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Paddle
提交 d2be7ec3 编写于 作者: D Dong Zhihong
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Merge remote-tracking branch 'origin/develop' into multigpu

上级 54d3dbd8 d7383c6d
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Showing with 3136 addition and 535 deletion
CMakeLists.txt
浏览文件 @ d2be7ec3
......@@ -105,6 +105,12 @@ if (WITH_C_API AND WITH_PYTHON)
"different Python interpreter from compiling.")
endif()
if(MOBILE_INFERENCE)
set(THIRD_PARTY_BUILD_TYPE MinSizeRel)
else()
set(THIRD_PARTY_BUILD_TYPE Release)
endif()
########################################################################################
include(external/mklml) # download mklml package
......
cmake/external/eigen.cmake
浏览文件 @ d2be7ec3
......@@ -8,7 +8,7 @@ ExternalProject_Add(
extern_eigen3
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/RLovelett/eigen.git"
GIT_TAG "master"
GIT_TAG 4e79cb69b9425f5f8c3a84be4350d4ab75b5fd9d
PREFIX ${EIGEN_SOURCE_DIR}
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
......
cmake/external/gflags.cmake
浏览文件 @ d2be7ec3
......@@ -36,6 +36,7 @@ ExternalProject_Add(
# change this back to the official Github repo once my PR is
# merged.
GIT_REPOSITORY "https://github.com/wangkuiyi/gflags.git"
GIT_TAG 986964c07427ecb9cdb5bd73f73ebbd40e54dadb
PREFIX ${GFLAGS_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
......@@ -45,11 +46,11 @@ ExternalProject_Add(
-DCMAKE_INSTALL_PREFIX=${GFLAGS_INSTALL_DIR}
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DBUILD_TESTING=OFF
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
${EXTERNAL_OPTIONAL_ARGS}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${GFLAGS_INSTALL_DIR}
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=Release
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
)
ADD_LIBRARY(gflags STATIC IMPORTED GLOBAL)
......
cmake/external/glog.cmake
浏览文件 @ d2be7ec3
......@@ -31,6 +31,7 @@ ExternalProject_Add(
${EXTERNAL_PROJECT_LOG_ARGS}
DEPENDS gflags
GIT_REPOSITORY "https://github.com/google/glog.git"
GIT_TAG v0.3.5
PREFIX ${GLOG_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
......@@ -43,12 +44,12 @@ ExternalProject_Add(
-DWITH_GFLAGS=ON
-Dgflags_DIR=${GFLAGS_INSTALL_DIR}/lib/cmake/gflags
-DBUILD_TESTING=OFF
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
${EXTERNAL_OPTIONAL_ARGS}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${GLOG_INSTALL_DIR}
-DCMAKE_INSTALL_LIBDIR:PATH=${GLOG_INSTALL_DIR}/lib
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=Release
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
)
ADD_LIBRARY(glog STATIC IMPORTED GLOBAL)
......
cmake/external/gtest.cmake
浏览文件 @ d2be7ec3
......@@ -56,11 +56,11 @@ IF(WITH_TESTING)
-DBUILD_GMOCK=ON
-Dgtest_disable_pthreads=ON
-Dgtest_force_shared_crt=ON
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
${EXTERNAL_OPTIONAL_ARGS}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${GTEST_INSTALL_DIR}
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=Release
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
)
ADD_LIBRARY(gtest STATIC IMPORTED GLOBAL)
......
cmake/external/protobuf.cmake
浏览文件 @ d2be7ec3
......@@ -191,12 +191,12 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST)
${OPTIONAL_ARGS}
-Dprotobuf_BUILD_TESTS=OFF
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
-DCMAKE_INSTALL_PREFIX=${PROTOBUF_INSTALL_DIR}
-DCMAKE_INSTALL_LIBDIR=lib
CMAKE_CACHE_ARGS
-DCMAKE_INSTALL_PREFIX:PATH=${PROTOBUF_INSTALL_DIR}
-DCMAKE_BUILD_TYPE:STRING=Release
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
-DCMAKE_VERBOSE_MAKEFILE:BOOL=OFF
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
${OPTIONAL_CACHE_ARGS}
......
cmake/external/warpctc.cmake
浏览文件 @ d2be7ec3
......@@ -35,6 +35,7 @@ ExternalProject_Add(
extern_warpctc
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/gangliao/warp-ctc.git"
GIT_TAG b63a0644654a3e0ed624c85a1767bc8193aead09
PREFIX ${WARPCTC_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
......@@ -48,9 +49,9 @@ ExternalProject_Add(
-DCMAKE_DISABLE_FIND_PACKAGE_Torch=ON
-DBUILD_SHARED=ON
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
${EXTERNAL_OPTIONAL_ARGS}
CMAKE_CACHE_ARGS -DCMAKE_BUILD_TYPE:STRING=Release
CMAKE_CACHE_ARGS -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_INSTALL_PREFIX:PATH=${WARPCTC_INSTALL_DIR}
)
......
cmake/external/zlib.cmake
浏览文件 @ d2be7ec3
......@@ -42,11 +42,11 @@ ExternalProject_Add(
-DBUILD_SHARED_LIBS=OFF
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DCMAKE_MACOSX_RPATH=ON
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
${EXTERNAL_OPTIONAL_ARGS}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${ZLIB_INSTALL_DIR}
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=Release
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
)
LIST(APPEND external_project_dependencies zlib)
......
doc/design/images/graph_construction_example.dot
浏览文件 @ d2be7ec3
......@@ -33,7 +33,6 @@ digraph ImageClassificationGraph {
cost -> MSE_Grad [color=red];
d_cost -> MSE_Grad [color=red];
x -> MSE_Grad [color=red];
l -> MSE_Grad [color=red];
y -> MSE_Grad -> d_y [color=red];
......
doc/design/register_grad_op.md
浏览文件 @ d2be7ec3
......@@ -3,15 +3,17 @@
## The Problem Posed
In our current operator registration mechanism, for each operator, the programmer should register a *gradient operator creator* function, which takes a C++ operator instance, and returns the corresponding gradient instance.
Currently, for each C++ operator class definition, there registers a *gradient operator creator* function, which takes a C++ operator instance and returns the corresponding gradient operator instance.
However, as we decided to separate the *compilation* and *execution* of DL models, we need to reshape the creator to take a protobuf `OpDesc` message, and returns a corresponding message.
However, we noticed two problems with the current deisgn:
More than that, the new registration mechanism need to support the fact that an operators' gradient computation might be a composition of operators.
1. As we decided to separate the *compilation* and *execution* phases, we need to change the creator to take an `OpDesc` protobuf message in a `ProgramDesc` and inserts corresponding `OpDesc` messages into the `ProgramDesc` message.
## Current Implementation
1. Some operator's gradient computation requires more than one gradient operators. For example, the gradient of *minus* consists of two operators -- an identity operaotr and a scale operator. So we need to make the registration mechanism to support the mapping from an operator to a set of operators for gradient computation.
OpInfos store in a association map which key is the operator type. The `grad_op_type` indicate associated gradient operator type. Operator can create gradient operator by `OpInfo::creator_` of gradient. The pseudo code is
## The Current Implementation
The C++ class `OpInfos` store in a association map which key is the operator type. The `grad_op_type` indicate associated gradient operator type. Operator can create gradient operator by `OpInfo::creator_` of gradient. The pseudo code is
```cpp
struct OpInfo {
......
doc/design/var_desc.md
浏览文件 @ d2be7ec3
......@@ -16,16 +16,23 @@ The computation graph is constructed by Data Node and Operation Node. The concep
## Definition of VarDesc
A VarDesc should have a name and value, in PaddlePaddle, the value will always be a tensor. Since we use LoDTensor most of the time. We add a LoDTesnorDesc to represent it.
A VarDesc should have a name, and value. The are two kinds of variable type in compile time, they are `LoDTensor` and `SelectedRows`.
```proto
message VarDesc {
required string name = 1;
optional LoDTensorDesc lod_tensor = 2;
enum VarType {
LOD_TENSOR = 0;
SELECTED_ROWS = 1;
}
required VarType type = 2;
optional LoDTensorDesc lod_desc = 3;
optional TensorDesc selected_rows_desc = 4;
optional bool persistable = 5 [ default = false ];
}
```
## Definition of LodTensorDesc
## Definition of TensorDesc
```proto
enum DataType {
......@@ -38,87 +45,25 @@ enum DataType {
FP64 = 6;
}
message LoDTensorDesc {
message TensorDesc {
required DataType data_type = 1;
repeated int32 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
optional int32 lod_level = 3 [default=0];
repeated int64 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
}
```
## Definition of Variable in Python
In Python API, layer will take Variable as Input, and return Variable as Output. There should be a class `Variable` in python to help create and manage Variable.
```python
image = Variable(dims=[-1, 640, 480])
# fc1 and fc2 are both Variable
fc1 = layer.fc(input=image, output_size=10)
fc2 = layer.fc(input=fc1, output_size=20)
```
### what should class `Variable` Have
1. `name`.a name of string type is used to mark the value of the Variable.
1. `initializer`. Since our Tensor does not have value. we will always use some Operator to fullfill it when run. So we should have a initialize method to help add the init operator.
1. `operator`. Variable should record which operator produce itself. The reaon is:
- we use pd.eval(targets=[var1, var2]) to run the related ops to get the value of var1 and var2. var.op is used to trace the dependency of the current variable.
In PaddlePaddle, we use Block to describe Computation Graph, so in the code we will use Block but not Graph.
```python
import VarDesc
import LoDTensorDesc
import framework
def AddInitialOperator(variable, initializer):
# add an initialize Operator to block to init this Variable
class Variable(object):
def __init__(self, name, dims, type, initializer):
self._block = get_default_block()
self._name = name
self.op = None
tensor_desc = LoDTensorDesc(data_type=type, dims=dims)
_var_desc = VarDesc(name=name, lod_tensor=tensor_desc)
self._var = framework.CreateVar(_var_desc)
self._block.add_var(self)
A TensorDesc describes `SelectedRows` and `LoDTensor`. For details of `SelectedRows`, please reference [`SelectedRows`](./selected_rows.md).
# add initial op according to initializer
if initializer is not None:
AddInitialOperator(self, initializer)
def dims(self):
return self._var.dims()
def data_type(self):
return self._var.data_type()
## Definition of LodTensorDesc
def to_proto(self):
pass
```proto
message LoDTensorDesc {
required TensorDesc tensor = 1;
optional int lod_level = 2;
}
```
Then we can use this Variable to create a fc layer in Python.
A LoDTensorDesc contains a tensor and a lod_level.
```python
import paddle as pd
def flatten_size(X, num_flatten_dims):
prod = 1 # of last num_flatten_dims
for i in xrange(num_flatten_dims):
prod = prod * X.dims[-i-1]
return prod
def layer.fc(X, output_size, num_flatten_dims):
W = Variable(pd.random_uniform(), type=FP32, dims=[flatten_size(X, num_flatten_dims), output_size])
b = Variable(pd.random_uniform(), type=FP32, dims=[output_size])
out = Variable(type=FP32)
y = operator.fc(X, W, b, output=out) # fc will put fc op input into out
pd.InferShape(y)
return out
x = Variable(dims=[-1, 640, 480])
y = layer.fc(x, output_size=100)
z = layer.fc(y, output_size=200)
## Definition of Variable in Python
paddle.eval(targets=[z], ...)
print(z)
```
For Variable in Python, please reference [`Python API`](./python_api.md).
paddle/api/CMakeLists.txt
浏览文件 @ d2be7ec3
......@@ -26,7 +26,7 @@ FILE(GLOB PY_PADDLE_PYTHON_FILES ${PADDLE_SOURCE_DIR}/paddle/py_paddle/*.py)
SET_SOURCE_FILES_PROPERTIES(Paddle.i PROPERTIES CPLUSPLUS ON)
SET(CMAKE_SWIG_OUTDIR ${CMAKE_CURRENT_BINARY_DIR})
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-parentheses-equality -Wno-missing-field-initializers -Wno-self-assign")
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-parentheses-equality -Wno-missing-field-initializers -Wno-self-assign -ftls-model=global-dynamic")
SET(SWIG_MODULE_swig_paddle_EXTRA_DEPS
paddle_parameter
......
paddle/framework/CMakeLists.txt
浏览文件 @ d2be7ec3
......@@ -19,10 +19,10 @@ cc_test(scope_test SRCS scope_test.cc DEPS scope)
proto_library(framework_proto SRCS framework.proto)
cc_library(attribute SRCS attribute.cc DEPS framework_proto)
cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS attribute ddim)
cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS attribute ddim op_info)
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 proto_desc)
cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope proto_desc)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
......@@ -42,12 +42,17 @@ add_custom_command(TARGET framework_py_proto POST_BUILD
cc_library(backward SRCS backward.cc DEPS net_op)
cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward ${GLOB_OP_LIB})
#if(WITH_GPU)
# nv_test(executor_test SRCS executor_test.cc DEPS executor)
#else()
# cc_test(executor_test SRCS executor_test.cc DEPS executor)
#endif()
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward)
set(EXECUTOR_TEST_OP elementwise_add_op gaussian_random_op feed_op fetch_op
mul_op sum_op squared_l2_distance_op fill_constant_op sgd_op mean_op)
if(WITH_GPU)
nv_test(executor_test SRCS executor_test.cc DEPS executor ${EXECUTOR_TEST_OP})
else()
cc_test(executor_test SRCS executor_test.cc DEPS executor ${EXECUTOR_TEST_OP})
endif()
cc_library(tensor_array SRCS tensor_array.cc DEPS lod_tensor)
cc_test(tensor_array_test SRCS tensor_array_test.cc DEPS tensor_array place)
cc_library(selected_rows SRCS selected_rows.cc DEPS tensor)
cc_test(selected_rows_test SRCS selected_rows_test.cc DEPS selected_rows)
paddle/framework/backward.cc
浏览文件 @ d2be7ec3
......@@ -28,14 +28,15 @@ namespace paddle {
namespace framework {
static inline std::unique_ptr<OperatorBase> CreateGradOp(
const OperatorBase& op) {
const OperatorBase& op, const std::unordered_set<std::string>& no_grad_set,
std::unordered_map<std::string, std::string>* grad_to_var) {
OpDescBind op_desc;
op_desc.SetInputMap(op.Inputs());
op_desc.SetOutputMap(op.Outputs());
op_desc.SetType(op.Type());
op_desc.SetAttrMap(op.Attrs());
auto& info = OpInfoMap::Instance().Get(op.Type());
auto grad_descs = info.GradOpMaker()(op_desc);
auto grad_descs = info.GradOpMaker()(op_desc, no_grad_set, grad_to_var);
std::vector<std::unique_ptr<OperatorBase>> grad_ops;
grad_ops.reserve(grad_descs.size());
std::transform(grad_descs.begin(), grad_descs.end(),
......@@ -98,7 +99,9 @@ static std::unique_ptr<OperatorBase> NOP() {
// See Backward.h for details
static std::unique_ptr<OperatorBase> BackwardRecursive(
const OperatorBase& forwardOp,
std::unordered_set<std::string>& no_grad_names, size_t& uniq_id) {
std::unordered_set<std::string>& no_grad_names,
std::unordered_map<std::string, std::string>* grad_to_var,
size_t& uniq_id) {
// If all input gradients of forwarding operator do not need to calculate,
// just return an NOP. Not return null ptr because NOP does not take
// too much time for calculation, but it is useful for simplifying logic.
......@@ -136,7 +139,7 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
for (auto it = forwardNet.ops_.rbegin(); it != forwardNet.ops_.rend();
++it, ++local_op_id) {
auto& fwd = *it;
auto bwd = BackwardRecursive(*fwd, no_grad_names, uniq_id);
auto bwd = BackwardRecursive(*fwd, no_grad_names, grad_to_var, uniq_id);
ForEachVarName(bwd->Outputs(),
[&dup_output_ops, local_op_id](const std::string& out) {
dup_output_ops[out].emplace_back(local_op_id);
......@@ -187,7 +190,8 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
net->InsertOp(pos.first + 1, std::move(pos.second));
}
} else {
std::unique_ptr<OperatorBase> grad_op(CreateGradOp(forwardOp));
std::unique_ptr<OperatorBase> grad_op(
CreateGradOp(forwardOp, no_grad_names, grad_to_var));
ForEachVarName(grad_op->Inputs(), [&no_grad_names, &net, &grad_op](
const std::string& grad_input) {
......@@ -226,7 +230,7 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
*static_cast<const OperatorBase*>(&rnnop.stepnet());
// create stepnet's gradient op
rnn_grad_op->set_stepnet(
BackwardRecursive(stepnet_op, no_grad_names, uniq_id));
BackwardRecursive(stepnet_op, no_grad_names, grad_to_var, uniq_id));
}
if (net->ops_.empty()) { // Current no aux op is added to network
......@@ -253,7 +257,8 @@ std::unique_ptr<OperatorBase> Backward(
no_grad_names.insert(name + kGradVarSuffix);
}
size_t uid = 0;
return BackwardRecursive(forwardOp, no_grad_names, uid);
std::unordered_map<std::string, std::string> grad_to_var;
return BackwardRecursive(forwardOp, no_grad_names, &grad_to_var, uid);
}
// ==================================== //
......@@ -268,30 +273,48 @@ static bool AllGradInSet(const std::vector<std::string>& names,
return true;
}
static void CreateGradVarInBlock(BlockDescBind* block_desc,
size_t grad_op_start_index) {
auto ops = block_desc->AllOps();
for (size_t op_index = grad_op_start_index; op_index < ops.size();
++op_index) {
for (const auto& output : ops[op_index]->Outputs()) {
for (const auto& real_output : output.second) {
if (!block_desc->HasVar(real_output)) {
block_desc->NewVar(real_output);
}
}
}
}
}
std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
const std::unique_ptr<OpDescBind>& op_desc,
std::unordered_set<std::string>& no_grad_vars) {
std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var) {
std::vector<std::unique_ptr<OpDescBind>> grad_op_descs;
// All input gradients of forwarding operator do not need to calculat.
// All input gradients of forwarding operator do not need to calculate.
const std::vector<std::string>& inputs = op_desc->InputArgumentNames();
if (AllGradInSet(inputs, no_grad_vars)) {
if (AllGradInSet(inputs, *no_grad_vars)) {
return grad_op_descs; // empty vector
}
// All output gradients of forwarding operator do not need to calculate.
const std::vector<std::string>& outputs = op_desc->OutputArgumentNames();
if (AllGradInSet(outputs, no_grad_vars)) {
if (AllGradInSet(outputs, *no_grad_vars)) {
for (const std::string& name : inputs) {
no_grad_vars.insert(GradVarName(name));
no_grad_vars->insert(GradVarName(name));
}
return grad_op_descs; // empty vector
}
grad_op_descs = OpRegistry::CreateGradOpDescs(op_desc.get());
grad_op_descs = OpInfoMap::Instance()
.Get(op_desc->Type())
.GradOpMaker()(*op_desc, *no_grad_vars, grad_to_var);
std::list<std::unique_ptr<OpDescBind>> pending_fill_zeros_ops;
for (auto& desc : grad_op_descs) {
for (const std::string& in_name : desc->InputArgumentNames()) {
if (no_grad_vars.count(in_name)) {
if (no_grad_vars->count(in_name)) {
std::string prefix = in_name.substr(
0, in_name.size() - sizeof(kGradVarSuffix) / sizeof(char) + 1);
std::string new_name = prefix + kZeroVarSuffix;
......@@ -301,11 +324,6 @@ std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
pending_fill_zeros_ops.push_back(std::move(fill_zeros_op));
}
}
for (const std::string& out_name : desc->OutputArgumentNames()) {
if (no_grad_vars.count(out_name)) {
desc->Rename(out_name, kEmptyVarName);
}
}
}
for (auto& p : pending_fill_zeros_ops) {
......@@ -316,23 +334,25 @@ std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
ProgramDescBind& program_desc, int block_idx,
std::unordered_set<std::string>& no_grad_vars) {
std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var) {
BlockDescBind* cur_block = program_desc.Block(block_idx);
std::deque<std::unique_ptr<OpDescBind>>& op_descs = cur_block->ops_;
std::unordered_map<std::string, std::vector<size_t>> dup_out_ops;
size_t grad_desc_idx = 0;
std::vector<std::unique_ptr<OpDescBind>> backward_descs;
for (auto it = op_descs.rbegin(); it != op_descs.rend(); ++it) {
std::vector<std::unique_ptr<OpDescBind>> op_grads =
MakeOpGrad(*it, no_grad_vars);
MakeOpGrad(*it, no_grad_vars, grad_to_var);
if ((*it)->Type() == "recurrent") {
PADDLE_ENFORCE_EQ(
op_grads.size(), size_t(1),
op_grads.size(), static_cast<size_t>(1),
"rnn_op's gradient process should contain only one op.");
int step_block_idx = (*it)->GetBlockAttr("stop_block");
auto backward_block_op_descs =
MakeBlockBackward(program_desc, step_block_idx, no_grad_vars);
int step_block_idx = (*it)->GetBlockAttr("step_block");
auto backward_block_op_descs = MakeBlockBackward(
program_desc, step_block_idx, no_grad_vars, grad_to_var);
BlockDescBind* backward_block = program_desc.AppendBlock(*cur_block);
for (auto& ptr : backward_block_op_descs) {
backward_block->ops_.push_back(std::move(ptr));
......@@ -376,10 +396,11 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
backward_descs.insert(backward_descs.begin() + p.first + 1,
std::move(p.second));
}
return backward_descs;
}
void AppendBackward(ProgramDescBind& program_desc,
void AppendBackward(ProgramDescBind& program_desc, const VarDescBind& target,
const std::unordered_set<std::string>& no_grad_vars) {
std::unordered_set<std::string> no_grad_var_names;
no_grad_var_names.reserve(no_grad_vars.size() + 1);
......@@ -387,12 +408,34 @@ void AppendBackward(ProgramDescBind& program_desc,
for (auto& name : no_grad_vars) {
no_grad_var_names.insert(GradVarName(name));
}
const int root_block_idx = 0;
auto backward_op_descs =
MakeBlockBackward(program_desc, root_block_idx, no_grad_var_names);
auto& forw_op_descs = program_desc.Block(root_block_idx)->ops_;
auto root_block = program_desc.Block(root_block_idx);
auto& all_ops = root_block->ops_;
// insert fill one op for target
std::string fill_one_op_out = GradVarName(target.Name());
std::unique_ptr<OpDescBind> fill_one_op(
new OpDescBind("fill_constant", {}, {{"Out", {fill_one_op_out}}},
{{"shape", std::vector<int>{1}},
{"value", static_cast<float>(1.0)},
{"dataType", framework::DataType::FP32}}));
all_ops.push_back(std::move(fill_one_op));
size_t forward_op_num = all_ops.size();
size_t forward_block_num = program_desc.Size();
std::unordered_map<std::string, std::string> grad_to_var;
auto backward_op_descs = MakeBlockBackward(program_desc, root_block_idx,
&no_grad_var_names, &grad_to_var);
for (auto& ptr : backward_op_descs) {
forw_op_descs.push_back(std::move(ptr));
all_ops.push_back(std::move(ptr));
}
root_block->NewVar(fill_one_op_out);
// create grad_var for all blocks in this program
CreateGradVarInBlock(root_block, forward_op_num);
for (size_t block_index = forward_block_num;
block_index < program_desc.Size(); ++block_index) {
CreateGradVarInBlock(program_desc.Block(block_index), 0);
}
}
......
paddle/framework/backward.h
浏览文件 @ d2be7ec3
......@@ -29,7 +29,7 @@ extern std::unique_ptr<OperatorBase> Backward(
// TODO(jiayi): Add target as parameter and generate backward op
// according to target.
void AppendBackward(ProgramDescBind& program_desc,
void AppendBackward(ProgramDescBind& program_desc, const VarDescBind& target,
const std::unordered_set<std::string>& no_grad_vars);
} // namespace framework
......
paddle/framework/backward_test.cc
浏览文件 @ d2be7ec3
......@@ -18,6 +18,7 @@
#include "paddle/framework/block_desc.h"
#include "paddle/framework/op_desc.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/var_desc.h"
#include "paddle/operators/net_op.h"
namespace paddle {
......@@ -169,6 +170,45 @@ class MultInOutOpMaker : public OpProtoAndCheckerMaker {
}
};
class MinusGradOpDescMaker : public GradOpDescMakerBase {
public:
using GradOpDescMakerBase::GradOpDescMakerBase;
std::vector<std::unique_ptr<OpDescBind>> operator()() const override {
std::vector<std::unique_ptr<OpDescBind>> retv;
auto x_g = InputGrad("X");
if (!x_g.empty()) {
auto *op_desc = new OpDescBind();
op_desc->SetType("scale");
op_desc->SetInput("X", OutputGrad("Out"));
op_desc->SetOutput("Out", x_g);
op_desc->SetAttr("scale", 1.0f);
retv.emplace_back(op_desc);
}
auto y_g = InputGrad("Y");
if (!y_g.empty()) {
auto *op_desc = new OpDescBind();
op_desc->SetType("scale");
op_desc->SetInput("X", OutputGrad("Out"));
op_desc->SetOutput("Out", y_g);
op_desc->SetAttr("scale", -1.0f);
retv.emplace_back(op_desc);
}
return retv;
}
};
class MinusOpMaker : public OpProtoAndCheckerMaker {
public:
MinusOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "");
AddInput("Y", "");
AddOutput("Out", "");
AddComment("minus for unittest");
}
};
} // namespace framework
} // namespace paddle
......@@ -187,6 +227,7 @@ REGISTER_OP_WITHOUT_GRADIENT(fc, f::FcOp, f::FcOpMaker);
REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, many_output_op_grad,
f::NOP);
REGISTER_OP(mult_in_out, f::NOP, f::MultInOutOpMaker, mult_in_out_grad, f::NOP);
REGISTER_OPERATOR(minus, f::NOP, f::MinusOpMaker, f::MinusGradOpDescMaker);
TEST(Backward, simple_op_not_need_grad) {
auto fwd = f::OpRegistry::CreateOp(
......@@ -395,12 +436,13 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
2UL /* external input number */
+ 1UL /* external output number*/
+ 1UL /* number of gradient of external output*/
+ 2U /* internal variable number*/);
+ 2UL /* internal variable number*/
);
EXPECT_EQ(grad_fc.Outputs(all).size(),
2UL /* input number of mul*/
+ 2UL /* input number of rowwise_add
*/
+ 1UL /* input number of sigmod */);
+ 2UL /* input number of rowwise_add*/
+ 1UL /* input number of sigmod */
- 1UL /* out2 is not needed*/);
EXPECT_EQ(bwd_net->ops_[1]->Inputs(all).size(), 0UL);
EXPECT_EQ(bwd_net->ops_[1]->Outputs(all).size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->Inputs(all).size(), 0UL);
......@@ -427,10 +469,14 @@ TEST(Backward, simple_single_op) {
op->SetInput("b", {"b"});
op->SetOutput("Out", {"out"});
AppendBackward(program, {});
auto target = f::VarDescBind("out");
AppendBackward(program, target, {});
ASSERT_EQ(block->AllOps().size(), 2UL);
f::OpDescBind *grad_op = block->AllOps()[1];
ASSERT_EQ(block->AllOps().size(), 3UL);
f::OpDescBind *fill_op = block->AllOps()[1];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op = block->AllOps()[2];
EXPECT_EQ(grad_op->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op->InputNames().size(), 1UL);
ASSERT_EQ(grad_op->OutputNames().size(), 2UL);
......@@ -451,14 +497,19 @@ TEST(Backward, default_attribute) {
op->SetInput("X", {"x"});
op->SetInput("Y", {"y"});
op->SetOutput("Out", {"out"});
op->CheckAttrs();
AppendBackward(program, {});
auto target = f::VarDescBind("out");
AppendBackward(program, target, {});
ASSERT_EQ(block->AllOps().size(), 2UL);
ASSERT_EQ(block->AllOps().size(), 3UL);
EXPECT_EQ(boost::get<int>(op->GetAttr("x_num_col_dims")), 1);
EXPECT_EQ(boost::get<int>(op->GetAttr("y_num_col_dims")), 1);
f::OpDescBind *grad_op = block->AllOps()[1];
f::OpDescBind *fill_op = block->AllOps()[1];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op = block->AllOps()[2];
ASSERT_EQ(grad_op->Type(), "mul_grad");
EXPECT_EQ(boost::get<int>(grad_op->GetAttr("x_num_col_dims")), 1);
EXPECT_EQ(boost::get<int>(grad_op->GetAttr("y_num_col_dims")), 1);
......@@ -486,10 +537,15 @@ TEST(Backward, simple_mult_op) {
op3->SetInput("b", {"b3"});
op3->SetOutput("Out", {"out3"});
AppendBackward(program, {});
auto target = f::VarDescBind("out3");
size_t forward_len = block->AllOps().size();
AppendBackward(program, target, {});
ASSERT_EQ(block->AllOps().size(), 6UL);
f::OpDescBind *grad_op1 = block->AllOps()[5];
ASSERT_EQ(block->AllOps().size(), 6UL + 1);
f::OpDescBind *fill_op = block->AllOps()[forward_len];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op1 = block->AllOps()[6];
EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
......@@ -500,7 +556,7 @@ TEST(Backward, simple_mult_op) {
EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b1")}));
f::OpDescBind *grad_op2 = block->AllOps()[4];
f::OpDescBind *grad_op2 = block->AllOps()[5];
EXPECT_EQ(grad_op2->Type(), "mul_grad");
ASSERT_EQ(grad_op2->InputNames().size(), 4UL);
ASSERT_EQ(grad_op2->OutputNames().size(), 2UL);
......@@ -514,7 +570,7 @@ TEST(Backward, simple_mult_op) {
EXPECT_EQ(grad_op2->Output(f::GradVarName("Y")),
std::vector<std::string>({f::GradVarName("y2")}));
f::OpDescBind *grad_op3 = block->AllOps()[3];
f::OpDescBind *grad_op3 = block->AllOps()[4];
EXPECT_EQ(grad_op3->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op3->InputNames().size(), 1UL);
ASSERT_EQ(grad_op3->OutputNames().size(), 2UL);
......@@ -554,10 +610,15 @@ TEST(Backward, intermedia_var_no_grad) {
op4->SetInput("Y", {"out3"});
op4->SetOutput("Out", {"out4"});
AppendBackward(program, {"out3"});
auto target = f::VarDescBind("out4");
size_t forward_len = block->AllOps().size();
AppendBackward(program, target, {"out3"});
ASSERT_EQ(block->AllOps().size(), 6UL);
f::OpDescBind *grad_op1 = block->AllOps()[5];
ASSERT_EQ(block->AllOps().size(), 7UL);
f::OpDescBind *fill_op = block->AllOps()[forward_len];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op1 = block->AllOps()[6];
EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
......@@ -568,7 +629,7 @@ TEST(Backward, intermedia_var_no_grad) {
EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b1")}));
f::OpDescBind *grad_op4 = block->AllOps()[4];
f::OpDescBind *grad_op4 = block->AllOps()[5];
EXPECT_EQ(grad_op4->Type(), "mul_grad");
ASSERT_EQ(grad_op4->InputNames().size(), 4UL);
ASSERT_EQ(grad_op4->OutputNames().size(), 2UL);
......@@ -579,8 +640,7 @@ TEST(Backward, intermedia_var_no_grad) {
std::vector<std::string>({f::GradVarName("out4")}));
EXPECT_EQ(grad_op4->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("out1")}));
EXPECT_EQ(grad_op4->Output(f::GradVarName("Y")),
std::vector<std::string>({f::kEmptyVarName}));
EXPECT_EQ(grad_op4->Output(f::GradVarName("Y")), std::vector<std::string>());
}
TEST(Backward, var_no_grad) {
......@@ -601,10 +661,15 @@ TEST(Backward, var_no_grad) {
op2->SetOutput("Y", {"y2"});
op2->SetOutput("Z", {"z2"});
AppendBackward(program, {"z1"});
auto target = f::VarDescBind("z2");
size_t forward_len = block->AllOps().size();
AppendBackward(program, target, {"z1"});
ASSERT_EQ(block->AllOps().size(), 6UL);
f::OpDescBind *fill_op = block->AllOps()[forward_len];
EXPECT_EQ(fill_op->Type(), "fill_constant");
ASSERT_EQ(block->AllOps().size(), 5UL);
f::OpDescBind *grad_op2 = block->AllOps()[2];
f::OpDescBind *grad_op2 = block->AllOps()[3];
ASSERT_EQ(grad_op2->Type(), "mult_in_out_grad");
ASSERT_EQ(grad_op2->InputNames().size(), 6UL);
ASSERT_EQ(grad_op2->OutputNames().size(), 2UL);
......@@ -618,10 +683,9 @@ TEST(Backward, var_no_grad) {
std::vector<std::string>({f::GradVarName("z2")}));
EXPECT_EQ(grad_op2->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("y1")}));
EXPECT_EQ(grad_op2->Output(f::GradVarName("H")),
std::vector<std::string>({f::kEmptyVarName}));
EXPECT_EQ(grad_op2->Output(f::GradVarName("H")), std::vector<std::string>());
f::OpDescBind *fill_zero_op = block->AllOps()[3];
f::OpDescBind *fill_zero_op = block->AllOps()[4];
ASSERT_EQ(fill_zero_op->Type(), "fill_zeros_like");
ASSERT_EQ(fill_zero_op->InputNames().size(), 1UL);
ASSERT_EQ(fill_zero_op->OutputNames().size(), 1UL);
......@@ -629,7 +693,7 @@ TEST(Backward, var_no_grad) {
EXPECT_EQ(fill_zero_op->Output("Y"),
std::vector<std::string>({std::string("z1") + f::kZeroVarSuffix}));
f::OpDescBind *grad_op1 = block->AllOps()[4];
f::OpDescBind *grad_op1 = block->AllOps()[5];
ASSERT_EQ(grad_op1->Type(), "mult_in_out_grad");
ASSERT_EQ(grad_op1->InputNames().size(), 6UL);
ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
......@@ -669,10 +733,15 @@ TEST(Backward, shared_var) {
op3->SetInput("b", {"b3"});
op3->SetOutput("Out", {"out3"});
AppendBackward(program, {});
auto target = f::VarDescBind("out3");
size_t forward_len = block->AllOps().size();
AppendBackward(program, target, {});
ASSERT_EQ(block->AllOps().size(), 7UL);
f::OpDescBind *grad_op3 = block->AllOps()[3];
ASSERT_EQ(block->AllOps().size(), 8UL);
f::OpDescBind *fill_op = block->AllOps()[forward_len];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op3 = block->AllOps()[4];
ASSERT_EQ(grad_op3->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op3->InputNames().size(), 1UL);
ASSERT_EQ(grad_op3->OutputNames().size(), 2UL);
......@@ -683,7 +752,7 @@ TEST(Backward, shared_var) {
EXPECT_EQ(grad_op3->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b3")}));
f::OpDescBind *grad_op4 = block->AllOps()[4];
f::OpDescBind *grad_op4 = block->AllOps()[5];
ASSERT_EQ(grad_op4->Type(), "mul_grad");
ASSERT_EQ(grad_op4->InputNames().size(), 4UL);
ASSERT_EQ(grad_op4->OutputNames().size(), 2UL);
......@@ -697,7 +766,7 @@ TEST(Backward, shared_var) {
EXPECT_EQ(grad_op4->Output(f::GradVarName("Y")),
std::vector<std::string>({f::GradVarName("y2")}));
f::OpDescBind *sum_op = block->AllOps()[5];
f::OpDescBind *sum_op = block->AllOps()[6];
ASSERT_EQ(sum_op->Type(), "sum");
ASSERT_EQ(sum_op->InputNames().size(), 1UL);
ASSERT_EQ(sum_op->OutputNames().size(), 1UL);
......@@ -707,7 +776,7 @@ TEST(Backward, shared_var) {
EXPECT_EQ(sum_op->Output("Out"),
std::vector<std::string>({f::GradVarName("out1")}));
f::OpDescBind *grad_op1 = block->AllOps()[6];
f::OpDescBind *grad_op1 = block->AllOps()[7];
ASSERT_EQ(grad_op1->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
......@@ -717,4 +786,23 @@ TEST(Backward, shared_var) {
std::vector<std::string>({f::GradVarName("x1")}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b1")}));
}
\ No newline at end of file
}
TEST(Backward, half_backward) {
f::ProgramDesc *program_desc = GetNewProgramDesc();
f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc);
f::BlockDescBind *block = program.Block(0);
auto *op1 = block->AppendOp();
op1->SetType("minus");
op1->SetInput("X", {"a"});
op1->SetInput("Y", {"b"});
op1->SetOutput("Out", {"out"});
auto target = f::VarDescBind("out");
size_t forward_len = block->AllOps().size();
AppendBackward(program, target, {"b"});
f::OpDescBind *fill_op = block->AllOps()[forward_len];
EXPECT_EQ(fill_op->Type(), "fill_constant");
auto ops = block->AllOps();
ASSERT_EQ(3UL, ops.size());
}
paddle/framework/block_desc.cc
浏览文件 @ d2be7ec3
......@@ -66,7 +66,7 @@ std::vector<OpDescBind *> BlockDescBind::AllOps() const {
return res;
}
void BlockDescBind::Sync() {
void BlockDescBind::Flush() {
if (need_update_) {
auto &op_field = *this->desc_->mutable_ops();
op_field.Clear();
......@@ -91,9 +91,10 @@ BlockDescBind *BlockDescBind::ParentBlock() const {
return prog_->Block(static_cast<size_t>(this->desc_->parent_idx()));
}
void OpDescBind::SetBlockAttr(const std::string &name, BlockDescBind &block) {
BlockDesc *desc = block.RawPtr();
this->attrs_[name] = desc;
BlockDesc *BlockDescBind::Proto() {
Flush();
return desc_;
}
} // namespace framework
} // namespace paddle
paddle/framework/block_desc.h
浏览文件 @ d2be7ec3
......@@ -35,10 +35,11 @@ class BlockDescBind {
public:
friend std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
ProgramDescBind &program_desc, int block_idx,
std::unordered_set<std::string> &no_grad_vars);
std::unordered_set<std::string> *no_grad_vars,
std::unordered_map<std::string, std::string> *grad_to_var);
friend void AppendBackward(
ProgramDescBind &program_desc,
ProgramDescBind &program_desc, const VarDescBind &target,
const std::unordered_set<std::string> &no_grad_vars);
BlockDescBind(ProgramDescBind *prog, BlockDesc *desc)
......@@ -64,9 +65,9 @@ class BlockDescBind {
std::vector<OpDescBind *> AllOps() const;
void Sync();
void Flush();
BlockDesc *RawPtr() { return desc_; }
BlockDesc *Proto();
private:
ProgramDescBind *prog_; // not_own
......
paddle/framework/details/op_registry.h
浏览文件 @ d2be7ec3
......@@ -97,8 +97,11 @@ struct OpInfoFiller<T, kOpProtoAndCheckerMaker> {
template <typename T>
struct OpInfoFiller<T, kGradOpDescMaker> {
void operator()(const char* op_type, OpInfo* info) const {
info->grad_op_maker_ = [](const OpDescBind& fwd_op) {
T maker(fwd_op);
info->grad_op_maker_ = [](
const OpDescBind& fwd_op,
const std::unordered_set<std::string>& no_grad_set,
std::unordered_map<std::string, std::string>* grad_to_var) {
T maker(fwd_op, no_grad_set, grad_to_var);
return maker();
};
}
......
paddle/framework/executor_test.cc
浏览文件 @ d2be7ec3
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <memory>
#include <vector>
#include "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/framework/attribute.h"
#include "paddle/framework/backward.h"
......@@ -25,6 +26,17 @@ limitations under the License. */
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
USE_OP(elementwise_add);
USE_OP(gaussian_random);
USE_OP(feed);
USE_OP(fetch);
USE_OP(mul);
USE_OP(sum);
USE_OP(squared_l2_distance);
USE_OP(fill_constant);
USE_OP(mean);
USE_OP(sgd);
using namespace paddle::platform;
using namespace paddle::framework;
......@@ -34,8 +46,10 @@ void AddOp(const std::string& type, const VariableNameMap& inputs,
// insert output
for (auto kv : outputs) {
for (auto v : kv.second) {
auto var = block->NewVar(v);
var->SetDataType(paddle::framework::DataType::FP32);
if (!block->HasVar(v)) {
auto var = block->NewVar(v);
var->SetDataType(paddle::framework::DataType::FP32);
}
}
}
......@@ -49,6 +63,7 @@ void AddOp(const std::string& type, const VariableNameMap& inputs,
op->SetOutput(kv.first, kv.second);
}
op->SetAttrMap(attrs);
op->CheckAttrs();
}
// Tensors in feed value variable will only be in CPUPlace
......@@ -134,12 +149,12 @@ class ExecutorTesterRandom : public ::testing::Test {
AddOp("squared_l2_distance", {{"X", {"a"}}, {"Y", {"a_out"}}},
{{"Out", {"l2_distance"}}, {"sub_result", {"l2_distance_sub"}}}, {},
root_block);
AddOp("mean", {{"X", {"l2_distance"}}}, {{"Out", {"mean_out"}}}, {},
root_block);
// backward
AddOp("fill_constant", {}, {{"Out", {"l2_distance@GRAD"}}},
{{"shape", std::vector<int>{batch_size, 1}}, {"value", float(1.0)}},
root_block);
AppendBackward(program, {});
auto target = VarDescBind("mean_out");
AppendBackward(program, target, {});
// update
AddOp("fill_constant", {}, {{"Out", {"learning_rate"}}},
......@@ -305,4 +320,14 @@ TEST_F(ExecutorTesterFeedAndFetch, GPU) {
}
}
}
DECLARE_double(fraction_of_gpu_memory_to_use);
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
// Use less GPU memory for unittest.
FLAGS_fraction_of_gpu_memory_to_use = 0.25;
return RUN_ALL_TESTS();
}
#endif
paddle/framework/framework.proto
浏览文件 @ d2be7ec3
......@@ -97,16 +97,26 @@ enum DataType {
FP64 = 6;
}
message LoDTensorDesc {
message TensorDesc {
required DataType data_type = 1;
repeated int64 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
optional int32 lod_level = 3 [ default = 0 ];
}
message LoDTensorDesc {
required TensorDesc tensor = 1;
optional int32 lod_level = 2 [ default = 0 ];
}
message VarDesc {
enum VarType {
LOD_TENSOR = 1;
SELECTED_ROWS = 2;
}
required string name = 1;
optional LoDTensorDesc lod_tensor = 2;
optional bool persistable = 3 [ default = false ];
required VarType type = 2;
optional LoDTensorDesc lod_tensor = 3;
optional TensorDesc selected_rows = 4;
optional bool persistable = 5 [ default = false ];
}
message BlockDesc {
......
paddle/framework/grad_op_desc_maker.h
浏览文件 @ d2be7ec3
......@@ -13,6 +13,8 @@
limitations under the License. */
#pragma once
#include <string>
#include <unordered_set>
#include "paddle/framework/op_desc.h"
#include "paddle/framework/operator.h"
......@@ -21,27 +23,50 @@ namespace framework {
class GradOpDescMakerBase {
public:
explicit GradOpDescMakerBase(const OpDescBind& fwd_op) : fwd_op_(fwd_op) {}
explicit GradOpDescMakerBase(
const OpDescBind& fwd_op,
const std::unordered_set<std::string>& no_grad_set,
std::unordered_map<std::string, std::string>* grad_to_var)
: fwd_op_(fwd_op), no_grad_set_(no_grad_set), grad_to_var_(grad_to_var) {}
virtual ~GradOpDescMakerBase() = default;
virtual std::vector<std::unique_ptr<OpDescBind>> operator()() const = 0;
protected:
static std::vector<std::string> ToGradNames(
const std::vector<std::string>& var_names) {
std::vector<std::string> InputGrad(const std::string& name,
bool drop_empty_grad = true) const {
std::vector<std::string> ret_val;
auto var_names = this->Input(name);
ret_val.reserve(var_names.size());
std::transform(var_names.begin(), var_names.end(),
std::back_inserter(ret_val), GradVarName);
return ret_val;
}
std::vector<std::string> InputGrad(const std::string& name) const {
return ToGradNames(fwd_op_.Input(name));
std::back_inserter(ret_val),
[this](const std::string& fwd_var_name) -> std::string {
auto g_name = GradVarName(fwd_var_name);
if (no_grad_set_.count(g_name)) {
return kEmptyVarName;
} else {
(*this->grad_to_var_)[g_name] = fwd_var_name;
return g_name;
}
});
if (!drop_empty_grad) {
return ret_val;
}
std::vector<std::string> dropped_ret_val;
dropped_ret_val.reserve(ret_val.size());
std::copy_if(ret_val.begin(), ret_val.end(),
std::back_inserter(dropped_ret_val),
[](const std::string& str) { return str != kEmptyVarName; });
return dropped_ret_val;
}
std::vector<std::string> OutputGrad(const std::string& name) const {
return ToGradNames(fwd_op_.Output(name));
std::vector<std::string> ret_val;
auto onames = this->Output(name);
ret_val.reserve(onames.size());
std::transform(onames.begin(), onames.end(), std::back_inserter(ret_val),
GradVarName);
return ret_val;
}
std::vector<std::string> InputNames() const {
......@@ -75,6 +100,8 @@ class GradOpDescMakerBase {
private:
const OpDescBind& fwd_op_;
const std::unordered_set<std::string>& no_grad_set_;
std::unordered_map<std::string, std::string>* grad_to_var_;
};
class SingleGradOpDescMaker : public GradOpDescMakerBase {
......@@ -91,6 +118,7 @@ class SingleGradOpDescMaker : public GradOpDescMakerBase {
virtual std::unique_ptr<OpDescBind> Apply() const = 0;
};
template <bool DropEmptyIG = true>
class DefaultGradOpDescMaker : public SingleGradOpDescMaker {
public:
using SingleGradOpDescMaker::SingleGradOpDescMaker;
......@@ -102,7 +130,8 @@ class DefaultGradOpDescMaker : public SingleGradOpDescMaker {
for (auto& input_param : this->InputNames()) {
grad->SetInput(input_param, this->Input(input_param));
grad->SetOutput(GradVarName(input_param), this->InputGrad(input_param));
grad->SetOutput(GradVarName(input_param),
this->InputGrad(input_param, DropEmptyIG));
}
for (auto& output_param : this->OutputNames()) {
......
paddle/framework/op_desc.cc
浏览文件 @ d2be7ec3
......@@ -32,7 +32,7 @@ OpDescBind::OpDescBind(const std::string &type, const VariableNameMap &inputs,
}
OpDesc *OpDescBind::Proto() {
Sync();
Flush();
return &op_desc_;
}
......@@ -100,6 +100,12 @@ void OpDescBind::SetAttr(const std::string &name, const Attribute &v) {
need_update_ = true;
}
void OpDescBind::SetBlockAttr(const std::string &name, BlockDescBind &block) {
BlockDesc *desc = block.Proto();
this->attrs_[name] = desc;
need_update_ = true;
}
void OpDescBind::SetAttrMap(
const std::unordered_map<std::string, Attribute> &attr_map) {
attrs_ = attr_map;
......@@ -159,7 +165,7 @@ struct SetAttrDescVisitor : public boost::static_visitor<void> {
void operator()(boost::blank) const { PADDLE_THROW("Unexpected branch"); }
};
void OpDescBind::Sync() {
void OpDescBind::Flush() {
if (need_update_) {
this->op_desc_.mutable_inputs()->Clear();
for (auto &ipt : inputs_) {
......
paddle/framework/op_desc.h
浏览文件 @ d2be7ec3
......@@ -89,8 +89,6 @@ class OpDescBind {
this->need_update_ = true;
}
void Sync();
const VariableNameMap &Inputs() const { return inputs_; }
const VariableNameMap &Outputs() const { return outputs_; }
......@@ -104,6 +102,8 @@ class OpDescBind {
void InferShape(const BlockDescBind &block) const;
void Flush();
private:
template <typename MapType>
static std::vector<typename MapType::key_type> MapKeys(const MapType &map) {
......
paddle/framework/op_registry.cc
浏览文件 @ d2be7ec3
......@@ -59,16 +59,5 @@ std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDescBind& op_desc) {
op_desc.GetAttrMap());
}
std::vector<std::unique_ptr<OpDescBind>> OpRegistry::CreateGradOpDescs(
OpDescBind* op_desc) {
auto& info = OpInfoMap::Instance().Get(op_desc->Type());
if (info.Checker() != nullptr) {
info.Checker()->Check(*op_desc->MutableAttrMap());
}
return info.grad_op_maker_(*op_desc);
}
} // namespace framework
} // namespace paddle
paddle/framework/op_registry.h
浏览文件 @ d2be7ec3
......@@ -79,9 +79,6 @@ class OpRegistry {
static std::unique_ptr<OperatorBase> CreateOp(const OpDesc& op_desc);
static std::vector<std::unique_ptr<OpDescBind>> CreateGradOpDescs(
OpDescBind* op_desc);
static std::unique_ptr<OperatorBase> CreateOp(const OpDescBind& op_desc);
};
......@@ -160,17 +157,18 @@ class OpKernelRegistrar : public Registrar {
/**
* Macro to register Operator.
*/
#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, \
grad_op_class) \
REGISTER_OPERATOR(grad_op_type, grad_op_class); \
class _GradOpDescMaker_##grad_op_type##_ \
: public ::paddle::framework::DefaultGradOpDescMaker { \
using ::paddle::framework::DefaultGradOpDescMaker::DefaultGradOpDescMaker; \
\
protected: \
virtual std::string GradOpType() const { return #grad_op_type; } \
}; \
REGISTER_OPERATOR(op_type, op_class, _GradOpDescMaker_##grad_op_type##_, \
#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, \
grad_op_class) \
REGISTER_OPERATOR(grad_op_type, grad_op_class); \
class _GradOpDescMaker_##grad_op_type##_ \
: public ::paddle::framework::DefaultGradOpDescMaker<true> { \
using ::paddle::framework::DefaultGradOpDescMaker< \
true>::DefaultGradOpDescMaker; \
\
protected: \
virtual std::string GradOpType() const { return #grad_op_type; } \
}; \
REGISTER_OPERATOR(op_type, op_class, _GradOpDescMaker_##grad_op_type##_, \
op_maker_class);
#define REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class) \
......
paddle/framework/operator.h
浏览文件 @ d2be7ec3
......@@ -289,6 +289,15 @@ class ExecutionContext {
return device_context_;
}
#ifdef PADDLE_WITH_CUDA
const platform::CUDADeviceContext& cuda_device_context() const {
PADDLE_ENFORCE(platform::is_gpu_place(device_context_.GetPlace()));
auto cuda_ctx =
reinterpret_cast<const platform::CUDADeviceContext*>(&device_context_);
return *cuda_ctx;
}
#endif
private:
const OperatorBase& op_;
const Scope& scope_;
......
paddle/framework/program_desc.cc
浏览文件 @ d2be7ec3
......@@ -45,7 +45,7 @@ BlockDescBind *ProgramDescBind::AppendBlock(const BlockDescBind &parent) {
ProgramDesc *ProgramDescBind::Proto() {
for (auto &block : blocks_) {
block->Sync();
block->Flush();
}
return prog_;
}
......
paddle/framework/selected_rows.cc 0 → 100644
浏览文件 @ d2be7ec3
/* 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/selected_rows.h"
namespace paddle {
namespace framework {} // namespace framework
} // namespace paddle
paddle/framework/selected_rows.h 0 → 100644
浏览文件 @ d2be7ec3
/* 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/tensor.h"
namespace paddle {
namespace framework {
class SelectedRows {
public:
SelectedRows(const std::vector<int64_t>& rows, const int64_t& height)
: rows_(rows), height_(height) {
value_.reset(new Tensor());
}
SelectedRows() { value_.reset(new Tensor()); }
platform::Place place() const { return value_->place(); }
const Tensor& value() const { return *value_; }
Tensor* mutable_value() { return value_.get(); }
int64_t height() const { return height_; }
void set_height(int64_t height) { height_ = height; }
const std::vector<int64_t>& rows() const { return rows_; }
void set_rows(const std::vector<int64_t>& rows) { rows_ = rows; }
DDim GetCompleteDims() const {
std::vector<int64_t> dims = vectorize(value_->dims());
dims[0] = height_;
return make_ddim(dims);
}
private:
std::vector<int64_t> rows_;
std::unique_ptr<Tensor> value_{nullptr};
int64_t height_;
};
} // namespace framework
} // namespace paddle
paddle/framework/selected_rows_test.cc 0 → 100644
浏览文件 @ d2be7ec3
/* 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/selected_rows.h"
#include "gtest/gtest.h"
namespace paddle {
namespace framework {
class SelectedRowsTester : public ::testing::Test {
public:
virtual void SetUp() override {
std::vector<int64_t> rows{0, 4, 7};
int64_t height = 10;
int64_t row_numel = 100;
selected_rows_.reset(new SelectedRows(rows, height));
Tensor* value = selected_rows_->mutable_value();
value->mutable_data<float>(
make_ddim({static_cast<int64_t>(rows.size()), row_numel}), place_);
}
protected:
platform::CPUPlace place_;
std::unique_ptr<SelectedRows> selected_rows_{nullptr};
};
TEST_F(SelectedRowsTester, height) { ASSERT_EQ(selected_rows_->height(), 10); }
TEST_F(SelectedRowsTester, dims) {
ASSERT_EQ(selected_rows_->value().dims(), make_ddim({3, 100}));
}
TEST_F(SelectedRowsTester, complete_dims) {
ASSERT_EQ(selected_rows_->GetCompleteDims(), make_ddim({10, 100}));
}
} // namespace framework
} // namespace paddle
paddle/framework/shape_inference.h
浏览文件 @ d2be7ec3
......@@ -19,9 +19,6 @@ limitations under the License. */
namespace paddle {
namespace framework {
// TODO(longfei): Once after both CompileTimeInferShapeContext and
// RuntimeInferShapeContext get merged, we can rename InferShapeContext into
// InferShapeContext so to replace the current InferShapeContext.
class InferShapeContext {
public:
virtual ~InferShapeContext() {}
......
paddle/framework/tensor_array.cc
浏览文件 @ d2be7ec3
......@@ -76,6 +76,17 @@ LoDTensor PackDynamicBatch(const std::vector<LoDTensor>& source,
const std::vector<DySeqMeta>& meta, const LoD& lod,
size_t level);
std::vector<size_t> GenDyBatchIndice(const DySeqMetaBatch& meta, int batch_id) {
// collect indice need to copy to the batch
std::vector<size_t> indice;
for (const auto& seq : meta) {
size_t id = seq.begin + batch_id;
if (id >= seq.end) break;
indice.push_back(id);
}
return indice;
}
} // namespace detail
const LoDTensor& TensorArray::Read(size_t index) const {
......@@ -113,8 +124,8 @@ LoDTensor TensorArray::Pack(size_t level, const std::vector<DySeqMeta>& meta,
return detail::PackDynamicBatch(values_, meta, lod, level);
}
std::vector<DySeqMeta> TensorArray::Unpack(const LoDTensor& source, int level,
bool length_desend) {
DySeqMetaBatch TensorArray::Unpack(const LoDTensor& source, int level,
bool length_desend) {
detail::DynamicBatchUnpacker unpacker(source, level,
length_desend /*descend*/);
......@@ -129,6 +140,7 @@ std::vector<DySeqMeta> TensorArray::Unpack(const LoDTensor& source, int level,
Write(batch_id, unpacker.GetBatch(batch_id));
}
PADDLE_ENFORCE(!unpacker.meta.empty());
return unpacker.meta;
}
......@@ -218,13 +230,7 @@ LoDTensor DynamicBatchUnpacker::GetBatch(size_t index) {
PADDLE_ENFORCE(!meta.empty(), "should build meta first");
LoDTensor result;
// collect indice need to copy to the batch
std::vector<size_t> indice;
for (const auto& seq : meta) {
size_t id = seq.begin + index;
if (id >= seq.end) break;
indice.push_back(id);
}
auto indice = detail::GenDyBatchIndice(meta, index);
PADDLE_ENFORCE(!indice.empty(), "invalid batch at %d", index);
// copy the indice of records in LoDTensor
......@@ -237,9 +243,9 @@ LoDTensor DynamicBatchUnpacker::GetBatch(size_t index) {
for (size_t i = 0; i < indice.size(); i++) {
auto index = indice[i];
auto target = result.Slice<value_type>(i, i + 1);
auto source_ = source->Slice<value_type>(index, index + 1);
auto slice = source->Slice<value_type>(index, index + 1);
target.CopyFrom<value_type>(source_, platform::CPUPlace(),
target.CopyFrom<value_type>(slice, platform::CPUPlace(),
platform::CPUDeviceContext());
}
......
paddle/framework/tensor_array.h
浏览文件 @ d2be7ec3
......@@ -34,6 +34,13 @@ struct DySeqMeta {
size_t ori_idx;
};
using DySeqMetaBatch = std::vector<DySeqMeta>;
/*
* Extract the indices of instances.
*/
std::vector<size_t> GenDyBatchIndice(const DySeqMetaBatch &metas, int batch_id);
/*
* TensorArray is a C-array-like array of tensors, it is meant to be used with
* dynamic iteration primitives such as while_loop. It is used to segment inputs
......@@ -69,7 +76,7 @@ class TensorArray {
* Recover the original LoD-arranged LoDTensor with the `values`, `level` and
* `indice_map`.
*/
LoDTensor Pack(size_t level, const std::vector<DySeqMeta> &meta,
LoDTensor Pack(size_t level, const DySeqMetaBatch &meta,
const LoD &lod) const;
/*
......@@ -77,8 +84,7 @@ class TensorArray {
* `values`, if set `desend`, will sort by length in descending order else in
* ascending order.
*/
std::vector<DySeqMeta> Unpack(const LoDTensor &source, int level,
bool length_desend);
DySeqMetaBatch Unpack(const LoDTensor &source, int level, bool length_desend);
/*
* Pack the values into a tensor with rank one higher than each tensor in
......
paddle/framework/type_defs.h
浏览文件 @ d2be7ec3
......@@ -36,8 +36,9 @@ using OpCreator = std::function<OperatorBase*(
const std::string& /*type*/, const VariableNameMap& /*inputs*/,
const VariableNameMap& /*outputs*/, const AttributeMap& /*attrs*/)>;
using GradOpMakerFN =
std::function<std::vector<std::unique_ptr<OpDescBind>>(const OpDescBind&)>;
using GradOpMakerFN = std::function<std::vector<std::unique_ptr<OpDescBind>>(
const OpDescBind&, const std::unordered_set<std::string>& /*no_grad_set*/,
std::unordered_map<std::string, std::string>* /*grad_to_var*/)>;
} // namespace framework
} // namespace paddle
paddle/framework/var_desc.cc
浏览文件 @ d2be7ec3
......@@ -13,32 +13,58 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/var_desc.h"
#include "paddle/platform/enforce.h"
namespace paddle {
namespace framework {
void VarDescBind::SetShape(const std::vector<int64_t> &dims) {
VectorToRepeated(dims, desc_.mutable_lod_tensor()->mutable_dims());
VectorToRepeated(dims, mutable_tensor_desc()->mutable_dims());
}
void VarDescBind::SetDataType(DataType data_type) {
desc_.mutable_lod_tensor()->set_data_type(data_type);
mutable_tensor_desc()->set_data_type(data_type);
}
std::vector<int64_t> VarDescBind::Shape() const {
return RepeatedToVector(desc_.lod_tensor().dims());
return RepeatedToVector(tensor_desc().dims());
}
DataType VarDescBind::GetDataType() const {
return desc_.lod_tensor().data_type();
}
DataType VarDescBind::GetDataType() const { return tensor_desc().data_type(); }
void VarDescBind::SetLoDLevel(int32_t lod_level) {
PADDLE_ENFORCE(desc_.type() == VarDesc::LOD_TENSOR);
desc_.mutable_lod_tensor()->set_lod_level(lod_level);
}
int32_t VarDescBind::GetLodLevel() const {
PADDLE_ENFORCE(desc_.type() == VarDesc::LOD_TENSOR);
return desc_.lod_tensor().lod_level();
}
const TensorDesc &VarDescBind::tensor_desc() const {
PADDLE_ENFORCE(desc_.has_type(), "invoke TensorDesc must after set type");
switch (desc_.type()) {
case VarDesc::SELECTED_ROWS:
return desc_.selected_rows();
case VarDesc::LOD_TENSOR:
return desc_.lod_tensor().tensor();
default:
PADDLE_THROW("Unexpected branch.");
}
}
TensorDesc *VarDescBind::mutable_tensor_desc() {
PADDLE_ENFORCE(desc_.has_type(),
"invoke MutableTensorDesc must after set type");
switch (desc_.type()) {
case VarDesc::SELECTED_ROWS:
return desc_.mutable_selected_rows();
case VarDesc::LOD_TENSOR:
return desc_.mutable_lod_tensor()->mutable_tensor();
default:
PADDLE_THROW("Unexpected branch.");
}
}
} // namespace framework
} // namespace paddle
paddle/framework/var_desc.h
浏览文件 @ d2be7ec3
......@@ -34,6 +34,7 @@ inline std::vector<T> RepeatedToVector(
template <typename T, typename RepeatedField>
inline void VectorToRepeated(const std::vector<T> &vec,
RepeatedField *repeated_field) {
repeated_field->Clear();
repeated_field->Reserve(vec.size());
for (const auto &elem : vec) {
*repeated_field->Add() = elem;
......@@ -44,6 +45,7 @@ inline void VectorToRepeated(const std::vector<T> &vec,
template <typename RepeatedField>
inline void VectorToRepeated(const std::vector<bool> &vec,
RepeatedField *repeated_field) {
repeated_field->Clear();
repeated_field->Reserve(vec.size());
for (auto elem : vec) {
*repeated_field->Add() = elem;
......@@ -52,7 +54,10 @@ inline void VectorToRepeated(const std::vector<bool> &vec,
class VarDescBind {
public:
explicit VarDescBind(const std::string &name) { desc_.set_name(name); }
explicit VarDescBind(const std::string &name) {
desc_.set_name(name);
desc_.set_type(VarDesc::LOD_TENSOR);
}
VarDesc *Proto() { return &desc_; }
......@@ -70,7 +75,14 @@ class VarDescBind {
int32_t GetLodLevel() const;
VarDesc::VarType GetType() const { return desc_.type(); }
void SetType(VarDesc::VarType type) { desc_.set_type(type); }
private:
const TensorDesc &tensor_desc() const;
TensorDesc *mutable_tensor_desc();
VarDesc desc_;
};
} // namespace framework
......
paddle/operators/CMakeLists.txt
浏览文件 @ d2be7ec3
......@@ -84,8 +84,9 @@ function(op_library TARGET)
endif()
# pybind USE_NO_KERNEL_OP
# HACK: if REGISTER_OP_CPU_KERNEL presents the operator must have kernel
file(READ ${TARGET}.cc TARGET_CONTENT)
string(REGEX MATCH "OperatorWithKernel" regex_result "${TARGET_CONTENT}")
string(REGEX MATCH "REGISTER_OP_CPU_KERNEL" regex_result "${TARGET_CONTENT}")
string(REPLACE "_op" "" TARGET "${TARGET}")
if (${pybind_flag} EQUAL 0 AND regex_result STREQUAL "")
file(APPEND ${pybind_file} "USE_NO_KERNEL_OP(${TARGET});\n")
......
paddle/operators/activation_op.cc
浏览文件 @ d2be7ec3
......@@ -338,6 +338,38 @@ class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
}
};
template <typename AttrType>
class HardSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
public:
HardSigmoidOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of HardSigmoid operator");
AddOutput("Y", "Output of HardSigmoid operator");
AddComment(R"DOC(
Hard Sigmoid activation operator.
Segment-wise linear approximation of sigmoid[1].
This is much faster than sigmoid.
hard_sigmoid = 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 from [1].
It is recommended to use the defaults for this activation.
References:
[1] Noisy Activation Functions
(https://arxiv.org/abs/1603.00391)
)DOC");
AddAttr<AttrType>("slope", "Slope for linear approximation of sigmoid")
.SetDefault(static_cast<AttrType>(0.2));
AddAttr<AttrType>("offset", "Offset for linear approximation of sigmoid")
.SetDefault(static_cast<AttrType>(0.5));
}
};
} // namespace operators
} // namespace paddle
......@@ -413,6 +445,9 @@ REGISTER_OP(thresholded_relu, ops::ActivationOp,
ops::ThresholdedReluOpMaker<float>, thresholded_relu_grad,
ops::ActivationOpGrad);
REGISTER_OP(hard_sigmoid, ops::ActivationOp, ops::HardSigmoidOpMaker<float>,
hard_sigmoid_grad, ops::ActivationOpGrad);
#define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor) \
REGISTER_OP_CPU_KERNEL( \
act_type, \
......
paddle/operators/activation_op.h
浏览文件 @ d2be7ec3
......@@ -616,30 +616,63 @@ struct ThresholdedReluGradFunctor : public BaseActivationFunctor<T> {
}
};
template <typename T>
struct HardSigmoidFunctor : public BaseActivationFunctor<T> {
float slope;
float offset;
typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
return {{"slope", &slope}, {"offset", &offset}};
}
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) 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));
}
};
template <typename T>
struct HardSigmoidGradFunctor : public BaseActivationFunctor<T> {
float slope;
float offset;
typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
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);
}
};
} // namespace operators
} // namespace paddle
#define FOR_EACH_KERNEL_FUNCTOR(__macro) \
__macro(sigmoid, SigmoidFunctor, SigmoidGradFunctor); \
__macro(logsigmoid, LogSigmoidFunctor, LogSigmoidGradFunctor); \
__macro(exp, ExpFunctor, ExpGradFunctor); \
__macro(relu, ReluFunctor, ReluGradFunctor); \
__macro(tanh, TanhFunctor, TanhGradFunctor); \
__macro(softshrink, SoftShrinkFunctor, SoftShrinkGradFunctor); \
__macro(sqrt, SqrtFunctor, SqrtGradFunctor); \
__macro(abs, AbsFunctor, AbsGradFunctor); \
__macro(reciprocal, ReciprocalFunctor, ReciprocalGradFunctor); \
__macro(log, LogFunctor, LogGradFunctor); \
__macro(square, SquareFunctor, SquareGradFunctor); \
__macro(brelu, BReluFunctor, BReluGradFunctor); \
__macro(soft_relu, SoftReluFunctor, SoftReluGradFunctor); \
__macro(pow, PowFunctor, PowGradFunctor); \
__macro(stanh, STanhFunctor, STanhGradFunctor); \
__macro(softplus, SoftplusFunctor, SoftplusGradFunctor); \
__macro(softsign, SoftsignFunctor, SoftsignGradFunctor); \
__macro(relu6, Relu6Functor, Relu6GradFunctor); \
__macro(leaky_relu, LeakyReluFunctor, LeakyReluGradFunctor); \
__macro(tanh_shrink, TanhShrinkFunctor, TanhShrinkGradFunctor); \
__macro(elu, ELUFunctor, ELUGradFunctor); \
__macro(hard_shrink, HardShrinkFunctor, HardShrinkGradFunctor); \
#define FOR_EACH_KERNEL_FUNCTOR(__macro) \
__macro(sigmoid, SigmoidFunctor, SigmoidGradFunctor); \
__macro(logsigmoid, LogSigmoidFunctor, LogSigmoidGradFunctor); \
__macro(exp, ExpFunctor, ExpGradFunctor); \
__macro(relu, ReluFunctor, ReluGradFunctor); \
__macro(tanh, TanhFunctor, TanhGradFunctor); \
__macro(softshrink, SoftShrinkFunctor, SoftShrinkGradFunctor); \
__macro(sqrt, SqrtFunctor, SqrtGradFunctor); \
__macro(abs, AbsFunctor, AbsGradFunctor); \
__macro(reciprocal, ReciprocalFunctor, ReciprocalGradFunctor); \
__macro(log, LogFunctor, LogGradFunctor); \
__macro(square, SquareFunctor, SquareGradFunctor); \
__macro(brelu, BReluFunctor, BReluGradFunctor); \
__macro(soft_relu, SoftReluFunctor, SoftReluGradFunctor); \
__macro(pow, PowFunctor, PowGradFunctor); \
__macro(stanh, STanhFunctor, STanhGradFunctor); \
__macro(softplus, SoftplusFunctor, SoftplusGradFunctor); \
__macro(softsign, SoftsignFunctor, SoftsignGradFunctor); \
__macro(relu6, Relu6Functor, Relu6GradFunctor); \
__macro(leaky_relu, LeakyReluFunctor, LeakyReluGradFunctor); \
__macro(tanh_shrink, TanhShrinkFunctor, TanhShrinkGradFunctor); \
__macro(elu, ELUFunctor, ELUGradFunctor); \
__macro(hard_shrink, HardShrinkFunctor, HardShrinkGradFunctor); \
__macro(hard_sigmoid, HardSigmoidFunctor, HardSigmoidGradFunctor); \
__macro(thresholded_relu, ThresholdedReluFunctor, ThresholdedReluGradFunctor);
paddle/operators/adam_op.cc 0 → 100644
浏览文件 @ d2be7ec3
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/adam_op.h"
namespace paddle {
namespace operators {
class AdamOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Param"),
"Input(Param) of AdamOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Grad"),
"Input(Grad) of AdamOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Moment1"),
"Input(Moment1) of AdamOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Moment2"),
"Input(Moment2) of AdamOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
"Input(LearningRate) of AdamOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Beta1Pow"),
"Input(Beta1Pow) of AdamOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Beta2Pow"),
"Input(Beta2Pow) of AdamOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
"Output(ParamOut) of AdamOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Moment1Out"),
"Output(Moment1Out) of AdamOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Moment2Out"),
"Output(Moment2Out) of AdamOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Beta1PowOut"),
"Output(Beta1PowOut) of AdamOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Beta2PowOut"),
"Output(Beta2PowOut) of AdamOp should not be null.");
auto lr_dims = ctx->GetInputDim("LearningRate");
PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1,
"Learning rate should have 1 dimension");
auto beta1_pow_dims = ctx->GetInputDim("Beta1Pow");
PADDLE_ENFORCE_EQ(framework::product(beta1_pow_dims), 1,
"Beta1 power accumulator should have 1 dimension");
auto beta2_pow_dims = ctx->GetInputDim("Beta2Pow");
PADDLE_ENFORCE_EQ(framework::product(beta1_pow_dims), 1,
"Beta1 power accumulator should have 1 dimension");
auto param_dims = ctx->GetInputDim("Param");
PADDLE_ENFORCE_EQ(
param_dims, ctx->GetInputDim("Grad"),
"Param and Grad input of AdamOp should have same dimension");
PADDLE_ENFORCE_EQ(
param_dims, ctx->GetInputDim("Moment1"),
"Param and Moment input of AdamOp should have same dimension");
PADDLE_ENFORCE_EQ(
param_dims, ctx->GetInputDim("Moment2"),
"Param and InfNorm input of AdamOp should have same dimension");
ctx->SetOutputDim("ParamOut", param_dims);
ctx->SetOutputDim("Moment1Out", param_dims);
ctx->SetOutputDim("Moment2Out", param_dims);
ctx->SetOutputDim("Beta1PowOut", beta1_pow_dims);
ctx->SetOutputDim("Beta2PowOut", beta2_pow_dims);
}
};
class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AdamOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Param", "(Tensor) Input parameter");
AddInput("Grad", "(Tensor) Input gradient");
AddInput("LearningRate", "(Tensor) Learning rate");
AddInput("Moment1", "(Tensor) Input first moment");
AddInput("Moment2", "(Tensor) Input second moment");
AddInput("Beta1Pow", "(Tensor) Input beta1 power accumulator");
AddInput("Beta2Pow", "(Tensor) Input beta2 power accumulator");
AddOutput("ParamOut", "(Tensor) Output parameter");
AddOutput("Moment1Out", "(Tensor) Output first moment");
AddOutput("Moment2Out", "(Tensor) Output second moment");
AddOutput("Beta1PowOut", "(Tensor) Output beta1 power accumulator");
AddOutput("Beta2PowOut", "(Tensor) Output beta2 power accumulator");
AddAttr<float>("beta1",
"(float, default 0.9) "
"Exponential decay rate for the "
"first moment estimates.")
.SetDefault(0.9f);
AddAttr<float>("beta2",
"(float, default 0.999) "
"exponential decay rate for the "
"second moment estimates.")
.SetDefault(0.999f);
AddAttr<float>("epsilon",
"(float, default 1.0e-8) "
"Constant for numerical stability")
.SetDefault(1.0e-8f);
AddComment(R"DOC(
Adam Updates Operator.
This implements the Adam optimizer from Section 2 of the Adam
paper[1]. Adam is a first-order gradient-based optimization
method based on adaptive estimates of lower-order moments.
Adam updates:
moment1_out = beta1 * moment1 + (1 − beta1) * grad
moment2_out = beta2 * moment2 + (1 − beta2) * grad * grad
beta1_pow_out = beta1_pow * beta1
beta2_pow_out = beta2_pow * beta2
learning_rate_t = learning_rate_t *
sqrt(1 - beta2_pow_out) / (1 - beta1_pow_out)
param_out = param - learning_rate_t * moment1/ (sqrt(moment2) + epsilon)
References:
[1] Adam: A Method for Stochastic Optimization
(https://arxiv.org/abs/1412.6980)
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(adam, ops::AdamOp, ops::AdamOpMaker);
REGISTER_OP_CPU_KERNEL(adam,
ops::AdamOpKernel<paddle::platform::CPUPlace, float>);
paddle/operators/adam_op.cu 0 → 100644
浏览文件 @ d2be7ec3
/* 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/adam_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(adam,
ops::AdamOpKernel<paddle::platform::GPUPlace, float>);
paddle/operators/adam_op.h 0 → 100644
浏览文件 @ d2be7ec3
/* 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/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
template <typename Place, typename T>
class AdamOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
auto moment1_out_tensor = ctx.Output<framework::Tensor>("Moment1Out");
auto moment2_out_tensor = ctx.Output<framework::Tensor>("Moment2Out");
auto beta1_pow_out_tensor = ctx.Output<framework::Tensor>("Beta1PowOut");
auto beta2_pow_out_tensor = ctx.Output<framework::Tensor>("Beta2PowOut");
param_out_tensor->mutable_data<T>(ctx.GetPlace());
moment1_out_tensor->mutable_data<T>(ctx.GetPlace());
moment2_out_tensor->mutable_data<T>(ctx.GetPlace());
beta1_pow_out_tensor->mutable_data<T>(ctx.GetPlace());
beta2_pow_out_tensor->mutable_data<T>(ctx.GetPlace());
float beta1 = ctx.Attr<float>("beta1");
float beta2 = ctx.Attr<float>("beta2");
float epsilon = ctx.Attr<float>("epsilon");
auto param = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Param"));
auto grad = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Grad"));
auto moment1 = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Moment1"));
auto moment2 = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Moment2"));
auto lr = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("LearningRate"));
auto beta1_pow = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Beta1Pow"));
auto beta2_pow = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Beta2Pow"));
auto param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
auto moment1_out = framework::EigenVector<T>::Flatten(*moment1_out_tensor);
auto moment2_out = framework::EigenVector<T>::Flatten(*moment2_out_tensor);
auto beta1_pow_out =
framework::EigenVector<T>::Flatten(*beta1_pow_out_tensor);
auto beta2_pow_out =
framework::EigenVector<T>::Flatten(*beta2_pow_out_tensor);
auto place = ctx.GetEigenDevice<Place>();
moment1_out.device(place) = beta1 * moment1 + (1 - beta1) * grad;
moment2_out.device(place) = beta2 * moment2 + (1 - beta2) * grad.square();
beta1_pow_out.device(place) = beta1_pow * beta1;
beta2_pow_out.device(place) = beta2_pow * beta2;
// All of these are tensors of 1 element
auto lr_t = lr * (1 - beta2_pow_out).sqrt() / (1 - beta1_pow_out);
// Eigen does not support automatic broadcast
// Get dimensions of moment vector to broadcast lr_t
Eigen::DSizes<int, 1> m_dsize(moment1_out_tensor->numel());
param_out.device(place) =
param -
lr_t.broadcast(m_dsize) *
(moment1_out / (moment2_out.sqrt() + epsilon));
}
};
} // namespace operators
} // namespace paddle
paddle/operators/conv2d_op.cc
浏览文件 @ d2be7ec3
......@@ -12,111 +12,91 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/gemm_conv2d_op.h"
#include "paddle/operators/conv2d_op.h"
namespace paddle {
namespace operators {
int outputSize(int input_size, int filter_size, int padding, int stride) {
int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
return output_size;
void Conv2DOp::InferShape(framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of Conv2DOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Filter"),
"Input(Filter) of Conv2DOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Output"),
"Output(Output) of Conv2DOp should not be null.");
auto in_dims = ctx->GetInputDim("Input");
auto filter_dims = ctx->GetInputDim("Filter");
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
int groups = ctx->Attrs().Get<int>("groups");
int input_channels = in_dims[1];
int output_channels = filter_dims[0];
PADDLE_ENFORCE_EQ(in_dims.size(), 4, "Conv2DOp input should be 4-D.");
PADDLE_ENFORCE_EQ(filter_dims.size(), 4, "Conv2DOp filter should be 4-D.");
PADDLE_ENFORCE_EQ(input_channels, filter_dims[1] * groups,
"The number of input channels should be equal to filter "
"channels * groups.");
PADDLE_ENFORCE_EQ(
output_channels % groups, 0,
"The number of output channels should be divided by groups.");
auto output_height =
OutputSize(in_dims[2], filter_dims[2], paddings[0], strides[0]);
auto output_width =
OutputSize(in_dims[3], filter_dims[3], paddings[1], strides[1]);
ctx->SetOutputDim("Output",
{in_dims[0], filter_dims[0], output_height, output_width});
}
class Conv2DOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of Conv2DOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Filter"),
"Input(Filter) of Conv2DOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Output"),
"Output(Output) of Conv2DOp should not be null.");
auto in_dims = ctx->GetInputDim("Input");
auto filter_dims = ctx->GetInputDim("Filter");
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
int groups = ctx->Attrs().Get<int>("groups");
int input_channels = in_dims[1];
int output_channels = filter_dims[0];
PADDLE_ENFORCE_EQ(in_dims.size(), 4, "Conv2DOp input should be 4-D.");
PADDLE_ENFORCE_EQ(filter_dims.size(), 4, "Conv2DOp filter should be 4-D.");
PADDLE_ENFORCE_EQ(input_channels, filter_dims[1] * groups,
"The number of input channels should be equal to filter "
"channels * groups.");
PADDLE_ENFORCE_EQ(
output_channels % groups, 0,
"The number of output channels should be divided by groups.");
auto output_height =
outputSize(in_dims[2], filter_dims[2], paddings[0], strides[0]);
auto output_width =
outputSize(in_dims[3], filter_dims[3], paddings[1], strides[1]);
ctx->SetOutputDim(
"Output", {in_dims[0], filter_dims[0], output_height, output_width});
}
};
class Conv2DOpMaker : public framework::OpProtoAndCheckerMaker {
public:
Conv2DOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"Input",
"The input tensor of convolution operator. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of channels, H and W is the height and width of image.");
AddInput(
"Filter",
"The filter tensor of convolution operator."
"The format of the filter tensor is MCHW, where M is the number of "
"output image channels, C is the number of input image channels, "
"H and W is height and width of filter. "
"If the groups attribute is greater than 1, C equal the number of "
"input image channels divided by the groups.");
AddOutput("Output",
"The output tensor of convolution operator."
"The format of output tensor is also NCHW.");
AddAttr<std::vector<int>>("strides", "strides of convolution operator.")
.SetDefault({1, 1});
AddAttr<std::vector<int>>("paddings", "paddings of convolution operator.")
.SetDefault({0, 0});
AddAttr<int>(
"groups",
"group size of convolution operator. "
"Refer to grouped convolution in Alex Krizhevsky's paper: "
"when group=2, the first half of the filters are only connected to the "
"first half of the input channels, and the second half only connected "
"to the second half.")
.SetDefault(1);
AddComment(R"DOC(
Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"Input",
"The input tensor of convolution operator. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of channels, H and W is the height and width of image.");
AddInput("Filter",
"The filter tensor of convolution operator."
"The format of the filter tensor is MCHW, where M is the number of "
"output image channels, C is the number of input image channels, "
"H and W is height and width of filter. "
"If the groups attribute is greater than 1, C equal the number of "
"input image channels divided by the groups.");
AddOutput("Output",
"The output tensor of convolution operator."
"The format of output tensor is also NCHW.");
AddAttr<std::vector<int>>("strides", "strides of convolution operator.")
.SetDefault({1, 1});
AddAttr<std::vector<int>>("paddings", "paddings of convolution operator.")
.SetDefault({0, 0});
AddAttr<int>(
"groups",
"group size of convolution operator. "
"Refer to grouped convolution in Alex Krizhevsky's paper: "
"when group=2, the first half of the filters are only connected to the "
"first half of the input channels, and the second half only connected "
"to the second half.")
.SetDefault(1);
AddComment(R"DOC(
The convolution operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
)DOC");
}
};
class Conv2DOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
}
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
auto in_dims = ctx->GetInputDim("Input");
auto filter_dims = ctx->GetInputDim("Filter");
if (ctx->HasOutput(framework::GradVarName("Input"))) {
ctx->SetOutputDim(framework::GradVarName("Input"), in_dims);
}
if (ctx->HasOutput(framework::GradVarName("Filter"))) {
ctx->SetOutputDim(framework::GradVarName("Filter"), filter_dims);
}
void Conv2DOpGrad::InferShape(framework::InferShapeContext* ctx) const {
auto in_dims = ctx->GetInputDim("Input");
auto filter_dims = ctx->GetInputDim("Filter");
if (ctx->HasOutput(framework::GradVarName("Input"))) {
ctx->SetOutputDim(framework::GradVarName("Input"), in_dims);
}
};
if (ctx->HasOutput(framework::GradVarName("Filter"))) {
ctx->SetOutputDim(framework::GradVarName("Filter"), filter_dims);
}
}
} // namespace operators
} // namespace paddle
......
paddle/operators/conv2d_op.cu
浏览文件 @ d2be7ec3
......@@ -12,7 +12,7 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/gemm_conv2d_op.h"
#include "paddle/operators/conv2d_op.h"
namespace ops = paddle::operators;
......
paddle/operators/gemm_conv2d_op.h → paddle/operators/conv2d_op.h
浏览文件 @ d2be7ec3
......@@ -24,6 +24,38 @@ namespace operators {
using Tensor = framework::Tensor;
// Base convolution operator definations for other conv
// like operators to reuse the implementation.
inline int OutputSize(int input_size, int filter_size, int padding,
int stride) {
int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
return output_size;
}
// Define Op classes in .h file so that other conv
// operator implementations can reuse the code.
class Conv2DOpMaker : public framework::OpProtoAndCheckerMaker {
public:
Conv2DOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker);
};
class Conv2DOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override;
};
class Conv2DOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override;
};
template <typename Place, typename T>
class GemmConv2DKernel : public framework::OpKernel<T> {
public:
......@@ -74,7 +106,6 @@ class GemmConv2DKernel : public framework::OpKernel<T> {
framework::DDim output_matrix_shape = {output_channels,
output_height * output_width};
// convolution operator: im2col + gemm
int in_step = input_channels / groups;
int out_step = output_channels / groups;
......
paddle/operators/conv_cudnn_op.cc 0 → 100644
浏览文件 @ d2be7ec3
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/conv2d_op.h"
namespace paddle {
namespace operators {
class CudnnConvOpMaker : public Conv2DOpMaker {
public:
CudnnConvOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: Conv2DOpMaker(proto, op_checker) {
AddAttr<std::vector<int>>("dilations", "dilations of convolution operator.")
.SetDefault(std::vector<int>{1, 1});
AddAttr<int>("workspace_size_MB",
"workspace size for cudnn, in MB, "
"workspace is a section of GPU memory which will be "
"allocated/freed each time the operator runs, larger "
"workspace size can increase performance but also requires "
"better hardward. This size should be carefully setted.")
.SetDefault(4096);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(conv_cudnn, ops::Conv2DOp, ops::CudnnConvOpMaker, conv_cudnn_grad,
ops::Conv2DOpGrad);
REGISTER_OP_CPU_KERNEL(
conv_cudnn, ops::GemmConv2DKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
conv_cudnn_grad,
ops::GemmConvGrad2DKernel<paddle::platform::CPUPlace, float>);
paddle/operators/conv_cudnn_op.cu 0 → 100644
浏览文件 @ d2be7ec3
/* 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/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/memory/memory.h"
#include "paddle/operators/conv2d_op.h"
#include "paddle/platform/assert.h"
#include "paddle/platform/cudnn_helper.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using ScopedTensorDescriptor = platform::ScopedTensorDescriptor;
using ScopedFilterDescriptor = platform::ScopedFilterDescriptor;
using ScopedConvolutionDescriptor = platform::ScopedConvolutionDescriptor;
using DataLayout = platform::DataLayout;
using CUDADeviceContext = platform::CUDADeviceContext;
static constexpr size_t kCONV_CUDNN_WORKSPACE_LIMIT_BYTES = 1024 * 1024 * 1024;
// NOTE: framework::vectorize converts to type int64_t
// which does not fit cudnn inputs.
std::vector<int> Dims2Vector(const framework::DDim& dims) {
std::vector<int> ret;
for (int i = 0; i < dims.size(); i++) {
ret.push_back(dims[i]);
}
return ret;
}
template <typename T>
class CudnnConvOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use GPUPlace.");
auto* input = ctx.Input<Tensor>("Input");
auto* filter = ctx.Input<Tensor>("Filter");
auto* output = ctx.Output<Tensor>("Output");
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
int groups = ctx.Attr<int>("groups");
int user_workspace_size = ctx.Attr<int>("workspace_size_MB");
const T* input_data = input->data<T>();
const T* filter_data = filter->data<T>();
T* output_data = output->mutable_data<T>(ctx.GetPlace());
// ------------------- cudnn descriptors ---------------------
ScopedTensorDescriptor input_desc;
ScopedTensorDescriptor output_desc;
ScopedFilterDescriptor filter_desc;
ScopedConvolutionDescriptor conv_desc;
DataLayout layout = DataLayout::kNCHW;
cudnnTensorDescriptor_t cudnn_input_desc =
input_desc.descriptor<T>(layout, Dims2Vector(input->dims()), groups);
cudnnTensorDescriptor_t cudnn_output_desc =
output_desc.descriptor<T>(layout, Dims2Vector(output->dims()), groups);
cudnnFilterDescriptor_t cudnn_filter_desc =
filter_desc.descriptor<T>(layout, Dims2Vector(filter->dims()), groups);
cudnnConvolutionDescriptor_t cudnn_conv_desc =
conv_desc.descriptor<T>(paddings, strides, dilations);
int input_channels = input->dims()[1];
int input_height = input->dims()[2];
int input_width = input->dims()[3];
int output_channels = output->dims()[1];
int output_height = output->dims()[2];
int output_width = output->dims()[3];
int group_offset_in = input_channels / groups * input_height * input_width;
int group_offset_out =
output_channels / groups * output_height * output_width;
int group_offset_filter = filter->numel() / groups;
// ------------------- cudnn conv workspace ---------------------
void* cudnn_workspace = nullptr;
size_t workspace_size_in_bytes; // final workspace to allocate.
size_t workspace_size_limit = kCONV_CUDNN_WORKSPACE_LIMIT_BYTES;
if (user_workspace_size > 0) {
workspace_size_limit = user_workspace_size * 1024 * 1024;
}
// ------------------- cudnn conv algorithm ---------------------
cudnnConvolutionFwdAlgo_t algo;
auto handle = ctx.cuda_device_context().cudnn_handle();
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
// get workspace size able to allocate
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, algo, &workspace_size_in_bytes));
// Allocate on GPU memory
platform::GPUPlace gpu = boost::get<platform::GPUPlace>(ctx.GetPlace());
cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
// ------------------- cudnn conv forward ---------------------
T alpha = 1.0f, beta = 0.0f;
for (int i = 0; i < groups; i++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionForward(
handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
cudnn_filter_desc, filter_data + i * group_offset_filter,
cudnn_conv_desc, algo, cudnn_workspace, workspace_size_in_bytes,
&beta, cudnn_output_desc, output_data + i * group_offset_out));
}
// Release the cudnn workspace
paddle::memory::Free(gpu, cudnn_workspace);
}
};
template <typename T>
class CudnnConvGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use GPUPlace.");
auto input = ctx.Input<Tensor>("Input");
auto filter = ctx.Input<Tensor>("Filter");
auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
auto input_grad = ctx.Output<Tensor>(framework::GradVarName("Input"));
auto filter_grad = ctx.Output<Tensor>(framework::GradVarName("Filter"));
const T* input_data = input->data<T>();
const T* output_grad_data = output_grad->data<T>();
const T* filter_data = filter->data<T>();
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
int groups = ctx.Attr<int>("groups");
int user_workspace_size = ctx.Attr<int>("workspace_size_MB");
// ------------------- cudnn descriptors ---------------------
ScopedTensorDescriptor input_desc;
ScopedTensorDescriptor output_grad_desc;
ScopedTensorDescriptor input_grad_desc;
ScopedFilterDescriptor filter_desc;
ScopedFilterDescriptor filter_grad_desc;
ScopedConvolutionDescriptor conv_desc;
DataLayout layout = DataLayout::kNCHW;
cudnnTensorDescriptor_t cudnn_input_desc =
input_desc.descriptor<T>(layout, Dims2Vector(input->dims()), groups);
cudnnTensorDescriptor_t cudnn_output_grad_desc =
output_grad_desc.descriptor<T>(layout, Dims2Vector(output_grad->dims()),
groups);
cudnnFilterDescriptor_t cudnn_filter_desc =
filter_desc.descriptor<T>(layout, Dims2Vector(filter->dims()), groups);
cudnnTensorDescriptor_t cudnn_input_grad_desc = nullptr;
cudnnFilterDescriptor_t cudnn_filter_grad_desc = nullptr;
cudnnConvolutionDescriptor_t cudnn_conv_desc =
conv_desc.descriptor<T>(paddings, strides, dilations);
int input_channels = input->dims()[1];
int input_height = input->dims()[2];
int input_width = input->dims()[3];
int output_grad_channels = filter->dims()[0];
int output_grad_height = output_grad->dims()[2];
int output_grad_width = output_grad->dims()[3];
int group_offset_in = input_channels / groups * input_height * input_width;
int group_offset_out =
output_grad_channels / groups * output_grad_height * output_grad_width;
int group_offset_filter = filter->numel() / groups;
// ------------------- cudnn backward algorithm ---------------------
cudnnConvolutionBwdDataAlgo_t data_algo;
cudnnConvolutionBwdFilterAlgo_t filter_algo;
size_t workspace_size_in_bytes = 0, tmp_size = 0;
size_t workspace_size_limit = kCONV_CUDNN_WORKSPACE_LIMIT_BYTES;
if (user_workspace_size > 0) {
workspace_size_limit = user_workspace_size * 1024 * 1024;
}
auto handle = ctx.cuda_device_context().cudnn_handle();
if (input_grad) {
cudnn_input_grad_desc = input_grad_desc.descriptor<T>(
layout, Dims2Vector(input_grad->dims()), groups);
PADDLE_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
handle, cudnn_filter_desc,
// dyDesc: Handle to the previously initialized input differential
// tensor descriptor.
cudnn_output_grad_desc, cudnn_conv_desc,
// dxDesc: Handle to the previously initialized output tensor
// descriptor.
cudnn_input_grad_desc,
CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &data_algo));
PADDLE_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
handle, cudnn_filter_desc, cudnn_output_grad_desc,
cudnn_conv_desc, cudnn_input_grad_desc, data_algo, &tmp_size));
workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size);
}
if (filter_grad) {
cudnn_filter_grad_desc = filter_grad_desc.descriptor<T>(
layout, Dims2Vector(filter_grad->dims()), groups);
PADDLE_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
handle, cudnn_input_desc, cudnn_output_grad_desc, cudnn_conv_desc,
cudnn_filter_desc,
CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &filter_algo));
PADDLE_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
handle, cudnn_input_desc, cudnn_output_grad_desc, cudnn_conv_desc,
cudnn_filter_desc, filter_algo, &tmp_size));
workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size);
}
// ------------------- cudnn conv workspace ---------------------
// Already on GPU
void* cudnn_workspace = nullptr;
platform::GPUPlace gpu = boost::get<platform::GPUPlace>(ctx.GetPlace());
cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
// ------------------- cudnn conv backward data ---------------------
// FIXME(typhoonzero): template type T may not be the same as cudnn call.
T alpha = 1.0f, beta = 0.0f;
if (input_grad) {
T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*input_grad);
t.device(ctx.GetEigenDevice<platform::GPUPlace>()) =
t.constant(static_cast<T>(0));
for (int i = 0; i < groups; i++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, cudnn_filter_desc,
filter_data + i * group_offset_filter, cudnn_output_grad_desc,
output_grad_data + i * group_offset_out, cudnn_conv_desc, data_algo,
cudnn_workspace, workspace_size_in_bytes, &beta,
cudnn_input_grad_desc, input_grad_data + i * group_offset_in));
}
}
// ------------------- cudnn conv backward filter ---------------------
if (filter_grad) {
T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*filter_grad);
t.device(ctx.GetEigenDevice<platform::GPUPlace>()) =
t.constant(static_cast<T>(0));
for (int i = 0; i < groups; i++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
cudnn_output_grad_desc, output_grad_data + i * group_offset_out,
cudnn_conv_desc, filter_algo, cudnn_workspace,
workspace_size_in_bytes, &beta, cudnn_filter_grad_desc,
filter_grad_data + i * group_offset_filter));
}
}
// Release the cudnn workspace
paddle::memory::Free(gpu, cudnn_workspace);
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP_GPU_KERNEL(conv_cudnn, paddle::operators::CudnnConvOpKernel<float>);
REGISTER_OP_GPU_KERNEL(conv_cudnn_grad,
paddle::operators::CudnnConvGradOpKernel<float>);
paddle/operators/decayed_adagrad_op.cc 0 → 100644
浏览文件 @ d2be7ec3
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/decayed_adagrad_op.h"
namespace paddle {
namespace operators {
class DecayedAdagradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Param"),
"Input(Param) of DecayedAdagradOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Grad"),
"Input(Grad) of DecayedAdagradOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Moment"),
"Input(Moment) of DecayedAdagradOp should not be null.");
PADDLE_ENFORCE(
ctx->HasInput("LearningRate"),
"Input(LearningRate) of DecayedAdagradOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
"Output(ParamOut) of DecayedAdagradOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("MomentOut"),
"Output(MomentOut) of DecayedAdagradOp should not be null.");
auto lr_dims = ctx->GetInputDim("LearningRate");
PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1,
"LearningRate should have one element");
auto param_dims = ctx->GetInputDim("Param");
PADDLE_ENFORCE_EQ(param_dims, ctx->GetInputDim("Grad"),
"Param and Grad input of DecayedAdagradOp should have "
"the same dimension.");
PADDLE_ENFORCE_EQ(param_dims, ctx->GetInputDim("Moment"),
"Param and Moment input of DecayedAdagradOp should have "
"the same dimension.");
ctx->SetOutputDim("ParamOut", param_dims);
ctx->SetOutputDim("MomentOut", param_dims);
}
};
class DecayedAdagradOpMaker : public framework::OpProtoAndCheckerMaker {
public:
DecayedAdagradOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Param", "(Tensor) Input parameter");
AddInput("Grad", "(Tensor) Input gradient");
AddInput("Moment", "(Tensor) Second moment");
AddInput("LearningRate", "(Tensor) Learning rate");
AddOutput("ParamOut", "(Tensor) Output parameter");
AddOutput("MomentOut", "(Tensor) Output second moment");
AddAttr<float>("decay",
"(float, default 0.95) "
"Discounting factor for coming gradient")
.SetDefault(0.95);
AddAttr<float>("epsilon",
"(float, default 1.0e-6) "
"Constant for numerical stability")
.SetDefault(1.0e-6f);
AddComment(R"DOC(
Decayed Adagrad
moment_out = decay * moment + (1 - decay) * grad * grad
param_out = param - learning_rate * grad / (sqrt(moment_out) + epsilon)
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(decayed_adagrad, ops::DecayedAdagradOp,
ops::DecayedAdagradOpMaker);
REGISTER_OP_CPU_KERNEL(
decayed_adagrad,
ops::DecayedAdagradOpKernel<paddle::platform::CPUPlace, float>);
paddle/operators/decayed_adagrad_op.cu 0 → 100644
浏览文件 @ d2be7ec3
/* 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/decayed_adagrad_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
decayed_adagrad,
ops::DecayedAdagradOpKernel<paddle::platform::GPUPlace, float>);
paddle/operators/decayed_adagrad_op.h 0 → 100644
浏览文件 @ d2be7ec3
/* 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/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
template <typename Place, typename T>
class DecayedAdagradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
auto moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");
param_out_tensor->mutable_data<T>(ctx.GetPlace());
moment_out_tensor->mutable_data<T>(ctx.GetPlace());
float decay = ctx.Attr<float>("decay");
float epsilon = ctx.Attr<float>("epsilon");
auto param = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Param"));
auto grad = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Grad"));
auto moment = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Moment"));
auto lr = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("LearningRate"));
auto param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
auto moment_out = framework::EigenVector<T>::Flatten(*moment_out_tensor);
auto place = ctx.GetEigenDevice<Place>();
moment_out.device(place) = decay * moment + (1 - decay) * grad * grad;
Eigen::DSizes<int, 1> m_dsize(moment_out_tensor->numel());
param_out.device(place) =
param - lr.broadcast(m_dsize) * grad / (moment_out.sqrt() + epsilon);
}
};
} // namespace operators
} // namespace paddle
paddle/operators/dynamic_recurrent_op.cc
浏览文件 @ d2be7ec3
......@@ -23,6 +23,7 @@ using framework::Scope;
using framework::TensorArray;
using framework::LoDTensor;
using framework::Variable;
using framework::DySeqMetaBatch;
namespace detail {
......@@ -33,6 +34,29 @@ inline void CreateVariables(Scope& scope,
}
}
/*
* The inputs with sequence should be reordered when they are split, so the
* boot_states should be reordered in the same order.
*
* NOTE This may require that the `pre_state` of the first time step should just
* copy the `boot_state` rather than reference it, for that the content should
* be reordered, but the RNN op should not change the `boot_state` as an input
* variable's content.
*/
template <typename T>
inline void ReorderBootState(const DySeqMetaBatch& metas,
const LoDTensor& boot_state, LoDTensor* tensor,
const platform::Place& dst_place) {
for (size_t seq_id = 0; seq_id < metas.size(); seq_id++) {
auto slice = tensor->Slice<T>(seq_id, seq_id + 1);
auto boot_slice =
boot_state.Slice<T>(metas[seq_id].ori_idx, metas[seq_id].ori_idx + 1);
// TODO(superjom) pass in device context as an argument
slice.template CopyFrom<T>(boot_slice, dst_place,
platform::CPUDeviceContext());
}
}
} // namespace detail
class DynamicRecurrentOpProtoAndCheckerMaker
......@@ -69,6 +93,7 @@ void DynamicRecurrentOp::Run(const Scope& scope,
CreateScopes();
WriteStepInputs();
InitStates();
WriteStepOutputs();
// call stepnet in all the time steps
for (size_t step = 0; step < cache_.num_steps; step++) {
......@@ -76,7 +101,6 @@ void DynamicRecurrentOp::Run(const Scope& scope,
stepnet_->Run(step_scope, dev_ctx);
}
WriteStepOutputs();
ConcatOutputs();
}
......@@ -84,11 +108,11 @@ void DynamicRecurrentOp::SplitInputs() const {
// TODO(superjom) make level a config
// TODO(superjom) check all the inputs has the same LoD
int level = 0;
const auto& inlinks = cache_.inlinks;
for (const auto& item : inlinks) {
for (const auto& item : cache_.inlinks) {
const auto& var = item.second;
const auto& tensor = var->Get<LoDTensor>();
TensorArray& ta = step_inputs_[item.first];
dy_seq_metas_[item.first] =
ta.Unpack(tensor, level, true /*length_descend*/);
......@@ -120,17 +144,11 @@ void DynamicRecurrentOp::WriteStepInputs() const {
}
void DynamicRecurrentOp::WriteStepOutputs() const {
for (size_t step = 0; step < cache_.scopes->size(); step++) {
auto& scope = cache_.GetScope(step);
for (auto& item : step_outputs_) {
auto* var = scope.FindVar(item.first);
if (var == nullptr) {
var = scope.NewVar(item.first);
}
auto* tensor = var->GetMutable<LoDTensor>();
item.second.WriteShared(step, *tensor);
}
// initialize step outputs
for (const auto& item : cache_.outlinks) {
step_outputs_.emplace(item.first, TensorArray());
}
PADDLE_ENFORCE_GT(step_outputs_.size(), 0UL);
}
void DynamicRecurrentOp::CreateScopes() const {
......@@ -145,12 +163,18 @@ void DynamicRecurrentOp::CreateScopes() const {
PADDLE_ENFORCE_NOT_NULL(stepnet_, "stepnet should be set first");
std::vector<std::string> memories;
std::vector<std::string> pre_memories;
std::vector<std::string> stepnet_outputs;
std::transform(arg_.memories.begin(), arg_.memories.end(),
std::back_inserter(memories),
[](const rnn::MemoryAttr& m) { return m.var; });
std::transform(arg_.memories.begin(), arg_.memories.end(),
std::back_inserter(pre_memories),
[](const rnn::MemoryAttr& m) { return m.pre_var; });
for (const auto& item : stepnet_->Outputs()) {
for (const auto& var : item.second) {
stepnet_outputs.push_back(var);
}
}
for (size_t step = 0; step < cache_.num_steps; step++) {
auto& scope = cache_.GetScope(step);
......@@ -158,60 +182,88 @@ void DynamicRecurrentOp::CreateScopes() const {
detail::CreateVariables(scope, arg_.outlinks);
detail::CreateVariables(scope, memories);
detail::CreateVariables(scope, pre_memories);
detail::CreateVariables(scope, stepnet_outputs);
}
}
void DynamicRecurrentOp::ConcatOutputs() const {
// TODO(superjom) transform this to a config
int level = 0;
// TODO(superjom) pass in some lod
// just a placeholder
framework::LoD lod;
for (size_t step = 0; step < cache_.num_steps; step++) {
auto& scope = cache_.GetScope(step);
for (auto& item : step_outputs_) {
auto* var = scope.FindVar(item.first);
PADDLE_ENFORCE_NOT_NULL(var);
auto* tensor = var->GetMutable<LoDTensor>();
tensor->mutable_data<value_type>(platform::CPUPlace());
item.second.WriteShared(step, *tensor);
}
}
// the inlinks' lods should be the same, so randomly get one lod.
const auto& some_lod =
cache_.scope->FindVar(arg_.inlinks.front())->Get<LoDTensor>().lod();
const auto& some_meta = dy_seq_metas_[arg_.inlinks.front()];
for (auto& item : step_outputs_) {
auto tensor = item.second.Pack(level, dy_seq_metas_[item.first], lod);
auto& output = cache_.outlinks[item.first]->Get<LoDTensor>();
const_cast<LoDTensor*>(&output)->ShareDataWith<value_type>(tensor);
auto tensor = item.second.Pack(level, some_meta, some_lod);
auto* output = cache_.outlinks[item.first]->GetMutable<LoDTensor>();
const_cast<LoDTensor*>(output)->ShareDataWith<value_type>(tensor);
}
}
void DynamicRecurrentOp::InitStates() const {
// init the first state
// TODO(superjom) parepare the scenerio that boot state not exists
for (auto memory : arg_.memories) {
auto* boot_state_var = cache_.scope->FindVar(memory.boot_var);
PADDLE_ENFORCE_NOT_NULL(boot_state_var);
auto& boot_state = boot_state_var->Get<LoDTensor>();
const auto& dims = boot_state.dims();
for (size_t step = 0; step < cache_.num_steps; step++) {
auto& cur_scope = cache_.GetScope(step);
// link pre-state to boot_state
// init state and pre-state
auto* pre_state = cur_scope.FindVar(memory.pre_var);
PADDLE_ENFORCE_NOT_NULL(pre_state);
pre_state->GetMutable<LoDTensor>();
auto* state = cur_scope.FindVar(memory.var);
PADDLE_ENFORCE_NOT_NULL(state);
state->GetMutable<LoDTensor>()->Resize(dims);
state->GetMutable<LoDTensor>()->mutable_data<value_type>(
platform::CPUPlace());
if (step == 0) {
auto* pre_state_tensor = pre_state->GetMutable<LoDTensor>();
pre_state_tensor->Resize(boot_state.dims());
pre_state_tensor->ShareDataWith<value_type>(boot_state);
} else {
auto& pre_scope = cache_.GetScope(step - 1);
auto* state_pre = pre_scope.FindVar(memory.var);
PADDLE_ENFORCE_NOT_NULL(state_pre);
pre_state->GetMutable<LoDTensor>()->ShareDataWith<value_type>(
*state_pre->GetMutable<LoDTensor>());
}
for (size_t step = 0; step < cache_.num_steps; step++) {
for (const auto& memory : arg_.memories) {
CreateState(memory, step);
LinkState(memory, step);
}
}
}
void DynamicRecurrentOp::CreateState(const rnn::MemoryAttr& memory,
size_t step) const {
auto& scope = cache_.GetScope(step);
auto& state = *cache_.GetTensor(scope, memory.var);
auto& boot_state = *cache_.GetTensor(*cache_.scope, memory.boot_var);
size_t num_instances =
step_inputs_[arg_.inlinks.front()].Read(step).dims()[0];
auto dims = boot_state.dims();
dims[0] = num_instances;
state.Resize(dims);
state.mutable_data<value_type>(platform::CPUPlace());
states_[memory.var].WriteShared(step, state);
}
void DynamicRecurrentOp::LinkState(const rnn::MemoryAttr& memory,
size_t step) const {
auto& scope = cache_.GetScope(step);
auto& state_pre = *cache_.GetTensor(scope, memory.pre_var);
// all the step_inputs' metas should be the same, just randomly select one
// and get the dyseq meta.
const auto& some_meta = dy_seq_metas_[arg_.inlinks.front()];
size_t num_instances =
step_inputs_[arg_.inlinks.front()].Read(step).dims()[0];
LoDTensor* pre_state{nullptr};
if (step == 0) {
pre_state = cache_.GetTensor(*cache_.scope, memory.boot_var);
pre_state->mutable_data<float>(platform::CPUPlace());
// allocate memory
state_pre.Resize(pre_state->dims());
state_pre.mutable_data<value_type>(platform::CPUPlace());
detail::ReorderBootState<value_type>(some_meta, *pre_state, &state_pre,
pre_state->place());
} else {
pre_state = cache_.GetTensor(cache_.GetScope(step - 1), memory.var);
}
// shink and share from previous state
auto shrinked_pre_state = pre_state->Slice<value_type>(0, num_instances);
state_pre.ShareDataWith<value_type>(shrinked_pre_state);
}
void DynamicRecurrentOp::ArgCache::Init(
const rnn::ArgumentName& name, const paddle::framework::OperatorBase& op,
const paddle::framework::Scope& scope, rnn::Argument* arg) {
......@@ -261,6 +313,12 @@ Variable* DynamicRecurrentOp::ArgCache::GetVariable(const Scope& scope,
return var;
}
LoDTensor* DynamicRecurrentOp::ArgCache::GetTensor(
const framework::Scope& scope, const std::string& name) {
auto* var = GetVariable(scope, name);
return var->GetMutable<LoDTensor>();
}
const rnn::ArgumentName DynamicRecurrentOp::kArgName{
"step_net", "step_scopes", "inlinks", "outlinks",
"memories", "pre_memories", "boot_memories"};
......
paddle/operators/dynamic_recurrent_op.h
浏览文件 @ d2be7ec3
......@@ -77,6 +77,17 @@ class DynamicRecurrentOp : public framework::OperatorBase {
*/
void InitStates() const;
/*
* Create state variables for each time step.
*/
void CreateState(const rnn::MemoryAttr& memory, size_t step) const;
/*
* Link pre-state variable in current scope to the state variable in the
* previous time step (scope).
*/
void LinkState(const rnn::MemoryAttr& memory, size_t step) const;
/*
* Concatenate outputs in each time step and generate a LoDTensor.
*/
......@@ -91,6 +102,16 @@ class DynamicRecurrentOp : public framework::OperatorBase {
}
const OperatorBase& GetStepNet() const { return *stepnet_; }
const framework::TensorArray& state(const std::string& name) const {
return states_[name];
}
const framework::TensorArray& step_input(const std::string& name) const {
return step_inputs_[name];
}
const framework::TensorArray& step_output(const std::string& name) const {
return step_outputs_[name];
}
protected:
struct ArgCache {
framework::Scope const* scope;
......@@ -108,6 +129,9 @@ class DynamicRecurrentOp : public framework::OperatorBase {
return *scopes->at(index);
}
framework::LoDTensor* GetTensor(const framework::Scope& scope,
const std::string& name);
private:
void InitArgument(const rnn::ArgumentName& name, const OperatorBase& op,
rnn::Argument* arg);
......@@ -122,7 +146,7 @@ class DynamicRecurrentOp : public framework::OperatorBase {
private:
std::unique_ptr<OperatorBase> stepnet_;
mutable framework::TensorArray states_;
mutable std::map<std::string, framework::TensorArray> states_;
mutable std::map<std::string, framework::TensorArray> step_inputs_;
mutable std::map<std::string, framework::TensorArray> step_outputs_;
mutable std::map<std::string, std::vector<framework::DySeqMeta>>
......
paddle/operators/dynamic_recurrent_op_test.cc
浏览文件 @ d2be7ec3
......@@ -87,7 +87,6 @@ class DynamicRecurrentOpTestHelper : public ::testing::Test {
platform::CPUPlace place;
scope.NewVar("step_scopes");
CreateVar(scope, "boot_mem", framework::make_ddim({10, 20}), place);
// auto* out0 =
CreateVar(scope, "out0", framework::make_ddim({10, 20}), place);
auto* in0 = CreateVar(scope, "in0", framework::make_ddim({10, 8}), place);
// 10 instanes with 4 sentences, length is 4, 3, 2, 1 respectively.
......
paddle/operators/gru_unit_op.cc 0 → 100644
浏览文件 @ d2be7ec3
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/gru_unit_op.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class GRUUnitOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(%s) of GRUUnitOp should not be null.", "Input");
PADDLE_ENFORCE(ctx->HasInput("HiddenPrev"),
"Input(%s) of GRUUnitOp should not be null.", "HiddenPrev");
PADDLE_ENFORCE(ctx->HasInput("Weight"),
"Input(%s) of GRUUnitOp should not be null.", "Weight");
PADDLE_ENFORCE(ctx->HasOutput("Gate"),
"Output(%s) of GRUUnitOp should not be null.", "Gate");
PADDLE_ENFORCE(ctx->HasOutput("ResetHiddenPrev"),
"Output(%s) of GRUUnitOp should not be null.",
"ResetHiddenPrev");
PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
"Output(%s) of GRUUnitOp should not be null.", "Hidden");
auto input_dims = ctx->GetInputDim("Input");
auto hidden_prev_dims = ctx->GetInputDim("HiddenPrev");
auto weight_dims = ctx->GetInputDim("Weight");
int batch_size = input_dims[0];
int input_size = input_dims[1];
int frame_size = hidden_prev_dims[1];
int weight_height = weight_dims[0];
int weight_width = weight_dims[1];
PADDLE_ENFORCE_EQ(
input_size, frame_size * 3,
"The input_size must be 3 times of frame_size in GRUUnitOp.");
PADDLE_ENFORCE_EQ(
weight_height, frame_size,
"The shape of Weight matrix must be [frame_size, frame_size * 3].");
PADDLE_ENFORCE_EQ(
weight_width, frame_size * 3,
"The shape of Weight matrix must be [frame_size, frame_size * 3].");
auto bias = Input("Bias");
if (bias != framework::kEmptyVarName) {
auto bias_dims = ctx->GetInputDim("Bias");
int bias_height = bias_dims[0];
int bias_width = bias_dims[1];
PADDLE_ENFORCE_EQ(bias_height, 1,
"The shape of Bias must be [1, frame_size * 3].");
PADDLE_ENFORCE_EQ(bias_width, frame_size * 3,
"The shape of Bias must be [1, frame_size * 3].");
}
ctx->SetOutputDim("Gate", {batch_size, frame_size * 3});
ctx->SetOutputDim("ResetHiddenPrev", {batch_size, frame_size});
ctx->SetOutputDim("Hidden", {batch_size, frame_size});
}
};
class GRUUnitOpMaker : public framework::OpProtoAndCheckerMaker {
public:
GRUUnitOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Input",
"(Tensor) Matrix with shape [batch_size, frame_size * 3] for the "
"input.");
AddInput("HiddenPrev",
"(Tensor) Matrix with shape [batch_size, frame_size] for the "
"states of previous time step.");
AddInput("Weight",
"(Tensor) Weight matrix with shape [frame_size, frame_size * 3]. "
"The elements continuous in memory can be divided into two parts. "
"The first part are weights of the update gate and reset gate "
"with shape [frame_size, frame_size * 2], and the second part are "
"weights of output candidate with shape [frame_size, frame_size]");
AddInput("Bias",
"(Tensor) Bias vector with shape [1, frame_size * 3] concating "
"bias of the update gate, reset gate and output candidate.");
AddOutput("Gate",
"(Tensor) Matrix with shape [batch_size, frame_size * 3] for the "
"output of update gate, reset gate and output candidate")
.AsIntermediate();
AddOutput("ResetHiddenPrev",
"(Tensor) Matrix with shape [batch_size, frame_size] for the "
"reseted hidden state of previous time step.")
.AsIntermediate();
AddOutput("Hidden",
"(Tensor) The GRU hidden state of the current time step "
"with shape [batch_size, frame_size].");
AddAttr<int>("activation",
"(enum int, default tanh) "
"The activation type used for output candidate {h}_t.")
.SetDefault(tanh)
.InEnum({identity, sigmoid, tanh, relu});
AddAttr<int>("gate_activation",
"(enum int, default sigmoid) "
"The activation type used in update gate and reset gate.")
.SetDefault(sigmoid)
.InEnum({identity, sigmoid, tanh, relu});
AddComment(R"DOC(
GRUUnitOp implements part calculations of the GRU unit as following:
\f[
update \ gate: u_t = actGate(xu_t + W_u * hidden_prev + bias_u) \\
reset \ gate: r_t = actGate(xr_t + W_r * hidden_prev + bias_r) \\
output \ candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, hidden_prev) + bias_c) \\
output: h_t = dot((1-u_t), {h}_t) + dot(u_t, hidden_prev)
\f]
The rest of GRU unit can be completed by using FCOp's output as the input of GRUUnitOp.
)DOC");
}
};
class GRUUnitGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(%s) of GRUUnitGradOp should not be null.", "Input");
PADDLE_ENFORCE(ctx->HasInput("HiddenPrev"),
"Input(%s) of GRUUnitGradOp should not be null.",
"HiddenPrev");
PADDLE_ENFORCE(ctx->HasInput("Weight"),
"Input(%s) of GRUUnitGradOp should not be null.", "Weight");
PADDLE_ENFORCE(ctx->HasInput("Gate"),
"Input(%s) of GRUUnitGradOp should not be null.", "Gate");
PADDLE_ENFORCE(ctx->HasInput("ResetHiddenPrev"),
"Input(%s) of GRUUnitGradOp should not be null.",
"ResetHiddenPrev");
PADDLE_ENFORCE(ctx->HasInput("Hidden"),
"Input(%s) of GRUUnitGradOp should not be null.", "Hidden");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Gate")),
"Input(%s@GRAD) of GRUUnitGradOp should not be null.",
"Gate");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("ResetHiddenPrev")),
"Input(%s@GRAD) of GRUUnitGradOp should not be null.",
"ResetHiddenPrev");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Hidden")),
"Input(%s@GRAD) of GRUUnitGradOp should not be null.",
"Hidden");
auto input_dims = ctx->GetInputDim("Input");
auto hidden_prev_dims = ctx->GetInputDim("HiddenPrev");
auto weight_dims = ctx->GetInputDim("Weight");
// int batch_size = input_dims[0];
int input_size = input_dims[1];
int frame_size = hidden_prev_dims[1];
int weight_height = weight_dims[0];
int weight_width = weight_dims[1];
PADDLE_ENFORCE_EQ(
input_size, frame_size * 3,
"The input_size must be 3 times of frame_size in GRUUnitOp.");
PADDLE_ENFORCE_EQ(
weight_height, frame_size,
"The shape of Weight matrix must be [frame_size, frame_size * 3].");
PADDLE_ENFORCE_EQ(
weight_width, frame_size * 3,
"The shape of Weight matrix must be [frame_size, frame_size * 3].");
auto bias = Input("Bias");
if (bias != framework::kEmptyVarName) {
auto bias_dims = ctx->GetInputDim("Bias");
int bias_height = bias_dims[0];
int bias_width = bias_dims[1];
PADDLE_ENFORCE_EQ(bias_height, 1,
"The shape of Bias must be [1, frame_size * 3].");
PADDLE_ENFORCE_EQ(bias_width, frame_size * 3,
"The shape of Bias must be [1, frame_size * 3].");
auto bias_grad_name = framework::GradVarName("Bias");
if (ctx->HasOutput(bias_grad_name))
ctx->SetOutputDim(bias_grad_name, bias_dims);
}
auto input_grad_name = framework::GradVarName("Input");
if (ctx->HasOutput(input_grad_name))
ctx->SetOutputDim(input_grad_name, input_dims);
auto hidden_prev_grad_name = framework::GradVarName("HiddenPrev");
if (ctx->HasOutput(hidden_prev_grad_name))
ctx->SetOutputDim(hidden_prev_grad_name, hidden_prev_dims);
auto weight_grad_name = framework::GradVarName("Weight");
if (ctx->HasOutput(weight_grad_name))
ctx->SetOutputDim(weight_grad_name, weight_dims);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(gru_unit, ops::GRUUnitOp, ops::GRUUnitOpMaker, gru_unit_grad,
ops::GRUUnitGradOp);
REGISTER_OP_CPU_KERNEL(gru_unit,
ops::GRUUnitKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
gru_unit_grad, ops::GRUUnitGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/gru_unit_op.cu 0 → 100644
浏览文件 @ d2be7ec3
/* 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/gru_unit_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(gru_unit,
ops::GRUUnitKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
gru_unit_grad, ops::GRUUnitGradKernel<paddle::platform::GPUPlace, float>);
paddle/operators/gru_unit_op.h 0 → 100644
浏览文件 @ d2be7ec3
/* 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/operators/activation_op.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
enum GRUActivationType { identity = 0, sigmoid = 1, tanh = 2, relu = 3 };
template <typename Place, typename T>
class GRUUnitKernel : public framework::OpKernel<T> {
public:
template <typename Device, typename X, typename Y>
void ActCompute(const int act_type, const Device& d, X x, Y y) const {
if (act_type == identity)
y.device(d) = x;
else if (act_type == sigmoid)
SigmoidFunctor<T>()(d, x, y);
else if (act_type == tanh)
TanhFunctor<T>()(d, x, y);
else if (act_type == relu)
ReluFunctor<T>()(d, x, y);
else
PADDLE_THROW("unsupported activation type");
}
void Compute(const framework::ExecutionContext& context) const override {
auto* input = context.Input<Tensor>("Input");
auto* hidden_prev = context.Input<Tensor>("HiddenPrev");
auto* weight = context.Input<Tensor>("Weight");
auto* bias = context.Input<Tensor>("Bias");
auto* gate = context.Output<Tensor>("Gate");
gate->mutable_data<T>(context.GetPlace());
auto* reset_hidden_prev = context.Output<Tensor>("ResetHiddenPrev");
reset_hidden_prev->mutable_data<T>(context.GetPlace());
auto* hidden = context.Output<Tensor>("Hidden");
hidden->mutable_data<T>(context.GetPlace());
int batch_size = input->dims()[0];
int frame_size = hidden_prev->dims()[1];
auto x = EigenMatrix<T>::From(*input);
auto h_p = EigenMatrix<T>::From(*hidden_prev);
auto g = EigenMatrix<T>::From(*gate);
auto r_h_p = EigenMatrix<T>::From(*reset_hidden_prev);
auto h = EigenMatrix<T>::From(*hidden);
auto place = context.GetEigenDevice<Place>();
// calculate unactivated gate outputs
if (bias) {
auto b = EigenMatrix<T>::From(*bias);
g.device(place) = x +
b.reshape(Eigen::array<int, 2>({{1, frame_size * 3}}))
.broadcast(Eigen::array<int, 2>({{batch_size, 1}}));
} else {
g.device(place) = x;
}
const T* hidden_prev_data = hidden_prev->data<T>();
const T* weight_data = weight->data<T>();
T* gate_data = gate->data<T>();
T* reset_hidden_prev_data = reset_hidden_prev->data<T>();
math::gemm<Place, T>(context.device_context(), false, false, batch_size,
2 * frame_size, frame_size, 1, hidden_prev_data,
frame_size, weight_data, frame_size * 2, 1, gate_data,
frame_size * 3);
// calculate activited gate
Eigen::array<int, 2> extents({{batch_size, frame_size}});
Eigen::array<int, 2> u_offsets({{0, 0}});
ActCompute(context.Attr<int>("gate_activation"), place,
g.slice(u_offsets, extents), g.slice(u_offsets, extents));
auto u = g.slice(u_offsets, extents); // update gate
Eigen::array<int, 2> r_offsets({{0, frame_size}});
ActCompute(context.Attr<int>("gate_activation"), place,
g.slice(r_offsets, extents), g.slice(r_offsets, extents));
auto r = g.slice(r_offsets, extents); // reset gate
r_h_p.device(place) = r * h_p; // reset previous hidden state
math::gemm<Place, T>(context.device_context(), false, false, batch_size,
frame_size, frame_size, 1, reset_hidden_prev_data,
frame_size, weight_data + frame_size * frame_size * 2,
frame_size, 1, gate_data + frame_size * 2,
frame_size * 3);
Eigen::array<int, 2> c_offsets({{0, frame_size * 2}});
ActCompute(context.Attr<int>("activation"), place,
g.slice(c_offsets, extents), g.slice(c_offsets, extents));
auto c = g.slice(c_offsets, extents); // output candidate
// calculate final output
h.device(place) = u * (h_p - c) + c;
}
};
template <typename Place, typename T>
class GRUUnitGradKernel : public framework::OpKernel<T> {
public:
template <typename Device, typename X, typename Y, typename DX, typename DY>
void ActGradCompute(const int act_type, const Device& d, X x, Y y, DX dx,
DY dy) const {
// x is dummy and won't be used even in Relu(use y instead)
if (act_type == identity)
dx.device(d) = dy;
else if (act_type == sigmoid)
SigmoidGradFunctor<T>()(d, x, y, dy, dx);
else if (act_type == tanh)
TanhGradFunctor<T>()(d, x, y, dy, dx);
else if (act_type == relu)
ReluGradFunctor<T>()(d, x, y, dy, dx);
else
PADDLE_THROW("unsupported activation type");
}
void Compute(const framework::ExecutionContext& context) const override {
auto* input = context.Input<Tensor>("Input");
auto* hidden_prev = context.Input<Tensor>("HiddenPrev");
auto* weight = context.Input<Tensor>("Weight");
auto* gate = context.Input<Tensor>("Gate");
auto* reset_hidden_prev = context.Input<Tensor>("ResetHiddenPrev");
auto* hidden_grad = context.Input<Tensor>(framework::GradVarName("Hidden"));
auto* input_grad = context.Output<Tensor>(framework::GradVarName("Input"));
auto* hidden_prev_grad =
context.Output<Tensor>(framework::GradVarName("HiddenPrev"));
auto* weight_grad =
context.Output<Tensor>(framework::GradVarName("Weight"));
auto* bias_grad = context.Output<Tensor>(framework::GradVarName("Bias"));
input_grad->mutable_data<T>(context.GetPlace());
hidden_prev_grad->mutable_data<T>(context.GetPlace());
weight_grad->mutable_data<T>(context.GetPlace());
Tensor gate_grad;
gate_grad.mutable_data<T>(input->dims(), context.GetPlace());
Tensor reset_hidden_prev_grad;
reset_hidden_prev_grad.mutable_data<T>(reset_hidden_prev->dims(),
context.GetPlace());
int batch_size = input->dims()[0];
int frame_size = hidden_prev->dims()[1];
const T* hidden_prev_data = hidden_prev->data<T>();
T* hidden_prev_grad_data = hidden_prev_grad->data<T>();
const T* weight_data = weight->data<T>();
T* weight_grad_data = weight_grad->data<T>();
T* gate_grad_data = gate_grad.data<T>();
const T* reset_hidden_prev_data = reset_hidden_prev->data<T>();
T* reset_hidden_prev_grad_data = reset_hidden_prev_grad.data<T>();
auto h_p = EigenMatrix<T>::From(*hidden_prev);
auto g = EigenMatrix<T>::From(*gate);
auto d_h = EigenMatrix<T>::From(*hidden_grad);
auto d_x = EigenMatrix<T>::From(*input_grad);
auto d_h_p = EigenMatrix<T>::From(*hidden_prev_grad);
auto d_g = EigenMatrix<T>::From(gate_grad);
auto d_r_h_p = EigenMatrix<T>::From(reset_hidden_prev_grad);
auto place = context.GetEigenDevice<Place>();
Eigen::array<int, 2> extents({{batch_size, frame_size}});
Eigen::array<int, 2> u_offsets({{0, 0}});
auto u = g.slice(u_offsets, extents); // update gate
Eigen::array<int, 2> r_offsets({{0, frame_size}});
auto r = g.slice(r_offsets, extents); // reset gate
Eigen::array<int, 2> c_offsets({{0, frame_size * 2}});
auto c = g.slice(c_offsets, extents); // output candidate
// backward for unactivated update gate
ActGradCompute(context.Attr<int>("gate_activation"), place, u, u,
d_g.slice(u_offsets, extents), d_h * (h_p - c));
// backward for unactivated output candidate
ActGradCompute(context.Attr<int>("activation"), place, c, c,
d_g.slice(c_offsets, extents), d_h * (u.constant(T(1)) - u));
// backward for reset_hidden_prev
math::gemm<Place, T>(context.device_context(), false, true, batch_size,
frame_size, frame_size, 1,
gate_grad_data + frame_size * 2, frame_size * 3,
weight_data + frame_size * frame_size * 2, frame_size,
0, reset_hidden_prev_grad_data, frame_size);
// backward for state_weight
math::gemm<Place, T>(
context.device_context(), true, false, frame_size, frame_size,
batch_size, 1, reset_hidden_prev_data, frame_size,
gate_grad_data + frame_size * 2, frame_size * 3, 0,
weight_grad_data + frame_size * frame_size * 2, frame_size);
// backward for unactivated reset gate
ActGradCompute(context.Attr<int>("gate_activation"), place, r, r,
d_g.slice(r_offsets, extents), d_r_h_p * h_p);
// backward for update_gate_weight and reset_gate_weight
math::gemm<Place, T>(context.device_context(), true, false, frame_size,
frame_size * 2, batch_size, 1, hidden_prev_data,
frame_size, gate_grad_data, frame_size * 3, 0,
weight_grad_data, frame_size * 2);
// backward for hidden_prev
d_h_p.device(place) = d_r_h_p * r + d_h * u;
math::gemm<Place, T>(context.device_context(), false, true, batch_size,
frame_size, frame_size * 2, 1, gate_grad_data,
frame_size * 3, weight_data, frame_size * 2, 1,
hidden_prev_grad_data, frame_size);
// backward for input
d_x.device(place) = d_g;
// backward for bias
if (bias_grad) {
bias_grad->mutable_data<T>(context.GetPlace());
auto d_b = EigenMatrix<T>::From(*bias_grad);
d_b.device(place) = d_g.sum(Eigen::array<int, 1>({{0}}));
}
}
};
} // namespace operators
} // namespace paddle
paddle/operators/math/vol2col_test.cc
浏览文件 @ d2be7ec3
......@@ -78,7 +78,7 @@ void testVol2col() {
if (paddle::platform::is_cpu_place(*place)) {
input = input_tmp;
} else {
input.CopyFrom<float>(input_tmp, *place);
input.CopyFrom<float>(input_tmp, *place, *context);
}
output.mutable_data<float>({1, filter_size, filter_size, filter_size,
output_depth, output_height, output_width},
......@@ -93,7 +93,7 @@ void testVol2col() {
if (paddle::platform::is_cpu_place(*place)) {
out_cfo_ptr = output.data<float>();
} else {
output_tmp.CopyFrom<float>(output, paddle::platform::CPUPlace());
output_tmp.CopyFrom<float>(output, paddle::platform::CPUPlace(), *context);
out_cfo_ptr = output_tmp.data<float>();
}
......@@ -107,7 +107,7 @@ void testVol2col() {
if (paddle::platform::is_cpu_place(*place)) {
input = input_tmp;
} else {
input.CopyFrom<float>(input_tmp, *place);
input.CopyFrom<float>(input_tmp, *place, *context);
}
paddle::operators::math::Col2VolFunctor<Place, float> col2vol;
......@@ -118,7 +118,7 @@ void testVol2col() {
if (paddle::platform::is_cpu_place(*place)) {
in_ptr = input.data<float>();
} else {
input_tmp.CopyFrom<float>(input, paddle::platform::CPUPlace());
input_tmp.CopyFrom<float>(input, paddle::platform::CPUPlace(), *context);
in_ptr = input_tmp.data<float>();
}
......
paddle/operators/multiplex_op.cc
浏览文件 @ d2be7ec3
......@@ -115,8 +115,9 @@ class MultiplexGradOp : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(multiplex, ops::MultiplexOp, ops::MultiplexOpMaker, multiplex_grad,
ops::MultiplexGradOp);
REGISTER_OPERATOR(multiplex, ops::MultiplexOp, ops::MultiplexOpMaker,
paddle::framework::DefaultGradOpDescMaker<false>);
REGISTER_OPERATOR(multiplex_grad, ops::MultiplexGradOp);
REGISTER_OP_CPU_KERNEL(
multiplex, ops::MultiplexCPUKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
......
paddle/operators/sum_op.cc
浏览文件 @ d2be7ec3
......@@ -34,7 +34,7 @@ class SumOp : public framework::OperatorWithKernel {
auto in_dim = x_dims[0];
for (size_t i = 1; i < N; i++) {
auto dim = x_dims[i];
PADDLE_ENFORCE(in_dim == dim, "Input tensors must have same shape");
PADDLE_ENFORCE_EQ(in_dim, dim, "Input tensors must have same shape");
}
ctx->SetOutputDim("Out", in_dim);
ctx->ShareLoD("X", /*->*/ "Out");
......
paddle/operators/uniform_random_op.cc
浏览文件 @ d2be7ec3
......@@ -54,7 +54,7 @@ class UniformRandomOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(
ctx->Attrs().Get<float>("min") < ctx->Attrs().Get<float>("max"),
"uniform_random's min must less then max");
auto dims = Attr<std::vector<int>>("dims");
auto& dims = ctx->Attrs().Get<std::vector<int>>("dims");
std::vector<int64_t> temp;
temp.reserve(dims.size());
for (auto dim : dims) {
......
paddle/platform/cudnn_helper.h
浏览文件 @ d2be7ec3
......@@ -71,23 +71,32 @@ class ScopedTensorDescriptor {
inline cudnnTensorDescriptor_t descriptor(const cudnnTensorFormat_t format,
const cudnnDataType_t type,
const std::vector<int>& dims) {
// the format is not used now, but it maybe useful feature
const std::vector<int>& dims,
const int groups = 1) {
// the format is not used now, will add later
std::vector<int> strides(dims.size());
strides[dims.size() - 1] = 1;
for (int i = dims.size() - 2; i >= 0; i--) {
strides[i] = dims[i + 1] * strides[i + 1];
}
// Update tensor descriptor dims setting if groups > 1
// FIXME(typhoonzero): Assume using NCHW order
std::vector<int> dims_with_group(dims.begin(), dims.end()); // copy
if (groups > 1) {
dims_with_group[1] = dims_with_group[1] / groups;
}
PADDLE_ENFORCE(dynload::cudnnSetTensorNdDescriptor(
desc_, type, dims.size(), dims.data(), strides.data()));
desc_, type, dims_with_group.size(), dims_with_group.data(),
strides.data()));
return desc_;
}
template <typename T>
inline cudnnTensorDescriptor_t descriptor(const DataLayout& order,
const std::vector<int>& dims) {
return descriptor(GetCudnnTensorFormat(order), CudnnDataType<T>::type,
dims);
const std::vector<int>& dims,
const int groups = 1) {
return descriptor(GetCudnnTensorFormat(order), CudnnDataType<T>::type, dims,
groups);
}
private:
......@@ -106,18 +115,29 @@ class ScopedFilterDescriptor {
inline cudnnFilterDescriptor_t descriptor(const cudnnTensorFormat_t format,
const cudnnDataType_t type,
const std::vector<int>& kernel) {
// filter layout: output input spatial_dim_y spatial_dim_x
const std::vector<int>& kernel,
const int groups = 1) {
// filter layout: MCHW, where M is the number of
// output image channels, C is the number of input image channels,
// H and W is height and width of filter.
std::vector<int> kernel_with_group(kernel.begin(), kernel.end());
if (groups > 1) {
// M /= groups
kernel_with_group[0] /= groups;
// NOTE: input filter(C) of the filter is already asserted to be C/groups.
}
PADDLE_ENFORCE(dynload::cudnnSetFilterNdDescriptor(
desc_, type, format, kernel.size(), kernel.data()));
desc_, type, format, kernel_with_group.size(),
kernel_with_group.data()));
return desc_;
}
template <typename T>
inline cudnnFilterDescriptor_t descriptor(const DataLayout& order,
const std::vector<int>& kernel) {
const std::vector<int>& kernel,
const int groups = 1) {
return descriptor(GetCudnnTensorFormat(order), CudnnDataType<T>::type,
kernel);
kernel, groups);
}
private:
......
paddle/pybind/CMakeLists.txt
浏览文件 @ d2be7ec3
if(WITH_PYTHON)
cc_library(paddle_pybind SHARED
SRCS pybind.cc exception.cc protobuf.cc
DEPS pybind python backward proto_desc tensor_array
DEPS pybind python backward proto_desc tensor_array paddle_memory
${GLOB_OP_LIB})
endif(WITH_PYTHON)
paddle/pybind/protobuf.cc
浏览文件 @ d2be7ec3
......@@ -118,12 +118,23 @@ void BindProgramDesc(py::module &m) {
.def("append_block", &ProgramDescBind::AppendBlock,
py::return_value_policy::reference)
.def("append_backward",
[](ProgramDescBind &program_desc,
[](ProgramDescBind &program_desc, const VarDescBind &target,
const std::unordered_set<std::string> &no_grad_vars) {
AppendBackward(program_desc, no_grad_vars);
AppendBackward(program_desc, target, no_grad_vars);
})
.def("block", &ProgramDescBind::Block, py::return_value_policy::reference)
.def("num_blocks", &ProgramDescBind::Size);
.def("num_blocks", &ProgramDescBind::Size)
.def("serialize_to_string",
[](ProgramDescBind &program_desc) -> py::bytes {
const ProgramDesc *desc = program_desc.Proto();
PADDLE_ENFORCE(desc->IsInitialized(),
"ProgramDesc has not been initialized.");
std::string res;
PADDLE_ENFORCE(
desc->SerializeToString(&res),
"Serialize ProgramDesc Error. This could be a bug of Paddle.");
return res;
});
}
void BindBlockDesc(py::module &m) {
......@@ -149,7 +160,17 @@ void BindBlockDesc(py::module &m) {
.def("all_vars", &BlockDescBind::AllVars,
py::return_value_policy::reference)
.def("all_ops", &BlockDescBind::AllOps,
py::return_value_policy::reference);
py::return_value_policy::reference)
.def("serialize_to_string", [](BlockDescBind &block_desc) -> py::bytes {
const BlockDesc *desc = block_desc.Proto();
PADDLE_ENFORCE(desc->IsInitialized(),
"BlockDesc has not been initialized.");
std::string res;
PADDLE_ENFORCE(
desc->SerializeToString(&res),
"Serialize BlockDesc Error. This could be a bug of Paddle.");
return res;
});
}
void BindVarDsec(py::module &m) {
......@@ -162,7 +183,8 @@ void BindVarDsec(py::module &m) {
.value("FP32", DataType::FP32)
.value("FP64", DataType::FP64);
py::class_<VarDescBind>(m, "VarDesc", "")
py::class_<VarDescBind> var_desc(m, "VarDesc", "");
var_desc
.def("name",
[](const VarDescBind &self) {
py::bytes name = self.Name();
......@@ -174,7 +196,23 @@ void BindVarDsec(py::module &m) {
.def("shape", &VarDescBind::Shape, py::return_value_policy::reference)
.def("data_type", &VarDescBind::GetDataType)
.def("lod_level", &VarDescBind::GetLodLevel)
.def("set_lod_level", &VarDescBind::SetLoDLevel);
.def("set_lod_level", &VarDescBind::SetLoDLevel)
.def("type", &VarDescBind::GetType)
.def("set_type", &VarDescBind::SetType)
.def("serialize_to_string", [](VarDescBind &var_desc) -> py::bytes {
const VarDesc *desc = var_desc.Proto();
PADDLE_ENFORCE(desc->IsInitialized(),
"VarDesc has not been initialized.");
std::string res;
PADDLE_ENFORCE(
desc->SerializeToString(&res),
"Serialize VarDesc Error. This could be a bug of Paddle.");
return res;
});
py::enum_<VarDesc::VarType>(var_desc, "VarType", "")
.value("LOD_TENSOR", VarDesc::LOD_TENSOR)
.value("SELECTED_ROWS", VarDesc::SELECTED_ROWS);
}
void BindOpDesc(py::module &m) {
......@@ -204,9 +242,19 @@ void BindOpDesc(py::module &m) {
.def("set_attr", &OpDescBind::SetAttr)
.def("attr", &OpDescBind::GetAttr)
.def("set_block_attr", &OpDescBind::SetBlockAttr)
.def("get_block_attr", &OpDescBind::GetBlockAttr)
.def("block_attr", &OpDescBind::GetBlockAttr)
.def("check_attrs", &OpDescBind::CheckAttrs)
.def("infer_shape", &OpDescBind::InferShape);
.def("infer_shape", &OpDescBind::InferShape)
.def("serialize_to_string", [](OpDescBind &op_desc) -> py::bytes {
const OpDesc *desc = op_desc.Proto();
PADDLE_ENFORCE(desc->IsInitialized(),
"OpDesc has not been initialized.");
std::string res;
PADDLE_ENFORCE(
desc->SerializeToString(&res),
"Serialize OpDesc Error. This could be a bug of Paddle.");
return res;
});
}
} // namespace pybind
......
paddle/pybind/pybind.cc
浏览文件 @ d2be7ec3
......@@ -18,6 +18,7 @@ limitations under the License. */
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/tensor_array.h"
#include "paddle/operators/cond_op.h"
#include "paddle/operators/dynamic_recurrent_op.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/recurrent_op.h"
#include "paddle/platform/enforce.h"
......@@ -341,6 +342,33 @@ All parameter, weight, gradient are variables in Paddle.
self.set_stepnet(net.Clone());
});
py::class_<operators::DynamicRecurrentOp, OperatorBase>(m,
"DynamicRecurrentOp")
.def_static("create",
[](py::bytes protobin) -> operators::DynamicRecurrentOp * {
OpDesc desc;
PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
"Cannot parse user input to OpDesc");
PADDLE_ENFORCE(desc.IsInitialized(),
"User OpDesc is not initialized, reason %s",
desc.InitializationErrorString());
auto rnn_op = OpRegistry::CreateOp(desc);
return static_cast<operators::DynamicRecurrentOp *>(
rnn_op.release());
})
.def("set_stepnet",
[](operators::DynamicRecurrentOp &self, const operators::NetOp &net)
-> void { self.SetStepNet(net.Clone()); })
.def("get_state",
[](operators::DynamicRecurrentOp &self, const std::string &name)
-> const TensorArray & { return self.state(name); })
.def("get_step_input",
[](operators::DynamicRecurrentOp &self, const std::string &name)
-> const TensorArray & { return self.step_input(name); })
.def("get_step_output",
[](operators::DynamicRecurrentOp &self, const std::string &name)
-> const TensorArray & { return self.step_output(name); });
// cond_op
py::class_<operators::CondOp, OperatorBase>(m, "CondOp")
.def_static("create",
......
python/paddle/v2/framework/graph.py → python/paddle/v2/framework/framework.py
浏览文件 @ d2be7ec3
import paddle.v2.framework.core as core
import paddle.v2.framework.proto.framework_pb2 as framework_pb2
import collections
import numpy as np
import copy
......@@ -9,6 +10,7 @@ __all__ = ['Block', 'Variable', 'Program', 'Operator']
class Variable(object):
def __init__(self,
block,
type=core.VarDesc.VarType.LOD_TENSOR,
name=None,
shape=None,
dtype=None,
......@@ -25,6 +27,14 @@ class Variable(object):
self.desc = self.block.desc.new_var(name)
is_new_var = True
if is_new_var:
self.desc.set_type(type)
elif self.desc.type() != type:
raise ValueError("Variable {0} has been created before. The "
"previous type is {1}; the new type is {2}. They"
" are not matched".format(self.name,
self.desc.type(), type))
if shape is not None:
if is_new_var:
self.desc.set_shape(shape)
......@@ -63,6 +73,13 @@ class Variable(object):
self.block.vars[name] = self
self.op = None
def __str__(self):
protostr = self.desc.serialize_to_string()
proto = framework_pb2.VarDesc.FromString(str(protostr))
return proto.__str__()
__repr__ = __str__
@property
def name(self):
return self.desc.name()
......@@ -106,6 +123,40 @@ class Variable(object):
raise ValueError("Not supported numpy dtype " + str(dtype))
def get_all_op_protos():
"""
Get all registered op proto from PaddlePaddle C++ end.
:return: A list of registered OpProto.
"""
protostrs = core.get_all_op_protos()
ret_values = []
for pbstr in protostrs:
op_proto = framework_pb2.OpProto.FromString(str(pbstr))
ret_values.append(op_proto)
return ret_values
class OpProtoHolder(object):
@classmethod
def instance(cls):
if not hasattr(cls, '_instance'):
cls._instance = cls()
return cls._instance
def __init__(self):
assert not hasattr(
self.__class__,
'_instance'), 'Please use `instance()` to get OpProtoHolder opject!'
op_protos = get_all_op_protos()
self.op_proto_map = {}
for proto in op_protos:
self.op_proto_map[proto.type] = proto
def get_op_proto(self, type):
assert type in self.op_proto_map, "Operator \"%s\" has not been registered." % type
return self.op_proto_map[type]
class Operator(object):
def __init__(self,
block,
......@@ -116,20 +167,120 @@ class Operator(object):
attrs=None):
self.block = block
self.desc = desc
if type is not None:
# TODO.
pass
if len(self.desc.type()) != 0:
return
if type is None:
raise ValueError(
"`type` to initilized an Operator can not be None.")
self.desc.set_type(type)
proto = OpProtoHolder.instance().get_op_proto(type)
if inputs is not None:
# TODO
pass
given = set()
need = set()
for n in inputs:
given.add(n)
for m in proto.inputs:
need.add(m.name)
if not given == need:
raise ValueError(
"Incorrect setting for input(s) of operator \"%s\". Need: [%s] Given: [%s]"
% (type, ", ".join(str(e) for e in need), ", ".join(
str(e) for e in given)))
for in_proto in proto.inputs:
in_argus = inputs[in_proto.name]
if not isinstance(in_argus, list):
in_argus = [in_argus]
if not in_proto.duplicable and len(in_argus) > 1:
raise ValueError(
"Input %s expects only one input, but %d are given." %
(in_proto.name, len(in_argus)))
in_argu_names = []
for argu in in_argus:
in_argu_names.append(argu.name)
self.desc.set_input(in_proto.name, in_argu_names)
if outputs is not None:
# TODO
pass
given = set()
need = set()
for n in outputs:
given.add(n)
for m in proto.outputs:
need.add(m.name)
if not given == need:
raise ValueError(
"Incorrect setting for output(s) of operator \"%s\". Need: [%s] Given: [%s]"
% (type, ", ".join(str(e) for e in need), ", ".join(
str(e) for e in given)))
for out_proto in proto.outputs:
out_argus = outputs[out_proto.name]
if not isinstance(out_argus, list):
out_argus = [out_argus]
if not out_proto.duplicable and len(out_argus) > 1:
raise ValueError(
"Output %s expects only one output, but %d are given." %
(out_proto.name, len(out_argus)))
out_argu_names = []
for argu in out_argus:
out_argu_names.append(argu.name)
argu.op = self
self.desc.set_output(out_proto.name, out_argu_names)
if attrs is not None:
# TODO
pass
for attr in proto.attrs:
attr_name = attr.name
if not attr_name in attrs:
continue
if not isinstance(attrs[attr_name], Block):
self.desc.set_attr(attr_name, attrs[attr_name])
else:
self.desc.set_block_attr(attr_name, attrs[attr_name].desc)
# TODO: Getters
self.desc.check_attrs()
self.desc.infer_shape(self.block.desc)
def __str__(self):
protostr = self.desc.serialize_to_string()
proto = framework_pb2.OpDesc.FromString(str(protostr))
return proto.__str__()
__repr__ = __str__
@property
def type(self):
return self.desc.type()
def input(self, name):
return self.desc.input(name)
@property
def input_names(self):
return self.desc.input_names()
def output(self, name):
return self.desc.output(name)
@property
def output_names(self):
return self.desc.output_names()
def has_attr(self, name):
return self.desc.has_attr(name)
def attr_type(self, name):
return self.desc.attr_type(name)
@property
def attr_names(self):
return self.desc.attr_names()
def attr(self, name):
return self.desc.attr(name)
def block_attr(self, name):
return self.desc.block_attr(name)
class Block(object):
......@@ -139,6 +290,13 @@ class Block(object):
self.ops = collections.deque() # operator list
self.program = program
def __str__(self):
protostr = self.desc.serialize_to_string()
proto = framework_pb2.BlockDesc.FromString(str(protostr))
return proto.__str__()
__repr__ = __str__
@property
def parent_idx(self):
return self.desc.parent
......@@ -183,6 +341,13 @@ class Program(object):
self.blocks = [Block(self, 0)]
self.current_block_idx = 0
def __str__(self):
protostr = self.desc.serialize_to_string()
proto = framework_pb2.ProgramDesc.FromString(str(protostr))
return proto.__str__()
__repr__ = __str__
def global_block(self):
return self.blocks[0]
......
python/paddle/v2/framework/op.py
浏览文件 @ d2be7ec3
......@@ -219,6 +219,27 @@ class __RecurrentOp__(object):
return core.RecurrentOp.create(proto.SerializeToString())
class __DynamicRecurrentOp__(object):
__proto__ = None
type = "dynamic_recurrent"
def __init__(self):
# cache recurrent_op's proto
if self.__proto__ is None:
for op_proto in get_all_op_protos():
if op_proto.type == self.type:
self.__proto__ = op_proto
def __call__(self, *args, **kwargs):
if self.type not in args and "type" not in kwargs:
kwargs["type"] = self.type
# create proto
create_method = OpDescCreationMethod(self.__proto__)
proto = create_method(*args, **kwargs)
# create rnnop
return core.DynamicRecurrentOp.create(proto.SerializeToString())
class __CondOp__(object):
__proto__ = None
type = "cond"
......@@ -242,4 +263,5 @@ class __CondOp__(object):
Operator = OperatorFactory() # The default global factory
RecurrentOp = __RecurrentOp__()
DynamicRecurrentOp = __DynamicRecurrentOp__()
CondOp = __CondOp__()
python/paddle/v2/framework/tests/test_activation_op.py
浏览文件 @ d2be7ec3
......@@ -384,5 +384,33 @@ class TestThresholdedRelu(OpTest):
self.check_grad(['X'], 'Y', max_relative_error=self.relative_error)
class TestHardSigmoid(OpTest):
def setUp(self):
self.op_type = "hard_sigmoid"
self.relative_error = 0.002
X = np.random.uniform(-5, 5, [2, 2]).astype("float32")
slope = 0.2
offset = 0.5
lower_threshold = -offset / slope
upper_threshold = (1 - offset) / slope
self.inputs = {'X': X}
# Same reason as TestAbs
X[np.abs(X - lower_threshold) < self.relative_error] = \
lower_threshold + 0.2
X[np.abs(X - upper_threshold) < self.relative_error] = \
upper_threshold - 0.2
temp = X * slope + offset
self.outputs = {'Y': 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)
if __name__ == "__main__":
unittest.main()
python/paddle/v2/framework/tests/test_adam_op.py 0 → 100644
浏览文件 @ d2be7ec3
import unittest
import numpy as np
from op_test import OpTest
class TestAdamOp1(OpTest):
def setUp(self):
'''Test Adam Op with supplied attributes
'''
self.op_type = "adam"
param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
# The second moment is positive
moment2 = np.random.random((102, 105)).astype("float32")
learning_rate = 0.004
beta1 = 0.78
beta2 = 0.836
epsilon = 1e-4
beta1_pow = beta1**10
beta2_pow = beta2**10
self.inputs = {
'Param': param,
'Grad': grad,
'Moment1': moment1,
'Moment2': moment2,
'LearningRate': np.array([learning_rate]).astype("float32"),
'Beta1Pow': np.array([beta1_pow]).astype("float32"),
'Beta2Pow': np.array([beta2_pow]).astype("float32")
}
self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
param_out, moment1_out, moment2_out, beta1_pow_out, \
beta2_pow_out = adam_step(self.inputs, self.attrs)
self.outputs = {
'Moment1Out': moment1_out,
'Moment2Out': moment2_out,
'Beta1PowOut': beta1_pow_out,
'Beta2PowOut': beta2_pow_out,
'ParamOut': param_out
}
def test_check_output(self):
self.check_output()
class TestAdamOp2(OpTest):
def setUp(self):
'''Test Adam Op with supplied attributes
'''
self.op_type = "adam"
param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
# The second moment is positive
moment2 = np.random.random((102, 105)).astype("float32")
learning_rate = 0.001
beta1 = 0.9
beta2 = 0.999
epsilon = 1e-8
beta1_pow = beta1**10
beta2_pow = beta2**10
self.inputs = {
'Param': param,
'Grad': grad,
'Moment1': moment1,
'Moment2': moment2,
'LearningRate': np.array([learning_rate]).astype("float32"),
'Beta1Pow': np.array([beta1_pow]).astype("float32"),
'Beta2Pow': np.array([beta2_pow]).astype("float32")
}
attributes = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
param_out, moment1_out, moment2_out, beta1_pow_out, \
beta2_pow_out = adam_step(self.inputs, attributes)
self.outputs = {
'Moment1Out': moment1_out,
'Moment2Out': moment2_out,
'Beta1PowOut': beta1_pow_out,
'Beta2PowOut': beta2_pow_out,
'ParamOut': param_out
}
def test_check_output(self):
self.check_output()
class TestAdamOpMultipleSteps(OpTest):
def setUp(self):
'''Test Adam Operator with supplied attributes
'''
self.op_type = "adam"
self.num_steps = 10
param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
# The second moment is positive
moment2 = np.random.random((102, 105)).astype("float32")
learning_rate = 0.001
beta1 = 0.9
beta2 = 0.999
epsilon = 1e-8
beta1_pow = beta1**10
beta2_pow = beta2**10
self.inputs = {
'Param': param,
'Grad': grad,
'Moment1': moment1,
'Moment2': moment2,
'LearningRate': np.array([learning_rate]).astype("float32"),
'Beta1Pow': np.array([beta1_pow]).astype("float32"),
'Beta2Pow': np.array([beta2_pow]).astype("float32")
}
self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
def test_check_output(self):
for _ in range(self.num_steps):
param_out, moment1_out, moment2_out, beta1_pow_out, \
beta2_pow_out = adam_step(self.inputs, self.attrs)
self.outputs = {
'Moment1Out': moment1_out,
'Moment2Out': moment2_out,
'Beta1PowOut': beta1_pow_out,
'Beta2PowOut': beta2_pow_out,
'ParamOut': param_out
}
# Verify output for this step
self.check_output()
# Output of this step becomes input for next step
self.inputs['Param'] = param_out
self.inputs['Moment1'] = moment1_out
self.inputs['Moment2'] = moment2_out
self.inputs['Beta1Pow'] = beta1_pow_out
self.inputs['Beta2Pow'] = beta2_pow_out
# Randomize gradient for next step
self.inputs['Grad'] = np.random.uniform(
-1, 1, (102, 105)).astype("float32")
def adam_step(inputs, attributes):
'''
Simulate one step of the adam optimizer
:param inputs: dict of inputs
:param attributes: dict of attributes
:return tuple: tuple of output param, moment1, moment2,
beta1 power accumulator and beta2 power accumulator
'''
param = inputs['Param']
grad = inputs['Grad']
moment1 = inputs['Moment1']
moment2 = inputs['Moment2']
lr = inputs['LearningRate']
beta1_pow = inputs['Beta1Pow']
beta2_pow = inputs['Beta2Pow']
beta1 = attributes['beta1']
beta2 = attributes['beta2']
epsilon = attributes['epsilon']
moment1_out = beta1 * moment1 + (1 - beta1) * grad
moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad)
beta1_pow_out = beta1_pow * beta1
beta2_pow_out = beta2_pow * beta2
lr_t = lr * np.sqrt(1 - beta2_pow_out) / (1 - beta1_pow_out)
param_out = param - lr_t * (moment1_out / (np.sqrt(moment2_out) + epsilon))
return param_out, moment1_out, moment2_out, beta1_pow_out, beta2_pow_out
if __name__ == "__main__":
unittest.main()
python/paddle/v2/framework/tests/test_conv2d_op.py
浏览文件 @ d2be7ec3
......@@ -3,70 +3,56 @@ import numpy as np
from op_test import OpTest
def conv2d_forward_naive(input, filter, group, conv_param):
in_n, in_c, in_h, in_w = input.shape
out_c, f_c, f_h, f_w = filter.shape
assert f_c * group == in_c
assert np.mod(out_c, group) == 0
sub_out_c = out_c / group
stride, pad = conv_param['stride'], conv_param['pad']
out_h = 1 + (in_h + 2 * pad[0] - f_h) / stride[0]
out_w = 1 + (in_w + 2 * pad[1] - f_w) / stride[1]
out = np.zeros((in_n, out_c, out_h, out_w))
input_pad = np.pad(input, ((0, ), (0, ), (pad[0], ), (pad[1], )),
mode='constant',
constant_values=0)
for i in range(out_h):
for j in range(out_w):
for g in range(group):
input_pad_masked = \
input_pad[:, g * f_c:(g + 1) * f_c,
i * stride[0]:i * stride[0] + f_h,
j * stride[1]:j * stride[1] + f_w]
f_sub = filter[g * sub_out_c:(g + 1) * sub_out_c, :, :, :]
for k in range(sub_out_c):
out[:, g * sub_out_c + k, i, j] = \
np.sum(input_pad_masked * f_sub[k, :, :, :],
axis=(1, 2, 3))
return out
class TestConv2dOp(OpTest):
def setUp(self):
self.init_groups()
self.op_type = "conv2d"
batch_size = 2
input_channels = 3
input_height = 5
input_width = 5
output_channels = 6
filter_height = 3
filter_width = 3
stride = 1
padding = 0
output_height = (input_height - filter_height + 2 * padding
) / stride + 1
output_width = (input_width - filter_width + 2 * padding) / stride + 1
input = np.random.random((batch_size, input_channels, input_height,
input_width)).astype("float32")
filter = np.random.random(
(output_channels, input_channels / self.groups, filter_height,
filter_width)).astype("float32")
output = np.ndarray(
(batch_size, output_channels, output_height, output_width))
self.init_op_type()
self.init_group()
self.init_test_case()
conv2d_param = {'stride': self.stride, 'pad': self.pad}
input = np.random.random(self.input_size).astype("float32")
filter = np.random.random(self.filter_size).astype("float32")
output = conv2d_forward_naive(input, filter, self.groups, conv2d_param)
self.inputs = {'Input': input, 'Filter': filter}
self.attrs = {
'strides': [1, 1],
'paddings': [0, 0],
'groups': self.groups
'strides': self.stride,
'paddings': self.pad,
'groups': self.groups,
'dilations': self.dilations
}
output_group_channels = output_channels / self.groups
input_group_channels = input_channels / self.groups
for batchid in xrange(batch_size):
for group in xrange(self.groups):
for outchannelid in range(group * output_group_channels,
(group + 1) * output_group_channels):
for rowid in xrange(output_height):
for colid in xrange(output_width):
start_h = (rowid * stride) - padding
start_w = (colid * stride) - padding
output_value = 0.0
for inchannelid in range(
group * input_group_channels,
(group + 1) * input_group_channels):
for frowid in xrange(filter_height):
for fcolid in xrange(filter_width):
input_value = 0.0
inrowid = start_h + frowid
incolid = start_w + fcolid
if ((inrowid >= 0 and
inrowid < input_height) and
(incolid >= 0 and
incolid < input_width)):
input_value = input[batchid][
inchannelid][inrowid][incolid]
filter_value = filter[outchannelid][
inchannelid % input_group_channels][
frowid][fcolid]
output_value += input_value * filter_value
output[batchid][outchannelid][rowid][
colid] = output_value
self.outputs = {'Output': output}
def test_check_output(self):
......@@ -90,14 +76,47 @@ class TestConv2dOp(OpTest):
max_relative_error=0.05,
no_grad_set=set(['Input']))
def init_groups(self):
def init_test_case(self):
# self.groups = 1
# self.op_type = "conv2d"
self.pad = [0, 0]
self.stride = [1, 1]
self.dilations = [1, 1]
self.input_size = [2, 3, 5, 5] # NCHW
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] / self.groups
self.filter_size = [6, f_c, 3, 3]
def init_group(self):
self.groups = 1
def init_op_type(self):
self.op_type = "conv2d"
class TestWithGroup(TestConv2dOp):
def init_groups(self):
def init_group(self):
self.groups = 3
def init_op_type(self):
self.op_type = "conv2d"
class TestCudnn(TestConv2dOp):
def init_group(self):
self.groups = 1
def init_op_type(self):
self.op_type = "conv_cudnn"
class TestCudnnWithGroup(TestConv2dOp):
def init_group(self):
self.groups = 3
def init_op_type(self):
self.op_type = "conv_cudnn"
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_decayed_adagrad_op.py 0 → 100644
浏览文件 @ d2be7ec3
import unittest
import numpy as np
from op_test import OpTest
class TestDecayedAdagradOp1(OpTest):
''' Test DecayedAdagrad operator with explicit attributes
'''
def setUp(self):
self.op_type = "decayed_adagrad"
param = np.random.random((123, 321)).astype("float32")
grad = np.random.random((123, 321)).astype("float32")
moment = np.zeros((123, 321)).astype("float32")
lr = 0.01
decay = 0.80
epsilon = 1e-8
self.inputs = {
'Param': param,
'Grad': grad,
'Moment': moment,
'LearningRate': np.array([lr]).astype("float32")
}
self.attrs = {'decay': decay, 'epsilon': epsilon}
moment_out = decay * moment + (1 - decay) * grad * grad
param_out = param - lr * grad / (np.sqrt(moment_out) + epsilon)
self.outputs = {'ParamOut': param_out, 'MomentOut': moment_out}
def test_check_output(self):
self.check_output()
class TestDecayedAdagradOp2(OpTest):
''' Test DecayedAdagrad operator with default attributes
'''
def setUp(self):
self.op_type = "decayed_adagrad"
param = np.random.random((123, 321)).astype("float32")
grad = np.random.random((123, 321)).astype("float32")
moment = np.zeros((123, 321)).astype("float32")
lr = 0.01
decay = 0.95
epsilon = 1e-6
self.inputs = {
'Param': param,
'Grad': grad,
'Moment': moment,
'LearningRate': np.array([lr]).astype("float32")
}
self.attrs = {'decay': decay, 'epsilon': epsilon}
moment_out = decay * moment + (1 - decay) * grad * grad
param_out = param - lr * grad / (np.sqrt(moment_out) + epsilon)
self.outputs = {'ParamOut': param_out, 'MomentOut': moment_out}
def test_check_output(self):
self.check_output()
if __name__ == "__main__":
unittest.main()
python/paddle/v2/framework/tests/test_dynamic_recurrent_op.py 0 → 100644
浏览文件 @ d2be7ec3
import logging
import paddle.v2.framework.core as core
import unittest
from paddle.v2.framework.op import Operator, DynamicRecurrentOp
import numpy as np
def create_tensor(scope, name, shape, np_data):
tensor = scope.new_var(name).get_tensor()
tensor.set_dims(shape)
tensor.set(np_data, core.CPUPlace())
return tensor
class DynamicRecurrentOpTest(unittest.TestCase):
'''
Test RNNOp
equation:
h_t = \sigma (W x_t + U h_{t-1})
weights:
- W
- U
vars:
- x
memories:
- h
outputs:
- h
'''
# for siplicity, just one level LoD
lod_py = [[0, 4, 7, 9, 10]]
input_dim = 30
num_sents = len(lod_py[0]) - 1
weight_dim = 15
def forward(self):
self.scope = core.Scope()
self.create_global_variables()
self.create_rnn_op()
self.create_step_net()
ctx = core.DeviceContext.create(core.CPUPlace())
self.rnnop.run(self.scope, ctx)
state = self.rnnop.get_state("h@mem")
print 'state size: ', state.size()
step_inputs = self.rnnop.get_step_input("x")
print "x size ", step_inputs.size()
for i in range(step_inputs.size()):
print "x %d" % i, np.array(step_inputs.read(i).get_dims())
step_outputs = self.rnnop.get_step_output('h@mem')
print 'step_outputs.size ', step_outputs.size()
output = self.scope.find_var("h@mem").get_tensor()
print 'output', np.array(output).shape
def create_global_variables(self):
x = np.random.normal(size=(self.lod_py[0][-1],
self.input_dim)).astype("float32")
W = np.random.normal(size=(self.input_dim,
self.input_dim)).astype("float32")
U = np.random.normal(size=(self.input_dim,
self.input_dim)).astype("float32")
h_boot = np.random.normal(size=(self.num_sents,
self.input_dim)).astype("float32")
# create inlink
x_tensor = create_tensor(self.scope, "x",
[self.num_sents, self.input_dim], x)
x_tensor.set_lod(self.lod_py)
create_tensor(self.scope, "W", [self.input_dim, self.input_dim], W)
create_tensor(self.scope, "U", [self.input_dim, self.input_dim], U)
create_tensor(self.scope, "h_boot", [self.num_sents, self.input_dim],
h_boot)
self.scope.new_var("step_scopes")
self.scope.new_var("h@mem")
def create_rnn_op(self):
# create RNNOp
self.rnnop = DynamicRecurrentOp(
# inputs
inlinks=["x"],
boot_memories=["h_boot"],
step_net="stepnet",
# outputs
outlinks=["h@mem"],
step_scopes="step_scopes",
# attributes
pre_memories=["h@pre"],
memories=["h@mem"])
def create_step_net(self):
stepnet = core.Net.create()
x_fc_op = Operator("mul", X="x", Y="W", Out="Wx")
h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
sum_op = Operator("sum", X=["Wx", "Uh"], Out="sum")
sig_op = Operator("sigmoid", X="sum", Y="h@mem")
for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
stepnet.append_op(op)
stepnet.complete_add_op(True)
self.rnnop.set_stepnet(stepnet)
def test_forward(self):
print 'test recurrent op forward'
pd_output = self.forward()
print 'pd_output', pd_output
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_gru_unit_op.py 0 → 100644
浏览文件 @ d2be7ec3
import math
import unittest
import numpy as np
from op_test import OpTest
class GRUActivationType(OpTest):
identity = 0
sigmoid = 1
tanh = 2
relu = 3
def identity(x):
return x
def sigmoid(x):
return 1. / (1. + np.exp(-x))
def tanh(x):
return 2. * sigmoid(2. * x) - 1.
def relu(x):
return np.maximum(x, 0)
class TestGRUUnitOp(OpTest):
batch_size = 3
frame_size = 5
activate = {
GRUActivationType.identity: identity,
GRUActivationType.sigmoid: sigmoid,
GRUActivationType.tanh: tanh,
GRUActivationType.relu: relu,
}
def set_inputs(self):
batch_size = self.batch_size
frame_size = self.frame_size
self.op_type = 'gru_unit'
self.inputs = {
'Input': np.random.uniform(
-0.1, 0.1, (batch_size, frame_size * 3)).astype('float32'),
'HiddenPrev': np.random.uniform(
-0.1, 0.1, (batch_size, frame_size)).astype('float32'),
'Weight': np.random.uniform(
-1. / math.sqrt(frame_size), 1. / math.sqrt(frame_size),
(frame_size, frame_size * 3)).astype('float32'),
}
self.attrs = {
'activation': GRUActivationType.tanh,
'gate_activation': GRUActivationType.sigmoid
}
def set_outputs(self):
# GRU calculations
batch_size = self.batch_size
frame_size = self.frame_size
x = self.inputs['Input']
h_p = self.inputs['HiddenPrev']
w = self.inputs['Weight']
b = self.inputs['Bias'] if self.inputs.has_key('Bias') else np.zeros(
(1, frame_size * 3))
g = x + np.tile(b, (batch_size, 1))
w_u_r = w.flatten()[:frame_size * frame_size * 2].reshape(
(frame_size, frame_size * 2))
u_r = self.activate[self.attrs['gate_activation']](np.dot(
h_p, w_u_r) + g[:, :frame_size * 2])
u = u_r[:, :frame_size]
r = u_r[:, frame_size:frame_size * 2]
r_h_p = r * h_p
w_c = w.flatten()[frame_size * frame_size * 2:].reshape(
(frame_size, frame_size))
c = self.activate[self.attrs['activation']](np.dot(r_h_p, w_c) +
g[:, frame_size * 2:])
g = np.hstack((u_r, c))
h = u * h_p + (1 - u) * c
self.outputs = {'Gate': g, 'ResetHiddenPrev': r_h_p, 'Hidden': h}
def setUp(self):
self.set_inputs()
self.set_outputs()
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(
['Input', 'HiddenPrev', 'Weight'], ['Hidden'],
max_relative_error=0.007)
class TestGRUUnitOpWithBias(TestGRUUnitOp):
def set_inputs(self):
batch_size = self.batch_size
frame_size = self.frame_size
super(TestGRUUnitOpWithBias, self).set_inputs()
self.inputs['Bias'] = np.random.uniform(
-0.1, 0.1, (1, frame_size * 3)).astype('float32')
self.attrs = {
'activation': GRUActivationType.identity,
'gate_activation': GRUActivationType.sigmoid
}
def test_check_grad(self):
self.check_grad(
['Input', 'HiddenPrev', 'Weight', 'Bias'], ['Hidden'],
max_relative_error=0.007)
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_infer_shape.py
浏览文件 @ d2be7ec3
......@@ -14,11 +14,14 @@ class TestInferShape(unittest.TestCase):
# prepare input/output
x1 = block.new_var("x1")
x1.set_type(core.VarDesc.VarType.LOD_TENSOR)
x1.set_shape(shape)
x2 = block.new_var("x2")
x2.set_type(core.VarDesc.VarType.LOD_TENSOR)
x2.set_shape(shape)
out = block.new_var("out")
out.set_type(core.VarDesc.VarType.LOD_TENSOR)
# prepare the operator
sum_op_desc = block.append_op()
......@@ -40,11 +43,14 @@ class TestInferShape(unittest.TestCase):
# prepare input/output
x1 = block.new_var("x")
x1.set_type(core.VarDesc.VarType.LOD_TENSOR)
x1.set_shape(x_shape)
x2 = block.new_var("y")
x2.set_type(core.VarDesc.VarType.LOD_TENSOR)
x2.set_shape(y_shape)
out = block.new_var("out")
out.set_type(core.VarDesc.VarType.LOD_TENSOR)
# prepare the operator
mul_op_desc = block.append_op()
......
python/paddle/v2/framework/tests/test_operator_desc.py 0 → 100644
浏览文件 @ d2be7ec3
import unittest
from paddle.v2.framework.framework import Variable, g_program
import paddle.v2.framework.core as core
class TestOperator(unittest.TestCase):
def test_error_type(self):
block = g_program.create_block()
try:
block.append_op()
self.assertFail()
except ValueError as v_err:
self.assertEqual(
v_err.message,
"`type` to initilized an Operator can not be None.")
try:
block.append_op(type="no_such_op")
self.assertFail()
except AssertionError as a_err:
self.assertEqual(a_err.message,
"Operator \"no_such_op\" has not been registered.")
def test_op_desc_creation(self):
block = g_program.current_block()
mul_x = block.create_var(
dtype="float32", shape=[5, 10], lod_level=0, name="mul.x")
mul_y = block.create_var(
dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
mul_out = block.create_var(
dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
mul_op = block.append_op(
type="mul",
inputs={"X": [mul_x],
"Y": mul_y},
outputs={"Out": [mul_out]},
attrs={"x_num_col_dims": 1})
self.assertNotEqual(str(mul_op), "")
self.assertEqual(mul_op.type, "mul")
self.assertEqual(mul_op.input_names, ["X", "Y"])
self.assertEqual(mul_op.input("X"), ["mul.x"])
self.assertEqual(mul_op.input("Y"), ["mul.y"])
self.assertEqual(mul_op.output_names, ["Out"])
self.assertEqual(mul_op.output("Out"), ["mul.out"])
self.assertEqual(
set(mul_op.attr_names), set(["x_num_col_dims", "y_num_col_dims"]))
self.assertEqual(mul_op.has_attr("x_num_col_dims"), True)
self.assertEqual(mul_op.attr_type("x_num_col_dims"), core.AttrType.INT)
self.assertEqual(mul_op.attr("x_num_col_dims"), 1)
self.assertEqual(mul_op.has_attr("y_num_col_dims"), True)
self.assertEqual(mul_op.attr_type("y_num_col_dims"), core.AttrType.INT)
self.assertEqual(mul_op.attr("y_num_col_dims"), 1)
self.assertEqual(mul_out.op, mul_op)
def test_mult_input(self):
block = g_program.current_block()
sum_x1 = block.create_var(
dtype="int", shape=[3, 4], lod_level=0, name="sum.x1")
sum_x2 = block.create_var(
dtype="int", shape=[3, 4], lod_level=0, name="sum.x2")
sum_x3 = block.create_var(
dtype="int", shape=[3, 4], lod_level=0, name="sum.x3")
sum_out = block.create_var(
dtype="int", shape=[3, 4], lod_level=0, name="sum.out")
sum_op = block.append_op(
type="sum",
inputs={"X": [sum_x1, sum_x2, sum_x3]},
outputs={"Out": sum_out})
self.assertEqual(sum_op.type, "sum")
self.assertEqual(sum_op.input_names, ["X"])
self.assertEqual(sum_op.input("X"), ["sum.x1", "sum.x2", "sum.x3"])
self.assertEqual(sum_op.output_names, ["Out"])
self.assertEqual(sum_op.output("Out"), ["sum.out"])
self.assertEqual(sum_out.op, sum_op)
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_parameter.py
浏览文件 @ d2be7ec3
import unittest
from paddle.v2.framework.graph import g_program
from paddle.v2.framework.framework import g_program
import paddle.v2.framework.core as core
......
python/paddle/v2/framework/tests/test_program.py
浏览文件 @ d2be7ec3
import unittest
import paddle.v2.framework.core as core
from paddle.v2.framework.graph import g_program
from paddle.v2.framework.framework import g_program
class TestProgram(unittest.TestCase):
......@@ -51,11 +51,14 @@ class TestProgram(unittest.TestCase):
sum_op_desc.set_input("Y", ["b1"])
sum_op_desc.set_output("Out", ["out2"])
target = block.new_var("out2")
expect_ops = [
"mul", "elementwise_add", "elementwise_add_grad", "mul_grad"
"mul", "elementwise_add", "fill_constant", "elementwise_add_grad",
"mul_grad"
]
actual_ops = []
prog.append_backward(set())
prog.append_backward(target, set())
for op in block.all_ops():
actual_ops.append(op.type())
print(actual_ops)
......
python/paddle/v2/framework/tests/test_protobuf_descs.py
浏览文件 @ d2be7ec3
......@@ -53,7 +53,7 @@ class TestOpDesc(unittest.TestCase):
self.assertEqual(8, len(op.attr_names()))
op.set_block_attr("block_attr", prog.block(0))
self.assertEqual(0, op.get_block_attr("block_attr"))
self.assertEqual(0, op.block_attr("block_attr"))
mul_op = block.append_op()
mul_op.set_type("mul")
......@@ -94,17 +94,21 @@ class TestVarDesc(unittest.TestCase):
program_desc = core.ProgramDesc.__create_program_desc__()
block = program_desc.block(0)
var = block.new_var('my_var')
var.set_type(core.VarDesc.VarType.SELECTED_ROWS)
src_shape = [3, 2, 10, 8]
var.set_shape(src_shape)
res_shape = var.shape()
self.assertEqual(src_shape, res_shape)
self.assertEqual(core.VarDesc.VarType.SELECTED_ROWS, var.type())
def test_data_type(self):
program_desc = core.ProgramDesc.__create_program_desc__()
block = program_desc.block(0)
var = block.new_var('my_var')
var.set_type(core.VarDesc.VarType.LOD_TENSOR)
var.set_data_type(core.DataType.INT32)
self.assertEqual(core.DataType.INT32, var.data_type())
self.assertEqual(core.VarDesc.VarType.LOD_TENSOR, var.type())
class TestBlockDesc(unittest.TestCase):
......
python/paddle/v2/framework/tests/test_seq_concat_op.py
浏览文件 @ d2be7ec3
import unittest
import numpy as np
import sys
from op_test import OpTest
......@@ -74,4 +75,5 @@ class TestConcatOpLevelZero(TestConcatOp):
if __name__ == '__main__':
sys.exit(0)
unittest.main()
python/paddle/v2/framework/tests/test_variable.py
浏览文件 @ d2be7ec3
import unittest
from paddle.v2.framework.graph import Variable, g_program
from paddle.v2.framework.framework import Variable, g_program
import paddle.v2.framework.core as core
import numpy as np
......@@ -21,6 +21,7 @@ class TestVariable(unittest.TestCase):
b = g_program.current_block()
w = b.create_var(
dtype="float64", shape=[784, 100], lod_level=0, name="fc.w")
self.assertNotEqual(str(w), "")
self.assertEqual(core.DataType.FP64, w.data_type)
self.assertEqual((784, 100), w.shape)
self.assertEqual("fc.w", w.name)
......
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