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提交 dc488c17 编写于 作者: Y Yang Yu
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Merge branch 'develop' of github.com:baidu/Paddle into feature/parallel_for_unittest

上级 12aca860 87f9b583
隐藏空白更改
内联 并排
Showing with 1930 addition and 572 deletion
paddle/framework/CMakeLists.txt
浏览文件 @ dc488c17
......@@ -32,10 +32,12 @@ cc_test(threadpool_test SRCS threadpool_test.cc DEPS threadpool)
cc_library(scope SRCS scope.cc DEPS glog threadpool)
cc_test(scope_test SRCS scope_test.cc DEPS scope)
cc_library(device_data_transform SRCS device_data_transform.cc DEPS tensor)
cc_library(data_device_transform SRCS data_device_transform.cc DEPS tensor)
cc_library(data_type_transform SRCS data_type_transform.cc DEPS tensor)
cc_library(data_layout_transform SRCS data_layout_transform.cc DEPS tensor math_function)
cc_library(data_transform SRCS data_transform.cc DEPS math_function tensor framework_proto selected_rows device_data_transform)
cc_test(data_transform_test SRCS data_transform_test.cc DEPS data_transform device_context)
cc_library(data_transform SRCS data_transform.cc DEPS math_function tensor
framework_proto selected_rows data_device_transform data_type_transform data_layout_transform)
cc_library(attribute SRCS attribute.cc DEPS framework_proto)
cc_test(program_desc_test SRCS program_desc_test.cc DEPS proto_desc
......@@ -80,5 +82,5 @@ cc_test(init_test SRCS init_test.cc DEPS init)
cc_test(op_kernel_type_test SRCS op_kernel_type_test.cc DEPS place device_context framework_proto)
cc_test(cow_ptr_tests SRCS details/cow_ptr_test.cc)
nv_test(device_data_transform_test SRCS device_data_transform_test.cu
nv_test(data_device_transform_test SRCS data_device_transform_test.cu
DEPS operator op_registry init math_function)
paddle/framework/device_data_transform.cc → paddle/framework/data_device_transform.cc
浏览文件 @ dc488c17
......@@ -11,7 +11,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/device_data_transform.h"
#include "paddle/framework/data_device_transform.h"
namespace paddle {
namespace framework {
......
paddle/framework/data_layout.h
浏览文件 @ dc488c17
......@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/platform/enforce.h"
#include <iostream>
#include "paddle/platform/enforce.h"
......
paddle/framework/data_layout_transform.cc 0 → 100644
浏览文件 @ dc488c17
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/data_layout_transform.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace framework {
struct CastDataLayout {
CastDataLayout(const platform::DeviceContext* ctx,
const std::vector<int>& axis, const framework::Tensor& in,
framework::Tensor* out)
: in_(in), out_(out), ctx_(ctx), axis_(axis) {}
const framework::Tensor in_;
framework::Tensor* out_;
const platform::DeviceContext* ctx_;
const std::vector<int> axis_;
template <typename T>
void operator()() {
auto place = ctx_->GetPlace();
if (platform::is_cpu_place(place)) {
operators::math::Transpose<platform::CPUDeviceContext, T, 4> trans4;
auto* context = static_cast<const platform::CPUDeviceContext*>(ctx_);
trans4(*context, in_, out_, axis_);
} else {
PADDLE_THROW("Unsupport CPU <-> GPU!");
}
}
};
void TransDataLayout(const std::vector<int>& axis,
const platform::DeviceContext* ctx,
const KernelTypePair& kernel_pair, const Variable& in,
Variable* out) {
PADDLE_ENFORCE(in.IsType<Tensor>(), "Only support Tensor transform!.");
PADDLE_ENFORCE(
platform::places_are_same_class(kernel_pair.first.place_,
kernel_pair.second.place_),
"TransDataLayout only support DataLayout transform on same place!");
PADDLE_ENFORCE(kernel_pair.first.data_type_ == kernel_pair.second.data_type_,
"TransDataLayout only support Datatype are same!");
auto src = in.Get<Tensor>();
auto* dst = out->GetMutable<Tensor>();
PADDLE_ENFORCE(arity(src.dims()) == 4, "Input Arity Only Suppport 4!");
auto src_dim = src.dims();
std::vector<int64_t> dst_dim;
dst_dim.resize(axis.size());
for (size_t i = 0; i < axis.size(); i++) {
dst_dim[i] = src_dim[axis[i]];
}
dst->Resize(make_ddim(dst_dim));
auto place = kernel_pair.second.place_;
dst->mutable_data(place, src.type());
auto src_type = kernel_pair.first.data_type_;
framework::VisitDataType(src_type, CastDataLayout(ctx, axis, src, dst));
dst->set_layout(kernel_pair.second.data_layout_);
}
} // namespace framework
} // namespace paddle
paddle/operators/detail/recv_impl.cc → paddle/framework/data_layout_transform.h
浏览文件 @ dc488c17
......@@ -12,54 +12,20 @@ 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 "send_recv_impl.h"
#pragma once
namespace paddle {
namespace operators {
namespace detail {
Status SendRecvServerImpl::SendVariable(ServerContext *context,
const VariableMessage *in_var,
VoidMessage *out_var) {
MessageWithName msg_with_name =
std::make_pair(in_var->varname(), std::move(*in_var));
var_recv_queue_.Push(std::move(msg_with_name));
return Status::OK;
}
Status SendRecvServerImpl::GetVariable(ServerContext *context,
const VariableMessage *in_var,
VariableMessage *out_var) {
std::string get_var_name = in_var->varname();
auto *var = scope_->FindVar(get_var_name);
#include "paddle/framework/op_kernel_type.h"
#include "paddle/framework/variable.h"
SerializeToMessage(get_var_name, var, platform::CPUDeviceContext(), out_var);
return Status::OK;
}
Status SendRecvServerImpl::Wait(ServerContext *context,
const VoidMessage *in_var,
VoidMessage *out_var) {
{
std::unique_lock<std::mutex> lock(this->mutex_);
condition_.wait(lock, [=] { return this->done_ == true; });
}
return Status::OK;
}
namespace paddle {
namespace framework {
void SendRecvServerImpl::Reset() {
std::lock_guard<std::mutex> lock(this->mutex_);
done_ = false;
}
using KernelTypePair = std::pair<OpKernelType, OpKernelType>;
void SendRecvServerImpl::Done() {
{
std::lock_guard<std::mutex> lock(this->mutex_);
done_ = true;
}
condition_.notify_all();
}
void TransDataLayout(const std::vector<int>& axis,
const platform::DeviceContext* ctx,
const KernelTypePair& kernel_pair, const Variable& in,
Variable* out);
} // namespace detail
} // namespace operators
} // namespace framework
} // namespace paddle
paddle/framework/data_transform.cc
浏览文件 @ dc488c17
......@@ -11,22 +11,14 @@ 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 <functional>
#include "paddle/framework/data_transform.h"
#include "paddle/framework/device_data_transform.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/selected_rows.h"
#include "paddle/platform/device_context.h"
#include "paddle/framework/data_device_transform.h"
namespace paddle {
namespace framework {
DataTransformFnMap& DataTransformFnMap::Instance() {
static DataTransformFnMap data_transform_map;
return data_transform_map;
}
Tensor* DataTransform(const OpKernelType& expected_kernel_type,
const OpKernelType& kernel_type_for_var,
const Tensor& input_tensor) {
......@@ -58,134 +50,5 @@ void CopyVariableWithTensor(const Variable& in_var, const Tensor& tensor,
}
}
auto KernelFP32 = OpKernelType(proto::DataType::FP32, platform::CPUPlace(),
DataLayout::kNHWC, LibraryType::kPlain);
auto KernelFP64 = OpKernelType(proto::DataType::FP64, platform::CPUPlace(),
DataLayout::kNHWC, LibraryType::kPlain);
auto KernelNHWC = OpKernelType(proto::DataType::FP64, platform::CPUPlace(),
DataLayout::kNHWC, LibraryType::kPlain);
auto KernelNCHW = OpKernelType(proto::DataType::FP64, platform::CPUPlace(),
DataLayout::kNCHW, LibraryType::kPlain);
// TODO(dzhwinter): Only for testing multiple op kernel.
// Dummy transform function for library_type
// should be removed.
auto KernelPlain = OpKernelType(proto::DataType::FP32, platform::CUDAPlace(0),
DataLayout::kAnyLayout, LibraryType::kPlain);
auto KernelCUDNN = OpKernelType(proto::DataType::FP32, platform::CUDAPlace(0),
DataLayout::kAnyLayout, LibraryType::kCUDNN);
void DummyTrans(const platform::DeviceContext* ctx,
const KernelTypePair& kernel_pair, const Variable& in,
Variable* out) {
PADDLE_ENFORCE(in.IsType<Tensor>(), "Only Support Tensor transform!.");
PADDLE_ENFORCE(
platform::places_are_same_class(kernel_pair.first.place_,
kernel_pair.second.place_),
"TransDataType Only Support DataType transform on same place!");
auto src = in.Get<Tensor>();
auto* dst = out->GetMutable<Tensor>();
*dst = src;
}
void TransDataType(const platform::DeviceContext* ctx,
const KernelTypePair& kernel_pair, const Variable& in,
Variable* out) {
PADDLE_ENFORCE(in.IsType<Tensor>(), "Only Support Tensor transform!.");
PADDLE_ENFORCE(
platform::places_are_same_class(kernel_pair.first.place_,
kernel_pair.second.place_),
"TransDataType Only Support DataType transform on same place!");
auto src = in.Get<Tensor>();
auto* dst = out->GetMutable<Tensor>();
auto dims = src.dims();
dst->Resize(dims);
auto dst_type = kernel_pair.second.data_type_;
auto src_type = kernel_pair.first.data_type_;
switch (src_type) {
case proto::DataType::FP32:
framework::VisitDataType(dst_type, CastDataType<float>(src, dst, ctx));
break;
case proto::DataType::FP64:
framework::VisitDataType(dst_type, CastDataType<double>(src, dst, ctx));
break;
case proto::DataType::INT32:
framework::VisitDataType(dst_type, CastDataType<int>(src, dst, ctx));
break;
case proto::DataType::INT64:
framework::VisitDataType(dst_type, CastDataType<int64_t>(src, dst, ctx));
break;
case proto::DataType::BOOL:
framework::VisitDataType(dst_type, CastDataType<bool>(src, dst, ctx));
break;
default:
PADDLE_THROW("Not support type %d", src_type);
}
}
void TransDataLayout(const std::vector<int>& axis,
const platform::DeviceContext* ctx,
const KernelTypePair& kernel_pair, const Variable& in,
Variable* out) {
PADDLE_ENFORCE(in.IsType<Tensor>(), "Only support Tensor transform!.");
PADDLE_ENFORCE(
platform::places_are_same_class(kernel_pair.first.place_,
kernel_pair.second.place_),
"TransDataLayout only support DataLayout transform on same place!");
PADDLE_ENFORCE(kernel_pair.first.data_type_ == kernel_pair.second.data_type_,
"TransDataLayout only support Datatype are same!");
auto src = in.Get<Tensor>();
auto* dst = out->GetMutable<Tensor>();
PADDLE_ENFORCE(arity(src.dims()) == 4, "Input Arity Only Suppport 4!");
auto src_dim = src.dims();
std::vector<int64_t> dst_dim;
dst_dim.resize(axis.size());
for (size_t i = 0; i < axis.size(); i++) {
dst_dim[i] = src_dim[axis[i]];
}
dst->Resize(make_ddim(dst_dim));
auto place = kernel_pair.second.place_;
dst->mutable_data(place, src.type());
auto src_type = kernel_pair.first.data_type_;
framework::VisitDataType(src_type, CastDataLayout(ctx, axis, src, dst));
dst->set_layout(kernel_pair.second.data_layout_);
}
} // namespace framework
} // namespace paddle
namespace f = paddle::framework;
namespace {
std::vector<int> NHWC2NCHW = {0, 3, 1, 2};
std::vector<int> NCHW2NHWC = {0, 2, 3, 1};
}
REGISTER_DATA_TRANSFORM_FN(f::KernelFP32, f::KernelFP64, f::TransDataType);
REGISTER_DATA_TRANSFORM_FN(f::KernelPlain, f::KernelCUDNN, f::DummyTrans);
REGISTER_DATA_TRANSFORM_FN(f::KernelCUDNN, f::KernelPlain, f::DummyTrans);
REGISTER_DATA_TRANSFORM_FN(f::KernelNHWC, f::KernelNCHW,
std::bind(f::TransDataLayout, NHWC2NCHW,
std::placeholders::_1,
std::placeholders::_2,
std::placeholders::_3,
std::placeholders::_4));
REGISTER_DATA_TRANSFORM_FN(f::KernelNCHW, f::KernelNHWC,
std::bind(f::TransDataLayout, NCHW2NHWC,
std::placeholders::_1,
std::placeholders::_2,
std::placeholders::_3,
std::placeholders::_4));
paddle/framework/data_transform.h
浏览文件 @ dc488c17
......@@ -30,26 +30,6 @@ limitations under the License. */
namespace paddle {
namespace framework {
using KernelTypePair = std::pair<OpKernelType, OpKernelType>;
using DataTransformFn =
std::function<void(const platform::DeviceContext*, const KernelTypePair&,
const Variable&, Variable*)>;
struct KernelTypePairHash {
static void HashCombine(const OpKernelType& t, std::size_t* seed) {
OpKernelType::Hash kernel_type_hasher;
(*seed) ^= kernel_type_hasher(t) + 0x9e3779b9 + (*seed << 6) + (*seed >> 2);
}
size_t operator()(const KernelTypePair& kernel_pair) const {
std::size_t seed = 0;
HashCombine(kernel_pair.first, &seed);
HashCombine(kernel_pair.second, &seed);
return seed;
}
};
Tensor* DataTransform(const OpKernelType& expected_kernel_type,
const OpKernelType& kernel_type_for_var,
const Tensor& input_tensor);
......@@ -57,125 +37,5 @@ Tensor* DataTransform(const OpKernelType& expected_kernel_type,
void CopyVariableWithTensor(const Variable& in_var, const Tensor& tensor,
Variable& out_var);
template <typename InType, typename OutType>
struct CastDataTypeFunctor {
HOSTDEVICE inline OutType operator()(InType in) const {
return static_cast<OutType>(in);
}
};
template <typename InType>
struct CastDataType {
CastDataType(const framework::Tensor& in, framework::Tensor* out,
const platform::DeviceContext* ctx)
: in_(in), out_(out), ctx_(ctx) {}
const framework::Tensor in_;
framework::Tensor* out_;
const platform::DeviceContext* ctx_;
template <typename OutType>
void operator()() {
auto place = ctx_->GetPlace();
auto* in_begin = in_.data<InType>();
auto numel = in_.numel();
auto* in_end = in_begin + numel;
auto* out_begin = out_->mutable_data<OutType>(place);
if (platform::is_cpu_place(place)) {
platform::Transform<platform::CPUDeviceContext> trans;
auto* context = static_cast<const platform::CPUDeviceContext*>(ctx_);
trans(*context, in_begin, in_end, out_begin,
CastDataTypeFunctor<InType, OutType>());
} else {
// TODO(dzhwinter): enhance Copy CPU<->GPU with different data type?
PADDLE_THROW("Unsupport CPU <-> GPU!");
}
}
};
struct CastDataLayout {
CastDataLayout(const platform::DeviceContext* ctx,
const std::vector<int>& axis, const framework::Tensor& in,
framework::Tensor* out)
: in_(in), out_(out), ctx_(ctx), axis_(axis) {}
const framework::Tensor in_;
framework::Tensor* out_;
const platform::DeviceContext* ctx_;
const std::vector<int> axis_;
template <typename T>
void operator()() {
auto place = ctx_->GetPlace();
if (platform::is_cpu_place(place)) {
operators::math::Transpose<platform::CPUDeviceContext, T, 4> trans4;
auto* context = static_cast<const platform::CPUDeviceContext*>(ctx_);
trans4(*context, in_, out_, axis_);
} else {
PADDLE_THROW("Unsupport CPU <-> GPU!");
}
}
};
using DataTransformMap =
std::unordered_map<KernelTypePair, DataTransformFn, KernelTypePairHash>;
class DataTransformFnMap {
public:
static DataTransformFnMap& Instance();
bool Has(const KernelTypePair& key_pair) const {
return map_.find(key_pair) != map_.end();
}
void Insert(const OpKernelType& left, const OpKernelType& right,
const DataTransformFn& data_tranform_fn) {
Insert(std::make_pair(left, right), data_tranform_fn);
}
void Insert(const KernelTypePair& kernel_type_pair,
const DataTransformFn& data_tranform_fn) {
PADDLE_ENFORCE(!Has(kernel_type_pair),
"KernelTypePair %s has been registered", "");
map_.insert({kernel_type_pair, data_tranform_fn});
}
const DataTransformFn& Get(const KernelTypePair& key_pair) const {
auto data_transformer = GetNullable(key_pair);
PADDLE_ENFORCE_NOT_NULL(data_transformer,
"DataTransformFn should not be NULL");
return *data_transformer;
}
const DataTransformFn* GetNullable(const KernelTypePair& key_pair) const {
auto it = map_.find(key_pair);
if (it == map_.end()) {
return nullptr;
} else {
return &(it->second);
}
}
const DataTransformMap& Map() const { return map_; }
private:
DataTransformFnMap() = default;
DataTransformMap map_;
DISABLE_COPY_AND_ASSIGN(DataTransformFnMap);
};
// generate unique name with __LINE__
// refs https://stackoverflow.com/questions/1597007
#define TOKENPASTE(x, y) x##y
#define TOKENPASTE2(x, y) TOKENPASTE(x, y)
#define REGISTER_DATA_TRANSFORM_FN(from, to, fn) \
static int TOKENPASTE2(fn_, __LINE__)() { \
::paddle::framework::DataTransformFnMap::Instance().Insert(from, to, fn); \
return 0; \
} \
static int TOKENPASTE2(var_, __LINE__) __attribute__((unused)) = \
TOKENPASTE2(fn_, __LINE__)()
} // namespace framework
} // namespace paddle
paddle/framework/data_transform_test.cc 已删除 100644 → 0
浏览文件 @ 12aca860
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <array>
#include <vector>
#include <gtest/gtest.h>
#include "paddle/framework/data_transform.h"
#include "paddle/platform/device_context.h"
namespace paddle {
namespace framework {
using namespace platform;
/**
* @brief cross validation of different kernel type transform
* We use four bit map represent different combination.
* If the field has multiple possible value, only choose two of them.
* For DataType, only test the FP32(float), FP64(double).
* e.g. 0000 -> FP32, CPUPlace, kNHWC, kPlain
* 1111 -> FP64, GPUPlace, kNCHW, kMKLDNN
*/
std::array<proto::DataType, 2> kDataType = {
{proto::DataType::FP32, proto::DataType::FP64}};
std::array<Place, 2> kPlace = {{CPUPlace(), CUDAPlace(0)}};
std::array<DataLayout, 2> kDataLayout = {{
DataLayout::kNHWC, DataLayout::kNCHW,
}};
std::array<LibraryType, 2> kLibraryType = {{
LibraryType::kPlain, LibraryType::kMKLDNN,
}};
OpKernelType GenFromBit(const std::vector<bool> bits) {
return OpKernelType(kDataType[bits[0]], kPlace[bits[1]], kDataLayout[bits[2]],
kLibraryType[bits[3]]);
}
int test_value = 0;
auto kernel0 = GenFromBit({0, 0, 0, 0});
auto kernel1 = GenFromBit({0, 0, 0, 1});
auto kernel2 = GenFromBit({0, 0, 1, 0});
auto kernel3 = GenFromBit({0, 0, 1, 1});
void TransDataType_t(const platform::DeviceContext* ctx,
const KernelTypePair& p, const Variable& in,
Variable* out) {
test_value++;
}
void TransDataLayout_t(const platform::DeviceContext* ctx,
const KernelTypePair& p, const Variable& in,
Variable* out) {
test_value--;
}
void TransLibraryType_t(const platform::DeviceContext* ctx,
const KernelTypePair& p, const Variable& in,
Variable* out) {
test_value += 2;
}
} // namespace framework
} // namespace paddle
namespace frw = paddle::framework;
REGISTER_DATA_TRANSFORM_FN(frw::kernel0, frw::kernel1, frw::TransDataType_t);
REGISTER_DATA_TRANSFORM_FN(frw::kernel1, frw::kernel2, frw::TransDataLayout_t);
REGISTER_DATA_TRANSFORM_FN(frw::kernel0, frw::kernel2, frw::TransLibraryType_t);
TEST(DataTransform, Register) {
using namespace paddle::framework;
using namespace paddle::platform;
auto& instance = DataTransformFnMap::Instance();
paddle::framework::Variable in;
paddle::framework::Variable out;
DeviceContext* ctx = new CPUDeviceContext();
auto pair0 = std::make_pair(frw::kernel0, frw::kernel1);
instance.Get(pair0)(ctx, pair0, in, &out);
ASSERT_EQ(test_value, 1);
auto pair1 = std::make_pair(frw::kernel1, frw::kernel2);
instance.Get(pair1)(ctx, pair1, in, &out);
ASSERT_EQ(test_value, 0);
auto pair3 = std::make_pair(frw::kernel0, frw::kernel2);
instance.Get(pair3)(ctx, pair3, in, &out);
ASSERT_EQ(test_value, 2);
}
TEST(DataTransform, DataLayout) {
using namespace paddle::framework;
using namespace paddle::platform;
auto& instance = DataTransformFnMap::Instance();
Variable in;
Variable out;
Tensor* src = in.GetMutable<Tensor>();
src->mutable_data<double>(make_ddim({2, 3, 1, 2}), CPUPlace());
src->set_layout(DataLayout::kNHWC);
DeviceContext* ctx = new CPUDeviceContext();
{
auto kernel1 = GenFromBit({1, 0, 0, 0});
auto kernel2 = GenFromBit({1, 0, 1, 0});
auto pair0 = std::make_pair(kernel1, kernel2);
instance.Get(pair0)(ctx, pair0, in, &out);
}
Tensor dst = out.Get<Tensor>();
EXPECT_TRUE(dst.layout() == DataLayout::kNCHW);
EXPECT_TRUE(dst.dims() == make_ddim({2, 2, 3, 1}));
{
auto kernel1 = GenFromBit({1, 0, 1, 0});
auto kernel2 = GenFromBit({1, 0, 0, 0});
auto pair0 = std::make_pair(kernel1, kernel2);
instance.Get(pair0)(ctx, pair0, out, &in);
}
EXPECT_TRUE(src->layout() == DataLayout::kNHWC);
EXPECT_TRUE(src->dims() == make_ddim({2, 3, 1, 2}));
}
TEST(DataTransform, DataType) {
using namespace paddle::framework;
using namespace paddle::platform;
auto& instance = DataTransformFnMap::Instance();
DeviceContext* ctx = new CPUDeviceContext();
Variable in;
Variable out;
Tensor* src = in.GetMutable<Tensor>();
float* ptr = src->mutable_data<float>(make_ddim({2, 3}), CPUPlace());
for (int i = 0; i < 6; ++i) {
ptr[i] = i / 3;
}
{
auto kernel1 = GenFromBit({0, 0, 0, 0});
auto kernel2 = GenFromBit({1, 0, 0, 0});
auto pair0 = std::make_pair(kernel1, kernel2);
instance.Get(pair0)(ctx, pair0, in, &out);
}
Tensor dst = out.Get<Tensor>();
EXPECT_TRUE(dst.data<double>() != nullptr);
}
paddle/framework/data_type_transform.cc 0 → 100644
浏览文件 @ dc488c17
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/data_type_transform.h"
#include "paddle/framework/selected_rows.h"
#include "paddle/platform/transform.h"
namespace paddle {
namespace framework {
template <typename InType, typename OutType>
struct CastDataTypeFunctor {
HOSTDEVICE inline OutType operator()(InType in) const {
return static_cast<OutType>(in);
}
};
template <typename InType>
struct CastDataType {
CastDataType(const framework::Tensor& in, framework::Tensor* out,
const platform::DeviceContext* ctx)
: in_(in), out_(out), ctx_(ctx) {}
const framework::Tensor in_;
framework::Tensor* out_;
const platform::DeviceContext* ctx_;
template <typename OutType>
void operator()() {
auto place = ctx_->GetPlace();
auto* in_begin = in_.data<InType>();
auto numel = in_.numel();
auto* in_end = in_begin + numel;
auto* out_begin = out_->mutable_data<OutType>(place);
if (platform::is_cpu_place(place)) {
platform::Transform<platform::CPUDeviceContext> trans;
auto* context = static_cast<const platform::CPUDeviceContext*>(ctx_);
trans(*context, in_begin, in_end, out_begin,
CastDataTypeFunctor<InType, OutType>());
} else {
// TODO(dzhwinter): enhance Copy CPU<->GPU with different data type?
PADDLE_THROW("Unsupport CPU <-> GPU!");
}
}
};
void TransDataType(const platform::DeviceContext* ctx,
const KernelTypePair& kernel_pair, const Variable& in,
Variable* out) {
PADDLE_ENFORCE(in.IsType<Tensor>(), "Only Support Tensor transform!.");
PADDLE_ENFORCE(
platform::places_are_same_class(kernel_pair.first.place_,
kernel_pair.second.place_),
"TransDataType Only Support DataType transform on same place!");
auto src = in.Get<Tensor>();
auto* dst = out->GetMutable<Tensor>();
auto dims = src.dims();
dst->Resize(dims);
auto dst_type = kernel_pair.second.data_type_;
auto src_type = kernel_pair.first.data_type_;
switch (src_type) {
case proto::DataType::FP32:
framework::VisitDataType(dst_type, CastDataType<float>(src, dst, ctx));
break;
case proto::DataType::FP64:
framework::VisitDataType(dst_type, CastDataType<double>(src, dst, ctx));
break;
case proto::DataType::INT32:
framework::VisitDataType(dst_type, CastDataType<int>(src, dst, ctx));
break;
case proto::DataType::INT64:
framework::VisitDataType(dst_type, CastDataType<int64_t>(src, dst, ctx));
break;
case proto::DataType::BOOL:
framework::VisitDataType(dst_type, CastDataType<bool>(src, dst, ctx));
break;
default:
PADDLE_THROW("Not support type %d", src_type);
}
}
} // namespace framework
} // namespace paddle
paddle/framework/data_type_transform.h 0 → 100644
浏览文件 @ dc488c17
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/op_kernel_type.h"
#include "paddle/framework/variable.h"
#include "paddle/platform/device_context.h"
namespace paddle {
namespace framework {
using KernelTypePair = std::pair<OpKernelType, OpKernelType>;
void TransDataType(const platform::DeviceContext* ctx,
const KernelTypePair& kernel_pair, const Variable& in,
Variable* out);
} // namespace framework
} // namespace paddle
paddle/framework/operator.cc
浏览文件 @ dc488c17
......@@ -16,7 +16,6 @@ limitations under the License. */
#include <algorithm>
#include "paddle/framework/data_transform.h"
#include "paddle/framework/device_data_transform.h"
#include "paddle/framework/executor.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/shape_inference.h"
......
paddle/gserver/layers/MKLDNNLayer.cpp
浏览文件 @ dc488c17
......@@ -132,6 +132,8 @@ void MKLDNNLayer::reshapeInput(int& batchsize,
if (w != 0) {
width = w;
}
height = height != 0 ? height : 1;
width = width != 0 ? width : 1;
}
void MKLDNNLayer::reshapeOutput(size_t height, size_t width) {
......
paddle/gserver/layers/MKLDNNLayer.h
浏览文件 @ dc488c17
......@@ -98,6 +98,8 @@ protected:
public:
explicit MKLDNNLayer(const LayerConfig& config)
: Layer(config),
ih_(0),
iw_(0),
condition_(0),
needResetBwd_(true),
outputOnlyMKLDNN_(false),
......
paddle/operators/detail/CMakeLists.txt
浏览文件 @ dc488c17
grpc_library(sendrecvop_grpc SRCS recv_impl.cc send_impl.cc PROTO send_recv.proto DEPS lod_tensor selected_rows)
grpc_library(sendrecvop_grpc SRCS sendrecvop_utils.cc grpc_client.cc grpc_server.cc PROTO send_recv.proto DEPS lod_tensor selected_rows)
paddle/operators/detail/grpc_client.cc 0 → 100644
浏览文件 @ dc488c17
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "grpc_client.h"
namespace paddle {
namespace operators {
namespace detail {
bool RPCClient::AsyncSendVariable(const std::string& ep,
const platform::DeviceContext& ctx,
const framework::Scope& scope,
const std::string& var_name,
int64_t time_out) {
sendrecv::VariableMessage req;
auto* var = scope.FindVar(var_name);
SerializeToMessage(var_name, var, ctx, &req);
// varhandle
VarHandle var_h;
var_h.ep = ep;
var_h.scope = &scope;
var_h.name = var_name;
var_h.ctx = &ctx;
// stub context
auto ch = GetChannel(ep);
SendProcessor* s = new SendProcessor(ch);
s->Prepare(var_h, time_out);
s->response_call_back_ = NULL;
auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, (void*)s);
req_count_++;
return true;
}
void ProcGetResponse(const VarHandle& var_h,
const sendrecv::VariableMessage& ret_msg) {
auto* outvar = var_h.scope->FindVar(var_h.name);
std::istringstream iss(ret_msg.serialized());
DeserializeFromMessage(ret_msg, *var_h.ctx, outvar);
}
bool RPCClient::AsyncGetVariable(const std::string& ep,
const platform::DeviceContext& ctx,
const framework::Scope& scope,
const std::string& var_name,
int64_t time_out) {
sendrecv::VariableMessage req;
req.set_varname(var_name);
auto* var = scope.FindVar(var_name);
SerializeToMessage(var_name, var, ctx, &req);
// varhandle
VarHandle var_h;
var_h.ep = ep;
var_h.scope = &scope;
var_h.name = var_name;
var_h.ctx = &ctx;
// stub context
auto ch = GetChannel(ep);
GetProcessor* s = new GetProcessor(ch);
s->Prepare(var_h, time_out);
s->response_call_back_ = ProcGetResponse;
auto rpc = s->stub_->AsyncGetVariable(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, (void*)s);
req_count_++;
return true;
}
bool RPCClient::wait() {
bool ok = true;
while (true) {
if (req_count_ <= 0) {
break;
}
if (!Proceed()) {
LOG(ERROR) << "Get meets CompletionQueue error";
return false;
}
}
return ok;
}
bool RPCClient::Proceed() {
void* tag = NULL;
bool ok = false;
// request counts.
if (!cq_.Next(&tag, &ok)) {
return false;
}
req_count_--;
GPR_ASSERT(ok);
PADDLE_ENFORCE(tag);
// TODO(gongwb): add more retries.
ClientBase* c = static_cast<ClientBase*>(tag);
if (!c->status_.ok()) {
delete c;
return true;
}
c->Process();
delete c;
return true;
}
std::shared_ptr<grpc::Channel> RPCClient::GetChannel(const std::string& ep) {
auto it = channels_.find(ep);
if (it != channels_.end()) {
return it->second;
}
auto ch = std::shared_ptr<grpc::Channel>(
grpc::CreateChannel(ep, grpc::InsecureChannelCredentials()));
channels_[ep] = ch;
return ch;
}
} // namespace detail
} // namespace operators
} // namespace paddle
paddle/operators/detail/grpc_client.h 0 → 100644
浏览文件 @ dc488c17
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <grpc++/grpc++.h>
#include <grpc/support/log.h>
#include <time.h>
#include <chrono>
#include <ctime>
#include <functional>
#include <iostream>
#include <map>
#include <string>
#include <vector>
#include "paddle/framework/data_type.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/selected_rows.h"
#include "paddle/operators/detail/sendrecvop_utils.h"
#include "paddle/operators/detail/simple_block_queue.h"
namespace paddle {
namespace operators {
namespace detail {
struct VarHandle {
std::string ep;
const platform::DeviceContext* ctx;
const framework::Scope* scope;
std::string name;
std::string String() const {
std::ostringstream s;
s << "name:[" << name << "] ep:[" << ep << "]";
return s.str();
}
};
void ProcGetResponse(const VarHandle& var_h,
const sendrecv::VariableMessage& msg);
class ClientBase {
public:
explicit ClientBase(std::shared_ptr<grpc::Channel> ch) {
stub_ = sendrecv::SendRecvService::NewStub(ch);
context_ = NULL;
}
virtual ~ClientBase() {}
virtual void Prepare(const VarHandle& var_info, int64_t time_out) {
context_.reset(new grpc::ClientContext());
var_h_ = var_info;
std::chrono::system_clock::time_point deadline =
std::chrono::system_clock::now() + std::chrono::milliseconds(time_out);
context_->set_deadline(deadline);
}
virtual void Process() = 0;
std::unique_ptr<sendrecv::SendRecvService::Stub> stub_;
std::unique_ptr<grpc::ClientContext> context_;
grpc::Status status_;
VarHandle var_h_;
};
typedef std::function<void(const VarHandle&, const sendrecv::VoidMessage&)>
RequestSendCallBack;
class SendProcessor : public ClientBase {
public:
explicit SendProcessor(std::shared_ptr<grpc::Channel> ch) : ClientBase(ch) {}
virtual ~SendProcessor() {}
virtual void Process() {
if (response_call_back_) {
response_call_back_(var_h_, reply_);
}
}
sendrecv::VoidMessage reply_;
RequestSendCallBack response_call_back_ = NULL;
};
typedef std::function<void(const VarHandle&, const sendrecv::VariableMessage&)>
RequestGetCallBack;
class GetProcessor : public ClientBase {
public:
explicit GetProcessor(std::shared_ptr<grpc::Channel> ch) : ClientBase(ch) {}
virtual ~GetProcessor() {}
virtual void Process() {
if (response_call_back_) {
response_call_back_(var_h_, reply_);
}
}
sendrecv::VariableMessage reply_;
RequestGetCallBack response_call_back_ = ProcGetResponse;
};
class RPCClient {
public:
bool AsyncSendVariable(const std::string& ep,
const platform::DeviceContext& ctx,
const framework::Scope& scope,
const std::string& var_name,
int64_t time_out = 600 * 1000);
bool AsyncGetVariable(const std::string& ep,
const platform::DeviceContext& ctx,
const framework::Scope& scope,
const std::string& var_name,
int64_t time_out = 600 * 1000);
bool wait();
private:
bool Proceed();
std::shared_ptr<grpc::Channel> GetChannel(const std::string& ep);
private:
grpc::CompletionQueue cq_;
std::map<std::string, std::shared_ptr<grpc::Channel>> channels_;
int64_t req_count_ = 0;
};
} // namespace detail
} // namespace operators
} // namespace paddle
paddle/operators/detail/grpc_server.cc 0 → 100644
浏览文件 @ dc488c17
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/detail/grpc_server.h"
using grpc::ServerAsyncResponseWriter;
namespace paddle {
namespace operators {
namespace detail {
enum CallStatus { PROCESS = 0, FINISH };
// reference:
// https://stackoverflow.com/questions/41732884/grpc-multiple-services-in-cpp-async-server
class RequestBase {
public:
explicit RequestBase(sendrecv::SendRecvService::AsyncService* service,
grpc::ServerCompletionQueue* cq)
: service_(service), cq_(cq), status_(PROCESS) {}
virtual ~RequestBase() {}
virtual void Process() { assert(false); }
CallStatus Status() { return status_; }
void SetStatus(CallStatus status) { status_ = status; }
protected:
grpc::ServerContext ctx_;
sendrecv::SendRecvService::AsyncService* service_;
grpc::ServerCompletionQueue* cq_;
CallStatus status_;
};
typedef std::pair<std::string, sendrecv::VariableMessage> MessageWithName;
class RequestSend final : public RequestBase {
public:
explicit RequestSend(sendrecv::SendRecvService::AsyncService* service,
grpc::ServerCompletionQueue* cq,
SimpleBlockQueue<MessageWithName>* queue)
: RequestBase(service, cq), queue_(queue), responder_(&ctx_) {
service_->RequestSendVariable(&ctx_, &request_, &responder_, cq_, cq_,
this);
}
virtual ~RequestSend() {}
virtual void Process() {
MessageWithName msg_with_name =
std::make_pair(request_.varname(), std::move(request_));
queue_->Push(std::move(msg_with_name));
// TODO(gongwb): check var's info.
responder_.Finish(reply_, grpc::Status::OK, this);
}
protected:
sendrecv::VariableMessage request_;
sendrecv::VoidMessage reply_;
SimpleBlockQueue<MessageWithName>* queue_;
ServerAsyncResponseWriter<sendrecv::VoidMessage> responder_;
};
class RequestGet final : public RequestBase {
public:
explicit RequestGet(sendrecv::SendRecvService::AsyncService* service,
grpc::ServerCompletionQueue* cq, framework::Scope* scope)
: RequestBase(service, cq), responder_(&ctx_), scope_(scope) {
service_->RequestGetVariable(&ctx_, &request_, &responder_, cq_, cq_, this);
}
virtual ~RequestGet() {}
virtual void Process() {
// proc request.
std::string var_name = request_.varname();
auto* var = scope_->FindVar(var_name);
SerializeToMessage(var_name, var, platform::CPUDeviceContext(), &reply_);
// TODO(gongwb): check var's info.
responder_.Finish(reply_, grpc::Status::OK, this);
}
protected:
sendrecv::VariableMessage request_;
sendrecv::VariableMessage reply_;
ServerAsyncResponseWriter<sendrecv::VariableMessage> responder_;
framework::Scope* scope_;
};
void AsyncGRPCServer::RunSyncUpdate() {
grpc::ServerBuilder builder;
builder.AddListeningPort(address_, grpc::InsecureServerCredentials());
builder.RegisterService(&service_);
cq_send_ = builder.AddCompletionQueue();
cq_get_ = builder.AddCompletionQueue();
server_ = builder.BuildAndStart();
LOG(INFO) << "Server listening on " << address_ << std::endl;
std::function<void()> send_register =
std::bind(&AsyncGRPCServer::TryToRegisterNewSendOne, this);
std::function<void()> get_register =
std::bind(&AsyncGRPCServer::TryToRegisterNewGetOne, this);
t_send_.reset(
new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this, false,
cq_send_.get(), "cq_send", send_register)));
t_get_.reset(
new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this, true,
cq_get_.get(), "cq_get", get_register)));
// wait server
server_->Wait();
t_send_->join();
t_get_->join();
}
void AsyncGRPCServer::ShutdownQueue() {
std::unique_lock<std::mutex> lock(cq_mutex_);
cq_send_->Shutdown();
cq_get_->Shutdown();
is_shut_down_ = true;
}
// This URL explains why shutdown is complicate:
// https://stackoverflow.com/questions/35708348/grpc-what-is-the-recommended-way-to-shut-down-an-asynchronous-server-in-c
void AsyncGRPCServer::ShutDown() {
server_->Shutdown();
ShutdownQueue();
}
void AsyncGRPCServer::TryToRegisterNewSendOne() {
std::unique_lock<std::mutex> lock(cq_mutex_);
if (is_shut_down_) {
return;
}
RequestSend* send =
new RequestSend(&service_, cq_send_.get(), &var_recv_queue_);
VLOG(4) << "create RequestSend status:" << send->Status();
}
void AsyncGRPCServer::TryToRegisterNewGetOne() {
std::unique_lock<std::mutex> lock(cq_mutex_);
if (is_shut_down_) {
return;
}
RequestGet* get = new RequestGet(&service_, cq_get_.get(), scope_);
VLOG(4) << "create Requestget status:" << get->Status();
}
void AsyncGRPCServer::SetFinishOrDelete(RequestBase*& last) {
std::unique_lock<std::mutex> lock(cq_mutex_);
if (is_shut_down_) {
delete last;
last = NULL;
return;
}
last->SetStatus(FINISH);
return;
}
void AsyncGRPCServer::HandleRequest(bool wait, grpc::ServerCompletionQueue* cq,
std::string cq_name,
std::function<void()> TryToRegisterNewOne) {
TryToRegisterNewOne();
void* tag = NULL;
bool ok = false;
while (true) {
if (!cq->Next(&tag, &ok)) {
LOG(INFO) << cq_name << " get CompletionQueue shutdown!";
break;
}
if (wait && !done_) {
Wait();
}
RequestBase* base = (RequestBase*)tag;
if (!ok) {
VLOG(4) << cq_name << " recv no regular event";
TryToRegisterNewOne();
delete base;
continue;
}
switch (base->Status()) {
case PROCESS: {
VLOG(4) << cq_name << " status:" << base->Status();
TryToRegisterNewOne();
base->Process();
SetFinishOrDelete(base);
break;
}
case FINISH: {
VLOG(4) << cq_name << " status:" << base->Status();
delete base;
break;
}
default: { assert(false); }
}
}
}
void AsyncGRPCServer::Wait() {
std::unique_lock<std::mutex> lock(this->mutex_);
condition_.wait(lock, [=] { return this->done_ == true; });
}
void AsyncGRPCServer::Reset() {
std::lock_guard<std::mutex> lock(this->mutex_);
done_ = false;
}
void AsyncGRPCServer::Done() {
{
std::lock_guard<std::mutex> lock(this->mutex_);
done_ = true;
}
condition_.notify_all();
}
} // namespace detail
} // namespace operators
} // namespace paddle
paddle/operators/detail/grpc_server.h 0 → 100644
浏览文件 @ dc488c17
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/selected_rows.h"
#include "paddle/framework/var_type.h"
#include "paddle/operators/detail/simple_block_queue.h"
#include "paddle/operators/detail/send_recv.grpc.pb.h"
#include "paddle/operators/detail/send_recv.pb.h"
#include <grpc++/grpc++.h>
#include <grpc/support/log.h>
#include <thread>
#include "paddle/operators/detail/sendrecvop_utils.h"
namespace paddle {
namespace operators {
namespace detail {
typedef std::pair<std::string, sendrecv::VariableMessage> MessageWithName;
class RequestBase;
class AsyncGRPCServer final : public sendrecv::SendRecvService::Service {
public:
explicit AsyncGRPCServer(std::string address) { address_ = address; }
void RunSyncUpdate();
void Reset();
void Done();
void SetScope(framework::Scope *scope) { scope_ = scope; }
const MessageWithName Get() { return this->var_recv_queue_.Pop(); }
void Push(const MessageWithName &msg) { this->var_recv_queue_.Push(msg); }
void ShutDown();
protected:
void Wait();
void HandleRequest(bool wait, grpc::ServerCompletionQueue *cq,
std::string cq_name,
std::function<void()> TryToRegisterNewOne);
void TryToRegisterNewSendOne();
void TryToRegisterNewGetOne();
void SetFinishOrDelete(RequestBase *&last);
void ShutdownQueue();
private:
std::mutex cq_mutex_;
volatile bool is_shut_down_ = false;
std::unique_ptr<grpc::ServerCompletionQueue> cq_send_;
std::unique_ptr<grpc::ServerCompletionQueue> cq_get_;
sendrecv::SendRecvService::AsyncService service_;
std::unique_ptr<grpc::Server> server_;
std::string address_;
framework::Scope *scope_;
// received variable from RPC, operators fetch variable from this queue.
SimpleBlockQueue<MessageWithName> var_recv_queue_;
// condition of the sub program
std::mutex mutex_;
volatile mutable bool done_;
std::condition_variable condition_;
std::unique_ptr<std::thread> t_send_;
std::unique_ptr<std::thread> t_get_;
};
}; // namespace detail
}; // namespace operators
}; // namespace paddle
paddle/operators/detail/send_recv.proto
浏览文件 @ dc488c17
......@@ -21,8 +21,6 @@ service SendRecvService {
rpc SendVariable(VariableMessage) returns (VoidMessage) {}
// Argument VariableMessage for GetVariable should only contain varname.
rpc GetVariable(VariableMessage) returns (VariableMessage) {}
// wait for one execution of the program
rpc Wait(VoidMessage) returns (VoidMessage) {}
}
// VariableMessage is serialized paddle variable message.
......
paddle/operators/detail/send_recv_impl.h → paddle/operators/detail/sendrecvop_utils.cc
浏览文件 @ dc488c17
......@@ -12,87 +12,15 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/selected_rows.h"
#include "paddle/framework/var_type.h"
#include "paddle/operators/detail/simple_block_queue.h"
#include "paddle/operators/detail/send_recv.grpc.pb.h"
#include "paddle/operators/detail/send_recv.pb.h"
#include <grpc++/grpc++.h>
using grpc::Channel;
using grpc::Server;
using grpc::ServerContext;
using grpc::ServerReader;
using grpc::ServerBuilder;
using grpc::ClientContext;
using grpc::ClientReader;
using grpc::ClientReaderWriter;
using grpc::ClientWriter;
using grpc::Status;
using sendrecv::SendRecvService;
using sendrecv::VariableMessage;
using sendrecv::VoidMessage;
#include "paddle/operators/detail/sendrecvop_utils.h"
namespace paddle {
namespace operators {
namespace detail {
typedef std::pair<std::string, sendrecv::VariableMessage> MessageWithName;
class SendRecvServerImpl final : public SendRecvService::Service {
public:
explicit SendRecvServerImpl() {}
Status SendVariable(ServerContext *context, const VariableMessage *in_var,
VoidMessage *out_var) override;
Status GetVariable(ServerContext *context, const VariableMessage *in_var,
VariableMessage *out_var) override;
Status Wait(ServerContext *context, const VoidMessage *in_var,
VoidMessage *out_var) override;
void Reset();
void Done();
void SetScope(framework::Scope *scope) { scope_ = scope; };
const MessageWithName Get() { return this->var_recv_queue_.Pop(); }
void Push(const MessageWithName &msg) { this->var_recv_queue_.Push(msg); }
private:
// received variable from RPC, operators fetch variable from this queue.
SimpleBlockQueue<MessageWithName> var_recv_queue_;
framework::Scope *scope_;
// condition of the sub program
std::mutex mutex_;
bool done_;
std::condition_variable condition_;
};
// RPCClient is a class to send tensors to pserver sub-network
// using different hashing methods.
class RPCClient {
public:
RPCClient(std::shared_ptr<Channel> channel)
: stub_(SendRecvService::NewStub(channel)) {}
bool SendVariable(const framework::Scope &scope, const std::string &inname);
bool GetVariable(const framework::Scope &scope, const std::string &outname);
void Wait();
private:
std::unique_ptr<SendRecvService::Stub> stub_;
};
inline void SerializeToMessage(const std::string &name,
const framework::Variable *var,
const platform::DeviceContext &ctx,
VariableMessage *msg) {
void SerializeToMessage(const std::string& name, const framework::Variable* var,
const platform::DeviceContext& ctx,
sendrecv::VariableMessage* msg) {
msg->set_varname(name);
std::ostringstream oss;
switch (framework::ToVarType(var->Type())) {
......@@ -114,10 +42,9 @@ inline void SerializeToMessage(const std::string &name,
msg->set_serialized(oss.str());
}
inline void DeserializeFromMessage(const VariableMessage &msg,
const platform::DeviceContext &ctx,
framework::Variable *var) {
using namespace paddle::framework::proto;
void DeserializeFromMessage(const sendrecv::VariableMessage& msg,
const platform::DeviceContext& ctx,
framework::Variable* var) {
std::istringstream iss(msg.serialized());
switch (msg.type()) {
case sendrecv::VarType::LOD_TENSOR:
......
paddle/operators/detail/send_impl.cc → paddle/operators/detail/sendrecvop_utils.h
浏览文件 @ dc488c17
......@@ -12,56 +12,31 @@ 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 "send_recv_impl.h"
#pragma once
#include <iostream>
#include <string>
#include <vector>
#include "paddle/framework/data_type.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/selected_rows.h"
#include "paddle/framework/var_type.h"
#include "paddle/operators/detail/send_recv.grpc.pb.h"
#include "paddle/operators/detail/send_recv.pb.h"
namespace paddle {
namespace operators {
namespace detail {
bool RPCClient::SendVariable(const framework::Scope& scope,
const std::string& inname) {
ClientContext context;
VariableMessage msg;
VoidMessage out_msg;
// FIXME(typhoonzero): pass device context to here.
auto ctx = platform::CPUDeviceContext();
auto* var = scope.FindVar(inname);
PADDLE_ENFORCE(var);
SerializeToMessage(inname, var, ctx, &msg);
Status status = stub_->SendVariable(&context, msg, &out_msg);
if (!status.ok()) {
LOG(ERROR) << "gRPC error: " << status.error_message();
return false;
}
return true;
}
bool RPCClient::GetVariable(const framework::Scope& scope,
const std::string& outname) {
ClientContext context;
VariableMessage call_msg, ret_msg;
call_msg.set_varname(outname);
auto ctx = platform::CPUDeviceContext();
Status status = stub_->GetVariable(&context, call_msg, &ret_msg);
auto* outvar = scope.FindVar(outname);
if (!status.ok()) {
LOG(ERROR) << "gRPC error: " << status.error_message();
return false;
}
std::istringstream iss(ret_msg.serialized());
DeserializeFromMessage(ret_msg, ctx, outvar);
return true;
}
void RPCClient::Wait() {
ClientContext context;
VoidMessage call_msg, ret_msg;
stub_->Wait(&context, call_msg, &ret_msg);
}
void SerializeToMessage(const std::string& name, const framework::Variable* var,
const platform::DeviceContext& ctx,
sendrecv::VariableMessage* msg);
void DeserializeFromMessage(const sendrecv::VariableMessage& msg,
const platform::DeviceContext& ctx,
framework::Variable* var);
} // namespace detail
} // namespace operators
} // namespace paddle
paddle/operators/edit_distance_op.cc 0 → 100644
浏览文件 @ dc488c17
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/edit_distance_op.h"
namespace paddle {
namespace operators {
class EditDistanceOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Hyps"), "Input(Hyps) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("Refs"), "Input(Refs) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) shouldn't be null.");
auto hyp_dims = ctx->GetInputDim("Hyps");
auto ref_dims = ctx->GetInputDim("Refs");
PADDLE_ENFORCE(hyp_dims.size() == 2 && hyp_dims[1] == 1,
"Input(Hyps) must be a 2-D LoDTensor with the 2nd dimension "
"equal to 1.");
PADDLE_ENFORCE(ref_dims.size() == 2 && ref_dims[1] == 1,
"Input(Refs) must be a 2-D LoDTensor with the 2nd dimension "
"equal to 1.");
ctx->SetOutputDim("Out", ctx->GetInputDim("Refs"));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(framework::proto::DataType::FP32,
ctx.device_context());
}
};
class EditDistanceOpMaker : public framework::OpProtoAndCheckerMaker {
public:
EditDistanceOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Hyps",
"(2-D LoDTensor<int>, 2nd dim. equal to 1) "
"The indices for hypothesis strings.");
AddInput("Refs",
"(2-D LoDTensor<int>, 2nd dim. equal to 1) "
"The indices for reference strings.");
AddAttr<bool>("normalized",
"(bool, default false) Indicated whether to normalize "
"the edit distance by the length of reference string.")
.SetDefault(false);
AddOutput("Out",
"(2-D Tensor with shape [`batch_size` x 1]) "
"The output edit distances of EditDistance operator.");
AddComment(R"DOC(
EditDistance operator computes the edit distances between a batch of hypothesis
strings and their references.
Edit distance, also called Levenshtein distance, measures how dissimilar two strings
are by counting the minimum number of operations to transform one string into anthor.
Here the operations include insertion, deletion, and substitution. For example,
given hypothesis string A = "kitten" and reference B = "sitting", the edit distance
is 3 for A will be transformed into B at least after two substitutions and one
insertion:
"kitten" -> "sitten" -> "sittin" -> "sitting"
Input(Hyps) is a LoDTensor consisting of all the hypothesis strings with the total
number denoted by `batch_size`, and the separation is specified by the LoD information.
And the `batch_size` reference strings are arranged in order in the same way in the
LoDTensor Input(Refs).
Output(Out) contains the `batch_size` results and each stands for the edit stance
for a pair of strings respectively. If Attr(normalized) is true, the edit distance
will be divided by the length of reference string.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(edit_distance, ops::EditDistanceOp, ops::EditDistanceOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
edit_distance, ops::EditDistanceKernel<paddle::platform::CPUPlace, float>);
paddle/operators/edit_distance_op.cu 0 → 100644
浏览文件 @ dc488c17
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <algorithm>
#include "paddle/framework/op_registry.h"
#include "paddle/platform/cuda_helper.h"
#include "paddle/platform/gpu_info.h"
namespace paddle {
namespace operators {
using platform::PADDLE_CUDA_NUM_THREADS;
template <typename T>
__global__ void FillFirstRow(T* dist, const int N) {
int idx = blockDim.x * blockIdx.x + threadIdx.x;
if (idx < N + 1) {
dist[idx] = idx;
}
}
template <typename T>
__global__ void FillFirstColumn(T* dist, const int M, const int N) {
int idx = blockDim.x * blockIdx.x + threadIdx.x;
if (idx < M + 1) {
dist[idx * (N + 1)] = idx;
}
}
template <typename T>
__global__ void Levenshtein(T* dist, const int* x1, const int* x2, const int M,
const int N, const int start) {
int idx = blockDim.x * blockIdx.x + threadIdx.x;
int offset = N;
int index = start + idx * offset;
int row = index / (N + 1);
int col = index % (N + 1);
if (row > 0 && col > 0 && row < M + 1 && col < N + 1) {
int cost = x1[row - 1] == x2[col - 1] ? 0 : 1;
int dels = dist[(row - 1) * (N + 1) + col] + 1;
int ins = dist[row * (N + 1) + col - 1] + 1;
int subs = dist[(row - 1) * (N + 1) + (col - 1)] + cost;
dist[index] = min(dels, min(ins, subs));
}
}
template <typename T>
__global__ void SetOutput(T* out, const T* dist, const int M, const int N,
bool normalized) {
int idx = blockDim.x * blockIdx.x + threadIdx.x;
if (idx == 0) {
out[0] = normalized ? dist[M * (N + 1) + N] / N : dist[M * (N + 1) + N];
}
}
template <typename Place, typename T>
class EditDistanceGPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* out_t = ctx.Output<framework::Tensor>("Out");
auto* x1_t = ctx.Input<framework::LoDTensor>("Hyps");
auto* x2_t = ctx.Input<framework::LoDTensor>("Refs");
auto normalized = ctx.Attr<bool>("normalized");
auto stream = reinterpret_cast<const platform::CUDADeviceContext&>(
ctx.device_context())
.stream();
auto hyp_lod = x1_t->lod()[0];
auto ref_lod = x2_t->lod()[0];
PADDLE_ENFORCE(
hyp_lod.size() == ref_lod.size(),
"Input(Hyps) and Input(Refs) must have the same batch size.");
for (size_t i = 1; i < ref_lod.size(); ++i) {
PADDLE_ENFORCE(ref_lod[i] > ref_lod[i - 1],
"Reference string %d is empty.", i);
}
auto num_strs = hyp_lod.size() - 1;
out_t->Resize({static_cast<int64_t>(num_strs), 1});
out_t->mutable_data<T>(ctx.GetPlace());
auto out = out_t->data<T>();
T distance = 0.0;
for (size_t num = 0; num < num_strs; num++) {
auto m = static_cast<int64_t>(hyp_lod[num + 1] - hyp_lod[num]);
auto n = static_cast<int64_t>(ref_lod[num + 1] - ref_lod[num]);
if (m == 0 || n == 0) {
distance = std::max(m, n);
if (normalized) {
PADDLE_ENFORCE(n > 0,
"The reference string (#%d) cannot be empty "
"when Attr(normalized) is enabled.",
n);
distance = distance / n;
}
memory::Copy(boost::get<Place>(ctx.GetPlace()), out + num,
platform::CPUPlace(), &distance, sizeof(T), stream);
} else {
framework::Tensor dist_t;
dist_t.Resize({m + 1, n + 1});
dist_t.mutable_data<T>(ctx.GetPlace());
auto dist = dist_t.data<T>();
auto x1 = x1_t->data<int>() + hyp_lod[num];
auto x2 = x2_t->data<int>() + ref_lod[num];
FillFirstColumn<T><<<1 + m / PADDLE_CUDA_NUM_THREADS,
PADDLE_CUDA_NUM_THREADS, 0, stream>>>(dist, m, n);
FillFirstRow<T><<<1 + n / PADDLE_CUDA_NUM_THREADS,
PADDLE_CUDA_NUM_THREADS, 0, stream>>>(dist, n);
// Compute the elements of distance matrix in the anti-diagonal diretion
for (int64_t slice = 2; slice < m + n + 1; ++slice) {
int z_m = slice < m + 1 ? 0 : slice - m;
int z_n = slice < n + 1 ? 0 : slice - n;
int size = slice - (z_m + z_n) + 1; // number of elments in the same
// anti-diagonal line to update
// the start index at which computes from
int start = slice < n + 1 ? slice : (z_n + 1) * (n + 1) - 1;
Levenshtein<T><<<1 + (size - 1) / PADDLE_CUDA_NUM_THREADS,
PADDLE_CUDA_NUM_THREADS, 0, stream>>>(dist, x1, x2,
m, n, start);
}
SetOutput<T><<<1, 1, 0, stream>>>(out + num, dist, m, n, normalized);
}
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
edit_distance,
ops::EditDistanceGPUKernel<paddle::platform::CUDAPlace, float>);
paddle/operators/edit_distance_op.h 0 → 100644
浏览文件 @ dc488c17
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <algorithm>
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
template <typename Place, typename T>
class EditDistanceKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* out_t = ctx.Output<framework::Tensor>("Out");
auto* x1_t = ctx.Input<framework::LoDTensor>("Hyps");
auto* x2_t = ctx.Input<framework::LoDTensor>("Refs");
auto normalized = ctx.Attr<bool>("normalized");
auto hyp_lod = x1_t->lod()[0];
auto ref_lod = x2_t->lod()[0];
PADDLE_ENFORCE(
hyp_lod.size() == ref_lod.size(),
"Input(Hyps) and Input(Refs) must have the same batch size.");
for (size_t i = 1; i < ref_lod.size(); ++i) {
PADDLE_ENFORCE(ref_lod[i] > ref_lod[i - 1],
"Reference string %d is empty.", i);
}
auto num_strs = hyp_lod.size() - 1;
out_t->Resize({static_cast<int64_t>(num_strs), 1});
out_t->mutable_data<float>(ctx.GetPlace());
auto out = out_t->data<T>();
T distance = 0.0;
for (size_t num = 0; num < num_strs; ++num) {
auto m = static_cast<int64_t>(hyp_lod[num + 1] - hyp_lod[num]);
auto n = static_cast<int64_t>(ref_lod[num + 1] - ref_lod[num]);
if (m == 0) {
distance = n;
} else if (n == 0) {
distance = m;
} else {
framework::Tensor dist_t;
dist_t.Resize({m + 1, n + 1});
dist_t.mutable_data<T>(ctx.GetPlace());
auto dist = dist_t.data<T>();
auto x1 = x1_t->data<int>() + hyp_lod[num];
auto x2 = x2_t->data<int>() + ref_lod[num];
for (int64_t i = 0; i < m + 1; ++i) {
dist[i * (n + 1)] = i;
}
for (int64_t j = 0; j < n + 1; ++j) {
dist[j] = j;
}
for (int64_t i = 1; i < m + 1; ++i) {
for (int64_t j = 1; j < n + 1; ++j) {
int cost = x1[i - 1] == x2[j - 1] ? 0 : 1;
int dels = dist[(i - 1) * (n + 1) + j] + 1;
int ins = dist[i * (n + 1) + (j - 1)] + 1;
int subs = dist[(i - 1) * (n + 1) + (j - 1)] + cost;
dist[i * (n + 1) + j] = std::min(dels, std::min(ins, subs));
}
}
distance = dist[m * (n + 1) + n];
}
if (normalized) {
PADDLE_ENFORCE(n > 0,
"The reference string (#%d) cannot be empty "
"when Attr(normalized) is enabled.",
n);
distance = distance / n;
}
out[num] = distance;
}
}
};
} // namespace operators
} // namespace paddle
paddle/operators/recv_op.cc
浏览文件 @ dc488c17
......@@ -24,7 +24,8 @@ limitations under the License. */
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/proto_desc.h"
#include "paddle/operators/detail/send_recv_impl.h"
#include "paddle/operators/detail/grpc_server.h"
#include "paddle/operators/detail/sendrecvop_utils.h"
#include "paddle/operators/detail/simple_block_queue.h"
#define LISTEN_TERMINATE_MESSAGE "TERMINATE@RECV"
......@@ -32,6 +33,11 @@ limitations under the License. */
namespace paddle {
namespace operators {
void RunServer(std::shared_ptr<detail::AsyncGRPCServer> service) {
service->RunSyncUpdate();
VLOG(4) << "RunServer thread end";
}
static void CreateTensorFromMessageType(framework::Variable *var,
sendrecv::VarType var_type) {
if (var_type == sendrecv::VarType::LOD_TENSOR) {
......@@ -46,18 +52,6 @@ static void CreateTensorFromMessageType(framework::Variable *var,
}
}
void RunServer(Server **rpc_server,
std::shared_ptr<detail::SendRecvServerImpl> service,
const std::string &server_address) {
ServerBuilder builder;
builder.AddListeningPort(server_address, grpc::InsecureServerCredentials());
builder.RegisterService(service.get());
std::unique_ptr<Server> server(builder.BuildAndStart());
*rpc_server = server.get();
LOG(INFO) << "Server listening on " << server_address;
server->Wait();
}
class RecvOp : public framework::OperatorBase {
public:
RecvOp(const std::string &type, const framework::VariableNameMap &inputs,
......@@ -65,10 +59,9 @@ class RecvOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {
if (!rpc_service_) {
rpc_service_.reset(new detail::SendRecvServerImpl());
std::string endpoint = Attr<std::string>("endpoint");
server_thread_.reset(
new std::thread(RunServer, &rpc_server_, rpc_service_, endpoint));
rpc_service_.reset(new detail::AsyncGRPCServer(endpoint));
server_thread_.reset(new std::thread(RunServer, rpc_service_));
}
}
......@@ -76,7 +69,7 @@ class RecvOp : public framework::OperatorBase {
detail::MessageWithName term_msg;
term_msg.first = LISTEN_TERMINATE_MESSAGE;
rpc_service_->Push(term_msg);
rpc_server_->Shutdown();
rpc_service_->ShutDown();
server_thread_->join();
}
......@@ -99,10 +92,12 @@ class RecvOp : public framework::OperatorBase {
auto grad_list = Attr<std::vector<std::string>>("GradList");
auto trainer_count = Attr<int>("Trainers");
size_t param_count = param_list.size();
rpc_service_->Reset();
// TODO(typhoonzero): change this to a while_op for every cluster-batch.
bool exit_flag = false;
while (!exit_flag) {
// TODO(gognwb): simply this loop.
// Get from multiple trainers, we don't care about order in which
// the gradient arrives, just add suffix 0~n then average the gradient.
for (size_t i = 0; i < param_count * trainer_count; ++i) {
......@@ -110,6 +105,7 @@ class RecvOp : public framework::OperatorBase {
const detail::MessageWithName &v = rpc_service_->Get();
auto grad_var_name = v.first;
if (grad_var_name == LISTEN_TERMINATE_MESSAGE) {
VLOG(4) << "received LISTEN_TERMINATE_MESSAGE and RunOp.Run() exit";
exit_flag = true;
break;
}
......@@ -118,10 +114,12 @@ class RecvOp : public framework::OperatorBase {
if (it != grad_list.end()) {
param_var_name = param_list[it - grad_list.begin()];
} else {
LOG(ERROR) << "grad have no paired param found!";
LOG(ERROR) << "grad have no paired param found!\"" << grad_var_name
<< "\"";
}
VLOG(3) << "recved grad: " << grad_var_name
<< " updating param: " << param_var_name;
auto *merged_grad = recv_scope.FindVar(grad_var_name);
if (merged_grad == nullptr) {
auto *ptr = recv_scope.Var(grad_var_name);
......@@ -141,9 +139,11 @@ class RecvOp : public framework::OperatorBase {
auto &dev_ctx = *pool.Get(dev_place);
detail::DeserializeFromMessage(v.second, dev_ctx, var);
}
if (exit_flag) {
break;
}
rpc_service_->Reset();
std::string program_str = Attr<std::string>("OptimizeProgram");
......@@ -158,17 +158,14 @@ class RecvOp : public framework::OperatorBase {
} catch (std::exception &e) {
LOG(ERROR) << "run sub program error " << e.what();
}
rpc_service_->Done();
grads_counter_.clear();
} // while(true)
}
protected:
// grpc server instance to track status and gracefully shutdown.
// borrow an pointer from server thread.
Server *rpc_server_{nullptr};
// grpc send/recv service implement to register.
std::shared_ptr<detail::SendRecvServerImpl> rpc_service_;
std::shared_ptr<detail::AsyncGRPCServer> rpc_service_;
std::shared_ptr<std::thread> server_thread_;
mutable std::unordered_map<std::string, int> grads_counter_;
};
......
paddle/operators/send_op.cc
浏览文件 @ dc488c17
......@@ -19,59 +19,45 @@ limitations under the License. */
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/detail/send_recv_impl.h"
#include "paddle/operators/detail/simple_block_queue.h"
#include <future>
#include "paddle/operators/detail/grpc_client.h"
namespace paddle {
namespace operators {
// TODO(typhoonzero): this is a simple implementation which only send
// one tensor
class SendOp : public framework::OperatorBase {
public:
SendOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {
// init client when the operator is created at runtime.
std::vector<std::string> endpoints =
Attr<std::vector<std::string>>("endpoints");
for (auto ep : endpoints) {
client_map_[ep].reset(new detail::RPCClient(
grpc::CreateChannel(ep, grpc::InsecureChannelCredentials())));
}
}
SendOp(const std::string& type, const framework::VariableNameMap& inputs,
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::Place &dev_place) const override {
void Run(const framework::Scope& scope,
const platform::Place& dev_place) const override {
auto ins = Inputs("X");
auto outs = Outputs("Out");
std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
// TODO(typhoonzero): use async calls to send multiple variable asyncly.
for (size_t i = 0; i < ins.size(); ++i) {
bool ret = client_map_[epmap[i]]->SendVariable(scope, ins[i]);
if (!ret) {
LOG(ERROR) << "send variable error: " << ins[i];
}
// FIXME(gongwb): DeviceContext?
auto ctx = platform::CPUDeviceContext();
for (size_t i = 0; i < ins.size(); i++) {
client_.AsyncSendVariable(epmap[i], ctx, scope, ins[i]);
}
// TODO(typhoonzero): support async optimization
client_map_[epmap[0]]->Wait();
for (size_t i = 0; i < outs.size(); ++i) {
bool ret = client_map_[epmap[i]]->GetVariable(scope, outs[i]);
if (!ret) {
LOG(ERROR) << "GetVariable error: " << outs[i];
}
for (size_t i = 0; i < outs.size(); i++) {
client_.AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
}
client_.wait();
}
protected:
mutable std::unordered_map<std::string, std::shared_ptr<detail::RPCClient>>
client_map_;
private:
mutable detail::RPCClient client_;
};
class SendOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SendOpMaker(OpProto *proto, OpAttrChecker *op_checker)
SendOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor) Input tensor to be send").AsDuplicable();
AddOutput("Out", "(Tensor) Output tensor to get from server")
......
paddle/operators/send_recv_op_test.cc
浏览文件 @ dc488c17
......@@ -140,7 +140,7 @@ void StartServerNet(bool is_sparse) {
TEST(SendRecvOp, CPUDense) {
std::thread server_thread(StartServerNet, false);
sleep(3); // wait server to start
sleep(10); // wait server to start
// local net
f::Scope scope;
p::CPUPlace place;
......
paddle/operators/sequence_erase_op.cc 0 → 100644
浏览文件 @ dc488c17
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/sequence_erase_op.h"
namespace paddle {
namespace operators {
class SequenceEraseOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequenceEraseOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SequenceEraseOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE(x_dims.size() == 2 && x_dims[1] == 1,
"Input(X) of SequenceEraseOp should be a 2-D LoDTensor "
"with the 2nd dimension equal to 1.");
ctx->SetOutputDim("Out", x_dims);
}
};
class SequenceEraseOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SequenceEraseOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(2-D LoDTensor with the 2nd dim. equal to 1) "
"Input LoDTensor of SequenceEraseOp.");
AddOutput("Out",
"(2-D LoDTensor with the 2nd dim. equal to 1) "
"Output LoDTensor of SequenceEraseOp.");
AddAttr<std::vector<int>>("tokens",
"(vector<int>) Tokens need to be erased from "
"input sequences.");
AddComment(R"DOC(
Sequence Erase Operator.
Sequence erase operator erases tokens specified by Attr(tokens) from the input
sequences Input(X), and outputs the remaining data and modifies the LoD
information at the same time. For example, given a 2-D LoDTensor
X = [[2, 2, 6, 1, 3, 9, 6, 1, 0, 1]]^T
with lod = [[0, 3, 6, 10]], there are three sequences in the input:
X1 = [[2, 2, 6]]^T, X2 = [[1, 3, 9]]^T and X3 = [[6, 1, 0, 1]]^T.
If the tokens to be erased are Attr(tokens) = [2, 3, 5], after the erasing
operation, the three sequences become
X1' = [[6]]^T, X2' = [[1, 9]]^T and X3' = [[6, 1, 0, 1]]^T.
Hence the LoDTensor Output(Out) should be
Out = [[6, 1, 9, 6, 1, 0, 1]]^T,
with lod = [[0, 1, 3, 7]].
An example usage for this operator is to remove the special tokens when
computing the edit distance between two strings, such as blank, start token,
and end token.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(sequence_erase, ops::SequenceEraseOp,
ops::SequenceEraseOpMaker);
REGISTER_OP_CPU_KERNEL(
sequence_erase,
ops::SequenceEraseKernel<paddle::platform::CPUDeviceContext, int32_t>);
paddle/operators/sequence_erase_op.cu 0 → 100644
浏览文件 @ dc488c17
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include "paddle/operators/sequence_erase_op.h"
#include "paddle/platform/cuda_helper.h"
namespace paddle {
namespace operators {
using platform::PADDLE_CUDA_NUM_THREADS;
using LoDTensor = framework::LoDTensor;
template <typename T>
__global__ void LabelErasedIdx(const T* in_dat, const int in_len,
const T* tokens, const int tokens_len,
int* num_erased) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < in_len) {
int erased = 0;
for (int i = 0; i < tokens_len; ++i) {
if (in_dat[index] == tokens[i]) {
erased = 1;
}
}
num_erased[index + 1] = erased;
if (index == 0) {
num_erased[0] = 0;
}
}
}
template <typename T>
__global__ void GetOutLod(const T* num_erased, const int* in_lod,
const int lod_len, int* out_lod0) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < lod_len) {
out_lod0[index] = in_lod[index] - num_erased[in_lod[index]];
}
}
template <typename T>
__global__ void SetOutput(const T* in_dat, const int in_len,
const int* num_erased, T* out_dat) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < in_len) {
if (in_dat[index] != in_dat[index + 1]) {
out_dat[index - num_erased[index]] = in_dat[index];
}
}
}
template <typename T>
class SequenceEraseOpCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<LoDTensor>("X");
auto* out = ctx.Output<LoDTensor>("Out");
auto lod = in->lod();
PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
PADDLE_ENFORCE_EQ(lod[0].back(), (size_t)in->numel(),
"The actual size mismatches with the LoD information.");
auto tokens = ctx.Attr<std::vector<T>>("tokens");
auto tokens_len = tokens.size();
auto in_len = in->numel();
auto in_dat = in->data<T>();
auto lod0 = lod[0];
thrust::host_vector<T> host_tokens(tokens_len);
for (size_t i = 0; i < tokens.size(); ++i) {
host_tokens[i] = tokens[i];
}
thrust::device_vector<T> dev_tokens = host_tokens;
thrust::device_vector<int> num_erased(in_len + 1);
T* dev_tokens_ptr = thrust::raw_pointer_cast(dev_tokens.data());
int* num_erased_ptr = thrust::raw_pointer_cast(num_erased.data());
auto stream = ctx.cuda_device_context().stream();
LabelErasedIdx<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
in_dat, in_len, dev_tokens_ptr, tokens_len, num_erased_ptr);
thrust::inclusive_scan(num_erased.begin() + 1, num_erased.end(),
num_erased.begin() + 1);
// Calc LoD
auto lod_len = lod0.size();
thrust::host_vector<int> host_lod(lod_len);
for (size_t i = 0; i < lod_len; ++i) {
host_lod[i] = lod0[i];
}
thrust::device_vector<int> dev_in_lod = host_lod;
thrust::device_vector<int> dev_out_lod(lod_len);
int* dev_in_lod_ptr = thrust::raw_pointer_cast(dev_in_lod.data());
int* dev_out_lod_ptr = thrust::raw_pointer_cast(dev_out_lod.data());
GetOutLod<<<(lod_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
num_erased_ptr, dev_in_lod_ptr, lod_len, dev_out_lod_ptr);
thrust::host_vector<int> host_out_lod = dev_out_lod;
std::vector<int> out_lod0(lod_len, 0);
for (size_t i = 0; i < lod_len; i++) {
out_lod0[i] = host_out_lod[i];
}
framework::LoD out_lod;
out_lod.push_back(out_lod0);
out->set_lod(out_lod);
// Set output
out->Resize({out_lod0.back(), 1});
auto out_dat = out->mutable_data<T>(ctx.GetPlace());
SetOutput<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
PADDLE_CUDA_NUM_THREADS, 0, stream>>>(in_dat, in_len,
num_erased_ptr, out_dat);
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP_CUDA_KERNEL(sequence_erase,
paddle::operators::SequenceEraseOpCUDAKernel<int32_t>);
paddle/operators/sequence_erase_op.h 0 → 100644
浏览文件 @ dc488c17
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class SequenceEraseKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<framework::LoDTensor>("X");
auto* out = ctx.Output<framework::LoDTensor>("Out");
auto lod = in->lod();
PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
PADDLE_ENFORCE_EQ(lod[0].back(), (size_t)in->numel(),
"The actual size mismatches with the LoD information.");
auto tokens = ctx.Attr<std::vector<int>>("tokens");
auto in_len = in->numel();
auto in_dat = in->data<T>();
auto lod0 = lod[0];
std::vector<size_t> num_erased(in_len + 1, 0);
std::vector<size_t> out_lod0(1, 0);
for (size_t i = 0; i < lod0.size() - 1; ++i) {
size_t num_out = 0;
for (auto j = lod0[i] + 1; j <= lod0[i + 1]; ++j) {
num_erased[j] = num_erased[j - 1];
if (std::find(tokens.begin(), tokens.end(), in_dat[j - 1]) !=
tokens.end()) {
num_erased[j] += 1;
} else {
num_out += 1;
}
}
out_lod0.push_back(out_lod0.back() + num_out);
}
auto out_len = in_len - num_erased[in_len];
out->Resize({static_cast<int64_t>(out_len), 1});
auto out_dat = out->mutable_data<T>(ctx.GetPlace());
for (int64_t i = 0; i < in_len; ++i) {
if (num_erased[i] == num_erased[i + 1]) {
out_dat[i - num_erased[i]] = in_dat[i];
}
}
framework::LoD out_lod;
out_lod.push_back(out_lod0);
out->set_lod(out_lod);
}
};
} // namespace operators
} // namespace paddle
paddle/operators/shrink_rnn_memory_op.cc
浏览文件 @ dc488c17
......@@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/lod_rank_table.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/operators/array_operator.h"
#include "paddle/operators/math/math_function.h"
......@@ -46,8 +47,21 @@ class ShrinkRNNMemoryOp : public ArrayOp {
auto *out_var = scope.FindVar(Output("Out"));
PADDLE_ENFORCE(out_var != nullptr, "Output Out must be set");
auto &out_tensor = *out_var->GetMutable<framework::LoDTensor>();
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)) {
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 (dst_num_rows != 0) {
out_tensor.ShareDataWith(x_tensor.Slice(0, dst_num_rows));
out_tensor.ShareDataWith(x_tensor.Slice(0, height));
}
}
};
......@@ -64,11 +78,11 @@ class ShrinkRNNMemoryOpProtoMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Out", "(LoDTensor) The shrinked RNN step memory.");
AddComment(
R"DOC(
In dynamic RNN, we are able to handle sequences of different lengths.
Because of the multiple lengths, the size of each step input can be
In dynamic RNN, we are able to handle sequences of different lengths.
Because of the multiple lengths, the size of each step input can be
different, which may lead to a mismatching between the input of
the current step and the memory generated by the previous one. This
operator shrinks memory according to the size of the next step input,
the current step and the memory generated by the previous one. This
operator shrinks memory according to the size of the next step input,
to make sure that they can match each other.
)DOC");
}
......@@ -132,6 +146,7 @@ class ShrinkRNNMemoryGradInferShape : public framework::InferShapeBase {
PADDLE_ENFORCE(context->HasOutput(framework::GradVarName("X")));
context->SetOutputDim(framework::GradVarName("X"),
context->GetInputDim("X"));
context->ShareLoD("X", framework::GradVarName("X"));
}
};
......
paddle/scripts/submit_local.sh.in
浏览文件 @ dc488c17
......@@ -92,6 +92,9 @@ function threads_config() {
if [ -z "$OPENBLAS_NUM_THREADS" ]; then
export OPENBLAS_NUM_THREADS=$threads
fi
if [ $threads -gt 1 ] && [ -z "$OPENBLAS_MAIN_FREE" ]; then
export OPENBLAS_MAIN_FREE=1
fi
fi
}
......
python/paddle/v2/__init__.py
浏览文件 @ dc488c17
......@@ -62,12 +62,15 @@ __all__ = [
cp.begin_parse()
def set_omp_mkl_env_vars(trainer_count):
def set_env_vars(trainer_count):
'''Auto set CPU environment if have not set before.
export KMP_AFFINITY, OMP_DYNAMIC according to the Hyper Threading status.
export OMP_NUM_THREADS, MKL_NUM_THREADS according to trainer_count.
For MKL:
export KMP_AFFINITY, OMP_DYNAMIC according to the Hyper Threading status.
export OMP_NUM_THREADS, MKL_NUM_THREADS according to trainer_count.
For OpenBLAS:
export OPENBLAS_NUM_THREADS, OPENBLAS_MAIN_FREE according to trainer_count.
'''
import platform
import platform, paddle
if not platform.system() in ['Linux', 'Darwin']:
return
......@@ -103,16 +106,22 @@ def set_omp_mkl_env_vars(trainer_count):
num_cores = num_physical_cores()
num_processors = num_logical_processors()
if num_processors > num_cores: # Hyper Threading is enabled
set_env("OMP_DYNAMIC", "true")
set_env("KMP_AFFINITY", "granularity=fine,compact,1,0")
else:
set_env("OMP_DYNAMIC", "false")
set_env("KMP_AFFINITY", "granularity=fine,compact,0,0")
if paddle.version.mkl() == 'ON':
if num_processors > num_cores: # Hyper Threading is enabled
set_env("OMP_DYNAMIC", "true")
set_env("KMP_AFFINITY", "granularity=fine,compact,1,0")
else:
set_env("OMP_DYNAMIC", "false")
set_env("KMP_AFFINITY", "granularity=fine,compact,0,0")
threads = num_processors / trainer_count
threads = '1' if threads < 1 else str(threads)
set_env("OMP_NUM_THREADS", threads)
set_env("MKL_NUM_THREADS", threads)
if paddle.version.mkl() == 'ON':
set_env("OMP_NUM_THREADS", threads)
set_env("MKL_NUM_THREADS", threads)
else:
set_env("OPENBLAS_NUM_THREADS", threads)
if threads > 1:
set_env("OPENBLAS_MAIN_FREE", '1')
def init(**kwargs):
......@@ -129,7 +138,7 @@ def init(**kwargs):
for key in args_dict.keys():
args.append('--%s=%s' % (key, str(args_dict[key])))
set_omp_mkl_env_vars(kwargs.get('trainer_count', 1))
set_env_vars(kwargs.get('trainer_count', 1))
if 'use_gpu' in kwargs:
cp.g_command_config_args['use_gpu'] = kwargs['use_gpu']
......
python/paddle/v2/fluid/layers/nn.py
浏览文件 @ dc488c17
......@@ -14,7 +14,7 @@ __all__ = [
'chunk_eval', 'sequence_conv', 'conv2d', 'sequence_pool', 'pool2d',
'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'sequence_expand',
'lstm_unit', 'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min',
'sequence_first_step', 'sequence_last_step'
'sequence_first_step', 'sequence_last_step', 'dropout'
]
......@@ -386,6 +386,21 @@ def cos_sim(X, Y, **kwargs):
return out
def dropout(x, dropout_prob, is_test=False, seed=0, **kwargs):
helper = LayerHelper('dropout', **kwargs)
out = helper.create_tmp_variable(dtype=x.dtype)
mask = helper.create_tmp_variable(dtype=x.dtype, stop_gradient=True)
helper.append_op(
type='dropout',
inputs={'X': [x]},
outputs={'Out': [out],
'Mask': [mask]},
attrs={'dropout_prob': dropout_prob,
'is_test': is_test,
'seed': seed})
return out
def cross_entropy(input, label, **kwargs):
"""
**Cross Entropy Layer**
......
python/paddle/v2/fluid/layers/ops.py
浏览文件 @ dc488c17
from ..registry import register_layer
__activations__ = [
'abs',
'ceil',
'exp',
'floor',
'log',
'relu',
'round',
'sigmoid',
'sqrt',
'square',
'tanh',
'abs', 'tanh', 'sigmoid', 'relu', 'sqrt', 'ceil', 'floor', 'log', 'round'
]
__all__ = [
'mean',
'mul',
'dropout',
'reshape',
'scale',
'transpose',
......
python/paddle/v2/fluid/tests/test_edit_distance_op.py 0 → 100644
浏览文件 @ dc488c17
import unittest
import numpy as np
from op_test import OpTest
def Levenshtein(hyp, ref):
""" Compute the Levenshtein distance between two strings.
:param hyp: hypothesis string in index
:type hyp: list
:param ref: reference string in index
:type ref: list
"""
m = len(hyp)
n = len(ref)
if m == 0:
return n
if n == 0:
return m
dist = np.zeros((m + 1, n + 1)).astype("float32")
for i in range(0, m + 1):
dist[i][0] = i
for j in range(0, n + 1):
dist[0][j] = j
for i in range(1, m + 1):
for j in range(1, n + 1):
cost = 0 if hyp[i - 1] == ref[j - 1] else 1
deletion = dist[i - 1][j] + 1
insertion = dist[i][j - 1] + 1
substitution = dist[i - 1][j - 1] + cost
dist[i][j] = min(deletion, insertion, substitution)
return dist[m][n]
class TestEditDistanceOp(OpTest):
def setUp(self):
self.op_type = "edit_distance"
normalized = False
x1 = np.array([[0, 12, 3, 5, 8, 2]]).astype("int32")
x2 = np.array([[0, 12, 4, 7, 8]]).astype("int32")
x1 = np.transpose(x1)
x2 = np.transpose(x2)
x1_lod = [0, 1, 5]
x2_lod = [0, 3, 4]
num_strs = len(x1_lod) - 1
distance = np.zeros((num_strs, 1)).astype("float32")
for i in range(0, num_strs):
distance[i] = Levenshtein(
hyp=x1[x1_lod[i]:x1_lod[i + 1]],
ref=x2[x2_lod[i]:x2_lod[i + 1]])
if normalized is True:
len_ref = x2_lod[i + 1] - x2_lod[i]
distance[i] = distance[i] / len_ref
self.attrs = {'normalized': normalized}
self.inputs = {'Hyps': (x1, [x1_lod]), 'Refs': (x2, [x2_lod])}
self.outputs = {'Out': distance}
def test_check_output(self):
self.check_output()
class TestEditDistanceOpNormalized(OpTest):
def setUp(self):
self.op_type = "edit_distance"
normalized = True
x1 = np.array([[0, 10, 3, 6, 5, 8, 2]]).astype("int32")
x2 = np.array([[0, 10, 4, 6, 7, 8]]).astype("int32")
x1 = np.transpose(x1)
x2 = np.transpose(x2)
x1_lod = [0, 1, 3, 6]
x2_lod = [0, 2, 3, 5]
num_strs = len(x1_lod) - 1
distance = np.zeros((num_strs, 1)).astype("float32")
for i in range(0, num_strs):
distance[i] = Levenshtein(
hyp=x1[x1_lod[i]:x1_lod[i + 1]],
ref=x2[x2_lod[i]:x2_lod[i + 1]])
if normalized is True:
len_ref = x2_lod[i + 1] - x2_lod[i]
distance[i] = distance[i] / len_ref
self.attrs = {'normalized': normalized}
self.inputs = {'Hyps': (x1, [x1_lod]), 'Refs': (x2, [x2_lod])}
self.outputs = {'Out': distance}
def test_check_output(self):
self.check_output()
if __name__ == '__main__':
unittest.main()
python/paddle/v2/fluid/tests/test_sequence_erase_op.py 0 → 100644
浏览文件 @ dc488c17
import unittest
import numpy as np
from op_test import OpTest
def sequence_erase(in_seq, lod0, tokens):
new_lod0 = [0]
out_seq = []
for i in range(0, len(lod0) - 1):
num_out = 0
for dat in in_seq[lod0[i]:lod0[i + 1]]:
if dat not in tokens:
out_seq.append(dat)
num_out += 1
new_lod0.append(new_lod0[-1] + num_out)
return np.array(out_seq).astype("int32"), new_lod0
class TestSequenceEraseOp(OpTest):
def setUp(self):
self.op_type = "sequence_erase"
in_seq = np.random.randint(0, 10, (30, 1)).astype("int32")
lod = [[0, 9, 13, 24, 30]]
tokens = [2, 3, 5]
out_seq, new_lod0 = sequence_erase(in_seq, lod[0], tokens)
self.attrs = {'tokens': tokens}
self.inputs = {'X': (in_seq, lod)}
self.outputs = {'Out': (out_seq, [new_lod0])}
def test_check_output(self):
self.check_output()
if __name__ == '__main__':
unittest.main()
python/paddle/v2/fluid/tests/test_shrink_rnn_memory.py
浏览文件 @ dc488c17
......@@ -3,43 +3,86 @@ import paddle.v2.fluid.core as core
from paddle.v2.fluid.executor import Executor
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.backward import append_backward
from paddle.v2.fluid.framework import default_main_program
import numpy
from paddle.v2.fluid.framework import default_main_program, switch_main_program
from paddle.v2.fluid.framework import Program
import numpy as np
main_program = default_main_program()
class TestShrinkRNNMemory(unittest.TestCase):
def test_shrink_rnn_memory(self):
class TestShrinkRNNMemoryBase(unittest.TestCase):
def setUp(self):
self.main_program = Program()
switch_main_program(self.main_program)
x = layers.data('x', shape=[100], dtype='float32')
x.stop_gradient = False
table = layers.lod_rank_table(x=x)
rank_table_tensor = layers.data(
'rank_table_tensor', shape=[1], dtype='float32', lod_level=1)
table = layers.lod_rank_table(x=rank_table_tensor)
i = layers.zeros(dtype='int64', shape=[1])
mem1 = layers.shrink_memory(x=x, i=i, table=table)
self.mem1 = layers.shrink_memory(x=x, i=i, table=table)
i = layers.increment(x=i)
i.stop_gradient = True
mem2 = layers.shrink_memory(x=mem1, i=i, table=table)
self.mem2 = layers.shrink_memory(x=self.mem1, i=i, table=table)
i = layers.increment(x=i)
i.stop_gradient = True
mem3 = layers.shrink_memory(x=mem2, i=i, table=table)
self.mem3 = layers.shrink_memory(x=self.mem2, i=i, table=table)
mem3_mean = layers.mean(x=self.mem3)
append_backward(loss=mem3_mean)
self.x_grad = self.main_program.global_block().var('x@GRAD')
def sum_lodtensor(self, tensor):
sum_res = 0.0
for i in xrange(np.product(tensor.get_dims())):
sum_res += tensor.get_float_element(i)
return sum_res
class TestShrinkRNNMemoryReferLoD(TestShrinkRNNMemoryBase):
def test_refer_lod(self):
cpu = core.CPUPlace()
tensor = core.LoDTensor()
tensor.set_lod([[0, 2, 5, 6]])
tensor_np = numpy.random.random(size=(3, 100)).astype('float32')
tensor.set(tensor_np, cpu)
x_tensor = core.LoDTensor()
x_tensor.set_lod([[0, 2, 5, 6]])
tensor_np = np.random.random(size=(6, 100)).astype('float32')
x_tensor.set(tensor_np, cpu)
rank_table_tensor = core.LoDTensor()
rank_table_tensor.set_lod([[0, 1, 3, 6]])
rank_table_tensor.set(np.random.random(size=(6, 1)).astype('float32'),
cpu)
exe = Executor(cpu)
outs = exe.run(feed={'x': tensor}, fetch_list=[mem1, mem2, mem3])
self.assertTrue(numpy.allclose(tensor_np[0:3], outs[0]))
self.assertTrue(numpy.allclose(tensor_np[0:2], outs[1]))
self.assertTrue(numpy.allclose(tensor_np[0:1], outs[2]))
outs = exe.run(
feed={'x': x_tensor,
'rank_table_tensor': rank_table_tensor},
fetch_list=[self.mem1, self.mem2, self.mem3, self.x_grad],
return_numpy=False)
self.assertTrue(np.allclose(tensor_np[0:6], outs[0]))
self.assertTrue(np.allclose(tensor_np[0:5], outs[1]))
self.assertTrue(np.allclose(tensor_np[0:2], outs[2]))
self.assertAlmostEqual(1.0, self.sum_lodtensor(outs[3]), delta=0.01)
mem3_mean = layers.mean(x=mem3)
append_backward(loss=mem3_mean)
x_grad = exe.run(
feed={'x': tensor},
fetch_list=[main_program.global_block().var('x@GRAD')])[0]
self.assertAlmostEqual(1.0, x_grad.sum(), delta=0.1)
class TestShrinkRNNMemoryNoLoD(TestShrinkRNNMemoryBase):
def test_no_lod(self):
cpu = core.CPUPlace()
x_tensor = core.LoDTensor()
tensor_np = np.random.random(size=(3, 100)).astype('float32')
x_tensor.set(tensor_np, cpu)
rank_table_tensor = core.LoDTensor()
rank_table_tensor.set_lod([[0, 1, 3, 6]])
rank_table_tensor.set(np.random.random(size=(6, 1)).astype('float32'),
cpu)
exe = Executor(cpu)
outs = exe.run(
feed={'x': x_tensor,
'rank_table_tensor': rank_table_tensor},
fetch_list=[self.mem1, self.mem2, self.mem3, self.x_grad],
return_numpy=False)
self.assertTrue(np.allclose(tensor_np[0:3], outs[0]))
self.assertTrue(np.allclose(tensor_np[0:2], outs[1]))
self.assertTrue(np.allclose(tensor_np[0:1], outs[2]))
self.assertAlmostEqual(1.0, self.sum_lodtensor(outs[3]), delta=0.01)
if __name__ == '__main__':
......
python/setup.py.in
浏览文件 @ dc488c17
......@@ -31,6 +31,7 @@ patch = '%(patch)d'
rc = '%(rc)d'
istaged = %(istaged)s
commit = '%(commit)s'
with_mkl = '%(with_mkl)s'
def show():
if istaged:
......@@ -41,6 +42,9 @@ def show():
print 'rc:', rc
else:
print 'commit:', commit
def mkl():
return with_mkl
'''
commit = git_commit()
with open(filename, 'w') as f:
......@@ -51,7 +55,8 @@ def show():
'rc': RC,
'version': '${PADDLE_VERSION}',
'commit': commit,
'istaged': ISTAGED})
'istaged': ISTAGED,
'with_mkl': '@WITH_MKL@'})
write_version_py(filename='@PADDLE_SOURCE_DIR@/python/paddle/version.py')
......
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