未验证 提交 e7d44a20 编写于 作者: Q Qiao Longfei 提交者: GitHub

Nmt model (#7340)

neural machine translation model support beam search with while op
上级 d8b923ab
......@@ -22,7 +22,7 @@ The current `LoDTensor` is designed to store levels of variable-length sequences
The integers in each level represent the begin and end (not inclusive) offset of a sequence **in the underlying tensor**,
let's call this format the **absolute-offset LoD** for clarity.
The relative-offset LoD can retrieve any sequence very quickly but fails to represent empty sequences, for example, a two-level LoD is as follows
The absolute-offset LoD can retrieve any sequence very quickly but fails to represent empty sequences, for example, a two-level LoD is as follows
```python
[[0, 3, 9]
[0, 2, 3, 3, 3, 9]]
......@@ -119,7 +119,7 @@ def generate():
encoder_ctx_expanded = pd.lod_expand(encoder_ctx, target_word)
decoder_input = pd.fc(
act=pd.activation.Linear(),
input=[target_word, encoder_ctx],
input=[target_word, encoder_ctx_expanded],
size=3 * decoder_dim)
gru_out, cur_mem = pd.gru_step(
decoder_input, mem=decoder_mem, size=decoder_dim)
......
......@@ -116,8 +116,9 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
for (auto& op_desc : block.AllOps()) {
auto op = paddle::framework::OpRegistry::CreateOp(*op_desc);
VLOG(3) << op->DebugStringEx(local_scope);
VLOG(4) << op->DebugStringEx(local_scope);
op->Run(*local_scope, place_);
VLOG(3) << op->DebugStringEx(local_scope);
if (FLAGS_do_memory_benchmark) {
VLOG(2) << "Memory used after operator " + op->Type() + " running: "
<< memory::memory_usage(place_);
......
......@@ -107,9 +107,10 @@ LoD ToAbsOffset(const LoD &in) {
// the lowest level stores relative offsets
if (in.empty() || in.size() == 1) return in;
LoD result = in;
for (int level = result.size() - 2; level >= 0; level--) {
for (auto &ele : result[level]) {
ele = result[level + 1][ele];
for (auto level = static_cast<int>(in.size() - 2); level >= 0; level--) {
for (size_t i = 0; i < in[level].size(); ++i) {
size_t index = in[level][i];
result[level][i] = result[level + 1][index];
}
}
return result;
......
......@@ -24,8 +24,18 @@ namespace operators {
void BeamSearch::operator()(const framework::LoDTensor &pre_ids,
framework::LoDTensor *selected_ids,
framework::LoDTensor *selected_scores) {
auto abs_lod = framework::ToAbsOffset(ids_->lod());
auto &high_level = abs_lod[lod_level_];
auto items = SelectTopBeamSizeItems();
auto selected_items = ToMap(items);
auto selected_items = ToMap(items, high_level.back());
VLOG(3) << "selected_items:";
for (size_t i = 0; i < selected_items.size(); ++i) {
VLOG(3) << "offset:" << i;
for (auto &item : selected_items[i]) {
VLOG(3) << ItemToString(item);
}
}
PruneEndidCandidates(pre_ids, &selected_items);
// calculate the output tensor's height
size_t num_instances = std::accumulate(
......@@ -63,11 +73,12 @@ void BeamSearch::operator()(const framework::LoDTensor &pre_ids,
low_level.push_back(low_offset);
// fill lod
auto abs_lod = framework::ToAbsOffset(ids_->lod());
auto &high_level = abs_lod[lod_level_];
framework::LoD lod(2);
lod[0].assign(high_level.begin(), high_level.end());
lod[1].assign(low_level.begin(), low_level.end());
if (!framework::CheckLoD(lod)) {
PADDLE_THROW("lod %s is not right", framework::LoDToString(lod));
}
selected_ids->set_lod(lod);
selected_scores->set_lod(lod);
}
......@@ -90,13 +101,11 @@ int BeamSearch::PruneEndidCandidates(const framework::LoDTensor &pre_ids,
}
std::vector<std::vector<BeamSearch::Item>> BeamSearch::ToMap(
const std::vector<std::vector<Item>> &items) {
const std::vector<std::vector<Item>> &items, size_t element_num) {
std::vector<std::vector<Item>> result;
result.resize(element_num);
for (auto &entries : items) {
for (const auto &item : entries) {
if (item.offset >= result.size()) {
result.resize(item.offset + 1);
}
result[item.offset].push_back(item);
}
}
......@@ -122,6 +131,14 @@ BeamSearch::SelectTopBeamSizeItems() {
}
result.emplace_back(items);
}
VLOG(3) << "SelectTopBeamSizeItems result size " << result.size();
for (auto &items : result) {
VLOG(3) << "item set:";
for (auto &item : items) {
VLOG(3) << ItemToString(item);
}
}
return result;
}
......@@ -159,6 +176,22 @@ bool BeamSearch::NextItemSet(std::vector<BeamSearch::Item> *items) {
return true;
}
std::ostream &operator<<(std::ostream &os, const BeamSearch::Item &item) {
os << "{";
os << "offset: " << item.offset << ", ";
os << "id: " << item.id << ", ";
os << "score: " << item.score << "";
os << "}";
return os;
}
std::string ItemToString(const BeamSearch::Item &item) {
std::ostringstream stream;
stream << item;
return stream.str();
}
class BeamSearchProtoAndCheckerMaker
: public framework::OpProtoAndCheckerMaker {
public:
......@@ -186,8 +219,40 @@ class BeamSearchProtoAndCheckerMaker
}
};
class BeamSearchInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
for (const std::string &arg :
std::vector<std::string>({"pre_ids", "ids", "scores"})) {
PADDLE_ENFORCE(context->HasInput(arg),
"BeamSearch need input argument '%s'", arg);
}
for (const std::string &arg :
std::vector<std::string>({"selected_ids", "selected_scores"})) {
PADDLE_ENFORCE(context->HasOutput(arg),
"BeamSearch need output argument '%s'", arg);
}
}
};
class BeamSearchInferVarType : public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
for (auto &o : op_desc.Output("selected_ids")) {
block->Var(o)->SetType(framework::proto::VarDesc::LOD_TENSOR);
}
for (auto &o : op_desc.Output("selected_scores")) {
block->Var(o)->SetType(framework::proto::VarDesc::LOD_TENSOR);
}
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP_WITHOUT_GRADIENT(beam_search, paddle::operators::BeamSearchOp,
paddle::operators::BeamSearchProtoAndCheckerMaker);
REGISTER_OPERATOR(beam_search, paddle::operators::BeamSearchOp,
paddle::operators::BeamSearchProtoAndCheckerMaker,
paddle::operators::BeamSearchInferShape,
paddle::operators::BeamSearchInferVarType,
paddle::framework::EmptyGradOpMaker);
......@@ -136,8 +136,6 @@ class BeamSearch {
void operator()(const framework::LoDTensor& pre_ids,
framework::LoDTensor* selected_ids,
framework::LoDTensor* selected_scores);
protected:
/*
* The basic items help to sort.
*/
......@@ -155,6 +153,7 @@ class BeamSearch {
score_t score;
};
protected:
/*
* Delete all the records that follows the end token.
*/
......@@ -166,7 +165,7 @@ class BeamSearch {
* NOTE low performance
*/
std::vector<std::vector<Item>> ToMap(
const std::vector<std::vector<Item>>& inputs);
const std::vector<std::vector<Item>>& inputs, size_t element_num);
/*
* For each source, select top beam_size records.
......@@ -187,6 +186,10 @@ class BeamSearch {
int end_id_{0};
};
std::ostream& operator<<(std::ostream& os, const BeamSearch::Item& item);
std::string ItemToString(const BeamSearch::Item& item);
class BeamSearchOp : public framework::OperatorBase {
public:
BeamSearchOp(const std::string& type,
......@@ -203,7 +206,6 @@ class BeamSearchOp : public framework::OperatorBase {
void Run(const framework::Scope& scope,
const platform::Place& dev_place) const override {
LOG(INFO) << "run beam search op";
auto ids_var = scope.FindVar(Input("ids"));
auto scores_var = scope.FindVar(Input("scores"));
auto pre_ids_var = scope.FindVar(Input("pre_ids"));
......@@ -217,10 +219,8 @@ class BeamSearchOp : public framework::OperatorBase {
size_t level = Attr<int>("level");
size_t beam_size = Attr<int>("beam_size");
int end_id = Attr<int>("end_id");
LOG(INFO) << "init beam search";
BeamSearch alg(ids, scores, level, beam_size, end_id);
LOG(INFO) << "after beam search";
auto selected_ids_var = scope.FindVar(Output("selected_ids"));
auto selected_scores_var = scope.FindVar(Output("selected_scores"));
PADDLE_ENFORCE_NOT_NULL(selected_ids_var);
......@@ -229,9 +229,7 @@ class BeamSearchOp : public framework::OperatorBase {
*selected_ids_var->GetMutable<framework::LoDTensor>();
auto& selected_scores_tensor =
*selected_scores_var->GetMutable<framework::LoDTensor>();
LOG(INFO) << "run beam search";
alg(pre_ids, &selected_ids_tensor, &selected_scores_tensor);
LOG(INFO) << "finish beam search";
}
};
......
......@@ -32,6 +32,7 @@ class SequenceExpandKernel : public framework::OpKernel<T> {
const T* x_data = x->data<T>();
auto x_dims = x->dims();
auto* y = context.Input<LoDTensor>("Y");
PADDLE_ENFORCE(!y->lod().empty(), "y should have lod");
PADDLE_ENFORCE_EQ(static_cast<size_t>(x_dims[0]),
y->lod().back().size() - 1,
"The size of last lod level in Input(Y)"
......
......@@ -22,6 +22,7 @@ namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
......@@ -33,9 +34,9 @@ class TopkKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
// Get the top k elements of each row of input tensor
// FIXME: only deal with matrix(2d tensor).
auto* input = ctx.Input<Tensor>("X");
auto* output = ctx.Output<Tensor>("Out");
auto* indices = ctx.Output<Tensor>("Indices");
auto* input = ctx.Input<LoDTensor>("X");
auto* output = ctx.Output<LoDTensor>("Out");
auto* indices = ctx.Output<LoDTensor>("Indices");
// k is determined by Attr
const size_t k = static_cast<int>(ctx.Attr<int>("k"));
......
......@@ -100,7 +100,8 @@ class LayerHelper(object):
if dtype is None:
dtype = each.dtype
elif dtype != each.dtype:
raise ValueError("Data Type mismatch")
raise ValueError("Data Type mismatch: %d to %d" %
(dtype, each.dtype))
return dtype
def create_parameter(self,
......
......@@ -769,7 +769,7 @@ def topk(input, k):
array = fluid.layers.topk(x, k)
"""
helper = LayerHelper('topk', **locals())
topk_out = helper.create_tmp_variable(dtype=input.data_type)
topk_out = helper.create_tmp_variable(dtype=input.dtype)
topk_indices = helper.create_tmp_variable(dtype='int64')
helper.append_op(
type='top_k',
......
......@@ -61,6 +61,7 @@ __all__ = [
'transpose',
'im2sequence',
'nce',
'beam_search',
]
......@@ -163,10 +164,8 @@ def fc(input,
tmp = helper.create_tmp_variable(dtype)
helper.append_op(
type="mul",
inputs={
"X": input_var,
"Y": w,
},
inputs={"X": input_var,
"Y": w},
outputs={"Out": tmp},
attrs={"x_num_col_dims": num_flatten_dims,
"y_num_col_dims": 1})
......@@ -1551,6 +1550,38 @@ def sequence_expand(x, y, name=None):
return tmp
def beam_search(pre_ids, ids, scores, beam_size, end_id, level=0):
'''
This function implements the beam search algorithm.
'''
helper = LayerHelper('beam_search', **locals())
score_type = scores.dtype
id_type = ids.dtype
selected_scores = helper.create_tmp_variable(dtype=score_type)
selected_ids = helper.create_tmp_variable(dtype=id_type)
helper.append_op(
type='beam_search',
inputs={
'pre_ids': pre_ids,
'ids': ids,
'scores': scores,
},
outputs={
'selected_ids': selected_ids,
'selected_scores': selected_scores,
},
attrs={
# TODO(ChunweiYan) to assure other value support
'level': level,
'beam_size': beam_size,
'end_id': end_id,
})
return selected_ids, selected_scores
def lstm_unit(x_t,
hidden_t_prev,
cell_t_prev,
......
......@@ -17,7 +17,7 @@ import paddle.v2 as paddle
import paddle.v2.fluid as fluid
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.layers as pd
from paddle.v2.fluid.executor import Executor
dict_size = 30000
......@@ -26,53 +26,136 @@ src_dict, trg_dict = paddle.dataset.wmt14.get_dict(dict_size)
hidden_dim = 32
word_dim = 16
IS_SPARSE = True
batch_size = 10
max_length = 50
batch_size = 2
max_length = 8
topk_size = 50
trg_dic_size = 10000
beam_size = 2
decoder_size = hidden_dim
place = core.CPUPlace()
def encoder_decoder():
def encoder():
# encoder
src_word_id = layers.data(
src_word_id = pd.data(
name="src_word_id", shape=[1], dtype='int64', lod_level=1)
src_embedding = layers.embedding(
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 = fluid.layers.fc(input=src_embedding, size=hidden_dim * 4, act='tanh')
lstm_hidden0, lstm_0 = layers.dynamic_lstm(input=fc1, size=hidden_dim * 4)
encoder_out = layers.sequence_last_step(input=lstm_hidden0)
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):
# decoder
trg_language_word = layers.data(
trg_language_word = pd.data(
name="target_language_word", shape=[1], dtype='int64', lod_level=1)
trg_embedding = layers.embedding(
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 = fluid.layers.DynamicRNN()
rnn = pd.DynamicRNN()
with rnn.block():
current_word = rnn.step_input(trg_embedding)
mem = rnn.memory(init=encoder_out)
fc1 = fluid.layers.fc(input=[current_word, mem],
pre_state = rnn.memory(init=context)
current_state = pd.fc(input=[current_word, pre_state],
size=decoder_size,
act='tanh')
out = fluid.layers.fc(input=fc1, size=target_dict_dim, act='softmax')
rnn.update_memory(mem, fc1)
rnn.output(out)
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):
init_state = context
array_len = pd.fill_constant(shape=[1], dtype='int64', value=max_length)
counter = pd.zeros(shape=[1], dtype='int64')
# 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_ids_emb, pre_state_expanded],
size=decoder_size,
act='tanh')
# use score to do beam search
current_score = pd.fc(input=current_state,
size=target_dict_dim,
act='softmax')
topk_scores, topk_indices = pd.topk(current_score, k=50)
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 = core.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
......@@ -88,12 +171,13 @@ def to_lodtensor(data, place):
return res
def main():
rnn_out = encoder_decoder()
label = layers.data(
def train_main():
context = encoder()
rnn_out = decoder_train(context)
label = pd.data(
name="target_language_next_word", shape=[1], dtype='int64', lod_level=1)
cost = layers.cross_entropy(input=rnn_out, label=label)
avg_cost = fluid.layers.mean(x=cost)
cost = pd.cross_entropy(input=rnn_out, label=label)
avg_cost = pd.mean(x=cost)
optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4)
optimizer.minimize(avg_cost)
......@@ -103,13 +187,12 @@ def main():
paddle.dataset.wmt14.train(dict_size), buf_size=1000),
batch_size=batch_size)
place = core.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
batch_id = 0
for pass_id in xrange(2):
for pass_id in xrange(1):
for data in train_data():
word_data = to_lodtensor(map(lambda x: x[0], data), place)
trg_word = to_lodtensor(map(lambda x: x[1], data), place)
......@@ -125,9 +208,48 @@ def main():
print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
" avg_cost=" + str(avg_cost_val))
if batch_id > 3:
exit(0)
break
batch_id += 1
def decode_main():
context = encoder()
translation_ids, translation_scores = decoder_decode(context)
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
if __name__ == '__main__':
main()
# train_main()
decode_main()
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