提交 b0868af5 编写于 作者: N Nicky

Simplify Machine Translation demo by using Trainer API

上级 868bdc97
......@@ -8,3 +8,4 @@ endforeach()
add_subdirectory(fit_a_line)
add_subdirectory(recognize_digits)
add_subdirectory(machine_translation)
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
# default test
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed 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 contextlib
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.framework as framework
import paddle.fluid.layers as pd
from paddle.fluid.executor import Executor
from functools import partial
import unittest
import os
dict_size = 30000
source_dict_dim = target_dict_dim = dict_size
hidden_dim = 32
word_dim = 16
batch_size = 2
max_length = 8
topk_size = 50
trg_dic_size = 10000
beam_size = 2
decoder_size = hidden_dim
def encoder(is_sparse):
# encoder
src_word_id = pd.data(
name="src_word_id", shape=[1], dtype='int64', lod_level=1)
src_embedding = pd.embedding(
input=src_word_id,
size=[dict_size, word_dim],
dtype='float32',
is_sparse=is_sparse,
param_attr=fluid.ParamAttr(name='vemb'))
fc1 = pd.fc(input=src_embedding, size=hidden_dim * 4, act='tanh')
lstm_hidden0, lstm_0 = pd.dynamic_lstm(input=fc1, size=hidden_dim * 4)
encoder_out = pd.sequence_last_step(input=lstm_hidden0)
return encoder_out
def decoder_train(context, is_sparse):
# decoder
trg_language_word = pd.data(
name="target_language_word", shape=[1], dtype='int64', lod_level=1)
trg_embedding = pd.embedding(
input=trg_language_word,
size=[dict_size, word_dim],
dtype='float32',
is_sparse=is_sparse,
param_attr=fluid.ParamAttr(name='vemb'))
rnn = pd.DynamicRNN()
with rnn.block():
current_word = rnn.step_input(trg_embedding)
pre_state = rnn.memory(init=context)
current_state = pd.fc(input=[current_word, pre_state],
size=decoder_size,
act='tanh')
current_score = pd.fc(input=current_state,
size=target_dict_dim,
act='softmax')
rnn.update_memory(pre_state, current_state)
rnn.output(current_score)
return rnn()
def decoder_decode(context, is_sparse):
init_state = context
array_len = pd.fill_constant(shape=[1], dtype='int64', value=max_length)
counter = pd.zeros(shape=[1], dtype='int64', force_cpu=True)
# fill the first element with init_state
state_array = pd.create_array('float32')
pd.array_write(init_state, array=state_array, i=counter)
# ids, scores as memory
ids_array = pd.create_array('int64')
scores_array = pd.create_array('float32')
init_ids = pd.data(name="init_ids", shape=[1], dtype="int64", lod_level=2)
init_scores = pd.data(
name="init_scores", shape=[1], dtype="float32", lod_level=2)
pd.array_write(init_ids, array=ids_array, i=counter)
pd.array_write(init_scores, array=scores_array, i=counter)
cond = pd.less_than(x=counter, y=array_len)
while_op = pd.While(cond=cond)
with while_op.block():
pre_ids = pd.array_read(array=ids_array, i=counter)
pre_state = pd.array_read(array=state_array, i=counter)
pre_score = pd.array_read(array=scores_array, i=counter)
# expand the lod of pre_state to be the same with pre_score
pre_state_expanded = pd.sequence_expand(pre_state, pre_score)
pre_ids_emb = pd.embedding(
input=pre_ids,
size=[dict_size, word_dim],
dtype='float32',
is_sparse=is_sparse)
# use rnn unit to update rnn
current_state = pd.fc(input=[pre_state_expanded, pre_ids_emb],
size=decoder_size,
act='tanh')
current_state_with_lod = pd.lod_reset(x=current_state, y=pre_score)
# use score to do beam search
current_score = pd.fc(input=current_state_with_lod,
size=target_dict_dim,
act='softmax')
topk_scores, topk_indices = pd.topk(current_score, k=topk_size)
selected_ids, selected_scores = pd.beam_search(
pre_ids, topk_indices, topk_scores, beam_size, end_id=10, level=0)
pd.increment(x=counter, value=1, in_place=True)
# update the memories
pd.array_write(current_state, array=state_array, i=counter)
pd.array_write(selected_ids, array=ids_array, i=counter)
pd.array_write(selected_scores, array=scores_array, i=counter)
pd.less_than(x=counter, y=array_len, cond=cond)
translation_ids, translation_scores = pd.beam_search_decode(
ids=ids_array, scores=scores_array)
# return init_ids, init_scores
return translation_ids, translation_scores
def set_init_lod(data, lod, place):
res = fluid.LoDTensor()
res.set(data, place)
res.set_lod(lod)
return res
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def train_program(is_sparse):
context = encoder(is_sparse)
rnn_out = decoder_train(context, is_sparse)
label = pd.data(
name="target_language_next_word", shape=[1], dtype='int64', lod_level=1)
cost = pd.cross_entropy(input=rnn_out, label=label)
avg_cost = pd.mean(cost)
return avg_cost
def train(use_cuda, is_sparse, is_local=True):
EPOCH_NUM = 1
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.wmt14.train(dict_size), buf_size=1000),
batch_size=batch_size)
feed_order = [
'src_word_id', 'target_language_word', 'target_language_next_word'
]
def event_handler(event):
if isinstance(event, fluid.EndStepEvent):
print('pass_id=' + str(event.epoch) + ' batch=' + str(event.step))
if event.step == 10:
trainer.stop()
trainer = fluid.Trainer(
train_func=partial(train_program, is_sparse),
optimizer=fluid.optimizer.Adagrad(
learning_rate=1e-4,
regularization=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.1)),
place=place)
trainer.train(
reader=train_reader,
num_epochs=EPOCH_NUM,
event_handler=event_handler,
feed_order=feed_order)
def decode_main(use_cuda, is_sparse):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
context = encoder(is_sparse)
translation_ids, translation_scores = decoder_decode(context, is_sparse)
exe = Executor(place)
exe.run(framework.default_startup_program())
init_ids_data = np.array([1 for _ in range(batch_size)], dtype='int64')
init_scores_data = np.array(
[1. for _ in range(batch_size)], dtype='float32')
init_ids_data = init_ids_data.reshape((batch_size, 1))
init_scores_data = init_scores_data.reshape((batch_size, 1))
init_lod = [i for i in range(batch_size)] + [batch_size]
init_lod = [init_lod, init_lod]
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.wmt14.train(dict_size), buf_size=1000),
batch_size=batch_size)
for _, data in enumerate(train_data()):
init_ids = set_init_lod(init_ids_data, init_lod, place)
init_scores = set_init_lod(init_scores_data, init_lod, place)
src_word_data = to_lodtensor(map(lambda x: x[0], data), place)
result_ids, result_scores = exe.run(
framework.default_main_program(),
feed={
'src_word_id': src_word_data,
'init_ids': init_ids,
'init_scores': init_scores
},
fetch_list=[translation_ids, translation_scores],
return_numpy=False)
print result_ids.lod()
break
class TestMachineTranslation(unittest.TestCase):
pass
@contextlib.contextmanager
def scope_prog_guard():
prog = fluid.Program()
startup_prog = fluid.Program()
scope = fluid.core.Scope()
with fluid.scope_guard(scope):
with fluid.program_guard(prog, startup_prog):
yield
def inject_test_train(use_cuda, is_sparse):
f_name = 'test_{0}_{1}_train'.format('cuda' if use_cuda else 'cpu', 'sparse'
if is_sparse else 'dense')
def f(*args):
with scope_prog_guard():
train(use_cuda, is_sparse)
setattr(TestMachineTranslation, f_name, f)
def inject_test_decode(use_cuda, is_sparse, decorator=None):
f_name = 'test_{0}_{1}_decode'.format('cuda'
if use_cuda else 'cpu', 'sparse'
if is_sparse else 'dense')
def f(*args):
with scope_prog_guard():
decode_main(use_cuda, is_sparse)
if decorator is not None:
f = decorator(f)
setattr(TestMachineTranslation, f_name, f)
for _use_cuda_ in (False, True):
for _is_sparse_ in (False, True):
inject_test_train(_use_cuda_, _is_sparse_)
for _use_cuda_ in (False, True):
for _is_sparse_ in (False, True):
_decorator_ = None
if _use_cuda_:
_decorator_ = unittest.skip(
reason='Beam Search does not support CUDA!')
inject_test_decode(
is_sparse=_is_sparse_, use_cuda=_use_cuda_, decorator=_decorator_)
if __name__ == '__main__':
unittest.main()
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