, + token_embedding_dim=WORD_EMBEDDING_DIMENSION, + train_token_embedding=TRAIN_WORD_EMBEDDINGS_LAYER, + condition_embedding_dim=CONDITION_EMBEDDING_DIMENSION, + input_seq_len=INPUT_SEQUENCE_LENGTH, + input_context_size=INPUT_CONTEXT_SIZE, + output_seq_len=OUTPUT_SEQUENCE_LENGTH, hidden_layer_dim=HIDDEN_LAYER_DIMENSION, - encoder_depth=ENCODER_DEPTH, - decoder_depth=DECODER_DEPTH, - init_embedding=None, - word_embedding_dim=WORD_EMBEDDING_DIMENSION, - train_word_embedding=TRAIN_WORD_EMBEDDINGS_LAYER, + use_cudnn=USE_CUDNN, dense_dropout_ratio=DENSE_DROPOUT_RATIO, - condition_embedding_dim=CONDITION_EMBEDDING_DIMENSION, - is_reverse_model=False): + is_reverse_model=False, + reverse_model=None, + learning_rate=LEARNING_RATE, + grad_clip=GRAD_CLIP, + batch_size=BATCH_SIZE, + epochs_num=EPOCHS_NUM, + horovod=None, + tensorboard_log_dir=TENSORBOARD_LOG_DIR, + log_run_metadata=LOG_RUN_METADATA): """ - :param index_to_token: Dict with tokens and indices for neural network - :param model_init_path: Path to weights file to be used for model's intialization - :param skip_token: Token to skip with masking. Id of this token is inferred from index_to_token dictionary - :param learning_rate: Learning rate factor for the optimization algorithm - :param grad_clip: Clipping parameter to prevent gradient explosion - :param init_embedding: Matrix to initialize word-embedding layer. Default value is random standart-gaussian - initialization + :param index_to_token: Dict with mapping: tokens indices to tokens + :param index_to_condition: Dict with mapping: conditions indicies to conditions values + :param training_data_param: Instance of ModelParam, tuple (value, id) where value is a dataset used for training + and id is a name this dataset + :param validation_data_param: Instance of ModelParam, tuple (value, id) where value is a dataset used for + metrics calculation and id is a concatenation of these datasets' names + :param w2v_model_param: Instance of ModelParam, tuple (value, id) where value is a word2vec matrix of shape + (vocab_size, token_embedding_dim) with float values, used for initializing token embedding layers, and id is + the name of word2vec model + :param model_init_path: Path to a file with model's saved weights for layers intialization + :param model_resolver: Factory that takes model path and returns a file resolver object + :param model_name: String prefix that is prepended to automatically generated model's name. The prefix helps + distinguish the current experiment from other experiments with similar params. + :param corpus_name: File name of the training dataset (included into automatically generated model's name) + :param skip_token: Token to skip with masking, usually _pad_ token. Id of this token is inferred from + index_to_token dictionary + :param token_embedding_dim: Vectors dimensionality of tokens embeddings + :param train_token_embedding: Bool value indicating whether to train token embeddings along with other model's + weights or keep them freezed during training + :param condition_embedding_dim: Vectors dimensionality of conditions embeddings + :param input_seq_len: Max number of tokens in the context sentences + :param input_context_size: Max number of sentences in the context + :param output_seq_len: Max number of tokens in the output sentences + :param hidden_layer_dim: Vectors dimensionality of hidden layers in GRU and Dense layers + :param dense_dropout_ratio: Float value between 0 and 1, indicating the ratio of neurons that will be randomly + deactivated during training to prevent model's overfitting + :param is_reverse_model: Bool value indicating the type of model: + False (regular model) - predicts response for the given context + True (reverse model) - predicts context for the given response (actually, predict the last context sentence for + given response and the beginning of the context) - used for calculating Maximim Mutual Information metric + :param reverse_model: Trained reverse model used to generate predictions in *_reranking modes + :param learning_rate: Learning rate of the optimization algorithm + :param grad_clip: Clipping parameter of the optimization algorithm, used to prevent gradient explosion + :param batch_size: Number of samples to be used for gradient estimation on each train step + :param epochs_num: Number of full dataset passes during train + :param horovod: Initialized horovod module used for multi-gpu training. Trains on single gpu if horovod=None + :param tensorboard_log_dir: Path to tensorboard logs directory + :param log_run_metadata: Set 'True' to profile memory consumption and computation time on tensorboard """ + # Calculate batches number in each epoch. + # The last batch which may be smaller than batch size is included in this number + batches_num_per_epoch = math.ceil(training_data_param.value.x.shape[0] / batch_size) \ + if training_data_param.value else None + + # Create callbacks + callbacks = self._create_essential_callbacks(self, horovod) + callbacks.extend([ + # Custom callback for metrics calculation + CakeChatEvaluatorCallback(self, index_to_token, batch_size, batches_num_per_epoch) + ]) + + super(CakeChatModel, self).__init__( + model_resolver_factory=model_resolver, + metrics_plotter=TensorboardMetricsPlotter(tensorboard_log_dir), + horovod=horovod, + training_callbacks=callbacks) + WithLogger.__init__(self) + + self._model_name = 'reverse_{}'.format(model_name) if is_reverse_model else model_name + self._rnn_class = CuDNNGRU if use_cudnn else partial(GRU, reset_after=True) + + # tokens params self._index_to_token = index_to_token self._token_to_index = {v: k for k, v in index_to_token.items()} self._vocab_size = len(self._index_to_token) + self._skip_token_id = self._token_to_index[skip_token] + self._token_embedding_dim = token_embedding_dim + self._train_token_embedding = train_token_embedding + self._W_init_embedding = \ + self._build_embedding_matrix(self._token_to_index, w2v_model_param.value, token_embedding_dim) \ + if w2v_model_param.value else None + + # condition params self._index_to_condition = index_to_condition self._condition_to_index = {v: k for k, v in index_to_condition.items()} - self._condition_ids_num = len(self._condition_to_index) self._condition_embedding_dim = condition_embedding_dim - self._learning_rate = learning_rate - self._grad_clip = grad_clip + # data params + self._training_data = training_data_param.value + self._validation_data = validation_data_param.value + + # train params + self._batches_num_per_epoch = batches_num_per_epoch + self._model_init_path = model_init_path + self._horovod = horovod + + self._optimizer = optimizers.Adadelta(lr=learning_rate, clipvalue=grad_clip) + if self._horovod: + self._optimizer = horovod.DistributedOptimizer(self._optimizer) + + # gather model's params that define the experiment setting + self._params = create_namedtuple_instance( + name='Params', + corpus_name=corpus_name, + input_context_size=input_context_size, + input_seq_len=input_seq_len, + output_seq_len=output_seq_len, + token_embedding_dim=token_embedding_dim, + train_batch_size=batch_size, + hidden_layer_dim=hidden_layer_dim, + w2v_model=w2v_model_param.id, + is_reverse_model=is_reverse_model, + dense_dropout_ratio=dense_dropout_ratio, + voc_size=len(self._token_to_index), + training_data=training_data_param.id, + validation_data=validation_data_param.id, + epochs_num=epochs_num, + optimizer=self._optimizer.get_config()) + + # profiling params + self._run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) if log_run_metadata else None + self._run_metadata = tf.RunMetadata() if log_run_metadata else None + + # parts of computational graph + self._models = None + + # get trained reverse model used for inference + self._reverse_model = reverse_model - self._W_init_embedding = Normal() if init_embedding is None else init_embedding - self._word_embedding_dim = word_embedding_dim - self._train_word_embedding = train_word_embedding - self._skip_token_id = self._token_to_index[skip_token] + @property + def model_name(self): + return self._model_name - self._hidden_layer_dim = hidden_layer_dim - self._encoder_depth = encoder_depth - self._decoder_depth = decoder_depth - self._dense_dropout_ratio = dense_dropout_ratio + @property + def run_metadata(self): + return self._run_metadata - self._nn_models_dir = nn_models_dir - self._model_prefix = model_prefix - self._corpus_name = corpus_name - self._is_reverse_model = is_reverse_model + @property + def token_to_index(self): + return self._token_to_index - self._model_load_path = model_init_path or self.model_save_path + @property + def index_to_token(self): + return self._index_to_token - self._train_fn = None # Training functions are compiled as needed - self._build_model_computational_graph() - self._compile_theano_functions_for_prediction() + @property + def condition_to_index(self): + return self._condition_to_index @property - def params_str(self): - params_str = 'gru' \ - '_hd{hidden_dim}' \ - '_cdim{condition_dimension}' \ - '_drop{dropout_ratio}' \ - '_encd{encoder_depth}' \ - '_decd{decoder_depth}' \ - '_il{input_seq_len}' \ - '_cs{input_cont_size}' \ - '_ansl{output_seq_len}' \ - '_lr{learning_rate}' \ - '_gc{gradient_clip}' \ - '_{learn_emb}' - - return params_str.format( - hidden_dim=self._hidden_layer_dim, - condition_dimension=self._condition_embedding_dim, - encoder_depth=self._encoder_depth, - decoder_depth=self._decoder_depth, - input_seq_len=INPUT_SEQUENCE_LENGTH, - input_cont_size=INPUT_CONTEXT_SIZE, - output_seq_len=OUTPUT_SEQUENCE_LENGTH, - dropout_ratio=self._dense_dropout_ratio, - learning_rate=self._learning_rate, - gradient_clip=self._grad_clip, - learn_emb='learnemb' if self._train_word_embedding else 'fixemb' - ) + def index_to_condition(self): + return self._index_to_condition @property - def model_name(self): - suffix = ['reverse'] if self._is_reverse_model else [] - params_str = '_'.join([ - self._model_prefix, - self._corpus_name, - self.params_str - ] + suffix) - return params_str + def vocab_size(self): + return self._vocab_size @property - def model_save_path(self): - return os.path.join(self._nn_models_dir, self.model_name) - - def _build_model_computational_graph(self): - self._net = OrderedDict() - self._add_word_embeddings() - self._add_condition_embeddings() - self._add_utterance_encoder() - self._add_context_encoder() - self._add_decoder() - self._add_output_dense() - - def _compile_theano_functions_for_prediction(self): - self._temperature = T.fscalar('temperature') # theano variable needed for prediction - self.predict_prob = self._get_predict_fn(logarithm_output_probs=False) - self.predict_prob_one_step = self._get_predict_one_step_fn(logarithm_output_probs=False) - self.predict_log_prob = self._get_predict_fn(logarithm_output_probs=True) - self.predict_log_prob_one_step = self._get_predict_one_step_fn(logarithm_output_probs=True) - self.predict_sequence_score = self._get_predict_sequence_score_fn() - self.predict_sequence_score_by_thought_vector = self._get_predict_sequence_score_by_thought_vector_fn() - self.get_thought_vectors = self._get_thought_vectors_fn() - - def _add_word_embeddings(self): - self._net['input_x'] = InputLayer( - shape=(None, None, None), input_var=T.itensor3(name='input_x'), name='input_x') - - self._net['input_y'] = InputLayer(shape=(None, None), input_var=T.imatrix(name='input_y'), name='input_y') - - # Infer these variables from data passed to computation graph since batch shape may differ in training and - # prediction phases - self._batch_size = self._net['input_x'].input_var.shape[0] - self._input_context_size = self._net['input_x'].input_var.shape[1] - self._input_seq_len = self._net['input_x'].input_var.shape[2] - self._output_seq_len = self._net['input_y'].input_var.shape[1] - - self._net['input_x_batched'] = \ - reshape(self._net['input_x'], (self._batch_size * self._input_context_size, self._input_seq_len)) - - self._net['input_x_mask'] = NotEqualMaskLayer( - incoming=self._net['input_x_batched'], x=self._skip_token_id, name='mask_x') - - self._net['emb_x'] = EmbeddingLayer( - incoming=self._net['input_x_batched'], - input_size=self._vocab_size, - output_size=self._word_embedding_dim, - W=self._W_init_embedding, - name='emb_x') - # output shape (batch_size, input_context_size, input_seq_len, embedding_dimension) - - self._net['input_y_mask'] = NotEqualMaskLayer( - incoming=self._net['input_y'], x=self._skip_token_id, name='mask_y') - - self._net['emb_y'] = EmbeddingLayer( - incoming=self._net['input_y'], - input_size=self._vocab_size, - output_size=self._word_embedding_dim, - W=self._W_init_embedding, - name='emb_y') - # output shape (batch_size, output_seq_len, embedding_dimension) - - if not self._train_word_embedding: - self._net['emb_x'].params[self._net['emb_x'].W].remove('trainable') - self._net['emb_y'].params[self._net['emb_y'].W].remove('trainable') - - def _add_forward_backward_encoder_layer(self): - is_single_layer_encoder = self._encoder_depth == 1 - return_only_final_state = is_single_layer_encoder - - # input shape = (batch_size * input_context_size, input_seq_len, embedding_dimension) - self._net['enc_forward'] = GRULayer( - incoming=self._net['emb_x'], - num_units=self._hidden_layer_dim, - grad_clipping=self._grad_clip, - only_return_final=return_only_final_state, - name='encoder_forward', - mask_input=self._net['input_x_mask']) - # output shape = (batch_size * input_context_size, input_seq_len, hidden_layer_dimension) - # or (batch_size * input_context_size, hidden_layer_dimension) - - # input shape = (batch_size * input_context_size, input_seq_len, embedding_dimension) - self._net['enc_backward'] = GRULayer( - incoming=self._net['emb_x'], - num_units=self._hidden_layer_dim, - grad_clipping=self._grad_clip, - only_return_final=return_only_final_state, - backwards=True, - name='encoder_backward', - mask_input=self._net['input_x_mask']) - # output shape = (batch_size * input_context_size, input_seq_len, hidden_layer_dimension) - # or (batch_size * input_context_size, hidden_layer_dimension) - - self._net['enc_0'] = ConcatLayer( - incomings=[self._net['enc_forward'], self._net['enc_backward']], - axis=1 if return_only_final_state else 2, - name='encoder_bidirectional_concat') - # output shape = (batch_size * input_context_size, input_seq_len, 2 * hidden_layer_dimension) - # or (batch_size * input_context_size, 2 * hidden_layer_dimension) - - def _add_condition_embeddings(self): - self._net['input_condition_id'] = InputLayer( - shape=(None, ), input_var=T.ivector(name='in_condition_id'), name='input_condition_id') - - self._net['emb_condition_id'] = EmbeddingLayer( - incoming=self._net['input_condition_id'], - input_size=self._condition_ids_num, - output_size=self._condition_embedding_dim, - name='embedding_condition_id') - - def _add_utterance_encoder(self): - # input shape = (batch_size * input_context_size, input_seq_len, embedding_dimension) - self._add_forward_backward_encoder_layer() - - for enc_layer_id in xrange(1, self._encoder_depth): - is_last_encoder_layer = enc_layer_id == self._encoder_depth - 1 - return_only_final_state = is_last_encoder_layer - - # input shape = (batch_size * input_context_size, input_seq_len, embedding_dimension) - self._net['enc_' + str(enc_layer_id)] = GRULayer( - incoming=self._net['enc_' + str(enc_layer_id - 1)], - num_units=self._hidden_layer_dim, - grad_clipping=self._grad_clip, - only_return_final=return_only_final_state, - name='encoder_' + str(enc_layer_id), - mask_input=self._net['input_x_mask']) - - self._net['enc'] = self._net['enc_' + str(self._encoder_depth - 1)] - - # output shape = (batch_size * input_context_size, hidden_layer_dim) - - def _add_context_encoder(self): - self._net['batched_enc'] = reshape( - self._net['enc'], (self._batch_size, self._input_context_size, get_output_shape(self._net['enc'])[-1])) - - self._net['context_enc'] = GRULayer( - incoming=self._net['batched_enc'], - num_units=self._hidden_layer_dim, - grad_clipping=self._grad_clip, - only_return_final=True, - name='context_encoder') - - self._net['switch_enc_to_tv'] = T.iscalar(name='switch_enc_to_tv') - - self._net['thought_vector'] = InputLayer( - shape=(None, self._hidden_layer_dim), input_var=T.fmatrix(name='thought_vector'), name='thought_vector') - - self._net['enc_result'] = SwitchLayer( - incomings=[self._net['thought_vector'], self._net['context_enc']], condition=self._net['switch_enc_to_tv']) - - # We need the following to pass as 'givens' argument when compiling theano functions: - self._default_thoughts_vector = T.zeros((self._batch_size, self._hidden_layer_dim)) - self._default_input_x = T.zeros(shape=(self._net['thought_vector'].input_var.shape[0], 1, 1), dtype=np.int32) - - def _add_decoder(self): - """ - Decoder returns the batch of sequences of thought vectors, each corresponds to a decoded token - reshapes this 3d tensor to 2d matrix so that the next Dense layer can convert each thought vector to - a probability distribution vector - """ + def skip_token_id(self): + return self._skip_token_id - self._net['hid_states_decoder'] = InputLayer( - shape=(None, self._decoder_depth, None), - input_var=T.tensor3('hid_inits_decoder'), - name='hid_states_decoder') - - # repeat along the sequence axis output_seq_len times, where output_seq_len is inferred from input tensor - self._net['enc_repeated'] = RepeatLayer( - incoming=self._net['enc_result'], # input shape = (batch_size, encoder_output_dimension) - n=self._output_seq_len, - name='repeat_layer') - - self._net['emb_condition_id_repeated'] = RepeatLayer( - incoming=self._net['emb_condition_id'], n=self._output_seq_len, name='embedding_condition_id_repeated') - - self._net['dec_concated_input'] = ConcatLayer( - incomings=[self._net['emb_y'], self._net['enc_repeated'], self._net['emb_condition_id_repeated']], - axis=2, - name='decoder_concated_input') - # shape = (batch_size, input_seq_len, encoder_output_dimension) - - self._net['dec_0'] = self._net['dec_concated_input'] - - for dec_layer_id in xrange(1, self._decoder_depth + 1): - # input shape = (batch_size, input_seq_len, embedding_dimension + hidden_dimension) - self._net['dec_' + str(dec_layer_id)] = GRULayer( - incoming=self._net['dec_' + str(dec_layer_id - 1)], - num_units=self._hidden_layer_dim, - grad_clipping=self._grad_clip, - only_return_final=False, - name='decoder_' + str(dec_layer_id), - mask_input=self._net['input_y_mask'], - hid_init=SliceLayer(self._net['hid_states_decoder'], dec_layer_id - 1, axis=1)) - - self._net['dec'] = self._net['dec_' + str(self._decoder_depth)] - # output shape = (batch_size, output_seq_len, hidden_dimension) + @property + def hidden_layer_dim(self): + return self._params.hidden_layer_dim - @staticmethod - def _remove_all_last_tokens(input_layer, unflatten_sequences_shape, flatten_input=True): - """ - Helper function that creates a sequence of layers to clean up the tensor from all elements, - corresponding to the last token in the sequence - """ - new_flattened_shape = (unflatten_sequences_shape[0] * (unflatten_sequences_shape[1] - 1),) \ - + unflatten_sequences_shape[2:] + @property + def decoder_depth(self): + return self._decoder_depth - sliced = SliceLayer( - incoming=reshape(input_layer, unflatten_sequences_shape) if flatten_input else input_layer, - indices=slice(0, -1), - axis=1) # sequence axis - return reshape(sliced, new_flattened_shape) + @property + def is_reverse_model(self): + return self._params.is_reverse_model - @staticmethod - def _remove_all_first_tokens(input_layer, unflatten_sequences_shape, flatten_input=True): + @property + def reverse_model(self): + return self._reverse_model + + @property + def _model_dir(self): + return os.path.join(RESULTS_PATH, 'nn_models') + + @property + def _model_params(self): + return self._params._asdict() + + @property + def _model_progress_resource_path(self): + return os.path.join(self.model_path, self._MODEL_PROGRESS_RESOURCE_NAME) + + def _build_model(self): + # embeddings + x_tokens_emb_model = self._tokens_embedding_model(name='x_token_embedding') + y_tokens_emb_model = self._tokens_embedding_model(name='y_token_embedding') + with tf.variable_scope('condition_embedding_scope', reuse=True): + condition_emb_model = self._condition_embedding_model() + + # encoding + with tf.variable_scope('utterance_scope', reuse=True): + utterance_enc_model = self._utterance_encoder(x_tokens_emb_model) + with tf.variable_scope('encoder_scope', reuse=True): + context_enc_model = self._context_encoder(utterance_enc_model) + + # decoding + with tf.variable_scope('decoder_scope', reuse=True): + decoder_training_model, decoder_model = self._decoder(y_tokens_emb_model, condition_emb_model) + + # seq2seq + seq2seq_training_model, seq2seq_model = self._seq2seq(context_enc_model, decoder_training_model, decoder_model) + + self._models = dict( + utterance_encoder=utterance_enc_model, + context_encoder=context_enc_model, + decoder=decoder_model, + seq2seq=seq2seq_model, + seq2seq_training=seq2seq_training_model) + + return self._models['seq2seq_training'] + + def _get_training_model(self): + def sparse_categorical_crossentropy_logits(y_true, y_pred): + return K.sparse_categorical_crossentropy(y_true, y_pred, from_logits=True) + + self._logger.info('Compiling seq2seq for train...') + + self._models['seq2seq_training'].compile( + loss=sparse_categorical_crossentropy_logits, + optimizer=self._optimizer, + options=self._run_options, + run_metadata=self._run_metadata) + + return self._models['seq2seq_training'] + + def _tokens_embedding_model(self, name='token_embedding'): + self._logger.info('Building tokens_embedding_model...') + + tokens_ids = Input(shape=(None, ), dtype='int32', name=name + '_input') + # output shape == (batch_size, seq_len) + + tokens_embeddings = Embedding( + input_dim=self._vocab_size, + output_dim=self._token_embedding_dim, + trainable=self._train_token_embedding, + name=name, + weights=None if self._W_init_embedding is None else [self._W_init_embedding])(tokens_ids) + # output shape == (batch_size, seq_len, token_emb_size) + + return Model(tokens_ids, tokens_embeddings, name=name + '_model') + + def _condition_embedding_model(self): + self._logger.info('Building condition_embedding_model...') + + condition_id = Input(shape=(1, ), dtype='int32', name='condition_input') + # output shape == (batch_size, 1) + + condition_emb = Embedding( + input_dim=len(self._condition_to_index), + output_dim=self._condition_embedding_dim, + name='condition_embedding')(condition_id) + # output shape == (batch_size, 1, condition_emb_size) + + condition_emb_reshaped = Reshape( + target_shape=(self._condition_embedding_dim, ), name='condition_embedding_reshaped')(condition_emb) + # output shape == (batch_size, condition_emb_size) + + return Model(condition_id, condition_emb_reshaped, name='condition_emb_model') + + def _utterance_encoder(self, tokens_emb_model): + self._logger.info('Building utterance_encoder...') + + tokens_ids = tokens_emb_model.inputs[0] + # output shape == (batch_size, seq_len) + tokens_embeddings = tokens_emb_model(tokens_ids) + # output shape == (batch_size, seq_len, token_emb_size) + + bidir_enc = Bidirectional( + layer=self._rnn_class( + units=self._params.hidden_layer_dim, return_sequences=True, name='encoder'), + name='bidir_utterance_encoder')(tokens_embeddings) + # output shape == (batch_size, seq_len, 2 * hidden_layer_dim) + + utterance_encoding = self._rnn_class( + units=self._params.hidden_layer_dim, name='utterance_encoder_final')(bidir_enc) + # output shape == (batch_size, hidden_layer_dim) + + return Model(tokens_ids, utterance_encoding, name='utterance_encoder_model') + + def _context_encoder(self, utterance_enc_model): + self._logger.info('Building context_encoder...') + context_tokens_ids = Input( + shape=(self._params.input_context_size, self._params.input_seq_len), + dtype='int32', + name='context_tokens_ids') + # output shape == (batch_size, context_size, seq_len) + + context_utterance_embeddings = TimeDistributed( + layer=utterance_enc_model, input_shape=(self._params.input_context_size, + self._params.input_seq_len))(context_tokens_ids) + # output shape == (batch_size, context_size, utterance_encoding_dim) + + context_encoding = self._rnn_class( + units=self._params.hidden_layer_dim, name='context_encoder')(context_utterance_embeddings) + # output shape == (batch_size, hidden_layer_dim) + + return Model(context_tokens_ids, context_encoding, name='encoder_model') + + def _decoder(self, tokens_emb_model, condition_emb_model): + self._logger.info('Building decoder...') + + thought_vector = Input(shape=(self._params.hidden_layer_dim, ), dtype=K.floatx(), name='dec_thought_vector') + # output shape == (batch_size, hidden_layer_dim) + response_tokens_ids = tokens_emb_model.inputs[0] + # output shape == (batch_size, seq_len) + condition_id = condition_emb_model.inputs[0] + # output shape == (batch_size, 1) + temperature = Input(shape=(1, ), dtype='float32', name='dec_temperature') + # output shape == (batch_size, 1) + + # hardcode decoder's depth here: the general solution for any number of stacked rnn layers hs num is too bulky + # and we don't need it, so keep it simple, stupid + self._decoder_depth = 2 + # keep inputs for rnn decoder hidden states globally accessible for all model layers that are using them + # otherwise you may encounter a keras bug that affects rnn stateful models + # related discussion: https://github.com/keras-team/keras/issues/9385#issuecomment-365464721 + self._dec_hs_input = Input( + shape=(self._decoder_depth, self._params.hidden_layer_dim), dtype=K.floatx(), name='dec_hs') + # shape == (batch_size, dec_depth, hidden_layer_dim) + + response_tokens_embeddings = tokens_emb_model(response_tokens_ids) + # output shape == (batch_size, seq_len, token_emb_size) + condition_embedding = condition_emb_model(condition_id) + # output shape == (batch_size, cond_emb_size) + conditioned_tv = Concatenate(name='conditioned_tv')([thought_vector, condition_embedding]) + # output shape == (batch_size, hidden_layer_dim + cond_emb_size) + + # Temporary solution: + # use a custom lambda function for layer repeating and manually set output_shape + # otherwise the consequent Concatenate layer won't work + repeated_conditioned_tv = Lambda( + function=repeat_vector, + mask=lambda inputs, inputs_masks: inputs_masks[0], # function to get mask of the first input + output_shape=(None, self._params.hidden_layer_dim + self._condition_embedding_dim), + name='repeated_conditioned_tv')([conditioned_tv, response_tokens_ids]) + # output shape == (batch_size, seq_len, hidden_layer_dim + cond_emb_size) + + decoder_input = Concatenate(name='concat_emb_cond_tv')([response_tokens_embeddings, repeated_conditioned_tv]) + # output shape == (batch_size, seq_len, token_emb_size + hidden_layer_dim + cond_emb_size) + + # unpack hidden states to tensors + dec_hs_0 = Lambda( + function=lambda x: x[:, 0, :], output_shape=(self._params.hidden_layer_dim, ), + name='dec_hs_0')(self._dec_hs_input) + + dec_hs_1 = Lambda( + function=lambda x: x[:, 1, :], output_shape=(self._params.hidden_layer_dim, ), + name='dec_hs_1')(self._dec_hs_input) + + outputs_seq_0, updated_hs_seq_0 = self._rnn_class( + units=self._params.hidden_layer_dim, return_sequences=True, return_state=True, name='decoder_0')\ + (decoder_input, initial_state=dec_hs_0) + # outputs_seq_0 and updated_hs_seq_0 shapes == (batch_size, seq_len, hidden_layer_dim) + + outputs_seq_1, updated_hs_seq_1 = self._rnn_class( + units=self._params.hidden_layer_dim, return_sequences=True, return_state=True, name='decoder_1')\ + (outputs_seq_0, initial_state=dec_hs_1) + # outputs_seq_1 and updated_hs_seq_1 shapes == (batch_size, seq_len, hidden_layer_dim) + + outputs_dropout = Dropout(rate=self._params.dense_dropout_ratio)(outputs_seq_1) + # output shape == (batch_size, seq_len, hidden_layer_dim) + tokens_logits = Dense(self._vocab_size)(outputs_dropout) + # output shape == (batch_size, seq_len, vocab_size) + tokens_probs = softmax_with_temperature(tokens_logits, temperature) + # output shape == (batch_size, seq_len, vocab_size) + + # pack updated hidden states into one tensor + updated_hs = Concatenate( + axis=1, name='updated_hs')([ + Reshape((1, self._params.hidden_layer_dim))(updated_hs_seq_0), + Reshape((1, self._params.hidden_layer_dim))(updated_hs_seq_1) + ]) + + decoder_training_model = Model( + inputs=[thought_vector, response_tokens_ids, condition_id, self._dec_hs_input], + outputs=[tokens_logits], + name='decoder_training_model') + + decoder_model = Model( + inputs=[thought_vector, response_tokens_ids, condition_id, self._dec_hs_input, temperature], + outputs=[tokens_probs, updated_hs], + name='decoder_model') + + return decoder_training_model, decoder_model + + def _seq2seq(self, context_encoder, decoder_training, decoder): + self._logger.info('Building seq2seq...') + + context_tokens_ids = context_encoder.inputs[0] + # output shape == (batch_size, context_size, input_seq_len) + response_tokens_ids = decoder.inputs[1] + # output shape == (batch_size, output_seq_len - 1) + condition_id = decoder.inputs[2] + # output shape == (batch_size, 1) + temperature = decoder.inputs[4] + # output shape == (batch_size, 1) + + context_encoding = context_encoder(inputs=[context_tokens_ids]) + # output shape == (batch_size, hidden_layer_dim) + + tokens_logits = decoder_training( + inputs=[context_encoding, response_tokens_ids, condition_id, self._dec_hs_input]) + # tokens_probs shape == (batch_size, output_seq_len - 1, vocab_size) + + tokens_probs, _ = decoder( + inputs=[context_encoding, response_tokens_ids, condition_id, self._dec_hs_input, temperature]) + # tokens_probs shape == (batch_size, output_seq_len - 1, vocab_size) + + training_model = Model( + inputs=[context_tokens_ids, response_tokens_ids, condition_id, self._dec_hs_input], + outputs=[tokens_logits], + name='seq2seq_training_model') + + model = Model( + inputs=[context_tokens_ids, response_tokens_ids, condition_id, self._dec_hs_input, temperature], + outputs=[tokens_probs], + name='seq2seq_model') + + return training_model, model + + def _get_training_batch_generator(self): + # set unique random seed for different workers to correctly process batches in multi-gpu training + horovod_seed = self._horovod.rank() if self._horovod else 0 + epoch_id = 0 + + while True: # inifinite batches generator + epoch_id += 1 + + for train_batch in get_training_batch( + self._training_data, + self._params.train_batch_size, + random_permute=SHUFFLE_TRAINING_BATCHES, + random_seed=RANDOM_SEED * epoch_id + horovod_seed): + + context_tokens_ids, response_tokens_ids, condition_id = train_batch + # response tokens are wraped with _start_ and _end_ tokens + # output shape == (batch_size, seq_len) + + # get input response ids by removing last sequence token (_end_) + input_response_tokens_ids = response_tokens_ids[:, :-1] + # output shape == (batch_size, seq_len - 1) + + # get target response ids by removing the first (_start_) token of the sequence + target_response_tokens_ids = response_tokens_ids[:, 1:] + # output shape == (batch_size, seq_len - 1) + + # workaround for using sparse_categorical_crossentropy loss + # see https://github.com/tensorflow/tensorflow/issues/17150#issuecomment-399776510 + target_response_tokens_ids = np.expand_dims(target_response_tokens_ids, axis=-1) + # output shape == (batch_size, seq_len - 1, 1) + + init_dec_hs = np.zeros( + shape=(context_tokens_ids.shape[0], self._decoder_depth, self._params.hidden_layer_dim), + dtype=K.floatx()) + + yield [context_tokens_ids, input_response_tokens_ids, condition_id, + init_dec_hs], target_response_tokens_ids + + def _get_epoch_batches_num(self): + return self._batches_num_per_epoch + + def get_utterance_encoding(self, utterance_tokens_ids): """ - Helper function that creates a sequence of layers to clean up the tensor from all elements, - corresponding to the first token in the sequence + :param utterance_tokens_ids: shape == (batch_size, seq_len), int32 + :return: utterance_encoding shape == (batch_size, hidden_layer_dim), float32 """ - new_flattened_shape = (unflatten_sequences_shape[0] * (unflatten_sequences_shape[1] - 1),) \ - + unflatten_sequences_shape[2:] - - sliced = SliceLayer( - incoming=reshape(input_layer, unflatten_sequences_shape) if flatten_input else input_layer, - indices=slice(1, None), - axis=1) # sequence axis - return reshape(sliced, new_flattened_shape) + return self._models['utterance_encoder'](utterance_tokens_ids) - def _add_output_dense(self): + def get_thought_vectors(self, context_tokens_ids): """ - Adds a dense layer on top of the decoder to convert hidden_state vector to probs distribution over vocabulary. - For every prob sequence last prob vectors are cut off since they correspond - to the tokens that go after EOS_TOKEN and we are not interested in them. - Doesn't need to reshape back the cut tensor since it's convenient to compare - this "long" output with true one-hot vectors. + :param context_tokens_ids, shape == (batch_size, context_size, seq_len), int32 + :return: context_encoding, shape == (batch_size, hidden_layer_dim), float32 """ - self._net['dec_dropout'] = DropoutLayer( - incoming=reshape(self._net['dec'], (-1, self._hidden_layer_dim)), - p=self._dense_dropout_ratio, - name='decoder_dropout_layer') - - self._net['target'] = self._remove_all_first_tokens( - self._net['input_y'], - unflatten_sequences_shape=(self._batch_size, self._output_seq_len), - flatten_input=False) - - self._net['dec_dropout_nolast'] = self._remove_all_last_tokens( - self._net['dec_dropout'], - unflatten_sequences_shape=(self._batch_size, self._output_seq_len, self._hidden_layer_dim)) - - self._net['dist_nolast'] = DenseLayer( - incoming=self._net['dec_dropout_nolast'], - num_units=self._vocab_size, - nonlinearity=lasagne.nonlinearities.softmax, - name='dense_output_probs') - - dist_layer_params = get_all_params(self._net['dist_nolast']) - param_name_to_param = {p.name: p for p in dist_layer_params} - - self._net['dist'] = DenseLayer( - incoming=self._net['dec_dropout'], - num_units=self._vocab_size, - nonlinearity=lasagne.nonlinearities.softmax, - W=param_name_to_param['dense_output_probs.W'], - b=param_name_to_param['dense_output_probs.b'], - name='dense_output_probs') - # output tensor has shape (batch_size * (seq_len - 1), vocab_size) - - def _get_train_fn(self): - output_probs = get_output(self._net['dist_nolast']) - - mask = get_output(self._net['input_y_mask'])[:, 1:].flatten() - nonpad_ids = mask.nonzero() - - target_ids = get_output(self._net['target']) - loss_per_object = categorical_crossentropy(predictions=output_probs, targets=target_ids) - loss = loss_per_object[nonpad_ids].mean() - - all_params = get_all_params(self._net['dist'], trainable=True) - - _logger.info('Computing train updates...') - updates = lasagne.updates.adadelta(loss_or_grads=loss, params=all_params, learning_rate=self._learning_rate) - - _logger.info('Compiling train function...') - - train_fn = theano.function( - inputs=[ - self._net['input_x'].input_var, self._net['input_y'].input_var, - self._net['input_condition_id'].input_var - ], - givens={ - self._net['hid_states_decoder'].input_var: - T.zeros((self._batch_size, self._decoder_depth, self._hidden_layer_dim)), - self._net['thought_vector'].input_var: - self._default_thoughts_vector, - self._net['switch_enc_to_tv']: - np.cast[np.int32](False) # Doesn't compile without explicit casting here - }, - outputs=loss, - updates=updates) - return train_fn - - def train(self, *args): - if not self._train_fn: - self._train_fn = self._get_train_fn() - - return self._train_fn(*args) - - def _get_nn_output(self, remove_last_output=True): + return self._models['context_encoder'].predict(context_tokens_ids) + + def predict_prob(self, context_tokens_ids, response_tokens_ids, condition_id, temperature=1.0): """ - :param remove_last_output: If True, prediction for the last token in the sequence is removed. - If we predict all the outputs for loss calculation and scoring we need to throw away the last prediction - If we only want to get the distribution to predict the next token, this removing is unnecessary. + :param context_tokens_ids: shape == (batch_size, context_size, seq_len), int32 + :param response_tokens_ids: shape == (batch_size, seq_len), int32 + :param condition_id: shape == (batch_size, 1), int32 + :param temperature: float32 :return: + tokens_probs: shape == (batch_size, seq_len, vocab_size), float32 """ - if 'output_probs' not in self._net: - output_probs = get_output(self._net['dist'], deterministic=True) - output_probs = T.reshape(output_probs, (self._batch_size, -1, self._vocab_size)) - self._net['output_probs'] = output_probs - - # We remove the last probability in the sequence to match the input. - # output_probs has shape (batch_size * (seq_len - 1), vocab_size) - if remove_last_output: - return self._net['output_probs'][:, :-1, :] - # output_probs has shape (batch_size, seq_len - 1, vocab_size) - else: - return self._net['output_probs'] + # remove last token, but keep first token to match seq2seq decoder input's shape + response_tokens_ids = response_tokens_ids[:, :-1] + # shape == (batch_size, seq_len - 1) - @staticmethod - def _perform_temperature_transform(probs, temperature): - transformed_log_probs = T.log(probs) / temperature - # For numerical stability (e.g. for low temperatures: - transformed_log_probs = transformed_log_probs - T.max(transformed_log_probs, axis=2, keepdims=True) - # Normalization: - return T.exp(transformed_log_probs) / T.sum(T.exp(transformed_log_probs), axis=2, keepdims=True) - - def _get_predict_fn(self, logarithm_output_probs): - output_probs = self._get_nn_output() - - _logger.info('Compiling predict function (log_prob=%s)...' % logarithm_output_probs) - - predict_fn = theano.function( - inputs=[ - self._net['input_x'].input_var, self._net['input_y'].input_var, - self._net['input_condition_id'].input_var - ], - givens={ - self._net['hid_states_decoder'].input_var: - T.zeros((self._batch_size, self._decoder_depth, self._hidden_layer_dim)), - self._net['thought_vector'].input_var: - self._default_thoughts_vector, - self._net['switch_enc_to_tv']: - np.cast[np.int32](False) # Doesn't compile without explicit casting here - }, - outputs=T.log(output_probs) if logarithm_output_probs else output_probs) - return predict_fn - - def _get_predict_one_step_fn(self, logarithm_output_probs): - output_probs = self._get_nn_output(remove_last_output=False) - new_hiddens = [ - get_output(self._net['dec_{}'.format(layer_id)], deterministic=True) - for layer_id in xrange(1, self._decoder_depth + 1) - ] - - tranformed_output_probs = self._perform_temperature_transform(output_probs, self._temperature) - - _logger.info('Compiling one-step predict function (log_prob=%s)...' % logarithm_output_probs) - predict_one_step_fn = theano.function( - inputs=[ - self._net['thought_vector'].input_var, self._net['hid_states_decoder'].input_var, - self._net['input_y'].input_var, self._net['input_condition_id'].input_var, self._temperature - ], - givens={ - self._net['input_x'].input_var: self._default_input_x, - self._net['switch_enc_to_tv']: - np.cast[np.int32](True) # Doesn't compile without explicit casting here - }, - outputs=[ - T.concatenate(new_hiddens, axis=1), - T.log(tranformed_output_probs) if logarithm_output_probs else tranformed_output_probs - ], - name='predict_probs_one_step') - return predict_one_step_fn - - def _get_thought_vectors_fn(self): - thought_vector = get_output(self._net['context_enc']) - - thought_vector_fn = theano.function(inputs=[self._net['input_x'].input_var], outputs=thought_vector) - return thought_vector_fn - - def _get_sequence_scores(self): - # Calculate log-likelihood for batch of data on GPU in symbolic operationse - probs = self._get_nn_output() - mask = get_output(self._net['input_y_mask']) - # All shapes are symbolic and are evaluated on run-time only after input tensors are supplied - batch_size, seq_len, vocab_size = probs.shape - total_num_tokens = batch_size * seq_len + init_dec_hs = np.zeros( + shape=(context_tokens_ids.shape[0], self._decoder_depth, self._params.hidden_layer_dim), dtype=K.floatx()) + # shape == (batch_size, decoder_depth, hidden_layer_dim) - # We need reshape here to do effective slicing without any loops or scans - probs_long = probs.reshape((total_num_tokens, vocab_size)) - output_ids = self._net['input_y'].input_var[:, 1:] - mask = mask[:, 1:] # Do not use first tokens for likelihood computation - # (these are start tokens: we don't even have probabilities for them) - token_ids_long = output_ids.reshape((total_num_tokens, )) + temperature = np.full_like(condition_id, temperature, dtype=np.float32) + # shape == (batch_size, 1) - # Select probabilities of only observed tokens and reshape back - observed_tokens_probs = probs_long[T.arange(total_num_tokens), token_ids_long] - observed_tokens_log_probs = T.log(observed_tokens_probs) - nonpad_observed_tokens_log_probs = observed_tokens_log_probs.reshape((batch_size, seq_len)) * mask - batch_scores = nonpad_observed_tokens_log_probs.sum(axis=1) + tokens_probs = self._models['seq2seq'].predict( + [context_tokens_ids, response_tokens_ids, condition_id, init_dec_hs, temperature]) + # shape == (batch_size, seq_len - 1, vocab_size) + return tokens_probs - return batch_scores + def predict_prob_by_thought_vector(self, thought_vector, response_tokens_ids, condition_id, temperature=1.0): + """ + :param thought_vector: shape == (batch_size, hidden_layer_dim), float32 + :param response_tokens_ids: shape == (batch_size, seq_len), int32 + :param condition_id: shape == (batch_size, 1), int32 + :param temperature: float32 + :return: + tokens_probs: shape == (batch_size, seq_len, vocab_size), float32 + """ + # remove last token, but keep first token to match seq2seq decoder input's shape + response_tokens_ids = response_tokens_ids[:, :-1] + # output shape == (batch_size, seq_len - 1) - def _get_predict_sequence_score_fn(self): - batch_scores = self._get_sequence_scores() - _logger.info('Compiling sequence scoring function...') - predict_score_fn = theano.function( - inputs=[ - self._net['input_x'].input_var, self._net['input_y'].input_var, - self._net['input_condition_id'].input_var - ], - givens={ - self._net['hid_states_decoder'].input_var: - T.zeros((self._batch_size, self._decoder_depth, self._hidden_layer_dim)), - self._net['thought_vector'].input_var: - self._default_thoughts_vector, - self._net['switch_enc_to_tv']: - np.cast[np.int32](False) # Doesn't compile without explicit casting here - }, - outputs=batch_scores, - name='predict_sequence_score') - - return predict_score_fn - - def _get_predict_sequence_score_by_thought_vector_fn(self): - # We need batch_size symbolic variable to be independent of input_x, to avoid loops in the computational graph - batch_size = self._net['input_y'].input_var.shape[0] - batch_scores = self._get_sequence_scores() - _logger.info('Compiling sequence scoring function (with thought vectors as arguments)...') - predict_score_fn = theano.function( - inputs=[ - self._net['thought_vector'].input_var, - self._net['input_y'].input_var, - self._net['input_condition_id'].input_var, - ], - givens={ - self._net['input_x'].input_var: - self._default_input_x, - self._net['hid_states_decoder'].input_var: - T.zeros((batch_size, self._decoder_depth, self._hidden_layer_dim)), - self._net['switch_enc_to_tv']: - np.cast[np.int32](True) # Doesn't compile without explicit casting here - }, - outputs=batch_scores, - name='predict_sequence_score_by_thought_vector') - - return predict_score_fn + init_dec_hs = \ + np.zeros((thought_vector.shape[0], self._decoder_depth, self._params.hidden_layer_dim), dtype=K.floatx()) + # shape == (batch_size, decoder_depth, hidden_layer_dim) - @property - def index_to_token(self): - return self._index_to_token + temperature = np.full_like(condition_id, temperature, dtype=np.float32) + # shape == (batch_size, 1) - @property - def condition_to_index(self): - return self._condition_to_index + tokens_probs, _ = self._models['decoder'].predict( + [thought_vector, response_tokens_ids, condition_id, init_dec_hs, temperature]) + # shape == (batch_size, seq_len - 1, vocab_size) + return tokens_probs - @property - def index_to_condition(self): - return self._index_to_condition + def predict_prob_one_step(self, thought_vector, prev_hidden_states, prev_tokens_id, condition_id, temperature=1.0): + """ + :param thought_vector: shape == (batch_size, hidden_layer_dim), float32 + :param prev_hidden_states: shape == (batch_size, 2, hidden_layer_dim), float32 + :param prev_tokens_id: shape == (batch_size, 1), int32 + :param condition_id: shape == (batch_size, 1), int32 + :param temperature: float32 + :return: + updated_hidden_states: shape == (batch_size, 2, hidden_layer_dim), float32 + transformed_token_prob: shape == (batch_size, vocab_size), float32 + """ + temperature = np.full_like(prev_tokens_id, temperature, dtype=np.float32) + # shape == (batch_size, 1) - @property - def token_to_index(self): - return self._token_to_index + token_prob, updated_hidden_states = self._models['decoder'].predict( + [thought_vector, prev_tokens_id, condition_id, prev_hidden_states, temperature]) + return updated_hidden_states, token_prob - @property - def model_load_path(self): - return self._model_load_path + def predict_log_prob(self, context_tokens_ids, response_tokens_ids, condition_id, temperature=1.0): + """ + :param context_tokens_ids: shape == (batch_size, context_size, seq_len), int32 + :param response_tokens_ids: shape == (batch_size, seq_len), int32 + :param condition_id: shape == (batch_size, 1), int32 + :param temperature: float32 + :return: + tokens_probs: shape == (batch_size, seq_len, vocab_size), float32 + """ + tokens_probs = self.predict_prob(context_tokens_ids, response_tokens_ids, condition_id, temperature) - @property - def vocab_size(self): - return self._vocab_size + tokens_log_probs = np.log(tokens_probs) + return tokens_log_probs - @property - def skip_token_id(self): - return self._skip_token_id + def predict_log_prob_one_step(self, + thought_vector, + prev_hidden_states, + prev_tokens_id, + condition_id, + temperature=1.0): + """ + :param thought_vector: shape == (batch_size, hidden_layer_dim), float32 + :param prev_hidden_states: shape == (batch_size, 2 * hidden_layer_dim), float32 + :param prev_tokens_id: shape == (batch_size, 1), int32 + :param condition_id: shape == (batch_size, 1), int32 + :param temperature: float32 + :return: + updated_hidden_states: shape == (batch_size, 2 * hidden_layer_dim), float32 + transformed_token_prob: shape == (batch_size, vocab_size), float32 + """ + updated_hidden_states, token_prob = self.predict_prob_one_step(thought_vector, prev_hidden_states, + prev_tokens_id, condition_id, temperature) - @property - def hidden_layer_dim(self): - return self._hidden_layer_dim + token_log_prob = np.log(token_prob) + return updated_hidden_states, token_log_prob - @property - def decoder_depth(self): - return self._decoder_depth + def _compute_sequence_score(self, tokens_ids, tokens_probs): + """ + :param tokens_ids: shape == (batch_size, seq_len), int32 + :param tokens_probs: shape == (batch_size, seq_len - 1, vocab_size), float32 + :return: + """ + mask = tokens_ids != self._skip_token_id + # shape == (batch_size, seq_len) - @property - def is_reverse_model(self): - return self._is_reverse_model + # All shapes are symbolic and are evaluated on run-time only after input tensors are supplied + batch_size, truncated_seq_len, vocab_size = tokens_probs.shape + total_tokens_num = batch_size * truncated_seq_len - def load_weights(self): - _logger.info('\nLoading saved weights from file:\n{}\n'.format(self.model_load_path)) - saved_var_name_to_var = OrderedDict(np.load(self.model_load_path)) + # We need to reshape here for effective slicing without any loops or scans + probs_long = tokens_probs.reshape((total_tokens_num, vocab_size)) + # shape == (batch_size * (seq_len - 1), vocab_size), float32 - var_name_to_var = OrderedDict([(v.name, v) for v in get_all_params(self._net['dist'])]) - initialized_vars, missing_vars, mismatched_vars = [], [], [] + # Do not use first tokens for likelihood computation: + # these are _start_ tokens, we don't have probabilities for them + output_ids = tokens_ids[:, 1:] + # shape == (batch_size, seq_len - 1) + mask = mask[:, 1:] + # shape == (batch_size, seq_len - 1) - for var_name, var in iteritems(var_name_to_var): - if var_name not in saved_var_name_to_var: - missing_vars.append(var_name) - continue + token_ids_flattened = output_ids.reshape((total_tokens_num, )) + # shape == (batch_size * (seq_len - 1)) - default_var_value = var.get_value() - saved_var_value = saved_var_name_to_var[var_name] + # Select probabilities of only observed tokens and reshape back + observed_tokens_probs = probs_long[np.arange(total_tokens_num), token_ids_flattened] + # shape == (batch_size * (seq_len - 1), ) + observed_tokens_log_probs = np.log(observed_tokens_probs) + # shape == (batch_size * (seq_len - 1), ) + nonpad_observed_tokens_log_probs = observed_tokens_log_probs.reshape((batch_size, truncated_seq_len)) * mask + # shape == (batch_size, seq_len - 1) - if default_var_value.shape != saved_var_value.shape: - mismatched_vars.append((var_name, default_var_value.shape, saved_var_value.shape)) - continue + batch_scores = nonpad_observed_tokens_log_probs.sum(axis=1) + # shape == (batch_size, ) + return batch_scores + + def predict_sequence_score(self, context_tokens_ids, response_tokens_ids, condition_id): + """ + :param context_tokens_ids: shape == (batch_size, context_size, seq_len), int32 + :param response_tokens_ids: shape == (batch_size, seq_len), int32 + :param condition_id: shape == (batch_size, 1), int32 + :return: + sequence_score: shape == (batch_size, 1), float32 + """ + response_tokens_probs = self.predict_prob(context_tokens_ids, response_tokens_ids, condition_id) + # output shape == (batch_size, seq_len - 1, vocab_size) - # Checks passed, set parameter value - var.set_value(saved_var_value) - initialized_vars.append(var_name) - del saved_var_name_to_var[var_name] + return self._compute_sequence_score(response_tokens_ids, response_tokens_probs) - laconic_logger.info('\nRestored saved params:') - for var_name in initialized_vars: - laconic_logger.info('\t' + var_name) + def predict_sequence_score_by_thought_vector(self, thought_vector, response_tokens_ids, condition_id): + """ + :param thought_vector: shape == (batch_size, hidden_layer_dim), float32 + :param response_tokens_ids: shape == (batch_size, seq_len), int32 + :param condition_id: shape == (batch_size, 1), int32 + :return: + sequence_score: shape == (batch_size, 1), float32 + """ + response_tokens_probs = self.predict_prob_by_thought_vector(thought_vector, response_tokens_ids, condition_id) + # output shape == (batch_size, seq_len - 1, vocab_size) - laconic_logger.warning('\nMissing saved params:') - for var_name in missing_vars: - laconic_logger.warning('\t' + var_name) + return self._compute_sequence_score(response_tokens_ids, response_tokens_probs) - laconic_logger.warning('\nShapes-mismatched params (saved -> current):') - for var_name, default_shape, saved_shape in mismatched_vars: - laconic_logger.warning('\t{0:<40} {1:<12} -> {2:<12}'.format(var_name, saved_shape, default_shape)) + def _evaluate(self): + self._logger.info('Evaluating model\'s perplexity...') + metrics = {} - laconic_logger.warning('\nUnused saved params:') - for var_name in saved_var_name_to_var: - laconic_logger.warning('\t' + var_name) + for dataset_name, dataset in self._validation_data.items(): + perplexity = calculate_model_mean_perplexity(self, dataset) + metrics[dataset_name] = {'perplexity': perplexity} - laconic_logger.info('') + return metrics - def save_model(self, save_path): - ensure_dir(os.path.dirname(save_path)) - all_params = get_all_params(self._net['dist']) - with open(save_path, 'wb') as f: - params = {v.name: v.get_value() for v in all_params} - np.savez(f, **params) + @staticmethod + def _build_embedding_matrix(token_to_index, w2v_model, embedding_dim): + embedding_matrix = np.zeros((len(token_to_index), embedding_dim)) + for token, index in token_to_index.items(): + embedding_matrix[index] = get_token_vector(token, w2v_model, embedding_dim) - _logger.info('\nSaved model:\n{}\n'.format(save_path)) + return embedding_matrix @staticmethod - def delete_model(delete_path): - if not os.path.isfile(delete_path): - _logger.warning('Couldn\'t delete model. File not found:\n"{}"'.format(delete_path)) + def _get_metric_mean(metrics, metric_name): + return np.mean([metrics[dataset_name][metric_name] for dataset_name in metrics]) + + def _is_better_model(self, new_metrics, old_metrics): + return self._get_metric_mean(new_metrics, metric_name='perplexity') < \ + self._get_metric_mean(old_metrics, metric_name='perplexity') + + def _load_model_if_exists(self): + if self._model_init_path: + self._model = self._load_model(self._model, self._model_init_path) return - os.remove(delete_path) - _logger.info('\nModel is deleted:\n{}'.format(delete_path)) - - def print_layer_shapes(self): - laconic_logger.info('Net shapes:') - - layers = get_all_layers(self._net['dist']) - for l in layers: - laconic_logger.info('\t%-20s \t%s' % (l.name, get_output_shape(l))) - - def print_matrices_weights(self): - laconic_logger.info('\nNet matrices weights:') - params = get_all_params(self._net['dist']) - values = get_all_param_values(self._net['dist']) - - total_network_size = 0 - for p, v in zip(params, values): - param_size = float(v.nbytes) / 1024 / 1024 - # Work around numpy/python 3 regression: - # http://www.markhneedham.com/blog/2017/11/19/python-3-typeerror-unsupported-format-string-passed-to-numpy-ndarray-__format__/ - laconic_logger.info('\t{0:<40} dtype: {1:<10} shape: {2:<12} size: {3:<.2f}M'.format( - p.name, repr(v.dtype), repr(v.shape), param_size)) - total_network_size += param_size - laconic_logger.info('Total network size: {0:.1f} Mb'.format(total_network_size)) - - -def get_nn_model(index_to_token, index_to_condition, model_init_path=None, w2v_matrix=None, resolver_factory=None, - is_reverse_model=False): - model = CakeChatModel(index_to_token, - index_to_condition, - model_init_path=model_init_path, - init_embedding=w2v_matrix, - is_reverse_model=is_reverse_model) - - model.print_layer_shapes() - - # try to initialise model with pre-trained weights - resolver = resolver_factory(model.model_load_path) if resolver_factory else DummyFileResolver(model.model_load_path) - model_exists = resolver.resolve() - - if model_exists: - model.load_weights() - elif model_init_path: - raise FileNotFoundException('Can\'t initialize model from file:\n{}\n'.format(model_init_path)) - else: - _logger.info('\nModel will be built with initial weights.\n') - - model.print_matrices_weights() - _logger.info('\nModel is built\n') - - return model, model_exists + # proceed with usual process of weights loading if no _model_init_path is passed + super(CakeChatModel, self)._load_model_if_exists() diff --git a/cakechat/dialog_model/model_utils.py b/cakechat/dialog_model/model_utils.py index 41799ac..9cfe749 100644 --- a/cakechat/dialog_model/model_utils.py +++ b/cakechat/dialog_model/model_utils.py @@ -1,28 +1,30 @@ from itertools import islice import numpy as np -import theano -from six import text_type -from six.moves import xrange, map, zip -from cakechat.config import TRAIN_CORPUS_NAME, WORD_EMBEDDING_DIMENSION, INPUT_CONTEXT_SIZE, INPUT_SEQUENCE_LENGTH, DEFAULT_CONDITION, \ - S3_MODELS_BUCKET_NAME, S3_W2V_REMOTE_DIR, OUTPUT_SEQUENCE_LENGTH, W2V_WINDOW_SIZE, USE_SKIP_GRAM +from cakechat.config import INPUT_CONTEXT_SIZE, INPUT_SEQUENCE_LENGTH, DEFAULT_CONDITION, OUTPUT_SEQUENCE_LENGTH, \ + AUTOENCODER_MODE, RANDOM_SEED, INTX from cakechat.utils.data_types import Dataset from cakechat.utils.logger import get_logger -from cakechat.utils.s3 import S3FileResolver from cakechat.utils.tee_file import file_buffered_tee from cakechat.utils.text_processing import SPECIAL_TOKENS -from cakechat.utils.w2v import get_w2v_model _logger = get_logger(__name__) +class ModelLoaderException(Exception): + pass + + def transform_conditions_to_ids(conditions, condition_to_index, n_dialogs): condition_ids_iterator = map( lambda condition: condition_to_index.get(condition, condition_to_index[DEFAULT_CONDITION]), conditions) - condition_ids = np.full(n_dialogs, condition_to_index[DEFAULT_CONDITION], dtype=np.int32) + condition_ids = np.full(n_dialogs, condition_to_index[DEFAULT_CONDITION], dtype=INTX) + # shape == (n_dialogs, ) for sample_idx, condition_id in enumerate(condition_ids_iterator): condition_ids[sample_idx] = condition_id + + # shape == (n_dialogs, 1) return condition_ids @@ -48,7 +50,7 @@ def transform_contexts_to_token_ids(tokenized_contexts, :param max_context_len: maximum context length :param max_contexts_num: maximum number of contexts :param add_start_end: add start/end tokens to sequence - :return: X -- numpy array, dtype=np.int32, shape = (max_lines_num, max_context_len, max_line_len). + :return: X -- numpy array, dtype=INTX, shape = (max_lines_num, max_context_len, max_line_len). """ if max_contexts_num is None: @@ -56,8 +58,7 @@ def transform_contexts_to_token_ids(tokenized_contexts, raise TypeError('tokenized_lines should has list type if max_lines_num is not specified') max_contexts_num = len(tokenized_contexts) - X = np.full( - (max_contexts_num, max_context_len, max_line_len), token_to_index[SPECIAL_TOKENS.PAD_TOKEN], dtype=np.int32) + X = np.full((max_contexts_num, max_context_len, max_line_len), token_to_index[SPECIAL_TOKENS.PAD_TOKEN], dtype=INTX) for context_idx, context in enumerate(tokenized_contexts): if context_idx >= max_contexts_num: @@ -91,7 +92,7 @@ def transform_lines_to_token_ids(tokenized_lines, token_to_index, max_line_len, :param max_line_len: maximum number of tokens in a lineh :param max_lines_num: maximum number of lines :param add_start_end: add start/end tokens to sequence - :return: X -- numpy array, dtype=np.int32, shape = (max_lines_num, max_line_len). + :return: X -- numpy array, dtype=INTX, shape = (max_lines_num, max_line_len). """ if max_lines_num is None: @@ -99,7 +100,7 @@ def transform_lines_to_token_ids(tokenized_lines, token_to_index, max_line_len, raise TypeError('tokenized_lines should has list type if max_lines_num is not specified') max_lines_num = len(tokenized_lines) - X = np.full((max_lines_num, max_line_len), token_to_index[SPECIAL_TOKENS.PAD_TOKEN], dtype=np.int32) + X = np.full((max_lines_num, max_line_len), token_to_index[SPECIAL_TOKENS.PAD_TOKEN], dtype=INTX) for line_idx, line in enumerate(tokenized_lines): if line_idx >= max_lines_num: @@ -128,9 +129,9 @@ def transform_token_ids_to_sentences(y_ids, index_to_token): n_responses, n_tokens = y_ids.shape responses = [] - for resp_idx in xrange(n_responses): + for resp_idx in range(n_responses): response_tokens = [] - for token_idx in xrange(n_tokens): + for token_idx in range(n_tokens): token_to_add = index_to_token[y_ids[resp_idx, token_idx]] if token_to_add in [SPECIAL_TOKENS.EOS_TOKEN, SPECIAL_TOKENS.PAD_TOKEN]: @@ -140,9 +141,6 @@ def transform_token_ids_to_sentences(y_ids, index_to_token): response_tokens.append(token_to_add) response_str = ' '.join(response_tokens) - if not isinstance(response_str, text_type): - response_str = response_str.decode('utf-8') - responses.append(response_str) return responses @@ -160,11 +158,11 @@ def transform_context_token_ids_to_sentences(x_ids, index_to_token): n_samples, n_contexts, n_tokens = x_ids.shape samples = [] - for sample_idx in xrange(n_samples): + for sample_idx in range(n_samples): context_samples = [] - for cont_idx in xrange(n_contexts): + for cont_idx in range(n_contexts): sample_tokens = [] - for token_idx in xrange(n_tokens): + for token_idx in range(n_tokens): token_to_add = index_to_token[x_ids[sample_idx, cont_idx, token_idx]] if token_to_add == SPECIAL_TOKENS.EOS_TOKEN or token_to_add == SPECIAL_TOKENS.PAD_TOKEN: @@ -175,9 +173,6 @@ def transform_context_token_ids_to_sentences(x_ids, index_to_token): sample_tokens.append(token_to_add) sample_str = ' '.join(sample_tokens) - if not isinstance(sample_str, text_type): - sample_str = sample_str.decode('utf-8') - context_samples.append(sample_str) samples.append(' / '.join(context_samples)) return samples @@ -189,7 +184,7 @@ def _get_token_vector(token, w2v_model): elif token == SPECIAL_TOKENS.PAD_TOKEN: return np.zeros(w2v_model.vector_size) else: - _logger.warning('Can\'t find token [%s] in w2v dict' % token) + _logger.warning('Can\'t find token [{}] in w2v dict'.format(token)) if not hasattr(_get_token_vector, 'unk_vector'): if SPECIAL_TOKENS.UNKNOWN_TOKEN in w2v_model.wv.vocab: _get_token_vector.unk_vector = np.array(w2v_model[SPECIAL_TOKENS.UNKNOWN_TOKEN]) @@ -198,45 +193,22 @@ def _get_token_vector(token, w2v_model): return _get_token_vector.unk_vector -def transform_w2v_model_to_matrix(w2v_model, index_to_token): - _logger.info('Preparing embedding matrix based on w2v_model and index_to_token dict') - - token_to_index = {v: k for k, v in index_to_token.items()} - tokens_num = len(index_to_token) - output = np.zeros((tokens_num, WORD_EMBEDDING_DIMENSION), dtype=theano.config.floatX) - for token in index_to_token.values(): - idx = token_to_index[token] - output[idx] = _get_token_vector(token, w2v_model) - - return output - - -def get_w2v_embedding_matrix(tokenized_dialog_lines, index_to_token, add_start_end=False): - if add_start_end: - tokenized_dialog_lines = ( - [SPECIAL_TOKENS.START_TOKEN] + line + [SPECIAL_TOKENS.EOS_TOKEN] for line in tokenized_dialog_lines) - - w2v_resolver_factory = S3FileResolver.init_resolver(bucket_name=S3_MODELS_BUCKET_NAME, remote_dir=S3_W2V_REMOTE_DIR) - - w2v_model = get_w2v_model( - TRAIN_CORPUS_NAME, - len(index_to_token), - model_resolver_factory=w2v_resolver_factory, - tokenized_lines=tokenized_dialog_lines, - vec_size=WORD_EMBEDDING_DIMENSION, - window_size=W2V_WINDOW_SIZE, - skip_gram=USE_SKIP_GRAM) - w2v_matrix = transform_w2v_model_to_matrix(w2v_model, index_to_token) - return w2v_matrix - - -def get_training_batch(inputs, batch_size, random_permute=False): +def get_training_batch(inputs, batch_size, random_permute=False, random_seed=RANDOM_SEED): + """ + Generator that yields data in batches. The last batch may be incomplete, yield it as well. + :param inputs: tuple of numpy arrays, for example (contexts_ids, responses_ids, conditions_ids) + :param batch_size: length of numpy arrays to be yielded for each input + :param random_permute: if True input arrays data will be synchronously shuffled before yielding + :param random_seed: seed to ensure the identical shuffling of input data for experiments reproducibility + :return: generator that yields tuples of numpy arrays with batch_size length + """ n_samples = inputs[0].shape[0] n_batches = n_samples // batch_size batches_seq = np.arange(n_batches) samples_seq = np.arange(n_samples) if random_permute: + np.random.seed(random_seed) np.random.shuffle(samples_seq) for i in batches_seq: @@ -318,7 +290,7 @@ def _get_x_data_iterator_with_context(x_data_iterator, y_data_iterator, context_ last_y_line = y_line -def transform_lines_to_nn_input(tokenized_dialog_lines, token_to_index): +def transform_lines_to_nn_input(tokenized_dialog_lines, token_to_index, autoencoder_mode=AUTOENCODER_MODE): """ Splits lines (IterableSentences) and generates numpy arrays of token ids suitable for training. Doesn't store all lines in memory. @@ -328,9 +300,11 @@ def transform_lines_to_nn_input(tokenized_dialog_lines, token_to_index): _logger.info('Iterating through lines to get number of elements in the dataset') n_dialogs = sum(1 for _ in iterator_for_len_calc) - x_data_iterator = islice(x_data_iterator, 0, None, 2) - y_data_iterator = islice(y_data_iterator, 1, None, 2) - n_dialogs //= 2 + if not autoencoder_mode: + # seq2seq mode + x_data_iterator = islice(x_data_iterator, 0, None, 2) + y_data_iterator = islice(y_data_iterator, 1, None, 2) + n_dialogs //= 2 y_data_iterator, y_data_iterator_for_context = file_buffered_tee(y_data_iterator) x_data_iterator = _get_x_data_iterator_with_context(x_data_iterator, y_data_iterator_for_context) diff --git a/cakechat/dialog_model/quality/__init__.py b/cakechat/dialog_model/quality/__init__.py index 31c8ab4..994a101 100644 --- a/cakechat/dialog_model/quality/__init__.py +++ b/cakechat/dialog_model/quality/__init__.py @@ -3,5 +3,4 @@ from cakechat.dialog_model.quality.metrics.perplexity import calculate_model_mean_perplexity from cakechat.dialog_model.quality.metrics.ranking import compute_average_precision, compute_recall_k, \ compute_retrieval_metric_mean -from cakechat.dialog_model.quality.logging import save_metrics, save_test_results, calculate_and_log_val_metrics, \ - log_predictions +from cakechat.dialog_model.quality.logging import calculate_and_log_val_metrics, log_predictions diff --git a/cakechat/dialog_model/quality/logging.py b/cakechat/dialog_model/quality/logging.py index 051b780..80cd089 100644 --- a/cakechat/dialog_model/quality/logging.py +++ b/cakechat/dialog_model/quality/logging.py @@ -1,120 +1,57 @@ +import csv import os -import subprocess -import sys -import time -from collections import namedtuple from datetime import datetime -from six.moves import xrange import pandas as pd -# UnicodeCSV requires files to be opened as binary on Python3 by design. -# https://github.com/jdunck/python-unicodecsv/issues/65 -if sys.version_info[0] == 2: - import unicodecsv as csv -else: - import csv - -from cakechat.config import DATA_DIR, PREDICTION_MODE_FOR_TESTS, MAX_PREDICTIONS_LENGTH +from cakechat.config import PREDICTION_MODE_FOR_TESTS, MAX_PREDICTIONS_LENGTH from cakechat.dialog_model.inference import get_nn_responses from cakechat.dialog_model.model_utils import transform_context_token_ids_to_sentences from cakechat.dialog_model.quality import calculate_model_mean_perplexity, calculate_response_ngram_distinctness from cakechat.utils.files_utils import ensure_dir from cakechat.utils.logger import get_logger -from cakechat.utils.plotters import TensorboardMetricsPlotter - -_StatsInfo = namedtuple('StatsInfo', 'start_time, iteration_num, sents_batches_num') - -_TENSORBOARD_LOG_DIR = os.path.join(DATA_DIR, 'tensorboard') -_METRIC_NAMES = ['perplexity', 'unigram_distinctness', 'bigram_distinctness'] _logger = get_logger(__name__) -_tensorboard_metrics_plotter = TensorboardMetricsPlotter(_TENSORBOARD_LOG_DIR) - - -def _get_git_revision_short_hash(): - return subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD']) - - -def _get_formatted_time(seconds): - m, s = divmod(seconds, 60) - h, m = divmod(m, 60) - formatted_time = '%d:%02d:%02d' % (h, m, s) - - return formatted_time - - -def _get_iteration_stats(stats_info): - stats_str = 'Batch iteration number %s\n' % str(stats_info.iteration_num) - - total_elapsed_time = time.time() - stats_info.start_time - stats_str += 'Total elapsed time: %s\n' % _get_formatted_time(total_elapsed_time) - - elapsed_time_per_iteration = total_elapsed_time / (stats_info.iteration_num + 1) - stats_str += 'Elapsed time for a batch: %s\n' % _get_formatted_time(elapsed_time_per_iteration) - - estimated_time_for_full_pass = elapsed_time_per_iteration * stats_info.sents_batches_num - stats_str += 'Estimated time for a full dataset pass: %s\n' % _get_formatted_time(estimated_time_for_full_pass) - - return stats_str - - -def init_csv_writer(fh, output_seq_len, model_params_str): - csv_writer = csv.writer(fh, delimiter='\t') - csv_writer.writerow(['']) # empty row for better readability - csv_writer.writerow([model_params_str]) - csv_writer.writerow(['commit hash: %s' % _get_git_revision_short_hash()]) - csv_writer.writerow(['date: %s' % datetime.now().strftime("%Y-%m-%d_l%H:%M")]) - csv_writer.writerow(['%d maximum tokens in the response' % output_seq_len]) - - return csv_writer - - -def save_metrics(metrics, model_name): - for metric_name, metric_value in metrics.items(): - _tensorboard_metrics_plotter.plot(model_name, metric_name, float(metric_value)) def calculate_and_log_val_metrics(nn_model, - context_sensitive_val_subset, + context_sensitive_val, context_free_val, - prediction_mode=PREDICTION_MODE_FOR_TESTS): - val_metrics = dict() - - val_metrics['context_free_perplexity'] = calculate_model_mean_perplexity(nn_model, context_free_val) - _logger.info('Current val context-free perplexity: {0:.2f}'.format(val_metrics['context_free_perplexity'])) + prediction_mode=PREDICTION_MODE_FOR_TESTS, + calculate_ngram_distance=True): + metric_name_to_value = { + 'context_free_perplexity': calculate_model_mean_perplexity(nn_model, context_free_val), + 'context_sensitive_perplexity': calculate_model_mean_perplexity(nn_model, context_sensitive_val) + } - val_metrics['context_sensitive_perplexity'] = \ - calculate_model_mean_perplexity(nn_model, context_sensitive_val_subset) - _logger.info('Current val context-sensitive perplexity: {0:.2f}' - .format(val_metrics['context_sensitive_perplexity'])) + if calculate_ngram_distance: + for metric_name, ngram_len in [('unigram_distinctness', 1), ('bigram_distinctness', 2)]: + metric_name_to_value[metric_name] = calculate_response_ngram_distinctness( + context_sensitive_val.x, + nn_model, + ngram_len=ngram_len, + mode=prediction_mode, + condition_ids=context_sensitive_val.condition_ids) - val_metrics['unigram_distinctness'] = calculate_response_ngram_distinctness( - context_sensitive_val_subset.x, - nn_model, - ngram_len=1, - mode=prediction_mode, - condition_ids=context_sensitive_val_subset.condition_ids) + for metric_name, metric_value in metric_name_to_value.items(): + _logger.info('Val set {}: {:.3f}'.format(metric_name, metric_value)) - val_metrics['bigram_distinctness'] = calculate_response_ngram_distinctness( - context_sensitive_val_subset.x, - nn_model, - ngram_len=2, - mode=prediction_mode, - condition_ids=context_sensitive_val_subset.condition_ids) + return metric_name_to_value - _logger.info('Current val distinctness: uni={0:.3f}, bi={1:.3f}'.format(val_metrics['unigram_distinctness'], - val_metrics['bigram_distinctness'])) - return val_metrics +def _init_csv_writer(predictions_path, output_seq_len, model_name): + with open(predictions_path, 'w', encoding='utf-8') as fh: + csv_writer = csv.writer(fh, delimiter='\t') + csv_writer.writerow([model_name]) + csv_writer.writerow(['date: {}'.format(datetime.now().strftime('%Y-%m-%d %H:%M'))]) + csv_writer.writerow(['{} maximum tokens in the response'.format(output_seq_len)]) + csv_writer.writerow(['']) # empty row for better readability def log_predictions(predictions_path, - x_test, + contexts_token_ids, nn_model, - prediction_modes=(PREDICTION_MODE_FOR_TESTS,), - stats_info=None, - cur_perplexity=None, + prediction_modes, output_seq_len=MAX_PREDICTIONS_LENGTH, **kwargs): """ @@ -122,53 +59,29 @@ def log_predictions(predictions_path, several responses will be generated - one for each mode from the prediction_modes list. :param predictions_path: Generated responses will be saved to this file - :param x_test: context token ids, numpy array of shape (batch_size, context_len, INPUT_SEQUENCE_LENGTH) + :param contexts_token_ids: contexts token ids, numpy array of shape (batch_size, context_len, INPUT_SEQUENCE_LENGTH) :param nn_model: instance of CakeChatModel class - :param prediction_modes: Iterable of modes to be used for responses generation. See PREDICTION_MODES - for available options - :param stats_info: Info about current training status: total time passed, time spent on training, - processed batches number and estimated time for one epoch - :param cur_perplexity: Addition to stats_info - current perplexity metric calculated on validation dataset + :param prediction_modes: See PREDICTION_MODES for available options :param output_seq_len: Max number of tokens in generated responses """ - - _logger.info('Logging responses to test lines') + _logger.info('Logging responses for test set') # Create all the directories for the prediction path in case they don't exist - prediction_dir = os.path.dirname(predictions_path) - if prediction_dir: - ensure_dir(prediction_dir) - - with open(predictions_path, 'w') as test_res_fh: - csv_writer = init_csv_writer(test_res_fh, output_seq_len, nn_model.model_name) + ensure_dir(os.path.dirname(predictions_path)) - if cur_perplexity: - csv_writer.writerow(['Current perplexity: %.2f' % cur_perplexity]) - if stats_info: - csv_writer.writerow([_get_iteration_stats(stats_info)]) + _init_csv_writer(predictions_path, output_seq_len, nn_model.model_name) - contexts = transform_context_token_ids_to_sentences(x_test, nn_model.index_to_token) + contexts = transform_context_token_ids_to_sentences(contexts_token_ids, nn_model.index_to_token) predictions_data = pd.DataFrame() predictions_data['contexts'] = contexts - for pred_mode in prediction_modes: - responses_batch = get_nn_responses(x_test, nn_model, pred_mode, **kwargs) + for prediction_mode in prediction_modes: + predicted_responses = get_nn_responses(contexts_token_ids, nn_model, prediction_mode, **kwargs) # list of lists of strings, shape (contexts_num, 1) - first_responses_batch = [response[0] for response in responses_batch] + predicted_responses = [response[0] for response in predicted_responses] # list of strings, shape (contexts_num) - predictions_data[pred_mode] = first_responses_batch + predictions_data[prediction_mode] = predicted_responses predictions_data.to_csv(predictions_path, sep='\t', index=False, encoding='utf-8', mode='a', float_format='%.2f') - message = '\nSuccesfully dumped {} responses.'.format(len(contexts)) - message += '\nHere they are:\n{}\n'.format(predictions_path) - _logger.info(message) - - -def save_test_results(x_test, nn_model, start_time, current_batch_idx, all_batches_num, suffix='', cur_perplexity=None): - - stats_info = _StatsInfo(start_time, current_batch_idx, all_batches_num) - results_file_name = '{}_{}.tsv'.format(nn_model.model_name, suffix) - test_results_path = os.path.join(DATA_DIR, 'results', results_file_name) - - log_predictions(test_results_path, x_test, nn_model, stats_info=stats_info, cur_perplexity=cur_perplexity) + _logger.info('Dumped {} predicted responses to {}'.format(len(contexts), predictions_path)) diff --git a/cakechat/dialog_model/quality/metrics/distinctness.py b/cakechat/dialog_model/quality/metrics/distinctness.py index 8b29c50..5ae84f3 100644 --- a/cakechat/dialog_model/quality/metrics/distinctness.py +++ b/cakechat/dialog_model/quality/metrics/distinctness.py @@ -1,9 +1,9 @@ import numpy as np -from six.moves import xrange from cakechat.config import PREDICTION_MODE_FOR_TESTS, DEFAULT_TEMPERATURE, BEAM_SIZE, \ PREDICTION_DISTINCTNESS_NUM_TOKENS from cakechat.dialog_model.inference import get_nn_response_ids, ServiceTokensIDs +from cakechat.utils.profile import timer def _calculate_distinct_ngrams(prediction_samples, ngram_len): @@ -14,7 +14,7 @@ def _calculate_distinct_ngrams(prediction_samples, ngram_len): ngrams = set() for y in prediction_samples: # Calculate all n-grams where n = ngram_len. (Get ngram_len cyclic shifts of y and transpose the result) - cur_ngrams = zip(*[y[i:] for i in xrange(ngram_len)]) # yapf: disable + cur_ngrams = list(zip(*[y[i:] for i in range(ngram_len)])) # yapf: disable # Aggregate statistics ngrams.update(cur_ngrams) @@ -22,6 +22,7 @@ def _calculate_distinct_ngrams(prediction_samples, ngram_len): return len(ngrams) +@timer def calculate_response_ngram_distinctness(x, nn_model, ngram_len, @@ -62,4 +63,4 @@ def calculate_response_ngram_distinctness(x, break distinct_ngrams = _calculate_distinct_ngrams(responses, ngram_len) - return float(distinct_ngrams) / num_tokens_to_generate + return distinct_ngrams / num_tokens_to_generate diff --git a/cakechat/dialog_model/quality/metrics/lexical_simlarity.py b/cakechat/dialog_model/quality/metrics/lexical_simlarity.py index db913ac..5e18254 100644 --- a/cakechat/dialog_model/quality/metrics/lexical_simlarity.py +++ b/cakechat/dialog_model/quality/metrics/lexical_simlarity.py @@ -2,13 +2,13 @@ from sklearn.feature_extraction.text import TfidfVectorizer -from cakechat.config import TRAIN_CORPUS_NAME, BASE_CORPUS_NAME, DATA_DIR +from cakechat.config import TRAIN_CORPUS_NAME, BASE_CORPUS_NAME, RESULTS_PATH from cakechat.utils.files_utils import get_persisted from cakechat.utils.text_processing import load_index_to_item, get_tokens_sequence, get_processed_corpus_path, \ load_processed_dialogs_from_json, FileTextLinesIterator, get_dialog_lines_and_conditions, \ get_alternated_dialogs_lines, get_index_to_token_path -_TFIDF_VECTORIZER_FULL_PATH = os.path.join(DATA_DIR, 'tfidf_vectorizer.pickle') +_TFIDF_VECTORIZER_FULL_PATH = os.path.join(RESULTS_PATH, 'tfidf_vectorizer.pickle') def _load_train_lines(corpus_name=TRAIN_CORPUS_NAME): diff --git a/cakechat/dialog_model/quality/metrics/plotters.py b/cakechat/dialog_model/quality/metrics/plotters.py new file mode 100644 index 0000000..9a23491 --- /dev/null +++ b/cakechat/dialog_model/quality/metrics/plotters.py @@ -0,0 +1,60 @@ +import os +from collections import Counter +import tensorflow as tf +from keras import backend as K + +from cakechat.utils.files_utils import get_cached, serialize + + +class DummyMetricsPlotter(object): + def plot(self, model_id, metric_name, metric_value): + pass + + +class TensorboardMetricsPlotter(object): + def __init__(self, log_dir): + self._log_dir = log_dir + self._writers = {} + self._steps_path = os.path.join(self._log_dir, 'steps') + self._steps = get_cached(Counter, self._steps_path) + + @staticmethod + def _get_model_specific_key(model_name, key): + """ + Build unique identifier for (model_name, key_name) pair. + """ + return '{}_{}'.format(model_name, key) + + def _get_model_writer(self, model_name): + if model_name not in self._writers: + self._writers[model_name] = \ + tf.summary.FileWriter(os.path.join(self._log_dir, model_name), K.get_session().graph) + + return self._writers[model_name] + + def plot(self, model_name, metric_name, metric_value): + summary = tf.Summary() + summary.value.add(tag=metric_name, simple_value=metric_value) # pylint: disable=maybe-no-member + + writer = self._get_model_writer(model_name) + metric_model_key = self._get_model_specific_key(model_name, metric_name) + writer.add_summary(summary, self._steps[metric_model_key]) + writer.flush() + + self._steps[metric_model_key] += 1 + serialize(self._steps_path, self._steps) + + def log_run_metadata(self, model_name, run_metadata): + run_metadata_model_key = self._get_model_specific_key(model_name, key='run_metadata') + run_tag = '{}_{}'.format(self._steps[run_metadata_model_key], run_metadata_model_key) + + writer = self._get_model_writer(model_name) + writer.add_run_metadata(run_metadata, run_tag, self._steps[run_metadata_model_key]) + writer.flush() + + self._steps[run_metadata_model_key] += 1 + serialize(self._steps_path, self._steps) + + @property + def log_dir(self): + return self._log_dir diff --git a/cakechat/dialog_model/quality/metrics/ranking.py b/cakechat/dialog_model/quality/metrics/ranking.py index 4ae7cbe..97f55db 100644 --- a/cakechat/dialog_model/quality/metrics/ranking.py +++ b/cakechat/dialog_model/quality/metrics/ranking.py @@ -20,7 +20,7 @@ def compute_recall_k(expected_answers, weighted_actual_answers, k): sorted_k_responses = sorted( weighted_actual_answers.keys(), key=lambda response: weighted_actual_answers[response], reverse=True)[:k] - recall_k = len(set(sorted_k_responses) & set(expected_answers)) / float(len(expected_answers)) + recall_k = len(set(sorted_k_responses) & set(expected_answers)) / len(expected_answers) return recall_k diff --git a/cakechat/dialog_model/quality/metrics/utils.py b/cakechat/dialog_model/quality/metrics/utils.py index f2064a0..a37a167 100644 --- a/cakechat/dialog_model/quality/metrics/utils.py +++ b/cakechat/dialog_model/quality/metrics/utils.py @@ -1,2 +1,24 @@ +import json + +from cakechat.utils.files_utils import ensure_file +from cakechat.utils.logger import get_logger + +_logger = get_logger(__name__) + + class MetricsException(Exception): pass + + +class MetricsSerializer(object): + @staticmethod + def load_metrics(metrics_resource_name): + _logger.info('Restoring metrics from {}'.format(metrics_resource_name)) + with open(metrics_resource_name, 'r', encoding='utf-8') as fh: + return json.load(fh) + + @staticmethod + def save_metrics(metrics_resource_name, metrics): + _logger.info('Saving metrics to {}'.format(metrics_resource_name)) + with ensure_file(metrics_resource_name, 'w', encoding='utf-8') as fh: + json.dump(metrics, fh, indent=2) diff --git a/cakechat/dialog_model/quality/tests/metrics.py b/cakechat/dialog_model/quality/tests/metrics.py index b11b538..df7cf46 100644 --- a/cakechat/dialog_model/quality/tests/metrics.py +++ b/cakechat/dialog_model/quality/tests/metrics.py @@ -1,7 +1,12 @@ +import os +import sys import unittest import numpy as np +sys.path.append( + os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))) + from cakechat.dialog_model.quality.metrics.perplexity import _calculate_mean_perplexity from cakechat.dialog_model.quality.metrics.utils import MetricsException diff --git a/cakechat/dialog_model/train.py b/cakechat/dialog_model/train.py deleted file mode 100644 index 19c5dfe..0000000 --- a/cakechat/dialog_model/train.py +++ /dev/null @@ -1,254 +0,0 @@ -import time - -from six.moves import xrange - -from cakechat.config import MAX_PREDICTIONS_LENGTH, BATCH_SIZE, EPOCHS_NUM, LOG_TO_TB_FREQUENCY_PER_BATCHES, \ - SCREEN_LOG_FREQUENCY_PER_BATCHES, SCREEN_LOG_NUM_TEST_LINES, SHUFFLE_TRAINING_BATCHES, PREDICTION_MODE_FOR_TESTS, \ - LOG_CANDIDATES_NUM, VAL_SUBSET_SIZE, AVG_LOSS_DECAY, LOG_TO_FILE_FREQUENCY_PER_BATCHES, \ - SAVE_MODEL_FREQUENCY_PER_BATCHES -from cakechat.dialog_model.inference import get_nn_responses -from cakechat.dialog_model.inference.service_tokens import ServiceTokensIDs -from cakechat.dialog_model.model_utils import transform_context_token_ids_to_sentences, get_training_batch, \ - reverse_nn_input -from cakechat.dialog_model.quality import save_metrics, save_test_results, calculate_and_log_val_metrics, \ - calculate_model_mean_perplexity -from cakechat.utils.data_types import TrainStats, DatasetsCollection -from cakechat.utils.dataset_loader import load_context_free_val, load_conditioned_train_set, \ - generate_subset, load_context_sensitive_val -from cakechat.utils.logger import get_logger, laconic_logger - -_logger = get_logger(__name__) - - -def _update_saved_nn_model(nn_model, val_metrics, best_perplexities, train_stats): - if train_stats.cur_batch_id % SAVE_MODEL_FREQUENCY_PER_BATCHES != 0: - # don't save model on this iteration - return best_perplexities - - # proceed with model saving - cur_perplexities = (val_metrics['context_free_perplexity'], val_metrics['context_sensitive_perplexity']) - is_perplexity_improved = cur_perplexities[0] < best_perplexities[0] and cur_perplexities[1] < best_perplexities[1] - - if is_perplexity_improved: - old_suffix = '_pp_free{0:.2f}_sensitive{1:.2f}'.format(*best_perplexities) - new_suffix = '_pp_free{0:.2f}_sensitive{1:.2f}'.format(*cur_perplexities) - best_perplexities = cur_perplexities - nn_model.save_model(nn_model.model_save_path + new_suffix) - - if new_suffix != old_suffix: - nn_model.delete_model(nn_model.model_save_path + old_suffix) - else: - nn_model.save_model(nn_model.model_save_path) - - return best_perplexities - - -def _calc_and_save_train_metrics(nn_model, train_subset, avg_loss): - train_metrics = dict() - train_metrics['train_perplexity'] = calculate_model_mean_perplexity(nn_model, train_subset) - train_metrics['train_loss'] = avg_loss - - save_metrics(train_metrics, nn_model.model_name) - - _logger.info('Current train perplexity: %s' % train_metrics['train_perplexity']) - return train_metrics - - -def _calc_and_save_val_metrics(nn_model, - context_sensitive_val_subset, - context_free_val, - prediction_mode=PREDICTION_MODE_FOR_TESTS): - - val_metrics = calculate_and_log_val_metrics(nn_model, context_sensitive_val_subset, context_free_val, - prediction_mode) - save_metrics(val_metrics, nn_model.model_name) - - return val_metrics - - -def _save_val_results(nn_model, - x_context_free_val, - x_context_sensitive_val_subset, - val_metrics, - train_stats, - suffix=''): - - context_free_perplexity = val_metrics['context_free_perplexity'] if val_metrics else None - context_sensitive_perplexity = val_metrics['context_sensitive_perplexity'] if val_metrics else None - - save_test_results( - x_context_free_val, - nn_model, - train_stats.start_time, - train_stats.cur_batch_id, - train_stats.batches_num, - suffix='_context_free' + suffix, - cur_perplexity=context_free_perplexity) - - save_test_results( - x_context_sensitive_val_subset, - nn_model, - train_stats.start_time, - train_stats.cur_batch_id, - train_stats.batches_num, - suffix='_context_sensitive' + suffix, - cur_perplexity=context_sensitive_perplexity) - - -def _log_sample_answers(x_test, nn_model, mode): - _logger.info('Start predicting responses of length {out_len} for {n_samples} samples with mode {mode}'.format( - out_len=MAX_PREDICTIONS_LENGTH, n_samples=x_test.shape[0], mode=mode)) - - questions = transform_context_token_ids_to_sentences(x_test, nn_model.index_to_token) - responses = get_nn_responses(x_test, nn_model, mode, output_candidates_num=LOG_CANDIDATES_NUM) - _logger.info('Finished predicting! Logging...') - - for i, (question_ids, question) in enumerate(zip(x_test, questions)): - laconic_logger.info('') # for better readability - for j, response in enumerate(responses[i]): - laconic_logger.info('%-35s\t --#=%02d--> \t%s' % (question, j + 1, response)) - - laconic_logger.info('') # for better readability - - -def _log_train_info_for_one_batch(train_stats): - total_time = (time.time() - train_stats.start_time) / 3600 # in hours - total_train_time = train_stats.total_training_time / 3600 # in hours - shifted_batch_id = train_stats.cur_batch_id + 1 - - progress = float(shifted_batch_id) / train_stats.batches_num - avr_time_per_batch = total_time / shifted_batch_id - expected_time_per_epoch = avr_time_per_batch * train_stats.batches_num - total_training_time_in_percent = total_train_time / total_time - - # use print here for better readability - _logger.info('batch {batch_id} / {batches_num} ({progress:.1%}) \t' - 'loss: {loss:.2f} \t ' - 'time: epoch {epoch_time:.1f} h | total {total_time:.1f} h | ' - 'train {train_time:.1f} h ({train_time_percent:.1%})' - .format( - batch_id=shifted_batch_id, - batches_num=train_stats.batches_num, - progress=progress, - loss=train_stats.cur_loss, - epoch_time=expected_time_per_epoch, - total_time=total_time, - train_time=total_train_time, - train_time_percent=total_training_time_in_percent - )) - - -def _analyse_model_performance_and_dump_results(nn_model, datasets, train_stats): - cur_best_val_perplexities = train_stats.best_val_perplexities - cur_val_metrics = train_stats.cur_val_metrics - - _log_train_info_for_one_batch(train_stats) - - if train_stats.cur_batch_id % SCREEN_LOG_FREQUENCY_PER_BATCHES == 0: - questions_for_sampling = datasets.context_free_val.x[:SCREEN_LOG_NUM_TEST_LINES] - _log_sample_answers(questions_for_sampling, nn_model, PREDICTION_MODE_FOR_TESTS) - - if train_stats.cur_batch_id % LOG_TO_TB_FREQUENCY_PER_BATCHES == 0: - # save metrics on train data - _calc_and_save_train_metrics(nn_model, - datasets.train_subset, - train_stats.cur_loss) - - # save metrics on validation data - cur_val_metrics = _calc_and_save_val_metrics( - nn_model, - datasets.context_sensitive_val_subset, - datasets.context_free_val, - prediction_mode=PREDICTION_MODE_FOR_TESTS) - - if train_stats.cur_batch_id % LOG_TO_FILE_FREQUENCY_PER_BATCHES == 0: - # save predictions on validation inputs - _save_val_results( - nn_model, - datasets.context_free_val.x, - datasets.context_sensitive_val_subset.x, - cur_val_metrics, - train_stats=train_stats) - - return cur_best_val_perplexities, cur_val_metrics - - -def _get_datasets(nn_model, is_reverse_model): - train = load_conditioned_train_set(nn_model.token_to_index, nn_model.condition_to_index) - context_free_val = load_context_free_val(nn_model.token_to_index) - - context_sensitive_val = load_context_sensitive_val(nn_model.token_to_index, nn_model.condition_to_index) - if is_reverse_model: - service_tokens = ServiceTokensIDs(nn_model.token_to_index) - train = reverse_nn_input(train, service_tokens) - context_free_val = reverse_nn_input(context_free_val, service_tokens) - context_sensitive_val = reverse_nn_input(context_sensitive_val, service_tokens) - - # Train subset of same size as a context-free val for metrics calculation - train_subset = generate_subset(train, VAL_SUBSET_SIZE) - - # Context-sensitive val subset of same size as a context-free val for metrics calculation - context_sensitive_val_subset = generate_subset(context_sensitive_val, VAL_SUBSET_SIZE) - - datasets_collection = DatasetsCollection( - train=train, - train_subset=train_subset, - context_free_val=context_free_val, - context_sensitive_val=context_sensitive_val, - context_sensitive_val_subset=context_sensitive_val_subset - ) - - return datasets_collection - - -def _get_decayed_avg_loss(avg_loss, new_loss, avg_loss_decay=AVG_LOSS_DECAY): - return avg_loss_decay * avg_loss + (1 - avg_loss_decay) * new_loss - - -def train_model(nn_model): - _logger.info('\nDefault model save path:\n{}\n'.format(nn_model.model_save_path)) - - datasets_collection = _get_datasets(nn_model, nn_model.is_reverse_model) - _logger.info('Finished preprocessing! Start training') - - batch_id = 0 - best_val_perplexities = (float('inf'), float('inf')) - cur_val_metrics = None - - batches_num = (datasets_collection.train.x.shape[0] + BATCH_SIZE - 1) // BATCH_SIZE - # The adding (BATCH_SIZE - 1) should be used here to count the last batch - # that may be smaller than BATCH_SIZE - - cur_loss = 0 - total_training_time = 0 - start_time = time.time() - - for epoch_id in xrange(EPOCHS_NUM): - _logger.info('Starting epoch #{}'.format(epoch_id)) - - for train_batch in get_training_batch(datasets_collection.train, - BATCH_SIZE, - random_permute=SHUFFLE_TRAINING_BATCHES): - train_stats = TrainStats( - cur_batch_id=batch_id, - batches_num=batches_num, - start_time=start_time, - total_training_time=total_training_time, - cur_loss=cur_loss, - best_val_perplexities=best_val_perplexities, - cur_val_metrics=cur_val_metrics - ) - - best_val_perplexities, cur_val_metrics = \ - _analyse_model_performance_and_dump_results(nn_model, datasets_collection, train_stats) - - best_val_perplexities = \ - _update_saved_nn_model(nn_model, cur_val_metrics, best_val_perplexities, train_stats) - - prev_time = time.time() - - loss = nn_model.train(*train_batch) - cur_loss = _get_decayed_avg_loss(cur_loss, loss) if batch_id else loss - - total_training_time += time.time() - prev_time - batch_id += 1 diff --git a/cakechat/utils/data_types.py b/cakechat/utils/data_types.py index 7b04a0d..881cb29 100644 --- a/cakechat/utils/data_types.py +++ b/cakechat/utils/data_types.py @@ -1,22 +1,4 @@ from collections import namedtuple - Dataset = namedtuple('Dataset', ['x', 'y', 'condition_ids']) - -DatasetsCollection = namedtuple('DatasetsCollection', [ - 'train', - 'train_subset', - 'context_free_val', - 'context_sensitive_val', - 'context_sensitive_val_subset' -]) - -TrainStats = namedtuple('TrainStats', [ - 'cur_batch_id', - 'batches_num', - 'start_time', - 'total_training_time', - 'cur_loss', - 'best_val_perplexities', - 'cur_val_metrics' -]) +ModelParam = namedtuple('ModelParam', ['value', 'id']) diff --git a/cakechat/utils/dataset_loader.py b/cakechat/utils/dataset_loader.py index 77566e0..2357c47 100644 --- a/cakechat/utils/dataset_loader.py +++ b/cakechat/utils/dataset_loader.py @@ -3,14 +3,17 @@ import numpy as np -from cakechat.config import TEST_DATA_DIR, TRAIN_CORPUS_NAME, CONTEXT_FREE_VAL_CORPUS_NAME, QUESTIONS_CORPUS_NAME, \ - CONTEXT_SENSITIVE_VAL_CORPUS_NAME, INPUT_SEQUENCE_LENGTH, INPUT_CONTEXT_SIZE, TRAIN_SUBSET_SIZE, \ - DEFAULT_CONDITION, RANDOM_SEED, MAX_VAL_LINES_NUM +from cakechat.config import TEST_DATA_DIR, CONTEXT_FREE_VAL_CORPUS_NAME, QUESTIONS_CORPUS_NAME, \ + CONTEXT_SENSITIVE_VAL_CORPUS_NAME, INPUT_SEQUENCE_LENGTH, INPUT_CONTEXT_SIZE, DEFAULT_CONDITION, RANDOM_SEED, \ + MAX_VAL_LINES_NUM, CONTEXT_SENSITIVE_TEST_CORPUS_NAME +from cakechat.dialog_model.inference import ServiceTokensIDs from cakechat.dialog_model.model_utils import lines_to_context, transform_contexts_to_token_ids, \ - transform_conditions_to_nn_input, transform_lines_to_nn_input + transform_conditions_to_nn_input, transform_lines_to_nn_input, reverse_nn_input +from cakechat.utils.data_structures import create_namedtuple_instance from cakechat.utils.data_types import Dataset from cakechat.utils.files_utils import load_file, is_non_empty_file from cakechat.utils.logger import get_logger +from cakechat.utils.profile import timer from cakechat.utils.text_processing import get_tokens_sequence, replace_out_of_voc_tokens, \ get_processed_corpus_path, load_processed_dialogs_from_json, FileTextLinesIterator, \ get_dialog_lines_and_conditions, ProcessedLinesIterator, get_alternated_dialogs_lines @@ -19,7 +22,7 @@ def get_tokenized_test_lines(corpus_name, tokens_voc): - corpus_path = os.path.join(TEST_DATA_DIR, '%s.txt' % corpus_name) + corpus_path = os.path.join(TEST_DATA_DIR, '{}.txt'.format(corpus_name)) if not is_non_empty_file(corpus_path): raise ValueError('Test corpus file doesn\'t exist: {}'.format(corpus_path)) test_lines = load_file(corpus_path) @@ -47,6 +50,36 @@ def load_questions_set(token_to_index): return _load_dataset_without_responses(QUESTIONS_CORPUS_NAME, token_to_index) +def get_validation_data_id(validation_sets_names): + return ','.join(sorted(validation_sets_names)) + + +def get_validation_sets_names(): + return [CONTEXT_FREE_VAL_CORPUS_NAME, CONTEXT_SENSITIVE_VAL_CORPUS_NAME] + + +def get_validation_dataset_name_to_data(validation_sets_names, token_to_index, condition_to_index, is_reverse_model): + _logger.info('Loading validations sets...') + factory = { + CONTEXT_FREE_VAL_CORPUS_NAME: lambda: load_context_free_val(token_to_index), + CONTEXT_SENSITIVE_VAL_CORPUS_NAME: lambda: load_context_sensitive_val(token_to_index, condition_to_index) + } + dataset_name_to_data = {val_set_name: factory[val_set_name]() for val_set_name in validation_sets_names} + _logger.info('Done loading validations sets') + + if is_reverse_model: + _logger.info('Reversing validations sets...') + service_tokens = ServiceTokensIDs(token_to_index) + dataset_name_to_data = { + val_set_name: reverse_nn_input(val_set, service_tokens) + for val_set_name, val_set in dataset_name_to_data.items() + } + _logger.info('Done reversing validations sets') + + return dataset_name_to_data + + +@timer def load_context_free_val(token_to_index): _logger.info('Transform context free validation lines to matrix of indexes') tokenized_validation_lines = get_tokenized_test_lines(CONTEXT_FREE_VAL_CORPUS_NAME, set(token_to_index.keys())) @@ -55,6 +88,7 @@ def load_context_free_val(token_to_index): return Dataset(x=x_validation, y=y_validation, condition_ids=None) +@timer def load_context_sensitive_val(token_to_index, condition_to_index): processed_val_corpus_path = get_processed_corpus_path(CONTEXT_SENSITIVE_VAL_CORPUS_NAME) context_sensitive_val_dialogs = load_processed_dialogs_from_json( @@ -78,12 +112,14 @@ def load_context_sensitive_val(token_to_index, condition_to_index): x=x_context_sensitive_val, y=y_context_sensitive_val, condition_ids=condition_ids_context_sensitive_val) -def load_conditioned_train_set(token_to_index, condition_to_index, train_subset_size=TRAIN_SUBSET_SIZE): - processed_corpus_path = get_processed_corpus_path(TRAIN_CORPUS_NAME) +@timer +def load_conditioned_dataset(corpus_name, token_to_index, condition_to_index, subset_size=None): + processed_corpus_path = get_processed_corpus_path(corpus_name) dialogs = load_processed_dialogs_from_json( FileTextLinesIterator(processed_corpus_path), text_field_name='text', condition_field_name='condition') - if train_subset_size: - dialogs = islice(dialogs, train_subset_size) + if subset_size: + _logger.info('Slicing dataset to the first {} entries'.format(subset_size)) + dialogs = islice(dialogs, subset_size) train_lines, train_conditions = get_dialog_lines_and_conditions( get_alternated_dialogs_lines(dialogs), text_field_name='text', condition_field_name='condition') tokenized_alternated_train_lines = ProcessedLinesIterator(train_lines, processing_callbacks=[get_tokens_sequence]) @@ -95,6 +131,23 @@ def load_conditioned_train_set(token_to_index, condition_to_index, train_subset_ return Dataset(x=x_train, y=y_train, condition_ids=condition_ids_train) +def get_training_dataset(train_corpus_name, + token_to_index, + condition_to_index, + is_reverse_model, + train_subset_size=None): + _logger.info('Loading training dataset...') + train_dataset = load_conditioned_dataset(train_corpus_name, token_to_index, condition_to_index, train_subset_size) + + if is_reverse_model: + _logger.info('Reversing training dataset...') + service_tokens = ServiceTokensIDs(token_to_index) + train_dataset = reverse_nn_input(train_dataset, service_tokens) + + return train_dataset + + +@timer def generate_subset(dataset, subset_size, random_seed=RANDOM_SEED): # Fix random seed here so that we get the same subsets every time the function is called np.random.seed(random_seed) @@ -108,24 +161,24 @@ def generate_subset(dataset, subset_size, random_seed=RANDOM_SEED): condition_ids=dataset.condition_ids[sample_idx] if dataset.condition_ids is not None else None) -def load_datasets(token_to_index, condition_to_index): - train = load_conditioned_train_set(token_to_index, condition_to_index) - validation = load_context_free_val(token_to_index) - questions = load_questions_set(token_to_index) - - validation_set_size = validation.x.shape[0] - - train_subset = generate_subset(train, validation_set_size) +def load_datasets(token_to_index, condition_to_index, test_corpus_name=CONTEXT_SENSITIVE_TEST_CORPUS_NAME): + # load context_sensitive_test dataset + cs_test = load_conditioned_dataset(test_corpus_name, token_to_index, condition_to_index) + # load context_free_validation dataset + cf_validation = load_context_free_val(token_to_index) - # prepare conditioned subset - defined_condition_mask = train.condition_ids != condition_to_index[DEFAULT_CONDITION] - defined_condition_dataset = Dataset( - x=train.x[defined_condition_mask], - y=train.y[defined_condition_mask], - condition_ids=train.condition_ids[defined_condition_mask]) + # load context sensitive testset for one selected condition + condition_mask = cs_test.condition_ids != condition_to_index[DEFAULT_CONDITION] + conditioned_test = Dataset( + x=cs_test.x[condition_mask], y=cs_test.y[condition_mask], condition_ids=cs_test.condition_ids[condition_mask]) - defined_condition_dataset_len = defined_condition_dataset.x.shape[0] - defined_condition_subset = generate_subset(defined_condition_dataset, - min(validation_set_size, defined_condition_dataset_len)) + # get a subset of conditioned_test of the same size as cf_validation; + # if there are no so many samples in conditioned_test, use all of the available conditioned_test samples + cs_test_one_condition = \ + generate_subset(conditioned_test, subset_size=min(cf_validation.x.shape[0], conditioned_test.x.shape[0])) - return train, questions, validation, train_subset, defined_condition_subset + return create_namedtuple_instance( + 'EvalMetricsDatasets', + cf_validation=cf_validation, + cs_test=cs_test, + cs_test_one_condition=cs_test_one_condition) diff --git a/cakechat/utils/env.py b/cakechat/utils/env.py index 0d13a2a..bf0c4d9 100644 --- a/cakechat/utils/env.py +++ b/cakechat/utils/env.py @@ -1,12 +1,13 @@ import os +import subprocess +import numpy as np +import tensorflow as tf +from keras.backend.tensorflow_backend import set_session -def _use_gpu_env(): - try: - use_gpu = os.environ['USE_GPU'] - return int(use_gpu) - except (KeyError, ValueError): - return None +from cakechat.utils.logger import get_logger + +_logger = get_logger(__name__) def is_dev_env(): @@ -17,34 +18,67 @@ def is_dev_env(): return False -def _init_cuda_env(): - # Set GPU device order the same as in nvidia-smi +def init_cuda_env(): + os.environ['PATH'] += ':/usr/local/cuda/bin' + os.environ['LD_LIBRARY_PATH'] = '/usr/local/cuda/lib64:/usr/local/nvidia/lib64/:/usr/local/cuda/extras/CUPTI/lib64' + os.environ['LIBRARY_PATH'] = '/usr/local/share/cudnn' + os.environ['CUDA_HOME'] = '/usr/local/cuda' os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' -def init_theano_env(gpu_id=_use_gpu_env(), cnmem=0, float_precision='float32', is_dev=is_dev_env()): +def try_import_horovod(): + try: + import horovod.keras as hvd + except ImportError: + return None + else: + return hvd + + +def init_keras(hvd=None): """ - :param gpu_id: ID of GPU to use, default is None (No GPU support, CPU-only); - :param cnmem: The value represents the start size (either in MB or the fraction of total GPU memory) of the memory - pool. Default: 0 (Preallocation of size 0, only cache the allocation) - :param float_precision: String specifying floating point precision. Can be 'float64', 'float32', or 'float16' - :param is_dev: Apply just a few graph optimizations and only use Python implementations. Default is False. - GPU is disabled, CPU only. Drastically speeds up theano graph compilation. Use for development purposes. - :return: + Set config for Horovod. Config params copied from official example: + https://github.com/uber/horovod/blob/master/examples/keras_mnist_advanced.py#L15 + + :param hvd: instance of horovod.keras """ - _init_cuda_env() - theano_flags = 'floatX={}'.format(float_precision) + init_cuda_env() + config = tf.ConfigProto() - if is_dev: - # Use fast_compile only in dev-env because it doesn't works on GPU with libgpuarray - theano_flags += ',device=cpu,mode=FAST_COMPILE' - elif gpu_id is None: - theano_flags += ',device=cpu' - else: - theano_flags += ',device=cuda{},gpuarray.preallocate={:0.2}'.format(gpu_id, float(cnmem)) + if hvd: + hvd.init() + config.gpu_options.allow_growth = True + config.gpu_options.visible_device_list = str(hvd.local_rank()) + + set_session(tf.Session(config=config)) + + +def set_keras_tf_session(gpu_memory_fraction): + config = tf.ConfigProto() + config.gpu_options.per_process_gpu_memory_fraction = float(gpu_memory_fraction) # pylint: disable=maybe-no-member + set_session(tf.Session(config=config)) + + +def run_horovod_train(train_cmd, gpu_ids): + os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' + os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(gpu_ids) + + cmd = 'mpirun -np {workers_nums} -H localhost:{workers_nums} {train_cmd}'.format( + workers_nums=len(gpu_ids), train_cmd=train_cmd) + process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) + + while process.poll() is None: + output = process.stdout.readline() + if output: + print(output.strip()) + + +def is_main_horovod_worker(horovod): + return horovod is None or horovod.rank() == 0 + + +def set_horovod_worker_random_seed(horovod): + seed = horovod.rank() if horovod else 0 + np.random.seed(seed) - if 'THEANO_FLAGS' in os.environ: - os.environ['THEANO_FLAGS'] = theano_flags + ',' + os.environ['THEANO_FLAGS'] - else: - os.environ['THEANO_FLAGS'] = theano_flags diff --git a/cakechat/utils/files_utils.py b/cakechat/utils/files_utils.py index fd28293..31d0257 100644 --- a/cakechat/utils/files_utils.py +++ b/cakechat/utils/files_utils.py @@ -1,17 +1,17 @@ import os -import codecs +import pickle +import tarfile +from functools import partial from abc import abstractmethod, ABCMeta -from six.moves import cPickle as pickle - -from cakechat.utils.logger import get_logger +from cakechat.utils.logger import get_logger, WithLogger _logger = get_logger(__name__) +DEFAULT_CSV_DELIMITER = ',' -class AbstractFileResolver(object): - __metaclass__ = ABCMeta +class AbstractFileResolver(object, metaclass=ABCMeta): def __init__(self, file_path): self._file_path = file_path @@ -47,8 +47,48 @@ def _resolve(self): return False +class PackageResolver(WithLogger): + def __init__(self, package_path, package_file_resolver_factory, package_file_ext, package_extractor): + """ + :param package_path: + :param package_file_resolver_factory: a factory creating package file resolver + :param package_file_ext: package file extension + :param package_extractor: a function taking package file, package path, and extracting contents to that path + :return: + """ + WithLogger.__init__(self) + + self._package_path = package_path + self._package_file_resolver_factory = package_file_resolver_factory + self._package_file_ext = package_file_ext + self._package_extractor = package_extractor + + @staticmethod + def init_resolver(**kwargs): + """ + Method helping to set once some parameters like package_file_resolver and package_extractor + + :param kwargs: + :return: partially initialized class object + """ + return partial(PackageResolver, **kwargs) + + def resolve(self): + if os.path.exists(self._package_path): + return True + + package_file_path = '{}.{}'.format(self._package_path, self._package_file_ext) + package_file_resolver = self._package_file_resolver_factory(package_file_path) + if package_file_resolver.resolve(): + self._logger.info('Extracting package {}'.format(package_file_resolver.file_path)) + self._package_extractor(package_file_resolver.file_path, self._package_path) + return True + else: + return False + + def load_file(file_path, filter_empty_lines=True): - with codecs.open(file_path, 'r', 'utf-8') as fh: + with open(file_path, 'r', encoding='utf-8') as fh: lines = [line.strip() for line in fh.readlines()] if filter_empty_lines: lines = list(filter(None, lines)) @@ -61,7 +101,7 @@ def ensure_dir(dir_name): os.makedirs(dir_name) -def serialize(filename, data, protocol=2): +def serialize(filename, data, protocol=pickle.HIGHEST_PROTOCOL): ensure_dir(os.path.dirname(filename)) with open(filename, 'wb') as f: pickle.dump(data, f, protocol) @@ -81,16 +121,14 @@ def get_persisted(factory, persisted_file_name, **kwargs): :param persisted_file_name: :return: """ - filename = persisted_file_name.encode('utf-8') - - if os.path.exists(filename): - _logger.info(u'Loading {}'.format(persisted_file_name)) - cached = deserialize(filename) + if os.path.exists(persisted_file_name): + _logger.info('Loading {}'.format(persisted_file_name)) + cached = deserialize(persisted_file_name) return cached - _logger.info(u'Creating {}'.format(persisted_file_name)) + _logger.info('Creating {}'.format(persisted_file_name)) data = factory() - serialize(filename, data, **kwargs) + serialize(persisted_file_name, data, **kwargs) return data @@ -100,3 +138,39 @@ def is_non_empty_file(file_path): class FileNotFoundException(Exception): pass + + +def extract_tar(source_path, destination_path, compression_type='gz'): + """ + :param source_path: + :param destination_path: + :param compression_type: None, gz or bzip2 + :return: + """ + mode = 'r:{}'.format(compression_type if compression_type else 'r') + with tarfile.open(source_path, mode) as fh: + fh.extractall(path=destination_path) + + +def ensure_file(file_name, mode, encoding=None): + ensure_dir(os.path.dirname(file_name)) + return open(file_name, mode, encoding=encoding) + + +def get_cached(factory, cache_file_name, **kwargs): + """ + Loads cache if exists, otherwise calls factory and stores the results in the specified cache file. + **kwargs are passed to the serialize() function + :param factory: + :param cache_file_name: + :return: + """ + if os.path.exists(cache_file_name): + _logger.info('Loading {}'.format(cache_file_name)) + cached = deserialize(cache_file_name) + return cached + + _logger.info('Creating {}'.format(cache_file_name)) + data = factory() + serialize(cache_file_name, data, **kwargs) + return data diff --git a/cakechat/utils/logger.py b/cakechat/utils/logger.py index 0ea6458..341a40a 100644 --- a/cakechat/utils/logger.py +++ b/cakechat/utils/logger.py @@ -1,6 +1,7 @@ import logging import logging.config + logging.config.dictConfig({ 'version': 1, 'disable_existing_loggers': False, diff --git a/cakechat/utils/offense_detector/detector.py b/cakechat/utils/offense_detector/detector.py index 6e1745f..c043083 100644 --- a/cakechat/utils/offense_detector/detector.py +++ b/cakechat/utils/offense_detector/detector.py @@ -1,10 +1,8 @@ import nltk -from six import string_types -from six.moves import xrange +from cakechat.utils.data_structures import flatten from cakechat.utils.files_utils import load_file from cakechat.utils.text_processing import get_tokens_sequence -from cakechat.utils.data_structures import flatten class OffenseDetector(object): @@ -23,16 +21,13 @@ def _build_offensive_ngrams(offensive_phrases_path): return set(offensive_ngrams) def _get_ngrams(self, tokenized_line): - ngrams = [nltk.ngrams(tokenized_line, i) for i in xrange(1, self._max_ngram_len + 1)] + ngrams = [nltk.ngrams(tokenized_line, i) for i in range(1, self._max_ngram_len + 1)] return flatten(ngrams, constructor=set) - def has_offensive_ngrams(self, text_or_tokenized_text): - if isinstance(text_or_tokenized_text, string_types): - tokenized_text = get_tokens_sequence(text_or_tokenized_text) - elif isinstance(text_or_tokenized_text, list): - tokenized_text = text_or_tokenized_text - else: - raise TypeError('text_or_tokenized_text must be string or list') - + def has_offensive_ngrams(self, text): + if not isinstance(text, str): + raise TypeError('"text" variable must be a string') + tokenized_text = get_tokens_sequence(text) text_ngrams = self._get_ngrams(tokenized_text) + return bool(text_ngrams & self._offensive_ngrams) diff --git a/cakechat/utils/plotters.py b/cakechat/utils/plotters.py deleted file mode 100644 index b9933b7..0000000 --- a/cakechat/utils/plotters.py +++ /dev/null @@ -1,25 +0,0 @@ -import os -from collections import Counter - -from tensorboard_logger import Logger as TensorboardLogger - -from cakechat.utils.files_utils import get_persisted, serialize - - -class TensorboardMetricsPlotter(object): - def __init__(self, log_dir): - self._log_dir = log_dir - self._writers = {} - self._steps_path = os.path.join(log_dir, 'steps') - self._steps = get_persisted(dict, self._steps_path) - - def plot(self, model_name, metric_name, metric_value): - if model_name not in self._writers: - self._writers[model_name] = TensorboardLogger(os.path.join(self._log_dir, model_name)) - if model_name not in self._steps: - self._steps[model_name] = Counter() - - self._writers[model_name].log_value(metric_name, metric_value, step=self._steps[model_name][metric_name]) - self._steps[model_name][metric_name] += 1 - - serialize(self._steps_path, self._steps) diff --git a/cakechat/utils/profile.py b/cakechat/utils/profile.py index e5e2804..0501e73 100644 --- a/cakechat/utils/profile.py +++ b/cakechat/utils/profile.py @@ -11,7 +11,7 @@ def _execute_and_profile(fn, *args, **kwargs): fn_result = fn(*args, **kwargs) execution_time = time.time() - start_time - _logger.info('Elapsed time for "%s": %f' % (fn.__name__, execution_time)) + _logger.info('Elapsed time for "{}": {}'.format(fn.__name__, execution_time)) return execution_time, fn_result diff --git a/cakechat/utils/s3/__init__.py b/cakechat/utils/s3/__init__.py index 5567bcd..ceda1d7 100644 --- a/cakechat/utils/s3/__init__.py +++ b/cakechat/utils/s3/__init__.py @@ -1,3 +1,2 @@ from cakechat.utils.s3.bucket import S3Bucket -from cakechat.utils.s3.utils import get_s3_resource -from cakechat.utils.s3.resolver import S3FileResolver +from cakechat.utils.s3.resolver import S3FileResolver, get_s3_resource, get_s3_model_resolver diff --git a/cakechat/utils/s3/resolver.py b/cakechat/utils/s3/resolver.py index f80df4b..2d480a6 100644 --- a/cakechat/utils/s3/resolver.py +++ b/cakechat/utils/s3/resolver.py @@ -1,10 +1,13 @@ import os from functools import partial -from cakechat.utils.files_utils import AbstractFileResolver +import boto3 +from botocore import UNSIGNED +from botocore.client import Config + +from cakechat.utils.files_utils import AbstractFileResolver, PackageResolver, extract_tar from cakechat.utils.logger import WithLogger -from cakechat.utils.s3.bucket import S3Bucket -from cakechat.utils.s3.utils import get_s3_resource +from cakechat.utils.s3 import S3Bucket class S3FileResolver(AbstractFileResolver, WithLogger): @@ -42,3 +45,14 @@ def _resolve(self): self._logger.warn('File can not be downloaded from AWS S3 because: %s' % str(e)) return False + + +def get_s3_resource(): + return boto3.resource('s3', config=Config(signature_version=UNSIGNED)) + + +def get_s3_model_resolver(bucket_name, remote_dir): + return PackageResolver.init_resolver( + package_file_resolver_factory=S3FileResolver.init_resolver(bucket_name=bucket_name, remote_dir=remote_dir), + package_file_ext='tar.gz', + package_extractor=extract_tar) diff --git a/cakechat/utils/s3/utils.py b/cakechat/utils/s3/utils.py deleted file mode 100644 index 4e9cfc8..0000000 --- a/cakechat/utils/s3/utils.py +++ /dev/null @@ -1,7 +0,0 @@ -import boto3 -from botocore import UNSIGNED -from botocore.client import Config - - -def get_s3_resource(): - return boto3.resource('s3', config=Config(signature_version=UNSIGNED)) diff --git a/cakechat/utils/tee_file.py b/cakechat/utils/tee_file.py index e6c8681..66eab52 100644 --- a/cakechat/utils/tee_file.py +++ b/cakechat/utils/tee_file.py @@ -1,11 +1,11 @@ import os +import pickle import tempfile -from six.moves import cPickle as pickle, xrange - # Pickle on HIGHEST_PROTOCOL breaks on Python 3.6.5 _PICKLE_PROTOCOL = 2 + def _pickle_iterable(filename, iterable): with open(filename, 'wb') as pickle_fh: pklr = pickle.Pickler(pickle_fh, _PICKLE_PROTOCOL) @@ -32,6 +32,6 @@ def file_buffered_tee(iterable, n=2): _, filename = tempfile.mkstemp() try: _pickle_iterable(filename, iterable) - return tuple(_unpickle_iterable(_open_pickle(filename)) for _ in xrange(n)) + return tuple(_unpickle_iterable(_open_pickle(filename)) for _ in range(n)) finally: os.remove(filename) diff --git a/cakechat/utils/telegram_bot_client.py b/cakechat/utils/telegram_bot_client.py index a50e8fd..4ea6430 100644 --- a/cakechat/utils/telegram_bot_client.py +++ b/cakechat/utils/telegram_bot_client.py @@ -2,12 +2,11 @@ import telepot import telepot.loop -from six import iteritems from cakechat.utils.logger import WithLogger -class AbstractTelegramChatSession(WithLogger): +class AbstractTelegramChatSession(WithLogger, metaclass=ABCMeta): """ Specific implementations of a chat session should overload default message handler `default_handle_message`, and possibly some of specific @@ -38,7 +37,6 @@ def default_handle_message(self, msg): TelegramBot(token).run(ReversedChatSession) """ - __metaclass__ = ABCMeta def __init__(self, bot, chat_id): super(AbstractTelegramChatSession, self).__init__() @@ -75,7 +73,7 @@ def _bot_info(): def _send_bot_help(self, _): help_lines = [self._bot_info(), '', 'List of available commands:'] - for command, (_, description) in iteritems(self._command_to_handler): + for command, (_, description) in self._command_to_handler.items(): help_lines.append('/{} - {}'.format(command, description)) return self._send_text('\n'.join(help_lines)) diff --git a/cakechat/utils/text_processing/__init__.py b/cakechat/utils/text_processing/__init__.py index 5a9780e..2367661 100644 --- a/cakechat/utils/text_processing/__init__.py +++ b/cakechat/utils/text_processing/__init__.py @@ -1,5 +1,5 @@ from cakechat.utils.text_processing.str_processor import get_tokens_sequence, replace_out_of_voc_tokens, \ - get_pretty_str_from_tokens_sequence + prettify_response from cakechat.utils.text_processing.config import SPECIAL_TOKENS from cakechat.utils.text_processing.utils import get_processed_corpus_path, get_index_to_token_path, \ get_index_to_condition_path, load_index_to_item diff --git a/cakechat/utils/text_processing/config.py b/cakechat/utils/text_processing/config.py index 24f95bd..dca491d 100644 --- a/cakechat/utils/text_processing/config.py +++ b/cakechat/utils/text_processing/config.py @@ -1,7 +1,7 @@ from cakechat.utils.data_structures import create_namedtuple_instance SPECIAL_TOKENS = create_namedtuple_instance( - 'SPECIAL_TOKENS', PAD_TOKEN=u'_pad_', UNKNOWN_TOKEN=u'_unk_', START_TOKEN=u'_start_', EOS_TOKEN=u'_end_') + 'SPECIAL_TOKENS', PAD_TOKEN='_pad_', UNKNOWN_TOKEN='_unk_', START_TOKEN='_start_', EOS_TOKEN='_end_') DIALOG_TEXT_FIELD = 'text' DIALOG_CONDITION_FIELD = 'condition' diff --git a/cakechat/utils/text_processing/corpus_iterator.py b/cakechat/utils/text_processing/corpus_iterator.py index 826700b..341852c 100644 --- a/cakechat/utils/text_processing/corpus_iterator.py +++ b/cakechat/utils/text_processing/corpus_iterator.py @@ -1,4 +1,3 @@ -import codecs import json from copy import copy @@ -8,12 +7,11 @@ class FileTextLinesIterator(object): - def __init__(self, filename, encoding='utf-8'): + def __init__(self, filename): self._filename = filename - self._encoding = encoding def __iter__(self): - for line in codecs.open(self._filename, 'r', self._encoding): + for line in open(self._filename, 'r', encoding='utf-8'): yield line.strip() def __copy__(self): @@ -44,7 +42,7 @@ def __iter__(self): try: yield json.loads(line.strip()) except ValueError: - _logger.warning('Skipped invalid json object: "%s"' % line.strip()) + _logger.warning('Skipped invalid json object: "{}"'.format(line.strip())) continue def __copy__(self): diff --git a/cakechat/utils/text_processing/dialog.py b/cakechat/utils/text_processing/dialog.py index e48cc66..6abf714 100644 --- a/cakechat/utils/text_processing/dialog.py +++ b/cakechat/utils/text_processing/dialog.py @@ -1,4 +1,3 @@ -from six.moves import map from operator import itemgetter from cakechat.utils.tee_file import file_buffered_tee diff --git a/cakechat/utils/text_processing/str_processor.py b/cakechat/utils/text_processing/str_processor.py index 3c55cfd..4994039 100644 --- a/cakechat/utils/text_processing/str_processor.py +++ b/cakechat/utils/text_processing/str_processor.py @@ -1,18 +1,17 @@ import re import nltk.tokenize -from six import text_type from cakechat.utils.text_processing.config import SPECIAL_TOKENS _END_CHARS = '.?!' -_tokenizer = nltk.tokenize.RegexpTokenizer(pattern=u'\w+|[^\w\s]') +_tokenizer = nltk.tokenize.RegexpTokenizer(pattern='\w+|[^\w\s]') def get_tokens_sequence(text, lower=True, check_unicode=True): - if check_unicode and not isinstance(text, text_type): - raise TypeError('text object should be unicode type') + if check_unicode and not isinstance(text, str): + raise TypeError('Text object should be unicode type. Got instead "{}" of type {}'.format(text, type(text))) if not text.strip(): return [] @@ -33,7 +32,7 @@ def _capitalize_first_chars(text): if not text: return text - chars_pos_to_capitalize = [0] + [m.end() - 1 for m in re.finditer('[%s] \w' % _END_CHARS, text)] + chars_pos_to_capitalize = [0] + [m.end() - 1 for m in re.finditer('[{}] \w'.format(_END_CHARS), text)] for char_pos in chars_pos_to_capitalize: text = text[:char_pos] + text[char_pos].upper() + text[char_pos + 1:] @@ -41,22 +40,18 @@ def _capitalize_first_chars(text): return text -def get_pretty_str_from_tokens_sequence(tokens_sequence): +def prettify_response(response): """ Prettify chatbot's answer removing excessive characters and capitalizing first words of sentences. - Before: "hello world ! nice to meet you , buddy . do you like me ? I ' ve been missing you for a while . . . $$$" + Before: "hello world ! nice to meet you , buddy . do you like me ? I ' ve been missing you for a while . . ." After: "Hello world! Nice to meet you, buddy. Do you like me? I've been missing you for a while..." """ - phrase = ' '.join(tokens_sequence) - - phrase = phrase.replace(SPECIAL_TOKENS.EOS_TOKEN, '') - phrase = phrase.replace(SPECIAL_TOKENS.START_TOKEN, '') - phrase = phrase.replace(' \' ', '\'') + response = response.replace(' \' ', '\'') for ch in set(_END_CHARS) | {','}: - phrase = phrase.replace(' ' + ch, ch) + response = response.replace(' ' + ch, ch) - phrase = _capitalize_first_chars(phrase) - phrase = phrase.strip() + response = _capitalize_first_chars(response) + response = response.strip() - return phrase + return response diff --git a/cakechat/utils/text_processing/utils.py b/cakechat/utils/text_processing/utils.py index a53abc4..fd9459e 100644 --- a/cakechat/utils/text_processing/utils.py +++ b/cakechat/utils/text_processing/utils.py @@ -1,8 +1,5 @@ -import os import json -import codecs - -from six import iteritems +import os from cakechat.config import PROCESSED_CORPUS_DIR, TOKEN_INDEX_DIR, CONDITION_IDS_INDEX_DIR @@ -20,8 +17,8 @@ def get_index_to_condition_path(processed_corpus_name): def load_index_to_item(items_index_path): - with codecs.open(items_index_path, 'r', 'utf-8') as item_index_fh: + with open(items_index_path, 'r', encoding='utf-8') as item_index_fh: index_to_item = json.load(item_index_fh) - index_to_item = {int(k): v for k, v in iteritems(index_to_item)} + index_to_item = {int(k): v for k, v in index_to_item.items()} return index_to_item diff --git a/cakechat/utils/w2v/__init__.py b/cakechat/utils/w2v/__init__.py index 09b40fb..8edd5c8 100644 --- a/cakechat/utils/w2v/__init__.py +++ b/cakechat/utils/w2v/__init__.py @@ -1,2 +1 @@ -from cakechat.utils.w2v.model import get_w2v_model -from cakechat.utils.w2v.utils import get_w2v_model_path, get_w2v_params_str +from cakechat.utils.w2v.utils import get_w2v_model_path, get_w2v_params_str, get_w2v_model_name diff --git a/cakechat/utils/w2v/model.py b/cakechat/utils/w2v/model.py index a374739..1fe55e5 100644 --- a/cakechat/utils/w2v/model.py +++ b/cakechat/utils/w2v/model.py @@ -1,13 +1,15 @@ import multiprocessing import os - from gensim.models import Word2Vec -from cakechat.config import WORD_EMBEDDING_DIMENSION, W2V_WINDOW_SIZE, MIN_WORD_FREQ, USE_SKIP_GRAM +from cakechat.config import TRAIN_CORPUS_NAME, VOCABULARY_MAX_SIZE, WORD_EMBEDDING_DIMENSION, S3_MODELS_BUCKET_NAME, \ + S3_W2V_REMOTE_DIR, USE_SKIP_GRAM, MIN_WORD_FREQ, TOKEN_REPRESENTATION_SIZE, W2V_WINDOW_SIZE +from cakechat.dialog_model.model_utils import ModelLoaderException from cakechat.utils.files_utils import DummyFileResolver, ensure_dir from cakechat.utils.logger import get_logger +from cakechat.utils.s3 import S3FileResolver from cakechat.utils.tee_file import file_buffered_tee -from cakechat.utils.w2v.utils import get_w2v_params_str, get_w2v_model_path +from cakechat.utils.w2v import get_w2v_model_name, get_w2v_params_str, get_w2v_model_path _WORKERS_NUM = multiprocessing.cpu_count() @@ -18,7 +20,7 @@ def _train_model(tokenized_lines, voc_size, vec_size, window_size, skip_gram): _logger.info('Word2Vec model will be trained now. It can take long, so relax and have fun.') params_str = get_w2v_params_str(voc_size, vec_size, window_size, skip_gram) - _logger.info('Parameters for training: %s' % params_str) + _logger.info('Parameters for training: {}'.format(params_str)) model = Word2Vec( window=window_size, @@ -41,26 +43,26 @@ def _train_model(tokenized_lines, voc_size, vec_size, window_size, skip_gram): def _save_model(model, model_path): - _logger.info('Saving model to %s' % model_path) + _logger.info('Saving model to {}'.format(model_path)) ensure_dir(os.path.dirname(model_path)) model.save(model_path, separately=[]) _logger.info('Model has been saved') def _load_model(model_path): - _logger.info('Loading model from %s' % model_path) + _logger.info('Loading model from {}'.format(model_path)) model = Word2Vec.load(model_path, mmap='r') - _logger.info('Model "%s" has been loaded.' % os.path.basename(model_path)) + _logger.info('Model "{}" has been loaded.'.format(os.path.basename(model_path))) return model -def get_w2v_model(corpus_name, - voc_size, - model_resolver_factory=None, - tokenized_lines=None, - vec_size=WORD_EMBEDDING_DIMENSION, - window_size=W2V_WINDOW_SIZE, - skip_gram=USE_SKIP_GRAM): +def _get_w2v_model(corpus_name, + voc_size, + model_resolver_factory=None, + tokenized_lines=None, + vec_size=TOKEN_REPRESENTATION_SIZE, + window_size=W2V_WINDOW_SIZE, + skip_gram=USE_SKIP_GRAM): _logger.info('Getting w2v model') model_path = get_w2v_model_path(corpus_name, voc_size, vec_size, window_size, skip_gram) @@ -68,7 +70,8 @@ def get_w2v_model(corpus_name, if not model_resolver.resolve(): if not tokenized_lines: - raise ValueError('Tokenized corpus \'%s\' was not provided, so w2v model can\'t be trained.' % model_path) + raise ModelLoaderException( + 'Tokenized corpus "{}" was not provided, so w2v model can\'t be trained.'.format(corpus_name)) # bin model is not present on the disk, so get it model = _train_model(tokenized_lines, voc_size, vec_size, window_size, skip_gram) @@ -80,3 +83,40 @@ def get_w2v_model(corpus_name, _logger.info('Successfully got w2v model\n') return model + +def get_w2v_model(fetch_from_s3=False, + corpus_name=TRAIN_CORPUS_NAME, + voc_size=VOCABULARY_MAX_SIZE, + vec_size=WORD_EMBEDDING_DIMENSION, + window_size=W2V_WINDOW_SIZE, + skip_gram=USE_SKIP_GRAM): + try: + model_resolver_factory = \ + S3FileResolver.init_resolver(bucket_name=S3_MODELS_BUCKET_NAME, remote_dir=S3_W2V_REMOTE_DIR) \ + if fetch_from_s3 else None + + w2v_model = _get_w2v_model( + corpus_name=corpus_name, + voc_size=voc_size, + model_resolver_factory=model_resolver_factory, + vec_size=vec_size, + window_size=window_size, + skip_gram=skip_gram) + + except ModelLoaderException: + raise ModelLoaderException('Word2vec model is absent. Please run `tools/train_w2v.py` to get the model.' + ' WARNING: If you compare different dialog models, be sure that they' + ' use the same w2v model (since each run of the w2v-trainer even with the same' + ' parameters leads to different w2v models)') + + return w2v_model + + +def get_w2v_model_id(corpus_name=TRAIN_CORPUS_NAME, + voc_size=VOCABULARY_MAX_SIZE, + vec_size=WORD_EMBEDDING_DIMENSION, + window_size=W2V_WINDOW_SIZE, + skip_gram=USE_SKIP_GRAM): + + return get_w2v_model_name( + corpus_name, voc_size=voc_size, vec_size=vec_size, window_size=window_size, skip_gram=skip_gram) diff --git a/cakechat/utils/w2v/utils.py b/cakechat/utils/w2v/utils.py index b2ecac7..12f8fa7 100644 --- a/cakechat/utils/w2v/utils.py +++ b/cakechat/utils/w2v/utils.py @@ -1,6 +1,17 @@ import os +import numpy as np -from cakechat.config import W2V_WINDOW_SIZE, W2V_MODEL_DIR, USE_SKIP_GRAM +from cakechat.config import W2V_MODEL_DIR, USE_SKIP_GRAM, W2V_WINDOW_SIZE, TOKEN_REPRESENTATION_SIZE +from cakechat.utils.logger import get_logger +from cakechat.utils.text_processing import SPECIAL_TOKENS + +_logger = get_logger(__name__) + + +def _get_w2v_model_name(corpus_name, voc_size, vec_size, window_size=W2V_WINDOW_SIZE, skip_gram=USE_SKIP_GRAM): + params_str = get_w2v_params_str(voc_size, vec_size, window_size, skip_gram) + model_name = '{}_{}'.format(corpus_name, params_str) + return model_name def get_w2v_params_str(voc_size, vec_size, window_size=W2V_WINDOW_SIZE, skip_gram=USE_SKIP_GRAM): @@ -9,13 +20,24 @@ def get_w2v_params_str(voc_size, vec_size, window_size=W2V_WINDOW_SIZE, skip_gra return params_str -def _get_w2v_model_name(corpus_name, voc_size, vec_size, window_size=W2V_WINDOW_SIZE, skip_gram=USE_SKIP_GRAM): +def get_w2v_model_path(corpus_name, voc_size, vec_size, window_size=W2V_WINDOW_SIZE, skip_gram=USE_SKIP_GRAM): + model_name = get_w2v_model_name(corpus_name, voc_size, vec_size, window_size, skip_gram) + model_path = os.path.join(W2V_MODEL_DIR, '{}.bin'.format(model_name)) + return model_path + + +def get_w2v_model_name(corpus_name, voc_size, vec_size, window_size=W2V_WINDOW_SIZE, skip_gram=USE_SKIP_GRAM): params_str = get_w2v_params_str(voc_size, vec_size, window_size, skip_gram) - model_name = '%s_%s' % (corpus_name, params_str) + model_name = '{}_{}'.format(corpus_name, params_str) return model_name -def get_w2v_model_path(corpus_name, voc_size, vec_size, window_size=W2V_WINDOW_SIZE, skip_gram=USE_SKIP_GRAM): - model_name = _get_w2v_model_name(corpus_name, voc_size, vec_size, window_size, skip_gram) - model_path = os.path.join(W2V_MODEL_DIR, '%s.bin' % model_name) - return model_path +def get_token_vector(token, model, token_vec_size=TOKEN_REPRESENTATION_SIZE): + if token in model.wv.vocab: + return np.array(model[token]) + + # generally we want have trained embeddings for all special tokens except the PAD one + if token != SPECIAL_TOKENS.PAD_TOKEN: + _logger.warn('Unknown embedding for token "{}"'.format(token)) + + return np.zeros(token_vec_size, dtype=np.float32) diff --git a/data/conditions_index/c_idx_processed_dialogs.json b/data/conditions_index/c_idx_processed_dialogs.json new file mode 100644 index 0000000..593b284 --- /dev/null +++ b/data/conditions_index/c_idx_processed_dialogs.json @@ -0,0 +1 @@ +{"0": "neutral", "1": "anger", "2": "joy", "3": "fear", "4": "sadness"} diff --git a/data/corpora_processed/test_processed_dialogs.txt b/data/corpora_processed/test_processed_dialogs.txt new file mode 100644 index 0000000..ec2f8ad --- /dev/null +++ b/data/corpora_processed/test_processed_dialogs.txt @@ -0,0 +1,20 @@ +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] \ No newline at end of file diff --git a/data/corpora_processed/train_processed_dialogs.txt b/data/corpora_processed/train_processed_dialogs.txt index 4f51756..7f83db0 100644 --- a/data/corpora_processed/train_processed_dialogs.txt +++ b/data/corpora_processed/train_processed_dialogs.txt @@ -1,100 +1,100 @@ -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] -[{"text": "Hi, Eddie, what's up?", "condition": "joy"}, {"text": "Not much, what about you?", "condition": "neutral"}, {"text": "Fine, thanks. Are you going to the movies tomorrow?", "condition": "neutral"}, {"text": "No, I've watched the new Star Wars already and it was just terrible!", "condition": "anger"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] +[{"text": "Hello", "condition": "neutral"}, {"text": "Oh, hi! :) How are you, my friend?", "condition": "joy"}, {"text": "Doing good", "condition": "neutral"}] +[{"text": "Justin Bieber is the best", "condition": "neutral"}, {"text": "Ok...", "condition": "sadness"}] diff --git a/data/quality/context_free_questions.txt b/data/quality/context_free_questions.txt index f4707f5..3686069 100644 --- a/data/quality/context_free_questions.txt +++ b/data/quality/context_free_questions.txt @@ -1,3 +1,4 @@ +how are you, my friend? how old are you ? are you a leader or a follower ? my name is david . what is my name ? @@ -294,4 +295,4 @@ should a company have a code of ethics ? how did he manage his staff ? do you think leaders are born or made ? what about setting up a counseling service , vincent ? some staff are under a lot of stress . -have you paid the invoice for the last shipment ? \ No newline at end of file +have you paid the invoice for the last shipment ? diff --git a/data/quality/context_free_validation_set.txt b/data/quality/context_free_validation_set.txt index 0738015..ff37b5c 100644 --- a/data/quality/context_free_validation_set.txt +++ b/data/quality/context_free_validation_set.txt @@ -1,3 +1,11 @@ +hi +hi! +How are you? +I'm fine +How old are you? +I'm 25 +How's the weather outside? +It's sunny I am sorry. you have the wrong number. Oh, i am sorry. The last time we went, you almost drowned. @@ -497,4 +505,4 @@ I am here to see you . What do you think about the turing test ? It's very good . Is paris the capital of china ? -No. \ No newline at end of file +No. diff --git a/data/tokens_index/t_idx_processed_dialogs.json b/data/tokens_index/t_idx_processed_dialogs.json new file mode 100644 index 0000000..e7cc3fa --- /dev/null +++ b/data/tokens_index/t_idx_processed_dialogs.json @@ -0,0 +1 @@ +{"0": "_pad_", "1": "fawk", "2": "🎍", "3": "sonja", "4": "woods", "5": "spiders", "6": "hanging", "7": "woody", "8": "tingly", "9": "comically", "10": "localized", "11": "sevens", "12": "canes", "13": "isaid", "14": "aileen", "15": "scold", "16": "originality", "17": "refunding", "18": "gaa", "19": "crossbar", "20": "rickman", "21": "hermano", "22": "aigooo", "23": "hahahah", "24": "eugenics", "25": "appropriation", "26": "holyrood", "27": "hahhha", "28": "broward", "29": "bringing", "30": "jrpg", "31": "advices", "32": "emilys", "33": "bassett", "34": "tssss", "35": "wooden", "36": "wednesday", "37": "sabotaged", "38": "crotch", "39": "asami", "40": "stereotypical", "41": "zzzzzz", 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"markers", "49179": "adjustment", "49180": "contributions", "49181": "magnifying", "49182": "cupboards", "49183": "tinpot", "49184": "sniped", "49185": "roshan", "49186": "whennnn", "49187": "squeezing", "49188": "ahhaha", "49189": "swelling", "49190": "hinduism", "49191": "ridiculousness", "49192": "fences", "49193": "snipes", "49194": "sniper", "49195": "abode", "49196": "skydive", "49197": "wished", "49198": "715", "49199": "bedrooms", "49200": "bowles", "49201": "711", "49202": "710", "49203": "alden", "49204": "leyland", "49205": "wishes", "49206": "bosses", "49207": "brayden", "49208": "tuts", "49209": "tutu", "49210": "duffy", "49211": "minee", "49212": "descend", "49213": "notes", "49214": "underscores", "49215": "leader", "49216": "deals", "49217": "healers", "49218": "dealt", "49219": "outdo", "49220": "noted", "49221": "claudia", "49222": "locate", "49223": "cfl", "49224": "cfm", "49225": "sayyyy", "49226": "cfb", "49227": "cfc", "49228": "claudio", "49229": "cfa", "49230": "bodybuilding", "49231": "4ward", "49232": "manipulating", "49233": "rrr", "49234": "😟", "49235": "universities", "49236": "hourly", "49237": "challs", "49238": "wkwkwk", "49239": "tiem", "49240": "flaking", "49241": "altima", "49242": "starvin", "49243": "waiting", "49244": "worships", "49245": "attire", "49246": "relocate", "49247": "ammunition", "49248": "gower", "49249": "everyting", "49250": "bazinga", "49251": "chimes", "49252": "flavoured", "49253": "minhyuk", "49254": "slut", "49255": "wm", "49256": "scrapped", "49257": "kotaku", "49258": "cyclist", "49259": "hellyeah", "49260": "metro", "49261": "gooners", "49262": "cleopatra", "49263": "adjusts", "49264": "spiced", "49265": "memba", "49266": "evading", "49267": "👳", "49268": "helpers", "49269": "stimulating", "49270": "typin", "49271": "simpleton", "49272": "spicer", "49273": "spices", "49274": "fuked", "49275": "initials", "49276": "girllll", "49277": "shovel", "49278": "apple", "49279": "scales", "49280": "forbid", "49281": "chipped", "49282": "confession", "49283": "offends", "49284": "thisss", "49285": "motor", "49286": "apply", "49287": "vayne", "49288": "shoves", "49289": "unprovoked", "49290": "txtn", "49291": "10hrs", "49292": "icee", "49293": "iced", "49294": "10k", "49295": "pinning", "49296": "covington", "49297": "weeping", "49298": "expanding", "49299": "icey", "49300": "babeeee", "49301": "automobile", "49302": "txts", "49303": "brazilians", "49304": "porch", "49305": "sidemen", "49306": "inclined", "49307": "cooperate", "49308": "rabbit", "49309": "briere", "49310": "penis", "49311": "women", "49312": "annoy", "49313": "7pm", "49314": "slaps", "49315": "tulane", "49316": "5years", "49317": "column", "49318": "lmfaoooooooo", "49319": "wildest", "49320": "cuomo", "49321": "angsty", "49322": "gabby", "49323": "biscuit", "49324": "purity", "49325": "tat", "49326": "tau", "49327": "tav", "49328": "hagrid", "49329": "tap", "49330": "tar", "49331": "tas", "49332": "raps", "49333": "matinee", "49334": "rapp", "49335": "tax", "49336": "tay", "49337": "taz", "49338": "tad", "49339": "tae", "49340": "tag", "49341": "taa", "49342": "tab", "49343": "tac", "49344": "tal", "49345": "tam", "49346": "serial", "49347": "tao", "49348": "tah", "49349": "tai", "49350": "taj", "49351": "tak", "49352": "gurr", "49353": "bisping", "49354": "legging", "49355": "twizzlers", "49356": "guru", "49357": "inaugural", "49358": "fortunate", "49359": "hoein", "49360": "cabbies", "49361": "fidgeting", "49362": "leggins", "49363": "heaux", "49364": "usain", "49365": "taylors", "49366": "panic", "49367": "gurl", "49368": "fonte", "49369": "hurdles", "49370": "heckling", "49371": "thorin", "49372": "discomfort", "49373": "ethnic", "49374": "bursary", "49375": "richland", "49376": "daycare", "49377": "interrupted", "49378": "wwii", "49379": "🐩", "49380": "crawling", "49381": "jolie", "49382": "gtta", "49383": "elie", "49384": "😈", "49385": "mookie", "49386": "fizzy", "49387": "unjustified", "49388": "dearest", "49389": "becuse", "49390": "fuccin", "49391": "gamescom", "49392": "xoxox", "49393": "underwater", "49394": "reeva", "49395": "complicate", "49396": "wven", "49397": "connors", "49398": "darkskins", "49399": "choosy", "49400": "byu", "49401": "choose", "49402": "reilly", "49403": "inconsistencies", "49404": "covered", "49405": "bye", "49406": "‼", "49407": "pending", "49408": "llsss", "49409": "violating", "49410": "yadi", "49411": "crash", "49412": "flour", "49413": "practice", "49414": "commended", "49415": "spikes", "49416": "puro", "49417": "zzzquil", "49418": "sinners", "49419": "salford", "49420": "trad", "49421": "notable", "49422": "drummers", "49423": "tram", "49424": "tisk", "49425": "asab", "49426": "trav", "49427": "fiddle", "49428": "trap", "49429": "champ", "49430": "arianator", "49431": "tray", "49432": "trax", "49433": "lobbies", "49434": "bentley", "49435": "🎆", "49436": "hyun", "49437": "testifying", "49438": "hyuk", "49439": "macaroons", "49440": "grower", "49441": "firepower", "49442": "hiiii", "49443": "mewls", "49444": "consecutively", "49445": "category", "49446": "semen", "49447": "generalizations", "49448": "uggla", "49449": "outings", "49450": "ows", "49451": "grits", "49452": "disapointed", "49453": "ponies", "49454": "wuddup", "49455": "anyhoo", "49456": "dictionary", "49457": "yoru", "49458": "jcp", "49459": "york", "49460": "dumpster", "49461": "implode", "49462": "conflicts", "49463": "thise", "49464": "anyhow", "49465": "yore", "49466": "bedazzled", "49467": "bigass", "49468": "kobes", "49469": "cait", "49470": "segregated", "49471": "barns", "49472": "fitzpatrick", "49473": "horace", "49474": "feasible", "49475": "conversation", "49476": "calvary", "49477": "riko", "49478": "adrian", "49479": "cain", "49480": "toasters", "49481": "timbers", "49482": "firehouse", "49483": "indesign", "49484": "throating", "49485": "beggining", "49486": "marksandspencer", "49487": "probation", "49488": "homers", "49489": "postseason", "49490": "donte", "49491": "droop", "49492": "jinjja", "49493": "overtime", "49494": "flourish", "49495": "overdraft", "49496": "myspace", "49497": "accent", "49498": "thiss", "49499": "dontt", "49500": "pakka", "49501": "bdutt", "49502": "drool", "49503": "oladipo", "49504": "dumbness", "49505": "headbands", "49506": "beezy", "49507": "doomed", "49508": "kiera", "49509": "smthing", "49510": "powdered", "49511": "thingies", "49512": "mattingly", "49513": "constituent", "49514": "commence", "49515": "5ish", "49516": "vadra", "49517": "mmkay", "49518": "duude", "49519": "riches", "49520": "richer", "49521": "tommy", "49522": "durwardmolotsis", "49523": "frugal", "49524": "tommo", "49525": "clemson", "49526": "fuuuuuuuuck", "49527": "sculpture", "49528": "beter", "49529": "lateness", "49530": "💊", "49531": "gabbert", "49532": "beatin", "49533": "yellin", "49534": "footwork", "49535": "alfonso", "49536": "kiefer", "49537": "clove", "49538": "afraid", "49539": "triggered", "49540": "ghe", "49541": "flareon", "49542": "skittles", "49543": "oozing", "49544": "agency", "49545": "bonnaroo", "49546": "bagel", "49547": "ahole", "49548": "ahold", "49549": "spawns", "49550": "wreckless", "49551": "divers", "49552": "youuuu", "49553": "⚾", "49554": "25000", "49555": "centre", "49556": "firecrackers", "49557": "mewtwo", "49558": "noggin", "49559": "deena", "49560": "delve", "49561": "highschool", "49562": "deadpan", "49563": "germantown", "49564": "deny", "49565": "sahi", "49566": "dent", "49567": "xavier", "49568": "dena", "49569": "deng", "49570": "fank", "49571": "urghhh", "49572": "smirked", "49573": "mileys", "49574": "99p", "49575": "mistook", "49576": "grindin", "49577": "escobar", "49578": "jazmin", "49579": "thuglife", "49580": "purely", "49581": "playbook", "49582": "meadowhall", "49583": "chicken", "49584": "debate", "49585": "griffey", "49586": "moes", "49587": "imsg", "49588": "superwoman", "49589": "dandruff", "49590": "craziness", "49591": "enticing", "49592": "cache", "49593": "cahoots", "49594": "humanitarian", "49595": "reminisce", "49596": "candidates", "49597": "iheart", "49598": "hiiiii", "49599": "thousands", "49600": "forfeit", "49601": "wordd", "49602": "sued", "49603": "workin", "49604": "sagittarius", "49605": "silas", "49606": "goosebumps", "49607": "toledo", "49608": "999", "49609": "woobin", "49610": "watercolor", "49611": "rockford", "49612": "woooooo", "49613": "ones", "49614": "sues", "49615": "words", "49616": "onew", "49617": "penetrate", "49618": "delgado", "49619": "wordy", "49620": "mouthful", "49621": "suey", "49622": "ghetto", "49623": "heretic", "49624": "jannah", "49625": "homosexuals", "49626": "truee", "49627": "postin", "49628": "jaime", "49629": "waterfalls", "49630": "cleavage", "49631": "postie", "49632": "11am", "49633": "generations", "49634": "truer", "49635": "conversions", "49636": "w0w", "49637": "ebi", "49638": "ebt", "49639": "taint", "49640": "queers", "49641": "merits", "49642": "chacos", "49643": "unfairly", "49644": "violet", "49645": "headingley", "49646": "gutters", "49647": "gbbo", "49648": "closer", "49649": "closes", "49650": "panto", "49651": "thers", "49652": "closet", "49653": "¨", "49654": "aveline", "49655": "superb", "49656": "xray", "49657": "genius", "49658": "mexicans", "49659": "ashleigh", "49660": "rumblings", "49661": "panty", "49662": "closed", "49663": "intimidated", "49664": "simms", "49665": "theta", "49666": "linebacker", "49667": "btfo", "49668": "pants", "49669": "gambino", "49670": "hutt", "49671": "arteta", "49672": "foetus", "49673": "zouk", "49674": "addie", "49675": "friendliest", "49676": "gooooooooood", "49677": "spoil", "49678": "disconcerting", "49679": "declining", "49680": "barrett", "49681": "tiers", "49682": "boiiiiii", "49683": "wilbur", "49684": "lentil", "49685": "profanity", "49686": "shoutgamers", "49687": "wnba", "49688": "postpaid", "49689": "grain", "49690": "atleast", "49691": "grail", "49692": "clogged", "49693": "safely", "49694": "reiner", "49695": "withdraws", "49696": "shoving", "49697": "hyphen", "49698": "worldly", "49699": "spasm", "49700": "withdrawn", "49701": "unhappiness", "49702": "softie", "49703": "bbn", "49704": "bbm", "49705": "bbl", "49706": "birkenstocks", "49707": "bbg", "49708": "bbe", "49709": "bbc", "49710": "broadcasts", "49711": "bba", "49712": "validating", "49713": "reindeer", "49714": "bbz", "49715": "bby", "49716": "bbw", "49717": "bbt", "49718": "bbs", "49719": "bbq", "49720": "furrytails", "49721": "butchers", "49722": "verizon", "49723": "playstation", "49724": "thrusting", "49725": "meaningless", "49726": "repetition", "49727": "tryyy", "49728": "wilt", "49729": "vanilla", "49730": "irate", "49731": "choices", "49732": "will", "49733": "h8r", "49734": "hovering", "49735": "bombarding", "49736": "wilk", "49737": "wild", "49738": "wile", "49739": "downgrade", "49740": "jehovah", "49741": "vape", "49742": "garda", "49743": "camerondallas", "49744": "ahahhaa", "49745": "cowgirls", "49746": "🔛", "49747": "ahahhah", "49748": "girrrrrrl", "49749": "whore", "49750": "halt", "49751": "liddat", "49752": "saintsfc", "49753": "pied", "49754": "stadiums", "49755": "vintage", "49756": "privileges", "49757": "headlight", "49758": "muchhhhh", "49759": "adverse", "49760": "merciful", "49761": "hippos", "49762": "contactless", "49763": "gnight", "49764": "zilch", "49765": "kstate", "49766": "elbows", "49767": "yoon", "49768": "premiere", "49769": "diploma", "49770": "octagon", "49771": "yoou", "49772": "yoos", "49773": "premiers", "49774": "arse", "49775": "deshawn", "49776": "⇨", "49777": "unpacked", "49778": "theses", "49779": "borini", "49780": "ㅋㅋㅋ", "49781": "english", "49782": "drogba", "49783": "breaker", "49784": "sensing", "49785": "contenders", "49786": "rocker", "49787": "rocket", "49788": "lameee", "49789": "boot", "49790": "wren", "49791": "bongo", "49792": "hali", "49793": "rachels", "49794": "rocked", "49795": "bautista", "49796": "azalea", "49797": "elliot", "49798": "disturbance", "49799": "sky", "49800": "unsigned", "49801": "morbidly", "49802": "sweetener", "49803": "googling", "49804": "hoppy", "49805": "clyde", "49806": "livingthedream", "49807": "adoption", "49808": "skl", "49809": "skn", "49810": "ski", "49811": "earthquakes", "49812": "¿", "49813": "identical", "49814": "saturation", "49815": "branch", "49816": "knoe", "49817": "mcginn", "49818": "protesters", "49819": "bettman", "49820": "know", "49821": "knot", "49822": "alisha", "49823": "cpac", "49824": "knox", "49825": "👜", "49826": "msft", "49827": "tragus", "49828": "feral", "49829": "panicking", "49830": "sk8", "49831": "taxpayers", "49832": "deftones", "49833": "dancer", "49834": "boof", "49835": "cutler", "49836": "starred", "49837": "traitors", "49838": "nigga", "49839": "shabby", "49840": "arigato", "49841": "sequence", "49842": "searching", "49843": "mosquitoes", "49844": "115", "49845": "niggs", "49846": "117", "49847": "116", "49848": "111", "49849": "110", "49850": "113", "49851": "112", "49852": "empire", "49853": "niggy", "49854": "neglecting", "49855": "118", "49856": "goodlooking", "49857": "affections", "49858": "leaf", "49859": "lead", "49860": "leak", "49861": "leah", "49862": "lean", "49863": "lear", "49864": "leas", "49865": "leap", "49866": "outrageously", "49867": "leav", "49868": "dunkindonuts", "49869": "murdoch", "49870": "murdock", "49871": "bitxh", "49872": "enuf", "49873": "poets", "49874": "murderer", "49875": "uggggh", "49876": "slur", "49877": "mitt", "49878": "handedly", "49879": "enuh", "49880": "wesley", "49881": "murdered", "49882": "11s", "49883": "slum", "49884": "fused", "49885": "tempered", "49886": "mite", "49887": "mitb", "49888": "slug", "49889": "throne", "49890": "incline", "49891": "squealed", "49892": "getting", "49893": "spilled", "49894": "5hours", "49895": "dependence", "49896": "shipping", "49897": "surge", "49898": "dependency", "49899": "epiphany", "49900": "wheats", "49901": "11ish", "49902": "spiller", "49903": "unlikable", "49904": "warranty", "49905": "10000000", "49906": "dede", "49907": "kano", "49908": "versed", "49909": "warrants", "49910": "headphone", "49911": "sherm", "49912": "brush", "49913": "registration", "49914": "cormier", "49915": "brilliantly", "49916": "maize", "49917": "uncontrollably", "49918": "okkk", "49919": "grapevine", "49920": "wildlands", "49921": "frickle", "49922": "cairo", "49923": "followme", "49924": "uncontrollable", "49925": "ensured", "49926": "darvish", "49927": "sugars", "49928": "kojima", "49929": "ohhhhhhhh", "49930": "funds", "49931": "baaaaad", "49932": "wight", "49933": "contradictory", "49934": "nassau", "49935": "jaffa", "49936": "yeeeeeah", "49937": "wating", "49938": "lve", "49939": "lvg", "49940": "9ish", "49941": "lvl", "49942": "broadly", "49943": "ex", "49944": "buffs", "49945": "ez", "49946": "buffy", "49947": "nukes", "49948": "spanks", "49949": "goodness", "49950": "airsoft", "49951": "domesticated", "49952": "counteract", "49953": "spanky", "49954": "vant", "49955": "kanu", "49956": "theon", "49957": "boop", "49958": "", "49959": "boos", "49960": "percent", "49961": "illinois", "49962": "roars", "49963": "book", "49964": "bool", "49965": "boom", "49966": "sick", "49967": "booo", "49968": "sice", "49969": "boob", "49970": "wheelers", "49971": "sica", "49972": "gau", "49973": "roleplay", "49974": "boog", "49975": "lance", "49976": "illegally", "49977": "juno", "49978": "cerys", "49979": "june", "49980": "howwww", "49981": "jung", "49982": "edclv", "49983": "medications", "49984": "sandwhich", "49985": "lancs", "49986": "boyband", "49987": "sheri", "49988": "junt", "49989": "pate", "49990": "repertoire", "49991": "rotting", "49992": "yummmmm", "49993": "workkkk", "49994": "emery", "49995": "gak", "49996": "expands", "49997": "dammmn", "49998": "untalented", "49999": "jawbone"} diff --git a/dockerfiles/Dockerfile.cpu b/dockerfiles/Dockerfile.cpu index e1ac377..4b26c4e 100644 --- a/dockerfiles/Dockerfile.cpu +++ b/dockerfiles/Dockerfile.cpu @@ -1,10 +1,8 @@ # Dockerfile for CPU-only CakeChat setup -FROM ubuntu:16.04 +FROM ubuntu:18.04 -# TensorBoard-related packages versions -ARG TENSORBOARD_VERSION=1.5.0 -ARG TENSORFLOW_VERSION=1.5.0 +ENV LANG C.UTF-8 # Install some dependencies RUN apt-get update && apt-get install -y \ @@ -19,26 +17,28 @@ RUN apt-get update && apt-get install -y \ unzip \ vim \ wget \ - python \ - python-dev \ - python-pip \ - python-setuptools \ + python3 \ + python3-dev \ + python3-pip \ + python3-setuptools \ && \ apt-get clean && \ apt-get autoremove && \ rm -rf /var/lib/apt/lists/* +# Link python to python3 (since python 2 is used by default in ubuntu docker image) +RUN ln -s /usr/bin/python3 /usr/bin/python + # Install up-to-date pip -RUN pip --no-cache-dir install -U pip +RUN pip3 --no-cache-dir install -U pip # setup cakechat and install dependencies RUN git clone https://github.com/lukalabs/cakechat.git /root/cakechat -RUN pip --no-cache-dir install -r /root/cakechat/requirements.txt - -# setup Tensorboard for monitoring the training process -RUN pip install tensorflow-tensorboard==$TENSORBOARD_VERSION tensorflow==$TENSORFLOW_VERSION +RUN pip3 --no-cache-dir install -r /root/cakechat/requirements.txt -r /root/cakechat/requirements-local.txt RUN mkdir -p /root/cakechat/data/tensorboard WORKDIR /root/cakechat -CMD (tensorboard --logdir=data/tensorboard 2>data/tensorboard/err.log &); \ +CMD git pull && \ + pip3 install -r requirements.txt -r /root/cakechat/requirements-local.txt && \ + (tensorboard --logdir=data/tensorboard 2>data/tensorboard/err.log &); \ /bin/bash diff --git a/dockerfiles/Dockerfile.gpu b/dockerfiles/Dockerfile.gpu index 858b902..3a5dc43 100644 --- a/dockerfiles/Dockerfile.gpu +++ b/dockerfiles/Dockerfile.gpu @@ -1,99 +1,32 @@ -# Dockerfile for GPU-enabled CakeChat with Theano and libgpuarray built from source +# Development GPU-powered dockerfile -FROM nvidia/cuda:8.0-cudnn5-devel-ubuntu16.04 +FROM horovod/horovod:0.16.0-tf1.12.0-torch1.0.0-mxnet1.4.0-py3.5 -# Theano-related packages versions -ARG THEANO_VERSION=rel-0.9.0 -ARG GPUARRAY_VERSION=v0.6.2 +ENV LANG C.UTF-8 -# TensorBoard-related packages versions -ARG TENSORBOARD_VERSION=1.5.0 -ARG TENSORFLOW_VERSION=1.5.0 - -# Install some dependencies RUN apt-get update && apt-get install -y \ - bc \ - build-essential \ - cmake \ - curl \ - g++ \ - gfortran \ - git \ - libffi-dev \ - libfreetype6-dev \ - libhdf5-dev \ - libjpeg-dev \ - liblcms2-dev \ - libopenblas-dev \ - liblapack-dev \ - libpng12-dev \ - libssl-dev \ - libtiff5-dev \ - libwebp-dev \ - libzmq3-dev \ - nano \ - pkg-config \ - python \ - python-dev \ - python-pip \ - python-setuptools \ - software-properties-common \ - unzip \ - vim \ - wget \ - zlib1g-dev \ - && \ - apt-get clean && \ - apt-get autoremove && \ - rm -rf /var/lib/apt/lists/* && \ -# Link BLAS library to use OpenBLAS using the alternatives mechanism (https://www.scipy.org/scipylib/building/linux.html#debian-ubuntu) - update-alternatives --set libblas.so.3 /usr/lib/openblas-base/libblas.so.3 + cabextract \ + libcurl4-openssl-dev \ + libmemcached-dev \ + libmysqlclient-dev \ + zlib1g-dev \ + libssl-dev \ + sudo \ + htop \ + tmux \ + man \ + less # Install up-to-date pip -RUN pip --no-cache-dir install -U pip - -# Build & install libgpuarray -RUN pip --no-cache-dir install \ - Cython==0.25.2 \ - numpy==1.12.1 \ - Mako==1.0.7 \ - nose==1.3.7 - -RUN git clone https://github.com/Theano/libgpuarray.git /root/libgpuarray && \ - cd /root/libgpuarray && git checkout $GPUARRAY_VERSION && mkdir Build && cd Build && \ - cmake .. -DCMAKE_BUILD_TYPE=Release && make -j16 && make install && \ - cd .. && python setup.py build && python setup.py install && ldconfig - -RUN rm -rf /root/libgpuarray - -# Install Theano and set up Theano config (.theanorc) for CUDA and OpenBLAS -RUN pip --no-cache-dir install git+git://github.com/Theano/Theano.git@$THEANO_VERSION && \ - \ - echo "[global]\ndevice=cuda\nfloatX=float32\noptimizer_including=cudnn\nmode=FAST_RUN \ - \n[gpuarray]\npreallocatem=0.95 \ - \n[blas]\nldflag = -L/usr/lib/openblas-base -lopenblas \ - \n[DebugMode]\ncheck_finite=1" \ - > /root/.theanorc - - -# Install CakeChat & development tools -RUN apt-get update && apt-get install -y \ - git \ - screen \ - tmux \ - vim \ - sudo \ - && \ - rm -rf /var/lib/apt/lists/* +RUN pip3 --no-cache-dir install -U pip # setup cakechat and install dependencies RUN git clone https://github.com/lukalabs/cakechat.git /root/cakechat -RUN pip --no-cache-dir install -r /root/cakechat/requirements.txt - -# setup Tensorboard for monitoring the training process -RUN pip install tensorflow-tensorboard==$TENSORBOARD_VERSION tensorflow==$TENSORFLOW_VERSION +RUN pip3 --no-cache-dir install -r /root/cakechat/requirements.txt -r /root/cakechat/requirements-local.txt RUN mkdir -p /root/cakechat/data/tensorboard WORKDIR /root/cakechat -CMD (tensorboard --logdir=data/tensorboard 2>data/tensorboard/err.log &); \ +CMD git pull && \ + pip3 install -r requirements.txt && \ + (tensorboard --logdir=data/tensorboard 2>data/tensorboard/err.log &); \ /bin/bash diff --git a/dockerfiles/Dockerfile3.cpu b/dockerfiles/Dockerfile3.cpu deleted file mode 100644 index b84f718..0000000 --- a/dockerfiles/Dockerfile3.cpu +++ /dev/null @@ -1,44 +0,0 @@ -# Dockerfile for CPU-only CakeChat setup - -FROM ubuntu:16.04 - -# TensorBoard-related packages versions -ARG TENSORBOARD_VERSION=1.5.0 -ARG TENSORFLOW_VERSION=1.5.0 - -# Install some dependencies -RUN apt-get update && apt-get install -y \ - curl \ - git \ - screen \ - tmux \ - sudo \ - nano \ - pkg-config \ - software-properties-common \ - unzip \ - vim \ - wget \ - python3 \ - python3-dev \ - python3-pip \ - python3-setuptools \ - && \ - apt-get clean && \ - apt-get autoremove && \ - rm -rf /var/lib/apt/lists/* - -# Install up-to-date pip -RUN pip3 --no-cache-dir install -U pip - -# setup cakechat and install dependencies -RUN git clone https://github.com/lukalabs/cakechat.git /root/cakechat -RUN pip3 --no-cache-dir install -r /root/cakechat/requirements.txt - -# setup Tensorboard for monitoring the training process -RUN pip3 install tensorflow-tensorboard==$TENSORBOARD_VERSION tensorflow==$TENSORFLOW_VERSION -RUN mkdir -p /root/cakechat/data/tensorboard - -WORKDIR /root/cakechat -CMD (tensorboard --logdir=data/tensorboard 2>data/tensorboard/err.log &); \ - /bin/bash diff --git a/dockerfiles/Dockerfile3.gpu b/dockerfiles/Dockerfile3.gpu deleted file mode 100644 index 1e3572d..0000000 --- a/dockerfiles/Dockerfile3.gpu +++ /dev/null @@ -1,102 +0,0 @@ -# Dockerfile for GPU-enabled CakeChat with Theano and libgpuarray built from source - -FROM nvidia/cuda:8.0-cudnn5-devel-ubuntu16.04 - -# Theano-related packages versions -ARG THEANO_VERSION=rel-0.9.0 -ARG GPUARRAY_VERSION=v0.6.2 - -# TensorBoard-related packages versions -ARG TENSORBOARD_VERSION=1.5.0 -ARG TENSORFLOW_VERSION=1.5.0 - -# Install some dependencies -RUN apt-get update && apt-get install -y \ - bc \ - build-essential \ - cmake \ - curl \ - g++ \ - gfortran \ - git \ - libffi-dev \ - libfreetype6-dev \ - libhdf5-dev \ - libjpeg-dev \ - liblcms2-dev \ - libopenblas-dev \ - liblapack-dev \ - libpng12-dev \ - libssl-dev \ - libtiff5-dev \ - libwebp-dev \ - libzmq3-dev \ - nano \ - pkg-config \ - python3 \ - python3-dev \ - python3-pip \ - python3-setuptools \ - software-properties-common \ - unzip \ - vim \ - wget \ - zlib1g-dev \ - && \ - apt-get clean && \ - apt-get autoremove && \ - rm -rf /var/lib/apt/lists/* && \ -# Link BLAS library to use OpenBLAS using the alternatives mechanism (https://www.scipy.org/scipylib/building/linux.html#debian-ubuntu) - update-alternatives --set libblas.so.3 /usr/lib/openblas-base/libblas.so.3 - -# Set python3 to default python, cmake fails without it -RUN update-alternatives --install /usr/bin/python python /usr/bin/python3 10 - -# Install up-to-date pip3 -RUN pip3 --no-cache-dir install -U pip - -# Build & install libgpuarray -RUN pip3 --no-cache-dir install \ - Cython==0.25.2 \ - numpy==1.12.1 \ - Mako==1.0.7 \ - nose==1.3.7 - -RUN git clone https://github.com/Theano/libgpuarray.git /root/libgpuarray && \ - cd /root/libgpuarray && git checkout $GPUARRAY_VERSION && mkdir Build && cd Build && \ - cmake .. -DCMAKE_BUILD_TYPE=Release && make -j16 && make install && \ - cd .. && python3 setup.py build && python3 setup.py install && ldconfig - -RUN rm -rf /root/libgpuarray - -# Install Theano and set up Theano config (.theanorc) for CUDA and OpenBLAS -RUN pip3 --no-cache-dir install git+git://github.com/Theano/Theano.git@$THEANO_VERSION && \ - \ - echo "[global]\ndevice=cuda\nfloatX=float32\noptimizer_including=cudnn\nmode=FAST_RUN \ - \n[gpuarray]\npreallocatem=0.95 \ - \n[blas]\nldflag = -L/usr/lib/openblas-base -lopenblas \ - \n[DebugMode]\ncheck_finite=1" \ - > /root/.theanorc - - -# Install CakeChat & development tools -RUN apt-get update && apt-get install -y \ - git \ - screen \ - tmux \ - vim \ - sudo \ - && \ - rm -rf /var/lib/apt/lists/* - -# setup cakechat and install dependencies -RUN git clone https://github.com/lukalabs/cakechat.git /root/cakechat -RUN pip3 --no-cache-dir install -r /root/cakechat/requirements.txt - -# setup Tensorboard for monitoring the training process -RUN pip3 install tensorflow-tensorboard==$TENSORBOARD_VERSION tensorflow==$TENSORFLOW_VERSION -RUN mkdir -p /root/cakechat/data/tensorboard - -WORKDIR /root/cakechat -CMD (tensorboard --logdir=data/tensorboard 2>data/tensorboard/err.log &); \ - /bin/bash diff --git a/requirements-local.txt b/requirements-local.txt new file mode 100644 index 0000000..6356118 --- /dev/null +++ b/requirements-local.txt @@ -0,0 +1,2 @@ +tensorflow==1.12.0 +tensorboard==1.12.0 diff --git a/requirements.txt b/requirements.txt index 6e22837..785c088 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,15 +1,16 @@ +# tensorflow and horovod should be presented in a base docker image or installed manually (see requirements-local.txt) flask==1.0.2 -numpy==1.14.4 -pandas==0.23.0 -Theano==0.9.0 -https://github.com/Lasagne/Lasagne/archive/master.zip +numpy==1.16.0 +pandas==0.23.4 unicodecsv==0.14.1 requests==2.21.0 -nltk==3.2.5 -scipy==0.19.1 -scikit-learn==0.19.1 -tensorboard-logger==0.0.4 -telepot==12.5 +nltk==3.4 +scipy==1.2.0 +scikit-learn==0.20.2 +keras==2.2.4 +telepot==12.6 # 12.6 is the latest version compatible (via aiohttp) with python 3.5.2 (ubuntu 16.04 default) gensim==1.0.1 -six==1.11.0 -cachetools==2.1.0 +cachetools==3.0.0 +# Internal requirements +gunicorn==19.9.0 +tqdm==4.30.0 diff --git a/tools/distributed_train.py b/tools/distributed_train.py new file mode 100644 index 0000000..2cefef8 --- /dev/null +++ b/tools/distributed_train.py @@ -0,0 +1,26 @@ +import os +import sys +import argparse + +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from cakechat.utils.env import run_horovod_train + + +def parse_args(): + argparser = argparse.ArgumentParser() + argparser.add_argument('-g', '--gpu-ids', action='store', nargs='+', required=True) + argparser.add_argument('-s', '--train-subset-size', action='store', type=int) + args = argparser.parse_args() + + return args + + +if __name__ == '__main__': + args = parse_args() + + train_cmd = 'python tools/train.py' + if args.train_subset_size: + train_cmd += ' -s {}'.format(args.train_subset_size) + + run_horovod_train(train_cmd, args.gpu_ids) diff --git a/tools/download_model.py b/tools/download_model.py deleted file mode 100644 index bf0fbbc..0000000 --- a/tools/download_model.py +++ /dev/null @@ -1,26 +0,0 @@ -#!/usr/bin/env python -""" -Gets trained model and warms it up (i.e. compiles and dumps corresponding prediction functions) -""" - -import os -import sys - -sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -from cakechat.utils.env import init_theano_env - -init_theano_env() - -from cakechat.dialog_model.factory import get_trained_model -from cakechat.utils.logger import get_tools_logger - -_logger = get_tools_logger(__file__) - -if __name__ == '__main__': - _logger.info('Fetching and pre-compiling pre-trained model...') - get_trained_model(fetch_from_s3=True) - _logger.info('Successfully resolved and compiled model.') - _logger.info('Fetching and pre-compiling additional reverse-model for MMI reranking...') - get_trained_model(fetch_from_s3=True, reverse=True) - _logger.info('Successfully resolved and compiled reverse-model.') diff --git a/tools/fetch.py b/tools/fetch.py new file mode 100644 index 0000000..5780b4a --- /dev/null +++ b/tools/fetch.py @@ -0,0 +1,46 @@ +#!/usr/bin/env python +""" +Gets trained model and warms it up (i.e. compiles and dumps corresponding prediction functions) +""" +import argparse +import os +import sys + +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from cakechat.utils.env import init_keras + +init_keras() + +from cakechat.dialog_model.factory import get_trained_model +from cakechat.utils.logger import get_tools_logger +from cakechat.utils.w2v.model import get_w2v_model + +_logger = get_tools_logger(__file__) + + +def parse_args(): + argparser = argparse.ArgumentParser() + argparser.add_argument( + '-m', + '--model', + action='store', + choices=['default', 'reverse', 'w2v', 'all'], + help='Fetch models from s3 to disk', + default='all') + args = argparser.parse_args() + + return args + + +if __name__ == '__main__': + args = parse_args() + + if args.model in {'default', 'all'}: + get_trained_model(fetch_from_s3=True) + + if args.model in {'reverse', 'all'}: + get_trained_model(fetch_from_s3=True, is_reverse_model=True) + + if args.model in {'w2v', 'all'}: + get_w2v_model(fetch_from_s3=True) diff --git a/tools/generate_predictions.py b/tools/generate_predictions.py index 4524aaa..1eb57dd 100644 --- a/tools/generate_predictions.py +++ b/tools/generate_predictions.py @@ -4,38 +4,37 @@ sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -from cakechat.utils.env import init_theano_env +from cakechat.utils.env import init_cuda_env -init_theano_env() +init_cuda_env() -from cakechat.dialog_model.model import get_nn_model +from cakechat.dialog_model.factory import get_reverse_model +from cakechat.dialog_model.model import CakeChatModel from cakechat.dialog_model.model_utils import transform_contexts_to_token_ids, lines_to_context from cakechat.dialog_model.quality import log_predictions, calculate_and_log_val_metrics from cakechat.utils.files_utils import is_non_empty_file from cakechat.utils.logger import get_tools_logger +from cakechat.utils.data_types import ModelParam from cakechat.utils.dataset_loader import get_tokenized_test_lines, load_context_free_val, \ - load_context_sensitive_val + load_context_sensitive_val, get_validation_data_id, get_validation_sets_names from cakechat.utils.text_processing import get_index_to_token_path, load_index_to_item, get_index_to_condition_path +from cakechat.utils.w2v.model import get_w2v_model_id from cakechat.config import BASE_CORPUS_NAME, QUESTIONS_CORPUS_NAME, INPUT_SEQUENCE_LENGTH, INPUT_CONTEXT_SIZE, \ - PREDICTION_MODES, DATA_DIR, DEFAULT_TEMPERATURE, PREDICTION_MODE_FOR_TESTS + PREDICTION_MODES, PREDICTION_MODE_FOR_TESTS, RESULTS_PATH, DEFAULT_TEMPERATURE, TRAIN_CORPUS_NAME, \ + USE_PRETRAINED_W2V_EMBEDDINGS_LAYER _logger = get_tools_logger(__file__) -def _save_test_results(test_dataset, predictions_filename, nn_model, prediction_modes, **kwargs): +def _save_test_results(test_dataset, predictions_filename, nn_model, prediction_mode, **kwargs): + context_sensitive_val = load_context_sensitive_val(nn_model.token_to_index, nn_model.condition_to_index) + context_free_val = load_context_free_val(nn_model.token_to_index) + calculate_and_log_val_metrics(nn_model, context_sensitive_val, context_free_val, prediction_mode, + calculate_ngram_distance=False) + test_dataset_ids = transform_contexts_to_token_ids( list(lines_to_context(test_dataset)), nn_model.token_to_index, INPUT_SEQUENCE_LENGTH, INPUT_CONTEXT_SIZE) - - calculate_and_log_val_metrics(nn_model, - load_context_sensitive_val(nn_model.token_to_index, nn_model.condition_to_index), - load_context_free_val(nn_model.token_to_index)) - - log_predictions( - predictions_filename, - test_dataset_ids, - nn_model, - prediction_modes, - **kwargs) + log_predictions(predictions_filename, test_dataset_ids, nn_model, prediction_modes=[prediction_mode], **kwargs) def predict(model_path, @@ -45,7 +44,6 @@ def predict(model_path, reverse_model_weights=None, temperatures=None, prediction_mode=None): - if not tokens_index_path: tokens_index_path = get_index_to_token_path(BASE_CORPUS_NAME) if not conditions_index_path: @@ -72,11 +70,22 @@ def predict(model_path, index_to_token = load_index_to_item(tokens_index_path) index_to_condition = load_index_to_item(conditions_index_path) + w2v_model_id = get_w2v_model_id() if USE_PRETRAINED_W2V_EMBEDDINGS_LAYER else None + + nn_model = CakeChatModel( + index_to_token, + index_to_condition, + training_data_param=ModelParam(value=None, id=TRAIN_CORPUS_NAME), + validation_data_param=ModelParam(value=None, id=get_validation_data_id(get_validation_sets_names())), + w2v_model_param=ModelParam(value=None, id=w2v_model_id), + model_init_path=model_path, + reverse_model=get_reverse_model(prediction_mode)) - nn_model, _ = get_nn_model(index_to_token, index_to_condition, model_init_path=model_path) + nn_model.init_model() + nn_model.resolve_model() if not default_predictions_path: - default_predictions_path = os.path.join(DATA_DIR, 'results', 'predictions_' + nn_model.model_name) + default_predictions_path = os.path.join(RESULTS_PATH, 'results', 'predictions_' + nn_model.model_name) # Get path for each combination of parameters predictions_paths = [] @@ -91,30 +100,24 @@ def predict(model_path, else: predictions_paths = [default_predictions_path + '.tsv'] - _logger.info('Model for prediction:\n{}'.format(nn_model.model_load_path)) - _logger.info('Tokens index:\n{}'.format(tokens_index_path)) - _logger.info('File with questions:\n{}'.format(QUESTIONS_CORPUS_NAME)) - _logger.info('Files to dump responses:\n{}'.format('\n'.join(predictions_paths))) - _logger.info('Prediction parameters\n{}'.format('\n'.join([str(x) for x in prediction_params]))) + _logger.info('Model for prediction: {}'.format(nn_model.model_path)) + _logger.info('Tokens index: {}'.format(tokens_index_path)) + _logger.info('File with questions: {}'.format(QUESTIONS_CORPUS_NAME)) + _logger.info('Files to dump responses: {}'.format('\n'.join(predictions_paths))) + _logger.info('Prediction parameters {}'.format('\n'.join([str(x) for x in prediction_params]))) processed_test_set = get_tokenized_test_lines(QUESTIONS_CORPUS_NAME, set(index_to_token.values())) - processed_test_set = list(processed_test_set) for cur_params, cur_path in zip(prediction_params, predictions_paths): _logger.info('Predicting with the following params: {}'.format(cur_params)) - _save_test_results(processed_test_set, cur_path, nn_model, prediction_modes=[prediction_mode]) + _save_test_results(processed_test_set, cur_path, nn_model, prediction_mode, **cur_params) def parse_args(): argparser = argparse.ArgumentParser() argparser.add_argument( - '-p', - '--prediction-mode', - action='store', - help='Prediction mode', - choices=PREDICTION_MODES, - default=None) + '-p', '--prediction-mode', action='store', help='Prediction mode', choices=PREDICTION_MODES, default=None) argparser.add_argument( '-m', @@ -154,13 +157,7 @@ def parse_args(): help='Reverse model score weight for prediction with MMI-reranking objective. Used only in *-reranking modes', default=None) - argparser.add_argument( - '-t', - '--temperatures', - action='append', - help='temperature values', - default=None, - type=float) + argparser.add_argument('-t', '--temperatures', action='append', help='temperature values', default=None, type=float) args = argparser.parse_args() diff --git a/tools/generate_predictions_for_condition.py b/tools/generate_predictions_for_condition.py index 0b09513..7ffa58f 100644 --- a/tools/generate_predictions_for_condition.py +++ b/tools/generate_predictions_for_condition.py @@ -1,5 +1,3 @@ -from __future__ import print_function - import argparse import os import sys @@ -8,12 +6,13 @@ sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -from cakechat.utils.env import init_theano_env -init_theano_env() +from cakechat.utils.env import init_cuda_env + +init_cuda_env() from cakechat.config import QUESTIONS_CORPUS_NAME, INPUT_SEQUENCE_LENGTH, INPUT_CONTEXT_SIZE, \ - PREDICTION_MODES, PREDICTION_MODE_FOR_TESTS, DEFAULT_CONDITION, RANDOM_SEED + PREDICTION_MODES, PREDICTION_MODE_FOR_TESTS, DEFAULT_CONDITION, RANDOM_SEED, INTX from cakechat.utils.text_processing import get_tokens_sequence, replace_out_of_voc_tokens from cakechat.utils.dataset_loader import get_tokenized_test_lines from cakechat.dialog_model.model_utils import transform_context_token_ids_to_sentences, \ @@ -39,7 +38,7 @@ def transform_lines_to_contexts_token_ids(tokenized_lines, nn_model): def predict_for_condition_id(nn_model, contexts, condition_id, prediction_mode=PREDICTION_MODE_FOR_TESTS): - condition_ids = np.array([condition_id] * contexts.shape[0], dtype=np.int32) + condition_ids = np.array([condition_id] * contexts.shape[0], dtype=INTX) responses = get_nn_responses( contexts, nn_model, mode=prediction_mode, output_candidates_num=1, condition_ids=condition_ids) return [candidates[0] for candidates in responses] @@ -51,8 +50,8 @@ def print_predictions(nn_model, contexts_token_ids, condition, prediction_mode=P nn_model, contexts_token_ids, nn_model.condition_to_index[condition], prediction_mode=prediction_mode) for x, y in zip(x_sents, y_sents): - print('condition: %s; context: %s' % (condition.encode('utf8'), x.encode('utf8'))) - print('response: %s' % y.encode('utf8')) + print('condition: {}; context: {}'.format(condition, x)) + print('response: {}'.format(y)) print() @@ -79,7 +78,7 @@ def parse_args(): nn_model = get_trained_model() if args.text: - tokenized_lines = process_text(nn_model, args.text.decode('utf8')) + tokenized_lines = process_text(nn_model, args.text) else: tokenized_lines = load_corpus(nn_model, args.data) diff --git a/tools/prepare_index_files.py b/tools/prepare_index_files.py index 3c2f33d..cd61fa0 100644 --- a/tools/prepare_index_files.py +++ b/tools/prepare_index_files.py @@ -3,22 +3,24 @@ sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -import codecs from collections import Counter import json +from tqdm import tqdm + from cakechat.utils.files_utils import is_non_empty_file, ensure_dir from cakechat.utils.text_processing import get_tokens_sequence, get_processed_corpus_path, get_index_to_token_path, \ get_index_to_condition_path, load_processed_dialogs_from_json, FileTextLinesIterator, SPECIAL_TOKENS -from cakechat.config import BASE_CORPUS_NAME, TRAIN_CORPUS_NAME, DEFAULT_CONDITION +from cakechat.config import BASE_CORPUS_NAME, TRAIN_CORPUS_NAME, DEFAULT_CONDITION, VOCABULARY_MAX_SIZE, MAX_CONDITIONS_NUM -MAX_TOKENS_NUM = 50000 -MAX_CONDITIONS_NUM = 5 TEXT_FIELD_NAME = 'text' CONDITION_FIELD_NAME = 'condition' +SIMPLE_TOKENIZE = False # Set to True if you want to use str.split() instead of get_tokens_sequence() for +# tokenization -def build_index_mappings(corpus_path, max_tokens_num=MAX_TOKENS_NUM, max_conditions_num=MAX_CONDITIONS_NUM): +def build_index_mappings(corpus_path, max_tokens_num=VOCABULARY_MAX_SIZE, max_conditions_num=MAX_CONDITIONS_NUM, + simple_tokenize=SIMPLE_TOKENIZE): if not is_non_empty_file(corpus_path): raise ValueError('Test corpus file doesn\'t exist: {}'.format(corpus_path)) @@ -28,12 +30,12 @@ def build_index_mappings(corpus_path, max_tokens_num=MAX_TOKENS_NUM, max_conditi tokens_counter = Counter() conditions_counter = Counter() - for dialog in dialogs: + for dialog in tqdm(dialogs): for utterance in dialog: - # Tokenize dialog utterance text and update tokens count - tokens = get_tokens_sequence(utterance[TEXT_FIELD_NAME]) - tokens_counter += Counter(tokens) - # Update conditions count + tokens = utterance[TEXT_FIELD_NAME].split() if simple_tokenize else \ + get_tokens_sequence(utterance[TEXT_FIELD_NAME]) + + tokens_counter.update(tokens) conditions_counter[utterance[CONDITION_FIELD_NAME]] += 1 # Build the tokens list @@ -45,7 +47,7 @@ def build_index_mappings(corpus_path, max_tokens_num=MAX_TOKENS_NUM, max_conditi # Validate the condition list if DEFAULT_CONDITION not in conditions: - raise Exception('No default condition "%s" found in the dataset condition list.' % DEFAULT_CONDITION) + raise Exception('No default condition "{}" found in the dataset condition list.'.format(DEFAULT_CONDITION)) # Return index_to_token and index_to_condition mappings return dict(enumerate(vocab)), dict(enumerate(conditions)) @@ -53,7 +55,7 @@ def build_index_mappings(corpus_path, max_tokens_num=MAX_TOKENS_NUM, max_conditi def dump_index_to_item(index_to_item, path): ensure_dir(os.path.dirname(path)) - with codecs.open(path, 'w', 'utf-8') as fh: + with open(path, 'w', encoding='utf-8') as fh: json.dump(index_to_item, fh, ensure_ascii=False) diff --git a/tools/quality/condition_quality.py b/tools/quality/condition_quality.py index dbae4b5..57f9a57 100644 --- a/tools/quality/condition_quality.py +++ b/tools/quality/condition_quality.py @@ -3,12 +3,9 @@ sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) -import numpy as np -from six import iteritems +from cakechat.utils.env import init_cuda_env -from cakechat.utils.env import init_theano_env - -init_theano_env() +init_cuda_env() from cakechat.utils.dataset_loader import load_datasets from cakechat.utils.data_types import Dataset @@ -18,9 +15,7 @@ from cakechat.dialog_model.inference import get_nn_responses from cakechat.dialog_model.quality import calculate_model_mean_perplexity, get_tfidf_vectorizer, \ calculate_lexical_similarity -from cakechat.config import PREDICTION_MODE_FOR_TESTS, DEFAULT_CONDITION, RANDOM_SEED - -np.random.seed(seed=RANDOM_SEED) +from cakechat.config import PREDICTION_MODE_FOR_TESTS, DEFAULT_CONDITION _logger = get_tools_logger(__file__) @@ -35,40 +30,42 @@ def _slice_condition_data(dataset, condition_id): x=dataset.x[condition_mask], y=dataset.y[condition_mask], condition_ids=dataset.condition_ids[condition_mask]) -def calc_perplexity_metrics(nn_model, train_subset, subset_with_conditions, validation): - ppl_non_conditioned_train_subset = calculate_model_mean_perplexity(nn_model, _make_non_conditioned(train_subset)) - ppl_train_subset = calculate_model_mean_perplexity(nn_model, train_subset) - - ppl_non_conditioned_subset_with_conditions = calculate_model_mean_perplexity( - nn_model, _make_non_conditioned(subset_with_conditions)) - ppl_subset_with_conditions = calculate_model_mean_perplexity(nn_model, subset_with_conditions) - - ppl_validation = calculate_model_mean_perplexity(nn_model, validation) - +def calc_perplexity_metrics(nn_model, eval_datasets): return { - 'perplexity_train_subset_no_cond': ppl_non_conditioned_train_subset, - 'perplexity_train_subset': ppl_train_subset, - 'perplexity_subset_with_conditions_no_cond': ppl_non_conditioned_subset_with_conditions, - 'perplexity_subset_with_conditions': ppl_subset_with_conditions, - 'perplexity_validation': ppl_validation, + 'ppl_cs_test': + calculate_model_mean_perplexity(nn_model, eval_datasets.cs_test), + 'ppl_cs_test_not_conditioned': + calculate_model_mean_perplexity(nn_model, _make_non_conditioned(eval_datasets.cs_test)), + 'ppl_cs_test_one_condition': + calculate_model_mean_perplexity(nn_model, eval_datasets.cs_test_one_condition), + 'ppl_cs_test_one_condition_not_conditioned': + calculate_model_mean_perplexity(nn_model, _make_non_conditioned(eval_datasets.cs_test_one_condition)), + 'ppl_cf_validation': + calculate_model_mean_perplexity(nn_model, eval_datasets.cf_validation) } -def calc_perplexity_by_condition_metrics(nn_model, train): +def calc_perplexity_for_conditions(nn_model, dataset): + cond_to_ppl_conditioned, cond_to_ppl_not_conditioned = {}, {} + for condition, condition_id in nn_model.condition_to_index.items(): if condition == DEFAULT_CONDITION: continue - dataset_with_conditions = _slice_condition_data(train, condition_id) + dataset_with_conditions = _slice_condition_data(dataset, condition_id) if not dataset_with_conditions.x.size: - _logger.warning('No dataset samples found with the given condition "%s", skipping metrics.' % condition) + _logger.warning( + 'No dataset samples found with the given condition "{}", skipping metrics.'.format(condition)) continue - ppl_non_conditioned = calculate_model_mean_perplexity(nn_model, _make_non_conditioned(dataset_with_conditions)) - ppl_conditioned = calculate_model_mean_perplexity(nn_model, dataset_with_conditions) + cond_to_ppl_conditioned[condition] = \ + calculate_model_mean_perplexity(nn_model, _make_non_conditioned(dataset_with_conditions)) + + cond_to_ppl_not_conditioned[condition] = \ + calculate_model_mean_perplexity(nn_model, dataset_with_conditions) - yield condition, (ppl_non_conditioned, ppl_conditioned) + return cond_to_ppl_conditioned, cond_to_ppl_not_conditioned def predict_for_condition_id(nn_model, x_val, condition_id=None): @@ -76,47 +73,69 @@ def predict_for_condition_id(nn_model, x_val, condition_id=None): return [candidates[0] for candidates in responses] -def calc_lexical_similarity_metrics(nn_model, train, questions, tfidf_vectorizer): - responses_baseline = predict_for_condition_id(nn_model, questions.x) +def calc_lexical_similarity_metrics(nn_model, testset, tfidf_vectorizer): + """ + For each condition calculate lexical similarity between ground-truth responses and + generated conditioned responses. Similarly compare ground-truth responses with non-conditioned generated responses. + If lex_sim(gt, cond_resp) > lex_sim(gt, non_cond_resp), the conditioning on extra information proves to be useful. + :param nn_model: trained model to evaluate + :param testset: context-sensitive testset, instance of Dataset + :param tfidf_vectorizer: instance of scikit-learn TfidfVectorizer, calculates lexical similariry for documents + according to TF-IDF metric + :return: two dictionaries: + {condition: lex_sim(gt, cond_resp)}, + {condition: lex_sim(gt, non_cond_resp)} + """ + gt_vs_cond_lex_sim, gt_vs_non_cond_lex_sim = {}, {} for condition, condition_id in nn_model.condition_to_index.items(): - if condition == DEFAULT_CONDITION: - continue + sample_mask_for_condition = testset.condition_ids == condition_id + contexts_for_condition = testset.x[sample_mask_for_condition] + responses_for_condition = testset.y[sample_mask_for_condition] - responses_token_ids_ground_truth = train.y[train.condition_ids == condition_id] - if not responses_token_ids_ground_truth.size: - _logger.warning('No dataset samples found with the given condition "%s", skipping metrics.' % condition) + if not responses_for_condition.size: + _logger.warning('No dataset samples found for condition "{}", skip it.'.format(condition)) continue - responses_ground_truth = transform_token_ids_to_sentences(responses_token_ids_ground_truth, - nn_model.index_to_token) - responses = predict_for_condition_id(nn_model, questions.x, condition_id) + gt_responses = transform_token_ids_to_sentences(responses_for_condition, nn_model.index_to_token) + conditioned_responses = predict_for_condition_id(nn_model, contexts_for_condition, condition_id) + non_conditioned_responses = predict_for_condition_id(nn_model, contexts_for_condition, condition_id=None) - lex_sim_conditioned_vs_non_conditioned = calculate_lexical_similarity(responses, responses_baseline, - tfidf_vectorizer) - lex_sim_conditioned_vs_groundtruth = calculate_lexical_similarity(responses, responses_ground_truth, - tfidf_vectorizer) + gt_vs_cond_lex_sim[condition] = \ + calculate_lexical_similarity(gt_responses, conditioned_responses, tfidf_vectorizer) - yield condition, (lex_sim_conditioned_vs_non_conditioned, lex_sim_conditioned_vs_groundtruth) + gt_vs_non_cond_lex_sim[condition] = \ + calculate_lexical_similarity(gt_responses, non_conditioned_responses, tfidf_vectorizer) + + return gt_vs_cond_lex_sim, gt_vs_non_cond_lex_sim if __name__ == '__main__': nn_model = get_trained_model() - train, questions, validation, train_subset, conditioned_subset = load_datasets(nn_model.token_to_index, - nn_model.condition_to_index) - tfidf_vectorizer = get_tfidf_vectorizer() - - for metric, perplexity in iteritems(calc_perplexity_metrics(nn_model, train_subset, conditioned_subset, - validation)): - _logger.info('Metric: {}, perplexity: {}'.format(metric, perplexity)) - - for condition, (ppl_non_conditioned, ppl_conditioned) in calc_perplexity_by_condition_metrics(nn_model, train): - _logger.info('Condition: {}, non-conditioned perplexity: {}, conditioned perplexity: {}'.format( - condition, ppl_non_conditioned, ppl_conditioned)) - - for condition, (lex_sim_conditioned_vs_non_conditioned, lex_sim_conditioned_vs_groundtruth) in \ - calc_lexical_similarity_metrics(nn_model, train, questions, tfidf_vectorizer): - _logger.info('Condition: {}, conditioned vs non-conditioned lexical similarity: {}'.format( - condition, lex_sim_conditioned_vs_non_conditioned)) - _logger.info('Condition: {}, conditioned vs groundtruth lexical similarity: {}'.format( - condition, lex_sim_conditioned_vs_groundtruth)) + eval_datasets = load_datasets(nn_model.token_to_index, nn_model.condition_to_index) + + print('\nPerplexity on datasets:') + for dataset, perplexity in calc_perplexity_metrics(nn_model, eval_datasets).items(): + print('\t{}: \t{:.1f}'.format(dataset, perplexity)) + + cond_to_ppl_conditioned, cond_to_ppl_not_conditioned = \ + calc_perplexity_for_conditions(nn_model, eval_datasets.cs_test) + + print('\nPerplexity on conditioned testset for conditions:') + for condition, perplexity in cond_to_ppl_conditioned.items(): + print('\t{}: \t{:.1f}'.format(condition, perplexity)) + + print('\nPerplexity on non-conditioned testset for conditions:') + for condition, perplexity in cond_to_ppl_not_conditioned.items(): + print('\t{}: \t{:.1f}'.format(condition, perplexity)) + + gt_vs_cond_lex_sim, gt_vs_non_cond_lex_sim = \ + calc_lexical_similarity_metrics(nn_model, eval_datasets.cs_test, get_tfidf_vectorizer()) + + print('\nLexical similarity, ground-truth vs. conditioned responses:') + for condition, lex_sim in gt_vs_cond_lex_sim.items(): + print('\t{}: \t{:.2f}'.format(condition, lex_sim)) + + print('\nLexical similarity, ground-truth vs. non-conditioned responses:') + for condition, lex_sim in gt_vs_non_cond_lex_sim.items(): + print('\t{}: \t{:.2f}'.format(condition, lex_sim)) diff --git a/tools/quality/prediction_distinctness.py b/tools/quality/prediction_distinctness.py index 8d6ae37..abdc468 100644 --- a/tools/quality/prediction_distinctness.py +++ b/tools/quality/prediction_distinctness.py @@ -1,25 +1,20 @@ -from __future__ import print_function - +import argparse import os import sys -import argparse - -from six.moves import xrange sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) import numpy as np -from cakechat.utils.env import init_theano_env +from cakechat.utils.env import init_cuda_env -init_theano_env() +init_cuda_env() -from cakechat.dialog_model.model import get_nn_model +from cakechat.dialog_model.factory import get_trained_model from cakechat.dialog_model.quality import calculate_response_ngram_distinctness from cakechat.utils.dataset_loader import load_datasets, load_questions_set -from cakechat.utils.text_processing import get_index_to_token_path, load_index_to_item, get_index_to_condition_path from cakechat.utils.logger import get_tools_logger -from cakechat.config import BASE_CORPUS_NAME, PREDICTION_MODES, PREDICTION_MODE_FOR_TESTS +from cakechat.config import PREDICTION_MODES, PREDICTION_MODE_FOR_TESTS _logger = get_tools_logger(__file__) @@ -28,7 +23,7 @@ def log_distinct_metrics(nn_model, x, condition_ids=None, samples_num=1, ngram_l for ngram_length in ngram_lengths: scores = [ calculate_response_ngram_distinctness(x, nn_model, ngram_len=ngram_length, condition_ids=condition_ids) - for _ in xrange(samples_num) + for _ in range(samples_num) ] scores_mean = np.mean(scores) scores_std = np.std(scores) @@ -38,50 +33,36 @@ def log_distinct_metrics(nn_model, x, condition_ids=None, samples_num=1, ngram_l print(result) -def load_model(model_path, tokens_index_path=None, conditions_index_path=None): - if tokens_index_path is None: - tokens_index_path = get_index_to_token_path(BASE_CORPUS_NAME) - if conditions_index_path is None: - conditions_index_path = get_index_to_condition_path(BASE_CORPUS_NAME) - - index_to_token = load_index_to_item(tokens_index_path) - index_to_condition = load_index_to_item(conditions_index_path) - nn_model, model_exists = get_nn_model(index_to_token, index_to_condition, model_path) - - if not model_exists: - raise ValueError('Couldn\'t find model: "{}".'.format(model_path)) - - return nn_model - - def evaluate_distinctness(args): if args.sample_size > 1 and PREDICTION_MODE_FOR_TESTS == PREDICTION_MODES.beamsearch: _logger.waring('Using sample_size > 1 is meaningless with prediction_mode=\'beamsearch\' because there\'s no ' 'randomness in the prediction. Use sample_size=1 instead.') - nn_model = load_model(args.model, args.tokens_index, args.conditions_index) + nn_model = get_trained_model() if args.validation_only: validation = load_questions_set(nn_model.token_to_index) validation_set_name = 'context free questions' else: - train, _, validation, train_subset, defined_condition_subset = load_datasets( - nn_model.token_to_index, nn_model.condition_to_index) + eval_datasets = load_datasets(nn_model.token_to_index, nn_model.condition_to_index) + validation = eval_datasets.cf_validation + cs_test = eval_datasets.cs_test + cs_test_one_condition = eval_datasets.cs_test_one_condition validation_set_name = 'validation set without conditions' - _logger.info('Evaluating distinctness for train subset without conditions') - log_distinct_metrics(nn_model, train_subset.x, samples_num=args.sample_size) + _logger.info('Evaluating distinctness for context sensitive testset without conditions') + log_distinct_metrics(nn_model, cs_test.x, samples_num=args.sample_size) - _logger.info('Evaluating distinctness for train subset with conditions') - log_distinct_metrics(nn_model, train_subset.x, train_subset.condition_ids, samples_num=args.sample_size) + _logger.info('Evaluating distinctness for context sensitive testset with conditions') + log_distinct_metrics(nn_model, cs_test.x, cs_test.condition_ids, samples_num=args.sample_size) _logger.info('Evaluating distinctness for defined-conditions-subset without conditions') - log_distinct_metrics(nn_model, defined_condition_subset.x, samples_num=args.sample_size) + log_distinct_metrics(nn_model, cs_test_one_condition.x, samples_num=args.sample_size) _logger.info('Evaluating distinctness for defined-conditions-subset with conditions') log_distinct_metrics( - nn_model, defined_condition_subset.x, defined_condition_subset.condition_ids, samples_num=args.sample_size) + nn_model, cs_test_one_condition.x, cs_test_one_condition.condition_ids, samples_num=args.sample_size) _logger.info('Evaluating distinctness for {}'.format(validation_set_name)) log_distinct_metrics(nn_model, validation.x, samples_num=args.sample_size) @@ -90,28 +71,6 @@ def evaluate_distinctness(args): if __name__ == '__main__': argparser = argparse.ArgumentParser() - argparser.add_argument( - '-m', - '--model', - action='store', - default=None, - help='Path to the file with your model. ' - 'Be careful, model parameters are inferred from the config, not from the filename') - - argparser.add_argument( - '-t', - '--tokens_index', - action='store', - default=None, - help='Path to the json file with index_to_token dictionary.') - - argparser.add_argument( - '-c', - '--conditions_index', - action='store', - default=None, - help='Path to the json file with index_to_condition dictionary.') - argparser.add_argument( '-s', '--sample_size', action='store', default=1, type=int, help='Number of samples to average over') diff --git a/tools/quality/ranking_quality.py b/tools/quality/ranking_quality.py index ba3a243..548b657 100644 --- a/tools/quality/ranking_quality.py +++ b/tools/quality/ranking_quality.py @@ -1,16 +1,11 @@ -from __future__ import print_function - import os import sys -from six import iteritems -from six.moves import xrange - sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) -from cakechat.utils.env import init_theano_env +from cakechat.utils.env import init_cuda_env -init_theano_env() +init_cuda_env() from collections import defaultdict @@ -30,7 +25,7 @@ def _read_testset(): test_lines = load_file(corpus_path) testset = defaultdict(set) - for i in xrange(0, len(test_lines) - 1, 2): + for i in range(0, len(test_lines) - 1, 2): context = test_lines[i].strip() response = test_lines[i + 1].strip() testset[context].add(response) @@ -74,9 +69,9 @@ def _compute_metrics(model, testset): compute_recall_k, testset, context_to_weighted_responses, top_count=test_set_size // 4) } - print('Test set size = %i' % test_set_size) - for metric_name, metric_value in iteritems(metrics): - print('%s = %s' % (metric_name, metric_value)) + print('Test set size = {}'.format(test_set_size)) + for metric_name, metric_value in metrics.items(): + print('{} = {}'.format(metric_name, metric_value)) if __name__ == '__main__': diff --git a/tools/telegram_bot.py b/tools/telegram_bot.py index e8f8a4a..6f3407e 100644 --- a/tools/telegram_bot.py +++ b/tools/telegram_bot.py @@ -6,9 +6,9 @@ import argparse from collections import deque -from cakechat.utils.env import init_theano_env +from cakechat.utils.env import init_cuda_env -init_theano_env() +init_cuda_env() from cakechat.api.response import get_response from cakechat.config import INPUT_CONTEXT_SIZE, DEFAULT_CONDITION @@ -32,7 +32,7 @@ def default_handle_message(self, msg): if __name__ == '__main__': argparser = argparse.ArgumentParser() - argparser.add_argument('--token', help='Bot token') + argparser.add_argument('-t', '--token', help='Bot token') args = argparser.parse_args() TelegramBot(token=args.token).run(CakeChatTelegramChatSession) diff --git a/tools/test_api.py b/tools/test_api.py index db24bef..4d67c6b 100644 --- a/tools/test_api.py +++ b/tools/test_api.py @@ -1,5 +1,3 @@ -from __future__ import print_function - import argparse import os import sys @@ -26,7 +24,7 @@ def parse_args(): if __name__ == '__main__': args = parse_args() - url = 'http://%s:%s/cakechat_api/v1/actions/get_response' % (args.fqdn, args.port) + url = 'http://{}:{}/cakechat_api/v1/actions/get_response'.format(args.fqdn, args.port) body = {'context': args.context, 'emotion': args.emotion} response = requests.post(url, json=body) diff --git a/tools/train.py b/tools/train.py index 1980643..0c23897 100644 --- a/tools/train.py +++ b/tools/train.py @@ -1,107 +1,119 @@ import argparse import os import sys -from six.moves import map sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -from cakechat.utils.env import init_theano_env +import random +import numpy +import tensorflow as tf -init_theano_env() +from cakechat.utils.env import init_keras, try_import_horovod -from cakechat.utils.text_processing import get_processed_corpus_path, get_index_to_token_path, \ - get_index_to_condition_path, load_processed_dialogs_from_json, load_index_to_item, FileTextLinesIterator, \ - get_flatten_dialogs, ProcessedLinesIterator, get_tokens_sequence -from cakechat.utils.files_utils import is_non_empty_file +hvd = try_import_horovod() +init_keras(hvd) + +# fix random seeds for experiments reproducibility +random.seed(42) +numpy.random.seed(42) +tf.set_random_seed(42) + +from cakechat.config import BASE_CORPUS_NAME, TRAIN_CORPUS_NAME, CONTEXT_SENSITIVE_VAL_CORPUS_NAME, \ + USE_PRETRAINED_W2V_EMBEDDINGS_LAYER, S3_MODELS_BUCKET_NAME, S3_NN_MODEL_REMOTE_DIR, PREDICTION_MODE_FOR_TESTS +from cakechat.dialog_model.factory import get_reverse_model +from cakechat.dialog_model.model import CakeChatModel +from cakechat.utils.data_types import ModelParam +from cakechat.utils.dataset_loader import get_validation_data_id, get_validation_sets_names, \ + get_validation_dataset_name_to_data, get_training_dataset +from cakechat.utils.files_utils import is_non_empty_file, FileNotFoundException from cakechat.utils.logger import get_tools_logger from cakechat.utils.s3 import S3FileResolver -from cakechat.dialog_model.train import train_model -from cakechat.dialog_model.model_utils import get_w2v_embedding_matrix -from cakechat.dialog_model.model import get_nn_model -from cakechat.config import BASE_CORPUS_NAME, TRAIN_CORPUS_NAME, CONTEXT_SENSITIVE_VAL_CORPUS_NAME, \ - USE_PRETRAINED_W2V_EMBEDDINGS_LAYER, S3_MODELS_BUCKET_NAME, S3_NN_MODEL_REMOTE_DIR +from cakechat.utils.text_processing import get_processed_corpus_path, get_index_to_token_path, \ + get_index_to_condition_path, load_index_to_item +from cakechat.utils.w2v.model import get_w2v_model_id, get_w2v_model _logger = get_tools_logger(__file__) -def _look_for_saved_model(nn_model_path): - if os.path.isfile(nn_model_path): - _logger.info('\nSaved model is found:\n{}\n'.format(nn_model_path)) - else: - _logger.info('\nCould not find previously saved model: \n{}\nWill train it from scratch\n'.format(nn_model_path)) - - def _look_for_saved_files(files_paths): for f_path in files_paths: if not is_non_empty_file(f_path): - raise Exception('\nCould not find the following file or it\'s empty: {0}'.format(f_path)) - - -def _get_w2v_embedding_matrix_by_corpus_path(processed_train_corpus_path, index_to_token): - if USE_PRETRAINED_W2V_EMBEDDINGS_LAYER: - _logger.info('Getting train iterator for w2v...') - dialogs_for_w2v = load_processed_dialogs_from_json( - FileTextLinesIterator(processed_train_corpus_path), - text_field_name='text', - condition_field_name='condition') - - _logger.info('Getting text-filtered train iterator...') - train_lines_for_w2v = map(lambda x: x['text'], get_flatten_dialogs(dialogs_for_w2v)) - - _logger.info('Getting tokenized train iterator...') - tokenized_train_lines_for_w2v = ProcessedLinesIterator( - train_lines_for_w2v, processing_callbacks=[get_tokens_sequence]) - - return get_w2v_embedding_matrix(tokenized_train_lines_for_w2v, index_to_token, add_start_end=True) - else: - return None - - -def train(init_path=None, is_reverse_model=False): - processed_train_corpus_path = get_processed_corpus_path(TRAIN_CORPUS_NAME) - processed_val_corpus_path = get_processed_corpus_path(CONTEXT_SENSITIVE_VAL_CORPUS_NAME) - index_to_token_path = get_index_to_token_path(BASE_CORPUS_NAME) - index_to_condition_path = get_index_to_condition_path(BASE_CORPUS_NAME) + raise FileNotFoundException('\nCould not find the following file or it\'s empty: {0}'.format(f_path)) + + +def train(model_init_path=None, + is_reverse_model=False, + train_subset_size=None, + use_pretrained_w2v=USE_PRETRAINED_W2V_EMBEDDINGS_LAYER, + train_corpus_name=TRAIN_CORPUS_NAME, + context_sensitive_val_corpus_name=CONTEXT_SENSITIVE_VAL_CORPUS_NAME, + base_corpus_name=BASE_CORPUS_NAME, + s3_models_bucket_name=S3_MODELS_BUCKET_NAME, + s3_nn_model_remote_dir=S3_NN_MODEL_REMOTE_DIR, + prediction_mode_for_tests=PREDICTION_MODE_FOR_TESTS): + processed_train_corpus_path = get_processed_corpus_path(train_corpus_name) + processed_val_corpus_path = get_processed_corpus_path(context_sensitive_val_corpus_name) + index_to_token_path = get_index_to_token_path(base_corpus_name) + index_to_condition_path = get_index_to_condition_path(base_corpus_name) # check the existence of all necessary files before compiling the model _look_for_saved_files(files_paths=[processed_train_corpus_path, processed_val_corpus_path, index_to_token_path]) + # load essentials for building model and training index_to_token = load_index_to_item(index_to_token_path) index_to_condition = load_index_to_item(index_to_condition_path) + token_to_index = {v: k for k, v in index_to_token.items()} + condition_to_index = {v: k for k, v in index_to_condition.items()} - w2v_matrix = _get_w2v_embedding_matrix_by_corpus_path(processed_train_corpus_path, index_to_token) + training_data_param = ModelParam( + value=get_training_dataset(train_corpus_name, token_to_index, condition_to_index, is_reverse_model, + train_subset_size), + id=train_corpus_name) - # get nn_model and train it - nn_model_resolver_factory = S3FileResolver.init_resolver(bucket_name=S3_MODELS_BUCKET_NAME, - remote_dir=S3_NN_MODEL_REMOTE_DIR) + val_sets_names = get_validation_sets_names() + validation_data_param = ModelParam( + value=get_validation_dataset_name_to_data(val_sets_names, token_to_index, condition_to_index, is_reverse_model), + id=get_validation_data_id(val_sets_names)) - nn_model, _ = get_nn_model(index_to_token, index_to_condition, model_init_path=init_path, w2v_matrix=w2v_matrix, - resolver_factory=nn_model_resolver_factory, is_reverse_model=is_reverse_model) + w2v_model_param = ModelParam(value=get_w2v_model(), id=get_w2v_model_id()) if use_pretrained_w2v \ + else ModelParam(value=None, id=None) - train_model(nn_model) + model_resolver_factory = S3FileResolver.init_resolver( + bucket_name=s3_models_bucket_name, remote_dir=s3_nn_model_remote_dir) + + reverse_model = get_reverse_model(prediction_mode_for_tests) if not is_reverse_model else None + + # build CakeChatModel + cakechat_model = CakeChatModel( + index_to_token, + index_to_condition, + training_data_param=training_data_param, + validation_data_param=validation_data_param, + w2v_model_param=w2v_model_param, + model_init_path=model_init_path, + model_resolver=model_resolver_factory, + is_reverse_model=is_reverse_model, + reverse_model=reverse_model, + horovod=hvd) + + # train model + cakechat_model.train_model() def parse_args(): argparser = argparse.ArgumentParser() argparser.add_argument( - '-r', - '--reverse', - action='store_true', - help='Pass this flag if you want to train reverse model.') - + '-r', '--reverse', action='store_true', help='Pass this flag if you want to train reverse model.') argparser.add_argument( '-i', '--init_weights', - help="Path to the file with weights that should be used for the model's initialisation") + help='Path to the file with weights that should be used for the model\'s initialisation') + argparser.add_argument('-s', '--train-subset-size', action='store', type=int) return argparser.parse_args() if __name__ == '__main__': - _logger.info('THEANO_FLAGS: {}'.format(os.environ['THEANO_FLAGS'])) - if 'SLICE_TRAINSET' in os.environ: - _logger.info('Slicing trainset to the first %d entries for faster training' % int(os.environ['SLICE_TRAINSET'])) - args = parse_args() - train(init_path=args.init_weights, is_reverse_model=args.reverse) + train(model_init_path=args.init_weights, is_reverse_model=args.reverse, train_subset_size=args.train_subset_size) diff --git a/tools/train_w2v.py b/tools/train_w2v.py new file mode 100644 index 0000000..01db8d4 --- /dev/null +++ b/tools/train_w2v.py @@ -0,0 +1,29 @@ +import os +import sys + +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from cakechat.utils.text_processing import get_processed_corpus_path, load_processed_dialogs_from_json, \ + FileTextLinesIterator, get_dialog_lines_and_conditions, ProcessedLinesIterator, get_flatten_dialogs +from cakechat.utils.w2v.model import _get_w2v_model as get_w2v_model +from cakechat.config import TRAIN_CORPUS_NAME, VOCABULARY_MAX_SIZE, WORD_EMBEDDING_DIMENSION, W2V_WINDOW_SIZE, \ + USE_SKIP_GRAM + +if __name__ == '__main__': + processed_corpus_path = get_processed_corpus_path(TRAIN_CORPUS_NAME) + + dialogs = load_processed_dialogs_from_json( + FileTextLinesIterator(processed_corpus_path), text_field_name='text', condition_field_name='condition') + + training_dialogs_lines_for_w2v, _ = get_dialog_lines_and_conditions( + get_flatten_dialogs(dialogs), text_field_name='text', condition_field_name='condition') + + tokenized_training_lines = ProcessedLinesIterator(training_dialogs_lines_for_w2v, processing_callbacks=[str.split]) + + get_w2v_model( + tokenized_lines=tokenized_training_lines, + corpus_name=TRAIN_CORPUS_NAME, + voc_size=VOCABULARY_MAX_SIZE, + vec_size=WORD_EMBEDDING_DIMENSION, + window_size=W2V_WINDOW_SIZE, + skip_gram=USE_SKIP_GRAM)