#!/usr/bin/env python3
# Copyright 2018-present, HKUST-KnowComp.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Document Reader model"""
import torch
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
import logging
import copy
from torch.autograd import Variable
from config import override_model_args
from r_net import R_Net
from rnn_reader import RnnDocReader
from m_reader import MnemonicReader
from data import Dictionary
logger = logging.getLogger(__name__)
class DocReader(object):
"""High level model that handles intializing the underlying network
architecture, saving, updating examples, and predicting examples.
"""
# --------------------------------------------------------------------------
# Initialization
# --------------------------------------------------------------------------
def __init__(self, args, word_dict, char_dict, feature_dict,
state_dict=None, normalize=True):
# Book-keeping.
self.args = args
self.word_dict = word_dict
self.char_dict = char_dict
self.args.vocab_size = len(word_dict)
self.args.char_size = len(char_dict)
self.feature_dict = feature_dict
self.args.num_features = len(feature_dict)
self.updates = 0
self.use_cuda = False
self.parallel = False
# Building network. If normalize if false, scores are not normalized
# 0-1 per paragraph (no softmax).
if args.model_type == 'rnn':
self.network = RnnDocReader(args, normalize)
elif args.model_type == 'r_net':
self.network = R_Net(args, normalize)
elif args.model_type == 'mnemonic':
self.network = MnemonicReader(args, normalize)
else:
raise RuntimeError('Unsupported model: %s' % args.model_type)
# Load saved state
if state_dict:
# Load buffer separately
if 'fixed_embedding' in state_dict:
fixed_embedding = state_dict.pop('fixed_embedding')
self.network.load_state_dict(state_dict)
self.network.register_buffer('fixed_embedding', fixed_embedding)
else:
self.network.load_state_dict(state_dict)
def expand_dictionary(self, words):
"""Add words to the DocReader dictionary if they do not exist. The
underlying embedding matrix is also expanded (with random embeddings).
Args:
words: iterable of tokens to add to the dictionary.
Output:
added: set of tokens that were added.
"""
to_add = {self.word_dict.normalize(w) for w in words
if w not in self.word_dict}
# Add words to dictionary and expand embedding layer
if len(to_add) > 0:
logger.info('Adding %d new words to dictionary...' % len(to_add))
for w in to_add:
self.word_dict.add(w)
self.args.vocab_size = len(self.word_dict)
logger.info('New vocab size: %d' % len(self.word_dict))
old_embedding = self.network.embedding.weight.data
self.network.embedding = torch.nn.Embedding(self.args.vocab_size,
self.args.embedding_dim,
padding_idx=0)
new_embedding = self.network.embedding.weight.data
new_embedding[:old_embedding.size(0)] = old_embedding
# Return added words
return to_add
def expand_char_dictionary(self, chars):
"""Add chars to the DocReader dictionary if they do not exist. The
underlying embedding matrix is also expanded (with random embeddings).
Args:
chars: iterable of tokens to add to the dictionary.
Output:
added: set of tokens that were added.
"""
to_add = {self.char_dict.normalize(w) for w in chars
if w not in self.char_dict}
# Add chars to dictionary and expand embedding layer
if len(to_add) > 0:
logger.info('Adding %d new chars to dictionary...' % len(to_add))
for w in to_add:
self.char_dict.add(w)
self.args.char_size = len(self.char_dict)
logger.info('New char size: %d' % len(self.char_dict))
old_char_embedding = self.network.char_embedding.weight.data
self.network.char_embedding = torch.nn.Embedding(self.args.char_size,
self.args.char_embedding_dim,
padding_idx=0)
new_char_embedding = self.network.char_embedding.weight.data
new_char_embedding[:old_char_embedding.size(0)] = old_char_embedding
# Return added chars
return to_add
def load_embeddings(self, words, embedding_file):
"""Load pretrained embeddings for a given list of words, if they exist.
Args:
words: iterable of tokens. Only those that are indexed in the
dictionary are kept.
embedding_file: path to text file of embeddings, space separated.
"""
words = {w for w in words if w in self.word_dict}
logger.info('Loading pre-trained embeddings for %d words from %s' %
(len(words), embedding_file))
embedding = self.network.embedding.weight.data
# When normalized, some words are duplicated. (Average the embeddings).
vec_counts = {}
with open(embedding_file) as f:
for line in f:
parsed = line.rstrip().split(' ')
assert(len(parsed) == embedding.size(1) + 1)
w = self.word_dict.normalize(parsed[0])
if w in words:
vec = torch.Tensor([float(i) for i in parsed[1:]])
if w not in vec_counts:
vec_counts[w] = 1
embedding[self.word_dict[w]].copy_(vec)
else:
logging.warning(
'WARN: Duplicate embedding found for %s' % w
)
vec_counts[w] = vec_counts[w] + 1
embedding[self.word_dict[w]].add_(vec)
for w, c in vec_counts.items():
embedding[self.word_dict[w]].div_(c)
logger.info('Loaded %d embeddings (%.2f%%)' %
(len(vec_counts), 100 * len(vec_counts) / len(words)))
def load_char_embeddings(self, chars, char_embedding_file):
"""Load pretrained embeddings for a given list of chars, if they exist.
Args:
chars: iterable of tokens. Only those that are indexed in the
dictionary are kept.
char_embedding_file: path to text file of embeddings, space separated.
"""
chars = {w for w in chars if w in self.char_dict}
logger.info('Loading pre-trained embeddings for %d chars from %s' %
(len(chars), char_embedding_file))
char_embedding = self.network.char_embedding.weight.data
# When normalized, some chars are duplicated. (Average the embeddings).
vec_counts = {}
with open(char_embedding_file) as f:
for line in f:
parsed = line.rstrip().split(' ')
assert(len(parsed) == char_embedding.size(1) + 1)
w = self.char_dict.normalize(parsed[0])
if w in chars:
vec = torch.Tensor([float(i) for i in parsed[1:]])
if w not in vec_counts:
vec_counts[w] = 1
char_embedding[self.char_dict[w]].copy_(vec)
else:
logging.warning(
'WARN: Duplicate char embedding found for %s' % w
)
vec_counts[w] = vec_counts[w] + 1
char_embedding[self.char_dict[w]].add_(vec)
for w, c in vec_counts.items():
char_embedding[self.char_dict[w]].div_(c)
logger.info('Loaded %d char embeddings (%.2f%%)' %
(len(vec_counts), 100 * len(vec_counts) / len(chars)))
def tune_embeddings(self, words):
"""Unfix the embeddings of a list of words. This is only relevant if
only some of the embeddings are being tuned (tune_partial = N).
Shuffles the N specified words to the front of the dictionary, and saves
the original vectors of the other N + 1:vocab words in a fixed buffer.
Args:
words: iterable of tokens contained in dictionary.
"""
words = {w for w in words if w in self.word_dict}
if len(words) == 0:
logger.warning('Tried to tune embeddings, but no words given!')
return
if len(words) == len(self.word_dict):
logger.warning('Tuning ALL embeddings in dictionary')
return
# Shuffle words and vectors
embedding = self.network.embedding.weight.data
for idx, swap_word in enumerate(words, self.word_dict.START):
# Get current word + embedding for this index
curr_word = self.word_dict[idx]
curr_emb = embedding[idx].clone()
old_idx = self.word_dict[swap_word]
# Swap embeddings + dictionary indices
embedding[idx].copy_(embedding[old_idx])
embedding[old_idx].copy_(curr_emb)
self.word_dict[swap_word] = idx
self.word_dict[idx] = swap_word
self.word_dict[curr_word] = old_idx
self.word_dict[old_idx] = curr_word
# Save the original, fixed embeddings
self.network.register_buffer(
'fixed_embedding', embedding[idx + 1:].clone()
)
def init_optimizer(self, state_dict=None):
"""Initialize an optimizer for the free parameters of the network.
Args:
state_dict: network parameters
"""
if self.args.fix_embeddings:
for p in self.network.embedding.parameters():
p.requires_grad = False
parameters = [p for p in self.network.parameters() if p.requires_grad]
if self.args.optimizer == 'sgd':
self.optimizer = optim.SGD(parameters, lr=self.args.learning_rate,
momentum=self.args.momentum,
weight_decay=self.args.weight_decay)
elif self.args.optimizer == 'adamax':
self.optimizer = optim.Adamax(parameters,
weight_decay=self.args.weight_decay)
elif self.args.optimizer == 'adadelta':
self.optimizer = optim.Adadelta(parameters, lr=self.args.learning_rate,
rho=self.args.rho, eps=self.args.eps,
weight_decay=self.args.weight_decay)
else:
raise RuntimeError('Unsupported optimizer: %s' %
self.args.optimizer)
# --------------------------------------------------------------------------
# Learning
# --------------------------------------------------------------------------
def update(self, ex):
"""Forward a batch of examples; step the optimizer to update weights."""
if not self.optimizer:
raise RuntimeError('No optimizer set.')
# Train mode
self.network.train()
# Transfer to GPU
if self.use_cuda:
inputs = [e if e is None else Variable(e.cuda(async=True)) for e in ex[:-3]]
target_s = Variable(ex[-3].cuda(async=True))
target_e = Variable(ex[-2].cuda(async=True))
else:
inputs = [e if e is None else Variable(e) for e in ex[:-3]]
target_s = Variable(ex[-3])
target_e = Variable(ex[-2])
# Run forward
score_s, score_e = self.network(*inputs)
# Compute loss and accuracies
loss = F.nll_loss(score_s, target_s) + F.nll_loss(score_e, target_e)
# Clear gradients and run backward
self.optimizer.zero_grad()
loss.backward()
# Clip gradients
torch.nn.utils.clip_grad_norm(self.network.parameters(),
self.args.grad_clipping)
# Update parameters
self.optimizer.step()
self.updates += 1
# Reset any partially fixed parameters (e.g. rare words)
self.reset_parameters()
return loss.data[0], ex[0].size(0)
def reset_parameters(self):
"""Reset any partially fixed parameters to original states."""
# Reset fixed embeddings to original value
if self.args.tune_partial > 0:
# Embeddings to fix are indexed after the special + N tuned words
offset = self.args.tune_partial + self.word_dict.START
if self.parallel:
embedding = self.network.module.embedding.weight.data
fixed_embedding = self.network.module.fixed_embedding
else:
embedding = self.network.embedding.weight.data
fixed_embedding = self.network.fixed_embedding
if offset < embedding.size(0):
embedding[offset:] = fixed_embedding
# --------------------------------------------------------------------------
# Prediction
# --------------------------------------------------------------------------
def predict(self, ex, candidates=None, top_n=1, async_pool=None):
"""Forward a batch of examples only to get predictions.
Args:
ex: the batch
candidates: batch * variable length list of string answer options.
The model will only consider exact spans contained in this list.
top_n: Number of predictions to return per batch element.
async_pool: If provided, non-gpu post-processing will be offloaded
to this CPU process pool.
Output:
pred_s: batch * top_n predicted start indices
pred_e: batch * top_n predicted end indices
pred_score: batch * top_n prediction scores
If async_pool is given, these will be AsyncResult handles.
"""
# Eval mode
self.network.eval()
# Transfer to GPU
if self.use_cuda:
inputs = [e if e is None else
Variable(e.cuda(async=True), volatile=True)
for e in ex[:8]]
else:
inputs = [e if e is None else Variable(e, volatile=True)
for e in ex[:8]]
# Run forward
score_s, score_e = self.network(*inputs)
del inputs
# Decode predictions
score_s = score_s.data.cpu()
score_e = score_e.data.cpu()
if candidates:
args = (score_s, score_e, candidates, top_n, self.args.max_len)
if async_pool:
return async_pool.apply_async(self.decode_candidates, args)
else:
return self.decode_candidates(*args)
else:
args = (score_s, score_e, top_n, self.args.max_len)
if async_pool:
return async_pool.apply_async(self.decode, args)
else:
return self.decode(*args)
@staticmethod
def decode(score_s, score_e, top_n=1, max_len=None):
"""Take argmax of constrained score_s * score_e.
Args:
score_s: independent start predictions
score_e: independent end predictions
top_n: number of top scored pairs to take
max_len: max span length to consider
"""
pred_s = []
pred_e = []
pred_score = []
max_len = max_len or score_s.size(1)
for i in range(score_s.size(0)):
# Outer product of scores to get full p_s * p_e matrix
scores = torch.ger(score_s[i], score_e[i])
# Zero out negative length and over-length span scores
scores.triu_().tril_(max_len - 1)
# Take argmax or top n
scores = scores.numpy()
scores_flat = scores.flatten()
if top_n == 1:
idx_sort = [np.argmax(scores_flat)]
elif len(scores_flat) < top_n:
idx_sort = np.argsort(-scores_flat)
else:
idx = np.argpartition(-scores_flat, top_n)[0:top_n]
idx_sort = idx[np.argsort(-scores_flat[idx])]
s_idx, e_idx = np.unravel_index(idx_sort, scores.shape)
pred_s.append(s_idx)
pred_e.append(e_idx)
pred_score.append(scores_flat[idx_sort])
del score_s, score_e
return pred_s, pred_e, pred_score
@staticmethod
def decode_candidates(score_s, score_e, candidates, top_n=1, max_len=None):
"""Take argmax of constrained score_s * score_e. Except only consider
spans that are in the candidates list.
"""
pred_s = []
pred_e = []
pred_score = []
for i in range(score_s.size(0)):
# Extract original tokens stored with candidates
tokens = candidates[i]['input']
cands = candidates[i]['cands']
if not cands:
# try getting from globals? (multiprocessing in pipeline mode)
from ..pipeline.wrmcqa import PROCESS_CANDS
cands = PROCESS_CANDS
if not cands:
raise RuntimeError('No candidates given.')
# Score all valid candidates found in text.
# Brute force get all ngrams and compare against the candidate list.
max_len = max_len or len(tokens)
scores, s_idx, e_idx = [], [], []
for s, e in tokens.ngrams(n=max_len, as_strings=False):
span = tokens.slice(s, e).untokenize()
if span in cands or span.lower() in cands:
# Match! Record its score.
scores.append(score_s[i][s] * score_e[i][e - 1])
s_idx.append(s)
e_idx.append(e - 1)
if len(scores) == 0:
# No candidates present
pred_s.append([])
pred_e.append([])
pred_score.append([])
else:
# Rank found candidates
scores = np.array(scores)
s_idx = np.array(s_idx)
e_idx = np.array(e_idx)
idx_sort = np.argsort(-scores)[0:top_n]
pred_s.append(s_idx[idx_sort])
pred_e.append(e_idx[idx_sort])
pred_score.append(scores[idx_sort])
del score_s, score_e, candidates
return pred_s, pred_e, pred_score
# --------------------------------------------------------------------------
# Saving and loading
# --------------------------------------------------------------------------
def save(self, filename):
state_dict = copy.copy(self.network.state_dict())
if 'fixed_embedding' in state_dict:
state_dict.pop('fixed_embedding')
params = {
'state_dict': state_dict,
'word_dict': self.word_dict,
'char_dict': self.char_dict,
'feature_dict': self.feature_dict,
'args': self.args,
}
try:
torch.save(params, filename)
except BaseException:
logger.warning('WARN: Saving failed... continuing anyway.')
def checkpoint(self, filename, epoch):
params = {
'state_dict': self.network.state_dict(),
'word_dict': self.word_dict,
'char_dict': self.char_dict,
'feature_dict': self.feature_dict,
'args': self.args,
'epoch': epoch,
'optimizer': self.optimizer.state_dict(),
}
try:
torch.save(params, filename)
except BaseException:
logger.warning('WARN: Saving failed... continuing anyway.')
@staticmethod
def load(filename, new_args=None, normalize=True):
logger.info('Loading model %s' % filename)
saved_params = torch.load(
filename, map_location=lambda storage, loc: storage
)
word_dict = saved_params['word_dict']
try:
char_dict = saved_params['char_dict']
except KeyError as e:
char_dict = Dictionary()
feature_dict = saved_params['feature_dict']
state_dict = saved_params['state_dict']
args = saved_params['args']
if new_args:
args = override_model_args(args, new_args)
return DocReader(args, word_dict, char_dict, feature_dict, state_dict, normalize)
@staticmethod
def load_checkpoint(filename, normalize=True):
logger.info('Loading model %s' % filename)
saved_params = torch.load(
filename, map_location=lambda storage, loc: storage
)
word_dict = saved_params['word_dict']
char_dict = saved_params['char_dict']
feature_dict = saved_params['feature_dict']
state_dict = saved_params['state_dict']
epoch = saved_params['epoch']
optimizer = saved_params['optimizer']
args = saved_params['args']
model = DocReader(args, word_dict, char_dict, feature_dict, state_dict, normalize)
model.init_optimizer(optimizer)
return model, epoch
# --------------------------------------------------------------------------
# Runtime
# --------------------------------------------------------------------------
def cuda(self):
self.use_cuda = True
self.network = self.network.cuda()
def cpu(self):
self.use_cuda = False
self.network = self.network.cpu()
def parallelize(self):
"""Use data parallel to copy the model across several gpus.
This will take all gpus visible with CUDA_VISIBLE_DEVICES.
"""
self.parallel = True
self.network = torch.nn.DataParallel(self.network)
