batchfirst_bptt.py

# Copyright 2018 The Texar Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from torchtext.data import BPTTIterator, Dataset, Batch


class BatchFirstBPTTIterator(BPTTIterator):
    """Defines an iterator for language modeling tasks that use BPTT.

    Provides contiguous streams of examples together with targets that are
    one timestep further forward, for language modeling training with
    backpropagation through time (BPTT). Expects a Dataset with a single
    example and a single field called 'text' and produces Batches with text and
    target attributes.

    All batches will have sizes [batch_size, bptt_len]

    Attributes:
        dataset: The Dataset object to load Examples from.
        batch_size: Batch size.
        bptt_len: Length of sequences for backpropagation through time.
        sort_key: A key to use for sorting examples in order to batch together
            examples with similar lengths and minimize padding. The sort_key
            provided to the Iterator constructor overrides the sort_key
            attribute of the Dataset, or defers to it if None.
        train: Whether the iterator represents a train set.
        repeat: Whether to repeat the iterator for multiple epochs.
        shuffle: Whether to shuffle examples between epochs.
        sort: Whether to sort examples according to self.sort_key.
            Note that repeat, shuffle, and sort default to train, train, and
            (not train).
        device: Device to create batches on. Use -1 for CPU and None for the
            currently active GPU device.
    """

    def __len__(self):
        return math.floor(
            (len(self.dataset[0].text) / self.batch_size - 1) / self.bptt_len)

    def __iter__(self):
        text = self.dataset[0].text
        TEXT = self.dataset.fields['text']
        TEXT.eos_token = None
        pad_num = int(math.ceil(len(text) / self.batch_size) *
                      self.batch_size - len(text))
        text = text + ([TEXT.pad_token] * pad_num)
        data = TEXT.numericalize([text], device=self.device)
        data = data.view(self.batch_size, -1).contiguous()
        dataset = Dataset(examples=self.dataset.examples,
                          fields=[('text', TEXT), ('target', TEXT)])
        while True:
            for i in range(0, len(self) * self.bptt_len, self.bptt_len):
                self.iterations += 1
                seq_len = self.bptt_len
                yield Batch.fromvars(
                    dataset, self.batch_size,
                    text=data[:, i:i + seq_len],
                    target=data[:, i + 1:i + 1 + seq_len])
            if not self.repeat:
                return