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提交 6f5e64af 编写于 作者: Y Yang Yu
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Merge branch 'feature/check_nan_executor' into feature/rnn_gradient_check

上级 87288850 5162c41a
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Showing with 882 addition and 435 deletion
paddle/framework/CMakeLists.txt
浏览文件 @ 6f5e64af
......@@ -21,6 +21,8 @@ cc_test(variable_test SRCS variable_test.cc)
cc_library(scope SRCS scope.cc DEPS glog)
cc_test(scope_test SRCS scope_test.cc DEPS scope)
cc_library(data_transform SRCS data_transform.cc DEPS tensor framework_proto)
cc_test(data_transform_test SRCS data_transform_test.cc DEPS data_transform device_context)
cc_library(attribute SRCS attribute.cc DEPS framework_proto)
cc_test(program_desc_test SRCS program_desc_test.cc DEPS proto_desc
......@@ -29,7 +31,8 @@ cc_library(op_proto_maker SRCS op_proto_maker.cc DEPS framework_proto attribute)
cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
cc_library(shape_inference SRCS shape_inference.cc DEPS ddim attribute)
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog shape_inference)
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
shape_inference data_transform)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry init)
cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS shape_inference op_info operator glog)
......@@ -64,4 +67,4 @@ cc_test(threadpool_test SRCS threadpool_test.cc DEPS threadpool)
cc_library(init SRCS init.cc DEPS gflags device_context place stringpiece)
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)
cc_test(op_kernel_type_test SRCS op_kernel_type_test.cc DEPS place device_context framework_proto)
paddle/framework/data_transform.cc 0 → 100644
浏览文件 @ 6f5e64af
/* 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_transform.h"
namespace paddle {
namespace framework {
DataTransformFnMap& DataTransformFnMap::Instance() {
static DataTransformFnMap data_transform_map;
return data_transform_map;
}
} // namespace framework
} // namespace paddle
paddle/framework/data_transform.h 0 → 100644
浏览文件 @ 6f5e64af
/* 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 <functional>
#include <utility>
#include <vector>
#include "paddle/framework/op_kernel_type.h"
#include "paddle/framework/tensor.h"
#include "paddle/framework/variable.h"
#include "paddle/platform/device_context.h"
#include "paddle/platform/macros.h"
namespace paddle {
namespace framework {
using DataTransformFN =
std::function<void(const std::vector<platform::DeviceContext*> ctx,
const Variable& in, Variable* out)>;
using KernelTypePair = std::pair<OpKernelType, OpKernelType>;
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;
}
};
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 0 → 100644
浏览文件 @ 6f5e64af
/* 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_transform.h"
#include <gtest/gtest.h>
namespace paddle {
namespace framework {
using namespace platform;
int test_value = 0;
OpKernelType kernel_type_1(proto::DataType::FP32, CPUPlace(), DataLayout::kNCHW,
LibraryType::kCUDNN);
OpKernelType kernel_type_2(proto::DataType::FP32, CUDAPlace(0),
DataLayout::kNCHW, LibraryType::kCUDNN);
OpKernelType kernel_type_3(proto::DataType::FP16, CUDAPlace(0),
DataLayout::kNCHW, LibraryType::kCUDNN);
void type1_to_type2(std::vector<platform::DeviceContext*> ctx,
const Variable& in, Variable* out) {
test_value++;
}
void type2_to_type3(std::vector<platform::DeviceContext*> ctx,
const Variable& in, Variable* out) {
test_value--;
}
void type1_to_type3(std::vector<platform::DeviceContext*> ctx,
const Variable& in, Variable* out) {
test_value += 2;
}
} // namespace framework
} // namespace paddle
namespace frw = paddle::framework;
REGISTER_DATA_TRANSFORM_FN(frw::kernel_type_1, frw::kernel_type_2,
frw::type1_to_type2);
REGISTER_DATA_TRANSFORM_FN(frw::kernel_type_2, frw::kernel_type_3,
frw::type2_to_type3);
REGISTER_DATA_TRANSFORM_FN(frw::kernel_type_1, frw::kernel_type_3,
frw::type1_to_type3);
TEST(DataTransform, Register) {
using namespace paddle::framework;
using namespace paddle::platform;
auto& instance = DataTransformFnMap::Instance();
ASSERT_EQ(instance.Map().size(), 3UL);
std::vector<DeviceContext*> ctx;
paddle::framework::Variable in;
paddle::framework::Variable out;
instance.Get(std::make_pair(frw::kernel_type_1, frw::kernel_type_2))(ctx, in,
&out);
ASSERT_EQ(test_value, 1);
instance.Get(std::make_pair(frw::kernel_type_2, frw::kernel_type_3))(ctx, in,
&out);
ASSERT_EQ(test_value, 0);
instance.Get(std::make_pair(frw::kernel_type_1, frw::kernel_type_3))(ctx, in,
&out);
ASSERT_EQ(test_value, 2);
}
paddle/framework/executor.cc
浏览文件 @ 6f5e64af
......@@ -14,18 +14,17 @@ limitations under the License. */
#include "paddle/framework/executor.h"
#include <algorithm>
#include <iostream>
#include <memory>
#include <set>
#include <vector>
#include "gflags/gflags.h"
#include "paddle/framework/feed_fetch_type.h"
#include "paddle/framework/lod_rank_table.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/lod_tensor_array.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/scope.h"
DEFINE_bool(check_nan_inf, false,
"Checking whether operator produce NAN/INF or not. It will be "
"extremely slow so please use this flag wisely.");
namespace paddle {
namespace framework {
......@@ -58,6 +57,19 @@ static void CreateTensor(Variable* var, proto::VarDesc::VarType var_type) {
}
}
static void CheckTensorNANOrInf(const std::string& name,
const framework::Tensor& tensor) {
if (tensor.type().hash_code() != typeid(float).hash_code() &&
tensor.type().hash_code() != typeid(double).hash_code()) {
return;
}
if (tensor.memory_size() == 0) {
return;
}
PADDLE_ENFORCE(!framework::HasInf(tensor), "Tensor %s has Inf", name);
PADDLE_ENFORCE(!framework::HasNAN(tensor), "Tensor %s has NAN", name);
}
void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
bool create_local_scope, bool create_vars) {
// TODO(tonyyang-svail):
......@@ -101,6 +113,15 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
auto op = paddle::framework::OpRegistry::CreateOp(*op_desc);
VLOG(3) << op->DebugString();
op->Run(*local_scope, place_);
if (FLAGS_check_nan_inf) {
for (auto& vname : op->OutputVars(true)) {
auto* var = local_scope->FindVar(vname);
if (var == nullptr) continue;
if (var->IsType<framework::LoDTensor>()) {
CheckTensorNANOrInf(vname, var->Get<framework::LoDTensor>());
}
}
}
}
if (create_local_scope) {
scope->DeleteScope(local_scope);
......
paddle/framework/init.cc
浏览文件 @ 6f5e64af
......@@ -71,7 +71,7 @@ bool InitDevices(const std::vector<std::string> &devices) {
places.emplace_back(platform::CPUPlace());
LOG(WARNING) << "Not specified CPU device, create CPU by Default.";
}
platform::DeviceContextPool::Create(places);
platform::DeviceContextPool::Init(places);
return true;
}
......
paddle/framework/operator.cc
浏览文件 @ 6f5e64af
......@@ -15,6 +15,7 @@ limitations under the License. */
#include <algorithm>
#include <atomic>
#include "paddle/framework/data_transform.h"
#include "paddle/framework/executor.h"
#include "paddle/framework/lod_tensor_array.h"
#include "paddle/framework/operator.h"
......@@ -387,8 +388,8 @@ void OperatorWithKernel::Run(const Scope& scope,
const platform::Place& place) const {
RuntimeInferShapeContext infer_shape_ctx(*this, scope);
this->InferShape(&infer_shape_ctx);
platform::DeviceContextPool& pool = platform::DeviceContextPool::Get();
auto dev_ctx = pool.Borrow(place);
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto dev_ctx = pool.Get(place);
// check if op[type] has kernel registered.
auto& all_op_kernels = AllOpKernels();
......@@ -411,7 +412,38 @@ void OperatorWithKernel::Run(const Scope& scope,
expected_kernel_key);
}
kernel_iter->second->Compute(ctx);
if (actual_kernel_key == expected_kernel_key) {
kernel_iter->second->Compute(ctx);
} else {
Scope& op_scope = scope.NewScope();
auto input_vars = this->InputVars();
for (auto var_name : input_vars) {
op_scope.Var(var_name);
}
// TODO(qijun) get appropriate DeviceContext from DeviceContext pool
platform::DeviceContext* trans_dev_ctx = nullptr;
std::vector<platform::DeviceContext*> trans_dev_ctx_vec{trans_dev_ctx};
// TODO(qijun) get appropriate DataTransformFN from global map
framework::DataTransformFN trans_fun = nullptr;
// Wait for transform starting
dev_ctx->Wait();
for (auto var_name : input_vars) {
trans_fun(trans_dev_ctx_vec, *(scope.FindVar(var_name)),
op_scope.FindVar(var_name));
}
// Wait for data transform finishing
for (auto ctx : trans_dev_ctx_vec) {
ctx->Wait();
}
// Create a new ExecutionContext
ExecutionContext op_ctx(*this, op_scope, *dev_ctx);
kernel_iter->second->Compute(op_ctx);
}
}
OpKernelType OperatorWithKernel::GetActualKernelType(
......
paddle/framework/tensor_util.h
浏览文件 @ 6f5e64af
......@@ -13,7 +13,10 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/data_type.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
namespace paddle {
namespace framework {
......@@ -205,5 +208,100 @@ inline void CopyToVector(const Tensor& src, std::vector<T>* dst) {
src_ptr, size);
}
template <typename Predicate, typename DevCtx>
struct AnyDTypeVisitor {
Predicate predicate_;
const Tensor& tensor_;
const DevCtx& ctx_;
Tensor* out_;
AnyDTypeVisitor(Predicate predicate, const Tensor& tensor, const DevCtx& ctx,
Tensor* out)
: predicate_(predicate), tensor_(tensor), ctx_(ctx), out_(out) {}
template <typename T>
void operator()() const {
auto t = EigenVector<T>::Flatten(tensor_);
auto o = EigenScalar<bool>::From(*out_);
o.device(*ctx_.eigen_device()) = predicate_(t).any();
}
};
template <typename Predicate, typename DevCtx>
inline void AnyImpl(Predicate predicate, const framework::Tensor& tensor,
const DevCtx& ctx, framework::Tensor* out) {
VisitDataType(ToDataType(tensor.type()), AnyDTypeVisitor<Predicate, DevCtx>(
predicate, tensor, ctx, out));
}
template <typename Predicate>
struct AnyVisitor : public boost::static_visitor<bool> {
const framework::Tensor& tensor_;
Predicate predicate_;
AnyVisitor(const framework::Tensor& tensor, Predicate predicate)
: tensor_(tensor), predicate_(std::move(predicate)) {}
template <typename Place>
bool operator()(const Place& place) const {
framework::Tensor out;
out.Resize({1});
out.mutable_data<bool>(place);
auto* ctx = platform::DeviceContextPool::Instance().GetByPlace(place);
AnyImpl(predicate_, tensor_, *ctx, &out);
return this->GetResult(out, place);
}
bool GetResult(const framework::Tensor& out,
const platform::CUDAPlace& gpu) const {
platform::CPUPlace cpu;
framework::Tensor tmp;
tmp.Resize({1});
tmp.mutable_data<bool>(cpu);
platform::DeviceContextPool::Instance().Get(gpu)->Wait();
CopyFrom(out, cpu, &tmp);
platform::DeviceContextPool::Instance().Get(gpu)->Wait();
return GetResult(tmp, cpu);
}
bool GetResult(const framework::Tensor& out,
const platform::CPUPlace& cpu) const {
return *out.data<bool>();
}
};
template <typename Predicate>
inline bool Any(const framework::Tensor& tensor, Predicate predicate) {
AnyVisitor<Predicate> visitor(tensor, predicate);
auto place = tensor.place();
return platform::VisitPlace(place, visitor);
}
struct HasNANPredicate {
template <typename T>
auto operator()(const T& eigen_vec) const
-> decltype(std::declval<T>().isnan()) {
return eigen_vec.isnan();
}
};
inline bool HasNAN(const framework::Tensor& tensor) {
HasNANPredicate predicate;
return Any(tensor, predicate);
}
struct HasInfPredicate {
template <typename T>
auto operator()(const T& eigen_vec) const
-> decltype(std::declval<T>().isinf()) {
return eigen_vec.isinf();
}
};
inline bool HasInf(const framework::Tensor& tensor) {
HasInfPredicate predicate;
return Any(tensor, predicate);
}
} // namespace framework
} // namespace paddle
paddle/framework/tensor_util_test.cc
浏览文件 @ 6f5e64af
......@@ -13,6 +13,7 @@
#include "paddle/framework/tensor_util.h"
#include <gtest/gtest.h>
#include <cmath>
#include <string>
namespace paddle {
......@@ -230,5 +231,28 @@ TEST(CopyToVector, Tensor) {
#endif
}
TEST(IsNAN, CPU) {
using namespace paddle::framework;
using namespace paddle::platform;
Tensor src;
float* buf = src.mutable_data<float>({3}, CPUPlace());
buf[0] = 0.0;
buf[1] = NAN;
buf[2] = 0.0;
ASSERT_TRUE(HasNAN(src));
}
TEST(IsInf, CPU) {
using namespace paddle::framework;
using namespace paddle::platform;
Tensor src;
double* buf = src.mutable_data<double>({3}, CPUPlace());
buf[0] = 1.0;
buf[1] = INFINITY;
buf[2] = 0.0;
ASSERT_TRUE(HasInf(src));
}
} // namespace framework
} // namespace paddle
paddle/gserver/layers/MKLDNNLRNLayer.cpp
浏览文件 @ 6f5e64af
......@@ -29,7 +29,7 @@ bool MKLDNNLRNLayer::init(const LayerMap& layerMap,
}
/* the size of inputs for norm-layer is 1 */
CHECK_EQ(config_.inputs_size(), 1UL);
CHECK_EQ(config_.inputs_size(), 1);
const NormConfig& conf = config_.inputs(0).norm_conf();
localSize_ = conf.size();
alpha_ = conf.scale();
......
paddle/operators/activation_op.cc
浏览文件 @ 6f5e64af
......@@ -22,8 +22,8 @@ class ActivationOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Y");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Out");
}
};
......@@ -32,7 +32,7 @@ class ActivationOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Y"));
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Out"));
}
};
......@@ -41,11 +41,11 @@ class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
SigmoidOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Sigmoid operator");
AddOutput("Y", "Output of Sigmoid operator");
AddOutput("Out", "Output of Sigmoid operator");
AddComment(R"DOC(
Sigmoid Activation Operator
$$y = \frac{1}{1 + e^{-x}}$$
$$out = \frac{1}{1 + e^{-x}}$$
)DOC");
}
......@@ -56,11 +56,11 @@ class LogSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
LogSigmoidOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of LogSigmoid operator");
AddOutput("Y", "Output of LogSigmoid operator");
AddOutput("Out", "Output of LogSigmoid operator");
AddComment(R"DOC(
Logsigmoid Activation Operator
$$y = \log \frac{1}{1 + e^{-x}}$$
$$out = \log \frac{1}{1 + e^{-x}}$$
)DOC");
}
......@@ -71,11 +71,11 @@ class ExpOpMaker : public framework::OpProtoAndCheckerMaker {
ExpOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Exp operator");
AddOutput("Y", "Output of Exp operator");
AddOutput("Out", "Output of Exp operator");
AddComment(R"DOC(
Exp Activation Operator.
$y = e^x$
$out = e^x$
)DOC");
}
......@@ -86,11 +86,11 @@ class ReluOpMaker : public framework::OpProtoAndCheckerMaker {
ReluOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Relu operator");
AddOutput("Y", "Output of Relu operator");
AddOutput("Out", "Output of Relu operator");
AddComment(R"DOC(
Relu Activation Operator.
$y = \max(x, 0)$
$out = \max(x, 0)$
)DOC");
}
......@@ -101,12 +101,12 @@ class LeakyReluOpMaker : public framework::OpProtoAndCheckerMaker {
LeakyReluOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of LeakyRelu operator");
AddOutput("Y", "Output of LeakyRelu operator");
AddOutput("Out", "Output of LeakyRelu operator");
AddAttr<float>("alpha", "The small negative slope").SetDefault(0.02f);
AddComment(R"DOC(
LeakyRelu Activation Operator.
$y = \max(x, \alpha * x)$
$out = \max(x, \alpha * x)$
)DOC");
}
......@@ -117,13 +117,13 @@ class SoftShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
SoftShrinkOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Softshrink operator");
AddOutput("Y", "Output of Softshrink operator");
AddOutput("Out", "Output of Softshrink operator");
AddAttr<float>("lambda", "non-negative offset").SetDefault(0.5f);
AddComment(R"DOC(
Softshrink Activation Operator.
$$
y = \begin{cases}
out = \begin{cases}
x - \lambda, \text{if } x > \lambda \\
x + \lambda, \text{if } x < -\lambda \\
0, \text{otherwise}
......@@ -139,11 +139,11 @@ class TanhOpMaker : public framework::OpProtoAndCheckerMaker {
TanhOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Tanh operator");
AddOutput("Y", "Output of Tanh operator");
AddOutput("Out", "Output of Tanh operator");
AddComment(R"DOC(
Tanh Activation Operator.
$$y = \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
$$out = \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
)DOC");
}
......@@ -154,11 +154,11 @@ class TanhShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
TanhShrinkOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of TanhShrink operator");
AddOutput("Y", "Output of TanhShrink operator");
AddOutput("Out", "Output of TanhShrink operator");
AddComment(R"DOC(
TanhShrink Activation Operator.
$$y = x - \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
$$out = x - \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
)DOC");
}
......@@ -169,14 +169,14 @@ class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
HardShrinkOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of HardShrink operator");
AddOutput("Y", "Output of HardShrink operator");
AddOutput("Out", "Output of HardShrink operator");
AddAttr<float>("threshold", "The value of threshold for HardShrink")
.SetDefault(0.5f);
AddComment(R"DOC(
HardShrink Activation Operator.
$$
y = \begin{cases}
out = \begin{cases}
x, \text{if } x > \lambda \\
x, \text{if } x < -\lambda \\
0, \text{otherwise}
......@@ -192,11 +192,11 @@ class SqrtOpMaker : public framework::OpProtoAndCheckerMaker {
SqrtOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Sqrt operator");
AddOutput("Y", "Output of Sqrt operator");
AddOutput("Out", "Output of Sqrt operator");
AddComment(R"DOC(
Sqrt Activation Operator.
$y = \sqrt{x}$
$out = \sqrt{x}$
)DOC");
}
......@@ -207,11 +207,11 @@ class AbsOpMaker : public framework::OpProtoAndCheckerMaker {
AbsOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Abs operator");
AddOutput("Y", "Output of Abs operator");
AddOutput("Out", "Output of Abs operator");
AddComment(R"DOC(
Abs Activation Operator.
$y = |x|$
$out = |x|$
)DOC");
}
......@@ -222,11 +222,11 @@ class CeilOpMaker : public framework::OpProtoAndCheckerMaker {
CeilOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Ceil operator");
AddOutput("Y", "Output of Ceil operator");
AddOutput("Out", "Output of Ceil operator");
AddComment(R"DOC(
Ceil Activation Operator.
$y = ceil(x)$
$out = ceil(x)$
)DOC");
}
......@@ -237,11 +237,11 @@ class FloorOpMaker : public framework::OpProtoAndCheckerMaker {
FloorOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Floor operator");
AddOutput("Y", "Output of Floor operator");
AddOutput("Out", "Output of Floor operator");
AddComment(R"DOC(
Floor Activation Operator.
$y = floor(x)$
$out = floor(x)$
)DOC");
}
......@@ -252,11 +252,11 @@ class RoundOpMaker : public framework::OpProtoAndCheckerMaker {
RoundOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Round operator");
AddOutput("Y", "Output of Round operator");
AddOutput("Out", "Output of Round operator");
AddComment(R"DOC(
Round Activation Operator.
$y = [x]$
$out = [x]$
)DOC");
}
......@@ -267,11 +267,11 @@ class ReciprocalOpMaker : public framework::OpProtoAndCheckerMaker {
ReciprocalOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Reciprocal operator");
AddOutput("Y", "Output of Reciprocal operator");
AddOutput("Out", "Output of Reciprocal operator");
AddComment(R"DOC(
Reciprocal Activation Operator.
$$y = \frac{1}{x}$$
$$out = \frac{1}{x}$$
)DOC");
}
......@@ -282,11 +282,11 @@ class LogOpMaker : public framework::OpProtoAndCheckerMaker {
LogOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Log operator");
AddOutput("Y", "Output of Log operator");
AddOutput("Out", "Output of Log operator");
AddComment(R"DOC(
Log Activation Operator.
$y = \ln(x)$
$out = \ln(x)$
Natural logarithm of x.
......@@ -299,11 +299,11 @@ class SquareOpMaker : public framework::OpProtoAndCheckerMaker {
SquareOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Square operator");
AddOutput("Y", "Output of Square operator");
AddOutput("Out", "Output of Square operator");
AddComment(R"DOC(
Square Activation Operator.
$y = x^2$
$out = x^2$
)DOC");
}
......@@ -314,11 +314,11 @@ class SoftplusOpMaker : public framework::OpProtoAndCheckerMaker {
SoftplusOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Softplus operator");
AddOutput("Y", "Output of Softplus operator");
AddOutput("Out", "Output of Softplus operator");
AddComment(R"DOC(
Softplus Activation Operator.
$y = \ln(1 + e^{x})$
$out = \ln(1 + e^{x})$
)DOC");
}
......@@ -329,11 +329,11 @@ class SoftsignOpMaker : public framework::OpProtoAndCheckerMaker {
SoftsignOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Softsign operator");
AddOutput("Y", "Output of Softsign operator");
AddOutput("Out", "Output of Softsign operator");
AddComment(R"DOC(
Softsign Activation Operator.
$$y = \frac{x}{1 + |x|}$$
$$out = \frac{x}{1 + |x|}$$
)DOC");
}
......@@ -344,7 +344,7 @@ class BReluOpMaker : public framework::OpProtoAndCheckerMaker {
BReluOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of BRelu operator");
AddOutput("Y", "Output of BRelu operator");
AddOutput("Out", "Output of BRelu operator");
AddAttr<float>("t_min", "The min marginal value of BRelu")
.SetDefault(static_cast<float>(0));
AddAttr<float>("t_max", "The max marginal value of BRelu")
......@@ -352,7 +352,7 @@ class BReluOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
BRelu Activation Operator.
$y = \max(\min(x, t_{min}), t_{max})$
$out = \max(\min(x, t_{min}), t_{max})$
)DOC");
}
......@@ -363,13 +363,13 @@ class SoftReluOpMaker : public framework::OpProtoAndCheckerMaker {
SoftReluOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of SoftRelu operator");
AddOutput("Y", "Output of SoftRelu operator");
AddOutput("Out", "Output of SoftRelu operator");
AddAttr<float>("threshold", "The threshold value of SoftRelu")
.SetDefault(40.0f);
AddComment(R"DOC(
SoftRelu Activation Operator.
$y = \ln(1 + \exp(\max(\min(x, threshold), threshold))$
$out = \ln(1 + \exp(\max(\min(x, threshold), threshold))$
)DOC");
}
......@@ -380,7 +380,7 @@ class ELUOpMaker : public framework::OpProtoAndCheckerMaker {
ELUOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of ELU operator");
AddOutput("Y", "Output of ELU operator");
AddOutput("Out", "Output of ELU operator");
AddAttr<float>("alpha", "The alpha value of ELU").SetDefault(1.0f);
AddComment(R"DOC(
ELU Activation Operator.
......@@ -388,7 +388,7 @@ ELU Activation Operator.
Applies the following element-wise computation on the input according to
https://arxiv.org/abs/1511.07289.
$y = \max(0, x) + \min(0, \alpha * (e^x - 1))$
$out = \max(0, x) + \min(0, \alpha * (e^x - 1))$
)DOC");
}
......@@ -399,13 +399,13 @@ class Relu6OpMaker : public framework::OpProtoAndCheckerMaker {
Relu6OpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Relu6 operator");
AddOutput("Y", "Output of Relu6 operator");
AddOutput("Out", "Output of Relu6 operator");
AddAttr<float>("threshold", "The threshold value of Relu6")
.SetDefault(6.0f);
AddComment(R"DOC(
Relu6 Activation Operator.
$y = \min(\max(0, x), 6)$
$out = \min(\max(0, x), 6)$
)DOC");
}
......@@ -416,12 +416,12 @@ class PowOpMaker : public framework::OpProtoAndCheckerMaker {
PowOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Pow operator");
AddOutput("Y", "Output of Pow operator");
AddOutput("Out", "Output of Pow operator");
AddAttr<float>("factor", "The exponential factor of Pow").SetDefault(1.0f);
AddComment(R"DOC(
Pow Activation Operator.
$y = x^{factor}$
$out = x^{factor}$
)DOC");
}
......@@ -432,7 +432,7 @@ class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
STanhOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of STanh operator");
AddOutput("Y", "Output of STanh operator");
AddOutput("Out", "Output of STanh operator");
AddAttr<float>("scale_a", "The scale parameter of a for the input")
.SetDefault(2.0f / 3.0f);
AddAttr<float>("scale_b", "The scale parameter of b for the input")
......@@ -440,7 +440,7 @@ class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
STanh Activation Operator.
$$y = b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
$$out = b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
)DOC");
}
......@@ -451,14 +451,14 @@ class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
ThresholdedReluOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of ThresholdedRelu operator");
AddOutput("Y", "Output of ThresholdedRelu operator");
AddOutput("Out", "Output of ThresholdedRelu operator");
AddAttr<float>("threshold", "The threshold location of activation")
.SetDefault(1.0f);
AddComment(R"DOC(
ThresholdedRelu Activation Operator.
$$
y = \begin{cases}
out = \begin{cases}
x, \text{if } x > threshold \\
0, \text{otherwise}
\end{cases}
......@@ -473,7 +473,7 @@ class HardSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
HardSigmoidOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of HardSigmoid operator");
AddOutput("Y", "Output of HardSigmoid operator");
AddOutput("Out", "Output of HardSigmoid operator");
AddAttr<float>("slope", "Slope for linear approximation of sigmoid")
.SetDefault(0.2f);
AddAttr<float>("offset", "Offset for linear approximation of sigmoid")
......@@ -484,7 +484,7 @@ HardSigmoid Activation Operator.
Segment-wise linear approximation of sigmoid(https://arxiv.org/abs/1603.00391),
which is much faster than sigmoid.
$y = \max(0, \min(1, slope * x + shift))$
$out = \max(0, \min(1, slope * x + shift))$
The slope should be positive. The offset can be either positive or negative.
The default slope and shift are set according to the above reference.
......@@ -499,12 +499,12 @@ class SwishOpMaker : public framework::OpProtoAndCheckerMaker {
SwishOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Swish operator");
AddOutput("Y", "Output of Swish operator");
AddOutput("Out", "Output of Swish operator");
AddAttr<float>("beta", "Constant beta of swish operator").SetDefault(1.0f);
AddComment(R"DOC(
Swish Activation Operator.
$$y = \frac{x}{1 + e^{- \beta x}}$$
$$out = \frac{x}{1 + e^{- \beta x}}$$
)DOC");
}
......
paddle/operators/activation_op.h
浏览文件 @ 6f5e64af
......@@ -27,11 +27,11 @@ class ActivationKernel
void Compute(const framework::ExecutionContext& context) const override {
auto* X = context.Input<framework::Tensor>("X");
auto* Y = context.Output<framework::Tensor>("Y");
Y->mutable_data<T>(context.GetPlace());
auto* Out = context.Output<framework::Tensor>("Out");
Out->mutable_data<T>(context.GetPlace());
auto x = framework::EigenVector<T>::Flatten(*X);
auto y = framework::EigenVector<T>::Flatten(*Y);
auto out = framework::EigenVector<T>::Flatten(*Out);
auto* place =
context.template device_context<DeviceContext>().eigen_device();
Functor functor;
......@@ -40,7 +40,7 @@ class ActivationKernel
for (auto& attr : attrs) {
*attr.second = context.Attr<float>(attr.first);
}
functor(*place, x, y);
functor(*place, x, out);
}
};
......@@ -51,14 +51,15 @@ class ActivationGradKernel
using T = typename Functor::ELEMENT_TYPE;
void Compute(const framework::ExecutionContext& context) const override {
auto* X = context.Input<framework::Tensor>("X");
auto* Y = context.Input<framework::Tensor>("Y");
auto* dY = context.Input<framework::Tensor>(framework::GradVarName("Y"));
auto* Out = context.Input<framework::Tensor>("Out");
auto* dOut =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
dX->mutable_data<T>(context.GetPlace());
auto dy = framework::EigenVector<T>::Flatten(*dY);
auto dout = framework::EigenVector<T>::Flatten(*dOut);
auto x = framework::EigenVector<T>::Flatten(*X);
auto y = framework::EigenVector<T>::Flatten(*Y);
auto out = framework::EigenVector<T>::Flatten(*Out);
auto dx = framework::EigenVector<T>::Flatten(*dX);
auto* place =
context.template device_context<DeviceContext>().eigen_device();
......@@ -67,7 +68,7 @@ class ActivationGradKernel
for (auto& attr : attrs) {
*attr.second = context.Attr<float>(attr.first);
}
functor(*place, x, y, dy, dx);
functor(*place, x, out, dout, dx);
}
};
......@@ -83,17 +84,18 @@ struct BaseActivationFunctor {
// sigmoid(x) = 1 / (1 + exp(-x))
template <typename T>
struct SigmoidFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = static_cast<T>(1) / (static_cast<T>(1) + (-x).exp());
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = static_cast<T>(1) / (static_cast<T>(1) + (-x).exp());
}
};
template <typename T>
struct SigmoidGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) = dy * y * (static_cast<T>(1) - y);
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * out * (static_cast<T>(1) - out);
}
};
......@@ -101,7 +103,7 @@ struct SigmoidGradFunctor : public BaseActivationFunctor<T> {
// For numerical stability, we can use the log-sum-exp trick:
// https://hips.seas.harvard.edu/blog/2013/01/09/computing-log-sum-exp/
// We can rewrite the above equation as:
// y = -log( exp(0) + exp(-x)) [since exp(0) = 1]
// out = -log( exp(0) + exp(-x)) [since exp(0) = 1]
// = -log( exp(max(-x, 0) - max(-x, 0)) + exp(-x + max(-x, 0) - max(-x, 0)))
// = -log( exp(max(-x, 0)) * exp(-max(-x, 0)) - exp(max(-x, 0)) * exp(-x -
// max(-x, 0)))
......@@ -112,10 +114,10 @@ struct SigmoidGradFunctor : public BaseActivationFunctor<T> {
// + exp(-x - max(-x, 0))))
template <typename T>
struct LogSigmoidFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
auto temp = (-x).cwiseMax(static_cast<T>(0)); // temp = max(-x, 0)
y.device(d) = -temp - (((-temp).exp() + (-x - temp).exp()).log());
out.device(d) = -temp - (((-temp).exp() + (-x - temp).exp()).log());
}
};
......@@ -124,62 +126,66 @@ struct LogSigmoidFunctor : public BaseActivationFunctor<T> {
// exp(-x - max(-x, 0)))
template <typename T>
struct LogSigmoidGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
auto temp = (-x).cwiseMax(static_cast<T>(0)); // temp = max(-x, 0)
dx.device(d) =
dy * ((-x - temp).exp() / ((-temp).exp() + (-x - temp).exp()));
dout * ((-x - temp).exp() / ((-temp).exp() + (-x - temp).exp()));
}
};
// exp(x) = e^x
template <typename T>
struct ExpFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.exp();
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.exp();
}
};
template <typename T>
struct ExpGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) = dy * y;
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * out;
}
};
// relu(x) = max(x, 0)
template <typename T>
struct ReluFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.cwiseMax(static_cast<T>(0));
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.cwiseMax(static_cast<T>(0));
}
};
template <typename T>
struct ReluGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) = dy * (x > static_cast<T>(0)).template cast<T>();
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * (x > static_cast<T>(0)).template cast<T>();
}
};
// tanh(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))
template <typename T>
struct TanhFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.tanh();
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.tanh();
}
};
template <typename T>
struct TanhGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) = dy * (static_cast<T>(1) - y * y);
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * (static_cast<T>(1) - out * out);
}
};
......@@ -187,17 +193,18 @@ struct TanhGradFunctor : public BaseActivationFunctor<T> {
// where tanh(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))
template <typename T>
struct TanhShrinkFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x - x.tanh();
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x - x.tanh();
}
};
template <typename T>
struct TanhShrinkGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) = dy * (x.tanh() * x.tanh());
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * (x.tanh() * x.tanh());
}
};
......@@ -210,11 +217,11 @@ struct HardShrinkFunctor : public BaseActivationFunctor<T> {
typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
return {{"threshold", &threshold}};
}
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
auto temp1 = (x < static_cast<T>(threshold * -1)).template cast<T>().eval();
auto temp2 = (x > static_cast<T>(threshold)).template cast<T>().eval();
y.device(d) = x * (temp1 + temp2);
out.device(d) = x * (temp1 + temp2);
}
};
......@@ -226,11 +233,12 @@ struct HardShrinkGradFunctor : public BaseActivationFunctor<T> {
return {{"threshold", &threshold}};
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
auto temp1 = (x < static_cast<T>(threshold * -1)).template cast<T>().eval();
auto temp2 = (x > static_cast<T>(threshold)).template cast<T>().eval();
dx.device(d) = dy * (temp1 + temp2).template cast<T>();
dx.device(d) = dout * (temp1 + temp2).template cast<T>();
}
};
......@@ -243,12 +251,12 @@ struct SoftShrinkFunctor : public BaseActivationFunctor<T> {
return {{"lambda", &lambda}};
}
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
auto lambdaT = static_cast<T>(lambda);
auto temp1 = (x > lambdaT).template cast<T>().eval();
auto temp2 = (x < -lambdaT).template cast<T>().eval();
y.device(d) = temp1 * (x - lambdaT) + temp2 * (x + lambdaT);
out.device(d) = temp1 * (x - lambdaT) + temp2 * (x + lambdaT);
}
};
......@@ -258,46 +266,49 @@ struct SoftShrinkGradFunctor : public BaseActivationFunctor<T> {
typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
return {{"lambda", &lambda}};
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
auto lambdaT = static_cast<T>(lambda);
auto temp1 = (x > lambdaT).template cast<T>().eval();
auto temp2 = (x < -lambdaT).template cast<T>().eval();
dx.device(d) = dy * (temp1 + temp2).template cast<T>();
dx.device(d) = dout * (temp1 + temp2).template cast<T>();
}
};
// sqrt(x) = x^(1/2)
template <typename T>
struct SqrtFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.sqrt();
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.sqrt();
}
};
template <typename T>
struct SqrtGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
const Y y_conj = Eigen::numext::conj(y);
dx.device(d) = static_cast<T>(0.5) * dy / y_conj;
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
const Out out_conj = Eigen::numext::conj(out);
dx.device(d) = static_cast<T>(0.5) * dout / out_conj;
}
};
// ceil(x) = ceiling(x)
template <typename T>
struct CeilFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.ceil();
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.ceil();
}
};
template <typename T>
struct ZeroGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = static_cast<T>(0) / x;
}
};
......@@ -305,86 +316,90 @@ struct ZeroGradFunctor : public BaseActivationFunctor<T> {
// floor(x) = flooring(x)
template <typename T>
struct FloorFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.ceil();
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.ceil();
}
};
// round(x) = [x]
template <typename T>
struct RoundFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.round();
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.round();
}
};
// abs(x) = |x|
template <typename T>
struct AbsFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.abs();
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.abs();
}
};
template <typename T>
struct AbsGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) = dy * x.sign();
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * x.sign();
}
};
// reciprocal(x) = 1 / x
template <typename T>
struct ReciprocalFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = static_cast<T>(1) / x;
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = static_cast<T>(1) / x;
}
};
template <typename T>
struct ReciprocalGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) = dy * static_cast<T>(-1) * y * y;
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * static_cast<T>(-1) * out * out;
}
};
// log(x) = natural logarithm of x
template <typename T>
struct LogFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.log();
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.log();
}
};
template <typename T>
struct LogGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) = dy * (static_cast<T>(1) / x);
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * (static_cast<T>(1) / x);
}
};
// square(x) = x^2
template <typename T>
struct SquareFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.square();
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.square();
}
};
template <typename T>
struct SquareGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) = dy * static_cast<T>(2) * x;
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * static_cast<T>(2) * x;
}
};
......@@ -399,9 +414,9 @@ struct BReluFunctor : public BaseActivationFunctor<T> {
return {{"t_min", &t_min}, {"t_max", &t_max}};
}
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) =
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) =
x.cwiseMax(static_cast<T>(t_min)).cwiseMin(static_cast<T>(t_max));
}
};
......@@ -413,9 +428,10 @@ struct BReluGradFunctor : public BaseActivationFunctor<T> {
typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
return {{"t_min", &t_min}, {"t_max", &t_max}};
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) = dy *
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout *
((x > static_cast<T>(t_min)) * (x < static_cast<T>(t_max)))
.template cast<T>();
}
......@@ -430,9 +446,9 @@ struct Relu6Functor : public BaseActivationFunctor<T> {
return {{"threshold", &threshold}};
}
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) =
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) =
x.cwiseMax(static_cast<T>(0)).cwiseMin(static_cast<T>(threshold));
}
};
......@@ -443,9 +459,10 @@ struct Relu6GradFunctor : public BaseActivationFunctor<T> {
typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
return {{"threshold", &threshold}};
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) = dy *
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout *
((x > static_cast<T>(0)) * (x < static_cast<T>(threshold)))
.template cast<T>();
}
......@@ -458,10 +475,10 @@ struct Relu6GradFunctor : public BaseActivationFunctor<T> {
// Then: softplus(x) = max(x, 0) + log(exp(-max(x, 0)) + exp(x - max(x, 0)))
template <typename T>
struct SoftplusFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) {
auto temp = x.cwiseMax(static_cast<T>(0)); // temp = max(x, 0)
y.device(d) = temp + (((-temp).exp() + (x - temp).exp()).log());
out.device(d) = temp + (((-temp).exp() + (x - temp).exp()).log());
}
};
......@@ -471,19 +488,21 @@ struct SoftplusFunctor : public BaseActivationFunctor<T> {
// exp(x - max(x, 0)))
template <typename T>
struct SoftplusGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) {
auto temp = x.cwiseMax(static_cast<T>(0)); // temp = max(x, 0)
dx.device(d) = dy * ((x - temp).exp() / ((-temp).exp() + (x - temp).exp()));
dx.device(d) =
dout * ((x - temp).exp() / ((-temp).exp() + (x - temp).exp()));
}
};
// softsign(x) = x / (1 + |x|)
template <typename T>
struct SoftsignFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) {
y.device(d) = x / (static_cast<T>(1) + x.abs());
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) {
out.device(d) = x / (static_cast<T>(1) + x.abs());
}
};
......@@ -491,10 +510,11 @@ struct SoftsignFunctor : public BaseActivationFunctor<T> {
// Taken from https://en.wikipedia.org/wiki/Activation_function
template <typename T>
struct SoftsignGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) {
dx.device(d) =
dy * (static_cast<T>(1) / (static_cast<T>(1) + x.abs()).square());
dout * (static_cast<T>(1) / (static_cast<T>(1) + x.abs()).square());
}
};
......@@ -505,11 +525,11 @@ struct SoftReluFunctor : public BaseActivationFunctor<T> {
return {{"threshold", &threshold}};
}
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
auto tmp = static_cast<T>(threshold);
auto temp = x.cwiseMax(-tmp).cwiseMin(tmp);
y.device(d) = (static_cast<T>(1) + temp.exp()).log();
out.device(d) = (static_cast<T>(1) + temp.exp()).log();
}
};
......@@ -519,11 +539,12 @@ struct SoftReluGradFunctor : public BaseActivationFunctor<T> {
typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
return {{"threshold", &threshold}};
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
auto tmp = static_cast<T>(threshold);
auto temp = ((x > -tmp) * (x < tmp)).template cast<T>().eval();
dx.device(d) = dy * (static_cast<T>(1) - (-y).exp()) * temp;
dx.device(d) = dout * (static_cast<T>(1) - (-out).exp()) * temp;
}
};
......@@ -534,9 +555,9 @@ struct LeakyReluFunctor : public BaseActivationFunctor<T> {
return {{"alpha", &alpha}};
}
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.cwiseMax(static_cast<T>(alpha) * x);
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.cwiseMax(static_cast<T>(alpha) * x);
}
};
......@@ -546,12 +567,13 @@ struct LeakyReluGradFunctor : public BaseActivationFunctor<T> {
typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
return {{"alpha", &alpha}};
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
auto temp1 = static_cast<T>(alpha) *
(x < static_cast<T>(0)).template cast<T>().eval();
auto temp2 = (x >= static_cast<T>(0)).template cast<T>().eval();
dx.device(d) = dy * (temp1 + temp2).template cast<T>();
dx.device(d) = dout * (temp1 + temp2).template cast<T>();
}
};
......@@ -562,11 +584,11 @@ struct ELUFunctor : public BaseActivationFunctor<T> {
return {{"alpha", &alpha}};
}
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.cwiseMax(static_cast<T>(0)) +
(static_cast<T>(alpha) * (x.exp() - static_cast<T>(1)))
.cwiseMin(static_cast<T>(0));
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.cwiseMax(static_cast<T>(0)) +
(static_cast<T>(alpha) * (x.exp() - static_cast<T>(1)))
.cwiseMin(static_cast<T>(0));
}
};
......@@ -576,10 +598,11 @@ struct ELUGradFunctor : public BaseActivationFunctor<T> {
typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
return {{"alpha", &alpha}};
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) = dy * (x > static_cast<T>(0)).template cast<T>() +
dy * (y + static_cast<T>(alpha)) *
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * (x > static_cast<T>(0)).template cast<T>() +
dout * (out + static_cast<T>(alpha)) *
(x < static_cast<T>(0)).template cast<T>();
}
};
......@@ -591,9 +614,9 @@ struct PowFunctor : public BaseActivationFunctor<T> {
typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
return {{"factor", &factor}};
}
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.pow(static_cast<T>(factor));
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.pow(static_cast<T>(factor));
}
};
......@@ -603,9 +626,10 @@ struct PowGradFunctor : public BaseActivationFunctor<T> {
typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
return {{"factor", &factor}};
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) = dy * static_cast<T>(factor) *
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * static_cast<T>(factor) *
x.pow(static_cast<T>(factor - static_cast<T>(1)));
}
};
......@@ -618,9 +642,9 @@ struct STanhFunctor : public BaseActivationFunctor<T> {
return {{"scale_a", &scale_a}, {"scale_b", &scale_b}};
}
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) =
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) =
static_cast<T>(scale_b) * (static_cast<T>(scale_a) * x).tanh();
}
};
......@@ -633,12 +657,13 @@ struct STanhGradFunctor : public BaseActivationFunctor<T> {
return {{"scale_a", &scale_a}, {"scale_b", &scale_b}};
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
auto a = static_cast<T>(scale_a);
auto b = static_cast<T>(scale_b);
auto temp = (a * x).tanh() * (a * x).tanh();
dx.device(d) = dy * a * b * (static_cast<T>(1) - temp);
dx.device(d) = dout * a * b * (static_cast<T>(1) - temp);
}
};
......@@ -649,10 +674,10 @@ struct ThresholdedReluFunctor : public BaseActivationFunctor<T> {
return {{"threshold", &threshold}};
}
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
auto th = static_cast<T>(threshold);
y.device(d) = (x > th).template cast<T>() * x;
out.device(d) = (x > th).template cast<T>() * x;
}
};
......@@ -663,10 +688,11 @@ struct ThresholdedReluGradFunctor : public BaseActivationFunctor<T> {
return {{"threshold", &threshold}};
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
auto th = static_cast<T>(threshold);
dx.device(d) = dy * (x > th).template cast<T>();
dx.device(d) = dout * (x > th).template cast<T>();
}
};
......@@ -678,10 +704,11 @@ struct HardSigmoidFunctor : public BaseActivationFunctor<T> {
return {{"slope", &slope}, {"offset", &offset}};
}
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
auto temp = x * static_cast<T>(slope) + static_cast<T>(offset);
y.device(d) = temp.cwiseMax(static_cast<T>(0)).cwiseMin(static_cast<T>(1));
out.device(d) =
temp.cwiseMax(static_cast<T>(0)).cwiseMin(static_cast<T>(1));
}
};
......@@ -693,12 +720,13 @@ struct HardSigmoidGradFunctor : public BaseActivationFunctor<T> {
return {{"slope", &slope}, {"offset", &offset}};
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) =
dy *
((y > static_cast<T>(0)) * (y < static_cast<T>(1))).template cast<T>() *
static_cast<T>(slope);
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout *
((out > static_cast<T>(0)) * (out < static_cast<T>(1)))
.template cast<T>() *
static_cast<T>(slope);
}
};
......@@ -709,9 +737,9 @@ struct SwishFunctor : public BaseActivationFunctor<T> {
return {{"beta", &beta}};
}
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x / (static_cast<T>(1) + (static_cast<T>(-beta) * x).exp());
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x / (static_cast<T>(1) + (static_cast<T>(-beta) * x).exp());
}
};
......@@ -722,12 +750,13 @@ struct SwishGradFunctor : public BaseActivationFunctor<T> {
return {{"beta", &beta}};
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
auto temp1 = static_cast<T>(1) /
(static_cast<T>(1) + (static_cast<T>(-beta) * x).exp());
auto temp2 = temp1 * (static_cast<T>(1) - (beta * y));
dx.device(d) = dy * ((beta * y) + temp2);
auto temp2 = temp1 * (static_cast<T>(1) - (beta * out));
dx.device(d) = dout * ((beta * out) + temp2);
}
};
......
paddle/operators/array_operator.h
浏览文件 @ 6f5e64af
......@@ -35,8 +35,8 @@ class ArrayOp : public framework::OperatorBase {
PADDLE_ENFORCE_EQ(i_tensor.numel(), 1);
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(place);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
size_t offset;
if (platform::is_gpu_place(i_tensor.place())) {
......
paddle/operators/array_to_lod_tensor_op.cc
浏览文件 @ 6f5e64af
......@@ -106,8 +106,9 @@ class ArrayToLoDTensorOp : public framework::OperatorBase {
}
auto slice = out->Slice(out_offset, out_offset + len);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(place);
platform::DeviceContextPool &pool =
platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
framework::CopyFrom(x[x_idx].Slice(start_offset, end_offset), place,
dev_ctx, &slice);
......
paddle/operators/assign_op.cc
浏览文件 @ 6f5e64af
......@@ -82,8 +82,8 @@ class AssignOp : public framework::OperatorBase {
out != nullptr,
"The Output(Out) should not be null if the Input(X) is set.");
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(place);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
framework::VisitVarType(*x, AssignFunctor(out, dev_ctx));
}
......
paddle/operators/beam_search_decode_op.cc
浏览文件 @ 6f5e64af
......@@ -57,8 +57,8 @@ class BeamSearchDecodeOp : public framework::OperatorBase {
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope& scope,
const platform::Place& dev_place) const override {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Get();
auto& dev_ctx = *pool.Borrow(dev_place);
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& dev_ctx = *pool.Get(dev_place);
framework::ExecutionContext ctx(*this, scope, dev_ctx);
......
paddle/operators/cond_op.cc
浏览文件 @ 6f5e64af
......@@ -195,8 +195,8 @@ void CondOp::MergeDataFromSubnet(const framework::Scope& scope,
void CondOp::Run(const Scope& scope, const platform::Place& place) const {
// get device context from pool
platform::DeviceContextPool& pool = platform::DeviceContextPool::Get();
auto& dev_ctx = *pool.Borrow(place);
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& dev_ctx = *pool.Get(place);
PrepareDataForSubnet(scope, dev_ctx);
std::vector<framework::Scope*>& sub_scopes = GetSubScopes(scope);
......
paddle/operators/feed_op.cc
浏览文件 @ 6f5e64af
......@@ -49,8 +49,8 @@ class FeedOp : public framework::OperatorBase {
auto *out_item = out_var->GetMutable<framework::FeedFetchType>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(place);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
framework::CopyFrom(feed_item, place, dev_ctx, out_item);
out_item->set_lod(feed_item.lod());
......
paddle/operators/fetch_op.cc
浏览文件 @ 6f5e64af
......@@ -52,8 +52,8 @@ class FetchOp : public framework::OperatorBase {
// FIXME(yuyang18): Should we assume the fetch operator always generate
// CPU outputs?
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(place);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
CopyFrom(src_item, platform::CPUPlace(), dev_ctx, &dst_item);
dev_ctx.Wait();
......
paddle/operators/fill_constant_op.cc
浏览文件 @ 6f5e64af
......@@ -49,8 +49,8 @@ class FillConstantOp : public framework::OperatorBase {
out.mutable_data(dev_place, framework::ToTypeIndex(data_type));
}
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(dev_place);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(dev_place);
math::set_constant(dev_ctx, &out, value);
}
};
......
paddle/operators/fill_op.cc
浏览文件 @ 6f5e64af
......@@ -69,8 +69,9 @@ class FillOp : public framework::OperatorBase {
if (!force_cpu && platform::is_gpu_place(place)) {
// Copy tensor to out
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(place);
platform::DeviceContextPool &pool =
platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
framework::CopyFrom(tensor, place, dev_ctx, &out);
}
}
......
paddle/operators/load_op.cc
浏览文件 @ 6f5e64af
......@@ -40,8 +40,8 @@ class LoadOp : public framework::OperatorBase {
auto *tensor = out_var->GetMutable<framework::LoDTensor>();
framework::DeserializeFromStream(fin, tensor);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(place);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
if (platform::is_gpu_place(place)) {
// copy CPU to GPU
......
paddle/operators/lod_tensor_to_array_op.cc
浏览文件 @ 6f5e64af
......@@ -88,8 +88,9 @@ class LoDTensorToArrayOp : public framework::OperatorBase {
auto slice = out[i].Slice(static_cast<int>(offset),
static_cast<int>(offset + len));
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(place);
platform::DeviceContextPool &pool =
platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
framework::CopyFrom(x.Slice(static_cast<int>(each_range.begin),
static_cast<int>(each_range.end)),
......
paddle/operators/merge_lod_tensor_op.cc
浏览文件 @ 6f5e64af
......@@ -30,8 +30,8 @@ class MergeLoDTensorOp : public framework::OperatorBase {
void Run(const framework::Scope &scope,
const platform::Place &dev_place) const override {
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(dev_place);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(dev_place);
auto &x = scope.FindVar(Input("X"))->Get<framework::LoDTensor>();
auto &mask = scope.FindVar(Input("Mask"))->Get<framework::LoDTensor>();
......
paddle/operators/nccl_op_test.cu.cc
浏览文件 @ 6f5e64af
......@@ -305,7 +305,7 @@ int main(int argc, char **argv) {
}
VLOG(0) << " DeviceCount " << count;
paddle::platform::DeviceContextPool::Create(places);
paddle::platform::DeviceContextPool::Init(places);
testing::InitGoogleTest(&argc, argv);
......
paddle/operators/recurrent_op.cc
浏览文件 @ 6f5e64af
......@@ -272,8 +272,9 @@ class RecurrentOp : public RecurrentBase {
false /*create_local_scope*/);
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(place);
platform::DeviceContextPool &pool =
platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
// Copy inside::output -> outside::output
// outside::output[seq_offset: seq_offset + 1] = inside::output
......@@ -326,8 +327,8 @@ class RecurrentGradOp : public RecurrentBase {
auto *program = block->Program();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(place);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
for (size_t step_id = 0; step_id < seq_len; ++step_id) {
size_t seq_offset = reverse ? step_id : seq_len - step_id - 1;
......
paddle/operators/reorder_lod_tensor_by_rank_op.cc
浏览文件 @ 6f5e64af
......@@ -131,8 +131,8 @@ class ReorderLoDTensorByRankTableBase : public framework::OperatorBase {
auto x_sliced = x.Slice(x_offset, x_offset + len);
auto out_sliced = out->Slice(out_offset, out_offset + len);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(place);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
framework::CopyFrom(x_sliced, out_sliced.place(), dev_ctx, &out_sliced);
out_offset += len;
return out_offset;
......
paddle/operators/save_op.cc
浏览文件 @ 6f5e64af
......@@ -91,8 +91,8 @@ class SaveOp : public framework::OperatorBase {
auto &tensor = var->Get<framework::LoDTensor>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(place);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
framework::SerializeToStream(fout, tensor, dev_ctx);
}
......
paddle/operators/shrink_rnn_memory_op.cc
浏览文件 @ 6f5e64af
......@@ -106,8 +106,8 @@ class ShrinkRNNMemoryGradOp : public ArrayOp {
dx_tensor.mutable_data(x_tensor.place(), x_tensor.type());
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(place);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
if (dout_var == nullptr) { // dx_tensor fill zero
math::set_constant(dev_ctx, &dx_tensor, 0.0f);
......
paddle/operators/softmax_op.cc
浏览文件 @ 6f5e64af
......@@ -24,13 +24,13 @@ class SoftmaxOp : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SoftmaxOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Y"),
"Output(Y) of SoftmaxOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SoftmaxOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE(x_dims.size() == 2UL,
"The input of softmax op must be a matrix.");
ctx->SetOutputDim("Y", x_dims);
ctx->SetOutputDim("Out", x_dims);
}
};
......@@ -41,7 +41,7 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("X",
"The input tensor of softmax. "
"2-D with shape [batch_size, input_feature_dimensions].");
AddOutput("Y", "The normalized values with the same shape as X.");
AddOutput("Out", "The normalized values with the same shape as X.");
AddComment(R"DOC(
Softmax Operator.
......@@ -59,7 +59,7 @@ exponential values of all the other dimensions is the output of the softmax
operator.
For each row $i$ and each column $j$ in Input(X), we have:
$$Y[i, j] = \frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])}$$
$$Out[i, j] = \frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])}$$
)DOC");
}
......@@ -70,12 +70,12 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should be not null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
"Input(Y@GRAD) should be not null.");
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Y"),
ctx->GetInputDim(framework::GradVarName("Y")),
"Input(Y) and its gradients should have a same shape.");
PADDLE_ENFORCE(ctx->HasInput("Out"), "Input(Out) should be not null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should be not null.");
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Out"),
ctx->GetInputDim(framework::GradVarName("Out")),
"Input(Out) and its gradients should have a same shape.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
......
paddle/operators/softmax_op.h
浏览文件 @ 6f5e64af
......@@ -26,13 +26,13 @@ class SoftmaxKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* X = context.Input<Tensor>("X");
auto* Y = context.Output<Tensor>("Y");
auto* Out = context.Output<Tensor>("Out");
// allocate memory on device.
Y->mutable_data<T>(context.GetPlace());
Out->mutable_data<T>(context.GetPlace());
math::SoftmaxFunctor<DeviceContext, T>()(
context.template device_context<DeviceContext>(), X, Y);
context.template device_context<DeviceContext>(), X, Out);
}
};
......@@ -40,15 +40,15 @@ template <typename DeviceContext, typename T>
class SoftmaxGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* Y = context.Input<Tensor>("Y");
auto* dY = context.Input<Tensor>(framework::GradVarName("Y"));
auto* Out = context.Input<Tensor>("Out");
auto* dOut = context.Input<Tensor>(framework::GradVarName("Out"));
auto* dX = context.Output<Tensor>(framework::GradVarName("X"));
// allocate memory on device.
dX->mutable_data<T>(context.GetPlace());
math::SoftmaxGradFunctor<DeviceContext, T>()(
context.template device_context<DeviceContext>(), Y, dY, dX);
context.template device_context<DeviceContext>(), Out, dOut, dX);
}
};
......
paddle/operators/split_lod_tensor_op.cc
浏览文件 @ 6f5e64af
......@@ -45,8 +45,8 @@ class SplitLoDTensorOp : public framework::OperatorBase {
auto &x_lod = x.lod();
auto &mask_dim = mask.dims();
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(dev_place);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(dev_place);
std::unique_ptr<framework::LoDTensor> cpu_mask{new framework::LoDTensor()};
if (platform::is_cpu_place(mask.place())) {
......
paddle/operators/tensor_array_read_write_op.cc
浏览文件 @ 6f5e64af
......@@ -40,8 +40,9 @@ class WriteToArrayOp : public ArrayOp {
if (x_tensor.memory_size() > 0) {
auto *out_tensor = &out->at(offset);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(place);
platform::DeviceContextPool &pool =
platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
CopyFrom(x_tensor, place, dev_ctx, out_tensor);
out_tensor->set_lod(x_tensor.lod());
......@@ -132,8 +133,9 @@ class ReadFromArrayOp : public ArrayOp {
auto *out_tensor = out->GetMutable<framework::LoDTensor>();
size_t offset = GetOffset(scope, place);
if (offset < x_array.size()) {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
auto &dev_ctx = *pool.Borrow(place);
platform::DeviceContextPool &pool =
platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
framework::CopyFrom(x_array[offset], place, dev_ctx, out_tensor);
out_tensor->set_lod(x_array[offset].lod());
} else {
......
paddle/platform/device_context.cc
浏览文件 @ 6f5e64af
......@@ -17,7 +17,7 @@ namespace platform {
DeviceContextPool* DeviceContextPool::pool = nullptr;
const platform::DeviceContext* DeviceContextPool::Borrow(
const platform::DeviceContext* DeviceContextPool::Get(
const platform::Place& place) {
auto it = device_contexts_.find(place);
if (it == device_contexts_.end()) {
......@@ -28,24 +28,6 @@ const platform::DeviceContext* DeviceContextPool::Borrow(
return it->second;
}
std::vector<const platform::DeviceContext*> DeviceContextPool::Borrow(
const std::vector<platform::Place>& places) {
PADDLE_ENFORCE_GT(places.size(), 0);
PADDLE_ENFORCE_LE(places.size(), device_contexts_.size());
std::vector<const platform::DeviceContext*> borrowed_contexts;
for (auto& place : places) {
auto it = device_contexts_.find(place);
if (it != device_contexts_.end()) {
borrowed_contexts.emplace_back(it->second);
} else {
PADDLE_THROW(
"'Place' is not supported, Please re-compile with WITH_GPU "
"option");
}
}
return borrowed_contexts;
}
DeviceContextPool::DeviceContextPool(
const std::vector<platform::Place>& places) {
PADDLE_ENFORCE_GT(places.size(), 0);
......
paddle/platform/device_context.h
浏览文件 @ 6f5e64af
......@@ -52,6 +52,14 @@ class CPUDeviceContext : public DeviceContext {
std::unique_ptr<Eigen::DefaultDevice> eigen_device_;
};
template <typename Place>
struct DefaultDeviceContextType;
template <>
struct DefaultDeviceContextType<platform::CPUPlace> {
using TYPE = CPUDeviceContext;
};
#ifdef PADDLE_WITH_CUDA
class EigenCudaStreamDevice;
......@@ -90,6 +98,11 @@ class CUDADeviceContext : public DeviceContext {
cublasHandle_t cublas_handle_;
};
template <>
struct DefaultDeviceContextType<platform::CUDAPlace> {
using T = CUDADeviceContext;
};
class CUDNNDeviceContext : public CUDADeviceContext {
public:
explicit CUDNNDeviceContext(CUDAPlace place);
......@@ -109,13 +122,13 @@ class DeviceContextPool {
public:
explicit DeviceContextPool(const std::vector<platform::Place>& places);
static DeviceContextPool& Get() {
static DeviceContextPool& Instance() {
PADDLE_ENFORCE_NOT_NULL(pool, "Need to Create DeviceContextPool first!");
return *pool;
}
/*! \brief Create should only called by Init function */
static DeviceContextPool& Create(const std::vector<platform::Place>& places) {
static DeviceContextPool& Init(const std::vector<platform::Place>& places) {
if (pool == nullptr) {
pool = new DeviceContextPool(places);
}
......@@ -123,13 +136,14 @@ class DeviceContextPool {
}
/*! \brief Return handle of single device context. */
const platform::DeviceContext* Borrow(const platform::Place& place);
/*! \brief Return handle of multi-device context. */
std::vector<const platform::DeviceContext*> Borrow(
const std::vector<platform::Place>& places);
const platform::DeviceContext* Get(const platform::Place& place);
~DeviceContextPool() {}
template <typename Place>
const typename DefaultDeviceContextType<Place>::TYPE* GetByPlace(
const Place& place) {
return reinterpret_cast<
const typename DefaultDeviceContextType<Place>::TYPE*>(Get(place));
}
private:
static DeviceContextPool* pool;
......
paddle/platform/device_context_test.cu
浏览文件 @ 6f5e64af
......@@ -71,35 +71,20 @@ TEST(Device, DeviceContextPool) {
using paddle::platform::CPUPlace;
using paddle::platform::CUDAPlace;
DeviceContextPool& pool = DeviceContextPool::Get();
auto cpu_dev_ctx1 = pool.Borrow(CPUPlace());
auto cpu_dev_ctx2 = pool.Borrow(CPUPlace());
EXPECT_TRUE(cpu_dev_ctx2 == cpu_dev_ctx1);
DeviceContextPool& pool = DeviceContextPool::Instance();
auto cpu_dev_ctx1 = pool.Get(CPUPlace());
auto cpu_dev_ctx2 = pool.Get(CPUPlace());
ASSERT_EQ(cpu_dev_ctx2, cpu_dev_ctx1);
std::vector<Place> gpu_places;
int count = paddle::platform::GetCUDADeviceCount();
for (int i = 0; i < count; ++i) {
gpu_places.emplace_back(CUDAPlace(i));
}
auto dev_ctxs = pool.Borrow(gpu_places);
for (size_t i = 0; i < dev_ctxs.size(); ++i) {
auto* dev_ctx = static_cast<const CUDADeviceContext*>(dev_ctxs[i]);
// check same as CUDAPlace(i)
CUDAPlace place = boost::get<CUDAPlace>(dev_ctx->GetPlace());
EXPECT_EQ(place.GetDeviceId(), static_cast<int>(i));
auto dev_ctx = pool.Get(CUDAPlace(i));
ASSERT_NE(dev_ctx, nullptr);
}
}
int main(int argc, char** argv) {
int dev_count = paddle::platform::GetCUDADeviceCount();
if (dev_count <= 1) {
LOG(WARNING) << "Cannot test multi-gpu DeviceContextPool, because the CUDA "
"device count is "
<< dev_count;
return 0;
}
std::vector<paddle::platform::Place> places;
places.emplace_back(paddle::platform::CPUPlace());
......@@ -109,7 +94,7 @@ int main(int argc, char** argv) {
}
VLOG(0) << " DeviceCount " << count;
paddle::platform::DeviceContextPool::Create(places);
paddle::platform::DeviceContextPool::Init(places);
testing::InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
......
paddle/platform/nccl_test.cu
浏览文件 @ 6f5e64af
......@@ -144,7 +144,7 @@ int main(int argc, char** argv) {
}
VLOG(0) << " DeviceCount " << count;
paddle::platform::DeviceContextPool::Create(places);
paddle::platform::DeviceContextPool::Init(places);
testing::InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
......
paddle/platform/place.h
浏览文件 @ 6f5e64af
......@@ -15,7 +15,7 @@ limitations under the License. */
#pragma once
#include <iostream>
#include "paddle/platform/enforce.h"
#include "paddle/platform/variant.h"
namespace paddle {
......@@ -64,5 +64,31 @@ bool places_are_same_class(const Place &, const Place &);
std::ostream &operator<<(std::ostream &, const Place &);
template <typename Visitor>
struct PlaceVisitorWrapper
: public boost::static_visitor<typename Visitor::result_type> {
const Visitor &visitor_;
explicit PlaceVisitorWrapper(const Visitor &visitor) : visitor_(visitor) {}
typename Visitor::result_type operator()(const CPUPlace &cpu) const {
return visitor_(cpu);
}
typename Visitor::result_type operator()(const CUDAPlace &cuda) const {
#ifdef PADDLE_WITH_CUDA
return visitor_(cuda);
#else
PADDLE_THROW("Paddle is not compiled with CUDA. Cannot visit cuda device");
return typename Visitor::result_type();
#endif
}
};
template <typename Visitor>
typename Visitor::result_type VisitPlace(const Place &place,
const Visitor &visitor) {
return boost::apply_visitor(PlaceVisitorWrapper<Visitor>(visitor), place);
}
} // namespace platform
} // namespace paddle
paddle/pybind/tensor_py.h
浏览文件 @ 6f5e64af
......@@ -63,9 +63,10 @@ struct CastToPyBufferImpl<true, I, ARGS...> {
auto *dst_ptr = static_cast<void *>(dst_tensor.mutable_data<CUR_TYPE>(
tensor.dims(), platform::CPUPlace()));
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
platform::DeviceContextPool &pool =
platform::DeviceContextPool::Instance();
auto dev_ctx = static_cast<const platform::CUDADeviceContext *>(
pool.Borrow(tensor.place()));
pool.Get(tensor.place()));
paddle::platform::GpuMemcpyAsync(
dst_ptr, src_ptr, sizeof(CUR_TYPE) * tensor.numel(),
......@@ -137,9 +138,9 @@ void PyCUDATensorSetFromArray(
self.Resize(framework::make_ddim(dims));
auto *dst = self.mutable_data<T>(place);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Get();
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto dev_ctx =
static_cast<const platform::CUDADeviceContext *>(pool.Borrow(place));
static_cast<const platform::CUDADeviceContext *>(pool.Get(place));
paddle::platform::GpuMemcpyAsync(dst, array.data(), sizeof(T) * array.size(),
cudaMemcpyHostToDevice, dev_ctx->stream());
}
......
python/paddle/v2/fluid/__init__.py
浏览文件 @ 6f5e64af
......@@ -36,7 +36,7 @@ def __read_gflags_from_env__():
"""
import sys
import core
read_env_flags = ['use_pinned_memory']
read_env_flags = ['use_pinned_memory', 'check_nan_inf']
if core.is_compile_gpu():
read_env_flags.append('fraction_of_gpu_memory_to_use')
core.init_gflags([sys.argv[0]] +
......
python/paddle/v2/fluid/io.py
浏览文件 @ 6f5e64af
......@@ -180,10 +180,22 @@ def save_inference_model(dirname,
:return: None
"""
if isinstance(feeded_var_names, basestring):
feeded_var_names = [feeded_var_names]
else:
if not (bool(feeded_var_names) and all(
isinstance(name, basestring) for name in feeded_var_names)):
raise ValueError("'feed_var_names' should be a list of str.")
if isinstance(target_vars, Variable):
feeded_var_names = [feeded_var_names]
else:
if not (bool(target_vars) and all(
isinstance(var, Variable) for var in target_vars)):
raise ValueError("'target_vars' should be a list of Variable.")
if main_program is None:
main_program = default_main_program()
if not isinstance(target_vars, list):
target_vars = [target_vars]
if not os.path.isdir(dirname):
os.makedirs(dirname)
......
python/paddle/v2/fluid/layer_helper.py
浏览文件 @ 6f5e64af
......@@ -184,7 +184,7 @@ class LayerHelper(object):
self.append_op(
type=act_type,
inputs={"X": [input_var]},
outputs={"Y": [tmp]},
outputs={"Out": [tmp]},
attrs=act)
return tmp
......
python/paddle/v2/fluid/layers/nn.py
浏览文件 @ 6f5e64af
......@@ -386,7 +386,8 @@ def square_error_cost(input, label, **kwargs):
square_out = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type='square', inputs={'X': [minus_out]}, outputs={'Y': [square_out]})
type='square', inputs={'X': [minus_out]},
outputs={'Out': [square_out]})
return square_out
......@@ -604,7 +605,7 @@ def sequence_pool(input, pool_type, **kwargs):
sqrt : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
max : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
Args:
input(variable): The input variable which is a LoDTensor.
pool_type (string): The pooling type of sequence_pool.
......@@ -616,7 +617,7 @@ def sequence_pool(input, pool_type, **kwargs):
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
avg_x = fluid.layers.sequence_pool(input=x, pool_type='average')
......@@ -654,7 +655,7 @@ def sequence_first_step(input, **kwargs):
out.dim = [3, 1]
with condition len(x.lod[-1]) - 1 == out.dims[0]
out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
Args:
input(variable): The input variable which is a LoDTensor.
......@@ -664,7 +665,7 @@ def sequence_first_step(input, **kwargs):
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
x_first_step = fluid.layers.sequence_first_step(input=x)
......@@ -687,7 +688,7 @@ def sequence_last_step(input, **kwargs):
out.dim = [3, 1]
with condition len(x.lod[-1]) - 1 == out.dims[0]
out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
Args:
input(variable): The input variable which is a LoDTensor.
......@@ -697,7 +698,7 @@ def sequence_last_step(input, **kwargs):
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
x_last_step = fluid.layers.sequence_last_step(input=x)
......@@ -1132,7 +1133,7 @@ def reduce_sum(input, dim=None, keep_dim=False):
Returns:
Variable: The reduced Tensor variable.
Examples:
.. code-block:: python
......@@ -1176,7 +1177,7 @@ def reduce_mean(input, dim=None, keep_dim=False):
Returns:
Variable: The reduced Tensor variable.
Examples:
.. code-block:: python
......
python/paddle/v2/fluid/tests/test_activation_op.py
浏览文件 @ 6f5e64af
......@@ -10,13 +10,13 @@ class TestExp(OpTest):
self.inputs = {
'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
}
self.outputs = {'Y': np.exp(self.inputs['X'])}
self.outputs = {'Out': np.exp(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestSigmoid(OpTest):
......@@ -25,13 +25,13 @@ class TestSigmoid(OpTest):
self.inputs = {
'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
}
self.outputs = {'Y': 1 / (1 + np.exp(-self.inputs['X']))}
self.outputs = {'Out': 1 / (1 + np.exp(-self.inputs['X']))}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.008)
self.check_grad(['X'], 'Out', max_relative_error=0.008)
class TestLogSigmoid(OpTest):
......@@ -40,13 +40,13 @@ class TestLogSigmoid(OpTest):
self.inputs = {
'X': np.random.uniform(-1, 1, [11, 17]).astype("float32")
}
self.outputs = {'Y': np.log(1 / (1 + np.exp(-self.inputs['X'])))}
self.outputs = {'Out': np.log(1 / (1 + np.exp(-self.inputs['X'])))}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.008)
self.check_grad(['X'], 'Out', max_relative_error=0.008)
class TestTanh(OpTest):
......@@ -55,13 +55,13 @@ class TestTanh(OpTest):
self.inputs = {
'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
}
self.outputs = {'Y': np.tanh(self.inputs['X'])}
self.outputs = {'Out': np.tanh(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestTanhShrink(OpTest):
......@@ -70,13 +70,13 @@ class TestTanhShrink(OpTest):
self.inputs = {
'X': np.random.uniform(0.1, 1, [10, 17]).astype("float32")
}
self.outputs = {'Y': self.inputs['X'] - np.tanh(self.inputs['X'])}
self.outputs = {'Out': self.inputs['X'] - np.tanh(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.008)
self.check_grad(['X'], 'Out', max_relative_error=0.008)
class TestHardShrink(OpTest):
......@@ -90,13 +90,13 @@ class TestHardShrink(OpTest):
t = np.copy(x)
t[(t >= -threshold) & (t <= threshold)] = 0
self.outputs = {'Y': t}
self.outputs = {'Out': t}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.005)
self.check_grad(['X'], 'Out', max_relative_error=0.005)
class TestSoftShrink(OpTest):
......@@ -110,13 +110,13 @@ class TestSoftShrink(OpTest):
y = np.copy(self.inputs['X'])
y = (y < -lambda_val) * (y + lambda_val) + (y > lambda_val) * (
y - lambda_val)
self.outputs = {'Y': y}
self.outputs = {'Out': y}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestSqrt(OpTest):
......@@ -125,13 +125,13 @@ class TestSqrt(OpTest):
self.inputs = {
'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
}
self.outputs = {'Y': np.sqrt(self.inputs['X'])}
self.outputs = {'Out': np.sqrt(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestAbs(OpTest):
......@@ -144,13 +144,13 @@ class TestAbs(OpTest):
# we should avoid this
x[np.abs(x) < 0.005] = 0.02
self.inputs = {'X': x}
self.outputs = {'Y': np.abs(self.inputs['X'])}
self.outputs = {'Out': np.abs(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestCeil(OpTest):
......@@ -158,13 +158,13 @@ class TestCeil(OpTest):
self.op_type = "ceil"
x = np.random.uniform(-1, 1, [4, 4]).astype("float32")
self.inputs = {'X': x}
self.outputs = {'Y': np.ceil(self.inputs['X'])}
self.outputs = {'Out': np.ceil(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestFloor(OpTest):
......@@ -173,13 +173,13 @@ class TestFloor(OpTest):
x = np.random.uniform(-1, 1, [4, 4]).astype("float32")
self.inputs = {'X': x}
# numpy floor need +1
self.outputs = {'Y': np.floor(self.inputs['X']) + 1.0}
self.outputs = {'Out': np.floor(self.inputs['X']) + 1.0}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestRound(OpTest):
......@@ -187,13 +187,13 @@ class TestRound(OpTest):
self.op_type = "round"
x = np.random.uniform(-1, 1, [4, 4]).astype("float32")
self.inputs = {'X': x}
self.outputs = {'Y': np.round(self.inputs['X'])}
self.outputs = {'Out': np.round(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestRelu(OpTest):
......@@ -203,13 +203,13 @@ class TestRelu(OpTest):
# The same reason with TestAbs
x[np.abs(x) < 0.005] = 0.02
self.inputs = {'X': x}
self.outputs = {'Y': np.maximum(self.inputs['X'], 0)}
self.outputs = {'Out': np.maximum(self.inputs['X'], 0)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestBRelu(OpTest):
......@@ -227,13 +227,13 @@ class TestBRelu(OpTest):
t = np.copy(x)
t[t < t_min] = t_min
t[t > t_max] = t_max
self.outputs = {'Y': t}
self.outputs = {'Out': t}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.02)
self.check_grad(['X'], 'Out', max_relative_error=0.02)
class TestRelu6(OpTest):
......@@ -248,14 +248,14 @@ class TestRelu6(OpTest):
self.inputs = {'X': x}
self.attrs = {'threshold': threshold}
self.outputs = {
'Y': np.minimum(np.maximum(self.inputs['X'], 0), threshold)
'Out': np.minimum(np.maximum(self.inputs['X'], 0), threshold)
}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.02)
self.check_grad(['X'], 'Out', max_relative_error=0.02)
class TestSoftRelu(OpTest):
......@@ -271,13 +271,13 @@ class TestSoftRelu(OpTest):
t = np.copy(x)
t[t < -threshold] = -threshold
t[t > threshold] = threshold
self.outputs = {'Y': np.log((np.exp(t) + 1))}
self.outputs = {'Out': np.log((np.exp(t) + 1))}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.02)
self.check_grad(['X'], 'Out', max_relative_error=0.02)
class TestELU(OpTest):
......@@ -290,27 +290,27 @@ class TestELU(OpTest):
self.inputs = {'X': x}
self.attrs = {'alpha': alpha}
self.outputs = {
'Y': np.maximum(0, x) + np.minimum(0, alpha * (np.exp(x) - 1))
'Out': np.maximum(0, x) + np.minimum(0, alpha * (np.exp(x) - 1))
}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.02)
self.check_grad(['X'], 'Out', max_relative_error=0.02)
class TestReciprocal(OpTest):
def setUp(self):
self.op_type = "reciprocal"
self.inputs = {'X': np.random.uniform(1, 2, [11, 17]).astype("float32")}
self.outputs = {'Y': np.reciprocal(self.inputs['X'])}
self.outputs = {'Out': np.reciprocal(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.01)
self.check_grad(['X'], 'Out', max_relative_error=0.01)
class TestLog(OpTest):
......@@ -319,13 +319,13 @@ class TestLog(OpTest):
self.inputs = {
'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
}
self.outputs = {'Y': np.log(self.inputs['X'])}
self.outputs = {'Out': np.log(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestSquare(OpTest):
......@@ -334,13 +334,13 @@ class TestSquare(OpTest):
self.inputs = {
'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
}
self.outputs = {'Y': np.square(self.inputs['X'])}
self.outputs = {'Out': np.square(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestPow(OpTest):
......@@ -348,13 +348,13 @@ class TestPow(OpTest):
self.op_type = "pow"
self.inputs = {'X': np.random.uniform(1, 2, [11, 17]).astype("float32")}
self.attrs = {'factor': 3.0}
self.outputs = {'Y': np.power(self.inputs['X'], 3)}
self.outputs = {'Out': np.power(self.inputs['X'], 3)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.02)
self.check_grad(['X'], 'Out', max_relative_error=0.02)
class TestSTanh(OpTest):
......@@ -366,13 +366,13 @@ class TestSTanh(OpTest):
scale_a = 2.0 / 3.0
scale_b = 1.7159
self.attrs = {'scale_a': scale_a, 'scale_b': scale_b}
self.outputs = {'Y': scale_b * np.tanh(self.inputs['X'] * scale_a)}
self.outputs = {'Out': scale_b * np.tanh(self.inputs['X'] * scale_a)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestSoftplus(OpTest):
......@@ -381,13 +381,13 @@ class TestSoftplus(OpTest):
self.inputs = {
'X': np.random.uniform(-1, 1, [11, 17]).astype("float64")
}
self.outputs = {'Y': np.log(1 + np.exp(self.inputs['X']))}
self.outputs = {'Out': np.log(1 + np.exp(self.inputs['X']))}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestSoftsign(OpTest):
......@@ -397,14 +397,14 @@ class TestSoftsign(OpTest):
'X': np.random.uniform(-1, 1, [11, 17]).astype("float32")
}
self.outputs = {
'Y': np.divide(self.inputs['X'], 1 + np.abs(self.inputs['X']))
'Out': np.divide(self.inputs['X'], 1 + np.abs(self.inputs['X']))
}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestThresholdedRelu(OpTest):
......@@ -419,13 +419,13 @@ class TestThresholdedRelu(OpTest):
self.inputs = {'X': X}
self.attrs = {'threshold': threshold}
self.outputs = {'Y': (X > threshold) * X}
self.outputs = {'Out': (X > threshold) * X}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=self.relative_error)
self.check_grad(['X'], 'Out', max_relative_error=self.relative_error)
class TestHardSigmoid(OpTest):
......@@ -447,13 +447,13 @@ class TestHardSigmoid(OpTest):
upper_threshold - 0.2
temp = X * slope + offset
self.outputs = {'Y': np.maximum(0.0, np.minimum(1.0, temp))}
self.outputs = {'Out': np.maximum(0.0, np.minimum(1.0, temp))}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.002)
self.check_grad(['X'], 'Out', max_relative_error=0.002)
class TestSwish(OpTest):
......@@ -462,13 +462,13 @@ class TestSwish(OpTest):
X = np.random.uniform(0.1, 1, [11, 17]).astype("float32")
self.inputs = {'X': X}
self.attrs = {'beta': 2.3}
self.outputs = {'Y': X * expit(self.attrs['beta'] * X)}
self.outputs = {'Out': X * expit(self.attrs['beta'] * X)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.008)
self.check_grad(['X'], 'Out', max_relative_error=0.008)
if __name__ == "__main__":
......
python/paddle/v2/fluid/tests/test_net.py
浏览文件 @ 6f5e64af
......@@ -7,7 +7,7 @@ def fc(X, W, Y):
ret_v = core.Net.create()
ret_v.append_op(Operator("mul", X="X", Y="W", Out="pre_activation"))
ret_v.append_op(Operator("sigmoid", X="pre_activation", Y=Y))
ret_v.append_op(Operator("sigmoid", X="pre_activation", Out=Y))
ret_v.complete_add_op(True)
return ret_v
......@@ -30,7 +30,7 @@ Op(plain_net), inputs:{all[W, X, Y]}, outputs:{all[Out, fc.out, pre_activation]}
Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
Op(mul), inputs:{X[X], Y[W]}, outputs:{Out[pre_activation]}.
Op(sigmoid), inputs:{X[pre_activation]}, outputs:{Y[fc.out]}.
Op(sigmoid), inputs:{X[pre_activation]}, outputs:{Out[fc.out]}.
'''
self.assertEqual(expected, "\n" + str(net))
......
python/paddle/v2/fluid/tests/test_softmax_op.py
浏览文件 @ 6f5e64af
......@@ -17,14 +17,14 @@ class TestSoftmaxOp(OpTest):
'X': np.random.uniform(0.1, 1, [10, 10]).astype("float32")
}
self.outputs = {
'Y': np.apply_along_axis(stable_softmax, 1, self.inputs['X'])
'Out': np.apply_along_axis(stable_softmax, 1, self.inputs['X'])
}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y')
self.check_grad(['X'], 'Out')
if __name__ == "__main__":
......
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