'''This script is experimental. Try pre-training the CNN component of the text categorizer using a cheap language modelling-like objective. Specifically, we load pre-trained vectors (from something like word2vec, GloVe, FastText etc), and use the CNN to predict the tokens' pre-trained vectors. This isn't as easy as it sounds: we're not merely doing compression here, because heavy dropout is applied, including over the input words. This means the model must often (50% of the time) use the context in order to predict the word. To evaluate the technique, we're pre-training with the 50k texts from the IMDB corpus, and then training with only 100 labels. Note that it's a bit dirty to pre-train with the development data, but also not *so* terrible: we're not using the development labels, after all --- only the unlabelled text. ''' from __future__ import print_function, unicode_literals import plac import random import numpy import time import ujson as json from pathlib import Path import sys from collections import Counter import spacy from spacy.attrs import ID from spacy.util import minibatch, minibatch_by_words, use_gpu, compounding, ensure_path from spacy._ml import Tok2Vec, flatten, chain, zero_init, create_default_optimizer from thinc.v2v import Affine def prefer_gpu(): used = spacy.util.use_gpu(0) if used is None: return False else: import cupy.random cupy.random.seed(0) return True def load_texts(path): '''Load inputs from a jsonl file. Each line should be a dict like {"text": "..."} ''' path = ensure_path(path) with path.open('r', encoding='utf8') as file_: texts = [json.loads(line)['text'] for line in file_] random.shuffle(texts) return texts def stream_texts(): for line in sys.stdin: yield json.loads(line)['text'] def make_update(model, docs, optimizer, drop=0.): """Perform an update over a single batch of documents. docs (iterable): A batch of `Doc` objects. drop (float): The droput rate. optimizer (callable): An optimizer. RETURNS loss: A float for the loss. """ predictions, backprop = model.begin_update(docs, drop=drop) loss, gradients = get_vectors_loss(model.ops, docs, predictions) backprop(gradients, sgd=optimizer) return loss def get_vectors_loss(ops, docs, prediction): """Compute a mean-squared error loss between the documents' vectors and the prediction. Note that this is ripe for customization! We could compute the vectors in some other word, e.g. with an LSTM language model, or use some other type of objective. """ # The simplest way to implement this would be to vstack the # token.vector values, but that's a bit inefficient, especially on GPU. # Instead we fetch the index into the vectors table for each of our tokens, # and look them up all at once. This prevents data copying. ids = ops.flatten([doc.to_array(ID).ravel() for doc in docs]) target = docs[0].vocab.vectors.data[ids] d_scores = (prediction - target) / prediction.shape[0] # Don't want to return a cupy object here loss = float((d_scores**2).sum()) return loss, d_scores def create_pretraining_model(nlp, tok2vec): '''Define a network for the pretraining. We simply add an output layer onto the tok2vec input model. The tok2vec input model needs to be a model that takes a batch of Doc objects (as a list), and returns a list of arrays. Each array in the output needs to have one row per token in the doc. ''' output_size = nlp.vocab.vectors.data.shape[1] output_layer = zero_init(Affine(output_size, drop_factor=0.0)) # This is annoying, but the parser etc have the flatten step after # the tok2vec. To load the weights in cleanly, we need to match # the shape of the models' components exactly. So what we cann # "tok2vec" has to be the same set of processes as what the components do. tok2vec = chain(tok2vec, flatten) model = chain( tok2vec, output_layer ) model.tok2vec = tok2vec model.output_layer = output_layer model.begin_training([nlp.make_doc('Give it a doc to infer shapes')]) return model class ProgressTracker(object): def __init__(self, frequency=100000): self.loss = 0. self.nr_word = 0 self.words_per_epoch = Counter() self.frequency = frequency self.last_time = time.time() self.last_update = 0 def update(self, epoch, loss, docs): self.loss += loss words_in_batch = sum(len(doc) for doc in docs) self.words_per_epoch[epoch] += words_in_batch self.nr_word += words_in_batch words_since_update = self.nr_word - self.last_update if words_since_update >= self.frequency: wps = words_since_update / (time.time() - self.last_time) self.last_update = self.nr_word self.last_time = time.time() status = (epoch, self.nr_word, '%.5f' % self.loss, int(wps)) return status else: return None @plac.annotations( texts_loc=("Path to jsonl file with texts to learn from", "positional", None, str), vectors_model=("Name or path to vectors model to learn from"), output_dir=("Directory to write models each epoch", "positional", None, str), width=("Width of CNN layers", "option", "cw", int), depth=("Depth of CNN layers", "option", "cd", int), embed_rows=("Embedding rows", "option", "er", int), dropout=("Dropout", "option", "d", float), seed=("Seed for random number generators", "option", "s", float), nr_iter=("Number of iterations to pretrain", "option", "i", int), ) def pretrain(texts_loc, vectors_model, output_dir, width=128, depth=4, embed_rows=1000, dropout=0.2, nr_iter=10, seed=0): """ Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components, using an approximate language-modelling objective. Specifically, we load pre-trained vectors, and train a component like a CNN, BiLSTM, etc to predict vectors which match the pre-trained ones. The weights are saved to a directory after each epoch. You can then pass a path to one of these pre-trained weights files to the 'spacy train' command. This technique may be especially helpful if you have little labelled data. However, it's still quite experimental, so your mileage may vary. To load the weights back in during 'spacy train', you need to ensure all settings are the same between pretraining and training. The API and errors around this need some improvement. """ config = dict(locals()) output_dir = ensure_path(output_dir) random.seed(seed) numpy.random.seed(seed) if not output_dir.exists(): output_dir.mkdir() with (output_dir / 'config.json').open('w') as file_: file_.write(json.dumps(config)) has_gpu = prefer_gpu() nlp = spacy.load(vectors_model) model = create_pretraining_model(nlp, Tok2Vec(width, embed_rows, conv_depth=depth, pretrained_vectors=nlp.vocab.vectors.name, bilstm_depth=0, # Requires PyTorch. Experimental. cnn_maxout_pieces=2, # You can try setting this higher subword_features=True)) # Set to False for character models, e.g. Chinese optimizer = create_default_optimizer(model.ops) tracker = ProgressTracker() print('Epoch', '#Words', 'Loss', 'w/s') texts = stream_texts() if texts_loc == '-' else load_texts(texts_loc) for epoch in range(nr_iter): for batch in minibatch(texts, size=64): docs = [nlp.make_doc(text) for text in batch] loss = make_update(model, docs, optimizer, drop=dropout) progress = tracker.update(epoch, loss, docs) if progress: print(*progress) if texts_loc == '-' and tracker.words_per_epoch[epoch] >= 10**6: break with model.use_params(optimizer.averages): with (output_dir / ('model%d.bin' % epoch)).open('wb') as file_: file_.write(model.tok2vec.to_bytes()) with (output_dir / 'log.jsonl').open('a') as file_: file_.write(json.dumps({'nr_word': tracker.nr_word, 'loss': tracker.loss, 'epoch': epoch})) if texts_loc != '-': texts = load_texts(texts_loc)