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mlnmt.py
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"""
Multi-way NMT setup and training loop.
"""
import logging
import os
import theano
from collections import OrderedDict
from theano import tensor
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
from blocks.extensions import Printing, FinishAfter, Timing
from blocks.model import Model
from mcg.models import EncoderDecoder, MultiEncoder, MultiDecoder
from mcg.utils import p_, get_enc_dec_ids, get_version
from mcg.sampling import Sampler
from mcg.algorithm import SGDMultiCG, MainLoopWithMultiCGnoBlocks
from mcg.extensions import (
CostMonitoringWithMultiCG, DumpWithMultiCG, LoadFromDumpMultiCG,
PrintMultiStream, LogProbComputer, IncrementalDump)
logger = logging.getLogger(__name__)
def train(config, tr_stream, logprob_stream):
trng = RandomStreams(config['seed'] if 'seed' in config else 1234)
enc_ids, dec_ids = get_enc_dec_ids(config['cgs'])
# Create Theano variables
floatX = theano.config.floatX
src_sel = tensor.matrix('src_selector', dtype=floatX)
trg_sel = tensor.matrix('trg_selector', dtype=floatX)
x = tensor.lmatrix('source')
y = tensor.lmatrix('target')
x_mask = tensor.matrix('source_mask')
y_mask = tensor.matrix('target_mask')
x_sampling = tensor.matrix('source', dtype='int64')
y_sampling = tensor.vector('target', dtype='int64')
prev_state = tensor.matrix('prev_state', dtype=floatX)
src_sel_sampling = tensor.matrix('src_selector', dtype=floatX)
trg_sel_sampling = tensor.matrix('trg_selector', dtype=floatX)
# for multi source - maximum is 10 for now
xs = [tensor.lmatrix('source%d' % i) for i in range(10)]
x_masks = [tensor.matrix('source%d_mask' % i) for i in range(10)]
xs_sampling = [tensor.matrix('source%d' % i, dtype='int64')
for i in range(10)]
# test values
"""
import numpy
theano.config.compute_test_value = 'warn'
src_sel.tag.test_value = numpy.zeros((80, 3), dtype=floatX)
trg_sel.tag.test_value = numpy.zeros((80, 1), dtype=floatX)
x.tag.test_value = numpy.random.randint(0, 10, size=(12, 80))
y.tag.test_value = numpy.random.randint(0, 10, size=(14, 80))
x_mask.tag.test_value = numpy.ones_like(x.tag.test_value).astype(floatX)
y_mask.tag.test_value = numpy.ones_like(y.tag.test_value).astype(floatX)
x_sampling.tag.test_value = numpy.random.randint(0, 10, size=(1, 1))
y_sampling.tag.test_value = numpy.random.randint(0, 10, size=(1,))
prev_state.tag.test_value = numpy.random.randn(1, 1000).astype(floatX)
src_sel_sampling.tag.test_value = numpy.zeros((1, 3), dtype=floatX)
trg_sel_sampling.tag.test_value = numpy.zeros((1, 1), dtype=floatX)
for i in range(10):
xs[i].tag.test_value = numpy.random.randint(0, 10, size=(12, 80))
x_masks[i].tag.test_value = \
numpy.ones_like(x.tag.test_value).astype(floatX)
xs_sampling[i].tag.test_value = \
numpy.random.randint(0, 10, size=(1, 1))
"""
# Create encoder-decoder architecture
enc_dec = EncoderDecoder(
encoder=MultiEncoder(enc_ids=enc_ids, **config),
decoder=MultiDecoder(**config))
# Build training computational graphs
probs, opt_rets = enc_dec.build_models(
x, x_mask, y, y_mask, src_sel, trg_sel, xs=xs, x_masks=x_masks,
trng=trng)
# Get costs
costs = enc_dec.get_costs(probs, y, y_mask,
decay_cs=config.get('decay_c', None),
opt_rets=opt_rets)
# Computation graphs
cgs = enc_dec.get_computational_graphs(costs)
# Build sampling models
f_inits, f_nexts, f_next_states = enc_dec.build_sampling_models(
x_sampling, y_sampling, src_sel_sampling, trg_sel_sampling, prev_state,
trng=trng, xs=xs_sampling)
# Some printing
enc_dec.print_params(cgs)
# Get training parameters with optional excludes
training_params, excluded_params = enc_dec.get_training_params(
cgs, exclude_encs=config['exclude_encs'],
additional_excludes=config['additional_excludes'],
readout_only=config.get('readout_only', None),
train_shared=config.get('train_shared', None))
# Some more printing
enc_dec.print_training_params(cgs, training_params)
# Set up training algorithm
algorithm = SGDMultiCG(
costs=costs, tparams=training_params, drop_input=config['drop_input'],
step_rule=config['step_rule'], learning_rate=config['learning_rate'],
clip_c=config['step_clipping'],
step_rule_kwargs=config.get('step_rule_kwargs', {}))
# Set up training model
training_models = OrderedDict()
for k, v in costs.iteritems():
training_models[k] = Model(costs[k])
# Set extensions
extensions = [
Timing(after_batch=True),
FinishAfter(after_n_batches=config['finish_after']),
CostMonitoringWithMultiCG(after_batch=True),
Printing(after_batch=True),
PrintMultiStream(after_batch=True),
DumpWithMultiCG(saveto=config['saveto'],
save_accumulators=config['save_accumulators'],
every_n_batches=config['save_freq'],
no_blocks=True)]
# Reload model if necessary
if config['reload'] and os.path.exists(config['saveto']):
extensions.append(
LoadFromDumpMultiCG(saveto=config['saveto'],
load_accumulators=config['load_accumulators'],
no_blocks=True))
# Add sampling to computational graphs
for i, (cg_name, cg) in enumerate(cgs.iteritems()):
eid, did = p_(cg_name)
if config['hook_samples'] > 0:
extensions.append(Sampler(
f_init=f_inits[cg_name], f_next=f_nexts[cg_name],
data_stream=tr_stream, num_samples=config['hook_samples'],
src_eos_idx=config['src_eos_idxs'][eid],
trg_eos_idx=config['trg_eos_idxs'][did],
enc_id=eid, dec_id=did,
every_n_batches=config['sampling_freq'],
cond_init_trg=config.get('cond_init_trg', False),
f_next_state=f_next_states.get(cg_name, None)))
# Save parameters incrementally without overwriting
if config.get('incremental_dump', False):
extensions.append(
IncrementalDump(saveto=config['saveto'],
burnin=config['val_burn_in'],
every_n_batches=config['save_freq']))
# Compute log probability on dev set
if 'log_prob_freq' in config:
extensions.append(
LogProbComputer(
cgs=config['cgs'],
f_log_probs=enc_dec.build_f_log_probs(
probs, x, x_mask, y, y_mask, src_sel, trg_sel,
xs=xs, x_masks=x_masks),
streams=logprob_stream,
every_n_batches=config['log_prob_freq']))
# Initialize main loop
main_loop = MainLoopWithMultiCGnoBlocks(
models=training_models,
algorithm=algorithm,
data_stream=tr_stream,
extensions=extensions,
num_encs=config['num_encs'],
num_decs=config['num_decs'])
# Train!
main_loop.run()
# Be patient, after a month :-)
print 'done'