提交 eae5c949 编写于 作者: Y Yi Wang

Merge branch 'develop' of https://github.com/paddlepaddle/paddle into const

......@@ -187,7 +187,13 @@ function(cc_library TARGET_NAME)
endif()
# cpplint code style
add_style_check_target(${TARGET_NAME} ${cc_library_SRCS})
foreach(source_file ${cc_library_SRCS})
string(REGEX REPLACE "\\.[^.]*$" "" source ${source_file})
if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
list(APPEND cc_library_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
endif()
endforeach()
add_style_check_target(${TARGET_NAME} ${cc_library_SRCS} ${cc_library_HEADERS})
else(cc_library_SRCS)
if (cc_library_DEPS)
......@@ -239,6 +245,14 @@ function(nv_library TARGET_NAME)
add_dependencies(${TARGET_NAME} ${nv_library_DEPS})
target_link_libraries(${TARGET_NAME} ${nv_library_DEPS})
endif()
# cpplint code style
foreach(source_file ${nv_library_SRCS})
string(REGEX REPLACE "\\.[^.]*$" "" source ${source_file})
if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
list(APPEND cc_library_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
endif()
endforeach()
add_style_check_target(${TARGET_NAME} ${nv_library_SRCS} ${nv_library_HEADERS})
else(nv_library_SRCS)
if (nv_library_DEPS)
merge_static_libs(${TARGET_NAME} ${nv_library_DEPS})
......
......@@ -25,18 +25,15 @@ limitations under the License. */
namespace paddle {
namespace framework {
namespace {
typedef boost::variant<Dim<1>, Dim<2>, Dim<3>, Dim<4>, Dim<5>, Dim<6>, Dim<7>,
Dim<8>, Dim<9>>
DDimVar;
}
/**
* \brief A dynamically sized dimension.
*
* The number of dimensions must be between [1, 9].
*/
struct DDim {
typedef boost::variant<Dim<1>, Dim<2>, Dim<3>, Dim<4>, Dim<5>, Dim<6>, Dim<7>,
Dim<8>, Dim<9>>
DDimVar;
DDimVar var;
DDim() : var(Dim<1>()) {}
......
......@@ -26,7 +26,7 @@ using VarIndexMap = std::unordered_map<std::string, int>;
enum class OpArgType { IN, OUT };
static std::vector<int>* GetOpFormat(OperatorBase* op, const OpArgType& type) {
std::string key = type == OpArgType::IN ? "input_format" : "output_name";
std::string key = type == OpArgType::IN ? "input_format" : "output_format";
return op->attrs_.count(key)
? &boost::get<std::vector<int>>(op->attrs_.at(key))
: nullptr;
......@@ -34,7 +34,7 @@ static std::vector<int>* GetOpFormat(OperatorBase* op, const OpArgType& type) {
static const std::vector<int>* GetOpFormat(const OperatorBase* op,
const OpArgType& type) {
std::string key = type == OpArgType::IN ? "input_format" : "output_name";
std::string key = type == OpArgType::IN ? "input_format" : "output_format";
return op->attrs_.count(key)
? &boost::get<std::vector<int>>(op->attrs_.at(key))
: nullptr;
......@@ -82,7 +82,7 @@ OperatorBase* BuildGradOp(const OperatorBase* op) {
grad_op->attrs_ = op->attrs_;
grad_op->attrs_.erase("input_format");
grad_op->attrs_.erase("output_format");
if (GetOpFormat(op, OpArgType::OUT) != nullptr) {
if (GetOpFormat(op, OpArgType::IN) != nullptr) {
grad_op->attrs_["output_format"] = std::vector<int>({0});
}
if (GetOpFormat(op, OpArgType::IN) != nullptr ||
......
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/operator.h"
......
......@@ -8,10 +8,49 @@ USE_OP(add_two);
namespace paddle {
namespace framework {
class NOP : public OperatorBase {
public:
void InferShape(const Scope &scope) const override {}
void Run(const Scope &scope,
const platform::DeviceContext &dev_ctx) const override {}
};
class MutiInOutOpMaker : public OpProtoAndCheckerMaker {
public:
MutiInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("In1", "a single input");
AddInput("In2_mult", "a multiple input").SetMultiple();
AddInput("In3", "another single input");
AddOutput("Out1", "a single output");
AddOutput("Out2_mult", "a multiple output").SetMultiple();
AddComment("test op with multiple inputs and outputs");
}
};
class IOIgnoredOpMaker : public OpProtoAndCheckerMaker {
public:
IOIgnoredOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("In1", "a single input");
AddInput("In2_mult", "a multiple input").SetMultiple().IgnoreGradient();
AddInput("In3_mult", "another multiple input").SetMultiple();
AddOutput("Out1_mult", "a multiple output").SetMultiple();
AddOutput("Out2", "a single output").IgnoreGradient();
AddComment("op with inputs and outputs ignored in gradient calculating");
}
};
} // namespace framework
} // namespace paddle
namespace f = paddle::framework;
TEST(GradOpBuilder, AddTwo) {
std::shared_ptr<OperatorBase> add_op(
OpRegistry::CreateOp("add_two", {"x", "y"}, {"out"}, {}));
std::shared_ptr<OperatorBase> grad_add_op = OpRegistry::CreateGradOp(*add_op);
std::shared_ptr<f::OperatorBase> add_op(
f::OpRegistry::CreateOp("add_two", {"x", "y"}, {"out"}, {}));
std::shared_ptr<f::OperatorBase> grad_add_op =
f::OpRegistry::CreateGradOp(*add_op);
EXPECT_EQ(static_cast<int>(grad_add_op->inputs_.size()), 4);
EXPECT_EQ(static_cast<int>(grad_add_op->outputs_.size()), 2);
EXPECT_EQ(grad_add_op->Input("X"), "x");
......@@ -22,5 +61,85 @@ TEST(GradOpBuilder, AddTwo) {
EXPECT_EQ(grad_add_op->Output("Y@GRAD"), "y@GRAD");
}
} // namespace framework
} // namespace paddle
\ No newline at end of file
REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker);
REGISTER_GRADIENT_OP(mult_io, mult_io_grad, f::NOP);
REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker);
REGISTER_GRADIENT_OP(io_ignored, io_ignored_grad, f::NOP);
TEST(GradOpBuilder, MutiInOut) {
f::AttributeMap attrs{{"input_format", std::vector<int>{0, 1, 4, 5}},
{"output_format", std::vector<int>{0, 1, 3}}};
std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
"mult_io", {"in1", "in2_1", "in2_2", "in2_3", "in3"},
{"out1", "out2_1", "out2_2"}, attrs));
std::shared_ptr<f::OperatorBase> grad_test_op =
f::OpRegistry::CreateGradOp(*test_op);
ASSERT_EQ(grad_test_op->inputs_.size(), 5UL + 3UL + 3UL);
EXPECT_EQ(grad_test_op->Input("In1"), "in1");
EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
std::vector<std::string>({"in2_1", "in2_2", "in2_3"}));
EXPECT_EQ(grad_test_op->Input("In3"), "in3");
EXPECT_EQ(grad_test_op->Input("Out1"), "out1");
EXPECT_EQ(grad_test_op->Inputs("Out2_mult"),
std::vector<std::string>({"out2_1", "out2_2"}));
EXPECT_EQ(grad_test_op->Input("Out1" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"out1" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(
grad_test_op->Inputs("Out2_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>(
{"out2_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"out2_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
EXPECT_EQ(grad_test_op->Output("In1" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"in1" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(
grad_test_op->Outputs("In2_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>({"in2_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"in2_2" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"in2_3" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
EXPECT_EQ(grad_test_op->Output("In3" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"in3" + f::OperatorBase::GRAD_VAR_SUFFIX());
}
TEST(GradOpBuilder, IOIgnoredInGradient) {
f::AttributeMap attrs{{"input_format", std::vector<int>{0, 1, 3, 5}},
{"output_format", std::vector<int>{0, 2, 3}}};
std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
"io_ignored", {"in1", "in2_1", "in2_2", "in3_1", "in3_2"},
{"out1_1", "out1_2", "out2"}, attrs));
std::shared_ptr<f::OperatorBase> grad_test_op =
f::OpRegistry::CreateGradOp(*test_op);
// 'In2' and 'Out2' are ignored in gradient calculating
ASSERT_EQ(grad_test_op->inputs_.size(), 5UL + 3UL + 3UL);
EXPECT_EQ(grad_test_op->Input("In1"), "in1");
EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
std::vector<std::string>({f::OperatorBase::EMPTY_VAR_NAME(),
f::OperatorBase::EMPTY_VAR_NAME()}));
EXPECT_EQ(grad_test_op->Inputs("In3_mult"),
std::vector<std::string>({"in3_1", "in3_2"}));
EXPECT_EQ(grad_test_op->Inputs("Out1_mult"),
std::vector<std::string>({"out1_1", "out1_2"}));
EXPECT_EQ(grad_test_op->Input("Out2"), f::OperatorBase::EMPTY_VAR_NAME());
EXPECT_EQ(
grad_test_op->Inputs("Out1_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>(
{"out1_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"out1_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
EXPECT_EQ(grad_test_op->Input("Out2" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"out2" + f::OperatorBase::GRAD_VAR_SUFFIX());
ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
EXPECT_EQ(grad_test_op->Output("In1" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"in1" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(
grad_test_op->Outputs("In2_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>({"in2_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"in2_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
EXPECT_EQ(
grad_test_op->Outputs("In3_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>({"in3_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"in3_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
}
......@@ -314,7 +314,7 @@ class OpRegistry {
static std::unordered_map<std::string, OpProto>& protos() {
static std::unordered_map<std::string, OpProto> protos_;
return protos_;
};
}
static std::unordered_map<std::string, std::string>& grad_ops() {
static std::unordered_map<std::string, std::string> grad_ops_;
......@@ -336,7 +336,7 @@ class OpRegistry {
static std::unordered_map<std::string, OpAttrChecker>& op_checkers() {
static std::unordered_map<std::string, OpAttrChecker> op_checkers_;
return op_checkers_;
};
}
static void GenerateTempVariableName(OperatorBase* op) {
static std::atomic<size_t> gUniqId(0UL);
......@@ -353,7 +353,7 @@ class OpRegistry {
template <typename OpType, typename ProtoMakerType>
class OpRegisterHelper {
public:
OpRegisterHelper(const char* op_type) {
explicit OpRegisterHelper(const char* op_type) {
OpRegistry::RegisterOp<OpType, ProtoMakerType>(op_type);
}
};
......
......@@ -285,7 +285,7 @@ class OperatorWithKernel : public OperatorBase {
platform::Place place_;
OpKernelKey() = default;
OpKernelKey(const platform::DeviceContext& dev_ctx) {
explicit OpKernelKey(const platform::DeviceContext& dev_ctx) {
place_ = dev_ctx.GetPlace();
}
......
......@@ -105,7 +105,16 @@ PYBIND11_PLUGIN(core) {
.def("set", PyCUDATensorSetFromArray<float>)
.def("set", PyCUDATensorSetFromArray<int>)
#endif
.def("shape", [](Tensor &self) { return vectorize(self.dims()); });
.def("shape", [](Tensor &self) { return vectorize(self.dims()); })
.def("set_float_element",
[](Tensor &self, size_t offset, float f) {
// TODO(yuyang18): Only support GPU now.
self.data<float>()[offset] = f;
})
.def("get_float_element", [](Tensor &self, size_t offset) -> float {
// TODO(yuyang18): Only support GPU now.
return self.data<float>()[offset];
});
py::class_<Variable>(m, "Variable", R"DOC(Variable Class.
......
......@@ -967,8 +967,9 @@ void RecurrentGradientMachine::generateSequence() {
size_t numSequences = getGenBatchSize();
resizeBootFrame(numSequences);
// We create only two sub-network in generation for alternate use.
// Thus, we can reduce total memory of output_ in layer forward.
// We create only two sub-network in generation, one stores states of all
// layers in previous time step and the other storing the states at current
// time step.
resizeOrCreateFrames(2);
// outFrameLines_.size() > 1UL
......@@ -1001,10 +1002,9 @@ void RecurrentGradientMachine::generateSequence() {
// init outArg
size_t resultNum = generator_.config.num_results_per_sample();
IVector::resizeOrCreate(
generator_.outArg.ids,
generator_.config.max_num_frames() * numSequences * resultNum,
false);
size_t maxGenWordCount =
generator_.config.max_num_frames() * numSequences * resultNum;
IVector::resizeOrCreate(generator_.outArg.ids, maxGenWordCount, false);
if (resultNum > 1) {
CHECK_LE(resultNum, static_cast<size_t>(generator_.config.beam_size()));
Matrix::resizeOrCreate(generator_.outArg.in,
......@@ -1012,6 +1012,11 @@ void RecurrentGradientMachine::generateSequence() {
/* width */ resultNum,
false,
/* useGpu */ false);
Matrix::resizeOrCreate(generator_.outArg.value,
/* height */ maxGenWordCount,
/* width */ 1,
false,
/* useGpu */ false);
}
ICpuGpuVector::resizeOrCreate(generator_.outArg.sequenceStartPositions,
numSequences + 1,
......@@ -1313,13 +1318,20 @@ void RecurrentGradientMachine::fillGenOutputs() {
starts[0] = 0;
if (numResults > 1) {
real* probs = generator_.outArg.in->getData();
real* idsProb = generator_.outArg.value->getData();
size_t curPos = 0;
for (size_t i = 0; i < finalPaths_.size(); ++i) {
for (size_t j = 0; j < finalPaths_[i].size(); ++j) {
Path& path = finalPaths_[i][j];
generator_.ids.push_back(path.ids.size()); // sequence size
size_t genLen = path.ids.size();
generator_.ids.push_back(genLen); // sequence size
generator_.ids.insert(
generator_.ids.end(), path.ids.begin(), path.ids.end());
generator_.ids.push_back(-1); // end of sequence
memcpy(idsProb + curPos, path.idsProb.data(), sizeof(real) * genLen);
curPos += genLen;
idsProb[curPos++] = -1.0;
probs[i * numResults + j] = path.logProb;
if (!j && dataArgsSize_) {
......
......@@ -189,6 +189,11 @@ public:
*/
std::vector<int> ids;
/**
* @brief idsProb, log probability of each generated words.
*/
std::vector<real> idsProb;
/**
* @brief logProb, current probability of path.
*/
......@@ -228,11 +233,13 @@ public:
*/
Path(Path& old, int newId, real logProb, int machineId, int topIndex)
: ids(old.ids),
idsProb(old.idsProb),
logProb(old.logProb + logProb),
machineId(machineId),
topIndex(topIndex),
seqId(old.seqId) {
ids.push_back(newId);
idsProb.push_back(logProb);
if (!old.probHistory.empty()) {
this->probHistory = old.probHistory;
// probHistory store current prob, not sum
......@@ -412,6 +419,7 @@ protected:
struct Generator {
GeneratorConfig config;
std::vector<int> ids; // store generated sequences
std::vector<real> idsProb; // log probability of each generated word
Argument outArg; // final output argument
};
bool generating_;
......
......@@ -400,7 +400,6 @@ void initDataLayer(TestConfig testConf,
const std::vector<int>& labelSeqStartPositions =
testConf.inputDefs[i].labelSeqStartPositions;
if (labelSeqStartPositions.size() != 0) {
CHECK(!sequenceStartPositions);
CHECK_GE(static_cast<int>(labelSeqStartPositions.size()), 2);
sequenceStartPositions =
......@@ -410,6 +409,19 @@ void initDataLayer(TestConfig testConf,
useGpu);
data.sequenceStartPositions = sequenceStartPositions;
}
const std::vector<int>& labelSubSeqStartPositions =
testConf.inputDefs[i].labelSubSeqStartPositions;
if (labelSubSeqStartPositions.size() != 0) {
CHECK_GE(static_cast<int>(labelSubSeqStartPositions.size()), 2);
subSequenceStartPositions =
ICpuGpuVector::create(labelSubSeqStartPositions.size(), useGpu);
subSequenceStartPositions->copyFrom(labelSubSeqStartPositions.data(),
labelSubSeqStartPositions.size(),
useGpu);
data.subSequenceStartPositions = subSequenceStartPositions;
}
break;
}
default:
......
......@@ -67,6 +67,7 @@ struct InputDef {
bool isStatic;
std::vector<int> labelInitValue;
std::vector<int> labelSeqStartPositions;
std::vector<int> labelSubSeqStartPositions;
MatrixPtr selfDefinedData;
InputDef(InputType type, string nameIn, size_t dimIn, size_t sizeIn) {
......@@ -81,8 +82,10 @@ struct InputDef {
InputDef(InputType type,
string nameIn,
MatrixPtr selfDefinedData,
std::vector<int> selfDefinedSeqStartPos = {})
std::vector<int> selfDefinedSeqStartPos = {},
std::vector<int> selfDefinedSubSeqStartPos = {})
: labelSeqStartPositions(selfDefinedSeqStartPos),
labelSubSeqStartPositions(selfDefinedSubSeqStartPos),
selfDefinedData(selfDefinedData) {
inputType = type;
name = nameIn;
......
......@@ -39,7 +39,7 @@ class BuddyAllocator {
public:
void* Alloc(size_t unaligned_size);
void Free(void*);
void Free(void* ptr);
size_t Used();
public:
......
......@@ -33,17 +33,17 @@ namespace detail {
*/
class MetadataCache {
public:
MetadataCache(bool uses_gpu);
explicit MetadataCache(bool uses_gpu);
public:
/*! \brief Load the associated metadata for the specified memory block. */
Metadata load(const MemoryBlock*);
Metadata load(const MemoryBlock* memory_block);
/*! \brief Store the associated metadata for the specified memory block. */
void store(MemoryBlock*, const Metadata&);
void store(MemoryBlock* memory_block, const Metadata& meta_data);
/*! \brief Indicate that the specified metadata will no longer be used. */
void invalidate(MemoryBlock*);
void invalidate(MemoryBlock* memory_block);
public:
MetadataCache(const MetadataCache&) = delete;
......
......@@ -68,7 +68,7 @@ class PODDeleter {
static_assert(std::is_pod<T>::value, "T must be POD");
public:
PODDeleter(Place place) : place_(place) {}
explicit PODDeleter(Place place) : place_(place) {}
void operator()(T* ptr) { Free(place_, static_cast<void*>(ptr)); }
private:
......
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/framework/op_registry.h"
#include "paddle/operators/add_op.h"
......
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/cross_entropy_op.h"
......
/* 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/op_registry.h"
#include "paddle/operators/fill_zeros_like_op.h"
......
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/mean_op.h"
......
......@@ -19,7 +19,7 @@
namespace paddle {
namespace operators {
using namespace paddle::framework;
using namespace paddle::framework; // NOLINT
namespace rnn {
......@@ -94,7 +94,7 @@ void InitArgument(const ArgumentName& name, Argument* arg);
}; // namespace rnn
// The sequence format in RecurrentOp is Tensor<seq_len, batch_size, dim> now.
// TODO:
// TODO(Yan Chunwei):
// 1. No-padding computing for sequences with indifinite length in one batch.
// 2. Hierarchical RNN for sequence with sub-sequence.
// 3. Internal Memory.
......@@ -172,11 +172,9 @@ public:
/**
* InferShape must be called before Run.
*/
virtual void InferShape(const Scope& scope) const override {
alg_.InferShape(scope);
}
void InferShape(const Scope& scope) const override { alg_.InferShape(scope); }
virtual void Run(const Scope& scope,
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
alg_.Run(scope, dev_ctx);
}
......@@ -194,11 +192,9 @@ public:
/**
* InferShape must be called before Run.
*/
virtual void InferShape(const Scope& scope) const override {
alg_.InferShape(scope);
}
void InferShape(const Scope& scope) const override { alg_.InferShape(scope); }
virtual void Run(const Scope& scope,
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
alg_.Run(scope, dev_ctx);
}
......
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/rowwise_add_op.h"
......
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/sgd_op.h"
......
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/sigmoid_op.h"
......
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/framework/op_registry.h"
#include "paddle/operators/softmax_op.h"
REGISTER_OP_GPU_KERNEL(softmax, ops::SoftmaxKernel<ops::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(softmax_grad, ops::SoftmaxGradKernel<ops::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(softmax_grad,
ops::SoftmaxGradKernel<ops::GPUPlace, float>);
......@@ -40,7 +40,7 @@ class DeviceContext {
class CPUDeviceContext : public DeviceContext {
public:
CPUDeviceContext();
CPUDeviceContext(CPUPlace);
explicit CPUDeviceContext(CPUPlace);
virtual ~CPUDeviceContext() {}
Eigen::DefaultDevice* eigen_device() const;
......@@ -55,7 +55,7 @@ class CPUDeviceContext : public DeviceContext {
class CUDADeviceContext : public DeviceContext {
public:
explicit CUDADeviceContext(GPUPlace);
CUDADeviceContext(GPUPlace); // NOLINT
virtual ~CUDADeviceContext();
/*! \brief Wait for all operations completion in the stream. */
......@@ -69,10 +69,10 @@ class CUDADeviceContext : public DeviceContext {
// clang-format off
/*! \brief Return cublas handle in the device context. */
cublasHandle_t cublas_handle ();
cublasHandle_t cublas_handle();
/*! \brief Return cudnn handle in the device context. */
cudnnHandle_t cudnn_handle ();
cudnnHandle_t cudnn_handle();
/*! \brief Return curand handle in the device context. */
curandGenerator_t curand_generator();
......
/* 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/platform/dynload/cublas.h>
namespace paddle {
......
/* 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/platform/dynload/cudnn.h>
namespace paddle {
......
/* 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/platform/dynload/curand.h>
namespace paddle {
......@@ -10,6 +24,7 @@ void *curand_dso_handle;
#define DEFINE_WRAP(__name) DynLoad__##__name __name
CURAND_RAND_ROUTINE_EACH(DEFINE_WRAP);
}
}
}
\ No newline at end of file
} // namespace dynload
} // namespace platform
} // namespace paddle
......@@ -162,5 +162,50 @@ inline void throw_on_error(T e) {
} \
} while (0)
/*
* Some enforce helpers here, usage:
* int a = 1;
* int b = 2;
* PADDLE_ENFORCE_EQ(a, b);
*
* will raise an expression described as follows:
* "enforce a == b failed, 1 != 2" with detailed stack infomation.
*
* extra messages is also supported, for example:
* PADDLE_ENFORCE(a, b, "some simple enforce failed between %d numbers", 2)
*/
#define PADDLE_ENFORCE_EQ(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, ==, !=, __VA_ARGS__)
#define PADDLE_ENFORCE_NE(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, !=, ==, __VA_ARGS__)
#define PADDLE_ENFORCE_GT(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, >, <=, __VA_ARGS__)
#define PADDLE_ENFORCE_GE(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, >=, <, __VA_ARGS__)
#define PADDLE_ENFORCE_LT(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, <, >=, __VA_ARGS__)
#define PADDLE_ENFORCE_LE(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, <=, >, __VA_ARGS__)
// if two values have different data types, choose a compatible type for them.
template <typename T1, typename T2>
struct CompatibleType {
static const bool t1_to_t2 = std::is_convertible<T1, T2>::value;
typedef typename std::conditional<t1_to_t2, T2, T1>::type type;
};
#define __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, __CMP, __INV_CMP, ...) \
PADDLE_ENFORCE(__COMPATIBLE_TYPE(__VAL0, __VAL1, __VAL0) \
__CMP __COMPATIBLE_TYPE(__VAL0, __VAL1, __VAL1), \
"enforce %s " #__CMP " %s failed, %s " #__INV_CMP " %s\n%s", \
#__VAL0, #__VAL1, std::to_string(__VAL0), \
std::to_string(__VAL1), \
paddle::string::Sprintf("" __VA_ARGS__));
#define __COMPATIBLE_TYPE(__VAL0, __VAL1, __VAL) \
typename paddle::platform::CompatibleType<decltype(__VAL0), \
decltype(__VAL1)>::type(__VAL)
} // namespace platform
} // namespace paddle
......@@ -34,3 +34,165 @@ TEST(ENFORCE, FAILED) {
}
ASSERT_TRUE(in_catch);
}
TEST(ENFORCE, NO_ARG_OK) {
int a = 2;
int b = 2;
PADDLE_ENFORCE_EQ(a, b);
// test enforce with extra message.
PADDLE_ENFORCE_EQ(a, b, "some thing wrong %s", "info");
}
TEST(ENFORCE_EQ, NO_EXTRA_MSG_FAIL) {
int a = 2;
bool in_catch = false;
try {
PADDLE_ENFORCE_EQ(a, 1 + 3);
} catch (paddle::platform::EnforceNotMet error) {
in_catch = true;
const std::string msg = "enforce a == 1 + 3 failed, 2 != 4";
const char* what = error.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
}
}
ASSERT_TRUE(in_catch);
}
TEST(ENFORCE_EQ, EXTRA_MSG_FAIL) {
int a = 2;
bool in_catch = false;
try {
PADDLE_ENFORCE_EQ(a, 1 + 3, "%s size not match", "their");
} catch (paddle::platform::EnforceNotMet error) {
in_catch = true;
const std::string msg =
"enforce a == 1 + 3 failed, 2 != 4\ntheir size not match";
const char* what = error.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
}
}
ASSERT_TRUE(in_catch);
}
TEST(ENFORCE_NE, OK) {
PADDLE_ENFORCE_NE(1, 2);
PADDLE_ENFORCE_NE(1.0, 2UL);
}
TEST(ENFORCE_NE, FAIL) {
bool in_catch = false;
try {
// 2UL here to check data type compatible
PADDLE_ENFORCE_NE(1.0, 1UL);
} catch (paddle::platform::EnforceNotMet error) {
in_catch = true;
const std::string msg = "enforce 1.0 != 1UL failed, 1.000000 == 1";
const char* what = error.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
}
}
ASSERT_TRUE(in_catch);
}
TEST(ENFORCE_GT, OK) { PADDLE_ENFORCE_GT(2, 1); }
TEST(ENFORCE_GT, FAIL) {
bool in_catch = false;
try {
// 2UL here to check data type compatible
PADDLE_ENFORCE_GT(1, 2UL);
} catch (paddle::platform::EnforceNotMet error) {
in_catch = true;
const std::string msg = "enforce 1 > 2UL failed, 1 <= 2";
const char* what = error.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
}
}
ASSERT_TRUE(in_catch);
}
TEST(ENFORCE_GE, OK) {
PADDLE_ENFORCE_GE(2, 2UL);
PADDLE_ENFORCE_GE(3, 2UL);
PADDLE_ENFORCE_GE(3, 2);
PADDLE_ENFORCE_GE(3.21, 2UL);
}
TEST(ENFORCE_GE, FAIL) {
bool in_catch = false;
try {
PADDLE_ENFORCE_GE(1, 2UL);
} catch (paddle::platform::EnforceNotMet error) {
in_catch = true;
const std::string msg = "enforce 1 >= 2UL failed, 1 < 2";
const char* what = error.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
}
}
ASSERT_TRUE(in_catch);
}
TEST(ENFORCE_LE, OK) {
PADDLE_ENFORCE_LE(1, 1);
PADDLE_ENFORCE_LE(1, 1UL);
PADDLE_ENFORCE_LE(2, 3UL);
PADDLE_ENFORCE_LE(2UL, 3);
PADDLE_ENFORCE_LE(2UL, 3.2);
}
TEST(ENFORCE_LE, FAIL) {
bool in_catch = false;
try {
PADDLE_ENFORCE_GT(1, 2UL);
} catch (paddle::platform::EnforceNotMet error) {
in_catch = true;
const std::string msg = "enforce 1 > 2UL failed, 1 <= 2";
const char* what = error.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
}
}
ASSERT_TRUE(in_catch);
}
TEST(ENFORCE_LT, OK) {
PADDLE_ENFORCE_LT(3, 10);
PADDLE_ENFORCE_LT(2, 3UL);
PADDLE_ENFORCE_LT(2UL, 3);
}
TEST(ENFORCE_LT, FAIL) {
bool in_catch = false;
try {
PADDLE_ENFORCE_LT(1UL, 0.12);
} catch (paddle::platform::EnforceNotMet error) {
in_catch = true;
const std::string msg = "enforce 1UL < 0.12 failed, 1 >= 0.12";
const char* what = error.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
}
}
ASSERT_TRUE(in_catch);
}
......@@ -32,7 +32,7 @@ struct CPUPlace {
struct GPUPlace {
GPUPlace() : GPUPlace(0) {}
GPUPlace(int d) : device(d) {}
GPUPlace(int d) : device(d) {} // NOLINT
// needed for variant equality comparison
inline bool operator==(const GPUPlace &o) const { return device == o.device; }
......
......@@ -39,8 +39,8 @@ public:
// size_ is 0.
Piece();
Piece(const char* d, size_t n);
Piece(const char* d);
Piece(const std::string& s);
Piece(const char* d); // NOLINT
Piece(const std::string& s); // NOLINT
const char* data() const { return data_; }
size_t len() const { return size_; }
......
......@@ -13,4 +13,5 @@ add_python_test(test_framework
test_sigmoid_op.py
test_softmax_op.py
test_rowwise_add_op.py
test_network.py)
test_network.py
gradient_checker.py)
import paddle.v2.framework.core as core
from paddle.v2.framework.create_op_creation_methods import op_creations
import numpy
import unittest
__all__ = ['get_numeric_gradient']
def get_numeric_gradient(op,
input_values,
output_name,
input_to_check,
delta=1e-2,
local_scope=None):
"""
Get Numeric Gradient for an operator's input.
:param op: C++ operator instance, could be an network
:param input_values: The input variables. Should be an dictionary, key is
variable name. Value is numpy array.
:param output_name: The final output variable name.
:param input_to_check: The input variable need to get gradient.
:param delta: The perturbation value for numeric gradient method. The
smaller delta is, the more accurate result will get. But if that delta is
too small, it could occur numerical stability problem.
:param local_scope: The local scope used for get_numeric_gradient.
:return: The gradient array in numpy format.
"""
if local_scope is None:
local_scope = core.Scope()
# Create all input variable in local_scope
for var_name in input_values:
var = local_scope.new_var(var_name)
tensor = var.get_tensor()
tensor.set_dims(input_values[var_name].shape)
tensor.alloc_float(core.CPUPlace())
tensor.set(input_values[var_name], core.CPUPlace())
# Create all output variable in local_scope
for output in op.outputs():
if local_scope.find_var(output) is None:
local_scope.new_var(output).get_tensor()
op.infer_shape(local_scope)
# allocate output memory
for output in op.outputs():
local_scope.find_var(output).get_tensor().alloc_float(core.CPUPlace())
# TODO(yuyang18): Only CPU is support now.
cpu_ctx = core.DeviceContext.create(core.CPUPlace())
def get_output():
op.run(local_scope, cpu_ctx)
return numpy.array(local_scope.find_var(output_name).get_tensor()).sum()
def product(dim):
return reduce(lambda a, b: a * b, dim, 1)
tensor_to_check = local_scope.find_var(input_to_check).get_tensor()
tensor_size = product(tensor_to_check.get_dims())
gradient_flat = numpy.zeros(shape=(tensor_size, ), dtype='float32')
for i in xrange(tensor_size):
origin = tensor_to_check.get_float_element(i)
x_pos = origin + delta
tensor_to_check.set_float_element(i, x_pos)
y_pos = get_output()
x_neg = origin - delta
tensor_to_check.set_float_element(i, x_neg)
y_neg = get_output()
tensor_to_check.set_float_element(i, origin) # restore old value
gradient_flat[i] = (y_pos - y_neg) / delta / 2
return gradient_flat.reshape(tensor_to_check.get_dims())
if __name__ == '__main__':
class GetNumericGradientTest(unittest.TestCase):
def test_add_op(self):
add_op = op_creations.add_two(X="X", Y="Y", Out="Z")
x = numpy.random.random((10, 1)).astype("float32")
y = numpy.random.random((10, 1)).astype("float32")
arr = get_numeric_gradient(add_op, {'X': x, "Y": y}, 'Z', 'X')
self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-2)
unittest.main()
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