提交 9298a9ec 编写于 作者: L Luo Tao

stride pooling for seqlastin and seqfirstin

上级 bfc33108
......@@ -25,6 +25,9 @@ namespace paddle {
* Input: a sequence
* If SequenceLevel = kNonseq:
* Output: a sequence containing only the last instance of the input sequence
* If stride_ > 0:
* Output: a shorten sequence containing several last instances of the
* input sequence with stride window.
* If SequenceLevel = kSeq:
* Check input sequence must has sub-sequence
* Output: a sequence containing only the last instance of each sub-sequence
......@@ -37,6 +40,8 @@ class SequenceLastInstanceLayer : public SequencePoolLayer {
protected:
MatrixPtr tmpSrc_;
MatrixPtr tmpDest_;
bool select_first_;
std::vector<int> insId_;
public:
explicit SequenceLastInstanceLayer(const LayerConfig& config)
......@@ -54,6 +59,7 @@ REGISTER_LAYER(seqlastins, SequenceLastInstanceLayer);
bool SequenceLastInstanceLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
SequencePoolLayer::init(layerMap, parameterMap);
select_first_ = config_.select_first();
tmpSrc_ =
Matrix::create(nullptr, /* height= */ 1, 1, /* trans= */ false, useGpu_);
......@@ -74,9 +80,13 @@ void SequenceLastInstanceLayer::forward(PassType passType) {
AsyncGpuBlock asyncGpuBlock;
REGISTER_TIMER_INFO("SequenceLastInstanceLayerForward", getName().c_str());
insId_.clear();
for (size_t seqId = 0; seqId < newBatchSize_; ++seqId) {
int insId =
config_.select_first() ? starts[seqId] : starts[seqId + 1] - 1;
int insId = (stride_ > 0)
? (select_first_ ? stridePositions_[seqId]
: stridePositions_[seqId + 1] - 1)
: (select_first_ ? starts[seqId] : starts[seqId + 1] - 1);
insId_.push_back(insId);
outputValue->subMatrix(seqId, 1, tmpDest_)
->assign(*(inputValue->subMatrix(insId, 1, tmpSrc_)));
......@@ -96,18 +106,13 @@ void SequenceLastInstanceLayer::backward(const UpdateCallback& callback) {
MatrixPtr inputGrad = getInputGrad(0);
MatrixPtr outputGrad = getOutputGrad();
const int* starts = startPositions_->getData(false);
size_t numSequences = startPositions_->getSize() - 1;
if (inputGrad) {
AsyncGpuBlock asyncGpuBlock;
REGISTER_TIMER_INFO("SequenceLastInstanceLayerBackward", getName().c_str());
for (size_t seqId = 0; seqId < numSequences; ++seqId) {
int insId =
config_.select_first() ? starts[seqId] : starts[seqId + 1] - 1;
inputGrad->subMatrix(insId, 1, tmpDest_)
for (size_t seqId = 0; seqId < newBatchSize_; ++seqId) {
inputGrad->subMatrix(insId_[seqId], 1, tmpDest_)
->add(*(outputGrad->subMatrix(seqId, 1, tmpSrc_)));
}
}
......
......@@ -37,6 +37,7 @@ bool SequencePoolLayer::init(const LayerMap& layerMap,
} else {
LOG(FATAL) << "Unknown trans_type: " << config_.trans_type();
}
stride_ = config_.seq_pool_stride();
setNeedSequenceInfo(false);
return true;
}
......@@ -55,8 +56,6 @@ void SequencePoolLayer::forward(PassType passType) {
CHECK_EQ(starts->getData()[newBatchSize_], input.getBatchSize());
CHECK_EQ(newBatchSize_, starts->getSize() - 1);
resetOutput(newBatchSize_, dim);
/* If type_ = kNonSeq, both seq has or not has sub-seq degrade to a non-seq,
* thus, in this case, output_ has no sequenceStartPositions.
* If type_ = kSeq, seq has sub-seq degrades to a seq, thus, only in this
......@@ -67,6 +66,14 @@ void SequencePoolLayer::forward(PassType passType) {
<< "when trans_type = seq, input must hasSubseq";
output_.degradeSequence(input);
}
if (stride_ > 0) {
CHECK_EQ(input.hasSubseq(), 0UL)
<< "sequence stride pooling is not suitable for hasSubseq now";
output_.poolSequenceWithStride(input, stride_, &stridePositions_);
newBatchSize_ = stridePositions_.size() - 1;
}
resetOutput(newBatchSize_, dim);
}
void SequencePoolLayer::backward(const UpdateCallback& callback) {
......
......@@ -26,6 +26,10 @@ namespace paddle {
* Output: output size is the number of input sequences (NOT input instances)
* output[i] = seqlastin/average/max_{for each instance in this
* sequence}{input[i]}
* If stride_ > 0:
* Check input sequence must don't have sub-sequence
* Output: a shorten sequence, pooling is performed upon a small local
* area
* If SequenceLevel = kSeq:
* Check input sequence must has sub-sequence
* Output: output size is the number of input sub-sequences
......@@ -42,6 +46,9 @@ protected:
enum SequenceLevel { kNonSeq = 0, kSeq = 1 };
size_t newBatchSize_;
ICpuGpuVectorPtr startPositions_;
int stride_;
// store the start position of each stride window
std::vector<int> stridePositions_;
public:
explicit SequencePoolLayer(const LayerConfig& config) : Layer(config) {}
......
......@@ -804,10 +804,14 @@ TEST(Layer, ExpandLayer) {
testExpandLayer("seq", true); // seq expand to hasSubseq
}
void testDegradeLayer(bool hasSubseq, string layer_type, string trans_type) {
void testDegradeLayer(bool hasSubseq,
string layer_type,
string trans_type,
int stride = -1) {
TestConfig config;
config.layerConfig.set_type(layer_type);
config.layerConfig.set_size(10);
config.layerConfig.set_seq_pool_stride(stride);
config.biasSize = 0;
config.inputDefs.push_back(
......@@ -827,12 +831,14 @@ void testDegradeLayer(bool hasSubseq, string layer_type, string trans_type) {
if (layer_type == "average") {
for (auto strategy : {"average", "sum", "squarerootn"}) {
LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type
<< " average_strategy=" << strategy;
<< " average_strategy=" << strategy
<< " seq_pool_stride=" << stride;
config.layerConfig.set_average_strategy(strategy);
testDegradeLayerGrad(config, layer_type);
}
} else {
LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type;
LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type
<< " seq_pool_stride=" << stride;
testDegradeLayerGrad(config, layer_type);
}
}
......@@ -847,6 +853,10 @@ TEST(Layer, SequenceLastInstanceLayer) {
testDegradeLayer(false,
"seqlastins",
"non-seq"); // seq seqlastins to non-seq
testDegradeLayer(false,
"seqlastins",
"non-seq",
5); // seq seqlastins to a shorten seq, stride window = 5
testDegradeLayer(true,
"seqlastins",
"non-seq"); // hasSubseq seqlastins to non-seq
......
......@@ -559,6 +559,46 @@ void Argument::degradeSequence(const Argument& input) {
tgtBuf[numSequences] = numSubSequences;
}
void Argument::poolSequenceWithStride(const Argument& input,
size_t stride,
std::vector<int>* stridePostions) {
/*
* If input.sequenceStartPositions = [0, 9, 14, 17, 30] and stride = 5,
* then sequenceStartPositions = [0, 2, 3, 4, 7],
* and stridePostions = [0, 5, 9, 14, 17, 22, 27, 30]
*/
CHECK(input.sequenceStartPositions);
CHECK_EQ(input.hasSubseq(), 0UL);
CHECK_GT(stride, 0) << "stride must larger than 0";
size_t numSequences = input.getNumSequences();
ICpuGpuVector::resizeOrCreate(
sequenceStartPositions, numSequences + 1, false);
const int* starts = input.sequenceStartPositions->getData(false);
int* tgtBuf = sequenceStartPositions->getMutableData(false);
// first index of target sequence and stride positions are both 0
tgtBuf[0] = 0;
(*stridePostions).clear();
for (size_t seqId = 0; seqId < numSequences; ++seqId) {
size_t seqLength = starts[seqId + 1] - starts[seqId];
(*stridePostions).emplace_back(starts[seqId]);
if (seqLength == 0) {
// empty sequence
tgtBuf[seqId + 1] = tgtBuf[seqId];
} else if (seqLength < stride) {
tgtBuf[seqId + 1] = tgtBuf[seqId] + 1;
} else {
tgtBuf[seqId + 1] = tgtBuf[seqId] + ceil((float)seqLength / stride);
int size =
(seqLength % stride) ? seqLength / stride : seqLength / stride - 1;
for (int i = 0; i < size; i++) {
(*stridePostions).emplace_back((*stridePostions).back() + stride);
}
}
}
(*stridePostions).emplace_back(starts[numSequences]);
CHECK_EQ((*stridePostions).size() - 1, tgtBuf[numSequences]);
}
void Argument::getValueString(
std::unordered_map<std::string, std::string>* out) const {
if (value) {
......
......@@ -291,6 +291,14 @@ struct Argument {
*/
void degradeSequence(const Argument& input);
/*
After pooling with stride n (n is smaller than sequence length),
a long sequence will be shorten.
This function is not suitable for sequence with sub-sequence now.
*/
void poolSequenceWithStride(const Argument& input,
size_t stride,
std::vector<int>* stridePositions);
/**
* @brief getValueString will return the argument's output in string. There
* are several kinds of output. The keys of output dictionary are 'value',
......
add_simple_unittest(test_common)
add_simple_unittest(test_argument)
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#include <gtest/gtest.h>
#include <paddle/parameter/Argument.h>
using namespace paddle; // NOLINT
TEST(Argument, poolSequenceWithStride) {
Argument input, output;
ICpuGpuVector::resizeOrCreate(input.sequenceStartPositions, 5, false);
int* inStart = input.sequenceStartPositions->getMutableData(false);
inStart[0] = 0;
inStart[1] = 9;
inStart[2] = 14;
inStart[3] = 17;
inStart[4] = 30;
std::vector<int> stridePositions;
stridePositions.clear();
output.poolSequenceWithStride(input, 5 /* stride */, &stridePositions);
const int* outStart = output.sequenceStartPositions->getData(false);
CHECK_EQ(outStart[0], 0);
CHECK_EQ(outStart[1], 2);
CHECK_EQ(outStart[2], 3);
CHECK_EQ(outStart[3], 4);
CHECK_EQ(outStart[4], 7);
CHECK_EQ(stridePositions.size(), 8);
int strideResult[] = {0, 5, 9, 14, 17, 22, 27, 30};
for (int i = 0; i < 8; i++) {
CHECK_EQ(stridePositions[i], strideResult[i]);
}
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
initMain(argc, argv);
return RUN_ALL_TESTS();
}
......@@ -441,6 +441,11 @@ message LayerConfig {
// blank label used in ctc loss
optional uint32 blank = 52 [default = 0];
// stride parameter for seqlastins layer, AverageLayer, MaxLayer, which
// controls the scope of pooling operation. can be set > 0.
// leave empty or set to -1 to disable this stride pooling.
optional int32 seq_pool_stride = 53 [default = -1];
}
message EvaluatorConfig {
......
......@@ -2480,6 +2480,7 @@ class SequenceLastInstanceLayer(LayerBase):
active_type='linear',
trans_type='non-seq',
bias=False,
stride=-1,
**xargs):
super(SequenceLastInstanceLayer, self).__init__(
name,
......@@ -2490,10 +2491,11 @@ class SequenceLastInstanceLayer(LayerBase):
**xargs)
config_assert(
len(inputs) == 1, 'SequenceLastInstanceLayer must have 1 input')
if trans_type == 'seq':
config_assert(stride == -1, 'subseq do not support stride window')
self.config.trans_type = trans_type
for input_index in xrange(len(self.inputs)):
input_layer = self.get_input_layer(input_index)
self.set_layer_size(input_layer.size)
self.config.seq_pool_stride = stride
self.set_layer_size(self.get_input_layer(0).size)
self.create_bias_parameter(bias, self.config.size)
......@@ -2505,10 +2507,16 @@ class SequenceFirstInstanceLayer(SequenceLastInstanceLayer):
active_type='linear',
trans_type='non-seq',
bias=False,
stride=-1,
**xargs):
super(SequenceFirstInstanceLayer, self).__init__(
name, inputs=inputs, active_type=active_type, bias=bias, **xargs)
self.config.trans_type = trans_type
name,
inputs=inputs,
active_type=active_type,
trans_type=trans_type,
bias=bias,
stride=stride,
**xargs)
self.config.select_first = True
......
......@@ -1301,10 +1301,15 @@ def grumemory(input,
def last_seq(input,
name=None,
agg_level=AggregateLevel.EACH_TIMESTEP,
stride=-1,
layer_attr=None):
"""
Get Last Timestamp Activation of a sequence.
If stride > 0, get last timestamp upon a stride window of sequence.
And a long sequence will be shorten. Note that for sequence with
sub-sequence, stride is default -1 now.
The simple usage is:
.. code-block:: python
......@@ -1316,6 +1321,8 @@ def last_seq(input,
:type name: basestring
:param input: Input layer name.
:type input: LayerOutput
:param stride: parameter of stride window.
:type stride: Int
:param layer_attr: extra layer attributes.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
......@@ -1327,11 +1334,15 @@ def last_seq(input,
" series information at all. Maybe you want to use"
" first_seq instead.")
if agg_level == AggregateLevel.EACH_SEQUENCE:
assert stride == -1
Layer(
name=name,
type=LayerType.SEQUENCE_LAST_INSTANCE,
inputs=[input.name],
trans_type=agg_level,
stride=stride,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
......@@ -1345,10 +1356,16 @@ def last_seq(input,
def first_seq(input,
name=None,
agg_level=AggregateLevel.EACH_TIMESTEP,
stride=-1,
layer_attr=None):
"""
Get First Timestamp Activation of a sequence.
If stride > 0, get first timestamp upon a stride window of sequence,
and a long sequence will be shorten. Note that for sequence with
sub-sequence, stride is default -1 now.
The simple usage is:
.. code-block:: python
......@@ -1372,11 +1389,15 @@ def first_seq(input,
' time series information at all. Maybe you want to use'
' last_seq instead.')
if agg_level == AggregateLevel.EACH_SEQUENCE:
assert stride == -1
Layer(
name=name,
type=LayerType.SEQUENCE_FIRST_INSTANCE,
inputs=[input.name],
trans_type=agg_level,
stride=stride,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
......
......@@ -14,4 +14,7 @@ for op in seq_op:
for al in agg_level:
opts.append(op(input=din, agg_level=al))
for op in seq_op:
opts.append(op(input=din, agg_level=AggregateLevel.EACH_TIMESTEP, stride=5))
outputs(opts)
......@@ -15,6 +15,7 @@ layers {
}
select_first: true
trans_type: "seq"
seq_pool_stride: -1
}
layers {
name: "__first_seq_1__"
......@@ -26,6 +27,7 @@ layers {
}
select_first: true
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__last_seq_0__"
......@@ -36,6 +38,7 @@ layers {
input_layer_name: "data"
}
trans_type: "seq"
seq_pool_stride: -1
}
layers {
name: "__last_seq_1__"
......@@ -46,12 +49,38 @@ layers {
input_layer_name: "data"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__first_seq_2__"
type: "seqlastins"
size: 30
active_type: "linear"
inputs {
input_layer_name: "data"
}
select_first: true
trans_type: "non-seq"
seq_pool_stride: 5
}
layers {
name: "__last_seq_2__"
type: "seqlastins"
size: 30
active_type: "linear"
inputs {
input_layer_name: "data"
}
trans_type: "non-seq"
seq_pool_stride: 5
}
input_layer_names: "data"
output_layer_names: "__first_seq_0__"
output_layer_names: "__first_seq_1__"
output_layer_names: "__last_seq_0__"
output_layer_names: "__last_seq_1__"
output_layer_names: "__first_seq_2__"
output_layer_names: "__last_seq_2__"
sub_models {
name: "root"
layer_names: "data"
......@@ -59,11 +88,15 @@ sub_models {
layer_names: "__first_seq_1__"
layer_names: "__last_seq_0__"
layer_names: "__last_seq_1__"
layer_names: "__first_seq_2__"
layer_names: "__last_seq_2__"
input_layer_names: "data"
output_layer_names: "__first_seq_0__"
output_layer_names: "__first_seq_1__"
output_layer_names: "__last_seq_0__"
output_layer_names: "__last_seq_1__"
output_layer_names: "__first_seq_2__"
output_layer_names: "__last_seq_2__"
is_recurrent_layer_group: false
}
......@@ -128,6 +128,7 @@ layers {
input_layer_name: "__simple_gru_0__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__last_seq_1__"
......@@ -138,6 +139,7 @@ layers {
input_layer_name: "__simple_gru_1__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__fc_layer_0__"
......
......@@ -210,6 +210,7 @@ layers {
input_layer_name: "__lstm_group_0__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__last_seq_1__"
......@@ -220,6 +221,7 @@ layers {
input_layer_name: "__lstm_group_1__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__fc_layer_0__"
......
......@@ -143,6 +143,7 @@ layers {
input_layer_name: "__recurrent_layer_0__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__first_seq_0__"
......@@ -154,6 +155,7 @@ layers {
}
select_first: true
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__last_seq_1__"
......@@ -164,6 +166,7 @@ layers {
input_layer_name: "__lstmemory_0__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__first_seq_1__"
......@@ -175,6 +178,7 @@ layers {
}
select_first: true
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__last_seq_2__"
......@@ -185,6 +189,7 @@ layers {
input_layer_name: "__gru_0__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__first_seq_2__"
......@@ -196,6 +201,7 @@ layers {
}
select_first: true
trans_type: "non-seq"
seq_pool_stride: -1
}
parameters {
name: "___fc_layer_0__.w0"
......
......@@ -96,6 +96,7 @@ layers {
input_layer_name: "rnn_forward"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__recurrent_group_1__"
......@@ -145,6 +146,7 @@ layers {
}
select_first: true
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__recurrent_group_2__"
......@@ -193,6 +195,7 @@ layers {
input_layer_name: "rnn_subseq_forward"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__lstm_group_0___recurrent_group"
......@@ -282,6 +285,7 @@ layers {
input_layer_name: "__lstm_group_0__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__gru_group_0___recurrent_group"
......@@ -330,6 +334,7 @@ layers {
input_layer_name: "__gru_group_0__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__recurrent_group_3__"
......@@ -378,6 +383,7 @@ layers {
input_layer_name: "__fc_layer_0__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
parameters {
name: "___mixed_0__.w0"
......
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册