#!/usr/bin/env python3
# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
# This code is ported from the following implementation written in Torch.
# https://github.com/chainer/chainer/blob/master/examples/ptb/train_ptb_custom_loop.py
import copy
import json
import logging
import chainer
import chainer.functions as F
import chainer.links as L
import numpy as np
from chainer import link, reporter, training
from chainer.dataset import convert
# for classifier link
from chainer.functions.loss import softmax_cross_entropy
from chainer.training import extensions
import espnet.nets.chainer_backend.deterministic_embed_id as DL
from espnet.lm.lm_utils import (
MakeSymlinkToBestModel,
ParallelSentenceIterator,
compute_perplexity,
count_tokens,
read_tokens,
)
from espnet.nets.lm_interface import LMInterface
from espnet.optimizer.factory import dynamic_import_optimizer
from espnet.scheduler.chainer import ChainerScheduler
from espnet.scheduler.scheduler import dynamic_import_scheduler
from espnet.utils.deterministic_utils import set_deterministic_chainer
from espnet.utils.training.evaluator import BaseEvaluator
from espnet.utils.training.iterators import ShufflingEnabler
from espnet.utils.training.tensorboard_logger import TensorboardLogger
from espnet.utils.training.train_utils import check_early_stop, set_early_stop
# TODO(karita): reimplement RNNLM with new interface
[docs]class DefaultRNNLM(LMInterface, link.Chain):
"""Default RNNLM wrapper to compute reduce framewise loss values.
Args:
n_vocab (int): The size of the vocabulary
args (argparse.Namespace): configurations. see `add_arguments`
"""
[docs] @staticmethod
def add_arguments(parser):
parser.add_argument(
"--type",
type=str,
default="lstm",
nargs="?",
choices=["lstm", "gru"],
help="Which type of RNN to use",
)
parser.add_argument(
"--layer", "-l", type=int, default=2, help="Number of hidden layers"
)
parser.add_argument(
"--unit", "-u", type=int, default=650, help="Number of hidden units"
)
return parser
[docs]class ClassifierWithState(link.Chain):
"""A wrapper for a chainer RNNLM
:param link.Chain predictor : The RNNLM
:param function lossfun: The loss function to use
:param int/str label_key:
"""
def __init__(
self,
predictor,
lossfun=softmax_cross_entropy.softmax_cross_entropy,
label_key=-1,
):
if not (isinstance(label_key, (int, str))):
raise TypeError("label_key must be int or str, but is %s" % type(label_key))
super(ClassifierWithState, self).__init__()
self.lossfun = lossfun
self.y = None
self.loss = None
self.label_key = label_key
with self.init_scope():
self.predictor = predictor
def __call__(self, state, *args, **kwargs):
"""Computes the loss value for an input and label pair.
It also computes accuracy and stores it to the attribute.
When ``label_key`` is ``int``, the corresponding element in ``args``
is treated as ground truth labels. And when it is ``str``, the
element in ``kwargs`` is used.
The all elements of ``args`` and ``kwargs`` except the groundtruth
labels are features.
It feeds features to the predictor and compare the result
with ground truth labels.
:param state : The LM state
:param list[chainer.Variable] args : Input minibatch
:param dict[chainer.Variable] kwargs : Input minibatch
:return loss value
:rtype chainer.Variable
"""
if isinstance(self.label_key, int):
if not (-len(args) <= self.label_key < len(args)):
msg = "Label key %d is out of bounds" % self.label_key
raise ValueError(msg)
t = args[self.label_key]
if self.label_key == -1:
args = args[:-1]
else:
args = args[: self.label_key] + args[self.label_key + 1 :]
elif isinstance(self.label_key, str):
if self.label_key not in kwargs:
msg = 'Label key "%s" is not found' % self.label_key
raise ValueError(msg)
t = kwargs[self.label_key]
del kwargs[self.label_key]
self.y = None
self.loss = None
state, self.y = self.predictor(state, *args, **kwargs)
self.loss = self.lossfun(self.y, t)
return state, self.loss
[docs] def predict(self, state, x):
"""Predict log probabilities for given state and input x using the predictor
:param state : the state
:param x : the input
:return a tuple (state, log prob vector)
:rtype cupy/numpy array
"""
if hasattr(self.predictor, "normalized") and self.predictor.normalized:
return self.predictor(state, x)
else:
state, z = self.predictor(state, x)
return state, F.log_softmax(z).data
[docs] def final(self, state):
"""Predict final log probabilities for given state using the predictor
:param state : the state
:return log probability vector
:rtype cupy/numpy array
"""
if hasattr(self.predictor, "final"):
return self.predictor.final(state)
else:
return 0.0
# Definition of a recurrent net for language modeling
[docs]class RNNLM(chainer.Chain):
"""A chainer RNNLM
:param int n_vocab: The size of the vocabulary
:param int n_layers: The number of layers to create
:param int n_units: The number of units per layer
:param str type: The RNN type
"""
def __init__(self, n_vocab, n_layers, n_units, typ="lstm"):
super(RNNLM, self).__init__()
with self.init_scope():
self.embed = DL.EmbedID(n_vocab, n_units)
self.rnn = (
chainer.ChainList(
*[L.StatelessLSTM(n_units, n_units) for _ in range(n_layers)]
)
if typ == "lstm"
else chainer.ChainList(
*[L.StatelessGRU(n_units, n_units) for _ in range(n_layers)]
)
)
self.lo = L.Linear(n_units, n_vocab)
for param in self.params():
param.data[...] = np.random.uniform(-0.1, 0.1, param.data.shape)
self.n_layers = n_layers
self.n_units = n_units
self.typ = typ
def __call__(self, state, x):
if state is None:
if self.typ == "lstm":
state = {"c": [None] * self.n_layers, "h": [None] * self.n_layers}
else:
state = {"h": [None] * self.n_layers}
h = [None] * self.n_layers
emb = self.embed(x)
if self.typ == "lstm":
c = [None] * self.n_layers
c[0], h[0] = self.rnn[0](state["c"][0], state["h"][0], F.dropout(emb))
for n in range(1, self.n_layers):
c[n], h[n] = self.rnn[n](
state["c"][n], state["h"][n], F.dropout(h[n - 1])
)
state = {"c": c, "h": h}
else:
if state["h"][0] is None:
xp = self.xp
with chainer.backends.cuda.get_device_from_id(self._device_id):
state["h"][0] = chainer.Variable(
xp.zeros((emb.shape[0], self.n_units), dtype=emb.dtype)
)
h[0] = self.rnn[0](state["h"][0], F.dropout(emb))
for n in range(1, self.n_layers):
if state["h"][n] is None:
xp = self.xp
with chainer.backends.cuda.get_device_from_id(self._device_id):
state["h"][n] = chainer.Variable(
xp.zeros(
(h[n - 1].shape[0], self.n_units), dtype=h[n - 1].dtype
)
)
h[n] = self.rnn[n](state["h"][n], F.dropout(h[n - 1]))
state = {"h": h}
y = self.lo(F.dropout(h[-1]))
return state, y
[docs]class BPTTUpdater(training.updaters.StandardUpdater):
"""An updater for a chainer LM
:param chainer.dataset.Iterator train_iter : The train iterator
:param optimizer:
:param schedulers:
:param int device : The device id
:param int accum_grad :
"""
def __init__(self, train_iter, optimizer, schedulers, device, accum_grad):
super(BPTTUpdater, self).__init__(train_iter, optimizer, device=device)
self.scheduler = ChainerScheduler(schedulers, optimizer)
self.accum_grad = accum_grad
# The core part of the update routine can be customized by overriding.
[docs] def update_core(self):
# When we pass one iterator and optimizer to StandardUpdater.__init__,
# they are automatically named 'main'.
train_iter = self.get_iterator("main")
optimizer = self.get_optimizer("main")
count = 0
sum_loss = 0
optimizer.target.cleargrads() # Clear the parameter gradients
for _ in range(self.accum_grad):
# Progress the dataset iterator for sentences at each iteration.
batch = train_iter.__next__()
x, t = convert.concat_examples(batch, device=self.device, padding=(0, -1))
# Concatenate the token IDs to matrices and send them to the device
# self.converter does this job
# (it is chainer.dataset.concat_examples by default)
xp = chainer.backends.cuda.get_array_module(x)
loss = 0
state = None
batch_size, sequence_length = x.shape
for i in range(sequence_length):
# Compute the loss at this time step and accumulate it
state, loss_batch = optimizer.target(
state, chainer.Variable(x[:, i]), chainer.Variable(t[:, i])
)
non_zeros = xp.count_nonzero(x[:, i])
loss += loss_batch * non_zeros
count += int(non_zeros)
# backward
loss /= batch_size * self.accum_grad # normalized by batch size
sum_loss += float(loss.data)
loss.backward() # Backprop
loss.unchain_backward() # Truncate the graph
reporter.report({"loss": sum_loss}, optimizer.target)
reporter.report({"count": count}, optimizer.target)
# update
optimizer.update() # Update the parameters
self.scheduler.step(self.iteration)
[docs]class LMEvaluator(BaseEvaluator):
"""A custom evaluator for a chainer LM
:param chainer.dataset.Iterator val_iter : The validation iterator
:param eval_model : The model to evaluate
:param int device : The device id to use
"""
def __init__(self, val_iter, eval_model, device):
super(LMEvaluator, self).__init__(val_iter, eval_model, device=device)
[docs] def evaluate(self):
val_iter = self.get_iterator("main")
target = self.get_target("main")
loss = 0
count = 0
for batch in copy.copy(val_iter):
x, t = convert.concat_examples(batch, device=self.device, padding=(0, -1))
xp = chainer.backends.cuda.get_array_module(x)
state = None
for i in range(len(x[0])):
state, loss_batch = target(state, x[:, i], t[:, i])
non_zeros = xp.count_nonzero(x[:, i])
loss += loss_batch.data * non_zeros
count += int(non_zeros)
# report validation loss
observation = {}
with reporter.report_scope(observation):
reporter.report({"loss": float(loss / count)}, target)
return observation
[docs]def train(args):
"""Train with the given args
:param Namespace args: The program arguments
"""
# TODO(karita): support this
if args.model_module != "default":
raise NotImplementedError("chainer backend does not support --model-module")
# display chainer version
logging.info("chainer version = " + chainer.__version__)
set_deterministic_chainer(args)
# check cuda and cudnn availability
if not chainer.cuda.available:
logging.warning("cuda is not available")
if not chainer.cuda.cudnn_enabled:
logging.warning("cudnn is not available")
# get special label ids
unk = args.char_list_dict["<unk>"]
eos = args.char_list_dict["<eos>"]
# read tokens as a sequence of sentences
train = read_tokens(args.train_label, args.char_list_dict)
val = read_tokens(args.valid_label, args.char_list_dict)
# count tokens
n_train_tokens, n_train_oovs = count_tokens(train, unk)
n_val_tokens, n_val_oovs = count_tokens(val, unk)
logging.info("#vocab = " + str(args.n_vocab))
logging.info("#sentences in the training data = " + str(len(train)))
logging.info("#tokens in the training data = " + str(n_train_tokens))
logging.info(
"oov rate in the training data = %.2f %%"
% (n_train_oovs / n_train_tokens * 100)
)
logging.info("#sentences in the validation data = " + str(len(val)))
logging.info("#tokens in the validation data = " + str(n_val_tokens))
logging.info(
"oov rate in the validation data = %.2f %%" % (n_val_oovs / n_val_tokens * 100)
)
use_sortagrad = args.sortagrad == -1 or args.sortagrad > 0
# Create the dataset iterators
train_iter = ParallelSentenceIterator(
train,
args.batchsize,
max_length=args.maxlen,
sos=eos,
eos=eos,
shuffle=not use_sortagrad,
)
val_iter = ParallelSentenceIterator(
val, args.batchsize, max_length=args.maxlen, sos=eos, eos=eos, repeat=False
)
epoch_iters = int(len(train_iter.batch_indices) / args.accum_grad)
logging.info("#iterations per epoch = %d" % epoch_iters)
logging.info("#total iterations = " + str(args.epoch * epoch_iters))
# Prepare an RNNLM model
rnn = RNNLM(args.n_vocab, args.layer, args.unit, args.type)
model = ClassifierWithState(rnn)
if args.ngpu > 1:
logging.warning("currently, multi-gpu is not supported. use single gpu.")
if args.ngpu > 0:
# Make the specified GPU current
gpu_id = 0
chainer.cuda.get_device_from_id(gpu_id).use()
model.to_gpu()
else:
gpu_id = -1
# Save model conf to json
model_conf = args.outdir + "/model.json"
with open(model_conf, "wb") as f:
logging.info("writing a model config file to " + model_conf)
f.write(
json.dumps(vars(args), indent=4, ensure_ascii=False, sort_keys=True).encode(
"utf_8"
)
)
# Set up an optimizer
opt_class = dynamic_import_optimizer(args.opt, args.backend)
optimizer = opt_class.from_args(model, args)
if args.schedulers is None:
schedulers = []
else:
schedulers = [dynamic_import_scheduler(v)(k, args) for k, v in args.schedulers]
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.GradientClipping(args.gradclip))
updater = BPTTUpdater(train_iter, optimizer, schedulers, gpu_id, args.accum_grad)
trainer = training.Trainer(updater, (args.epoch, "epoch"), out=args.outdir)
trainer.extend(LMEvaluator(val_iter, model, device=gpu_id))
trainer.extend(
extensions.LogReport(
postprocess=compute_perplexity,
trigger=(args.report_interval_iters, "iteration"),
)
)
trainer.extend(
extensions.PrintReport(
["epoch", "iteration", "perplexity", "val_perplexity", "elapsed_time"]
),
trigger=(args.report_interval_iters, "iteration"),
)
trainer.extend(extensions.ProgressBar(update_interval=args.report_interval_iters))
trainer.extend(extensions.snapshot(filename="snapshot.ep.{.updater.epoch}"))
trainer.extend(extensions.snapshot_object(model, "rnnlm.model.{.updater.epoch}"))
# MEMO(Hori): wants to use MinValueTrigger, but it seems to fail in resuming
trainer.extend(MakeSymlinkToBestModel("validation/main/loss", "rnnlm.model"))
if use_sortagrad:
trainer.extend(
ShufflingEnabler([train_iter]),
trigger=(args.sortagrad if args.sortagrad != -1 else args.epoch, "epoch"),
)
if args.resume:
logging.info("resumed from %s" % args.resume)
chainer.serializers.load_npz(args.resume, trainer)
set_early_stop(trainer, args, is_lm=True)
if args.tensorboard_dir is not None and args.tensorboard_dir != "":
try:
from tensorboardX import SummaryWriter
except Exception:
logging.error("Please install tensorboardx")
raise
writer = SummaryWriter(args.tensorboard_dir)
trainer.extend(
TensorboardLogger(writer), trigger=(args.report_interval_iters, "iteration")
)
trainer.run()
check_early_stop(trainer, args.epoch)
# compute perplexity for test set
if args.test_label:
logging.info("test the best model")
chainer.serializers.load_npz(args.outdir + "/rnnlm.model.best", model)
test = read_tokens(args.test_label, args.char_list_dict)
n_test_tokens, n_test_oovs = count_tokens(test, unk)
logging.info("#sentences in the test data = " + str(len(test)))
logging.info("#tokens in the test data = " + str(n_test_tokens))
logging.info(
"oov rate in the test data = %.2f %%" % (n_test_oovs / n_test_tokens * 100)
)
test_iter = ParallelSentenceIterator(
test, args.batchsize, max_length=args.maxlen, sos=eos, eos=eos, repeat=False
)
evaluator = LMEvaluator(test_iter, model, device=gpu_id)
with chainer.using_config("train", False):
result = evaluator()
logging.info("test perplexity: " + str(np.exp(float(result["main/loss"]))))