pointer.py

"""
Licensed by Merity et al.

Copyright (c) 2017,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""

## Modified from original code! Added support for logging per token losses.

import argparse
import time
import math
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import _pickle as pickle

import data
import model

from utils import batchify, get_batch, repackage_hidden

parser = argparse.ArgumentParser(description='PyTorch PennTreeBank RNN/LSTM Language Model')
parser.add_argument('--data', type=str, default='data/penn',
                    help='location of the data corpus')
parser.add_argument('--model', type=str, default='LSTM',
                    help='type of recurrent net (LSTM, QRNN)')
parser.add_argument('--save', type=str,default='best.pt',
                    help='model to use the pointer over')
parser.add_argument('--cuda', action='store_false',
                    help='use CUDA')
parser.add_argument('--bptt', type=int, default=5000,
                    help='sequence length')
parser.add_argument('--window', type=int, default=3785,
                    help='pointer window length')
parser.add_argument('--theta', type=float, default=0.6625523432485668,
                    help='mix between uniform distribution and pointer softmax distribution over previous words')
parser.add_argument('--lambdasm', type=float, default=0.12785920428335693,
                    help='linear mix between only pointer (1) and only vocab (0) distribution')
parser.add_argument('--logfile', type=str, default='cache.pkl',
                    help='location to write per token ppl')
parser.add_argument('--use_test', action='store_true', default=False,
                    help='Run on test set')
args = parser.parse_args()

###############################################################################
# Load data
###############################################################################

corpus = data.Corpus(args.data)

eval_batch_size = 1
test_batch_size = 1
#train_data = batchify(corpus.train, args.batch_size)
val_data = batchify(corpus.valid, test_batch_size, args)
test_data = batchify(corpus.test, test_batch_size, args)

###############################################################################
# Build the model
###############################################################################

ntokens = len(corpus.dictionary)
criterion = nn.CrossEntropyLoss()

def one_hot(idx, size, cuda=True):
    a = np.zeros((1, size), np.float32)
    a[0][idx] = 1
    v = Variable(torch.from_numpy(a))
    if cuda: v = v.cuda()
    return v

def evaluate(data_source, batch_size=10, window=args.window):
    # Turn on evaluation mode which disables dropout.
    if args.model == 'QRNN': model.reset()
    model.eval()
    total_loss = 0
    save_all_losses = []
    ntokens = len(corpus.dictionary)
    hidden = model.init_hidden(batch_size)
    next_word_history = None
    pointer_history = None
    for i in range(0, data_source.size(0) - 1, args.bptt):
        if i > 0: print(i, len(data_source), math.exp(total_loss / i))
        data, targets = get_batch(data_source, i, evaluation=True, args=args)
        output, hidden, rnn_outs, _ = model(data, hidden, return_h=True)
        rnn_out = rnn_outs[-1].squeeze()
        output_flat = output.view(-1, ntokens)
        ###
        # Fill pointer history
        start_idx = len(next_word_history) if next_word_history is not None else 0
        next_word_history = torch.cat([one_hot(t.data[0], ntokens) for t in targets]) if next_word_history is None else torch.cat([next_word_history, torch.cat([one_hot(t.data[0], ntokens) for t in targets])])
        #print(next_word_history)
        pointer_history = Variable(rnn_out.data) if pointer_history is None else torch.cat([pointer_history, Variable(rnn_out.data)], dim=0)
        #print(pointer_history)
        ###
        # Built-in cross entropy
        # total_loss += len(data) * criterion(output_flat, targets).data[0]
        ###
        # Manual cross entropy
        # softmax_output_flat = torch.nn.functional.softmax(output_flat)
        # soft = torch.gather(softmax_output_flat, dim=1, index=targets.view(-1, 1))
        # entropy = -torch.log(soft)
        # total_loss += len(data) * entropy.mean().data[0]
        ###
        # Pointer manual cross entropy
        loss = 0
        softmax_output_flat = torch.nn.functional.softmax(output_flat)
        for idx, vocab_loss in enumerate(softmax_output_flat):
            p = vocab_loss
            if start_idx + idx > window:
                valid_next_word = next_word_history[start_idx + idx - window:start_idx + idx]
                valid_pointer_history = pointer_history[start_idx + idx - window:start_idx + idx]
                logits = torch.mv(valid_pointer_history, rnn_out[idx])
                theta = args.theta
                ptr_attn = torch.nn.functional.softmax(theta * logits).view(-1, 1)
                ptr_dist = (ptr_attn.expand_as(valid_next_word) * valid_next_word).sum(0).squeeze()
                lambdah = args.lambdasm
                p = lambdah * ptr_dist + (1 - lambdah) * vocab_loss
            ###
            target_loss = p[targets[idx].data]
            intermediate_loss = (-torch.log(target_loss)).data[0]
            loss += intermediate_loss
            save_all_losses.append(intermediate_loss)
        total_loss += loss / batch_size
        ###
        hidden = repackage_hidden(hidden)
        next_word_history = next_word_history[-window:]
        pointer_history = pointer_history[-window:]

    with open(args.logfile, 'wb') as f:
        pickle.dump(args.window, f)
        pickle.dump(save_all_losses, f)
        print(len(save_all_losses))

    return total_loss / len(data_source)

# Load the best saved model.
with open(args.save, 'rb') as f:
    if not args.cuda:
        model = torch.load(f, map_location=lambda storage, loc: storage)
    else:
        model = torch.load(f)
print(model)

if not args.use_test:
    # Run on val data.
    print('On validation set!!!')
    val_loss = evaluate(val_data, test_batch_size)
    print('=' * 89)
    print('| End of pointer | val loss {:5.2f} | val ppl {:8.2f}'.format(
        val_loss, math.exp(val_loss)))
    print('=' * 89)
else:
    # Run on test data.
    print('On test set!!!')
    test_loss = evaluate(test_data, test_batch_size)
    print('=' * 89)
    print('| End of pointer | test loss {:5.2f} | test ppl {:8.2f}'.format(
        test_loss, math.exp(test_loss)))
    print('=' * 89)