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‎doc_cn/demo/quick_start/index.md

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -4,7 +4,7 @@
44

55
## 安装(Install)
66

7-
首先请参考<a href = "../../build_and_install/install/index.html">安装教程</a>安装PaddlePaddle。
7+
首先请参考<a href = "../../build_and_install/index.html">安装教程</a>安装PaddlePaddle。
88

99
## 使用概述(Overview)
1010

‎paddle/cuda/src/hl_cuda_cublas.cc

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -217,7 +217,7 @@ void hl_matrix_mul(real *A_d, hl_trans_op_t transa,
217217
} else {
218218
LOG(FATAL) << "parameter transa error!";
219219
}
220-
CHECK_EQ(stat, CUBLAS_STATUS_SUCCESS);
220+
CHECK_EQ(stat, CUBLAS_STATUS_SUCCESS) << hl_cublas_get_error_string(stat);
221221
CHECK_SYNC("hl_matrix_mul failed");
222222
}
223223

@@ -266,7 +266,7 @@ void hl_matrix_mul_vector(real *A_d, hl_trans_op_t trans,
266266
LOG(FATAL) << "parameter transa error!";
267267
}
268268

269-
CHECK_EQ(stat, CUBLAS_STATUS_SUCCESS);
269+
CHECK_EQ(stat, CUBLAS_STATUS_SUCCESS) << hl_cublas_get_error_string(stat);
270270
CHECK_SYNC("hl_matrix_mul_vector");
271271
}
272272

‎paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp

Lines changed: 25 additions & 16 deletions
Original file line numberDiff line numberDiff line change
@@ -497,20 +497,21 @@ void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
497497
int idSize = 0;
498498
// connect in_links
499499
for (size_t j = 0; j < inFrameLines_.size(); ++j) {
500+
Info& info = info_[shareInlinkInfo ? 0 : j];
500501
// idSize denotes the sum number of tokens in each length i
501-
idSize = info_[j].idIndex[i + 1] - info_[j].idIndex[i];
502+
idSize = info.idIndex[i + 1] - info.idIndex[i];
502503
InFrameLine inFrameLine = inFrameLines_[j];
503504
auto scatterAgent =
504505
dynamic_cast<ScatterAgentLayer*>(inFrameLine.agents[i].get());
505506
scatterAgent->setRealLayerAndOutput(inFrameLine.inLayer,
506-
inFrameLine.outArg, info_[j].allIds,
507-
info_[j].idIndex[i], idSize);
507+
inFrameLine.outArg, info.allIds,
508+
info.idIndex[i], idSize);
508509
if (hasSubseq) {
509510
// size: the length of subsequence
510511
int size =
511-
info_[j].seqStartPosIndex[i + 1] - info_[j].seqStartPosIndex[i];
512-
scatterAgent->setSequenceStartPositions(info_[j].sequenceStartPositions,
513-
info_[j].seqStartPosIndex[i],
512+
info.seqStartPosIndex[i + 1] - info.seqStartPosIndex[i];
513+
scatterAgent->setSequenceStartPositions(info.sequenceStartPositions,
514+
info.seqStartPosIndex[i],
514515
size);
515516
}
516517
}
@@ -744,16 +745,24 @@ void RecurrentGradientMachine::selectRowsOneTime(LayerPtr layer,
744745
const IVectorPtr& allIds,
745746
Argument* arg,
746747
PassType passType) {
747-
const MatrixPtr& realV = layer->getOutputValue();
748-
int height = realV->getHeight();
749-
int width = realV->getWidth();
750-
Matrix::resizeOrCreate(arg->value, height, width, /* trans */ false, useGpu_);
751-
arg->value->zeroMem();
752-
arg->value->selectRows(*realV, *allIds);
753-
if (passType != PASS_TEST) {
754-
Matrix::resizeOrCreate(arg->grad, height, width, /* trans */ false,
755-
useGpu_);
756-
arg->grad->zeroMem();
748+
Argument& src = layer->getOutput();
749+
if (src.value) {
750+
const MatrixPtr& realV = src.value;
751+
int height = realV->getHeight();
752+
int width = realV->getWidth();
753+
Matrix::resizeOrCreate(
754+
arg->value, height, width, /* trans */ false, useGpu_);
755+
arg->value->zeroMem();
756+
arg->value->selectRows(*realV, *allIds);
757+
if (passType != PASS_TEST) {
758+
Matrix::resizeOrCreate(arg->grad, height, width, /* trans */ false,
759+
useGpu_);
760+
arg->grad->zeroMem();
761+
}
762+
}
763+
if (src.ids) {
764+
IVector::resizeOrCreate(arg->ids, src.ids->getSize(), useGpu_);
765+
arg->ids->selectFrom(*src.ids, *allIds);
757766
}
758767
}
759768

‎paddle/gserver/layers/AgentLayer.cpp

Lines changed: 13 additions & 13 deletions
Original file line numberDiff line numberDiff line change
@@ -139,15 +139,16 @@ void ScatterAgentLayer::forward(PassType passType) {
139139
Layer::forward(passType);
140140
CHECK_EQ(realLayer_->getDeviceId(), this->getDeviceId());
141141

142-
if (realLayer_->getOutput().ids) { // ids scatter
143-
IVector::resizeOrCreate(output_.ids, ids_->getSize(), useGpu_);
144-
output_.ids->selectFrom(*realLayer_->getOutput().ids, *ids_);
145-
} else { // value scatter
146-
int width = this->getSize();
147-
if (realOutArg_.value) {
148-
output_.subArgFrom(realOutArg_, /* offset */ idIndex_ * width, idSize_,
149-
width, useGpu_);
150-
} else { // used in generation
142+
int width = this->getSize();
143+
if (realOutArg_.value || realOutArg_.ids) {
144+
output_.subArgFrom(realOutArg_, /* offset */ idIndex_, idSize_,
145+
width, useGpu_);
146+
} else { // used in generation
147+
if (realLayer_->getOutput().ids) {
148+
IVector::resizeOrCreate(output_.ids, ids_->getSize(), useGpu_);
149+
output_.ids->selectFrom(*realLayer_->getOutput().ids, *ids_);
150+
}
151+
if (realLayer_->getOutput().value) {
151152
int height = ids_->getSize();
152153
resetOutput(height, width);
153154

@@ -213,18 +214,17 @@ void SequenceGatherAgentLayer::forward(PassType passType) {
213214
void SequenceScatterAgentLayer::forward(PassType passType) {
214215
Layer::forward(passType);
215216
CHECK_EQ(realLayer_->getDeviceId(), this->getDeviceId());
216-
CHECK(!realLayer_->getOutput().ids) << "Not supported";
217217

218218
const Argument& input = realLayer_->getOutput();
219-
CHECK_EQ(input.value->getWidth(), this->getSize());
219+
CHECK_EQ(realLayer_->getSize(), this->getSize());
220220
int width = this->getSize();
221221

222222
AsyncGpuBlock asyncGpuBlock;
223223
REGISTER_TIMER_INFO("SequenceAgentLayerForward", getName().c_str());
224224

225-
if (realOutArg_.value) {
225+
if (realOutArg_.value || realOutArg_.ids) {
226226
CHECK(realOutArg_.sequenceStartPositions);
227-
output_.subArgFrom(realOutArg_, /* offset */ idIndex_ * width, idSize_,
227+
output_.subArgFrom(realOutArg_, /* offset */ idIndex_, idSize_,
228228
width, useGpu_, /* trans */ false, /* seqFlag */ true,
229229
/* seqStart */ seqStartPosIndex_,
230230
/* seqSize */ numSequences_);

‎paddle/gserver/layers/CRFLayer.h

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -25,7 +25,7 @@ namespace paddle {
2525
/**
2626
* A layer for calculating the cost of sequential conditional random field
2727
* model.
28-
* See LinearChainCRF.h for the detail of the CRF formulation.
28+
* See class LinearChainCRF for the detail of the CRF formulation.
2929
*/
3030
class CRFLayer : public Layer {
3131
public:

‎paddle/gserver/layers/LinearChainCRF.h

Lines changed: 24 additions & 24 deletions
Original file line numberDiff line numberDiff line change
@@ -21,39 +21,39 @@ namespace paddle {
2121

2222
class LinearChainCRF {
2323
public:
24-
/*
25-
The size of para and grad must be (numClasses + 2) * numClasses.
26-
The first numClasses values of para are for starting weights (a).
27-
The next numClasses values of para are for ending weights (b),
28-
The remaning values are for transition weights (w).
29-
30-
The probability of a state sequence s of length L is defined as:
31-
P(s) = (1/Z) exp(a_{s_1} + b_{s_L}
32-
+ \sum_{l=1}^L x_{s_l}
33-
+ \sum_{l=2}^L w_{s_{l-1},s_l})
34-
where Z is a normalization value so that the sum of P(s) over all possible
35-
sequences is 1, and x is the input feature to the CRF.
24+
/**
25+
* The size of para and grad must be \f$(numClasses + 2) * numClasses\f$.
26+
* The first numClasses values of para are for starting weights (\f$a\f$).
27+
* The next numClasses values of para are for ending weights (\f$b\f$),
28+
* The remaning values are for transition weights (\f$w\f$).
29+
*
30+
* The probability of a state sequence s of length \f$L\f$ is defined as:
31+
* \f$P(s) = (1/Z) exp(a_{s_1} + b_{s_L}
32+
* + \sum_{l=1}^L x_{s_l}
33+
* + \sum_{l=2}^L w_{s_{l-1},s_l})\f$
34+
* where \f$Z\f$ is a normalization value so that the sum of \f$P(s)\f$ over all possible
35+
* sequences is \f$1\f$, and \f$x\f$ is the input feature to the CRF.
3636
*/
3737
LinearChainCRF(int numClasses, real* para, real* grad);
3838

39-
/*
40-
Calculate the negative log likelihood of s given x.
41-
The size of x must be length * numClasses. Each consecutive numClasses
42-
values are the features for one time step.
39+
/**
40+
* Calculate the negative log likelihood of s given x.
41+
* The size of x must be length * numClasses. Each consecutive numClasses
42+
* values are the features for one time step.
4343
*/
4444
real forward(real* x, int* s, int length);
4545

46-
/*
47-
Calculate the gradient with respect to x, a, b, and w.
48-
The gradient of x will be stored in dx.
49-
backward() can only be called after a corresponding call to forward() with
50-
the same x, s and length.
51-
NOTE: The gradient is added to dx and grad (provided at constructor).
46+
/**
47+
* Calculate the gradient with respect to x, a, b, and w.
48+
* The gradient of x will be stored in dx.
49+
* backward() can only be called after a corresponding call to forward() with
50+
* the same x, s and length.
51+
* @note The gradient is added to dx and grad (provided at constructor).
5252
*/
5353
void backward(real* x, real* dx, int* s, int length);
5454

55-
/*
56-
Find the most probable sequence given x. The result will be stored in s.
55+
/**
56+
* Find the most probable sequence given x. The result will be stored in s.
5757
*/
5858
void decode(real* x, int* s, int length);
5959

‎paddle/gserver/tests/CMakeLists.txt

Lines changed: 0 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -56,7 +56,6 @@ add_test(NAME test_RecurrentGradientMachine
5656
COMMAND .set_python_path.sh -d
5757
${PROJ_ROOT}/python:${PROJ_ROOT}/paddle/gserver/tests
5858
${CMAKE_CURRENT_BINARY_DIR}/test_RecurrentGradientMachine
59-
--use_gpu=false
6059
WORKING_DIRECTORY ${PROJ_ROOT}/paddle)
6160

6261
add_unittest_without_exec(test_NetworkCompare

‎paddle/gserver/tests/sequence_nest_rnn_multi_input.conf

Lines changed: 77 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,77 @@
1+
#edit-mode: -*- python -*-
2+
# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
3+
#
4+
# Licensed under the Apache License, Version 2.0 (the "License");
5+
# you may not use this file except in compliance with the License.
6+
# You may obtain a copy of the License at
7+
#
8+
# http://www.apache.org/licenses/LICENSE-2.0
9+
#
10+
# Unless required by applicable law or agreed to in writing, software
11+
# distributed under the License is distributed on an "AS IS" BASIS,
12+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13+
# See the License for the specific language governing permissions and
14+
# limitations under the License.
15+
16+
from paddle.trainer_config_helpers import *
17+
18+
######################## data source ################################
19+
define_py_data_sources2(train_list='gserver/tests/Sequence/dummy.list',
20+
test_list=None,
21+
module='rnn_data_provider',
22+
obj='process_subseq')
23+
24+
25+
settings(batch_size=2, learning_rate=0.01)
26+
######################## network configure ################################
27+
dict_dim = 10
28+
word_dim = 8
29+
hidden_dim = 8
30+
label_dim = 3
31+
32+
data = data_layer(name="word", size=dict_dim)
33+
34+
emb = embedding_layer(input=data, size=word_dim)
35+
36+
# This hierachical RNN is designed to be equivalent to the simple RNN in
37+
# sequence_rnn.conf
38+
39+
def outer_step(wid, x):
40+
outer_mem = memory(name="outer_rnn_state", size=hidden_dim)
41+
def inner_step(y, wid):
42+
z = embedding_layer(input=wid, size=word_dim)
43+
inner_mem = memory(name="inner_rnn_state",
44+
size=hidden_dim,
45+
boot_layer=outer_mem)
46+
out = fc_layer(input=[y, z, inner_mem],
47+
size=hidden_dim,
48+
act=TanhActivation(),
49+
bias_attr=True,
50+
name="inner_rnn_state")
51+
return out
52+
53+
inner_rnn_output = recurrent_group(
54+
step=inner_step,
55+
name="inner",
56+
input=[x, wid])
57+
last = last_seq(input=inner_rnn_output, name="outer_rnn_state")
58+
59+
# "return last" should also work. But currently RecurrentGradientMachine
60+
# does not handle it correctly. Current implementation requires that
61+
# all the out links are from sequences. However, it does not report error
62+
# when the out links are not sequences.
63+
return inner_rnn_output
64+
65+
out = recurrent_group(
66+
name="outer",
67+
step=outer_step,
68+
input=[SubsequenceInput(data), SubsequenceInput(emb)])
69+
70+
rep = last_seq(input=out)
71+
prob = fc_layer(size=label_dim,
72+
input=rep,
73+
act=SoftmaxActivation(),
74+
bias_attr=True)
75+
76+
outputs(classification_cost(input=prob,
77+
label=data_layer(name="label", size=label_dim)))

‎paddle/gserver/tests/sequence_rnn_multi_input.conf

Lines changed: 58 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,58 @@
1+
#edit-mode: -*- python -*-
2+
# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
3+
#
4+
# Licensed under the Apache License, Version 2.0 (the "License");
5+
# you may not use this file except in compliance with the License.
6+
# You may obtain a copy of the License at
7+
#
8+
# http://www.apache.org/licenses/LICENSE-2.0
9+
#
10+
# Unless required by applicable law or agreed to in writing, software
11+
# distributed under the License is distributed on an "AS IS" BASIS,
12+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13+
# See the License for the specific language governing permissions and
14+
# limitations under the License.
15+
16+
from paddle.trainer_config_helpers import *
17+
18+
######################## data source ################################
19+
define_py_data_sources2(train_list='gserver/tests/Sequence/dummy.list',
20+
test_list=None,
21+
module='rnn_data_provider',
22+
obj='process_seq')
23+
24+
25+
settings(batch_size=2, learning_rate=0.01)
26+
######################## network configure ################################
27+
dict_dim = 10
28+
word_dim = 8
29+
hidden_dim = 8
30+
label_dim = 3
31+
32+
data = data_layer(name="word", size=dict_dim)
33+
34+
emb = embedding_layer(input=data, size=word_dim)
35+
36+
def step(y, wid):
37+
z = embedding_layer(input=wid, size=word_dim)
38+
mem = memory(name="rnn_state", size=hidden_dim)
39+
out = fc_layer(input=[y, z, mem],
40+
size=hidden_dim,
41+
act=TanhActivation(),
42+
bias_attr=True,
43+
name="rnn_state")
44+
return out
45+
46+
out = recurrent_group(
47+
name="rnn",
48+
step=step,
49+
input=[emb, data])
50+
51+
rep = last_seq(input=out)
52+
prob = fc_layer(size=label_dim,
53+
input=rep,
54+
act=SoftmaxActivation(),
55+
bias_attr=True)
56+
57+
outputs(classification_cost(input=prob,
58+
label=data_layer(name="label", size=label_dim)))

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