From 7122cbe2a981077bd46bb1442eb39df93270d2de Mon Sep 17 00:00:00 2001 From: Nikolay <99244955+Kolpnick@users.noreply.github.com> Date: Thu, 19 Jan 2023 11:45:23 +0300 Subject: [PATCH 01/32] Added model that solves NLI between bot replicas --- annotators/ConveRTBasedNLI/Dockerfile | 17 ++ .../ConveRTBasedNLI/convert_annotator.py | 183 ++++++++++++++++++ annotators/ConveRTBasedNLI/encoder.py | 42 ++++ annotators/ConveRTBasedNLI/model.h5 | Bin 0 -> 11044072 bytes annotators/ConveRTBasedNLI/requirements.txt | 13 ++ annotators/ConveRTBasedNLI/server.py | 44 +++++ annotators/ConveRTBasedNLI/test0.sh | 3 + annotators/ConveRTBasedNLI/test1.sh | 3 + annotators/ConveRTBasedNLI/test2.sh | 3 + 9 files changed, 308 insertions(+) create mode 100644 annotators/ConveRTBasedNLI/Dockerfile create mode 100644 annotators/ConveRTBasedNLI/convert_annotator.py create mode 100644 annotators/ConveRTBasedNLI/encoder.py create mode 100644 annotators/ConveRTBasedNLI/model.h5 create mode 100644 annotators/ConveRTBasedNLI/requirements.txt create mode 100644 annotators/ConveRTBasedNLI/server.py create mode 100644 annotators/ConveRTBasedNLI/test0.sh create mode 100644 annotators/ConveRTBasedNLI/test1.sh create mode 100644 annotators/ConveRTBasedNLI/test2.sh diff --git a/annotators/ConveRTBasedNLI/Dockerfile b/annotators/ConveRTBasedNLI/Dockerfile new file mode 100644 index 0000000000..5fd451a006 --- /dev/null +++ b/annotators/ConveRTBasedNLI/Dockerfile @@ -0,0 +1,17 @@ +FROM python:3.8.16 + +RUN mkdir /src +WORKDIR /src + +RUN mkdir cache + +COPY . . +RUN pip install -r requirements.txt + +RUN wget 'https://github.com/davidalami/ConveRT/releases/download/1.0/nocontext_tf_model.tar.gz' +RUN mkdir convert_model +RUN tar -xf nocontext_tf_model.tar.gz --directory convert_model + +ENV TRAINED_MODEL_PATH model.h5 +ENV CACHE_DIR cache +ENV CONVERT_MODEL_PATH convert_model diff --git a/annotators/ConveRTBasedNLI/convert_annotator.py b/annotators/ConveRTBasedNLI/convert_annotator.py new file mode 100644 index 0000000000..43440f9b21 --- /dev/null +++ b/annotators/ConveRTBasedNLI/convert_annotator.py @@ -0,0 +1,183 @@ +import os +import logging +import numpy as np +import itertools + +from encoder import Encoder +import tensorflow as tf +import tensorflow_hub as tfhub +import tensorflow_datasets as tfds + + +logging.basicConfig(format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.INFO) +logger = logging.getLogger(__name__) + +TRAINED_MODEL_PATH = os.environ.get("TRAINED_MODEL_PATH", None) +CACHE_DIR = os.environ.get("CACHE_DIR", None) + + +class DataGenerator(tf.compat.v2.keras.utils.Sequence): + def __init__(self, list_examples, shuffle=True): + self.list_examples = list_examples + self.shuffle = shuffle + self.on_epoch_end() + + def __len__(self): + return len(self.list_examples) + + def __getitem__(self, index): + pos = self.indexes[index] + premise, hypothesis, label = self.__data_generation(self.list_examples[pos]) + + return [premise, hypothesis], label + + def on_epoch_end(self): + self.indexes = np.arange(len(self.list_examples)) + if self.shuffle == True: + np.random.shuffle(self.indexes) + + def __data_generation(self, file_path): + premise = np.load(file_path)['arr_0'][0] + hypothesis = np.load(file_path)['arr_0'][1] + label = np.load(file_path)['arr_1'] + label = l.reshape((len(label), 1)) + return premise, hypothesis, label + + +class ConveRTAnnotator: + def __init__(self): + self.encoder = Encoder() + + try: + self.model_path = TRAINED_MODEL_PATH + except: + self.__prepare_data() + self.__create_model() + self.__train_model() + + def __prepare_data(self): + snli_dataset = tfds.text.Snli() + snli_dataset.download_and_prepare(download_dir=CACHE_DIR) + + datasets = snli_dataset.as_dataset() + train_dataset, test_dataset, val_dataset = datasets['train'], datasets['test'], datasets['validation'] + val_dataset = val_dataset.batch(self.batch_size).prefetch(tf.data.experimental.AUTOTUNE) + test_dataset = test_dataset.batch(self.batch_size).prefetch(tf.data.experimental.AUTOTUNE) + train_dataset = train_dataset.batch(self.batch_size).prefetch(tf.data.experimental.AUTOTUNE) + + common_path = CACHE_DIR + '/data' + val_path = common_path + '/validation' + test_path = common_path + '/test' + train_path = common_path + '/train' + if not os.path.exists(val_path): + os.makedirs(val_path) + if not os.path.exists(test_path): + os.makedirs(test_path) + if not os.path.exists(train_path): + os.makedirs(train_path) + + self.__vectorize_data(val_path+'/val_', val_dataset) + self.__vectorize_data(test_path+'/test_', test_dataset) + self.__vectorize_data(train_path+'/train_', train_dataset) + + train_examples = os.listdir(train_path) + train_examples = [train_path + f_name for f_name in train_examples] + test_examples = os.listdir(test_path) + test_examples = [test_path + f_name for f_name in test_examples] + val_examples = os.listdir(val_path) + val_examples = [val_path + f_name for f_name in val_examples] + + self.train_generator = DataGenerator(train_examples) + self.test_generator = DataGenerator(test_examples) + self.val_generator = DataGenerator(val_examples) + + logger.info(f"All datasets are made.") + + def __vectorize_data(self, data_path, dataset): + counter = 0 + logger.info(f"Started making {data_path[-4:-1]} dataset.") + for example in tfds.as_numpy(dataset): + counter += 1 + premise, hypothesis, label = example['premise'], example['hypothesis'], example['label'] + + useless_pos = np.where(label == -1)[0] + premise = np.delete(premise, useless_pos) + hypothesis = np.delete(hypothesis, useless_pos) + label = np.delete(label, useless_pos) + + premise_encoded = self.encoder.encode_sentences(premise) + hypothesis_encoded = self.encoder.encode_sentences(hypothesis) + np.savez(data_path+str(counter), [premise_encoded, hypothesis_encoded], label) + + if counter % self.log_freq == 0: + logger.info(f"Prepared {counter} files.") + logger.info(f"Prepared all files.") + + def __create_model(self): + inp_p = tf.keras.layers.Input(shape=(1024)) + inp_h = tf.keras.layers.Input(shape=(1024)) + combined = tf.keras.layers.concatenate([inp_p, inp_h]) + linear_1 = tf.keras.layers.Dense(1024, activation='relu')(combined) + dropout_1 = tf.keras.layers.Dropout(0.45)(linear_1) + linear_2 = tf.keras.layers.Dense(512, activation='relu')(dropout_1) + linear_3 = tf.keras.layers.Dense(256, activation='relu')(linear_2) + output = tf.keras.layers.Dense(3, activation='softmax')(linear_3) + + self.model = tf.keras.models.Model(inputs=[inp_p, inp_h], outputs=output) + self.model.compile( + loss=tf.keras.losses.SparseCategoricalCrossentropy(), + optimizer='adam', + metrics=['accuracy']) + + def __train_model(self): + log_dir = CACHE_DIR + '/logs/' + ch_path = CACHE_DIR + '/checkpoints/cp-{epoch:04d}.ckpt' + csv_logger = tf.keras.callbacks.CSVLogger(log_dir + 'log.csv') + model_checkpoint = tf.keras.callbacks.ModelCheckpoint( + filepath=ch_path, + save_weights_only=True) + early_stopping = tf.keras.callbacks.EarlyStopping( + monitor='val_loss', + patience=10) + + history = self.model.fit(x=self.train_generator, + validation_data=self.val_generator, + use_multiprocessing=True, + workers=6, epochs=100, + callbacks=[model_checkpoint, csv_logger, early_stopping]) + + self.model.save(CACHE_DIR + '/model.h5') + self.model_path = CACHE_DIR + '/model.h5' + + def candidate_selection(self, vectorized_history, candidates, threshold=0.8): + self.model = tf.keras.models.load_model(self.model_path) + labels = {0: 'entailment', 1: 'neutral', 2: 'contradiction'} + rez_dict = dict(zip(candidates, [{'decision': labels[0], labels[0]: 1.0, labels[1]: 0.0, labels[2]: 0.0}]*len(candidates))) + if vectorized_history: + vectorized_candidates = self.__multiple_responses_encoding(candidates) + combinations = list(itertools.product(vectorized_history, vectorized_candidates)) + history_arr = [] + candidates_arr = [] + for item in combinations: + history_arr.append(item[0]) + candidates_arr.append(item[1]) + pred_rez = self.model.predict([history_arr, candidates_arr]) + for i in range(len(pred_rez)): + j = i % len(candidates) + cand = candidates[j] + row_probab = pred_rez[i] + if row_probab[2] < threshold: + row_probab[2] = -row_probab[2] + label = np.argmax(row_probab, axis=-1) + if rez_dict[cand]['decision'] != 2: + rez_dict[cand] = {'decision': labels[label], labels[0]: row_probab[0], labels[1]: row_probab[1], labels[2]: np.abs(row_probab[2])} + return rez_dict + else: + return rez_dict + + def __multiple_responses_encoding(self, responses): + return self.encoder.encode_sentences(responses) + + def response_encoding(self, response): + encoded_response = self.encoder.encode_sentences(response)[0] + return encoded_response.tolist() diff --git a/annotators/ConveRTBasedNLI/encoder.py b/annotators/ConveRTBasedNLI/encoder.py new file mode 100644 index 0000000000..0cd125c241 --- /dev/null +++ b/annotators/ConveRTBasedNLI/encoder.py @@ -0,0 +1,42 @@ +import os +import numpy as np + +import tensorflow as tf +import tensorflow_text +import tensorflow_hub as tfhub + + +tf.compat.v1.disable_eager_execution() +tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) +CONVERT_MODEL_PATH = os.environ.get("CONVERT_MODEL_PATH", None) + + +class Encoder: + def __init__(self): + self.sess = tf.compat.v1.Session() + self.text_placeholder = tf.compat.v1.placeholder(dtype=tf.string, shape=[None]) + + self.module = tfhub.Module(CONVERT_MODEL_PATH) + self.context_encoding_tensor = self.module(self.text_placeholder, signature="encode_context") + self.encoding_tensor = self.module(self.text_placeholder) + self.response_encoding_tensor = self.module(self.text_placeholder, signature="encode_response") + + self.sess.run(tf.compat.v1.tables_initializer()) + self.sess.run(tf.compat.v1.global_variables_initializer()) + + def encode_sentences(self, sentences): + vectors = self.sess.run(self.encoding_tensor, feed_dict={self.text_placeholder: sentences}) + 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