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# regarding copyright ownership. The ASF licenses this file
# to you 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 argparse
import time
import math
import mxnet as mx
from mxnet import gluon, autograd
import model
import data
parser = argparse.ArgumentParser(description='MXNet Autograd PennTreeBank RNN/LSTM Language Model')
parser.add_argument('--data', type=str, default='./data/ptb.',
help='location of the data corpus')
parser.add_argument('--model', type=str, default='lstm',
help='type of recurrent net (rnn_tanh, rnn_relu, lstm, gru)')
parser.add_argument('--emsize', type=int, default=200,
help='size of word embeddings')
parser.add_argument('--nhid', type=int, default=200,
help='number of hidden units per layer')
parser.add_argument('--nlayers', type=int, default=2,
help='number of layers')
parser.add_argument('--lr', type=float, default=1.0,
help='initial learning rate')
parser.add_argument('--clip', type=float, default=0.2,
help='gradient clipping')
parser.add_argument('--epochs', type=int, default=40,
help='upper epoch limit')
parser.add_argument('--batch_size', type=int, default=32, metavar='N',
help='batch size')
parser.add_argument('--bptt', type=int, default=35,
help='sequence length')
parser.add_argument('--dropout', type=float, default=0.2,
help='dropout applied to layers (0 = no dropout)')
parser.add_argument('--tied', action='store_true',
help='tie the word embedding and softmax weights')
parser.add_argument('--cuda', action='store_true',
help='Whether to use gpu')
parser.add_argument('--log-interval', type=int, default=200, metavar='N',
help='report interval')
parser.add_argument('--save', type=str, default='model.params',
help='path to save the final model')
args = parser.parse_args()
###############################################################################
# Load data
###############################################################################
if args.cuda:
context = mx.gpu(0)
else:
context = mx.cpu(0)
corpus = data.Corpus(args.data)
def batchify(data, batch_size):
"""Reshape data into (num_example, batch_size)"""
nbatch = data.shape[0] // batch_size
data = data[:nbatch * batch_size]
data = data.reshape((batch_size, nbatch)).T
return data
train_data = batchify(corpus.train, args.batch_size).as_in_context(context)
val_data = batchify(corpus.valid, args.batch_size).as_in_context(context)
test_data = batchify(corpus.test, args.batch_size).as_in_context(context)
###############################################################################
# Build the model
###############################################################################
ntokens = len(corpus.dictionary)
model = model.RNNModel(args.model, ntokens, args.emsize, args.nhid,
args.nlayers, args.dropout, args.tied)
model.collect_params().initialize(mx.init.Xavier(), ctx=context)
trainer = gluon.Trainer(model.collect_params(), 'sgd',
{'learning_rate': args.lr,
'momentum': 0,
'wd': 0})
loss = gluon.loss.SoftmaxCrossEntropyLoss()
###############################################################################
# Training code
###############################################################################
def get_batch(source, i):
seq_len = min(args.bptt, source.shape[0] - 1 - i)
data = source[i:i+seq_len]
target = source[i+1:i+1+seq_len]
return data, target.reshape((-1,))
def detach(hidden):
if isinstance(hidden, (tuple, list)):
hidden = [i.detach() for i in hidden]
else:
hidden = hidden.detach()
return hidden
def eval(data_source):
total_L = 0.0
ntotal = 0
hidden = model.begin_state(func=mx.nd.zeros, batch_size=args.batch_size, ctx=context)
for i in range(0, data_source.shape[0] - 1, args.bptt):
data, target = get_batch(data_source, i)
output, hidden = model(data, hidden)
L = loss(output, target)
total_L += mx.nd.sum(L).asscalar()
ntotal += L.size
return total_L / ntotal
def train():
best_val = float("Inf")
for epoch in range(args.epochs):
total_L = 0.0
start_time = time.time()
hidden = model.begin_state(func=mx.nd.zeros, batch_size=args.batch_size, ctx=context)
for ibatch, i in enumerate(range(0, train_data.shape[0] - 1, args.bptt)):
data, target = get_batch(train_data, i)
hidden = detach(hidden)
with autograd.record():
output, hidden = model(data, hidden)
L = loss(output, target)
L.backward()
grads = [i.grad(context) for i in model.collect_params().values()]
# Here gradient is for the whole batch.
# So we multiply max_norm by batch_size and bptt size to balance it.
gluon.utils.clip_global_norm(grads, args.clip * args.bptt * args.batch_size)
trainer.step(args.batch_size)
total_L += mx.nd.sum(L).asscalar()
if ibatch % args.log_interval == 0 and ibatch > 0:
cur_L = total_L / args.bptt / args.batch_size / args.log_interval
print('[Epoch %d Batch %d] loss %.2f, ppl %.2f'%(
epoch, ibatch, cur_L, math.exp(cur_L)))
total_L = 0.0
val_L = eval(val_data)
print('[Epoch %d] time cost %.2fs, valid loss %.2f, valid ppl %.2f'%(
epoch, time.time()-start_time, val_L, math.exp(val_L)))
if val_L < best_val:
best_val = val_L
test_L = eval(test_data)
model.collect_params().save(args.save)
print('test loss %.2f, test ppl %.2f'%(test_L, math.exp(test_L)))
else:
args.lr = args.lr*0.25
trainer._init_optimizer('sgd',
{'learning_rate': args.lr,
'momentum': 0,
'wd': 0})
model.collect_params().load(args.save, context)
if __name__ == '__main__':
train()
model.collect_params().load(args.save, context)
test_L = eval(test_data)
print('Best test loss %.2f, test ppl %.2f'%(test_L, math.exp(test_L)))
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