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提交 85ddb5c7 编写于 作者: Q qiaolongfei
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into optimize-opyreader

上级 accf3f75 43a3af86
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Showing with 2499 addition and 690 deletion
doc/CMakeLists.txt
浏览文件 @ 85ddb5c7
add_custom_target(paddle_apis ALL
DEPENDS paddle_v2_apis paddle_fluid_apis)
DEPENDS paddle_v2_apis)
add_custom_target(paddle_docs ALL
DEPENDS paddle_v2_docs paddle_v2_docs_cn
paddle_fluid_docs paddle_fluid_docs_cn
paddle_mobile_docs paddle_mobile_docs_cn)
add_subdirectory(v2)
add_subdirectory(fluid)
add_subdirectory(mobile)
paddle/fluid/API.spec
浏览文件 @ 85ddb5c7
......@@ -153,6 +153,13 @@ paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'out', 'axis', 'use_
paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0))
paddle.fluid.layers.gaussian_random ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype', 'use_mkldnn'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32', False))
paddle.fluid.layers.sampling_id ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32'))
paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32'))
paddle.fluid.layers.sum ArgSpec(args=['x', 'use_mkldnn'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.slice ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.shape ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
......@@ -224,13 +231,6 @@ paddle.fluid.layers.logical_and ArgSpec(args=[], varargs='args', keywords='kwarg
paddle.fluid.layers.logical_or ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_xor ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_not ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.gaussian_random ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sampling_id ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sum ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.slice ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.shape ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.maxout ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.logsigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
......@@ -298,6 +298,7 @@ paddle.fluid.contrib.BeamSearchDecoder.early_stop ArgSpec(args=['self'], varargs
paddle.fluid.contrib.BeamSearchDecoder.read_array ArgSpec(args=['self', 'init', 'is_ids', 'is_scores'], varargs=None, keywords=None, defaults=(False, False))
paddle.fluid.contrib.BeamSearchDecoder.update_array ArgSpec(args=['self', 'array', 'value'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.memory_usage ArgSpec(args=['program', 'batch_size'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.op_freq_statistic ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
......
paddle/fluid/framework/details/cow_ptr.h
浏览文件 @ 85ddb5c7
......@@ -20,79 +20,37 @@ namespace paddle {
namespace framework {
namespace details {
// Change it to thread safe flags if needed.
class ThreadUnsafeOwnershipFlags {
template <class T>
class COWPtr {
public:
explicit ThreadUnsafeOwnershipFlags(bool flag) : flag_(flag) {}
ThreadUnsafeOwnershipFlags(const ThreadUnsafeOwnershipFlags& other) = delete;
ThreadUnsafeOwnershipFlags& operator=(
const ThreadUnsafeOwnershipFlags& other) = delete;
ThreadUnsafeOwnershipFlags(ThreadUnsafeOwnershipFlags&& other) = default;
void SetOwnership(bool flag) { flag_ = flag; }
// Invoke the callback if it is not owned.
template <typename Callback>
void AcquireOwnershipOnce(Callback acquire) {
if (!flag_) {
acquire();
flag_ = true;
}
}
typedef std::shared_ptr<T> RefPtr;
private:
bool flag_;
};
RefPtr m_sp;
// Copy-On-Write pointer.
// It will hold a T* pointer, and only copy once when `MutableData` is invoked.
//
// The template parameter OwnershipFlags should have:
// * a constructor takes a bool. True if own.
// * SetOwnership(bool flag).
// * AcquireOwnershipOnce(Callback). It will invoke the callback if it is not
// owned.
//
// https://en.wikipedia.org/wiki/Copy-on-write
template <typename T, typename OwnershipFlags = ThreadUnsafeOwnershipFlags>
class COWPtr {
public:
// Ctor from raw pointer.
explicit COWPtr(T* ptr) : payload_(ptr), ownership_{true} {}
COWPtr() : m_sp(nullptr) {}
explicit COWPtr(T* t) : m_sp(t) {}
// Move methods. Steal ownership from origin
COWPtr(COWPtr&& other)
: payload_(other.payload_), ownership_{std::move(other.ownership_)} {}
COWPtr& operator=(COWPtr&& origin) = default;
const T& Data() const { return *m_sp; }
// Copy methods. Not own payload
COWPtr(const COWPtr& other) : payload_(other.payload_), ownership_{false} {}
COWPtr& operator=(const COWPtr& other) {
payload_ = other.payload_;
ownership_.SetOwnership(false);
return *this;
}
// Access read only data.
const T& Data() const { return *payload_; }
// Access mutable data. If the data is not owned, the data will be copied
// before.
T* MutableData() {
ownership_.AcquireOwnershipOnce(
[this] { payload_.reset(new T(*payload_)); });
return payload_.get();
DetachIfNotUnique();
return m_sp.get();
}
private:
// Actual data pointer.
std::shared_ptr<T> payload_;
void DetachIfNotUnique() {
T* tmp = m_sp.get();
if (!(tmp == nullptr || m_sp.unique())) {
Detach();
}
}
// Ownership flag.
OwnershipFlags ownership_;
void Detach() {
T* tmp = m_sp.get();
m_sp = RefPtr(new T(*tmp));
}
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/cow_ptr_test.cc
浏览文件 @ 85ddb5c7
......@@ -30,6 +30,14 @@ TEST(COWPtr, all) {
ASSERT_EQ(ptr2.Data(), 10);
}
TEST(COWPtr, change_old) {
COWPtr<int> ptr(new int{0});
COWPtr<int> ptr2 = ptr;
*ptr.MutableData() = 10;
ASSERT_EQ(ptr2.Data(), 0);
ASSERT_EQ(ptr.Data(), 10);
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc
浏览文件 @ 85ddb5c7
......@@ -257,6 +257,22 @@ std::unique_ptr<ir::Graph> AttentionLSTMFusePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
PDPattern external_pattern, subblock_pattern;
// Use the following variables to tell whether this model is RNN1.
// This fuse can only works on the RNN1 model.
std::unordered_set<std::string> specified_vars({"data_lod_attention",
"cell_init", "hidden_init",
"data", "week", "minute"});
int count = 0;
for (auto* node : graph->Nodes()) {
if (node->IsVar() && specified_vars.count(node->Name())) {
++count;
}
}
if (count < specified_vars.size()) {
return graph;
}
// Continue to fuse.
FindWhileOp(graph.get());
return graph;
}
......
paddle/fluid/framework/mixed_vector.h
浏览文件 @ 85ddb5c7
......@@ -17,10 +17,13 @@
#include <algorithm>
#include <initializer_list>
#include <memory>
#include <mutex> // NOLINT
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/cow_ptr.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/memory/memcpy.h"
#include "glog/logging.h"
......@@ -28,206 +31,436 @@ namespace paddle {
namespace framework {
#if defined(PADDLE_WITH_CUDA)
namespace details {
struct CUDABuffer {
void *data_{nullptr};
size_t size_{0};
platform::CUDAPlace place_;
CUDABuffer() {}
CUDABuffer(platform::Place place, size_t size)
: size_(size), place_(boost::get<platform::CUDAPlace>(place)) {
data_ = memory::Alloc(place_, size);
}
~CUDABuffer() { ClearMemory(); }
CUDABuffer(const CUDABuffer &o) = delete;
CUDABuffer &operator=(const CUDABuffer &o) = delete;
void Resize(platform::Place place, size_t size) {
ClearMemory();
place_ = boost::get<platform::CUDAPlace>(place);
data_ = memory::Alloc(place_, size);
PADDLE_ENFORCE_NOT_NULL(data_);
size_ = size;
}
void Swap(CUDABuffer &o) {
std::swap(data_, o.data_);
std::swap(place_, o.place_);
std::swap(size_, o.size_);
}
private:
void ClearMemory() const {
if (data_ != nullptr) {
memory::Free(place_, data_);
}
}
};
} // namespace details
// Vector<T> implements the std::vector interface, and can get Data or
// MutableData from any place. The data will be synced implicitly inside.
template <typename T>
class Vector {
public:
using value_type = T;
using iterator = typename std::vector<T>::iterator;
using const_iterator = typename std::vector<T>::const_iterator;
// Default ctor. Create empty Vector
Vector() { InitEmpty(); }
private:
// The actual class to implement vector logic
class VectorData {
public:
VectorData() : flag_(kDataInCPU) {}
VectorData(size_t count, const T &value)
: cpu_(count, value), flag_(kDataInCPU) {}
VectorData(std::initializer_list<T> init) : cpu_(init), flag_(kDataInCPU) {}
template <typename U>
explicit VectorData(const std::vector<U> &dat)
: cpu_(dat), flag_(kDataInCPU) {}
~VectorData() {}
VectorData(const VectorData &o) {
o.ImmutableCPU();
cpu_ = o.cpu_;
flag_ = kDataInCPU;
}
// Fill vector with value. The vector size is `count`.
explicit Vector(size_t count, const T &value = T()) {
InitEmpty();
if (count != 0) {
resize(count);
T *ptr = begin();
for (size_t i = 0; i < count; ++i) {
ptr[i] = value;
VectorData &operator=(const VectorData &o) {
o.ImmutableCPU();
cpu_ = o.cpu_;
flag_ = kDataInCPU;
details::CUDABuffer null;
gpu_.Swap(null);
return *this;
}
T &operator[](size_t i) {
MutableCPU();
return cpu_[i];
}
const T &operator[](size_t i) const {
ImmutableCPU();
return cpu_[i];
}
size_t size() const { return cpu_.size(); }
iterator begin() {
MutableCPU();
return cpu_.begin();
}
iterator end() {
MutableCPU();
return cpu_.end();
}
T &front() {
MutableCPU();
return cpu_.front();
}
T &back() {
MutableCPU();
return cpu_.back();
}
const_iterator begin() const {
ImmutableCPU();
return cpu_.begin();
}
const_iterator end() const {
ImmutableCPU();
return cpu_.end();
}
const T &back() const {
ImmutableCPU();
return cpu_.back();
}
T *data() { return &(*this)[0]; }
const T *data() const { return &(*this)[0]; }
const T &front() const {
ImmutableCPU();
return cpu_.front();
}
// assign this from iterator.
// NOTE: the iterator must support `end-begin`
template <typename Iter>
void assign(Iter begin, Iter end) {
MutableCPU();
cpu_.assign(begin, end);
}
// push_back. If the previous capacity is not enough, the memory will
// double.
void push_back(T elem) {
MutableCPU();
cpu_.push_back(elem);
}
// extend a vector by iterator.
// NOTE: the iterator must support end-begin
template <typename It>
void Extend(It begin, It end) {
MutableCPU();
auto out_it = std::back_inserter<std::vector<T>>(this->cpu_);
std::copy(begin, end, out_it);
}
// resize the vector
void resize(size_t size) {
MutableCPU();
cpu_.resize(size);
}
// get cuda ptr. immutable
const T *CUDAData(platform::Place place) const {
PADDLE_ENFORCE(platform::is_gpu_place(place),
"CUDA Data must on CUDA place");
ImmutableCUDA(place);
return reinterpret_cast<T *>(gpu_.data_);
}
// get cuda ptr. mutable
T *CUDAMutableData(platform::Place place) {
const T *ptr = CUDAData(place);
flag_ = kDirty | kDataInCUDA;
return const_cast<T *>(ptr);
}
// clear
void clear() {
cpu_.clear();
flag_ = kDirty | kDataInCPU;
}
size_t capacity() const { return cpu_.capacity(); }
// reserve data
void reserve(size_t size) const { cpu_.reserve(size); }
// implicit cast operator. Vector can be cast to std::vector implicitly.
operator std::vector<T>() const {
ImmutableCPU();
return cpu_;
}
bool operator==(const VectorData &other) const {
ImmutableCPU();
other.ImmutableCPU();
return cpu_ == other.cpu_;
}
std::mutex &Mutex() const { return mtx_; }
std::unique_ptr<platform::CUDAPlace> CUDAPlace() const {
if (gpu_.data_ == nullptr) {
return nullptr;
} else {
return std::unique_ptr<platform::CUDAPlace>(
new platform::CUDAPlace(gpu_.place_));
}
}
}
// Ctor with init_list
Vector(std::initializer_list<T> init) {
if (init.size() == 0) {
InitEmpty();
} else {
InitByIter(init.size(), init.begin(), init.end());
private:
enum DataFlag {
kDataInCPU = 0x01,
kDataInCUDA = 0x02,
// kDirty means the data has been changed in one device.
kDirty = 0x10
};
void CopyToCPU() const {
// COPY GPU Data To CPU
auto *dev_ctx = static_cast<platform::CUDADeviceContext *>(
platform::DeviceContextPool::Instance().Get(
platform::Place(gpu_.place_)));
auto stream = dev_ctx->stream();
void *src = gpu_.data_;
void *dst = cpu_.data();
memory::Copy(platform::CPUPlace(), dst, gpu_.place_, src, gpu_.size_,
stream);
dev_ctx->Wait();
}
void MutableCPU() {
if (IsInCUDA() && IsDirty()) {
CopyToCPU();
}
flag_ = kDirty | kDataInCPU;
}
}
void ImmutableCUDA(platform::Place place) const {
if (IsDirty()) {
if (IsInCPU()) {
CopyCPUDataToCUDA(place);
UnsetFlag(kDirty);
SetFlag(kDataInCUDA);
} else if (IsInCUDA() &&
!(boost::get<platform::CUDAPlace>(place) == gpu_.place_)) {
PADDLE_THROW("This situation should not happen");
// Still dirty
} else {
// Dirty && DataInCUDA && Device is same
// Do nothing
}
} else {
if (!IsInCUDA()) {
// Even data is not dirty. However, data is not in CUDA. Copy data.
CopyCPUDataToCUDA(place);
SetFlag(kDataInCUDA);
} else if (!(boost::get<platform::CUDAPlace>(place) == gpu_.place_)) {
PADDLE_THROW("This situation should not happen.");
} else {
// Not Dirty && DataInCUDA && Device is same
// Do nothing.
}
}
}
void CopyCPUDataToCUDA(const platform::Place &place) const {
void *src = cpu_.data();
gpu_.Resize(place, cpu_.size() * sizeof(T));
void *dst = gpu_.data_;
auto *dev_ctx = static_cast<platform::CUDADeviceContext *>(
platform::DeviceContextPool::Instance().Get(place));
auto stream = dev_ctx->stream();
memory::Copy(gpu_.place_, dst, platform::CPUPlace(), src, gpu_.size_,
stream);
}
void ImmutableCPU() const {
if (IsDirty() && !IsInCPU()) { // If data has been changed in CUDA, or
// CPU has no data.
CopyToCPU();
UnsetFlag(kDirty);
}
SetFlag(kDataInCPU);
}
void UnsetFlag(int flag) const { flag_ &= ~flag; }
void SetFlag(int flag) const { flag_ |= flag; }
bool IsDirty() const { return flag_ & kDirty; }
bool IsInCUDA() const { return flag_ & kDataInCUDA; }
bool IsInCPU() const { return flag_ & kDataInCPU; }
mutable std::vector<T> cpu_;
mutable details::CUDABuffer gpu_;
mutable int flag_;
mutable std::mutex mtx_;
};
public:
// Default ctor. Create empty Vector
Vector() : m_(new VectorData()) {}
// Fill vector with value. The vector size is `count`.
explicit Vector(size_t count, const T &value = T())
: m_(new VectorData(count, value)) {}
// Ctor with init_list
Vector(std::initializer_list<T> init) : m_(new VectorData(init)) {}
// implicit cast from std::vector.
template <typename U>
Vector(const std::vector<U> &dat) { // NOLINT
if (dat.size() == 0) {
InitEmpty();
} else {
InitByIter(dat.size(), dat.begin(), dat.end());
}
Vector(const std::vector<U> &dat) : m_(new VectorData(dat)) { // NOLINT
}
// Copy ctor
Vector(const Vector<T> &other) { this->operator=(other); }
Vector(const Vector<T> &other) { m_ = other.m_; }
// Copy operator
Vector<T> &operator=(const Vector<T> &other) {
if (other.size() != 0) {
this->InitByIter(other.size(), other.begin(), other.end());
} else {
InitEmpty();
}
m_ = other.m_;
return *this;
}
// Move ctor
Vector(Vector<T> &&other) {
this->size_ = other.size_;
this->flag_ = other.flag_;
if (other.cuda_vec_.memory_size()) {
this->cuda_vec_.ShareDataWith(other.cuda_vec_);
}
if (other.cpu_vec_.memory_size()) {
this->cpu_vec_.ShareDataWith(other.cpu_vec_);
}
}
Vector(Vector<T> &&other) { m_ = std::move(other.m_); }
// CPU data access method. Mutable.
T &operator[](size_t i) {
MutableCPU();
return const_cast<T *>(cpu_vec_.data<T>())[i];
}
T &operator[](size_t i) { return (*m_.MutableData())[i]; }
// CPU data access method. Immutable.
const T &operator[](size_t i) const {
ImmutableCPU();
return cpu_vec_.data<T>()[i];
}
const T &operator[](size_t i) const { return m_.Data()[i]; }
// std::vector iterator methods. Based on CPU data access method
size_t size() const { return size_; }
size_t size() const { return m_.Data().size(); }
T *begin() { return capacity() == 0 ? &EmptyDummy() : &this->operator[](0); }
iterator begin() { return m_.MutableData()->begin(); }
T *end() {
return capacity() == 0 ? &EmptyDummy() : &this->operator[](size());
}
iterator end() { return m_.MutableData()->end(); }
T &front() { return *begin(); }
T &front() { return m_.MutableData()->front(); }
T &back() {
auto it = end();
--it;
return *it;
}
T &back() { return m_.MutableData()->back(); }
const T *begin() const {
return capacity() == 0 ? &EmptyDummy() : &this->operator[](0);
}
const_iterator begin() const { return m_.Data().begin(); }
const T *end() const {
return capacity() == 0 ? &EmptyDummy() : &this->operator[](size());
}
const_iterator end() const { return m_.Data().end(); }
const T *cbegin() const { return begin(); }
const_iterator cbegin() const { return begin(); }
const T *cend() const { return end(); }
const_iterator cend() const { return end(); }
const T &back() const {
auto it = end();
--it;
return *it;
}
const T &back() const { return m_.Data().back(); }
T *data() { return begin(); }
T *data() { return m_.MutableData()->data(); }
const T *data() const { return begin(); }
const T *data() const { return m_.Data().data(); }
const T &front() const { return *begin(); }
const T &front() const { return m_.Data().front(); }
// end of std::vector iterator methods
// assign this from iterator.
// NOTE: the iterator must support `end-begin`
template <typename Iter>
void assign(Iter begin, Iter end) {
InitByIter(end - begin, begin, end);
m_.MutableData()->assign(begin, end);
}
// push_back. If the previous capacity is not enough, the memory will
// double.
void push_back(T elem) {
if (size_ + 1 > capacity()) {
reserve((size_ + 1) << 1);
}
*end() = elem;
++size_;
}
void push_back(T elem) { m_.MutableData()->push_back(elem); }
// extend a vector by iterator.
// NOTE: the iterator must support end-begin
template <typename It>
void Extend(It begin, It end) {
size_t pre_size = size_;
resize(pre_size + (end - begin));
T *ptr = this->begin() + pre_size;
for (; begin < end; ++begin, ++ptr) {
*ptr = *begin;
}
m_.MutableData()->Extend(begin, end);
}
// resize the vector
void resize(size_t size) {
if (size + 1 <= capacity()) {
size_ = size;
} else {
MutableCPU();
Tensor cpu_tensor;
platform::Place cpu = platform::CPUPlace();
T *ptr = cpu_tensor.mutable_data<T>(
framework::make_ddim({static_cast<int64_t>(size)}), cpu);
const T *old_ptr =
cpu_vec_.memory_size() == 0 ? nullptr : cpu_vec_.data<T>();
if (old_ptr != nullptr) {
std::copy(old_ptr, old_ptr + size_, ptr);
}
size_ = size;
cpu_vec_.ShareDataWith(cpu_tensor);
if (m_.Data().size() != size) {
m_.MutableData()->resize(size);
}
}
// get cuda ptr. immutable
const T *CUDAData(platform::Place place) const {
PADDLE_ENFORCE(platform::is_gpu_place(place),
"CUDA Data must on CUDA place");
ImmutableCUDA(place);
return cuda_vec_.data<T>();
{
auto &mtx = m_.Data().Mutex();
std::lock_guard<std::mutex> guard(mtx);
auto cuda_place = m_.Data().CUDAPlace();
if (cuda_place == nullptr ||
*cuda_place == boost::get<platform::CUDAPlace>(place)) {
return m_.Data().CUDAData(place);
}
}
// If m_ contains CUDAData in a different place. Detach manually.
m_.Detach();
return CUDAData(place);
}
// get cuda ptr. mutable
T *CUDAMutableData(platform::Place place) {
const T *ptr = CUDAData(place);
flag_ = kDirty | kDataInCUDA;
return const_cast<T *>(ptr);
{
auto &mtx = m_.Data().Mutex();
std::lock_guard<std::mutex> guard(mtx);
auto cuda_place = m_.Data().CUDAPlace();
if (cuda_place == nullptr ||
*cuda_place == boost::get<platform::CUDAPlace>(place)) {
return m_.MutableData()->CUDAMutableData(place);
}
}
// If m_ contains CUDAData in a different place. Detach manually.
m_.Detach();
return CUDAMutableData(place);
}
// clear
void clear() {
size_ = 0;
flag_ = kDirty | kDataInCPU;
}
void clear() { m_.MutableData()->clear(); }
size_t capacity() const {
return cpu_vec_.memory_size() / SizeOfType(typeid(T));
}
size_t capacity() const { return m_.Data().capacity(); }
// reserve data
void reserve(size_t size) {
size_t pre_size = size_;
resize(size);
resize(pre_size);
}
void reserve(size_t size) { m_.Data().reserve(size); }
// the unify method to access CPU or CUDA data. immutable.
const T *Data(platform::Place place) const {
......@@ -248,12 +481,7 @@ class Vector {
}
// implicit cast operator. Vector can be cast to std::vector implicitly.
operator std::vector<T>() const {
std::vector<T> result;
result.resize(size());
std::copy(begin(), end(), result.begin());
return result;
}
operator std::vector<T>() const { return m_.Data(); }
bool operator==(const Vector<T> &other) const {
if (size() != other.size()) return false;
......@@ -267,118 +495,11 @@ class Vector {
return true;
}
private:
void InitEmpty() {
size_ = 0;
flag_ = kDataInCPU;
}
template <typename Iter>
void InitByIter(size_t size, Iter begin, Iter end) {
platform::Place cpu = platform::CPUPlace();
T *ptr = this->cpu_vec_.template mutable_data<T>(
framework::make_ddim({static_cast<int64_t>(size)}), cpu);
for (size_t i = 0; i < size; ++i) {
*ptr++ = *begin++;
}
flag_ = kDataInCPU | kDirty;
size_ = size;
}
enum DataFlag {
kDataInCPU = 0x01,
kDataInCUDA = 0x02,
// kDirty means the data has been changed in one device.
kDirty = 0x10
};
void CopyToCPU() const {
// COPY GPU Data To CPU
TensorCopy(cuda_vec_, platform::CPUPlace(), &cpu_vec_);
WaitPlace(cuda_vec_.place());
}
void MutableCPU() {
if (IsInCUDA() && IsDirty()) {
CopyToCPU();
}
flag_ = kDirty | kDataInCPU;
}
void ImmutableCUDA(platform::Place place) const {
if (IsDirty()) {
if (IsInCPU()) {
TensorCopy(cpu_vec_, boost::get<platform::CUDAPlace>(place),
&cuda_vec_);
WaitPlace(place);
UnsetFlag(kDirty);
SetFlag(kDataInCUDA);
} else if (IsInCUDA() && !(place == cuda_vec_.place())) {
framework::Tensor tmp;
TensorCopy(cuda_vec_, boost::get<platform::CUDAPlace>(place), &tmp);
WaitPlace(cuda_vec_.place());
cuda_vec_.ShareDataWith(tmp);
// Still dirty
} else {
// Dirty && DataInCUDA && Device is same
// Do nothing
}
} else {
if (!IsInCUDA()) {
// Even data is not dirty. However, data is not in CUDA. Copy data.
TensorCopy(cpu_vec_, boost::get<platform::CUDAPlace>(place),
&cuda_vec_);
WaitPlace(place);
SetFlag(kDataInCUDA);
} else if (!(place == cuda_vec_.place())) {
framework::Tensor tmp;
WaitPlace(cuda_vec_.place());
TensorCopy(cuda_vec_, boost::get<platform::CUDAPlace>(place), &tmp);
WaitPlace(cuda_vec_.place());
WaitPlace(place);
cuda_vec_.ShareDataWith(tmp);
} else {
// Not Dirty && DataInCUDA && Device is same
// Do nothing.
}
}
}
void ImmutableCPU() const {
if (IsDirty() &&
!IsInCPU()) { // If data has been changed in CUDA, or CPU has no data.
CopyToCPU();
UnsetFlag(kDirty);
}
SetFlag(kDataInCPU);
}
void UnsetFlag(int flag) const { flag_ &= ~flag; }
void SetFlag(int flag) const { flag_ |= flag; }
const void *Handle() const { return &m_.Data(); }
bool IsDirty() const { return flag_ & kDirty; }
bool IsInCUDA() const { return flag_ & kDataInCUDA; }
bool IsInCPU() const { return flag_ & kDataInCPU; }
static void WaitPlace(const platform::Place place) {
if (platform::is_gpu_place(place)) {
platform::DeviceContextPool::Instance()
.Get(boost::get<platform::CUDAPlace>(place))
->Wait();
}
}
static T &EmptyDummy() {
static T dummy = T();
return dummy;
}
mutable int flag_;
mutable Tensor cpu_vec_;
mutable Tensor cuda_vec_;
size_t size_;
private:
// Vector is an COW object.
mutable details::COWPtr<VectorData> m_;
};
#else // PADDLE_WITH_CUDA
......
paddle/fluid/framework/op_info.h
浏览文件 @ 85ddb5c7
......@@ -38,31 +38,27 @@ struct OpInfo {
OpAttrChecker* checker_{nullptr};
InferVarTypeFN infer_var_type_;
InferShapeFN infer_shape_;
std::string op_type_;
bool HasOpProtoAndChecker() const {
return proto_ != nullptr && checker_ != nullptr;
}
const proto::OpProto& Proto() const {
PADDLE_ENFORCE_NOT_NULL(proto_, "Operator %s Proto has not been registered",
op_type_);
PADDLE_ENFORCE_NOT_NULL(proto_, "Operator Proto has not been registered");
PADDLE_ENFORCE(proto_->IsInitialized(),
"Operator %s Proto must be initialized in op info",
op_type_);
"Operator Proto must be initialized in op info");
return *proto_;
}
const OpCreator& Creator() const {
PADDLE_ENFORCE_NOT_NULL(
creator_, "Operator %s Creator has not been registered", op_type_);
PADDLE_ENFORCE_NOT_NULL(creator_,
"Operator Creator has not been registered");
return creator_;
}
const GradOpMakerFN& GradOpMaker() const {
PADDLE_ENFORCE_NOT_NULL(grad_op_maker_,
"Operator %s GradOpMaker has not been registered.",
op_type_);
"Operator GradOpMaker has not been registered.");
return grad_op_maker_;
}
......@@ -77,9 +73,8 @@ class OpInfoMap {
return map_.find(op_type) != map_.end();
}
void Insert(const std::string& type, OpInfo info) {
void Insert(const std::string& type, const OpInfo& info) {
PADDLE_ENFORCE(!Has(type), "Operator %s has been registered", type);
info.op_type_ = type;
map_.insert({type, info});
}
......
paddle/fluid/framework/selected_rows_test.cc
浏览文件 @ 85ddb5c7
......@@ -27,8 +27,11 @@ class SelectedRowsTester : public ::testing::Test {
selected_rows_.reset(new SelectedRows(rows, height));
Tensor* value = selected_rows_->mutable_value();
value->mutable_data<float>(
auto* data = value->mutable_data<float>(
make_ddim({static_cast<int64_t>(rows.size()), row_numel}), place_);
for (int64_t i = 0; i < value->numel(); ++i) {
data[i] = static_cast<float>(i);
}
}
protected:
......@@ -60,6 +63,10 @@ TEST_F(SelectedRowsTester, SerializeAndDeseralize) {
ASSERT_EQ(selected_rows_->height(), dst_tensor.height());
ASSERT_EQ(selected_rows_->value().dims(), dst_tensor.value().dims());
ASSERT_EQ(selected_rows_->GetCompleteDims(), dst_tensor.GetCompleteDims());
auto* dst_data = dst_tensor.value().data<float>();
for (int64_t i = 0; i < dst_tensor.value().numel(); ++i) {
ASSERT_EQ(dst_data[i], static_cast<float>(i));
}
}
TEST(SelectedRows, SparseTable) {
......
paddle/fluid/inference/api/paddle_inference_api.h
浏览文件 @ 85ddb5c7
......@@ -212,10 +212,11 @@ struct AnalysisConfig : public NativeConfig {
kExclude // Specify the disabled passes in `ir_passes`.
};
// Determine whether to perform graph optimization.
bool enable_ir_optim = true;
// Manually determine the IR passes to run.
IrPassMode ir_mode{IrPassMode::kExclude};
// attention lstm fuse works only on some specific models, disable as default.
std::vector<std::string> ir_passes{"attention_lstm_fuse_pass"};
std::vector<std::string> ir_passes;
// NOTE this is just for internal development, please not use it.
bool _use_mkldnn{false};
......
paddle/fluid/operators/detection/CMakeLists.txt
浏览文件 @ 85ddb5c7
......@@ -30,7 +30,13 @@ detection_library(polygon_box_transform_op SRCS polygon_box_transform_op.cc
polygon_box_transform_op.cu)
detection_library(rpn_target_assign_op SRCS rpn_target_assign_op.cc)
detection_library(generate_proposal_labels_op SRCS generate_proposal_labels_op.cc)
detection_library(generate_proposals_op SRCS generate_proposals_op.cc)
if(WITH_GPU)
detection_library(generate_proposals_op SRCS generate_proposals_op.cc generate_proposals_op.cu DEPS memory cub)
else()
detection_library(generate_proposals_op SRCS generate_proposals_op.cc)
endif()
detection_library(roi_perspective_transform_op SRCS roi_perspective_transform_op.cc roi_perspective_transform_op.cu)
#Export local libraries to parent
set(DETECTION_LIBRARY ${LOCAL_DETECTION_LIBS} PARENT_SCOPE)
paddle/fluid/operators/detection/generate_proposals_op.cc
浏览文件 @ 85ddb5c7
......@@ -15,6 +15,7 @@ limitations under the License. */
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/gather.h"
#include "paddle/fluid/operators/math/math_function.h"
......@@ -69,7 +70,7 @@ class GenerateProposalsOp : public framework::OperatorWithKernel {
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("Anchors")->type()),
platform::CPUPlace());
ctx.device_context());
}
};
......@@ -162,7 +163,7 @@ void FilterBoxes(const platform::DeviceContext &ctx, Tensor *boxes,
const T *im_info_data = im_info.data<T>();
T *boxes_data = boxes->mutable_data<T>(ctx.GetPlace());
T im_scale = im_info_data[2];
keep->Resize({boxes->dims()[0], 1});
keep->Resize({boxes->dims()[0]});
min_size = std::max(min_size, 1.0f);
int *keep_data = keep->mutable_data<int>(ctx.GetPlace());
......@@ -463,7 +464,7 @@ class GenerateProposalsOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<int>("post_nms_topN", "post_nms_topN");
AddAttr<float>("nms_thresh", "nms_thres");
AddAttr<float>("min_size", "min size");
AddAttr<float>("eta", "eta");
AddAttr<float>("eta", "The parameter for adaptive NMS.");
AddComment(R"DOC(
Generate Proposals OP
......
paddle/fluid/operators/detection/generate_proposals_op.cu 0 → 100644
浏览文件 @ 85ddb5c7
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <stdio.h>
#include <string>
#include <vector>
#include "cub/cub.cuh"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/operators/gather.cu.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
namespace {
#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
#define CUDA_1D_KERNEL_LOOP(i, n) \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
i += blockDim.x * gridDim.x)
int const kThreadsPerBlock = sizeof(uint64_t) * 8;
template <typename T>
__global__ void RangeInitKernel(const T start, const T delta, const int size,
T *out) {
CUDA_1D_KERNEL_LOOP(i, size) { out[i] = start + i * delta; }
}
template <typename T>
void SortDescending(const platform::CUDADeviceContext &ctx, const Tensor &value,
Tensor *value_out, Tensor *index_out) {
int num = value.numel();
Tensor index_in_t;
int *idx_in = index_in_t.mutable_data<int>({num}, ctx.GetPlace());
int block = 512;
auto stream = ctx.stream();
RangeInitKernel<<<DIVUP(num, block), block, 0, stream>>>(0, 1, num, idx_in);
int *idx_out = index_out->mutable_data<int>({num}, ctx.GetPlace());
const T *keys_in = value.data<T>();
T *keys_out = value_out->mutable_data<T>({num}, ctx.GetPlace());
// Determine temporary device storage requirements
void *d_temp_storage = NULL;
size_t temp_storage_bytes = 0;
cub::DeviceRadixSort::SortPairsDescending<T, int>(
d_temp_storage, temp_storage_bytes, keys_in, keys_out, idx_in, idx_out,
num);
// Allocate temporary storage
auto place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
d_temp_storage = memory::Alloc(place, temp_storage_bytes);
// Run sorting operation
cub::DeviceRadixSort::SortPairsDescending<T, int>(
d_temp_storage, temp_storage_bytes, keys_in, keys_out, idx_in, idx_out,
num);
memory::Free(place, d_temp_storage);
}
template <typename T>
__device__ __forceinline__ T Min(T x, T y) {
return x < y ? x : y;
}
template <typename T>
__device__ __forceinline__ T Max(T x, T y) {
return x > y ? x : y;
}
template <typename T>
__global__ void BoxDecodeAndClipKernel(const T *anchor, const T *deltas,
const T *var, const int *index,
const T *im_info, const int num,
T *proposals) {
T kBBoxClipDefault = log(1000.0 / 16.0);
CUDA_1D_KERNEL_LOOP(i, num) {
int k = index[i] * 4;
T axmin = anchor[k];
T aymin = anchor[k + 1];
T axmax = anchor[k + 2];
T aymax = anchor[k + 3];
T w = axmax - axmin + 1.0;
T h = aymax - aymin + 1.0;
T cx = axmin + 0.5 * w;
T cy = aymin + 0.5 * h;
T dxmin = deltas[k];
T dymin = deltas[k + 1];
T dxmax = deltas[k + 2];
T dymax = deltas[k + 3];
T d_cx = 0., d_cy = 0., d_w = 0., d_h = 0.;
if (var) {
d_cx = cx + dxmin * w * var[k];
d_cy = cy + dymin * h * var[k + 1];
d_w = exp(Min<T>(dxmax * var[k + 2], kBBoxClipDefault)) * w;
d_h = exp(Min<T>(dymax * var[k + 3], kBBoxClipDefault)) * h;
} else {
d_cx = cx + dxmin * w;
d_cy = cy + dymin * h;
d_w = exp(Min<T>(dxmax, kBBoxClipDefault)) * w;
d_h = exp(Min<T>(dymax, kBBoxClipDefault)) * h;
}
T oxmin = d_cx - d_w * 0.5;
T oymin = d_cy - d_h * 0.5;
T oxmax = d_cx + d_w * 0.5 - 1.;
T oymax = d_cy + d_h * 0.5 - 1.;
proposals[i * 4] = Max<T>(Min<T>(oxmin, im_info[1] - 1.), 0.);
proposals[i * 4 + 1] = Max<T>(Min<T>(oymin, im_info[0] - 1.), 0.);
proposals[i * 4 + 2] = Max<T>(Min<T>(oxmax, im_info[1] - 1.), 0.);
proposals[i * 4 + 3] = Max<T>(Min<T>(oymax, im_info[0] - 1.), 0.);
}
}
template <typename T, int BlockSize>
__global__ void FilterBBoxes(const T *bboxes, const T *im_info,
const T min_size, const int num, int *keep_num,
int *keep) {
T im_h = im_info[0];
T im_w = im_info[1];
T im_scale = im_info[2];
int cnt = 0;
__shared__ int keep_index[BlockSize];
CUDA_1D_KERNEL_LOOP(i, num) {
keep_index[threadIdx.x] = -1;
__syncthreads();
int k = i * 4;
T xmin = bboxes[k];
T ymin = bboxes[k + 1];
T xmax = bboxes[k + 2];
T ymax = bboxes[k + 3];
T w = xmax - xmin + 1.0;
T h = ymax - ymin + 1.0;
T cx = xmin + w / 2.;
T cy = ymin + h / 2.;
T w_s = (xmax - xmin) / im_scale + 1.;
T h_s = (ymax - ymin) / im_scale + 1.;
if (w_s >= min_size && h_s >= min_size && cx <= im_w && cy <= im_h) {
keep_index[threadIdx.x] = i;
}
__syncthreads();
if (threadIdx.x == 0) {
int size = (num - i) < BlockSize ? num - i : BlockSize;
for (int j = 0; j < size; ++j) {
if (keep_index[j] > -1) {
keep[cnt++] = keep_index[j];
}
}
}
__syncthreads();
}
if (threadIdx.x == 0) {
keep_num[0] = cnt;
}
}
__device__ inline float IoU(const float *a, const float *b) {
float left = max(a[0], b[0]), right = min(a[2], b[2]);
float top = max(a[1], b[1]), bottom = min(a[3], b[3]);
float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f);
float inter_s = width * height;
float s_a = (a[2] - a[0] + 1) * (a[3] - a[1] + 1);
float s_b = (b[2] - b[0] + 1) * (b[3] - b[1] + 1);
return inter_s / (s_a + s_b - inter_s);
}
__global__ void NMSKernel(const int n_boxes, const float nms_overlap_thresh,
const float *dev_boxes, uint64_t *dev_mask) {
const int row_start = blockIdx.y;
const int col_start = blockIdx.x;
const int row_size =
min(n_boxes - row_start * kThreadsPerBlock, kThreadsPerBlock);
const int col_size =
min(n_boxes - col_start * kThreadsPerBlock, kThreadsPerBlock);
__shared__ float block_boxes[kThreadsPerBlock * 4];
if (threadIdx.x < col_size) {
block_boxes[threadIdx.x * 4 + 0] =
dev_boxes[(kThreadsPerBlock * col_start + threadIdx.x) * 4 + 0];
block_boxes[threadIdx.x * 4 + 1] =
dev_boxes[(kThreadsPerBlock * col_start + threadIdx.x) * 4 + 1];
block_boxes[threadIdx.x * 4 + 2] =
dev_boxes[(kThreadsPerBlock * col_start + threadIdx.x) * 4 + 2];
block_boxes[threadIdx.x * 4 + 3] =
dev_boxes[(kThreadsPerBlock * col_start + threadIdx.x) * 4 + 3];
}
__syncthreads();
if (threadIdx.x < row_size) {
const int cur_box_idx = kThreadsPerBlock * row_start + threadIdx.x;
const float *cur_box = dev_boxes + cur_box_idx * 4;
int i = 0;
uint64_t t = 0;
int start = 0;
if (row_start == col_start) {
start = threadIdx.x + 1;
}
for (i = start; i < col_size; i++) {
if (IoU(cur_box, block_boxes + i * 4) > nms_overlap_thresh) {
t |= 1ULL << i;
}
}
const int col_blocks = DIVUP(n_boxes, kThreadsPerBlock);
dev_mask[cur_box_idx * col_blocks + col_start] = t;
}
}
template <typename T>
void NMS(const platform::CUDADeviceContext &ctx, const Tensor &proposals,
const Tensor &sorted_indices, const T nms_threshold,
Tensor *keep_out) {
int boxes_num = proposals.dims()[0];
PADDLE_ENFORCE_EQ(boxes_num, sorted_indices.dims()[0]);
const int col_blocks = DIVUP(boxes_num, kThreadsPerBlock);
dim3 blocks(DIVUP(boxes_num, kThreadsPerBlock),
DIVUP(boxes_num, kThreadsPerBlock));
dim3 threads(kThreadsPerBlock);
const T *boxes = proposals.data<T>();
auto place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
int size_bytes = boxes_num * col_blocks * sizeof(uint64_t);
uint64_t *d_mask =
reinterpret_cast<uint64_t *>(memory::Alloc(place, size_bytes));
NMSKernel<<<blocks, threads>>>(boxes_num, nms_threshold, boxes, d_mask);
uint64_t *h_mask = reinterpret_cast<uint64_t *>(
memory::Alloc(platform::CPUPlace(), size_bytes));
memory::Copy(platform::CPUPlace(), h_mask, place, d_mask, size_bytes, 0);
std::vector<uint64_t> remv(col_blocks);
memset(&remv[0], 0, sizeof(uint64_t) * col_blocks);
std::vector<int> keep_vec;
int num_to_keep = 0;
for (int i = 0; i < boxes_num; i++) {
int nblock = i / kThreadsPerBlock;
int inblock = i % kThreadsPerBlock;
if (!(remv[nblock] & (1ULL << inblock))) {
++num_to_keep;
keep_vec.push_back(i);
uint64_t *p = &h_mask[0] + i * col_blocks;
for (int j = nblock; j < col_blocks; j++) {
remv[j] |= p[j];
}
}
}
int *keep = keep_out->mutable_data<int>({num_to_keep}, ctx.GetPlace());
memory::Copy(place, keep, platform::CPUPlace(), keep_vec.data(),
sizeof(int) * num_to_keep, 0);
memory::Free(place, d_mask);
memory::Free(platform::CPUPlace(), h_mask);
}
template <typename T>
std::pair<Tensor, Tensor> ProposalForOneImage(
const platform::CUDADeviceContext &ctx, const Tensor &im_info,
const Tensor &anchors, const Tensor &variances,
const Tensor &bbox_deltas, // [M, 4]
const Tensor &scores, // [N, 1]
int pre_nms_top_n, int post_nms_top_n, float nms_thresh, float min_size,
float eta) {
// 1. pre nms
Tensor scores_sort, index_sort;
SortDescending<T>(ctx, scores, &scores_sort, &index_sort);
int num = scores.numel();
int pre_nms_num = (pre_nms_top_n <= 0 || pre_nms_top_n > num) ? scores.numel()
: pre_nms_top_n;
scores_sort.Resize({pre_nms_num, 1});
index_sort.Resize({pre_nms_num, 1});
// 2. box decode and clipping
Tensor proposals;
proposals.mutable_data<T>({pre_nms_num, 4}, ctx.GetPlace());
int block = 512;
auto stream = ctx.stream();
BoxDecodeAndClipKernel<T><<<DIVUP(pre_nms_num, block), block, 0, stream>>>(
anchors.data<T>(), bbox_deltas.data<T>(), variances.data<T>(),
index_sort.data<int>(), im_info.data<T>(), pre_nms_num,
proposals.data<T>());
// 3. filter
Tensor keep_index, keep_num_t;
keep_index.mutable_data<int>({pre_nms_num}, ctx.GetPlace());
keep_num_t.mutable_data<int>({1}, ctx.GetPlace());
min_size = std::max(min_size, 1.0f);
FilterBBoxes<T, 512><<<1, 512, 0, stream>>>(
proposals.data<T>(), im_info.data<T>(), min_size, pre_nms_num,
keep_num_t.data<int>(), keep_index.data<int>());
int keep_num;
const auto gpu_place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
memory::Copy(platform::CPUPlace(), &keep_num, gpu_place,
keep_num_t.data<int>(), sizeof(int), 0);
keep_index.Resize({keep_num});
Tensor scores_filter, proposals_filter;
proposals_filter.mutable_data<T>({keep_num, 4}, ctx.GetPlace());
scores_filter.mutable_data<T>({keep_num, 1}, ctx.GetPlace());
GPUGather<T>(ctx, proposals, keep_index, &proposals_filter);
GPUGather<T>(ctx, scores_sort, keep_index, &scores_filter);
if (nms_thresh <= 0) {
return std::make_pair(proposals_filter, scores_filter);
}
// 4. nms
Tensor keep_nms;
NMS<T>(ctx, proposals_filter, keep_index, nms_thresh, &keep_nms);
if (post_nms_top_n > 0 && post_nms_top_n < keep_nms.numel()) {
keep_nms.Resize({post_nms_top_n});
}
Tensor scores_nms, proposals_nms;
proposals_nms.mutable_data<T>({keep_nms.numel(), 4}, ctx.GetPlace());
scores_nms.mutable_data<T>({keep_nms.numel(), 1}, ctx.GetPlace());
GPUGather<T>(ctx, proposals_filter, keep_nms, &proposals_nms);
GPUGather<T>(ctx, scores_filter, keep_nms, &scores_nms);
return std::make_pair(proposals_nms, scores_nms);
}
} // namespace
template <typename DeviceContext, typename T>
class CUDAGenerateProposalsKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *scores = context.Input<Tensor>("Scores");
auto *bbox_deltas = context.Input<Tensor>("BboxDeltas");
auto *im_info = context.Input<Tensor>("ImInfo");
auto *anchors = context.Input<Tensor>("Anchors");
auto *variances = context.Input<Tensor>("Variances");
auto *rpn_rois = context.Output<LoDTensor>("RpnRois");
auto *rpn_roi_probs = context.Output<LoDTensor>("RpnRoiProbs");
int pre_nms_top_n = context.Attr<int>("pre_nms_topN");
int post_nms_top_n = context.Attr<int>("post_nms_topN");
float nms_thresh = context.Attr<float>("nms_thresh");
float min_size = context.Attr<float>("min_size");
float eta = context.Attr<float>("eta");
PADDLE_ENFORCE_GE(eta, 1., "Not support adaptive NMS.");
auto &dev_ctx = context.template device_context<DeviceContext>();
auto scores_dim = scores->dims();
int64_t num = scores_dim[0];
int64_t c_score = scores_dim[1];
int64_t h_score = scores_dim[2];
int64_t w_score = scores_dim[3];
auto bbox_dim = bbox_deltas->dims();
int64_t c_bbox = bbox_dim[1];
int64_t h_bbox = bbox_dim[2];
int64_t w_bbox = bbox_dim[3];
Tensor bbox_deltas_swap, scores_swap;
bbox_deltas_swap.mutable_data<T>({num, h_bbox, w_bbox, c_bbox},
dev_ctx.GetPlace());
scores_swap.mutable_data<T>({num, h_score, w_score, c_score},
dev_ctx.GetPlace());
math::Transpose<DeviceContext, T, 4> trans;
std::vector<int> axis = {0, 2, 3, 1};
trans(dev_ctx, *bbox_deltas, &bbox_deltas_swap, axis);
trans(dev_ctx, *scores, &scores_swap, axis);
Tensor *anchor = const_cast<framework::Tensor *>(anchors);
anchor->Resize({anchors->numel() / 4, 4});
Tensor *var = const_cast<framework::Tensor *>(variances);
var->Resize({var->numel() / 4, 4});
rpn_rois->mutable_data<T>({bbox_deltas->numel() / 4, 4},
context.GetPlace());
rpn_roi_probs->mutable_data<T>({scores->numel(), 1}, context.GetPlace());
T *rpn_rois_data = rpn_rois->data<T>();
T *rpn_roi_probs_data = rpn_roi_probs->data<T>();
auto place = boost::get<platform::CUDAPlace>(dev_ctx.GetPlace());
int64_t num_proposals = 0;
std::vector<size_t> offset(1, 0);
for (int64_t i = 0; i < num; ++i) {
Tensor im_info_slice = im_info->Slice(i, i + 1);
Tensor bbox_deltas_slice = bbox_deltas_swap.Slice(i, i + 1);
Tensor scores_slice = scores_swap.Slice(i, i + 1);
bbox_deltas_slice.Resize({h_bbox * w_bbox * c_bbox / 4, 4});
scores_slice.Resize({h_score * w_score * c_score, 1});
std::pair<Tensor, Tensor> box_score_pair =
ProposalForOneImage<T>(dev_ctx, im_info_slice, *anchor, *var,
bbox_deltas_slice, scores_slice, pre_nms_top_n,
post_nms_top_n, nms_thresh, min_size, eta);
Tensor proposals = box_score_pair.first;
Tensor scores = box_score_pair.second;
memory::Copy(place, rpn_rois_data + num_proposals * 4, place,
proposals.data<T>(), sizeof(T) * proposals.numel(), 0);
memory::Copy(place, rpn_roi_probs_data + num_proposals, place,
scores.data<T>(), sizeof(T) * scores.numel(), 0);
num_proposals += proposals.dims()[0];
offset.emplace_back(num_proposals);
}
framework::LoD lod;
lod.emplace_back(offset);
rpn_rois->set_lod(lod);
rpn_roi_probs->set_lod(lod);
rpn_rois->Resize({num_proposals, 4});
rpn_roi_probs->Resize({num_proposals, 1});
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(generate_proposals,
ops::CUDAGenerateProposalsKernel<
paddle::platform::CUDADeviceContext, float>);
paddle/fluid/operators/detection_map_op.h
浏览文件 @ 85ddb5c7
......@@ -76,8 +76,8 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
auto ap_type = GetAPType(ctx.Attr<std::string>("ap_type"));
int class_num = ctx.Attr<int>("class_num");
auto label_lod = in_label->lod();
auto detect_lod = in_detect->lod();
auto& label_lod = in_label->lod();
auto& detect_lod = in_detect->lod();
PADDLE_ENFORCE_EQ(label_lod.size(), 1UL,
"Only support one level sequence now.");
PADDLE_ENFORCE_EQ(label_lod[0].size(), detect_lod[0].size(),
......@@ -166,11 +166,11 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
auto labels = framework::EigenTensor<T, 2>::From(input_label);
auto detect = framework::EigenTensor<T, 2>::From(input_detect);
auto label_lod = input_label.lod();
auto detect_lod = input_detect.lod();
auto& label_lod = input_label.lod();
auto& detect_lod = input_detect.lod();
int batch_size = label_lod[0].size() - 1;
auto label_index = label_lod[0];
auto& label_index = label_lod[0];
for (int n = 0; n < batch_size; ++n) {
std::map<int, std::vector<Box>> boxes;
......@@ -274,7 +274,6 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
output_true_pos->set_lod(true_pos_lod);
output_false_pos->set_lod(false_pos_lod);
return;
}
void GetInputPos(const framework::Tensor& input_pos_count,
......@@ -292,7 +291,7 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
auto SetData = [](const framework::LoDTensor& pos_tensor,
std::map<int, std::vector<std::pair<T, int>>>& pos) {
const T* pos_data = pos_tensor.data<T>();
auto pos_data_lod = pos_tensor.lod()[0];
auto& pos_data_lod = pos_tensor.lod()[0];
for (size_t i = 0; i < pos_data_lod.size() - 1; ++i) {
for (size_t j = pos_data_lod[i]; j < pos_data_lod[i + 1]; ++j) {
T score = pos_data[j * 2];
......@@ -317,20 +316,23 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
std::map<int, std::vector<std::pair<T, int>>>* false_pos) const {
int batch_size = gt_boxes.size();
for (int n = 0; n < batch_size; ++n) {
auto image_gt_boxes = gt_boxes[n];
for (auto it = image_gt_boxes.begin(); it != image_gt_boxes.end(); ++it) {
auto& image_gt_boxes = gt_boxes[n];
for (auto& image_gt_box : image_gt_boxes) {
size_t count = 0;
auto labeled_bboxes = it->second;
auto& labeled_bboxes = image_gt_box.second;
if (evaluate_difficult) {
count = labeled_bboxes.size();
} else {
for (size_t i = 0; i < labeled_bboxes.size(); ++i)
if (!(labeled_bboxes[i].is_difficult)) ++count;
for (auto& box : labeled_bboxes) {
if (!box.is_difficult) {
++count;
}
}
}
if (count == 0) {
continue;
}
int label = it->first;
int label = image_gt_box.first;
if (label_pos_count->find(label) == label_pos_count->end()) {
(*label_pos_count)[label] = count;
} else {
......
paddle/fluid/operators/extract_rows_op.cc
浏览文件 @ 85ddb5c7
......@@ -50,7 +50,7 @@ class ExtractRowsOp : public framework::OperatorBase {
auto &in = scope.FindVar(Input("X"))->Get<framework::SelectedRows>();
auto out = scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
auto in_rows = in.rows();
auto &in_rows = in.rows();
auto out_dim = framework::make_ddim(
std::vector<int64_t>{static_cast<int64_t>(in_rows.size()), 1});
auto dst_ptr = out->mutable_data<int64_t>(out_dim, in.place());
......
paddle/fluid/operators/lookup_table_op.cu
浏览文件 @ 85ddb5c7
......@@ -127,10 +127,8 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
auto gpu_place = boost::get<platform::CUDAPlace>(context.GetPlace());
// TODO(yuyang18): Strange code here.
memory::Copy(platform::CPUPlace(),
new_rows.CUDAMutableData(context.GetPlace()), gpu_place,
ids_data, ids_num * sizeof(int64_t), stream);
memory::Copy(gpu_place, new_rows.CUDAMutableData(context.GetPlace()),
gpu_place, ids_data, ids_num * sizeof(int64_t), stream);
d_table->set_rows(new_rows);
auto *d_table_value = d_table->mutable_value();
......
paddle/fluid/operators/math/selected_rows_functor.cu
浏览文件 @ 85ddb5c7
......@@ -60,11 +60,9 @@ struct SelectedRowsAdd<platform::CUDADeviceContext, T> {
auto out_place = context.GetPlace();
PADDLE_ENFORCE(platform::is_gpu_place(out_place));
memory::Copy(
boost::get<platform::CUDAPlace>(out_place), out_data,
boost::get<platform::CUDAPlace>(in1_place), in1_data,
in1_value.numel() * sizeof(T),
reinterpret_cast<const platform::CUDADeviceContext&>(context).stream());
memory::Copy(boost::get<platform::CUDAPlace>(out_place), out_data,
boost::get<platform::CUDAPlace>(in1_place), in1_data,
in1_value.numel() * sizeof(T), context.stream());
auto* in2_data = in2_value.data<T>();
memory::Copy(boost::get<platform::CUDAPlace>(out_place),
......@@ -148,7 +146,7 @@ struct SelectedRowsAddTo<platform::CUDADeviceContext, T> {
auto in1_height = input1.height();
PADDLE_ENFORCE_EQ(in1_height, input2->height());
framework::Vector<int64_t> in1_rows(input1.rows());
auto& in1_rows = input1.rows();
auto& in2_rows = *(input2->mutable_rows());
auto& in1_value = input1.value();
......
paddle/fluid/operators/read_op.cc
浏览文件 @ 85ddb5c7
......@@ -45,12 +45,10 @@ class ReadInferVarType : public framework::VarTypeInference {
framework::VarDesc* reader = block->FindVarRecursive(reader_name);
auto dtypes = reader->GetDataTypes();
PADDLE_ENFORCE_EQ(dtypes.size(), out_names.size());
auto lod_levels = reader->GetLoDLevels();
for (size_t i = 0; i < dtypes.size(); ++i) {
framework::VarDesc& out = block->FindRecursiveOrCreateVar(out_names[i]);
out.SetType(framework::proto::VarType::LOD_TENSOR);
out.SetDataType(dtypes[i]);
out.SetLoDLevel(lod_levels[i]);
}
}
};
......
paddle/fluid/operators/sampling_id_op.cc
浏览文件 @ 85ddb5c7
......@@ -53,15 +53,16 @@ class SamplingIdOpMaker : public framework::OpProtoAndCheckerMaker {
SamplingId Operator.
A layer for sampling id from multinomial distribution from the
input. Sampling one id for one sample.)DOC");
AddAttr<float>("min", "Minimum value of random. [default 0.0].")
AddAttr<float>("min", "Minimum value of random. (float, default 0.0).")
.SetDefault(0.0f);
AddAttr<float>("max", "Maximun value of random. [default 1.0].")
AddAttr<float>("max", "Maximun value of random. (float, default 1.0).")
.SetDefault(1.0f);
AddAttr<int>("seed",
"Random seed used for the random number engine. "
"0 means use a seed generated by the system."
"Note that if seed is not 0, this operator will always "
"generate the same random numbers every time. [default 0].")
AddAttr<int>(
"seed",
"Random seed used for the random number engine. "
"0 means use a seed generated by the system."
"Note that if seed is not 0, this operator will always "
"generate the same random numbers every time. (int, default 0).")
.SetDefault(0);
}
};
......
paddle/fluid/operators/scale_op.cc
浏览文件 @ 85ddb5c7
......@@ -77,8 +77,10 @@ class ScaleOpVarTypeInference : public framework::VarTypeInference {
auto out_var_name = op_desc.Output("Out").front();
auto *out_var = block->FindVarRecursive(out_var_name);
out_var->SetType(in_var.GetType());
out_var->SetDataType(in_var.GetDataType());
if (in_var_name != out_var_name) {
out_var->SetType(in_var.GetType());
out_var->SetDataType(in_var.GetDataType());
}
}
};
......
paddle/fluid/operators/sgd_op.cu
浏览文件 @ 85ddb5c7
......@@ -88,7 +88,7 @@ class SGDOpCUDAKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ(in_height, out_dims[0]);
auto& in_value = grad->value();
framework::Vector<int64_t> in_rows(grad->rows());
auto& in_rows = grad->rows();
int64_t in_row_numel = in_value.numel() / in_rows.size();
PADDLE_ENFORCE_EQ(in_row_numel, param_out->numel() / in_height);
......
paddle/fluid/operators/shrink_rnn_memory_op.cc
浏览文件 @ 85ddb5c7
......@@ -52,26 +52,16 @@ class ShrinkRNNMemoryOp : public ArrayOp {
size_t height = dst_num_rows;
// do shrink for the top level LoD
if (x_tensor.lod().size() > 0 &&
x_tensor.lod()[0].size() > static_cast<size_t>(dst_num_rows)) {
if (x_tensor.lod().size() > 1) { // MultiLevel LoD
auto lod_offset = framework::GetSubLoDAndAbsoluteOffset(
x_tensor.lod(), 0, dst_num_rows, 0);
height = lod_offset.second.second;
auto out_lod = out_tensor.mutable_lod();
framework::AppendLoD(out_lod, lod_offset.first);
} else {
// Shrink LoD
auto lod_item = x_tensor.lod()[0];
lod_item.resize(dst_num_rows + 1);
out_tensor.set_lod({lod_item});
const auto &const_lod_item = lod_item;
height = const_lod_item.back();
}
auto lod_offset = framework::GetSubLoDAndAbsoluteOffset(x_tensor.lod(), 0,
dst_num_rows, 0);
height = lod_offset.second.second;
auto out_lod = out_tensor.mutable_lod();
framework::AppendLoD(out_lod, lod_offset.first);
}
if (height != 0) {
if (dst_num_rows != 0) {
out_tensor.mutable_data(place, x_tensor.type());
auto dev_ctx = platform::DeviceContextPool::Instance().Get(place);
framework::TensorCopy(x_tensor.Slice(0, height), place, *dev_ctx,
......@@ -144,11 +134,8 @@ class ShrinkRNNMemoryGradOp : public ArrayOp {
} else {
auto &dout_tensor = dout_var->Get<framework::LoDTensor>();
auto height = dout_tensor.dims()[0];
if (height != 0) {
auto slice = dx_tensor.Slice(0, static_cast<int>(height));
framework::TensorCopy(dout_tensor, dout_tensor.place(), dev_ctx,
&slice);
}
auto slice = dx_tensor.Slice(0, static_cast<int>(height));
framework::TensorCopy(dout_tensor, dout_tensor.place(), dev_ctx, &slice);
if (dx_tensor.dims()[0] > height) {
auto rest_tensor = dx_tensor.Slice(
static_cast<int>(height), static_cast<int>(dx_tensor.dims()[0]));
......
paddle/fluid/operators/sum_op.h
浏览文件 @ 85ddb5c7
......@@ -32,7 +32,7 @@ class SumKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto in_vars = context.MultiInputVar("X");
int N = in_vars.size();
size_t in_num = in_vars.size();
auto out_var = context.OutputVar("Out");
bool in_place = out_var == in_vars[0];
......@@ -53,7 +53,7 @@ class SumKernel : public framework::OpKernel<T> {
auto &place =
*context.template device_context<DeviceContext>().eigen_device();
// If in_place, just skip the first tensor
for (int i = in_place ? 1 : 0; i < N; i++) {
for (size_t i = in_place ? 1 : 0; i < in_num; i++) {
if (in_vars[i]->IsType<framework::LoDTensor>()) {
auto &in_t = in_vars[i]->Get<framework::LoDTensor>();
if (in_t.numel() == 0) {
......@@ -101,13 +101,13 @@ class SumKernel : public framework::OpKernel<T> {
// Runtime InferShape
size_t first_dim = 0;
for (int i = 0; i < N; i++) {
for (size_t i = 0; i < in_num; i++) {
auto &sel_row = get_selected_row(i);
first_dim += sel_row.rows().size();
}
std::vector<int64_t> in_dim;
for (int i = 0; i < N; i++) {
for (size_t i = 0; i < in_num; i++) {
auto &sel_row = get_selected_row(i);
if (sel_row.rows().size() > 0) {
in_dim = framework::vectorize(sel_row.value().dims());
......@@ -116,14 +116,14 @@ class SumKernel : public framework::OpKernel<T> {
}
if (in_dim.empty()) {
VLOG(3) << "WARNING: all the inputs are empty";
in_dim = framework::vectorize(get_selected_row(N - 1).value().dims());
in_dim =
framework::vectorize(get_selected_row(in_num - 1).value().dims());
} else {
in_dim[0] = static_cast<int64_t>(first_dim);
}
out_value->Resize(framework::make_ddim(in_dim));
out_value->mutable_data<T>(context.GetPlace());
// if all the input sparse vars are empty, no need to
// merge these vars.
if (first_dim == 0UL) {
......@@ -133,7 +133,7 @@ class SumKernel : public framework::OpKernel<T> {
math::SelectedRowsAddTo<DeviceContext, T> functor;
int64_t offset = 0;
for (int i = 0; i < N; i++) {
for (size_t i = 0; i < in_num; i++) {
auto &sel_row = get_selected_row(i);
if (sel_row.rows().size() == 0) {
continue;
......
paddle/fluid/platform/device_context.cc
浏览文件 @ 85ddb5c7
......@@ -201,7 +201,6 @@ CUDADeviceContext::CUDADeviceContext(CUDAPlace place)
compute_capability = GetCUDAComputeCapability(place_.device);
multi_process = GetCUDAMultiProcessors(place_.device);
max_threads_per_mp = GetCUDAMaxThreadsPerMultiProcessor(place_.device);
grid_max_dims_ = GpuMaxGridDim(place_.device);
PADDLE_ENFORCE(cudaStreamCreate(&stream_));
eigen_stream_.reset(new EigenCudaStreamDevice());
eigen_stream_->Reinitialize(&stream_, place);
......@@ -240,10 +239,6 @@ int CUDADeviceContext::GetMaxPhysicalThreadCount() const {
return multi_process * max_threads_per_mp;
}
std::tuple<int, int, int> CUDADeviceContext::GetMaxGridDims() const {
return grid_max_dims_;
}
Eigen::GpuDevice* CUDADeviceContext::eigen_device() const {
return eigen_device_.get();
}
......
paddle/fluid/platform/device_context.h
浏览文件 @ 85ddb5c7
......@@ -13,7 +13,6 @@ limitations under the License. */
#include <memory>
#include <mutex> // NOLINT
#include <string>
#include <tuple>
#include <unordered_map>
#include <vector>
......@@ -92,8 +91,6 @@ class CUDADeviceContext : public DeviceContext {
/*! \brief Return the max physical thread count in the device context */
int GetMaxPhysicalThreadCount() const;
std::tuple<int, int, int> GetMaxGridDims() const;
/*! \brief Return eigen device in the device context. */
Eigen::GpuDevice* eigen_device() const;
......@@ -138,8 +135,6 @@ class CUDADeviceContext : public DeviceContext {
cudaStream_t stream_;
cublasHandle_t cublas_handle_;
std::tuple<int, int, int> grid_max_dims_;
int compute_capability;
int multi_process;
int max_threads_per_mp;
......
paddle/fluid/platform/for_range.h
浏览文件 @ 85ddb5c7
......@@ -48,54 +48,35 @@ __global__ static void ForRangeElemwiseOpGridIsOne(Function func) {
}
template <typename Function>
__global__ static void ForRangeElemwiseOp(Function func, size_t limit) {
__global__ static void ForRangeElemwiseOp(Function func, int limit) {
size_t idx = static_cast<size_t>(blockIdx.x * blockDim.x + threadIdx.x);
if (idx < limit) {
func(idx);
}
}
template <typename Function>
__global__ static void ForRangeElemwiseOpGridLarge(Function func, size_t limit,
int grid_dim) {
size_t idx = static_cast<size_t>(blockIdx.x * blockDim.x + threadIdx.x);
while (idx < limit) {
func(idx);
idx += grid_dim;
}
}
template <>
struct ForRange<CUDADeviceContext> {
ForRange(const CUDADeviceContext& dev_ctx, size_t limit)
: dev_ctx_(dev_ctx), limit_(limit) {}
: dev_ctx_(dev_ctx), limit_(static_cast<int>(limit)) {}
template <typename Function>
inline void operator()(Function func) const {
constexpr int num_threads = 1024;
int block_size = limit_ <= num_threads ? limit_ : num_threads;
size_t grid_size = (limit_ + num_threads - 1) / num_threads;
int max_grid_dim = std::get<0>(dev_ctx_.GetMaxGridDims());
if (grid_size < max_grid_dim) {
int grid_size_int = static_cast<int>(grid_size);
if (grid_size == 1) {
ForRangeElemwiseOpGridIsOne<<<1, block_size, 0, dev_ctx_.stream()>>>(
func);
} else {
ForRangeElemwiseOp<<<grid_size_int, block_size, 0, dev_ctx_.stream()>>>(
func, limit_);
}
int grid_size = (limit_ + num_threads - 1) / num_threads;
if (grid_size == 1) {
ForRangeElemwiseOpGridIsOne<<<1, block_size, 0, dev_ctx_.stream()>>>(
func);
} else {
ForRangeElemwiseOpGridLarge<<<max_grid_dim, block_size, 0,
dev_ctx_.stream()>>>(func, limit_,
max_grid_dim);
ForRangeElemwiseOp<<<grid_size, block_size, 0, dev_ctx_.stream()>>>(
func, limit_);
}
}
const CUDADeviceContext& dev_ctx_;
size_t limit_;
int limit_;
};
#endif
......
paddle/fluid/platform/gpu_info.cc
浏览文件 @ 85ddb5c7
......@@ -152,22 +152,5 @@ void GpuMemsetAsync(void *dst, int value, size_t count, cudaStream_t stream) {
PADDLE_ENFORCE(cudaMemsetAsync(dst, value, count, stream),
"cudaMemsetAsync failed in paddle::platform::GpuMemsetAsync");
}
std::tuple<int, int, int> GpuMaxGridDim(int id) {
std::tuple<int, int, int> result;
PADDLE_ENFORCE(
cudaDeviceGetAttribute(&std::get<0>(result), cudaDevAttrMaxBlockDimX, id),
"cudaDeviceGetAttribute failed in "
"cudaDevAttrMaxBlockDim");
PADDLE_ENFORCE(
cudaDeviceGetAttribute(&std::get<1>(result), cudaDevAttrMaxBlockDimY, id),
"cudaDeviceGetAttribute failed in "
"cudaDevAttrMaxBlockDim");
PADDLE_ENFORCE(
cudaDeviceGetAttribute(&std::get<2>(result), cudaDevAttrMaxBlockDimZ, id),
"cudaDeviceGetAttribute failed in "
"cudaDevAttrMaxBlockDim");
return result;
}
} // namespace platform
} // namespace paddle
paddle/fluid/platform/gpu_info.h
浏览文件 @ 85ddb5c7
......@@ -19,7 +19,6 @@ limitations under the License. */
#include <cuda_runtime.h>
#include <stddef.h>
#include <string>
#include <tuple>
namespace paddle {
namespace platform {
......@@ -73,8 +72,6 @@ void GpuMemcpyPeerSync(void *dst, int dst_device, const void *src,
//! Set memory dst with value count size asynchronously
void GpuMemsetAsync(void *dst, int value, size_t count, cudaStream_t stream);
std::tuple<int, int, int> GpuMaxGridDim(int id);
} // namespace platform
} // namespace paddle
......
paddle/scripts/paddle_build.sh
浏览文件 @ 85ddb5c7
......@@ -70,8 +70,8 @@ function cmake_gen() {
PYTHON_FLAGS=""
SYSTEM=`uname -s`
if [ "$SYSTEM" == "Darwin" ]; then
echo "Using python abi: $1"
if [[ "$1" == "cp27-cp27m" ]] || [[ "$1" == "" ]]; then
echo "using python abi: $1"
if [ -d "/Library/Frameworks/Python.framework/Versions/2.7" ]; then
export LD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/2.7
export DYLD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/2.7
......@@ -82,7 +82,18 @@ function cmake_gen() {
else
exit 1
fi
# TODO: qiyang add python3 part here
elif [ "$1" == "cp35-cp35m" ]; then
if [ -d "/Library/Frameworks/Python.framework/Versions/3.5" ]; then
export LD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.5/lib/
export DYLD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.5/lib/
export PATH=/Library/Frameworks/Python.framework/Versions/3.5/bin/:${PATH}
PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.5/bin/python3
-DPYTHON_INCLUDE_DIR:PATH=/Library/Frameworks/Python.framework/Versions/3.5/include/python3.5m/
-DPYTHON_LIBRARY:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.5/lib/libpython3.5m.dylib"
WITH_FLUID_ONLY=${WITH_FLUID_ONLY:-ON}
else
exit 1
fi
fi
else
if [ "$1" != "" ]; then
......@@ -629,10 +640,10 @@ EOF
function gen_capi_package() {
if [[ ${WITH_C_API} == "ON" ]]; then
install_prefix="${PADDLE_ROOT}/build/capi_output"
rm -rf $install_prefix
make DESTDIR="$install_prefix" install
cd $install_prefix/usr/local
capi_install_prefix=${INSTALL_PREFIX:-/paddle/build}/capi_output
rm -rf $capi_install_prefix
make DESTDIR="$capi_install_prefix" install
cd $capi_install_prefix/
ls | egrep -v "^Found.*item$" | xargs tar -czf ${PADDLE_ROOT}/build/paddle.tgz
fi
}
......
python/paddle/dataset/common.py
浏览文件 @ 85ddb5c7
......@@ -77,13 +77,14 @@ def download(url, module_name, md5sum, save_name=None):
retry_limit = 3
while not (os.path.exists(filename) and md5file(filename) == md5sum):
if os.path.exists(filename):
print("file md5", md5file(filename), md5sum)
sys.stderr.write("file %s md5 %s" % (md5file(filename), md5sum))
if retry < retry_limit:
retry += 1
else:
raise RuntimeError("Cannot download {0} within retry limit {1}".
format(url, retry_limit))
print("Cache file %s not found, downloading %s" % (filename, url))
sys.stderr.write("Cache file %s not found, downloading %s" %
(filename, url))
r = requests.get(url, stream=True)
total_length = r.headers.get('content-length')
......@@ -100,10 +101,11 @@ def download(url, module_name, md5sum, save_name=None):
dl += len(data)
f.write(data)
done = int(50 * dl / total_length)
sys.stdout.write("\r[%s%s]" % ('=' * done,
sys.stderr.write("\r[%s%s]" % ('=' * done,
' ' * (50 - done)))
sys.stdout.flush()
sys.stderr.write("\n")
sys.stdout.flush()
return filename
......
python/paddle/fluid/contrib/__init__.py
浏览文件 @ 85ddb5c7
......@@ -18,5 +18,10 @@ from . import decoder
from .decoder import *
from . import memory_usage_calc
from .memory_usage_calc import *
from . import op_frequence
from .op_frequence import *
__all__ = decoder.__all__ + memory_usage_calc.__all__
__all__ = []
__all__ += decoder.__all__
__all__ += memory_usage_calc.__all__
__all__ += op_frequence.__all__
python/paddle/fluid/contrib/op_frequence.py 0 → 100644
浏览文件 @ 85ddb5c7
# 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.
from __future__ import print_function
from collections import OrderedDict
from ..framework import Program
__all__ = ['op_freq_statistic']
def op_freq_statistic(program):
"""
Statistics of Op frequency.
Args:
program(Program): The current Program.
Returns:
uni_op_freq(dict): the single op frequency.
adj_2_op_freq(dict): the two adjacent ops frequency.
Examples:
>>> import paddle.fluid as fluid
>>> uni_op_freq, adj_2_op_freq = fluid.contrib.op_freq_statistic(
>>> fluid.default_main_program())
>>> for op_type, op_num in uni_op_freq:
>>> print("%s \t %d" % (op_type, op_num))
>>> for op_type, op_num in adj_2_op_freq:
>>> print("%s \t %d" % (op_type, op_num))
"""
if not isinstance(program, Program):
raise TypeError("The input type should be Porgram."
"But you passed in %s" % (type(program)))
uni_op_freq = OrderedDict()
adj_2_op_freq = OrderedDict()
op_in_ops = OrderedDict()
parameters = [p.name for p in program.blocks[0].all_parameters()]
# get uni_op_freq
for op in program.global_block().ops:
had_recorded = False
for var_name in op.output_arg_names:
if var_name in parameters:
continue
if not had_recorded and uni_op_freq.has_key(op.type):
uni_op_freq[op.type] += 1
had_recorded = True
elif not had_recorded:
uni_op_freq[op.type] = 1
had_recorded = True
# get adj_2_op_freq
var_gen_op = {}
for op in program.global_block().ops:
for var_name in op.input_arg_names:
if var_name in parameters:
continue
if var_gen_op.has_key(var_name):
assert len(var_gen_op[var_name]) > 0
if op_in_ops.has_key(op.type):
op_in_ops[op.type].append(var_gen_op[var_name][-1])
else:
op_in_ops[op.type] = [var_gen_op[var_name][-1]]
else:
print("Var's generate op is not found,%s, %s" %
(var_name, op.type))
for var_name in op.output_arg_names:
if var_gen_op.has_key(var_name):
var_gen_op[var_name].append(op.type)
else:
var_gen_op[var_name] = [op.type]
for op, in_ops in op_in_ops.iteritems():
for in_op in in_ops:
op_op = in_op + "->" + op
if adj_2_op_freq.has_key(op_op):
adj_2_op_freq[op_op] += 1
else:
adj_2_op_freq[op_op] = 1
uni_op_freq = sorted(
uni_op_freq.items(), key=lambda item: item[1], reverse=True)
adj_2_op_freq = sorted(
adj_2_op_freq.items(), key=lambda item: item[1], reverse=True)
return uni_op_freq, adj_2_op_freq
python/paddle/fluid/layers/detection.py
浏览文件 @ 85ddb5c7
......@@ -284,7 +284,7 @@ def detection_output(loc,
target_box=loc,
code_type='decode_center_size')
compile_shape = scores.shape
run_shape = ops.shape(scores)
run_shape = nn.shape(scores)
scores = nn.flatten(x=scores, axis=2)
scores = nn.softmax(input=scores)
scores = nn.reshape(x=scores, shape=compile_shape, actual_shape=run_shape)
......@@ -697,7 +697,7 @@ def ssd_loss(location,
raise ValueError("Only support mining_type == max_negative now.")
num, num_prior, num_class = confidence.shape
conf_shape = ops.shape(confidence)
conf_shape = nn.shape(confidence)
def __reshape_to_2d(var):
return nn.flatten(x=var, axis=2)
......@@ -724,7 +724,7 @@ def ssd_loss(location,
target_label.stop_gradient = True
conf_loss = nn.softmax_with_cross_entropy(confidence, target_label)
# 3. Mining hard examples
actual_shape = ops.slice(conf_shape, axes=[0], starts=[0], ends=[2])
actual_shape = nn.slice(conf_shape, axes=[0], starts=[0], ends=[2])
actual_shape.stop_gradient = True
conf_loss = nn.reshape(
x=conf_loss, shape=(num, num_prior), actual_shape=actual_shape)
......
python/paddle/fluid/layers/io.py
浏览文件 @ 85ddb5c7
......@@ -312,7 +312,6 @@ def _copy_reader_var_(block, var):
new_var = block.create_var(name=var.name, type=core.VarDesc.VarType.READER)
new_var.desc.set_shapes(var.desc.shapes())
new_var.desc.set_dtypes(var.desc.dtypes())
new_var.desc.set_lod_levels(var.desc.lod_levels())
new_var.persistable = True
return new_var
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 85ddb5c7
......@@ -29,110 +29,29 @@ from .. import unique_name
from functools import reduce
__all__ = [
'fc',
'embedding',
'dynamic_lstm',
'dynamic_lstmp',
'dynamic_gru',
'gru_unit',
'linear_chain_crf',
'crf_decoding',
'cos_sim',
'cross_entropy',
'square_error_cost',
'chunk_eval',
'sequence_conv',
'conv2d',
'conv3d',
'sequence_pool',
'sequence_softmax',
'softmax',
'pool2d',
'pool3d',
'batch_norm',
'beam_search_decode',
'conv2d_transpose',
'conv3d_transpose',
'sequence_expand',
'sequence_expand_as',
'sequence_pad',
'lstm_unit',
'reduce_sum',
'reduce_mean',
'reduce_max',
'reduce_min',
'reduce_prod',
'sequence_first_step',
'sequence_last_step',
'dropout',
'split',
'ctc_greedy_decoder',
'edit_distance',
'l2_normalize',
'matmul',
'topk',
'warpctc',
'sequence_reshape',
'transpose',
'im2sequence',
'nce',
'hsigmoid',
'beam_search',
'row_conv',
'multiplex',
'layer_norm',
'softmax_with_cross_entropy',
'smooth_l1',
'one_hot',
'autoincreased_step_counter',
'reshape',
'squeeze',
'unsqueeze',
'lod_reset',
'lrn',
'pad',
'pad_constant_like',
'label_smooth',
'roi_pool',
'dice_loss',
'image_resize',
'image_resize_short',
'resize_bilinear',
'gather',
'scatter',
'sequence_scatter',
'random_crop',
'mean_iou',
'relu',
'log',
'crop',
'rank_loss',
'elu',
'relu6',
'pow',
'stanh',
'hard_sigmoid',
'swish',
'prelu',
'brelu',
'leaky_relu',
'soft_relu',
'flatten',
'sequence_mask',
'stack',
'pad2d',
'unstack',
'sequence_enumerate',
'expand',
'sequence_concat',
'scale',
'elementwise_add',
'elementwise_div',
'elementwise_sub',
'elementwise_mul',
'elementwise_max',
'elementwise_min',
'elementwise_pow',
'fc', 'embedding', 'dynamic_lstm', 'dynamic_lstmp', 'dynamic_gru',
'gru_unit', 'linear_chain_crf', 'crf_decoding', 'cos_sim', 'cross_entropy',
'square_error_cost', 'chunk_eval', 'sequence_conv', 'conv2d', 'conv3d',
'sequence_pool', 'sequence_softmax', 'softmax', 'pool2d', 'pool3d',
'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'conv3d_transpose',
'sequence_expand', 'sequence_expand_as', 'sequence_pad', 'lstm_unit',
'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min', 'reduce_prod',
'sequence_first_step', 'sequence_last_step', 'dropout', 'split',
'ctc_greedy_decoder', 'edit_distance', 'l2_normalize', 'matmul', 'topk',
'warpctc', 'sequence_reshape', 'transpose', 'im2sequence', 'nce',
'hsigmoid', 'beam_search', 'row_conv', 'multiplex', 'layer_norm',
'softmax_with_cross_entropy', 'smooth_l1', 'one_hot',
'autoincreased_step_counter', 'reshape', 'squeeze', 'unsqueeze',
'lod_reset', 'lrn', 'pad', 'pad_constant_like', 'label_smooth', 'roi_pool',
'dice_loss', 'image_resize', 'image_resize_short', 'resize_bilinear',
'gather', 'scatter', 'sequence_scatter', 'random_crop', 'mean_iou', 'relu',
'log', 'crop', 'rank_loss', 'elu', 'relu6', 'pow', 'stanh', 'hard_sigmoid',
'swish', 'prelu', 'brelu', 'leaky_relu', 'soft_relu', 'flatten',
'sequence_mask', 'stack', 'pad2d', 'unstack', 'sequence_enumerate',
'expand', 'sequence_concat', 'scale', 'elementwise_add', 'elementwise_div',
'elementwise_sub', 'elementwise_mul', 'elementwise_max', 'elementwise_min',
'elementwise_pow', 'uniform_random_batch_size_like', 'gaussian_random',
'sampling_id', 'gaussian_random_batch_size_like', 'sum', 'slice', 'shape'
]
......@@ -6463,6 +6382,246 @@ def expand(x, expand_times, name=None):
return out
from paddle.fluid.framework import convert_np_dtype_to_dtype_
@templatedoc()
def uniform_random_batch_size_like(input,
shape,
dtype='float32',
input_dim_idx=0,
output_dim_idx=0,
min=-1.0,
max=1.0,
seed=0):
"""
${comment}
Args:
input (Variable): ${input_comment}
shape (tuple|list): ${shape_comment}
input_dim_idx (Int): ${input_dim_idx_comment}
output_dim_idx (Int): ${output_dim_idx_comment}
min (Float): ${min_comment}
max (Float): ${max_comment}
seed (Int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('uniform_random_batch_size_like', **locals())
out = helper.create_tmp_variable(dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
helper.append_op(
type='uniform_random_batch_size_like',
inputs={'Input': input},
outputs={'Out': out},
attrs={
'shape': shape,
'input_dim_idx': input_dim_idx,
'output_dim_idx': output_dim_idx,
'min': min,
'max': max,
'seed': seed,
'dtype': c_dtype
})
return out
@templatedoc()
def gaussian_random(shape,
mean=0.0,
std=1.0,
seed=0,
dtype='float32',
use_mkldnn=False):
"""
${comment}
Args:
shape (tuple|list): ${shape_comment}
mean (Float): ${mean_comment}
std (Float): ${std_comment}
seed (Int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): Output data type.
use_mkldnn (Bool): Only used in mkldnn kernel.
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('gaussian_random', **locals())
out = helper.create_tmp_variable(dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
helper.append_op(
type='gaussian_random',
outputs={'Out': out},
attrs={
'shape': shape,
'mean': mean,
'std': std,
'seed': seed,
'dtype': c_dtype,
'use_mkldnn': use_mkldnn
})
return out
@templatedoc()
def sampling_id(x, min=0.0, max=1.0, seed=0, dtype='float32'):
"""
${comment}
Args:
x (Variable): ${x_comment}
min (Float): ${min_comment}
max (Float): ${max_comment}
seed (Float): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): The type of output data : float32, float_16, int etc
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('sampling_id', **locals())
out = helper.create_tmp_variable(dtype)
helper.append_op(
type='sampling_id',
inputs={'X': x},
outputs={'Out': out},
attrs={'min': min,
'max': max,
'seed': seed})
return out
@templatedoc()
def gaussian_random_batch_size_like(input,
shape,
input_dim_idx=0,
output_dim_idx=0,
mean=0.0,
std=1.0,
seed=0,
dtype='float32'):
"""
${comment}
Args:
input (Variable): ${input_comment}
shape (tuple|list): ${shape_comment}
input_dim_idx (Int): ${input_dim_idx_comment}
output_dim_idx (Int): ${output_dim_idx_comment}
mean (Float): ${mean_comment}
std (Float): ${std_comment}
seed (Int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): The type of output data : float32, float_16, int etc
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('gaussian_random_batch_size_like', **locals())
out = helper.create_tmp_variable(dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
helper.append_op(
type='gaussian_random_batch_size_like',
inputs={'Input': input},
outputs={'Out': out},
attrs={
'shape': shape,
'input_dim_idx': input_dim_idx,
'output_dim_idx': output_dim_idx,
'mean': mean,
'std': std,
'seed': seed,
'dtype': c_dtype
})
return out
@templatedoc()
def sum(x, use_mkldnn=False):
"""
${comment}
Args:
x (Variable): ${x_comment}
use_mkldnn (Bool): ${use_mkldnn_comment}
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('sum', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype('x'))
helper.append_op(
type='sum',
inputs={'X': x},
outputs={'Out': out},
attrs={'use_mkldnn': use_mkldnn})
return out
@templatedoc()
def slice(input, axes, starts, ends):
"""
${comment}
Args:
input (Variable): ${input_comment}.
axes (List): ${axes_comment}
starts (List): ${starts_comment}
ends (List): ${ends_comment}
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('slice', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype('input'))
helper.append_op(
type='slice',
inputs={'Input': input},
outputs={'Out': out},
attrs={'axes': axes,
'starts': starts,
'ends': ends})
return out
@templatedoc()
def shape(input):
"""
${comment}
Args:
input (Variable): ${input_comment}
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('shape', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype('input'))
helper.append_op(
type='shape', inputs={'Input': input}, outputs={'Out': out})
return out
def _elementwise_op(helper):
op_type = helper.layer_type
x = helper.kwargs.get('x', None)
......
python/paddle/fluid/layers/ops.py
浏览文件 @ 85ddb5c7
......@@ -45,13 +45,6 @@ __all__ = [
'logical_or',
'logical_xor',
'logical_not',
'uniform_random_batch_size_like',
'gaussian_random',
'sampling_id',
'gaussian_random_batch_size_like',
'sum',
'slice',
'shape',
'maxout',
]
......@@ -63,6 +56,8 @@ for _OP in set(__all__):
# e.g.: test_program_code.py, test_dist_train.py
globals()['_scale'] = generate_layer_fn('scale')
globals()['_elementwise_div'] = generate_layer_fn('elementwise_div')
__all__ += __activations_noattr__
for _OP in set(__activations_noattr__):
......
python/paddle/fluid/optimizer.py
浏览文件 @ 85ddb5c7
......@@ -26,6 +26,7 @@ from .layer_helper import LayerHelper
from .regularizer import append_regularization_ops
from .clip import append_gradient_clip_ops, error_clip_callback
from contextlib import contextmanager
from .layers import ops
__all__ = [
'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', 'Ftrl',
......@@ -1301,7 +1302,7 @@ class ModelAverage(Optimizer):
x=tmp, dtype='float32' if self._dtype == None else self._dtype)
sum = layers.cast(
x=sum, dtype='float32' if self._dtype == None else self._dtype)
layers.elementwise_div(x=sum, y=tmp, out=param)
ops._elementwise_div(x=sum, y=tmp, out=param)
def _add_average_restore_op(self, block, param_grad):
param = block._clone_variable(param_grad[0])
......
python/paddle/fluid/tests/unittests/dist_ctr.py 0 → 100644
浏览文件 @ 85ddb5c7
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import paddle
import paddle.fluid as fluid
import dist_ctr_reader
from test_dist_base import TestDistRunnerBase, runtime_main
IS_SPARSE = True
# Fix seed for test
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1
class TestDistCTR2x2(TestDistRunnerBase):
def get_model(self, batch_size=2):
dnn_input_dim, lr_input_dim = dist_ctr_reader.load_data_meta()
""" network definition """
dnn_data = fluid.layers.data(
name="dnn_data",
shape=[-1, 1],
dtype="int64",
lod_level=1,
append_batch_size=False)
lr_data = fluid.layers.data(
name="lr_data",
shape=[-1, 1],
dtype="int64",
lod_level=1,
append_batch_size=False)
label = fluid.layers.data(
name="click",
shape=[-1, 1],
dtype="int64",
lod_level=0,
append_batch_size=False)
# build dnn model
dnn_layer_dims = [128, 64, 32, 1]
dnn_embedding = fluid.layers.embedding(
is_distributed=False,
input=dnn_data,
size=[dnn_input_dim, dnn_layer_dims[0]],
param_attr=fluid.ParamAttr(
name="deep_embedding",
initializer=fluid.initializer.Constant(value=0.01)),
is_sparse=IS_SPARSE)
dnn_pool = fluid.layers.sequence_pool(
input=dnn_embedding, pool_type="sum")
dnn_out = dnn_pool
for i, dim in enumerate(dnn_layer_dims[1:]):
fc = fluid.layers.fc(
input=dnn_out,
size=dim,
act="relu",
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01)),
name='dnn-fc-%d' % i)
dnn_out = fc
# build lr model
lr_embbding = fluid.layers.embedding(
is_distributed=False,
input=lr_data,
size=[lr_input_dim, 1],
param_attr=fluid.ParamAttr(
name="wide_embedding",
initializer=fluid.initializer.Constant(value=0.01)),
is_sparse=IS_SPARSE)
lr_pool = fluid.layers.sequence_pool(input=lr_embbding, pool_type="sum")
merge_layer = fluid.layers.concat(input=[dnn_out, lr_pool], axis=1)
predict = fluid.layers.fc(input=merge_layer, size=2, act='softmax')
acc = fluid.layers.accuracy(input=predict, label=label)
auc_var, batch_auc_var, auc_states = fluid.layers.auc(input=predict,
label=label)
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
inference_program = paddle.fluid.default_main_program().clone()
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.0001)
sgd_optimizer.minimize(avg_cost)
dataset = dist_ctr_reader.Dataset()
train_reader = paddle.batch(dataset.train(), batch_size=batch_size)
test_reader = paddle.batch(dataset.test(), batch_size=batch_size)
return inference_program, avg_cost, train_reader, test_reader, None, predict
if __name__ == "__main__":
runtime_main(TestDistCTR2x2)
python/paddle/fluid/tests/unittests/dist_ctr_reader.py 0 → 100644
浏览文件 @ 85ddb5c7
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import paddle
import tarfile
logging.basicConfig()
logger = logging.getLogger("paddle")
logger.setLevel(logging.INFO)
DATA_URL = "http://paddle-ctr-data.cdn.bcebos.com/avazu_ctr_data.tgz"
DATA_MD5 = "c11df99fbd14e53cd4bfa6567344b26e"
"""
avazu_ctr_data/train.txt
avazu_ctr_data/infer.txt
avazu_ctr_data/test.txt
avazu_ctr_data/data.meta.txt
"""
def read_data(file_name):
path = paddle.dataset.common.download(DATA_URL, "avazu_ctr_data", DATA_MD5)
tar = tarfile.open(path, "r:gz")
tar_info = None
for member in tar.getmembers():
if member.name.endswith(file_name):
tar_info = member
f = tar.extractfile(tar_info)
ret_lines = [_.decode('utf-8') for _ in f.readlines()]
return ret_lines
class TaskMode:
TRAIN_MODE = 0
TEST_MODE = 1
INFER_MODE = 2
def __init__(self, mode):
self.mode = mode
def is_train(self):
return self.mode == self.TRAIN_MODE
def is_test(self):
return self.mode == self.TEST_MODE
def is_infer(self):
return self.mode == self.INFER_MODE
@staticmethod
def create_train():
return TaskMode(TaskMode.TRAIN_MODE)
@staticmethod
def create_test():
return TaskMode(TaskMode.TEST_MODE)
@staticmethod
def create_infer():
return TaskMode(TaskMode.INFER_MODE)
class ModelType:
CLASSIFICATION = 0
REGRESSION = 1
def __init__(self, mode):
self.mode = mode
def is_classification(self):
return self.mode == self.CLASSIFICATION
def is_regression(self):
return self.mode == self.REGRESSION
@staticmethod
def create_classification():
return ModelType(ModelType.CLASSIFICATION)
@staticmethod
def create_regression():
return ModelType(ModelType.REGRESSION)
def load_dnn_input_record(sent):
return list(map(int, sent.split()))
def load_lr_input_record(sent):
res = []
for _ in [x.split(':') for x in sent.split()]:
res.append(int(_[0]))
return res
feeding_index = {'dnn_input': 0, 'lr_input': 1, 'click': 2}
class Dataset(object):
def train(self):
'''
Load trainset.
'''
file_name = "train.txt"
logger.info("load trainset from %s" % file_name)
mode = TaskMode.create_train()
return self._parse_creator(file_name, mode)
def test(self):
'''
Load testset.
'''
file_name = "test.txt"
logger.info("load testset from %s" % file_name)
mode = TaskMode.create_test()
return self._parse_creator(file_name, mode)
def infer(self):
'''
Load infer set.
'''
file_name = "infer.txt"
logger.info("load inferset from %s" % file_name)
mode = TaskMode.create_infer()
return self._parse_creator(file_name, mode)
def _parse_creator(self, file_name, mode):
'''
Parse dataset.
'''
def _parse():
data = read_data(file_name)
for line_id, line in enumerate(data):
fs = line.strip().split('\t')
dnn_input = load_dnn_input_record(fs[0])
lr_input = load_lr_input_record(fs[1])
if not mode.is_infer():
click = int(fs[2])
yield [dnn_input, lr_input, click]
else:
yield [dnn_input, lr_input]
return _parse
def load_data_meta():
'''
load data meta info from path, return (dnn_input_dim, lr_input_dim)
'''
lines = read_data('data.meta.txt')
err_info = "wrong meta format"
assert len(lines) == 2, err_info
assert 'dnn_input_dim:' in lines[0] and 'lr_input_dim:' in lines[
1], err_info
res = map(int, [_.split(':')[1] for _ in lines])
res = list(res)
logger.info('dnn input dim: %d' % res[0])
logger.info('lr input dim: %d' % res[1])
return res
python/paddle/fluid/tests/unittests/dist_mnist.py
浏览文件 @ 85ddb5c7
......@@ -47,7 +47,7 @@ def cnn_model(data):
pool_stride=2,
act="relu",
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
value=0.3)))
value=0.01)))
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
......@@ -56,7 +56,7 @@ def cnn_model(data):
pool_stride=2,
act="relu",
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
value=0.2)))
value=0.01)))
SIZE = 10
input_shape = conv_pool_2.shape
......@@ -68,7 +68,7 @@ def cnn_model(data):
size=SIZE,
act="softmax",
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
initializer=fluid.initializer.Constant(value=0.01)))
return predict
......
python/paddle/fluid/tests/unittests/dist_simnet_bow.py 0 → 100644
浏览文件 @ 85ddb5c7
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import numpy as np
import argparse
import time
import math
import random
import paddle
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
from paddle.fluid import core
import unittest
from multiprocessing import Process
import os
import signal
from functools import reduce
from test_dist_base import TestDistRunnerBase, runtime_main
DTYPE = "int64"
DATA_URL = 'http://paddle-dist-ce-data.bj.bcebos.com/simnet.train.1000'
DATA_MD5 = '24e49366eb0611c552667989de2f57d5'
# For Net
base_lr = 0.2
emb_lr = base_lr * 3
dict_dim = 1500
emb_dim = 128
hid_dim = 128
margin = 0.1
sample_rate = 1
# Fix seed for test
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1
def get_acc(cos_q_nt, cos_q_pt, batch_size):
cond = fluid.layers.less_than(cos_q_nt, cos_q_pt)
cond = fluid.layers.cast(cond, dtype='float64')
cond_3 = fluid.layers.reduce_sum(cond)
acc = fluid.layers.elementwise_div(
cond_3,
fluid.layers.fill_constant(
shape=[1], value=batch_size * 1.0, dtype='float64'),
name="simnet_acc")
return acc
def get_loss(cos_q_pt, cos_q_nt):
loss_op1 = fluid.layers.elementwise_sub(
fluid.layers.fill_constant_batch_size_like(
input=cos_q_pt, shape=[-1, 1], value=margin, dtype='float32'),
cos_q_pt)
loss_op2 = fluid.layers.elementwise_add(loss_op1, cos_q_nt)
loss_op3 = fluid.layers.elementwise_max(
fluid.layers.fill_constant_batch_size_like(
input=loss_op2, shape=[-1, 1], value=0.0, dtype='float32'),
loss_op2)
avg_cost = fluid.layers.mean(loss_op3)
return avg_cost
def get_optimizer():
# SGD optimizer
optimizer = fluid.optimizer.SGD(learning_rate=base_lr)
return optimizer
def train_network(batch_size, is_distributed=False, is_sparse=False):
# query
q = fluid.layers.data(
name="query_ids", shape=[1], dtype="int64", lod_level=1)
## embedding
q_emb = fluid.layers.embedding(
input=q,
is_distributed=is_distributed,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__emb__",
learning_rate=emb_lr),
is_sparse=is_sparse)
## vsum
q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
q_ss = fluid.layers.softsign(q_sum)
## fc layer after conv
q_fc = fluid.layers.fc(
input=q_ss,
size=hid_dim,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__q_fc__",
learning_rate=base_lr))
# label data
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
# pt
pt = fluid.layers.data(
name="pos_title_ids", shape=[1], dtype="int64", lod_level=1)
## embedding
pt_emb = fluid.layers.embedding(
input=pt,
is_distributed=is_distributed,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__emb__",
learning_rate=emb_lr),
is_sparse=is_sparse)
## vsum
pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
pt_ss = fluid.layers.softsign(pt_sum)
## fc layer
pt_fc = fluid.layers.fc(
input=pt_ss,
size=hid_dim,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__fc__",
learning_rate=base_lr),
bias_attr=fluid.ParamAttr(name="__fc_b__"))
# nt
nt = fluid.layers.data(
name="neg_title_ids", shape=[1], dtype="int64", lod_level=1)
## embedding
nt_emb = fluid.layers.embedding(
input=nt,
is_distributed=is_distributed,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__emb__",
learning_rate=emb_lr),
is_sparse=is_sparse)
## vsum
nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
nt_ss = fluid.layers.softsign(nt_sum)
## fc layer
nt_fc = fluid.layers.fc(
input=nt_ss,
size=hid_dim,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__fc__",
learning_rate=base_lr),
bias_attr=fluid.ParamAttr(name="__fc_b__"))
cos_q_pt = fluid.layers.cos_sim(q_fc, pt_fc)
cos_q_nt = fluid.layers.cos_sim(q_fc, nt_fc)
# loss
avg_cost = get_loss(cos_q_pt, cos_q_nt)
# acc
acc = get_acc(cos_q_nt, cos_q_pt, batch_size)
return [avg_cost, acc, cos_q_pt]
def combination(x, y):
res = [[[xi, yi] for yi in y] for xi in x]
return res[0]
def get_one_data(file_list):
for file in file_list:
contents = []
with open(file, "r") as fin:
for i in fin:
contents.append(i.strip())
for index, q in enumerate(contents):
try:
one_data = [[int(j) for j in i.split(" ")]
for i in q.split(";")[:-1]]
if one_data[1][0] + one_data[1][1] != len(one_data) - 3:
q = fin.readline()
continue
tmp = combination(one_data[3:3 + one_data[1][0]],
one_data[3 + one_data[1][0]:])
except Exception as e:
continue
for each in tmp:
yield [one_data[2], 0, each[0], each[1]]
def get_batch_reader(file_list, batch_size):
def batch_reader():
res = []
for i in get_one_data(file_list):
if random.random() <= sample_rate:
res.append(i)
if len(res) >= batch_size:
yield res
res = []
return batch_reader
def get_train_reader(batch_size):
# The training data set.
train_file = os.path.join(paddle.dataset.common.DATA_HOME, "simnet",
"train")
train_reader = get_batch_reader([train_file], batch_size)
train_feed = ["query_ids", "pos_title_ids", "neg_title_ids", "label"]
return train_reader, train_feed
class TestDistSimnetBow2x2(TestDistRunnerBase):
def get_model(self, batch_size=2):
# Train program
avg_cost, acc, predict = \
train_network(batch_size, bool(int(os.environ["IS_DISTRIBUTED"])), bool(int(os.environ["IS_SPARSE"])))
inference_program = fluid.default_main_program().clone()
# Optimization
opt = get_optimizer()
opt.minimize(avg_cost)
# Reader
train_reader, _ = get_train_reader(batch_size)
return inference_program, avg_cost, train_reader, train_reader, acc, predict
if __name__ == "__main__":
paddle.dataset.common.download(DATA_URL, 'simnet', DATA_MD5, "train")
runtime_main(TestDistSimnetBow2x2)
python/paddle/fluid/tests/unittests/dist_text_classification.py 0 → 100644
浏览文件 @ 85ddb5c7
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import numpy as np
import argparse
import time
import math
import paddle
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
from paddle.fluid import core
import unittest
from multiprocessing import Process
import os
import signal
import six
import tarfile
import string
import re
from functools import reduce
from test_dist_base import TestDistRunnerBase, runtime_main
DTYPE = "float32"
VOCAB_URL = 'http://paddle-dist-ce-data.bj.bcebos.com/imdb.vocab'
VOCAB_MD5 = '23c86a0533c0151b6f12fa52b106dcc2'
DATA_URL = 'http://paddle-dist-ce-data.bj.bcebos.com/text_classification.tar.gz'
DATA_MD5 = '29ebfc94f11aea9362bbb7f5e9d86b8a'
# Load dictionary.
def load_vocab(filename):
vocab = {}
if six.PY2:
with open(filename, 'r') as f:
for idx, line in enumerate(f):
vocab[line.strip()] = idx
else:
with open(filename, 'r', encoding="utf-8") as f:
for idx, line in enumerate(f):
vocab[line.strip()] = idx
return vocab
def get_worddict(dict_path):
word_dict = load_vocab(dict_path)
word_dict["<unk>"] = len(word_dict)
dict_dim = len(word_dict)
return word_dict, dict_dim
def conv_net(input,
dict_dim,
emb_dim=128,
window_size=3,
num_filters=128,
fc0_dim=96,
class_dim=2):
emb = fluid.layers.embedding(
input=input,
size=[dict_dim, emb_dim],
is_sparse=False,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
value=0.01)))
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=num_filters,
filter_size=window_size,
act="tanh",
pool_type="max",
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01)))
fc_0 = fluid.layers.fc(
input=[conv_3],
size=fc0_dim,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01)))
prediction = fluid.layers.fc(
input=[fc_0],
size=class_dim,
act="softmax",
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01)))
return prediction
def inference_network(dict_dim):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
out = conv_net(data, dict_dim)
return out
def get_reader(word_dict, batch_size):
# The training data set.
train_reader = paddle.batch(train(word_dict), batch_size=batch_size)
# The testing data set.
test_reader = paddle.batch(test(word_dict), batch_size=batch_size)
return train_reader, test_reader
def get_optimizer(learning_rate):
optimizer = fluid.optimizer.SGD(learning_rate=learning_rate)
return optimizer
class TestDistTextClassification2x2(TestDistRunnerBase):
def get_model(self, batch_size=2):
vocab = os.path.join(paddle.dataset.common.DATA_HOME,
"text_classification", "imdb.vocab")
word_dict, dict_dim = get_worddict(vocab)
# Input data
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# Train program
predict = conv_net(data, dict_dim)
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
acc = fluid.layers.accuracy(input=predict, label=label)
inference_program = fluid.default_main_program().clone()
# Optimization
opt = get_optimizer(learning_rate=0.001)
opt.minimize(avg_cost)
# Reader
train_reader, test_reader = get_reader(word_dict, batch_size)
return inference_program, avg_cost, train_reader, test_reader, acc, predict
def tokenize(pattern):
"""
Read files that match the given pattern. Tokenize and yield each file.
"""
with tarfile.open(
paddle.dataset.common.download(DATA_URL, 'text_classification',
DATA_MD5)) as tarf:
# Note that we should use tarfile.next(), which does
# sequential access of member files, other than
# tarfile.extractfile, which does random access and might
# destroy hard disks.
tf = tarf.next()
while tf != None:
if bool(pattern.match(tf.name)):
# newline and punctuations removal and ad-hoc tokenization.
yield tarf.extractfile(tf).read().rstrip(six.b(
"\n\r")).translate(
None, six.b(string.punctuation)).lower().split()
tf = tarf.next()
def reader_creator(pos_pattern, neg_pattern, word_idx):
UNK = word_idx['<unk>']
INS = []
def load(pattern, out, label):
for doc in tokenize(pattern):
out.append(([word_idx.get(w, UNK) for w in doc], label))
load(pos_pattern, INS, 0)
load(neg_pattern, INS, 1)
def reader():
for doc, label in INS:
yield doc, label
return reader
def train(word_idx):
"""
IMDB training set creator.
It returns a reader creator, each sample in the reader is an zero-based ID
sequence and label in [0, 1].
:param word_idx: word dictionary
:type word_idx: dict
:return: Training reader creator
:rtype: callable
"""
return reader_creator(
re.compile("train/pos/.*\.txt$"),
re.compile("train/neg/.*\.txt$"), word_idx)
def test(word_idx):
"""
IMDB test set creator.
It returns a reader creator, each sample in the reader is an zero-based ID
sequence and label in [0, 1].
:param word_idx: word dictionary
:type word_idx: dict
:return: Test reader creator
:rtype: callable
"""
return reader_creator(
re.compile("test/pos/.*\.txt$"),
re.compile("test/neg/.*\.txt$"), word_idx)
if __name__ == "__main__":
paddle.dataset.common.download(VOCAB_URL, 'text_classification', VOCAB_MD5)
paddle.dataset.common.download(DATA_URL, 'text_classification', DATA_MD5)
runtime_main(TestDistTextClassification2x2)
python/paddle/fluid/tests/unittests/dist_transformer.py
浏览文件 @ 85ddb5c7
......@@ -1699,10 +1699,9 @@ class DistTransformer2x2(TestDistRunnerBase):
exe.run(startup_prog)
exe.run(pserver_prog)
def run_trainer(self, use_cuda, args):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
TrainTaskConfig.use_gpu = use_cuda
sum_cost, avg_cost, predict, token_num, local_lr_scheduler, test_program = get_model(
def run_trainer(self, args):
TrainTaskConfig.use_gpu = args.use_cuda
sum_cost, avg_cost, predict, token_num, local_lr_scheduler = get_model(
args.is_dist, not args.sync_mode)
if args.is_dist:
......@@ -1718,6 +1717,11 @@ class DistTransformer2x2(TestDistRunnerBase):
TrainTaskConfig.batch_size = 20
trainer_prog = fluid.default_main_program()
if args.use_cuda:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
startup_exe = fluid.Executor(place)
TrainTaskConfig.local = not args.is_dist
......
python/paddle/fluid/tests/unittests/dist_word2vec.py
浏览文件 @ 85ddb5c7
......@@ -122,4 +122,7 @@ class TestDistWord2vec2x2(TestDistRunnerBase):
if __name__ == "__main__":
import os
os.environ['CPU_NUM'] = '1'
os.environ['USE_CUDA'] = "FALSE"
runtime_main(TestDistWord2vec2x2)
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ 85ddb5c7
......@@ -345,7 +345,7 @@ class OpTest(unittest.TestCase):
actual_t, expect_t, atol=atol, equal_nan=equal_nan),
"Output (" + out_name + ") has diff at " + str(place) +
"\nExpect " + str(expect_t) + "\n" + "But Got" +
str(actual_t))
str(actual_t) + " in class " + self.__class__.__name__)
if isinstance(expect, tuple):
self.assertListEqual(actual.recursive_sequence_lengths(),
expect[1], "Output (" + out_name +
......
python/paddle/fluid/tests/unittests/test_detection_map_op.py
浏览文件 @ 85ddb5c7
......@@ -20,6 +20,7 @@ import six
import sys
import collections
import math
import paddle.fluid as fluid
from op_test import OpTest
......@@ -32,7 +33,7 @@ class TestDetectionMAPOp(OpTest):
self.detect = np.array(self.detect).astype('float32')
self.mAP = np.array(self.mAP).astype('float32')
if (len(self.class_pos_count) > 0):
if len(self.class_pos_count) > 0:
self.class_pos_count = np.array(self.class_pos_count).astype(
'int32')
self.true_pos = np.array(self.true_pos).astype('float32')
......@@ -273,7 +274,7 @@ class TestDetectionMAPOp11Point(TestDetectionMAPOp):
class TestDetectionMAPOpMultiBatch(TestDetectionMAPOp):
def init_test_case(self):
super(TestDetectionMAPOpMultiBatch, self).init_test_case()
self.class_pos_count = [0, 2, 1]
self.class_pos_count = [0, 2, 1, 0]
self.true_pos_lod = [[0, 3, 2]]
self.true_pos = [[0.7, 1.], [0.3, 0.], [0.2, 1.], [0.8, 0.], [0.1, 1.]]
self.false_pos_lod = [[0, 3, 2]]
......
python/paddle/fluid/tests/unittests/test_dist_base.py
浏览文件 @ 85ddb5c7
......@@ -18,23 +18,27 @@ import time
import unittest
import os
import sys
import six
import signal
import subprocess
import six
import argparse
import paddle.fluid as fluid
RUN_STEP = 10
class TestDistRunnerBase(object):
def get_model(self, batch_size=2):
raise NotImplementedError(
"get_model should be implemented by child classes.")
def get_transpiler(self, trainer_id, main_program, pserver_endpoints,
trainers, sync_mode):
@staticmethod
def get_transpiler(trainer_id, main_program, pserver_endpoints, trainers,
sync_mode):
# NOTE: import fluid until runtime, or else forking processes will cause error.
import paddle
import paddle.fluid as fluid
t = fluid.DistributeTranspiler()
config = fluid.DistributeTranspilerConfig()
t = fluid.DistributeTranspiler(config=config)
t.transpile(
trainer_id=trainer_id,
program=main_program,
......@@ -44,9 +48,9 @@ class TestDistRunnerBase(object):
return t
def run_pserver(self, args):
import paddle
import paddle.fluid as fluid
self.get_model(batch_size=2)
if args.mem_opt:
fluid.memory_optimize(fluid.default_main_program())
t = self.get_transpiler(args.trainer_id,
......@@ -61,12 +65,10 @@ class TestDistRunnerBase(object):
exe.run(startup_prog)
exe.run(pserver_prog)
def run_trainer(self, use_cuda, args):
import paddle
import paddle.fluid as fluid
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
def run_trainer(self, args):
test_program, avg_cost, train_reader, test_reader, batch_acc, predict = \
self.get_model(batch_size=2)
if args.mem_opt:
fluid.memory_optimize(fluid.default_main_program())
if args.is_dist:
......@@ -74,16 +76,23 @@ class TestDistRunnerBase(object):
fluid.default_main_program(),
args.endpoints, args.trainers,
args.sync_mode)
trainer_prog = t.get_trainer_program()
else:
trainer_prog = fluid.default_main_program()
if args.use_cuda:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
startup_exe = fluid.Executor(place)
startup_exe.run(fluid.default_startup_program())
strategy = fluid.ExecutionStrategy()
strategy.num_threads = 1
strategy.allow_op_delay = False
build_stra = fluid.BuildStrategy()
if args.use_reduce:
......@@ -92,7 +101,7 @@ class TestDistRunnerBase(object):
build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.AllReduce
exe = fluid.ParallelExecutor(
use_cuda,
args.use_cuda,
loss_name=avg_cost.name,
exec_strategy=strategy,
build_strategy=build_stra)
......@@ -103,27 +112,26 @@ class TestDistRunnerBase(object):
]
feeder = fluid.DataFeeder(feed_var_list, place)
reader_generator = test_reader()
data = next(reader_generator)
first_loss, = exe.run(fetch_list=[avg_cost.name],
feed=feeder.feed(data))
print(first_loss)
reader_generator = train_reader()
for i in six.moves.xrange(5):
data = next(reader_generator)
loss, = exe.run(fetch_list=[avg_cost.name], feed=feeder.feed(data))
def get_data():
origin_batch = next(reader_generator)
if args.is_dist and args.use_reader_alloc:
new_batch = []
for offset, item in enumerate(origin_batch):
if offset % 2 == args.trainer_id:
new_batch.append(item)
return new_batch
else:
return origin_batch
data = next(reader_generator)
last_loss, = exe.run(fetch_list=[avg_cost.name], feed=feeder.feed(data))
print(last_loss)
for _ in six.moves.xrange(RUN_STEP):
loss, = exe.run(fetch_list=[avg_cost.name],
feed=feeder.feed(get_data()))
print(loss)
def runtime_main(test_class):
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
parser = argparse.ArgumentParser(description='Run dist test.')
parser.add_argument(
'--role', type=str, required=True, choices=['pserver', 'trainer'])
......@@ -135,7 +143,10 @@ def runtime_main(test_class):
'--current_endpoint', type=str, required=False, default="")
parser.add_argument('--sync_mode', action='store_true')
parser.add_argument('--mem_opt', action='store_true')
parser.add_argument('--use_cuda', action='store_true')
parser.add_argument('--use_reduce', action='store_true')
parser.add_argument(
'--use_reader_alloc', action='store_true', required=False, default=True)
args = parser.parse_args()
......@@ -143,8 +154,7 @@ def runtime_main(test_class):
if args.role == "pserver" and args.is_dist:
model.run_pserver(args)
else:
use_cuda = True if core.is_compiled_with_cuda() else False
model.run_trainer(use_cuda, args)
model.run_trainer(args)
import paddle.compat as cpt
......@@ -163,8 +173,10 @@ class TestDistBase(unittest.TestCase):
self._find_free_port(), self._find_free_port())
self._python_interp = "python"
self._sync_mode = True
self._use_cuda = True
self._mem_opt = False
self._use_reduce = False
self._use_reader_alloc = True
self._setup_config()
def _find_free_port(self):
......@@ -172,15 +184,15 @@ class TestDistBase(unittest.TestCase):
s.bind(('', 0))
return s.getsockname()[1]
def start_pserver(self, model_file, check_error_log):
def start_pserver(self, model_file, check_error_log, required_envs):
ps0_ep, ps1_ep = self._ps_endpoints.split(",")
ps_cmd = "%s %s --role pserver --endpoints %s --trainer_id 0 --current_endpoint %s --trainers %d --is_dist"
ps0_cmd = ps_cmd % \
(self._python_interp, model_file, self._ps_endpoints, ps0_ep,
self._trainers)
(self._python_interp, model_file, self._ps_endpoints, ps0_ep,
self._trainers)
ps1_cmd = ps_cmd % \
(self._python_interp, model_file, self._ps_endpoints, ps1_ep,
self._trainers)
(self._python_interp, model_file, self._ps_endpoints, ps1_ep,
self._trainers)
if self._sync_mode:
ps0_cmd += " --sync_mode"
......@@ -198,9 +210,15 @@ class TestDistBase(unittest.TestCase):
ps1_pipe = open("/tmp/ps1_err.log", "wb")
ps0_proc = subprocess.Popen(
ps0_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=ps0_pipe)
ps0_cmd.strip().split(" "),
stdout=subprocess.PIPE,
stderr=ps0_pipe,
env=required_envs)
ps1_proc = subprocess.Popen(
ps1_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=ps1_pipe)
ps1_cmd.strip().split(" "),
stdout=subprocess.PIPE,
stderr=ps1_pipe,
env=required_envs)
if not check_error_log:
return ps0_proc, ps1_proc, None, None
......@@ -222,59 +240,60 @@ class TestDistBase(unittest.TestCase):
(e, retry_times))
retry_times -= 1
def check_with_place(self, model_file, delta=1e-3, check_error_log=False):
# TODO(typhoonzero): should auto adapt GPU count on the machine.
required_envs = {
"PATH": os.getenv("PATH", ""),
"PYTHONPATH": os.getenv("PYTHONPATH", ""),
"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
"FLAGS_fraction_of_gpu_memory_to_use": "0.15",
"FLAGS_cudnn_deterministic": "1",
"CPU_NUM": "1"
}
def _run_local(self, model, envs, check_error_log):
if check_error_log:
required_envs["GLOG_v"] = "7"
required_envs["GLOG_logtostderr"] = "1"
cmd = "%s %s --role trainer" % (self._python_interp, model)
if self._use_cuda:
cmd += " --use_cuda"
env_local = {"CUDA_VISIBLE_DEVICES": "0"}
else:
env_local = {'CPU_NUM': '1'}
envs.update(env_local)
# Run local to get a base line
env_local = {"CUDA_VISIBLE_DEVICES": "0"}
env_local.update(required_envs)
local_cmd = "%s %s --role trainer" % (self._python_interp, model_file)
if not check_error_log:
err_log = open("/tmp/trainer.err.log", "wb")
local_proc = subprocess.Popen(
local_cmd.split(" "),
cmd.split(" "),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=env_local)
stderr=err_log,
env=envs)
else:
err_log = open("/tmp/trainer.err.log", "wb")
local_proc = subprocess.Popen(
local_cmd.split(" "),
cmd.split(" "),
stdout=subprocess.PIPE,
stderr=err_log,
env=env_local)
stderr=subprocess.PIPE,
env=envs)
local_proc.wait()
out, err = local_proc.communicate()
local_ret = cpt.to_text(out)
sys.stderr.write('local_loss: %s\n' % local_ret)
sys.stderr.write('local_stderr: %s\n' % err)
local_out, local_err = local_proc.communicate()
local_ret = cpt.to_text(local_out)
if check_error_log:
err_log.close()
sys.stderr.write('local_stdout: %s\n' % local_ret)
sys.stderr.write('local_stderr: %s\n' % local_err)
local_losses = local_ret.split("\n")
return local_losses
def _run_cluster(self, model, envs, check_error_log):
# Run dist train to compare with local results
ps0, ps1, ps0_pipe, ps1_pipe = self.start_pserver(model_file,
check_error_log)
ps0, ps1, ps0_pipe, ps1_pipe = self.start_pserver(model,
check_error_log, envs)
self._wait_ps_ready(ps0.pid)
self._wait_ps_ready(ps1.pid)
ps0_ep, ps1_ep = self._ps_endpoints.split(",")
tr_cmd = "%s %s --role trainer --endpoints %s --trainer_id %d --current_endpoint %s --trainers %d --is_dist"
tr0_cmd = tr_cmd % \
(self._python_interp, model_file, self._ps_endpoints,
0, ps0_ep, self._trainers)
(self._python_interp, model, self._ps_endpoints,
0, ps0_ep, self._trainers)
tr1_cmd = tr_cmd % \
(self._python_interp, model_file, self._ps_endpoints,
1, ps1_ep, self._trainers)
(self._python_interp, model, self._ps_endpoints,
1, ps1_ep, self._trainers)
if self._sync_mode:
tr0_cmd += " --sync_mode"
......@@ -285,18 +304,28 @@ class TestDistBase(unittest.TestCase):
if self._use_reduce:
tr0_cmd += " --use_reduce"
tr1_cmd += " --use_reduce"
if self._use_reader_alloc:
tr0_cmd += " --use_reader_alloc"
tr1_cmd += " --use_reader_alloc"
if self._use_cuda:
tr0_cmd += " --use_cuda"
tr1_cmd += " --use_cuda"
env0 = {"CUDA_VISIBLE_DEVICES": "0"}
env1 = {"CUDA_VISIBLE_DEVICES": "1"}
else:
env0 = {'CPU_NUM': '1'}
env1 = {'CPU_NUM': '1'}
env0.update(envs)
env1.update(envs)
env0 = {"CUDA_VISIBLE_DEVICES": "0"}
env1 = {"CUDA_VISIBLE_DEVICES": "1"}
env0.update(required_envs)
env1.update(required_envs)
FNULL = open(os.devnull, 'w')
tr0_pipe = subprocess.PIPE
tr1_pipe = subprocess.PIPE
if check_error_log:
print("tr0_cmd:", tr0_cmd)
print("tr1_cmd:", tr1_cmd)
print("tr0_cmd:{}, env0: {}".format(tr0_cmd, env0))
print("tr1_cmd:{}, env1: {}".format(tr1_cmd, env1))
tr0_pipe = open("/tmp/tr0_err.log", "wb")
tr1_pipe = open("/tmp/tr1_err.log", "wb")
......@@ -313,17 +342,11 @@ class TestDistBase(unittest.TestCase):
tr0_proc.wait()
tr1_proc.wait()
out, err = tr0_proc.communicate()
sys.stderr.write('dist_stderr: %s\n' % err)
loss_data0 = cpt.to_text(out)
sys.stderr.write('dist_loss: %s\n' % loss_data0)
lines = loss_data0.split("\n")
dist_first_loss = eval(lines[0].replace(" ", ","))[0]
dist_last_loss = eval(lines[1].replace(" ", ","))[0]
local_lines = local_ret.split("\n")
local_first_loss = eval(local_lines[0])[0]
local_last_loss = eval(local_lines[1])[0]
tr0_out, tr0_err = tr0_proc.communicate()
tr0_loss_text = cpt.to_text(tr0_out)
tr1_out, tr1_err = tr1_proc.communicate()
tr1_loss_text = cpt.to_text(tr1_out)
# close trainer file
if check_error_log:
......@@ -341,5 +364,47 @@ class TestDistBase(unittest.TestCase):
ps1.wait()
FNULL.close()
self.assertAlmostEqual(local_first_loss, dist_first_loss, delta=delta)
self.assertAlmostEqual(local_last_loss, dist_last_loss, delta=delta)
# print log
sys.stderr.write('trainer 0 stdout:\n %s\n' % tr0_loss_text)
sys.stderr.write('trainer 0 stderr:\n %s\n' % tr0_err)
sys.stderr.write('trainer 1 stdout: %s\n' % tr1_loss_text)
sys.stderr.write('trainer 1 stderr: %s\n' % tr1_err)
tr0_losses = tr0_loss_text.split("\n")
tr1_losses = tr1_loss_text.split("\n")
return tr0_losses, tr1_losses
def check_with_place(self,
model_file,
delta=1e-3,
check_error_log=False,
need_envs={}):
# TODO(typhoonzero): should auto adapt GPU count on the machine.
required_envs = {
"PATH": os.getenv("PATH", ""),
"PYTHONPATH": os.getenv("PYTHONPATH", ""),
"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
"FLAGS_fraction_of_gpu_memory_to_use": "0.15",
"FLAGS_cudnn_deterministic": "1",
}
required_envs.update(need_envs)
if check_error_log:
required_envs["GLOG_v"] = "7"
required_envs["GLOG_logtostderr"] = "1"
local_losses\
= self._run_local(model_file, required_envs,
check_error_log)
tr0_losses, tr1_losses = self._run_cluster(model_file, required_envs,
check_error_log)
for step_id in range(RUN_STEP):
local_loss = eval(local_losses[step_id])[0]
tr0_loss = eval(tr0_losses[step_id])[0]
tr1_loss = eval(tr1_losses[step_id])[0]
dist_loss = (tr0_loss + tr1_loss) / 2
print(str(local_loss) + ":" + str(dist_loss))
self.assertAlmostEqual(local_loss, dist_loss, delta=delta)
python/paddle/fluid/tests/unittests/test_dist_ctr.py 0 → 100644
浏览文件 @ 85ddb5c7
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import os
import unittest
from test_dist_base import TestDistBase
class TestDistCTR2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_cuda = False
def test_dist_ctr(self):
self.check_with_place("dist_ctr.py", delta=1e-7, check_error_log=False)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_dist_mnist.py
浏览文件 @ 85ddb5c7
......@@ -23,7 +23,7 @@ class TestDistMnist2x2(TestDistBase):
self._use_reduce = False
def test_dist_train(self):
self.check_with_place("dist_mnist.py", delta=1e-7)
self.check_with_place("dist_mnist.py", delta=1e-5)
class TestDistMnist2x2WithMemopt(TestDistBase):
......@@ -32,7 +32,7 @@ class TestDistMnist2x2WithMemopt(TestDistBase):
self._mem_opt = True
def test_dist_train(self):
self.check_with_place("dist_mnist.py", delta=1e-7)
self.check_with_place("dist_mnist.py", delta=1e-5)
class TestDistMnistAsync(TestDistBase):
......
python/paddle/fluid/tests/unittests/test_dist_se_resnext.py
浏览文件 @ 85ddb5c7
......@@ -20,9 +20,10 @@ from test_dist_base import TestDistBase
class TestDistSeResneXt2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_reader_alloc = False
def test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=1e-7)
self.check_with_place("dist_se_resnext.py", delta=100)
# TODO(typhoonzero): fix this test
......@@ -38,6 +39,7 @@ class TestDistSeResneXt2x2(TestDistBase):
class TestDistSeResneXt2x2Async(TestDistBase):
def _setup_config(self):
self._sync_mode = False
self._use_reader_alloc = False
def test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=100)
......
python/paddle/fluid/tests/unittests/test_dist_simnet_bow.py 0 → 100644
浏览文件 @ 85ddb5c7
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import os
import unittest
from test_dist_base import TestDistBase
class TestDistSimnetBowDense2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_cuda = False
def test_simnet_bow(self):
need_envs = {"IS_DISTRIBUTED": '0', "IS_SPARSE": '0'}
self.check_with_place(
"dist_simnet_bow.py",
delta=1e-5,
check_error_log=False,
need_envs=need_envs)
class TestDistSimnetBow2x2DenseAsync(TestDistBase):
def _setup_config(self):
self._sync_mode = False
self._use_cuda = False
def test_simnet_bow(self):
need_envs = {"IS_DISTRIBUTED": '0', "IS_SPARSE": '0'}
self.check_with_place(
"dist_simnet_bow.py",
delta=100,
check_error_log=False,
need_envs=need_envs)
class TestDistSimnetBowSparse2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_cuda = False
def test_simnet_bow(self):
need_envs = {"IS_DISTRIBUTED": '0', "IS_SPARSE": '1'}
self.check_with_place(
"dist_simnet_bow.py",
delta=1e-5,
check_error_log=False,
need_envs=need_envs)
class TestDistSimnetBow2x2SparseAsync(TestDistBase):
def _setup_config(self):
self._sync_mode = False
self._use_cuda = False
def test_simnet_bow(self):
need_envs = {"IS_DISTRIBUTED": '0', "IS_SPARSE": '1'}
self.check_with_place(
"dist_simnet_bow.py",
delta=100,
check_error_log=False,
need_envs=need_envs)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_dist_text_classification.py 0 → 100644
浏览文件 @ 85ddb5c7
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import os
import unittest
from test_dist_base import TestDistBase
class TestDistTextClassification2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_cuda = False
def test_text_classification(self):
self.check_with_place("dist_text_classification.py", delta=1e-6)
class TestDistTextClassification2x2Async(TestDistBase):
def _setup_config(self):
self._sync_mode = False
self._use_cuda = False
def test_se_resnext(self):
self.check_with_place("dist_text_classification.py", delta=100)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_dist_word2vec.py
浏览文件 @ 85ddb5c7
......@@ -39,7 +39,7 @@ class TestDistW2V2x2Async(TestDistBase):
self._sync_mode = False
def test_dist_train(self):
self.check_with_place("dist_word2vec.py", delta=1)
self.check_with_place("dist_word2vec.py", delta=100)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_generate_proposals_op.py
浏览文件 @ 85ddb5c7
......@@ -277,7 +277,6 @@ class TestGenerateProposalsOp(OpTest):
'eta': self.eta
}
print("lod = ", self.lod)
self.outputs = {
'RpnRois': (self.rpn_rois[0], [self.lod]),
'RpnRoiProbs': (self.rpn_roi_probs[0], [self.lod])
......@@ -295,7 +294,7 @@ class TestGenerateProposalsOp(OpTest):
self.post_nms_topN = 5000 # train 6000, test 1000
self.nms_thresh = 0.7
self.min_size = 3.0
self.eta = 0.8
self.eta = 1.
def init_test_input(self):
batch_size = 1
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 85ddb5c7
......@@ -541,7 +541,7 @@ class TestBook(unittest.TestCase):
with program_guard(program):
input = layers.data(
name="input", shape=[3, 100, 100], dtype="float32")
out = layers.shape(input, name="shape")
out = layers.shape(input)
self.assertIsNotNone(out)
print(str(program))
......@@ -758,6 +758,65 @@ class TestBook(unittest.TestCase):
out = layers.expand(x, [1, 2])
print(str(program))
def test_uniform_random_batch_size_like(self):
program = Program()
with program_guard(program):
input = layers.data(name="input", shape=[13, 11], dtype='float32')
out = layers.uniform_random_batch_size_like(input, [-1, 11])
self.assertIsNotNone(out)
print(str(program))
def test_gaussian_random(self):
program = Program()
with program_guard(program):
out = layers.gaussian_random(shape=[20, 30])
self.assertIsNotNone(out)
print(str(program))
def test_sampling_id(self):
program = Program()
with program_guard(program):
x = layers.data(
name="X",
shape=[13, 11],
dtype='float32',
append_batch_size=False)
out = layers.sampling_id(x)
self.assertIsNotNone(out)
print(str(program))
def test_gaussian_random_batch_size_like(self):
program = Program()
with program_guard(program):
input = layers.data(name="input", shape=[13, 11], dtype='float32')
out = layers.gaussian_random_batch_size_like(
input, shape=[-1, 11], mean=1.0, std=2.0)
self.assertIsNotNone(out)
print(str(program))
def test_sum(self):
program = Program()
with program_guard(program):
input = layers.data(name="input", shape=[13, 11], dtype='float32')
out = layers.sum(input)
self.assertIsNotNone(out)
print(str(program))
def test_slice(self):
starts = [1, 0, 2]
ends = [3, 3, 4]
axes = [0, 1, 2]
program = Program()
with program_guard(program):
input = layers.data(
name="input", shape=[3, 4, 5, 6], dtype='float32')
out = layers.slice(input, axes=axes, starts=starts, ends=ends)
def test_softshrink(self):
program = Program()
with program_guard(program):
......
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 85ddb5c7
......@@ -470,7 +470,10 @@ class DistributeTranspiler(object):
"""
# remove optimize ops and add a send op to main_program
# FIXME(typhoonzero): Also ops like clip_gradient, lrn_decay?
lr_ops = self._get_lr_ops()
delete_ops(self.origin_program.global_block(), self.optimize_ops)
delete_ops(self.origin_program.global_block(), lr_ops)
self.origin_program.__str__()
if wait_port:
......@@ -1487,7 +1490,6 @@ to transpile() call.")
per_trainer_name = "%s.trainer_%d" % \
(merged_var_name, i)
vars2merge.append(pserver_block.vars[per_trainer_name])
optimize_block.append_op(
type="sum",
inputs={"X": vars2merge},
......
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