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提交 b397febc 编写于 作者: Y Yang Yu
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Merge branch 'develop' of github.com:baidu/Paddle into test_parallel_do

上级 71cda02d 9609c17a
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Showing with 2116 addition and 217 deletion
CMakeLists.txt
浏览文件 @ b397febc
......@@ -31,9 +31,6 @@ if(NOT CMAKE_CROSSCOMPILING)
endif(NOT CMAKE_CROSSCOMPILING)
find_package(Git REQUIRED)
find_package(Threads REQUIRED)
if(NOT ANDROID AND NOT IOS)
find_package(Boost QUIET)
endif()
include(simd)
......@@ -140,6 +137,7 @@ include(external/openblas) # download, build, install openblas
include(external/mkldnn) # download, build, install mkldnn
include(external/swig) # download, build, install swig
include(external/warpctc) # download, build, install warpctc
include(external/boost) # download, build, install boost
include(external/any) # download libn::any
include(external/eigen) # download eigen3
include(external/pybind11) # download pybind11
......@@ -164,7 +162,6 @@ include_directories("${PADDLE_SOURCE_DIR}")
include_directories("${PADDLE_SOURCE_DIR}/paddle/cuda/include")
include_directories("${CMAKE_CURRENT_BINARY_DIR}/proto")
include_directories("${CMAKE_CURRENT_BINARY_DIR}/go/pserver/client/c")
include_directories(${Boost_INCLUDE_DIRS})
set(EXTERNAL_LIBS
${GFLAGS_LIBRARIES}
......
Dockerfile
浏览文件 @ b397febc
......@@ -27,7 +27,7 @@ RUN apt-get update && \
curl sed grep graphviz libjpeg-dev zlib1g-dev \
python-matplotlib gcc-4.8 g++-4.8 \
automake locales clang-format swig doxygen cmake \
liblapack-dev liblapacke-dev libboost-dev \
liblapack-dev liblapacke-dev \
clang-3.8 llvm-3.8 libclang-3.8-dev \
net-tools libtool && \
apt-get clean -y
......
cmake/external/boost.cmake 0 → 100644
浏览文件 @ b397febc
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
include(ExternalProject)
set(BOOST_PROJECT "extern_boost")
set(BOOST_VER "1.66.0")
set(BOOST_TAR "boost_1_66_0")
set(BOOST_URL "https://dl.bintray.com/boostorg/release/${BOOST_VER}/source/${BOOST_TAR}.tar.gz")
set(BOOST_SOURCES_DIR ${THIRD_PARTY_PATH}/boost)
set(BOOST_DOWNLOAD_DIR "${BOOST_SOURCES_DIR}/src/${BOOST_PROJECT}")
set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}/${BOOST_TAR}" CACHE PATH "boost include directory." FORCE)
include_directories(${BOOST_INCLUDE_DIR})
ExternalProject_Add(
${BOOST_PROJECT}
${EXTERNAL_PROJECT_LOG_ARGS}
DOWNLOAD_DIR ${BOOST_DOWNLOAD_DIR}
DOWNLOAD_COMMAND wget --no-check-certificate ${BOOST_URL} -c -q -O ${BOOST_TAR}.tar.gz
&& tar zxf ${BOOST_TAR}.tar.gz
DOWNLOAD_NO_PROGRESS 1
PREFIX ${BOOST_SOURCES_DIR}
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
INSTALL_COMMAND ""
UPDATE_COMMAND ""
)
if (${CMAKE_VERSION} VERSION_LESS "3.3.0")
set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/boost_dummy.c)
file(WRITE ${dummyfile} "const char *dummy = \"${dummyfile}\";")
add_library(boost STATIC ${dummyfile})
else()
add_library(boost INTERFACE)
endif()
add_dependencies(boost ${BOOST_PROJECT})
list(APPEND external_project_dependencies boost)
set(Boost_INCLUDE_DIR ${BOOST_INCLUDE_DIR})
doc/api/v2/fluid/layers.rst
浏览文件 @ b397febc
......@@ -18,6 +18,11 @@ dynamic_lstm
.. autofunction:: paddle.v2.fluid.layers.dynamic_lstm
:noindex:
dynamic_gru
-----------
.. autofunction:: paddle.v2.fluid.layers.dynamic_gru
:noindex:
data
----
.. autofunction:: paddle.v2.fluid.layers.data
......@@ -500,6 +505,11 @@ swish
.. autofunction:: paddle.v2.fluid.layers.swish
:noindex:
im2sequence
------
.. autofunction:: paddle.v2.fluid.layers.im2sequence
:noindex:
edit_distance
---------------
.. autofunction:: paddle.v2.fluid.layers.edit_distance_error
......
doc/design/csp.md 0 → 100644
浏览文件 @ b397febc
# Design Doc: CSP in PaddlePaddle Fluid
## Motivation
Concurrent programming is important for deep learning. Few example applications are:
1. The main thread keeps reading the next mini-batch while another thread uses the GPU for computing.
2. The main thread performs the computation while another thread uploads the local gradients from each trainer to the parameter server.
Most DL systems, including TensorFlow, Caffe2, and MxNet, can asynchronously execute operators in a graph. However, Fluid doesn't have the concept of a graph at all, as the design goal of Fluid is that of a programming language.
## Concurrent Programming Models
There were many concurrent programming models, implemented in various forms:
| concurrent programming model | implementation |
|-----|-----|
| mutex | types and functions in standard libraries |
| semaphore | types and functions in standard libraries |
| communicating sequential processes (CSP) | Go programming language |
| actor model | Erlang programming language |
| message passing | MPI |
| bulk synchronous parallel (BSP) | Pregel distributed programming framework |
Since Fluid was designed to be a programming language, we would like to implement CSP in Fluid.
### CSP v.s. Actor Model
A well-known implementation of Actor Model is the Erlang programming language. In Actor Model, *processes* could send messages to another process and receive messages from another process given the process IDs. We can find the three ingredients, process with ID, send, and recv, in MPI too. Indeed, we can rewrite Erlang programs in Python + MPI with possibly fewer lines of code. Our concern with Actor Model is that it doesn't seem reasonable to implement process management in a programming language's runtime library; instead, it should be the operating systems' responsibility to manage processes and libraries like MPI for send/recv.
## CSP in Fluid
Fluid has two fundamental control-flows: *if-else* and *while*. If we are to implement CSP, we need the following:
1. a new data type: *channel* and operators *send* and *recv*,
1. *goroutine* or thread, and
1. a new control-flow: select.
We also need Python wrappers for the above components.
The type *channel* is conceptually the blocking queue. In Go, its implemented is a [blocking circular queue](https://github.com/golang/go/blob/68ce117cf17b8debf5754bfd476345779b5b6616/src/runtime/chan.go#L31-L50), which supports send and recv.
The `select` operation has been in OS kernels long before Go language. All Unix kernels implement system calls *poll* and *select*. They monitor multiple file descriptors to see if I/O is possible on any of them. This takes O(N) time. Since Linux 2.6, a new system call, *epoll*, can do the same in O(1) time. In BSD systems, there is a similar system call *kqueue*. Go's Linux implementation uses epoll.
It might be a good idea to implement Fluid's select using epoll too. In this design doc, we start from the O(N) way, so we could focus on Python binding and the syntax.
### Type Channel
Fluid supports many data types:
1. Tensor,
1. Row-sparse Tensor
1. LoD Tensor,
1. Tensor array, etc
Each data type is registered in the [`framework.proto`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L117-L127) as an enum value. To add a new type channel, we need to add a new type enum.
To expose a C++ type to Python, we need to edit the [`pybind.cc`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc) file. [Here](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc#L120-L164) is an example how we expose C++ class LoDTensor.
## Syntax Design
### Create Channel
In Go, we create a channel by specifying the element type and buffer size:
```go
ch := make(chan int) // a channel without buffer
ch1 := make(chan int, 100) // a channel that can buffer 100 ints.
```
In Fluid, we should be able to do the same:
```python
ch = fluid.make_chan(dtype=INT)
ch1 = fluid.make_chan(dtype=INT, 100)
```
In addition to that, we want channels that can hold more complex element types, e.g., Tensors of float16:
```python
ch = fluid.make_chan(dtype=Tensor, etype=float16)
```
or Tensors of Tensors of float16 etc.
The point here is that we need a consistent way to compose types, like in C++ we can have `Tensor<Tensor<...<float16>...> >`.
### Send and Recv
### Select
## Example Programs
### 1. RPC between Trainers and Parameter Servers
### 2. Concurrent Minibatch Loading
doc/design/dist_refactor/parameter_server.md
浏览文件 @ b397febc
......@@ -9,16 +9,16 @@ different purposes.
## Background
The previous implementations of the parameter server does not run a
The previous implementations of the parameter server do not run a
fluid sub-program. Parameter initialization, optimizer computation, network
communication and checkpointing are implemented twice on both the
trainer and the parameter server.
trainer as well as the parameter server.
It would be great if we can write code once and use them on both the
trainer and the parameter server: reduces code duplication and
improves extensibility. Given that after the current refactor, we are
representing everything as a computing graph on the
trainer. Representing everything as a computing graph on the parameter
It would be great if we can write code once and use them on both: the
trainer and the parameter server, since this reduces code duplication and
improves extensibility. Given that after the current refactoring, we are
representing everything as a computation graph on the
trainer. Representing everything as a computation graph on the parameter
server becomes a natural extension.
## Design
......@@ -30,9 +30,9 @@ into sub-programs to be scheduled on different nodes with the following
steps:
1. OP placement: the OPs will be placed on different nodes according
to heuristic that minimizes estimated total computation
to a heuristic that minimizes the estimated total computation
time. Currently we will use a simple heuristic that puts parameter
varable on parameter server workers and everything else on trainer
variable on parameter server workers and everything else on trainer
workers.
1. Add communication OPs to enable the communication between nodes.
......@@ -47,22 +47,22 @@ After converting:
<img src="src/dist-graph.png" width="700"/>
1. The parameter variable W and it's optimizer program are placed on the parameter server.
1. The parameter variable W and its optimizer program are placed on the parameter server.
1. Operators are added to the program.
- *Send* sends data to the connected *Recv* operator. The
scheduler on the receive node will only schedule *Recv* operator
to run when the *Send* operator has ran (the *Send* OP will mark
the *Recv* OP runnable automatically).
- *Enueue* enqueues the input variable, it can block until space
- *Enqueue* enqueues the input variable, it can block until space
become available in the queue.
- *Dequeue* outputs configurable numbers of tensors from the
queue. It will block until the queue have the required number of
queue. It will block until the queue has the required number of
tensors.
### Benefits
- Model parallelism become easier to implement: it's an extension to
- Model parallelism becomes easier to implement: it is an extension to
the trainer - parameter server approach. We can have several "Transpilers"
to achieve different goals.
- User-defined optimizer is easier to add - user can now express it as
......@@ -72,22 +72,22 @@ After converting:
### Challenges
- It's important to balance the parameter shards of on multiple
parameter server. If a single parameter is very big (some
- It is important to balance the parameter shards on multiple
parameter servers. If a single parameter is very big (for example: some
word-embedding, fully connected, softmax layer), we need to
automatically partition the single parameter onto different
parameter servers when possible (only element-wise optimizer depends
on the parameter variable).
- In the "Aync SGD" figure, the "W" variable on the parameter server
could be read and wrote concurrently. See
- In the "Async SGD" figure, the "W" variable on the parameter server
could be read and written concurrently. See
[here](https://github.com/PaddlePaddle/Paddle/pull/6394) for more
details about concurrent program in fluid.
details about concurrent program in Fluid.
### Discussion
- Can the Enqueue OP be implemented under our current tensor design
(puts the input tensor into the queue tensor)?
- *Dequeue* OP will have variable numbers of output (depends on the
(put the input tensor into the queue tensor)?
- *Dequeue* OP will have variable numbers of output (depending on the
`min_count` attribute), does our current design support it? (similar
question for the *Add* OP)
......
doc/design/ops/sequence_decoder.md
浏览文件 @ b397febc
......@@ -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)
......
doc/howto/optimization/cpu_profiling.md
浏览文件 @ b397febc
......@@ -60,8 +60,7 @@ each column is as follows:
| column | meaning |
| --- | --- |
| ncalls | the number of calls into a function |
| tottime | the total execution time of the function, not including the
execution time of other functions called by the function |
| tottime | the total execution time of the function, not including the execution time of other functions called by the function |
| percall | tottime divided by ncalls |
| cumtime | the total execution time of the function, including the execution time of other functions being called |
| percall | cumtime divided by ncalls |
......
paddle/CMakeLists.txt
浏览文件 @ b397febc
......@@ -18,7 +18,7 @@ else()
add_subdirectory(capi)
endif()
if(Boost_FOUND)
if(NOT ANDROID AND NOT IOS)
add_subdirectory(memory)
add_subdirectory(platform)
add_subdirectory(framework)
......
paddle/framework/CMakeLists.txt
浏览文件 @ b397febc
# ddim lib
proto_library(framework_proto SRCS framework.proto)
cc_library(ddim SRCS ddim.cc DEPS eigen3)
cc_library(ddim SRCS ddim.cc DEPS eigen3 boost)
cc_test(ddim_test SRCS ddim_test.cc DEPS ddim)
nv_test(dim_test SRCS dim_test.cu DEPS ddim)
......@@ -45,7 +45,7 @@ cc_test(data_layout_transform_test SRCS data_layout_transform_test.cc DEPS data_
cc_library(data_transform SRCS data_transform.cc DEPS math_function tensor
framework_proto selected_rows data_device_transform data_type_transform data_layout_transform)
cc_library(attribute SRCS attribute.cc DEPS framework_proto)
cc_library(attribute SRCS attribute.cc DEPS framework_proto boost)
cc_test(program_desc_test SRCS program_desc_test.cc DEPS proto_desc
device_context)
cc_library(op_proto_maker SRCS op_proto_maker.cc DEPS framework_proto attribute)
......
paddle/framework/attribute.cc
浏览文件 @ b397febc
......@@ -61,6 +61,9 @@ Attribute GetAttrValue(const proto::OpDesc::Attr& attr_desc) {
}
return val;
}
case proto::AttrType::LONG: {
return attr_desc.l();
}
default:
PADDLE_THROW("Unsupport attr type %d", attr_desc.type());
}
......
paddle/framework/attribute.h
浏览文件 @ b397febc
......@@ -168,6 +168,32 @@ struct ExtractAttribute<bool> {
const std::string& attr_name_;
};
template <>
struct ExtractAttribute<int64_t> {
explicit ExtractAttribute(const std::string& attr_name)
: attr_name_(attr_name) {}
int64_t* operator()(Attribute& attr) const {
if (attr.type() == typeid(int)) { // NOLINT
int val = boost::get<int>(attr);
attr = static_cast<int64_t>(val);
} else if (attr.type() == typeid(float)) { // NOLINT
int val = boost::get<float>(attr);
attr = static_cast<int64_t>(val);
}
int64_t* attr_value = nullptr;
try {
attr_value = &boost::get<int64_t>(attr);
} catch (boost::bad_get& bad_get) {
PADDLE_THROW("Cannot get attribute %s by type int64_t, its type is %s",
attr_name_, attr.type().name());
}
return attr_value;
}
const std::string& attr_name_;
};
// check whether a certain attribute fit its limits
// an attribute can have more than one limits
template <typename T>
......
paddle/framework/block_desc.cc
浏览文件 @ b397febc
......@@ -75,7 +75,7 @@ std::vector<VarDesc *> BlockDesc::AllVars() const {
OpDesc *BlockDesc::AppendOp() {
need_update_ = true;
ops_.emplace_back(new OpDesc());
ops_.emplace_back(new OpDesc(this));
return ops_.back().get();
}
......@@ -86,7 +86,7 @@ void BlockDesc::AppendAllocatedOp(std::unique_ptr<OpDesc> &&op_desc) {
OpDesc *BlockDesc::PrependOp() {
need_update_ = true;
ops_.emplace_front(new OpDesc());
ops_.emplace_front(new OpDesc(this));
return ops_.front().get();
}
......@@ -153,7 +153,7 @@ BlockDesc::BlockDesc(ProgramDesc *prog, proto::BlockDesc *desc)
vars_[var_desc.name()].reset(new VarDesc(var_desc));
}
for (const proto::OpDesc &op_desc : desc_->ops()) {
ops_.emplace_back(new OpDesc(op_desc, prog));
ops_.emplace_back(new OpDesc(op_desc, prog, this));
}
}
......@@ -162,7 +162,7 @@ BlockDesc::BlockDesc(const BlockDesc &other, proto::BlockDesc *desc,
: prog_(prog), desc_(desc) {
need_update_ = true;
for (auto &op : other.ops_) {
ops_.emplace_back(new OpDesc(*op));
ops_.emplace_back(new OpDesc(*op, this));
}
for (auto &it : other.vars_) {
......
paddle/framework/executor.cc
浏览文件 @ b397febc
......@@ -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_);
......
paddle/framework/framework.proto
浏览文件 @ b397febc
......@@ -26,6 +26,7 @@ enum AttrType {
BOOLEAN = 6;
BOOLEANS = 7;
BLOCK = 8;
LONG = 9;
}
// OpDesc describes an instance of a C++ framework::OperatorBase
......@@ -44,6 +45,7 @@ message OpDesc {
optional bool b = 10;
repeated bool bools = 11;
optional int32 block_idx = 12;
optional int64 l = 13;
};
message Var {
......
paddle/framework/lod_tensor.cc
浏览文件 @ b397febc
......@@ -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;
......
paddle/framework/op_desc.cc
浏览文件 @ b397febc
......@@ -97,7 +97,7 @@ void OpDesc::CopyFrom(const OpDesc &op_desc) {
need_update_ = true;
}
OpDesc::OpDesc(const proto::OpDesc &desc, ProgramDesc *prog)
OpDesc::OpDesc(const proto::OpDesc &desc, ProgramDesc *prog, BlockDesc *block)
: desc_(desc), need_update_(false) {
// restore inputs_
int input_size = desc_.inputs_size();
......@@ -131,6 +131,7 @@ OpDesc::OpDesc(const proto::OpDesc &desc, ProgramDesc *prog)
attrs_[attr_name] = prog->MutableBlock(bid);
}
}
this->block_ = block;
}
proto::OpDesc *OpDesc::Proto() {
......@@ -282,6 +283,7 @@ struct SetAttrDescVisitor : public boost::static_visitor<void> {
VectorToRepeated(v, attr_->mutable_bools());
}
void operator()(BlockDesc *desc) const { attr_->set_block_idx(desc->ID()); }
void operator()(int64_t v) const { attr_->set_l(v); }
void operator()(boost::blank) const { PADDLE_THROW("Unexpected branch"); }
};
......
paddle/framework/op_desc.h
浏览文件 @ b397febc
......@@ -25,7 +25,6 @@ namespace framework {
class BlockDesc;
class ProgramDesc;
class OpDesc {
public:
OpDesc() {}
......@@ -33,7 +32,14 @@ class OpDesc {
OpDesc(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const AttributeMap &attrs);
OpDesc(const proto::OpDesc &desc, ProgramDesc *prog);
OpDesc(const proto::OpDesc &desc, ProgramDesc *prog, BlockDesc *block);
explicit OpDesc(BlockDesc *block) : block_(block) {}
OpDesc(const OpDesc &other, BlockDesc *block) {
*this = other;
block_ = block;
}
void CopyFrom(const OpDesc &op_desc);
......@@ -117,6 +123,10 @@ class OpDesc {
void Flush();
BlockDesc *Block() { return this->block_; }
void SetBlock(BlockDesc *block) { this->block_ = block; }
private:
template <typename MapType>
static std::vector<typename MapType::key_type> MapKeys(const MapType &map) {
......@@ -129,6 +139,7 @@ class OpDesc {
}
proto::OpDesc desc_;
BlockDesc *block_; // not_own
// input arg name => input variable names
VariableNameMap inputs_;
// output arg name => output variable names
......
paddle/framework/type_defs.h
浏览文件 @ b397febc
......@@ -35,7 +35,7 @@ using VariableNameMap = std::map<std::string, std::vector<std::string>>;
using Attribute =
boost::variant<boost::blank, int, float, std::string, std::vector<int>,
std::vector<float>, std::vector<std::string>, bool,
std::vector<bool>, BlockDesc*>;
std::vector<bool>, BlockDesc*, int64_t>;
using AttributeMap = std::unordered_map<std::string, Attribute>;
......
paddle/framework/var_desc.h
浏览文件 @ b397febc
......@@ -66,6 +66,8 @@ class VarDesc {
std::string Name() const { return desc_.name(); }
void SetName(std::string name) { desc_.set_name(name); }
void SetShape(const std::vector<int64_t> &dims);
void SetDataType(proto::DataType data_type);
......
paddle/gserver/layers/PriorBox.cpp
浏览文件 @ b397febc
......@@ -69,7 +69,7 @@ bool PriorBoxLayer::init(const LayerMap& layerMap,
if (maxSize_.size() > 0) CHECK_EQ(minSize_.size(), maxSize_.size());
// flip aspect ratios
for (int index = 0; index < tmp.size(); index++) {
for (unsigned index = 0; index < tmp.size(); index++) {
real ar = tmp[index];
if (fabs(ar - 1.) < 1e-6) continue;
aspectRatio_.push_back(ar);
......
paddle/memory/CMakeLists.txt
浏览文件 @ b397febc
add_subdirectory(detail)
cc_library(memory SRCS memory.cc DEPS place enforce)
cc_library(memcpy SRCS memcpy.cc)
cc_library(memcpy SRCS memcpy.cc DEPS place)
cc_library(paddle_memory
DEPS
......
paddle/operators/beam_search_op.cc
浏览文件 @ b397febc
......@@ -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);
paddle/operators/beam_search_op.h
浏览文件 @ b397febc
......@@ -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";
}
};
......
paddle/operators/ctc_align_op.h
浏览文件 @ b397febc
......@@ -51,7 +51,7 @@ class CTCAlignKernel : public framework::OpKernel<T> {
T prev_token = -1;
for (size_t i = input_lod[level][seq_idx];
i < input_lod[level][seq_idx + 1]; ++i) {
if (input_data[i] != blank &&
if ((unsigned)input_data[i] != blank &&
!(merge_repeated && input_data[i] == prev_token)) {
output_data[output_idx] = input_data[i];
++output_idx;
......
paddle/operators/iou_similarity_op.cc 0 → 100755
浏览文件 @ b397febc
/* 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 "paddle/operators/iou_similarity_op.h"
namespace paddle {
namespace operators {
class IOUSimilarityOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of IOUSimilarityOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"),
"Input(Y) of IOUSimilarityOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x_dims.size(), 2UL, "The rank of Input(X) must be 2.");
PADDLE_ENFORCE_EQ(x_dims[1], 4UL, "The shape of X is [N, 4]");
PADDLE_ENFORCE_EQ(y_dims.size(), 2UL, "The rank of Input(Y) must be 2.");
PADDLE_ENFORCE_EQ(y_dims[1], 4UL, "The shape of Y is [M, 4]");
ctx->ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", framework::make_ddim({x_dims[0], y_dims[0]}));
}
};
class IOUSimilarityOpMaker : public framework::OpProtoAndCheckerMaker {
public:
IOUSimilarityOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(LoDTensor, default LoDTensor<float>) "
"Box list X is a 2-D LoDTensor with shape [N, 4] holds N boxes, "
"each box is represented as [xmin, ymin, xmax, ymax], "
"the shape of X is [N, 4]. [xmin, ymin] is the left top "
"coordinate of the box if the input is image feature map, they "
"are close to the origin of the coordinate system. "
"[xmax, ymax] is the right bottom coordinate of the box. "
"This tensor can contain LoD information to represent a batch "
"of inputs. One instance of this batch can contain different "
"numbers of entities.");
AddInput("Y",
"(Tensor, default Tensor<float>) "
"Box list Y holds M boxes, each box is represented as "
"[xmin, ymin, xmax, ymax], the shape of X is [N, 4]. "
"[xmin, ymin] is the left top coordinate of the box if the "
"input is image feature map, and [xmax, ymax] is the right "
"bottom coordinate of the box.");
AddOutput("Out",
"(LoDTensor, the lod is same as input X) The output of "
"iou_similarity op, a tensor with shape [N, M] "
"representing pairwise iou scores.");
AddComment(R"DOC(
IOU Similarity Operator.
Computes intersection-over-union (IOU) between two box lists.
Box list 'X' should be a LoDTensor and 'Y' is a common Tensor,
boxes in 'Y' are shared by all instance of the batched inputs of X.
Given two boxes A and B, the calculation of IOU is as follows:
$$
IOU(A, B) =
\frac{area(A\cap B)}{area(A)+area(B)-area(A\cap B)}
$$
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(iou_similarity, ops::IOUSimilarityOp,
ops::IOUSimilarityOpMaker);
REGISTER_OP_CPU_KERNEL(
iou_similarity,
ops::IOUSimilarityKernel<paddle::platform::CPUDeviceContext, float>,
ops::IOUSimilarityKernel<paddle::platform::CPUDeviceContext, double>);
paddle/operators/iou_similarity_op.cu 0 → 100755
浏览文件 @ b397febc
/* 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 "paddle/operators/iou_similarity_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
iou_similarity,
ops::IOUSimilarityKernel<paddle::platform::CUDADeviceContext, float>,
ops::IOUSimilarityKernel<paddle::platform::CUDADeviceContext, double>);
paddle/operators/iou_similarity_op.h 0 → 100644
浏览文件 @ b397febc
/* 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. */
#pragma once
#include "paddle/framework/op_registry.h"
#include "paddle/platform/for_range.h"
template <typename T>
inline HOSTDEVICE T IOUSimilarity(T xmin1, T ymin1, T xmax1, T ymax1, T xmin2,
T ymin2, T xmax2, T ymax2) {
constexpr T zero = static_cast<T>(0);
T area1 = (ymax1 - ymin1) * (xmax1 - xmin1);
T area2 = (ymax2 - ymin2) * (xmax2 - xmin2);
T inter_xmax = xmax1 > xmax2 ? xmax2 : xmax1;
T inter_ymax = ymax1 > ymax2 ? ymax2 : ymax1;
T inter_xmin = xmin1 > xmin2 ? xmin1 : xmin2;
T inter_ymin = ymin1 > ymin2 ? ymin1 : ymin2;
T inter_height = inter_ymax - inter_ymin;
T inter_width = inter_xmax - inter_xmin;
inter_height = inter_height > zero ? inter_height : zero;
inter_width = inter_width > zero ? inter_width : zero;
T inter_area = inter_width * inter_height;
T union_area = area1 + area2 - inter_area;
T sim_score = inter_area / union_area;
return sim_score;
}
template <typename T>
struct IOUSimilarityFunctor {
IOUSimilarityFunctor(const T* x, const T* y, T* z, int cols)
: x_(x), y_(y), z_(z), cols_(static_cast<size_t>(cols)) {}
inline HOSTDEVICE void operator()(size_t row_id) const {
T x_min1 = x_[row_id * 4];
T y_min1 = x_[row_id * 4 + 1];
T x_max1 = x_[row_id * 4 + 2];
T y_max1 = x_[row_id * 4 + 3];
for (size_t i = 0; i < cols_; ++i) {
T x_min2 = y_[i * 4];
T y_min2 = y_[i * 4 + 1];
T x_max2 = y_[i * 4 + 2];
T y_max2 = y_[i * 4 + 3];
T sim = IOUSimilarity(x_min1, y_min1, x_max1, y_max1, x_min2, y_min2,
x_max2, y_max2);
z_[row_id * cols_ + i] = sim;
}
}
const T* x_;
const T* y_;
T* z_;
const size_t cols_;
};
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class IOUSimilarityKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const framework::LoDTensor* in_x = ctx.Input<framework::LoDTensor>("X");
const framework::Tensor* in_y = ctx.Input<framework::Tensor>("Y");
framework::LoDTensor* out = ctx.Output<framework::LoDTensor>("Out");
int x_n = in_x->dims()[0];
int y_n = in_y->dims()[0];
IOUSimilarityFunctor<T> functor(in_x->data<T>(), in_y->data<T>(),
out->mutable_data<T>(ctx.GetPlace()), y_n);
platform::ForRange<DeviceContext> for_range(
static_cast<const DeviceContext&>(ctx.device_context()), x_n);
for_range(functor);
}
}; // namespace operators
} // namespace operators
} // namespace paddle
paddle/operators/lookup_table_op.cc
浏览文件 @ b397febc
......@@ -66,6 +66,12 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
"(boolean, default false) "
"Sparse update")
.SetDefault(false);
AddAttr<int64_t>("padding_idx",
"(int64, default -1) "
"If the value is -1, it makes no effect to lookup. "
"Otherwise the given value indicates padding the output "
"with zeros whenever lookup encounters it in Ids.")
.SetDefault(-1);
AddComment(R"DOC(
Lookup Table Operator.
......
paddle/operators/lookup_table_op.cu
浏览文件 @ b397febc
......@@ -21,9 +21,11 @@ limitations under the License. */
namespace paddle {
namespace operators {
template <typename T, int BlockDimX, int BlockDimY, int GridDimX>
template <typename T, int BlockDimX, int BlockDimY, int GridDimX,
bool PaddingFlag>
__global__ void LookupTable(T* output, const T* table, const int64_t* ids,
const int64_t N, const int64_t K, const int64_t D) {
const int64_t N, const int64_t K, const int64_t D,
const int64_t padding_idx) {
int idx = threadIdx.x;
int idy = blockIdx.x + threadIdx.y * GridDimX;
......@@ -34,7 +36,14 @@ __global__ void LookupTable(T* output, const T* table, const int64_t* ids,
T* out = output + idy * D;
const T* tab = table + id * D;
for (int i = idx; i < D; i += BlockDimX) {
out[i] = tab[i];
if (PaddingFlag) {
if (id == padding_idx)
out[i] = static_cast<T>(0);
else
out[i] = tab[i];
} else {
out[i] = tab[i];
}
}
idy += BlockDimY * GridDimX;
}
......@@ -67,6 +76,7 @@ class LookupTableCUDAKernel : public framework::OpKernel<T> {
auto* table_t = context.Input<LoDTensor>("W");
auto* ids_t = context.Input<LoDTensor>("Ids");
auto* output_t = context.Output<LoDTensor>("Out");
int64_t padding_idx = context.Attr<int64_t>("padding_idx");
size_t N = table_t->dims()[0];
size_t D = table_t->dims()[1];
......@@ -77,10 +87,17 @@ class LookupTableCUDAKernel : public framework::OpKernel<T> {
dim3 threads(128, 8);
dim3 grids(8, 1);
LookupTable<
T, 128, 8,
8><<<grids, threads, 0, context.cuda_device_context().stream()>>>(
output, table, ids, N, K, D);
if (padding_idx == -1)
LookupTable<
T, 128, 8, 8,
false><<<grids, threads, 0, context.cuda_device_context().stream()>>>(
output, table, ids, N, K, D, padding_idx);
else
LookupTable<
T, 128, 8, 8,
true><<<grids, threads, 0, context.cuda_device_context().stream()>>>(
output, table, ids, N, K, D, padding_idx);
}
};
......@@ -91,6 +108,8 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
auto& dev_ctx =
context.template device_context<platform::CUDADeviceContext>();
bool is_sparse = context.Attr<bool>("is_sparse");
// Since paddings are not trainable and fixed in forward, the gradient of
// paddings makes no sense and we don't deal with it in backward.
if (is_sparse) {
auto* ids = context.Input<LoDTensor>("Ids");
auto* table = context.Input<LoDTensor>("W");
......
paddle/operators/lookup_table_op.h
浏览文件 @ b397febc
......@@ -32,16 +32,30 @@ class LookupTableKernel : public framework::OpKernel<T> {
auto* table_t = context.Input<LoDTensor>("W"); // float tensor
auto* ids_t = context.Input<LoDTensor>("Ids"); // int tensor
auto* output_t = context.Output<LoDTensor>("Out"); // float tensor
int64_t padding_idx = context.Attr<int64_t>("padding_idx");
int N = table_t->dims()[0];
int D = table_t->dims()[1];
auto* ids = ids_t->data<int64_t>();
auto* table = table_t->data<T>();
auto* output = output_t->mutable_data<T>(context.GetPlace());
for (int64_t i = 0; i < ids_t->numel(); ++i) {
PADDLE_ENFORCE_LT(ids[i], N);
PADDLE_ENFORCE_GE(ids[i], 0);
memcpy(output + i * D, table + ids[i] * D, D * sizeof(T));
if (padding_idx == -1) {
for (int64_t i = 0; i < ids_t->numel(); ++i) {
PADDLE_ENFORCE_LT(ids[i], N);
PADDLE_ENFORCE_GE(ids[i], 0);
memcpy(output + i * D, table + ids[i] * D, D * sizeof(T));
}
} else {
for (int64_t i = 0; i < ids_t->numel(); ++i) {
if (ids[i] == padding_idx) {
memset(output + i * D, 0, D * sizeof(T));
} else {
PADDLE_ENFORCE_LT(ids[i], N);
PADDLE_ENFORCE_GE(ids[i], 0);
memcpy(output + i * D, table + ids[i] * D, D * sizeof(T));
}
}
}
}
};
......@@ -51,6 +65,8 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
bool is_sparse = context.Attr<bool>("is_sparse");
// Since paddings are not trainable and fixed in forward, the gradient of
// paddings makes no sense and we don't deal with it in backward.
if (is_sparse) {
auto* ids = context.Input<LoDTensor>("Ids");
auto* table = context.Input<LoDTensor>("W");
......
paddle/operators/prior_box_op.cc 0 → 100644
浏览文件 @ b397febc
/* 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 "paddle/operators/prior_box_op.h"
namespace paddle {
namespace operators {
class PriorBoxOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of PriorBoxOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Image"),
"Input(Image) of PriorBoxOp should not be null.");
auto image_dims = ctx->GetInputDim("Image");
auto input_dims = ctx->GetInputDim("Input");
PADDLE_ENFORCE(image_dims.size() == 4, "The layout of image is NCHW.");
PADDLE_ENFORCE(input_dims.size() == 4, "The layout of input is NCHW.");
PADDLE_ENFORCE_LT(input_dims[2], image_dims[2],
"The height of input must smaller than image.");
PADDLE_ENFORCE_LT(input_dims[3], image_dims[3],
"The width of input must smaller than image.");
auto min_sizes = ctx->Attrs().Get<std::vector<int>>("min_sizes");
auto max_sizes = ctx->Attrs().Get<std::vector<int>>("max_sizes");
auto variances = ctx->Attrs().Get<std::vector<float>>("variances");
auto aspect_ratios = ctx->Attrs().Get<std::vector<float>>("aspect_ratios");
bool flip = ctx->Attrs().Get<bool>("flip");
PADDLE_ENFORCE_GT(min_sizes.size(), 0,
"Size of min_sizes must be at least 1.");
for (size_t i = 0; i < min_sizes.size(); ++i) {
PADDLE_ENFORCE_GT(min_sizes[i], 0, "min_sizes[%d] must be positive.", i);
}
std::vector<float> aspect_ratios_vec;
ExpandAspectRatios(aspect_ratios, flip, aspect_ratios_vec);
int num_priors = aspect_ratios_vec.size() * min_sizes.size();
if (max_sizes.size() > 0) {
PADDLE_ENFORCE_EQ(max_sizes.size(), min_sizes.size(),
"The number of min_size and max_size must be equal.");
for (size_t i = 0; i < min_sizes.size(); ++i) {
PADDLE_ENFORCE_GT(max_sizes[i], min_sizes[i],
"max_size[%d] must be greater than min_size[%d].", i,
i);
num_priors += 1;
}
}
PADDLE_ENFORCE_EQ(variances.size(), 4, "Must and only provide 4 variance.");
for (size_t i = 0; i < variances.size(); ++i) {
PADDLE_ENFORCE_GT(variances[i], 0.0,
"variance[%d] must be greater than 0.", i);
}
const float step_h = ctx->Attrs().Get<float>("step_h");
PADDLE_ENFORCE_GT(step_h, 0.0, "step_h should be larger than 0.");
const float step_w = ctx->Attrs().Get<float>("step_w");
PADDLE_ENFORCE_GT(step_w, 0.0, "step_w should be larger than 0.");
std::vector<int64_t> dim_vec(4);
dim_vec[0] = input_dims[2];
dim_vec[1] = input_dims[3];
dim_vec[2] = num_priors;
dim_vec[3] = 4;
ctx->SetOutputDim("Boxes", framework::make_ddim(dim_vec));
ctx->SetOutputDim("Variances", framework::make_ddim(dim_vec));
}
};
class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
public:
PriorBoxOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Input",
"(Tensor, default Tensor<float>), "
"the input feature data of PriorBoxOp, The layout is NCHW.");
AddInput("Image",
"(Tensor, default Tensor<float>), "
"the input image data of PriorBoxOp, The layout is NCHW.");
AddOutput("Boxes",
"(Tensor, default Tensor<float>), the output prior boxes of "
"PriorBoxOp. The layout is [H, W, num_priors, 4]. "
"H is the height of input, W is the width of input, num_priors "
"is the box count of each position.");
AddOutput("Variances",
"(Tensor, default Tensor<float>), the expanded variances of "
"PriorBoxOp. The layout is [H, W, num_priors, 4]. "
"H is the height of input, W is the width of input, num_priors "
"is the box count of each position.");
AddAttr<std::vector<int>>("min_sizes", "(vector<int>) ",
"List of min sizes of generated prior boxes.");
AddAttr<std::vector<int>>("max_sizes", "(vector<int>) ",
"List of max sizes of generated prior boxes.");
AddAttr<std::vector<float>>(
"aspect_ratios", "(vector<float>) ",
"List of aspect ratios of generated prior boxes.");
AddAttr<std::vector<float>>(
"variances", "(vector<float>) ",
"List of variances to be encoded in prior boxes.");
AddAttr<bool>("flip", "(bool) ", "Whether to flip aspect ratios.")
.SetDefault(true);
AddAttr<bool>("clip", "(bool) ", "Whether to clip out-of-boundary boxes.")
.SetDefault(true);
AddAttr<float>("step_w",
"Prior boxes step across width, 0 for auto calculation.")
.SetDefault(0.0);
AddAttr<float>("step_h",
"Prior boxes step across height, 0 for auto calculation.")
.SetDefault(0.0);
AddAttr<float>("offset",
"(float) "
"Prior boxes center offset.")
.SetDefault(0.5);
AddComment(R"DOC(
Prior box operator
Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
Each position of the input produce N prior boxes, N is determined by
the count of min_sizes, max_sizes and aspect_ratios, The size of the
box is in range(min_size, max_size) interval, which is generated in
sequence according to the aspect_ratios.
Please get more information from the following papers:
https://arxiv.org/abs/1512.02325.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(prior_box, ops::PriorBoxOp, ops::PriorBoxOpMaker);
REGISTER_OP_CPU_KERNEL(
prior_box, ops::PriorBoxOpKernel<paddle::platform::CPUPlace, float>,
ops::PriorBoxOpKernel<paddle::platform::CPUPlace, double>);
paddle/operators/prior_box_op.h 0 → 100644
浏览文件 @ b397febc
/* 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. */
#pragma once
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/platform/transform.h"
namespace paddle {
namespace operators {
inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratior,
bool flip,
std::vector<float>& output_aspect_ratior) {
constexpr float epsilon = 1e-6;
output_aspect_ratior.clear();
output_aspect_ratior.push_back(1.);
for (size_t i = 0; i < input_aspect_ratior.size(); ++i) {
float ar = input_aspect_ratior[i];
bool already_exist = false;
for (size_t j = 0; j < output_aspect_ratior.size(); ++j) {
if (fabs(ar - output_aspect_ratior[j]) < epsilon) {
already_exist = true;
break;
}
}
if (!already_exist) {
output_aspect_ratior.push_back(ar);
if (flip) {
output_aspect_ratior.push_back(1. / ar);
}
}
}
}
template <typename T>
struct ClipFunctor {
HOSTDEVICE T operator()(T in) const {
return std::min<T>(std::max<T>(in, 0.), 1.);
}
};
template <typename Place, typename T>
class PriorBoxOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<paddle::framework::Tensor>("Input");
auto* image = ctx.Input<paddle::framework::Tensor>("Image");
auto* boxes = ctx.Output<paddle::framework::Tensor>("Boxes");
auto* vars = ctx.Output<paddle::framework::Tensor>("Variances");
auto min_sizes = ctx.Attr<std::vector<int>>("min_sizes");
auto max_sizes = ctx.Attr<std::vector<int>>("max_sizes");
auto input_aspect_ratio = ctx.Attr<std::vector<float>>("aspect_ratios");
auto variances = ctx.Attr<std::vector<float>>("variances");
auto flip = ctx.Attr<bool>("flip");
auto clip = ctx.Attr<bool>("clip");
std::vector<float> aspect_ratios;
ExpandAspectRatios(input_aspect_ratio, flip, aspect_ratios);
T step_w = static_cast<T>(ctx.Attr<float>("step_w"));
T step_h = static_cast<T>(ctx.Attr<float>("step_h"));
T offset = static_cast<T>(ctx.Attr<float>("offset"));
auto img_width = image->dims()[3];
auto img_height = image->dims()[2];
auto feature_width = input->dims()[3];
auto feature_height = input->dims()[2];
T step_width, step_height;
if (step_w == 0 || step_h == 0) {
step_width = static_cast<T>(img_width) / feature_width;
step_height = static_cast<T>(img_height) / feature_height;
} else {
step_width = step_w;
step_height = step_h;
}
int num_priors = aspect_ratios.size() * min_sizes.size();
if (max_sizes.size() > 0) {
num_priors += max_sizes.size();
}
boxes->mutable_data<T>(ctx.GetPlace());
vars->mutable_data<T>(ctx.GetPlace());
auto e_boxes = framework::EigenTensor<T, 4>::From(*boxes);
for (int h = 0; h < feature_height; ++h) {
for (int w = 0; w < feature_width; ++w) {
T center_x = (w + offset) * step_width;
T center_y = (h + offset) * step_height;
T box_width, box_height;
int idx = 0;
for (size_t s = 0; s < min_sizes.size(); ++s) {
int min_size = min_sizes[s];
// first prior: aspect_ratio = 1, size = min_size
box_width = box_height = min_size;
// xmin
e_boxes(h, w, idx, 0) = (center_x - box_width / 2.) / img_width;
// ymin
e_boxes(h, w, idx, 1) = (center_y - box_height / 2.) / img_height;
// xmax
e_boxes(h, w, idx, 2) = (center_x + box_width / 2.) / img_width;
// ymax
e_boxes(h, w, idx, 3) = (center_y + box_height / 2.) / img_height;
idx++;
if (max_sizes.size() > 0) {
int max_size = max_sizes[s];
// second prior: aspect_ratio = 1,
// size = sqrt(min_size * max_size)
box_width = box_height = sqrt(min_size * max_size);
// xmin
e_boxes(h, w, idx, 0) = (center_x - box_width / 2.) / img_width;
// ymin
e_boxes(h, w, idx, 1) = (center_y - box_height / 2.) / img_height;
// xmax
e_boxes(h, w, idx, 2) = (center_x + box_width / 2.) / img_width;
// ymax
e_boxes(h, w, idx, 3) = (center_y + box_height / 2.) / img_height;
idx++;
}
// rest of priors
for (size_t r = 0; r < aspect_ratios.size(); ++r) {
float ar = aspect_ratios[r];
if (fabs(ar - 1.) < 1e-6) {
continue;
}
box_width = min_size * sqrt(ar);
box_height = min_size / sqrt(ar);
// xmin
e_boxes(h, w, idx, 0) = (center_x - box_width / 2.) / img_width;
// ymin
e_boxes(h, w, idx, 1) = (center_y - box_height / 2.) / img_height;
// xmax
e_boxes(h, w, idx, 2) = (center_x + box_width / 2.) / img_width;
// ymax
e_boxes(h, w, idx, 3) = (center_y + box_height / 2.) / img_height;
idx++;
}
}
}
}
if (clip) {
platform::Transform<platform::CPUDeviceContext> trans;
ClipFunctor<T> clip_func;
trans(ctx.template device_context<platform::CPUDeviceContext>(),
boxes->data<T>(), boxes->data<T>() + boxes->numel(),
boxes->data<T>(), clip_func);
}
framework::Tensor var_t;
var_t.mutable_data<T>(
framework::make_ddim({1, static_cast<int>(variances.size())}),
ctx.GetPlace());
auto var_et = framework::EigenTensor<T, 2>::From(var_t);
for (size_t i = 0; i < variances.size(); ++i) {
var_et(0, i) = variances[i];
}
int box_num = feature_height * feature_width * num_priors;
auto var_dim = vars->dims();
vars->Resize({box_num, static_cast<int>(variances.size())});
auto e_vars = framework::EigenMatrix<T, Eigen::RowMajor>::From(*vars);
e_vars = var_et.broadcast(Eigen::DSizes<int, 2>(box_num, 1));
vars->Resize(var_dim);
}
}; // namespace operators
} // namespace operators
} // namespace paddle
paddle/operators/sequence_expand_op.h
浏览文件 @ b397febc
......@@ -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)"
......
paddle/operators/sequence_reshape_op.h
浏览文件 @ b397febc
......@@ -35,7 +35,7 @@ class SequenceReshapeKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ(in_lod.size(), 1UL,
"Only support one level sequence now.");
PADDLE_ENFORCE_EQ(
in_dims[0], in_lod[0].back(),
(uint64_t)in_dims[0], in_lod[0].back(),
"Inconsistent size between X.shape[0] and X.lod()[0].back().");
auto in_lod_l0 = in_lod[0];
......
paddle/operators/top_k_op.h
浏览文件 @ b397febc
......@@ -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"));
......
paddle/platform/CMakeLists.txt
浏览文件 @ b397febc
......@@ -10,7 +10,7 @@ cc_test(cpu_info_test SRCS cpu_info_test.cc DEPS cpu_info)
nv_library(gpu_info SRCS gpu_info.cc DEPS gflags glog enforce)
cc_library(place SRCS place.cc DEPS enforce)
cc_library(place SRCS place.cc DEPS enforce boost)
cc_test(place_test SRCS place_test.cc DEPS place glog gflags)
add_subdirectory(dynload)
......
paddle/pybind/print_operators_doc.cc
浏览文件 @ b397febc
......@@ -64,6 +64,8 @@ std::string AttrType(paddle::framework::proto::AttrType at) {
return "bool array";
case paddle::framework::proto::BLOCK:
return "block id";
case paddle::framework::proto::LONG:
return "long";
}
return "UNKNOWN"; // not possible
}
......
paddle/pybind/protobuf.cc
浏览文件 @ b397febc
......@@ -212,6 +212,7 @@ void BindVarDsec(py::module &m) {
return name;
},
py::return_value_policy::reference)
.def("set_name", &VarDesc::SetName)
.def("set_shape", &VarDesc::SetShape)
.def("set_dtype", &VarDesc::SetDataType)
.def("shape", &VarDesc::Shape, py::return_value_policy::reference)
......@@ -280,7 +281,8 @@ void BindOpDesc(py::module &m) {
.def("check_attrs", &OpDesc::CheckAttrs)
.def("infer_shape", &OpDesc::InferShape)
.def("infer_var_type", &OpDesc::InferVarType)
.def("serialize_to_string", SerializeMessage<OpDesc>);
.def("serialize_to_string", SerializeMessage<OpDesc>)
.def("block", &OpDesc::Block, py::return_value_policy::reference);
}
} // namespace pybind
......
python/paddle/v2/fluid/layer_helper.py
浏览文件 @ b397febc
......@@ -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,
......
python/paddle/v2/fluid/layers/control_flow.py
浏览文件 @ b397febc
......@@ -770,7 +770,7 @@ 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',
......
python/paddle/v2/fluid/layers/nn.py
浏览文件 @ b397febc
......@@ -26,6 +26,7 @@ __all__ = [
'fc',
'embedding',
'dynamic_lstm',
'dynamic_gru',
'gru_unit',
'linear_chain_crf',
'crf_decoding',
......@@ -58,7 +59,9 @@ __all__ = [
'warpctc',
'sequence_reshape',
'transpose',
'im2sequence',
'nce',
'beam_search',
]
......@@ -161,10 +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})
......@@ -183,22 +184,35 @@ def fc(input,
return helper.append_activation(pre_activation)
def embedding(input, size, is_sparse=False, param_attr=None, dtype='float32'):
def embedding(input,
size,
is_sparse=False,
padding_idx=None,
param_attr=None,
dtype='float32'):
"""
**Embedding Layer**
This layer is used to lookup a vector of IDs, provided by *input*, in a lookup table.
The result of this lookup is the embedding of each ID in the *input*.
This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
a lookup table. The result of this lookup is the embedding of each ID in the
:attr:`input`.
All the input variables are passed in as local variables to the LayerHelper
constructor.
Args:
input(Variable): Input to the function
size(tuple|list|None): Shape of the look up table parameter
is_sparse(bool): Boolean flag that specifying whether the input is sparse
param_attr(ParamAttr): Parameters for this layer
dtype(np.dtype|core.DataType|str): The type of data : float32, float_16, int etc
input(Variable): The tensor variable containing the IDs.
size(tuple|list): The shape of the look up table parameter. It should
have two elements which indicate the size of the dictionary of
embeddings and the size of each embedding vector respectively.
is_sparse(bool): The flag indicating whether to use sparse update.
padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup.
Otherwise the given :attr:`padding_idx` indicates padding the output
with zeros whenever lookup encounters it in :attr:`input`. If
:math:`padding_idx < 0`, the padding_idx to use in lookup is
:math:`size[0] + dim`.
param_attr(ParamAttr): Parameters for this layer
dtype(np.dtype|core.DataType|str): The type of data : float32, float_16, int etc
Returns:
Variable: The tensor variable storing the embeddings of the \
......@@ -216,12 +230,15 @@ def embedding(input, size, is_sparse=False, param_attr=None, dtype='float32'):
w = helper.create_parameter(
attr=helper.param_attr, shape=size, dtype=dtype, is_bias=False)
tmp = helper.create_tmp_variable(dtype)
padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else (
size[0] + padding_idx)
helper.append_op(
type='lookup_table',
inputs={'Ids': input,
'W': w},
outputs={'Out': tmp},
attrs={'is_sparse': is_sparse})
attrs={'is_sparse': is_sparse,
'padding_idx': padding_idx})
return tmp
......@@ -368,6 +385,113 @@ def dynamic_lstm(input,
return hidden, cell
def dynamic_gru(input,
size,
param_attr=None,
bias_attr=None,
is_reverse=False,
gate_activation='sigmoid',
candidate_activation='tanh',
h_0=None):
"""
**Dynamic GRU Layer**
Refer to `Empirical Evaluation of Gated Recurrent Neural Networks on
Sequence Modeling <https://arxiv.org/abs/1412.3555>`_
The formula is as follows:
.. math::
u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)
r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)
\\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)
h_t & = (1-u_t) \odot h_{t-1} + u_t \odot \\tilde{h_t}
The :math:`\odot` is the element-wise product of the vectors. :math:`act_g`
is the update gate and reset gate activation function and :math:`sigmoid`
is usually used for it. :math:`act_c` is the activation function for
candidate hidden state and :math:`tanh` is usually used for it.
Note that these :math:`W_{ux}x_{t}, W_{rx}x_{t}, W_{cx}x_{t}` operations on
the input :math:`x_{t}` are NOT included in this operator. Users can choose
to use fully-connect layer before GRU layer.
Args:
input(Variable): The input of dynamic_gru layer, which supports
variable-time length input sequence. The underlying tensor in this
Variable is a matrix with shape :math:`(T \\times 3D)`, where
:math:`T` is the total time steps in this mini-batch, :math:`D`
is the hidden size.
size(int): The dimension of the gru cell.
param_attr(ParamAttr|None): The parameter attribute for the learnable
hidden-hidden weight matrix. Note:
- The shape of the weight matrix is :math:`(T \\times 3D)`, where
:math:`D` is the hidden size.
- All elements in the weight matrix can be divided into two parts.
The first part are weights of the update gate and reset gate with
shape :math:`(D \\times 2D)`, and the second part are weights for
candidate hidden state with shape :math:`(D \\times D)`.
bias_attr(ParamAttr): The parameter attribute for learnable the
hidden-hidden bias.
is_reverse(bool): Whether to compute reversed GRU, default
:attr:`False`.
gate_activation(str): The activation for update gate and reset gate.
Choices = ["sigmoid", "tanh", "relu", "identity"], default "sigmoid".
activation(str): The activation for candidate hidden state.
Choices = ["sigmoid", "tanh", "relu", "identity"], default "tanh".
Returns:
Variable: The hidden state of GRU. The shape is (T \\times D), and lod \
is the same with the input.
Examples:
.. code-block:: python
hidden_dim = 512
x = fluid.layers.fc(input=data, size=hidden_dim * 3)
hidden = fluid.layers.dynamic_gru(input=x, dim=hidden_dim)
"""
helper = LayerHelper('gru', **locals())
dtype = helper.input_dtype()
weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
bias = helper.create_parameter(
attr=helper.bias_attr, shape=[1, 3 * size], dtype=dtype, is_bias=True)
inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
if h_0 != None:
assert h_0.shape == (
size, size), 'The shape of h0 should be(%d, %d)' % (size, size)
inputs['h0'] = h_0
hidden = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype)
batch_reset_hidden_prev = helper.create_tmp_variable(dtype)
batch_hidden = helper.create_tmp_variable(dtype)
helper.append_op(
type='gru',
inputs=inputs,
outputs={
'Hidden': hidden,
'BatchGate': batch_gate,
'BatchResetHiddenPrev': batch_reset_hidden_prev,
'BatchHidden': batch_hidden
},
attrs={
'is_reverse': is_reverse,
'gate_activation': gate_activation,
'activation': candidate_activation
})
return hidden
def gru_unit(input,
hidden,
size,
......@@ -1426,6 +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,
......@@ -2283,3 +2439,128 @@ def transpose(x, perm, name=None):
outputs={'Out': [out]},
attrs={'axis': perm})
return out
def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
"""
Extracts image patches from the input tensor to form a tensor of shape
{input.batch_size * output_height * output_width, filter_size_H *
filter_size_W * input.channels} which is similar with im2col.
This op use filter / kernel to scan images and convert these images to
sequences. After expanding, the number of time step are
output_height * output_width for an image, in which output_height and
output_width are calculated by below equation:
.. math::
output\_size = 1 + \
(2 * padding + img\_size - block\_size + stride - 1) / stride
And the dimension of each time step is block_y * block_x * input.channels.
Args:
input (Variable): The input should be a tensor in NCHW format.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
padding(int|tuple): The padding size. If padding is a tuple, it can
contain two integers like (padding_H, padding_W) which means
padding_up = padding_down = padding_H and
padding_left = padding_right = padding_W. Or it can use
(padding_up, padding_left, padding_down, padding_right) to indicate
paddings of four direction. Otherwise, a scalar padding means
padding_up = padding_down = padding_left = padding_right = padding
Default: padding = 0.
name (int): The name of this layer. It is optional.
Returns:
output: The output is a LoDTensor with shape
{input.batch_size * output_height * output_width,
filter_size_H * filter_size_W * input.channels}.
If we regard output as a matrix, each row of this matrix is
a step of a sequence.
Examples:
As an example:
.. code-block:: text
Given:
x = [[[[ 6. 2. 1.]
[ 8. 3. 5.]
[ 0. 2. 6.]]
[[ 2. 4. 4.]
[ 6. 3. 0.]
[ 6. 4. 7.]]]
[[[ 6. 7. 1.]
[ 5. 7. 9.]
[ 2. 4. 8.]]
[[ 1. 2. 1.]
[ 1. 3. 5.]
[ 9. 0. 8.]]]]
x.dims = {2, 2, 3, 3}
And:
filter = [2, 2]
stride = [1, 1]
padding = [0, 0]
Then:
output.data = [[ 6. 2. 8. 3. 2. 4. 6. 3.]
[ 2. 1. 3. 5. 4. 4. 3. 0.]
[ 8. 3. 0. 2. 6. 3. 6. 4.]
[ 3. 5. 2. 6. 3. 0. 4. 7.]
[ 6. 7. 5. 7. 1. 2. 1. 3.]
[ 7. 1. 7. 9. 2. 1. 3. 5.]
[ 5. 7. 2. 4. 1. 3. 9. 0.]
[ 7. 9. 4. 8. 3. 5. 0. 8.]]
output.dims = {8, 9}
output.lod = [[0, 4, 8]]
The simple usage is:
.. code-block:: python
output = fluid.layers.im2sequence(input=layer, stride=[1, 1], filter_size=[2, 2])
"""
if isinstance(filter_size, int):
filter_size = [filter_size, filter_size]
if isinstance(stride, int):
stride = [stride, stride]
if isinstance(padding, int):
padding = [padding, padding]
if len(padding) == 2:
padding.append(padding[0])
padding.append(padding[1])
helper = LayerHelper('im2sequence', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='im2sequence',
inputs={'X': input},
outputs={'Out': out},
attrs={
'kernels': filter_size,
'strides': stride,
'paddings': padding,
})
return out
python/paddle/v2/fluid/memory_optimization_transpiler.py
浏览文件 @ b397febc
......@@ -31,10 +31,12 @@ dtype_to_size = {
class ControlFlowGraph(object):
def __init__(self, Program):
def __init__(self, Program, ops, forward_num):
self._program = Program
self._succesors = defaultdict(set)
self._presucessors = defaultdict(set)
self._ops = ops
self._forward_num = forward_num
self._successors = defaultdict(set)
self._presuccessors = defaultdict(set)
self._uses = defaultdict(set)
self._defs = defaultdict(set)
self._live_in = defaultdict(set)
......@@ -45,25 +47,16 @@ class ControlFlowGraph(object):
self._add(node1, node2)
def _add(self, node1, node2):
self._succesors[node1].add(node2)
self._presucessors[node2].add(node1)
self._successors[node1].add(node2)
self._presuccessors[node2].add(node1)
def _build_graph(self):
program_desc = self._program.get_desc()
block_size = program_desc.num_blocks()
# TODO(qijun) handle Program with if/while operators
self.global_block_desc = program_desc.block(0)
self.op_size = self.global_block_desc.op_size()
self.op_size = len(self._ops)
op_node_connections = [(i, i + 1) for i in range(self.op_size - 1)]
self._add_connections(op_node_connections)
self.ops = [self.global_block_desc.op(i) for i in range(self.op_size)]
for i in range(self.op_size):
self._uses[i].update(self.ops[i].input_arg_names())
self._defs[i].update(self.ops[i].output_arg_names())
self._uses[i].update(self._ops[i].input_arg_names())
self._defs[i].update(self._ops[i].output_arg_names())
def _update_graph(self, old_name, new_name, begin_idx=0):
for i in range(begin_idx, self.op_size):
......@@ -103,7 +96,7 @@ class ControlFlowGraph(object):
live_out[i] = set(self._live_out[i])
self._live_in[i] = self._uses[i] | (
self._live_out[i] - self._defs[i])
for s in self._succesors[i]:
for s in self._successors[i]:
self._live_out[i] |= self._live_in[s]
if self._reach_fixed_point(live_in, live_out):
......@@ -113,39 +106,76 @@ class ControlFlowGraph(object):
u = a & b
return a - u, b - u
def _has_var(self, block_desc, var_name, is_forward):
if is_forward:
return block_desc.has_var(str(var_name))
else:
return block_desc.has_var_recursive(str(var_name))
def _find_var(self, block_desc, var_name, is_forward):
if is_forward:
return block_desc.find_var(str(var_name))
else:
return block_desc.find_var_recursive(str(var_name))
def memory_optimize(self):
def check_var_validity(block_desc, x, is_forward):
if str(x) == "@EMPTY@":
return False
if not self._has_var(block_desc, x, is_forward):
return False
if self._find_var(block_desc, x, is_forward).persistable():
return False
if self._find_var(
block_desc, x,
is_forward).type() != core.VarDesc.VarType.LOD_TENSOR:
return False
return True
self._build_graph()
self._dataflow_analyze()
self.pool = []
for i in range(self.op_size):
op = self._ops[i]
if op.type() == "while" or op.type() == "while_grad":
continue
block_desc = op.block()
is_forward = i < self._forward_num
if self.pool:
out_pair = [(x, self.global_block_desc.var(str(x)).shape())
for x in self._defs[i]]
defs_can_optimize = filter(
lambda x: check_var_validity(block_desc, x, is_forward),
self._defs[i])
out_pair = [
(x, self._find_var(block_desc, x, is_forward).shape())
for x in defs_can_optimize
]
for x, x_shape in out_pair:
if not self.global_block_desc.var(str(x)).persistable():
for index, cache_pair in enumerate(self.pool):
cache_var = cache_pair[0]
cache_shape = cache_pair[1]
if x_shape == cache_shape:
x_dtype = self.global_block_desc.var(str(
x)).dtype()
cache_dtype = self.global_block_desc.var(
str(cache_var)).dtype()
for index, cache_pair in enumerate(self.pool):
cache_var = cache_pair[0]
cache_shape = cache_pair[1]
if x_shape == cache_shape:
if self._has_var(block_desc, cache_var, is_forward):
x_dtype = self._find_var(block_desc, x,
is_forward).dtype()
cache_dtype = self._find_var(
block_desc, cache_var, is_forward).dtype()
# TODO(qijun): actually, we should compare dtype_to_size[x_dtype]
# and dtype_to_size[cache_dtype]
if x_dtype == cache_dtype:
print(
("Hit Cache !!!! cache pool index "
"is %d, var name is %s, "
"cached var name is %s, "
"var shape is %s ") %
(index, x, cache_var, str(cache_shape)))
print(("Hit Cache !!!! cache pool index "
"is %d, var name is %s, "
"cached var name is %s, "
"var shape is %s ") %
(index, x, cache_var,
str(cache_shape)))
self.pool.pop(index)
if x == cache_var:
break
_rename_arg_(
self.ops, x, cache_var, begin_idx=i)
self._program.current_block().var(str(
x)).desc = self.global_block_desc.var(
str(cache_var))
self._ops, x, cache_var, begin_idx=i)
self._program.block(block_desc.id).var(
str(x)).desc = self._find_var(
block_desc, cache_var, is_forward)
self._update_graph(
x, cache_var, begin_idx=i)
break
......@@ -153,20 +183,70 @@ class ControlFlowGraph(object):
in_diff, out_diff = self._get_diff(self._live_in[i],
self._live_out[i])
can_optimize = filter(
lambda x: not self.global_block_desc.var(str(x)).persistable(),
lambda x: check_var_validity(block_desc, x, is_forward),
in_diff)
if can_optimize:
for var_name in can_optimize:
self.pool.append(
(var_name,
self.global_block_desc.var(str(var_name)).shape()))
def get_program(self):
return self._program
self.pool.append((var_name, self._find_var(
block_desc, var_name, is_forward).shape()))
def get_cfgs(input_program):
ops_list = []
pdesc = input_program.get_desc()
block_desc = pdesc.block(0)
op_size = block_desc.op_size()
# Get global block ops
ops_list.append(([block_desc.op(i) for i in range(op_size)], op_size))
while_sub_block_ids = []
while_grad_sub_block_ids = []
while_pair = []
for i in range(op_size):
op = block_desc.op(i)
if op.type() == "while":
while_sub_block_ids.append(op.attr("sub_block").id)
elif op.type() == "while_grad":
while_grad_sub_block_ids.append(op.attr("sub_block").id)
# Find while/while_grad block pair
for grad_id in while_grad_sub_block_ids:
parent_id = pdesc.block(grad_id).parent
if parent_id in while_sub_block_ids:
while_pair.append((parent_id, grad_id))
while_sub_block_ids.remove(parent_id)
# Get while/while_grad block ops
for parent_id, grad_id in while_pair:
while_block_ops = []
while_block = pdesc.block(parent_id)
while_block_op_size = while_block.op_size()
for i in range(while_block_op_size):
while_block_ops.append(while_block.op(i))
while_grad_block = pdesc.block(grad_id)
while_grad_block_op_size = while_grad_block.op_size()
for i in range(while_grad_block_op_size):
while_block_ops.append(while_grad_block.op(i))
ops_list.append((while_block_ops, while_block_op_size))
# Process rest while block ops
for parent_id in while_sub_block_ids:
while_block_ops = []
while_block = pdesc.block(parent_id)
while_block_op_size = while_block.op_size()
for i in range(while_block_op_size):
while_block_ops.append(while_block.op(i))
ops_list.append((while_block_ops, while_block_op_size))
cfgs = [ControlFlowGraph(input_program, i, j) for i, j in ops_list]
return cfgs
def memory_optimize(input_program):
graph = ControlFlowGraph(input_program)
graph.memory_optimize()
result_program = graph.get_program()
return result_program
cfgs = get_cfgs(input_program)
for cfg in cfgs:
cfg.memory_optimize()
python/paddle/v2/fluid/nets.py
浏览文件 @ b397febc
......@@ -56,7 +56,7 @@ def img_conv_group(input,
conv_act=None,
param_attr=None,
conv_with_batchnorm=False,
conv_batchnorm_drop_rate=None,
conv_batchnorm_drop_rate=0.0,
pool_stride=1,
pool_type=None,
use_cudnn=True):
......@@ -127,21 +127,21 @@ def sequence_conv_pool(input,
def glu(input, dim=-1):
"""
The gated linear unit composed by split, sigmoid activation and elementwise
multiplication. Specifically, Split the input into two equal sized parts
:math:`a` and :math:`b` along the given dimension and then compute as
The gated linear unit composed by split, sigmoid activation and elementwise
multiplication. Specifically, Split the input into two equal sized parts
:math:`a` and :math:`b` along the given dimension and then compute as
following:
.. math::
{GLU}(a, b)= a \otimes \sigma(b)
Refer to `Language Modeling with Gated Convolutional Networks
Refer to `Language Modeling with Gated Convolutional Networks
<https://arxiv.org/pdf/1612.08083.pdf>`_.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int): The dimension along which to split. If :math:`dim < 0`, the
dim (int): The dimension along which to split. If :math:`dim < 0`, the
dimension to split along is :math:`rank(input) + dim`.
Returns:
......@@ -164,24 +164,24 @@ def dot_product_attention(querys, keys, values):
"""
The dot-product attention.
Attention mechanism can be seen as mapping a query and a set of key-value
pairs to an output. The output is computed as a weighted sum of the values,
where the weight assigned to each value is computed by a compatibility
Attention mechanism can be seen as mapping a query and a set of key-value
pairs to an output. The output is computed as a weighted sum of the values,
where the weight assigned to each value is computed by a compatibility
function (dot-product here) of the query with the corresponding key.
The dot-product attention can be implemented through (batch) matrix
The dot-product attention can be implemented through (batch) matrix
multipication as follows:
.. math::
Attention(Q, K, V)= softmax(QK^\mathrm{T})V
Refer to `Attention Is All You Need
Refer to `Attention Is All You Need
<https://arxiv.org/pdf/1706.03762.pdf>`_.
Note that batch data containing sequences with different lengths is not
Note that batch data containing sequences with different lengths is not
supported by this because of the (batch) matrix multipication.
Args:
query (Variable): The input variable which is a Tensor or LoDTensor.
key (Variable): The input variable which is a Tensor or LoDTensor.
......
python/paddle/v2/fluid/tests/book/test_fit_a_line.py
浏览文件 @ b397febc
......@@ -49,7 +49,7 @@ for pass_id in range(PASS_NUM):
avg_loss_value, = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost])
print(avg_loss_value)
if avg_loss_value[0] < 10.0:
exit(0) # if avg cost less than 10.0, we think our code is good.
exit(1)
python/paddle/v2/fluid/tests/book/test_machine_translation.py
浏览文件 @ b397febc
......@@ -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()
python/paddle/v2/fluid/tests/book_distribute/notest_dist_fit_a_line.py
浏览文件 @ b397febc
......@@ -68,10 +68,10 @@ else:
fluid.io.save_persistables(exe, "./fit_a_line.model/")
fluid.io.load_persistables(exe, "./fit_a_line.model/")
for data in train_reader():
avg_loss_value, = exe.run(trainer_prog,
feed=feeder.feed(data),
fetch_list=[avg_cost])
avg_loss_value = exe.run(trainer_prog,
feed=feeder.feed(data),
fetch_list=[avg_cost])
print("loss:" + str(avg_loss_value))
if avg_loss_value[0] < 10.0:
exit(0)
exit(1)
python/paddle/v2/fluid/tests/book_distribute/notest_machine_translation.py 0 → 100644
浏览文件 @ b397febc
# Copyright (c) 2018 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.
import numpy as np
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
from paddle.v2.fluid.executor import Executor
import os
dict_size = 30000
source_dict_dim = target_dict_dim = dict_size
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
topk_size = 50
trg_dic_size = 10000
decoder_size = hidden_dim
def encoder_decoder():
# encoder
src_word_id = layers.data(
name="src_word_id", shape=[1], dtype='int64', lod_level=1)
src_embedding = layers.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)
# decoder
trg_language_word = layers.data(
name="target_language_word", shape=[1], dtype='int64', lod_level=1)
trg_embedding = layers.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()
with rnn.block():
current_word = rnn.step_input(trg_embedding)
mem = rnn.memory(init=encoder_out)
fc1 = fluid.layers.fc(input=[current_word, mem],
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)
return rnn()
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 = core.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def main():
rnn_out = encoder_decoder()
label = layers.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)
optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4)
optimize_ops, params_grads = optimizer.minimize(avg_cost)
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.wmt14.train(dict_size), buf_size=1000),
batch_size=batch_size)
place = core.CPUPlace()
exe = Executor(place)
t = fluid.DistributeTranspiler()
# all parameter server endpoints list for spliting parameters
pserver_endpoints = os.getenv("PSERVERS")
# server endpoint for current node
current_endpoint = os.getenv("SERVER_ENDPOINT")
# run as trainer or parameter server
training_role = os.getenv(
"TRAINING_ROLE", "TRAINER") # get the training role: trainer/pserver
t.transpile(
optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
if training_role == "PSERVER":
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
trainer_prog = t.get_trainer_program()
exe.run(framework.default_startup_program())
batch_id = 0
for pass_id in xrange(2):
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)
trg_word_next = to_lodtensor(map(lambda x: x[2], data), place)
outs = exe.run(trainer_prog,
feed={
'src_word_id': word_data,
'target_language_word': trg_word,
'target_language_next_word': trg_word_next
},
fetch_list=[avg_cost])
avg_cost_val = np.array(outs[0])
print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
" avg_cost=" + str(avg_cost_val))
if batch_id > 3:
exit(0)
batch_id += 1
else:
print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
if __name__ == '__main__':
main()
python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py
浏览文件 @ b397febc
......@@ -16,6 +16,11 @@ import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
# need to fix random seed and training data to compare the loss
# value accurately calculated by the default and the memory optimization
# version.
fluid.default_startup_program().random_seed = 111
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
......@@ -28,15 +33,18 @@ avg_cost = fluid.layers.mean(x=cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.1)
sgd_optimizer.minimize(avg_cost)
# memopt_program = fluid.default_main_program()
memopt_program = fluid.memory_optimize(fluid.default_main_program())
fluid.memory_optimize(fluid.default_main_program())
BATCH_SIZE = 200
# fix the order of training data
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.uci_housing.train(), buf_size=500),
batch_size=BATCH_SIZE)
paddle.dataset.uci_housing.train(), batch_size=BATCH_SIZE)
# train_reader = paddle.batch(
# paddle.reader.shuffle(
# paddle.dataset.uci_housing.train(), buf_size=500),
# batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
......@@ -49,7 +57,7 @@ for pass_id in range(PASS_NUM):
fluid.io.save_persistables(exe, "./fit_a_line.model/")
fluid.io.load_persistables(exe, "./fit_a_line.model/")
for data in train_reader():
avg_loss_value, = exe.run(memopt_program,
avg_loss_value, = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost])
......
python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py
浏览文件 @ b397febc
......@@ -19,6 +19,11 @@ import sys
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
# need to fix random seed and training data to compare the loss
# value accurately calculated by the default and the memory optimization
# version.
fluid.default_startup_program().random_seed = 111
def resnet_cifar10(input, depth=32):
def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
......@@ -117,31 +122,37 @@ opts = optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
# memopt_program = fluid.default_main_program()
memopt_program = fluid.memory_optimize(fluid.default_main_program())
fluid.memory_optimize(fluid.default_main_program())
BATCH_SIZE = 128
PASS_NUM = 1
# fix the order of training data
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
paddle.dataset.cifar.train10(), batch_size=BATCH_SIZE)
# train_reader = paddle.batch(
# paddle.reader.shuffle(
# paddle.dataset.cifar.train10(), buf_size=128 * 10),
# batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
exe.run(fluid.default_startup_program())
i = 0
for pass_id in range(PASS_NUM):
accuracy.reset(exe)
for data in train_reader():
loss, acc = exe.run(memopt_program,
loss, acc = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost] + accuracy.metrics)
pass_acc = accuracy.eval(exe)
print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
pass_acc))
# this model is slow, so if we can train two mini batch, we think it works properly.
exit(0)
if i > 2:
exit(0)
i += 1
exit(1)
python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_machine_translation.py 0 → 100644
浏览文件 @ b397febc
# Copyright (c) 2018 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.
import numpy as np
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
from paddle.v2.fluid.executor import Executor
dict_size = 30000
source_dict_dim = target_dict_dim = dict_size
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
topk_size = 50
trg_dic_size = 10000
decoder_size = hidden_dim
# need to fix random seed and training data to compare the loss
# value accurately calculated by the default and the memory optimization
# version.
fluid.default_startup_program().random_seed = 111
def encoder_decoder():
# encoder
src_word_id = layers.data(
name="src_word_id", shape=[1], dtype='int64', lod_level=1)
src_embedding = layers.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)
# decoder
trg_language_word = layers.data(
name="target_language_word", shape=[1], dtype='int64', lod_level=1)
trg_embedding = layers.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()
with rnn.block():
current_word = rnn.step_input(trg_embedding)
mem = rnn.memory(init=encoder_out)
fc1 = fluid.layers.fc(input=[current_word, mem],
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)
return rnn()
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 = core.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def main():
rnn_out = encoder_decoder()
label = layers.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)
optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4)
optimizer.minimize(avg_cost)
fluid.memory_optimize(fluid.default_main_program())
# fix the order of training data
train_data = paddle.batch(
paddle.dataset.wmt14.train(dict_size), batch_size=batch_size)
# train_data = paddle.batch(
# paddle.reader.shuffle(
# 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(10):
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)
trg_word_next = to_lodtensor(map(lambda x: x[2], data), place)
outs = exe.run(fluid.default_main_program(),
feed={
'src_word_id': word_data,
'target_language_word': trg_word,
'target_language_next_word': trg_word_next
},
fetch_list=[avg_cost])
avg_cost_val = np.array(outs[0])
print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
" avg_cost=" + str(avg_cost_val))
if batch_id > 2:
exit(0)
batch_id += 1
if __name__ == '__main__':
main()
python/paddle/v2/fluid/tests/test_detection_output_op.py
浏览文件 @ b397febc
......@@ -68,4 +68,6 @@ class TestUnpoolOp(OpTest):
if __name__ == '__main__':
unittest.main()
# FIXME: detection_output_op will be rewritten. This unittest should be
# enabled after rewriting.
exit(0) # temporary disable this unittest
python/paddle/v2/fluid/tests/test_iou_similarity_op.py 0 → 100755
浏览文件 @ b397febc
# Copyright (c) 2018 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.
import unittest
import numpy as np
import sys
import math
from op_test import OpTest
class TestIOUSimilarityOp(OpTest):
def test_check_output(self):
self.check_output()
def setUp(self):
self.op_type = "iou_similarity"
self.boxes1 = np.array(
[[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]]).astype('float32')
self.boxes2 = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
[0.0, 0.0, 20.0, 20.0]]).astype('float32')
self.output = np.array(
[[2.0 / 16.0, 0, 6.0 / 400.0],
[1.0 / 16.0, 0.0, 5.0 / 400.0]]).astype('float32')
self.inputs = {'X': self.boxes1, 'Y': self.boxes2}
self.outputs = {'Out': self.output}
class TestIOUSimilarityOpWithLoD(TestIOUSimilarityOp):
def test_check_output(self):
self.check_output()
def setUp(self):
super(TestIOUSimilarityOpWithLoD, self).setUp()
self.boxes1_lod = [[0, 1, 2]]
self.output_lod = [[0, 1, 2]]
self.inputs = {'X': (self.boxes1, self.boxes1_lod), 'Y': self.boxes2}
self.outputs = {'Out': (self.output, self.output_lod)}
if __name__ == '__main__':
unittest.main()
python/paddle/v2/fluid/tests/test_layers.py
浏览文件 @ b397febc
......@@ -226,6 +226,16 @@ class TestBook(unittest.TestCase):
self.assertIsNotNone(out)
print(str(program))
def test_im2sequence(self):
print("test_im2sequence")
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[3, 128, 128], dtype='float32')
output = layers.im2sequence(
input=x, stride=[1, 1], filter_size=[2, 2])
self.assertIsNotNone(output)
print(str(program))
@decorators.prog_scope()
def test_nce(self):
window_size = 5
......
python/paddle/v2/fluid/tests/test_lookup_table_op.py
浏览文件 @ b397febc
......@@ -33,5 +33,19 @@ class TestLookupTableOp(OpTest):
self.check_grad(['W'], 'Out', no_grad_set=set('Ids'))
class TestLookupTableOpWithPadding(TestLookupTableOp):
def test_check_output(self):
ids = np.squeeze(self.inputs['Ids'])
padding_idx = np.random.choice(ids, 1)[0]
self.outputs['Out'][ids == padding_idx] = np.zeros(31)
self.attrs = {'padding_idx': long(padding_idx)}
self.check_output()
def test_check_grad(self):
# Since paddings are not trainable and fixed in forward, the gradient of
# paddings makes no sense and we don't test the gradient here.
pass
if __name__ == "__main__":
unittest.main()
python/paddle/v2/fluid/tests/test_nce.py
浏览文件 @ b397febc
......@@ -109,4 +109,6 @@ class TestNCECase1(TestNCE):
if __name__ == '__main__':
# FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/7778
exit(0)
unittest.main()
python/paddle/v2/fluid/tests/test_prior_box_op.py 0 → 100644
浏览文件 @ b397febc
# Copyright (c) 2018 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.
import unittest
import numpy as np
import sys
import math
from op_test import OpTest
class TestPriorBoxOp(OpTest):
def set_data(self):
self.init_test_params()
self.init_test_input()
self.init_test_output()
self.inputs = {'Input': self.input, 'Image': self.image}
self.attrs = {
'min_sizes': self.min_sizes,
'max_sizes': self.max_sizes,
'aspect_ratios': self.aspect_ratios,
'variances': self.variances,
'flip': self.flip,
'clip': self.clip,
'step_w': self.step_w,
'step_h': self.step_h,
'offset': self.offset
}
self.outputs = {'Boxes': self.out_boxes, 'Variances': self.out_var}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
return
def setUp(self):
self.op_type = "prior_box"
self.set_data()
def init_test_params(self):
self.layer_w = 4
self.layer_h = 4
self.image_w = 20
self.image_h = 20
self.step_w = float(self.image_w) / float(self.layer_w)
self.step_h = float(self.image_h) / float(self.layer_h)
self.input_channels = 2
self.image_channels = 3
self.batch_size = 10
self.min_sizes = [2, 4]
self.min_sizes = np.array(self.min_sizes).astype('int64')
self.max_sizes = [5, 10]
self.max_sizes = np.array(self.max_sizes).astype('int64')
self.aspect_ratios = [2.0, 3.0]
self.flip = True
self.real_aspect_ratios = [1, 2.0, 1.0 / 2.0, 3.0, 1.0 / 3.0]
self.aspect_ratios = np.array(
self.aspect_ratios, dtype=np.float).flatten()
self.variances = [0.1, 0.1, 0.2, 0.2]
self.variances = np.array(self.variances, dtype=np.float).flatten()
self.clip = True
self.num_priors = len(self.real_aspect_ratios) * len(self.min_sizes)
if len(self.max_sizes) > 1:
self.num_priors += len(self.max_sizes)
self.offset = 0.5
def init_test_input(self):
self.image = np.random.random(
(self.batch_size, self.image_channels, self.image_w,
self.image_h)).astype('float32')
self.input = np.random.random(
(self.batch_size, self.input_channels, self.layer_w,
self.layer_h)).astype('float32')
def init_test_output(self):
out_dim = (self.layer_h, self.layer_w, self.num_priors, 4)
out_boxes = np.zeros(out_dim).astype('float32')
out_var = np.zeros(out_dim).astype('float32')
idx = 0
for h in range(self.layer_h):
for w in range(self.layer_w):
c_x = (w + self.offset) * self.step_w
c_y = (h + self.offset) * self.step_h
idx = 0
for s in range(len(self.min_sizes)):
min_size = self.min_sizes[s]
c_w = c_h = min_size / 2.
out_boxes[h, w, idx, :] = [
(c_x - c_w) / self.image_w, (c_y - c_h) / self.image_h,
(c_x + c_w) / self.image_w, (c_y + c_h) / self.image_h
]
idx += 1
if len(self.max_sizes) > 0:
max_size = self.max_sizes[s]
# second prior: aspect_ratio = 1,
c_w = c_h = math.sqrt(min_size * max_size) / 2
out_boxes[h, w, idx, :] = [(c_x - c_w) / self.image_w,
(c_y - c_h) / self.image_h,
(c_x + c_w) / self.image_w,
(c_y + c_h) / self.image_h]
idx += 1
# rest of priors
for r in range(len(self.real_aspect_ratios)):
ar = self.real_aspect_ratios[r]
if math.fabs(ar - 1.) < 1e-6:
continue
c_w = min_size * math.sqrt(ar) / 2
c_h = (min_size / math.sqrt(ar)) / 2
out_boxes[h, w, idx, :] = [(c_x - c_w) / self.image_w,
(c_y - c_h) / self.image_h,
(c_x + c_w) / self.image_w,
(c_y + c_h) / self.image_h]
idx += 1
# clip the prior's coordidate such that it is within[0, 1]
if self.clip:
out_boxes = np.clip(out_boxes, 0.0, 1.0)
# set the variance.
out_var = np.tile(self.variances, (self.layer_h, self.layer_w,
self.num_priors, 1))
self.out_boxes = out_boxes.astype('float32')
self.out_var = out_var.astype('float32')
if __name__ == '__main__':
unittest.main()
python/paddle/v2/image.py
浏览文件 @ b397febc
......@@ -176,7 +176,6 @@ def resize_short(im, size):
:param size: the shorter edge size of image after resizing.
:type size: int
"""
assert im.shape[-1] == 1 or im.shape[-1] == 3
h, w = im.shape[:2]
h_new, w_new = size, size
if h > w:
......@@ -267,7 +266,7 @@ def random_crop(im, size, is_color=True):
return im
def left_right_flip(im):
def left_right_flip(im, is_color=True):
"""
Flip an image along the horizontal direction.
Return the flipped image.
......@@ -278,13 +277,15 @@ def left_right_flip(im):
im = left_right_flip(im)
:paam im: input image with HWC layout
:param im: input image with HWC layout or HW layout for gray image
:type im: ndarray
:param is_color: whether input image is color or not
:type is_color: bool
"""
if len(im.shape) == 3:
if len(im.shape) == 3 and is_color:
return im[:, ::-1, :]
else:
return im[:, ::-1, :]
return im[:, ::-1]
def simple_transform(im,
......@@ -319,11 +320,12 @@ def simple_transform(im,
"""
im = resize_short(im, resize_size)
if is_train:
im = random_crop(im, crop_size)
im = random_crop(im, crop_size, is_color=is_color)
if np.random.randint(2) == 0:
im = left_right_flip(im)
im = left_right_flip(im, is_color)
else:
im = center_crop(im, crop_size)
im = center_crop(im, crop_size, is_color)
im = center_crop(im, crop_size, is_color=is_color)
if len(im.shape) == 3:
im = to_chw(im)
......@@ -331,8 +333,10 @@ def simple_transform(im,
if mean is not None:
mean = np.array(mean, dtype=np.float32)
# mean value, may be one value per channel
if mean.ndim == 1:
if mean.ndim == 1 and is_color:
mean = mean[:, np.newaxis, np.newaxis]
elif mean.ndim == 1:
mean = mean
else:
# elementwise mean
assert len(mean.shape) == len(im)
......@@ -372,6 +376,6 @@ def load_and_transform(filename,
mean values per channel.
:type mean: numpy array | list
"""
im = load_image(filename)
im = load_image(filename, is_color)
im = simple_transform(im, resize_size, crop_size, is_train, is_color, mean)
return im
tools/manylinux1/Dockerfile.x64
浏览文件 @ b397febc
......@@ -35,7 +35,7 @@ RUN cd /opt && wget -q --no-check-certificate https://github.com/google/protobuf
cd protobuf-3.1.0 && ./configure && make -j4 && make install && cd .. && rm -f protobuf-cpp-3.1.0.tar.gz
RUN yum install -y sqlite-devel zlib-devel openssl-devel boost boost-devel pcre-devel vim tk-devel tkinter libtool
RUN yum install -y sqlite-devel zlib-devel openssl-devel pcre-devel vim tk-devel tkinter libtool
RUN wget -O /root/requirements.txt https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/python/requirements.txt
......
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