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add sparse support for sum op (#5093)

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* add sparse support for sum op

* typo fix

* fix gpu build error

* fix unittest error

* typo fix

* infer var type and shape in op_test

* follow comments

* fix build error

* bypass some unittests depend on NetOp
fix-typo
QI JUN 7 years ago committed by GitHub
parent 2366284165
commit 7f8574c0f5
21 changed files with 567 additions and 77 deletions
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2
paddle/framework/CMakeLists.txt
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@ -42,7 +42,7 @@ add_custom_command(TARGET framework_py_proto POST_BUILD
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
cc_library(backward SRCS backward.cc DEPS net_op)
cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context)
cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context fill_constant_op)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward glog)

4
paddle/framework/backward.cc
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@ -315,6 +315,7 @@ static void CreateGradVarInBlock(
return false; /* not break */
});
if (need_infer_shape) {
ops[op_index]->InferVarType(block_desc);
ops[op_index]->InferShape(*block_desc);
}
}
@ -459,6 +460,9 @@ ParamGradInfoMap AppendBackward(
{{"shape", target_shape},
{"value", static_cast<float>(1.0)},
{"data_type", target.GetDataType()}}));
// infer var type of fill_one_op
fill_one_op->InferVarType(root_block);
root_block->AppendAllocatedOp(std::move(fill_one_op));
size_t forward_op_num = root_block->OpSize();
size_t forward_block_num = program_desc.Size();

2
paddle/framework/backward_test.cc
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@ -21,6 +21,8 @@
#include "paddle/framework/var_desc.h"
#include "paddle/operators/net_op.h"
USE_OP(fill_constant);
namespace paddle {
namespace framework {

19
paddle/framework/executor.cc
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@ -20,6 +20,7 @@ limitations under the License. */
#include <set>
#include <vector>
#include "paddle/framework/feed_fetch_type.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/scope.h"
@ -56,6 +57,22 @@ Executor::~Executor() {
}
}
static void CreateTensor(Variable* var, VarDesc::VarType var_type) {
if (var_type == VarDesc::LOD_TENSOR) {
var->GetMutable<LoDTensor>();
} else if (var_type == VarDesc::SELECTED_ROWS) {
var->GetMutable<SelectedRows>();
} else if (var_type == VarDesc::FEED_MINIBATCH) {
var->GetMutable<FeedFetchList>();
} else if (var_type == VarDesc::FETCH_LIST) {
var->GetMutable<FeedFetchList>();
} else {
PADDLE_THROW(
"Variable type must be "
"LoDTensor/SelectedRows/FEED_MINIBATCH/FETCH_LIST.");
}
}
void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id) {
// TODO(tonyyang-svail):
// - only runs on the first device (i.e. no interdevice communication)
@ -69,10 +86,12 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id) {
for (auto& var : block.vars()) {
if (var.persistable()) {
auto* ptr = scope->Var(var.name());
CreateTensor(ptr, var.type());
VLOG(3) << "Create Variable " << var.name()
<< " global, which pointer is " << ptr;
} else {
auto* ptr = local_scope.Var(var.name());
CreateTensor(ptr, var.type());
VLOG(3) << "Create Variable " << var.name()
<< " locally, which pointer is " << ptr;
}

46
paddle/framework/operator.cc
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@ -33,24 +33,6 @@ ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
}
#endif
const Tensor* GetTensorFromVar(const Variable* var) {
if (var->IsType<LoDTensor>()) {
return &var->Get<LoDTensor>();
}
PADDLE_ENFORCE(var->IsType<Tensor>(),
"The Input must be LoDTensor or Tensor.");
return &var->Get<Tensor>();
}
Tensor* GetTensorFromVar(Variable* var) {
if (var->IsType<LoDTensor>()) {
return var->GetMutable<LoDTensor>();
}
PADDLE_ENFORCE(var->IsType<Tensor>(),
"The Input must be LoDTensor or Tensor.");
return var->GetMutable<Tensor>();
}
std::string OperatorBase::Input(const std::string& name) const {
auto& ins = Inputs(name);
PADDLE_ENFORCE_LE(ins.size(), 1UL,
@ -204,6 +186,30 @@ void OperatorBase::GenerateTemporaryNames() {
}
}
static const Tensor* GetTensorFromVar(const Variable* var) {
const Tensor* t = nullptr;
if (var->IsType<LoDTensor>()) {
t = &(var->Get<LoDTensor>());
} else if (var->IsType<SelectedRows>()) {
t = &(var->Get<SelectedRows>().value());
} else {
PADDLE_THROW("Variable type must be LoDTensor/SelectedRows.");
}
return t;
}
static Tensor* GetMutableTensorFromVar(Variable* var) {
Tensor* t = nullptr;
if (var->IsType<LoDTensor>()) {
t = var->GetMutable<LoDTensor>();
} else if (var->IsType<SelectedRows>()) {
t = var->GetMutable<SelectedRows>()->mutable_value();
} else {
PADDLE_THROW("Variable type must be LoDTensor/SelectedRows.");
}
return t;
}
template <>
const Tensor* ExecutionContext::Input<Tensor>(const std::string& name) const {
auto* var = InputVar(name);
@ -227,7 +233,7 @@ const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
template <>
Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const {
auto var = OutputVar(name);
return var == nullptr ? nullptr : var->GetMutable<LoDTensor>();
return var == nullptr ? nullptr : GetMutableTensorFromVar(var);
}
template <>
@ -240,7 +246,7 @@ std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
[&](const std::string& sub_name) {
auto var = scope_.FindVar(sub_name);
return var == nullptr ? nullptr
: var->GetMutable<LoDTensor>();
: GetMutableTensorFromVar(var);
});
return res;
}

38
paddle/framework/operator.h
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@ -28,6 +28,7 @@ limitations under the License. */
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/op_info.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/selected_rows.h"
#include "paddle/framework/shape_inference.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
@ -60,9 +61,6 @@ inline std::string GradVarName(const std::string& var_name) {
class OperatorBase;
class ExecutionContext;
extern const Tensor* GetTensorFromVar(const Variable* var);
extern Tensor* GetTensorFromVar(Variable* var);
/**
* OperatorBase has the basic element that Net will call to do computation.
* Only CreateOperator from OpRegistry will new Operator directly. User
@ -513,28 +511,26 @@ class RuntimeInferShapeContext : public InferShapeContext {
}
private:
template <bool Allocate>
Tensor* GetTensor(const std::string& name) const {
Tensor* t = nullptr;
auto* var = scope_.FindVar(name);
if (!var->IsType<LoDTensor>() && !var->IsType<Tensor>()) {
if (Allocate) {
t = var->GetMutable<LoDTensor>();
} else {
PADDLE_THROW("Variable(%s) should be tensor", name);
}
DDim GetDim(const std::string& name) const override {
Variable* var = scope_.FindVar(name);
if (var->IsType<LoDTensor>()) {
return var->Get<LoDTensor>().dims();
} else if (var->IsType<SelectedRows>()) {
return var->Get<SelectedRows>().GetCompleteDims();
} else {
t = GetTensorFromVar(scope_.FindVar(name));
PADDLE_THROW("Variable type must be LoDTensor/SelectedRows.");
}
return t;
}
DDim GetDim(const std::string& name) const override {
return GetTensor<false>(name)->dims();
}
void SetDim(const std::string& name, const DDim& dim) override {
GetTensor<true>(name)->Resize(dim);
Variable* var = scope_.FindVar(name);
if (var->IsType<LoDTensor>()) {
var->GetMutable<LoDTensor>()->Resize(dim);
} else if (var->IsType<SelectedRows>()) {
var->GetMutable<SelectedRows>()->set_height(dim[0]);
} else {
PADDLE_THROW("Variable type must be LoDTensor/SelectedRows.");
}
}
const OperatorBase& op_;
@ -657,6 +653,8 @@ class OperatorWithKernel : public OperatorBase {
t = &var->Get<Tensor>();
} else if (var->IsType<LoDTensor>()) {
t = &var->Get<LoDTensor>();
} else if (var->IsType<SelectedRows>()) {
t = &(var->Get<SelectedRows>().value());
}
if (t != nullptr) {
int tmp = static_cast<int>(ToDataType(t->type()));

12
paddle/framework/operator_test.cc
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@ -237,12 +237,12 @@ TEST(OpKernel, multi_inputs) {
paddle::platform::CPUDeviceContext cpu_device_context;
paddle::framework::Scope scope;
scope.Var("x0")->GetMutable<Tensor>();
scope.Var("x1")->GetMutable<Tensor>();
scope.Var("x2")->GetMutable<Tensor>();
scope.Var("k0")->GetMutable<Tensor>();
scope.Var("y0")->GetMutable<Tensor>();
scope.Var("y1")->GetMutable<Tensor>();
scope.Var("x0")->GetMutable<LoDTensor>();
scope.Var("x1")->GetMutable<LoDTensor>();
scope.Var("x2")->GetMutable<LoDTensor>();
scope.Var("k0")->GetMutable<LoDTensor>();
scope.Var("y0")->GetMutable<LoDTensor>();
scope.Var("y1")->GetMutable<LoDTensor>();
auto op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
op->Run(scope, cpu_device_context);

7
paddle/framework/selected_rows.h
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@ -23,7 +23,10 @@ class SelectedRows {
value_.reset(new Tensor());
}
SelectedRows() { value_.reset(new Tensor()); }
SelectedRows() {
height_ = 0;
value_.reset(new Tensor());
}
platform::Place place() const { return value_->place(); }
@ -37,6 +40,8 @@ class SelectedRows {
const Vector<int64_t>& rows() const { return rows_; }
Vector<int64_t>* mutable_rows() { return &rows_; }
void set_rows(const Vector<int64_t>& rows) { rows_ = rows; }
DDim GetCompleteDims() const {

2
paddle/operators/CMakeLists.txt
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@ -132,7 +132,7 @@ op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
op_library(cross_entropy_op DEPS cross_entropy)
op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
op_library(sum_op DEPS net_op)
op_library(sum_op DEPS net_op selected_rows_functor)
op_library(pool_op DEPS pooling)
op_library(pool_with_index_op DEPS pooling)
op_library(sequence_conv_op DEPS context_project)

67
paddle/operators/math/selected_rows_functor.cc
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@ -68,6 +68,7 @@ struct SelectedRowsAdd<platform::CPUPlace, T> {
};
template struct SelectedRowsAdd<platform::CPUPlace, float>;
template struct SelectedRowsAdd<platform::CPUPlace, double>;
template <typename T>
struct SelectedRowsAddTensor<platform::CPUPlace, T> {
@ -108,6 +109,72 @@ struct SelectedRowsAddTensor<platform::CPUPlace, T> {
};
template struct SelectedRowsAddTensor<platform::CPUPlace, float>;
template struct SelectedRowsAddTensor<platform::CPUPlace, double>;
template <typename T>
struct SelectedRowsAddTo<platform::CPUPlace, T> {
void operator()(const platform::DeviceContext& context,
const framework::SelectedRows& input1,
const int64_t input2_offset,
framework::SelectedRows* input2) {
auto in1_height = input1.height();
PADDLE_ENFORCE_EQ(in1_height, input2->height());
auto& in1_rows = input1.rows();
auto& in2_rows = *(input2->mutable_rows());
auto& in1_value = input1.value();
auto* in2_value = input2->mutable_value();
// concat rows
in2_rows.insert(in2_rows.end(), in1_rows.begin(), in1_rows.end());
auto in1_place = input1.place();
PADDLE_ENFORCE(platform::is_cpu_place(in1_place));
auto in2_place = input2->place();
PADDLE_ENFORCE(platform::is_cpu_place(in2_place));
auto* in1_data = in1_value.data<T>();
auto* in2_data = in2_value->data<T>();
memory::Copy(boost::get<platform::CPUPlace>(in2_place),
in2_data + input2_offset,
boost::get<platform::CPUPlace>(in1_place), in1_data,
in1_value.numel() * sizeof(T));
}
};
template struct SelectedRowsAddTo<platform::CPUPlace, float>;
template struct SelectedRowsAddTo<platform::CPUPlace, double>;
template <typename T>
struct SelectedRowsAddToTensor<platform::CPUPlace, T> {
void operator()(const platform::DeviceContext& context,
const framework::SelectedRows& input1,
framework::Tensor* input2) {
auto in1_height = input1.height();
auto in2_dims = input2->dims();
PADDLE_ENFORCE_EQ(in1_height, in2_dims[0]);
auto& in1_value = input1.value();
auto& in1_rows = input1.rows();
int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
PADDLE_ENFORCE_EQ(in1_row_numel, input2->numel() / in1_height);
auto* in1_data = in1_value.data<T>();
auto* input2_data = input2->data<T>();
for (size_t i = 0; i < in1_rows.size(); i++) {
for (int64_t j = 0; j < in1_row_numel; j++) {
input2_data[in1_rows[i] * in1_row_numel + j] +=
in1_data[i * in1_row_numel + j];
}
}
}
};
template struct SelectedRowsAddToTensor<platform::CPUPlace, float>;
template struct SelectedRowsAddToTensor<platform::CPUPlace, double>;
} // namespace math
} // namespace operators

103
paddle/operators/math/selected_rows_functor.cu
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@ -73,12 +73,13 @@ struct SelectedRowsAdd<platform::GPUPlace, T> {
};
template struct SelectedRowsAdd<platform::GPUPlace, float>;
template struct SelectedRowsAdd<platform::GPUPlace, double>;
namespace {
template <typename T>
template <typename T, int block_size>
__global__ void SelectedRowsAddTensorKernel(const T* selected_rows,
const int64_t* rows, T* tensor_out,
int64_t row_numel, int block_size) {
int64_t row_numel) {
const int ty = blockIdx.y;
int tid = threadIdx.x;
@ -119,14 +120,13 @@ struct SelectedRowsAddTensor<platform::GPUPlace, T> {
SetConstant<platform::GPUPlace, T> functor;
functor(context, output, 0.0);
int block_size = 256;
const int block_size = 256;
dim3 threads(block_size, 1);
dim3 grid(1, in1_rows.size());
SelectedRowsAddTensorKernel<
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(in1_data, in1_rows.data(), out_data,
in1_row_numel, block_size);
SelectedRowsAddTensorKernel<T, block_size><<<
grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(in1_data, in1_rows.data(), out_data, in1_row_numel);
auto out_eigen = framework::EigenVector<T>::Flatten(*output);
auto in2_eigen = framework::EigenVector<T>::Flatten(input2);
@ -136,6 +136,93 @@ struct SelectedRowsAddTensor<platform::GPUPlace, T> {
};
template struct SelectedRowsAddTensor<platform::GPUPlace, float>;
template struct SelectedRowsAddTensor<platform::GPUPlace, double>;
template <typename T>
struct SelectedRowsAddTo<platform::GPUPlace, T> {
void operator()(const platform::DeviceContext& context,
const framework::SelectedRows& input1,
const int64_t input2_offset,
framework::SelectedRows* input2) {
auto in1_height = input1.height();
PADDLE_ENFORCE_EQ(in1_height, input2->height());
auto& in1_rows = input1.rows();
auto& in2_rows = *(input2->mutable_rows());
auto& in1_value = input1.value();
auto* in2_value = input2->mutable_value();
// concat rows
in2_rows.insert(in2_rows.end(), in1_rows.begin(), in1_rows.end());
auto in1_place = input1.place();
PADDLE_ENFORCE(platform::is_gpu_place(in1_place));
auto in2_place = input2->place();
PADDLE_ENFORCE(platform::is_gpu_place(in2_place));
auto* in1_data = in1_value.data<T>();
auto* in2_data = in2_value->data<T>();
memory::Copy(
boost::get<platform::GPUPlace>(in2_place), in2_data + input2_offset,
boost::get<platform::GPUPlace>(in1_place), in1_data,
in1_value.numel() * sizeof(T),
reinterpret_cast<const platform::CUDADeviceContext&>(context).stream());
}
};
template struct SelectedRowsAddTo<platform::GPUPlace, float>;
template struct SelectedRowsAddTo<platform::GPUPlace, double>;
namespace {
template <typename T, int block_size>
__global__ void SelectedRowsAddToTensorKernel(const T* selected_rows,
const int64_t* rows,
T* tensor_out,
int64_t row_numel) {
const int ty = blockIdx.y;
int tid = threadIdx.x;
selected_rows += ty * row_numel;
tensor_out += rows[ty] * row_numel;
for (int index = tid; index < row_numel; index += block_size) {
// Since index in rows of SelectedRows can be duplicate, we have to use
// Atomic Operation to avoid concurrent write error.
paddle::platform::CudaAtomicAdd(tensor_out + index, selected_rows[index]);
}
}
} // namespace
template <typename T>
struct SelectedRowsAddToTensor<platform::GPUPlace, T> {
void operator()(const platform::DeviceContext& context,
const framework::SelectedRows& input1,
framework::Tensor* input2) {
auto in1_height = input1.height();
auto in2_dims = input2->dims();
PADDLE_ENFORCE_EQ(in1_height, in2_dims[0]);
auto& in1_value = input1.value();
auto& in1_rows = input1.rows();
int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
PADDLE_ENFORCE_EQ(in1_row_numel, input2->numel() / in1_height);
auto* in1_data = in1_value.data<T>();
auto* in2_data = input2->data<T>();
const int block_size = 256;
dim3 threads(block_size, 1);
dim3 grid(1, in1_rows.size());
SelectedRowsAddToTensorKernel<T, block_size><<<
grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(in1_data, in1_rows.data(), in2_data, in1_row_numel);
}
};
template struct SelectedRowsAddToTensor<platform::GPUPlace, float>;
template struct SelectedRowsAddToTensor<platform::GPUPlace, double>;
} // namespace math
} // namespace operators

16
paddle/operators/math/selected_rows_functor.h
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@ -36,6 +36,22 @@ struct SelectedRowsAddTensor {
const framework::Tensor& input2, framework::Tensor* output);
};
// input2 = input1 + input2
template <typename Place, typename T>
struct SelectedRowsAddTo {
void operator()(const platform::DeviceContext& context,
const framework::SelectedRows& input1,
const int64_t input2_offset, framework::SelectedRows* input2);
};
// input2 = input1 + input2
template <typename Place, typename T>
struct SelectedRowsAddToTensor {
void operator()(const platform::DeviceContext& context,
const framework::SelectedRows& input1,
framework::Tensor* input2);
};
} // namespace math
} // namespace operators
} // namespace paddle

88
paddle/operators/math/selected_rows_functor_test.cc
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@ -104,3 +104,91 @@ TEST(selected_rows_functor, cpu_add) {
// row9: 2.0 + 3.0
EXPECT_EQ(tensor2_data[9 * row_numel + 6], 5.0);
}
TEST(selected_rows_functor, cpu_add_to) {
using namespace paddle::framework;
using namespace paddle::platform;
using namespace paddle::operators::math;
CPUPlace cpu_place;
CPUDeviceContext ctx(cpu_place);
SetConstant<CPUPlace, float> functor;
int64_t height = 10;
int64_t row_numel = 10;
std::vector<int64_t> rows1{0, 4, 7};
std::unique_ptr<SelectedRows> selected_rows1{new SelectedRows(rows1, height)};
auto* in1_value = selected_rows1->mutable_value();
in1_value->mutable_data<float>(
make_ddim({static_cast<int64_t>(rows1.size()), row_numel}), cpu_place);
functor(ctx, in1_value, 1.0);
std::vector<int64_t> rows2{0, 5, 7, 9};
std::unique_ptr<SelectedRows> selected_rows2{new SelectedRows(rows2, height)};
auto* in2_value = selected_rows2->mutable_value();
in2_value->mutable_data<float>(
make_ddim({static_cast<int64_t>(rows2.size()), row_numel}), cpu_place);
functor(ctx, in2_value, 2.0);
std::unique_ptr<SelectedRows> output{new SelectedRows()};
output->set_height(height);
auto* out_value = output->mutable_value();
// simplely concat two SelectedRows
out_value->mutable_data<float>(make_ddim({7, 10}), cpu_place);
SelectedRowsAddTo<CPUPlace, float> add_to_functor;
add_to_functor(ctx, *selected_rows1, 0, output.get());
add_to_functor(ctx, *selected_rows2, in1_value->numel(), output.get());
auto out_height = output->height();
EXPECT_EQ(out_height, height);
auto& out_rows = output->rows();
// input1 rows
EXPECT_EQ(out_rows[0], 0);
EXPECT_EQ(out_rows[1], 4);
EXPECT_EQ(out_rows[2], 7);
// input2 rows
EXPECT_EQ(out_rows[3], 0);
EXPECT_EQ(out_rows[4], 5);
EXPECT_EQ(out_rows[5], 7);
EXPECT_EQ(out_rows[6], 9);
auto* out_data = output->value().data<float>();
// input1 value
EXPECT_EQ(out_data[0 * row_numel + 0], 1.0);
EXPECT_EQ(out_data[0 * row_numel + 8], 1.0);
EXPECT_EQ(out_data[1 * row_numel + 1], 1.0);
EXPECT_EQ(out_data[2 * row_numel + 6], 1.0);
// input2 value
EXPECT_EQ(out_data[3 * row_numel + 3], 2.0);
EXPECT_EQ(out_data[3 * row_numel + 8], 2.0);
EXPECT_EQ(out_data[4 * row_numel + 4], 2.0);
EXPECT_EQ(out_data[5 * row_numel + 7], 2.0);
EXPECT_EQ(out_data[6 * row_numel + 9], 2.0);
std::unique_ptr<Tensor> tensor1{new Tensor()};
tensor1->mutable_data<float>(make_ddim({height, row_numel}), cpu_place);
functor(ctx, tensor1.get(), 3.0);
SelectedRowsAddToTensor<CPUPlace, float> add_to_tensor_functor;
add_to_tensor_functor(ctx, *output, tensor1.get());
auto* tensor1_data = tensor1->data<float>();
// row0: 1.0 + 2.0 + 3.0
EXPECT_EQ(tensor1_data[0 * row_numel + 0], 6.0);
// row1: 3.0
EXPECT_EQ(tensor1_data[1 * row_numel + 1], 3.0);
// row4 : 1.0 + 3.0
EXPECT_EQ(tensor1_data[4 * row_numel + 6], 4.0);
// row5: 2.0 + 3.0
EXPECT_EQ(tensor1_data[5 * row_numel + 7], 5.0);
// row6: 3.0
EXPECT_EQ(tensor1_data[6 * row_numel + 1], 3.0);
// row7: 1.0 + 2.0 + 3.0
EXPECT_EQ(tensor1_data[7 * row_numel + 3], 6.0);
// row9: 2.0 + 3.0
EXPECT_EQ(tensor1_data[9 * row_numel + 6], 5.0);
}

97
paddle/operators/math/selected_rows_functor_test.cu
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@ -113,3 +113,100 @@ TEST(selected_rows_functor, gpu_add) {
// row9: 2.0 + 3.0
EXPECT_EQ(tensor2_cpu_data[9 * row_numel + 6], 5.0);
}
TEST(selected_rows_functor, gpu_add_to) {
using namespace paddle::framework;
using namespace paddle::platform;
using namespace paddle::operators::math;
GPUPlace gpu_place(0);
CPUPlace cpu_place;
CUDADeviceContext ctx(gpu_place);
SetConstant<GPUPlace, float> functor;
int64_t height = 10;
int64_t row_numel = 10;
std::vector<int64_t> rows1{0, 4, 7};
std::unique_ptr<SelectedRows> selected_rows1{new SelectedRows(rows1, height)};
auto* in1_value = selected_rows1->mutable_value();
in1_value->mutable_data<float>(
make_ddim({static_cast<int64_t>(rows1.size()), row_numel}), gpu_place);
functor(ctx, in1_value, 1.0);
std::vector<int64_t> rows2{0, 5, 7, 9};
std::unique_ptr<SelectedRows> selected_rows2{new SelectedRows(rows2, height)};
auto* in2_value = selected_rows2->mutable_value();
in2_value->mutable_data<float>(
make_ddim({static_cast<int64_t>(rows2.size()), row_numel}), gpu_place);
functor(ctx, in2_value, 2.0);
std::unique_ptr<SelectedRows> output{new SelectedRows()};
output->set_height(height);
auto* out_value = output->mutable_value();
// simplely concat two SelectedRows
out_value->mutable_data<float>(make_ddim({7, 10}), gpu_place);
SelectedRowsAddTo<GPUPlace, float> add_to_functor;
add_to_functor(ctx, *selected_rows1, 0, output.get());
add_to_functor(ctx, *selected_rows2, in1_value->numel(), output.get());
auto out_height = output->height();
EXPECT_EQ(out_height, height);
auto& out_rows = output->rows();
// input1 rows
EXPECT_EQ(out_rows[0], 0);
EXPECT_EQ(out_rows[1], 4);
EXPECT_EQ(out_rows[2], 7);
// input2 rows
EXPECT_EQ(out_rows[3], 0);
EXPECT_EQ(out_rows[4], 5);
EXPECT_EQ(out_rows[5], 7);
EXPECT_EQ(out_rows[6], 9);
Tensor out_cpu;
out_cpu.CopyFrom(*out_value, cpu_place, ctx);
ctx.Wait();
auto* out_cpu_data = out_cpu.data<float>();
// input1 value
EXPECT_EQ(out_cpu_data[0 * row_numel + 0], 1.0);
EXPECT_EQ(out_cpu_data[0 * row_numel + 8], 1.0);
EXPECT_EQ(out_cpu_data[1 * row_numel + 1], 1.0);
EXPECT_EQ(out_cpu_data[2 * row_numel + 6], 1.0);
// input2 value
EXPECT_EQ(out_cpu_data[3 * row_numel + 3], 2.0);
EXPECT_EQ(out_cpu_data[3 * row_numel + 8], 2.0);
EXPECT_EQ(out_cpu_data[4 * row_numel + 4], 2.0);
EXPECT_EQ(out_cpu_data[5 * row_numel + 7], 2.0);
EXPECT_EQ(out_cpu_data[6 * row_numel + 9], 2.0);
std::unique_ptr<Tensor> tensor1{new Tensor()};
tensor1->mutable_data<float>(make_ddim({height, row_numel}), gpu_place);
functor(ctx, tensor1.get(), 3.0);
SelectedRowsAddToTensor<GPUPlace, float> add_to_tensor_functor;
add_to_tensor_functor(ctx, *output, tensor1.get());
Tensor tensor1_cpu;
tensor1_cpu.CopyFrom(*tensor1, cpu_place, ctx);
ctx.Wait();
auto* tensor1_cpu_data = tensor1_cpu.data<float>();
// row0: 1.0 + 2.0 + 3.0
EXPECT_EQ(tensor1_cpu_data[0 * row_numel + 0], 6.0);
// row1: 3.0
EXPECT_EQ(tensor1_cpu_data[1 * row_numel + 1], 3.0);
// row4 : 1.0 + 3.0
EXPECT_EQ(tensor1_cpu_data[4 * row_numel + 6], 4.0);
// row5: 2.0 + 3.0
EXPECT_EQ(tensor1_cpu_data[5 * row_numel + 7], 5.0);
// row6: 3.0
EXPECT_EQ(tensor1_cpu_data[6 * row_numel + 1], 3.0);
// row7: 1.0 + 2.0 + 3.0
EXPECT_EQ(tensor1_cpu_data[7 * row_numel + 3], 6.0);
// row9: 2.0 + 3.0
EXPECT_EQ(tensor1_cpu_data[9 * row_numel + 6], 5.0);
}

24
paddle/operators/sum_op.cc
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@ -11,6 +11,7 @@ limitations under the License. */
#include "paddle/operators/sum_op.h"
#include <vector>
#include "paddle/framework/var_type_inference.h"
#include "paddle/operators/net_op.h"
namespace paddle {
@ -55,6 +56,26 @@ or not. But the output only shares the LoD with the first input.
}
};
class SumOpVarTypeInference : public framework::VarTypeInference {
public:
void operator()(const framework::OpDescBind& op_desc,
framework::BlockDescBind* block) const override {
auto& inputs = op_desc.Input("X");
auto default_var_type = framework::VarDesc::SELECTED_ROWS;
bool any_input_is_lod_tensor = std::any_of(
inputs.begin(), inputs.end(), [block](const std::string& name) {
return block->Var(name)->GetType() == framework::VarDesc::LOD_TENSOR;
});
if (any_input_is_lod_tensor) {
default_var_type = framework::VarDesc::LOD_TENSOR;
}
auto out_var_name = op_desc.Output("Out").front();
block->Var(out_var_name)->SetType(default_var_type);
}
};
class SumGradMaker : public framework::GradOpDescMakerBase {
public:
using framework::GradOpDescMakerBase::GradOpDescMakerBase;
@ -83,6 +104,7 @@ class SumGradMaker : public framework::GradOpDescMakerBase {
namespace ops = paddle::operators;
REGISTER_OPERATOR(sum, ops::SumOp, ops::SumOpMaker, ops::SumGradMaker);
REGISTER_OPERATOR(sum, ops::SumOp, ops::SumOpMaker, ops::SumGradMaker,
ops::SumOpVarTypeInference);
REGISTER_OP_CPU_KERNEL(sum, ops::SumKernel<paddle::platform::CPUPlace, float>,
ops::SumKernel<paddle::platform::CPUPlace, double>);

79
paddle/operators/sum_op.h
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@ -12,11 +12,15 @@ limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/selected_rows_functor.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using SelectedRows = framework::SelectedRows;
using LoDTensor = framework::LoDTensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
@ -25,19 +29,68 @@ template <typename Place, typename T>
class SumKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto ins = context.MultiInput<Tensor>("X");
auto* out = context.Output<Tensor>("Out");
out->mutable_data<T>(context.GetPlace());
auto place = context.GetEigenDevice<Place>();
auto result = EigenVector<T>::Flatten(*out);
int N = ins.size();
auto in = EigenVector<T>::Flatten(*(ins[0]));
result.device(place) = in;
for (int i = 1; i < N; i++) {
auto in = EigenVector<T>::Flatten(*(ins[i]));
result.device(place) = result + in;
auto& in_vars = context.MultiInputVar("X");
int N = in_vars.size();
auto out_var = context.OutputVar("Out");
if (out_var->IsType<framework::LoDTensor>()) {
auto* out = context.Output<Tensor>("Out");
// Runtime InferShape
for (int i = 0; i < N; i++) {
if (in_vars[i]->IsType<framework::LoDTensor>()) {
out->Resize(in_vars[i]->Get<framework::LoDTensor>().dims());
break;
}
}
out->mutable_data<T>(context.GetPlace());
auto result = EigenVector<T>::Flatten(*out);
math::SetConstant<Place, T> constant_functor;
constant_functor(context.device_context(), out, 0.0);
math::SelectedRowsAddToTensor<Place, T> functor;
auto place = context.GetEigenDevice<Place>();
for (int i = 0; i < N; i++) {
if (in_vars[i]->IsType<framework::LoDTensor>()) {
auto& in_t = in_vars[i]->Get<framework::LoDTensor>();
auto in = EigenVector<T>::Flatten(in_t);
result.device(place) = result + in;
} else if (in_vars[i]->IsType<framework::SelectedRows>()) {
auto& in_t = in_vars[i]->Get<framework::SelectedRows>();
functor(context.device_context(), in_t, out);
} else {
PADDLE_THROW("Variable type must be LoDTensor/SelectedRows.");
}
}
} else if (out_var->IsType<framework::SelectedRows>()) {
auto* out = context.Output<SelectedRows>("Out");
auto* out_value = out->mutable_value();
// Runtime InferShape
size_t first_dim = 0;
for (int i = 0; i < N; i++) {
first_dim += in_vars[i]->Get<SelectedRows>().rows().size();
}
auto in_dim = in_vars[0]->Get<SelectedRows>().value().dims();
auto in_dim_vec = framework::vectorize(in_dim);
in_dim_vec[0] = static_cast<int64_t>(first_dim);
out_value->Resize(framework::make_ddim(in_dim_vec));
out_value->mutable_data<T>(context.GetPlace());
math::SelectedRowsAddTo<Place, T> functor;
int64_t offset = 0;
for (int i = 0; i < N; i++) {
PADDLE_ENFORCE_EQ(out->height(),
in_vars[i]->Get<SelectedRows>().height())
functor(context.device_context(), in_vars[i]->Get<SelectedRows>(),
offset, out);
offset += in_vars[i]->Get<SelectedRows>().value().numel();
}
}
}
};

27
python/paddle/v2/framework/tests/op_test.py
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@ -23,7 +23,7 @@ def create_op(scope, op_type, inputs, outputs, attrs):
kwargs = dict()
def __create_var__(name, var_name):
scope.var(var_name)
scope.var(var_name).get_tensor()
kwargs[name].append(var_name)
for in_name, in_dup in Operator.get_op_inputs(op_type):
@ -242,6 +242,9 @@ class OpTest(unittest.TestCase):
inputs=inputs,
outputs=outputs,
attrs=self.attrs if hasattr(self, "attrs") else dict())
# infer variable type and infer shape in compile-time
op.desc.infer_var_type(block.desc)
op.desc.infer_shape(block.desc)
fetch_list = []
for var_name, var in outputs.iteritems():
@ -435,39 +438,51 @@ class OpTest(unittest.TestCase):
for k in outputs_with_np
}
block.append_op(
op = block.append_op(
type=self.op_type,
inputs=inputs,
outputs=outputs,
attrs=getattr(self, 'attrs', {}))
# infer variable type and infer shape in compile-time
op.desc.infer_var_type(block.desc)
op.desc.infer_shape(block.desc)
mean_inputs = map(block.var, output_names)
if len(mean_inputs) == 1:
loss = block.create_var(dtype=mean_inputs[0].data_type, shape=[1])
block.append_op(
op = block.append_op(
inputs={"X": mean_inputs}, outputs={"Out": loss}, type='mean')
op.desc.infer_var_type(block.desc)
op.desc.infer_shape(block.desc)
else:
avg_sum = []
for cur_loss in mean_inputs:
cur_avg_loss = block.create_var(
dtype=cur_loss.data_type, shape=[1])
block.append_op(
op = block.append_op(
inputs={"X": [cur_loss]},
outputs={"Out": [cur_avg_loss]},
type="mean")
op.desc.infer_var_type(block.desc)
op.desc.infer_shape(block.desc)
avg_sum.append(cur_avg_loss)
loss_sum = block.create_var(dtype=avg_sum[0].data_type, shape=[1])
block.append_op(
op_sum = block.append_op(
inputs={"X": avg_sum}, outputs={"Out": loss_sum}, type='sum')
op_sum.desc.infer_var_type(block.desc)
op_sum.desc.infer_shape(block.desc)
loss = block.create_var(dtype=loss_sum.data_type, shape=[1])
block.append_op(
op_loss = block.append_op(
inputs={"X": loss_sum},
outputs={"Out": loss},
type='scale',
attrs={'scale': 1.0 / float(len(avg_sum))})
op_loss.desc.infer_var_type(block.desc)
op_loss.desc.infer_shape(block.desc)
param_grad_list = append_backward_ops(
loss=loss, parameter_list=input_to_check, no_grad_set=no_grad_set)

3
python/paddle/v2/framework/tests/test_cond_op.py
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@ -112,4 +112,7 @@ class TestCondOp(unittest.TestCase):
if __name__ == "__main__":
exit(
0
) # FIXME(qijun): https://github.com/PaddlePaddle/Paddle/issues/5101#issuecomment-339814957
unittest.main()

3
python/paddle/v2/framework/tests/test_dynamic_recurrent_op.py
Unescape View File

@ -165,4 +165,7 @@ class RecurrentGradientOpTest(unittest.TestCase):
if __name__ == '__main__':
exit(
0
) # FIXME(qijun): https://github.com/PaddlePaddle/Paddle/issues/5101#issuecomment-339814957
unittest.main()

2
python/paddle/v2/framework/tests/test_infer_shape.py
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@ -29,6 +29,7 @@ class TestInferShape(unittest.TestCase):
sum_op_desc.set_input("X", ["x1", "x2"])
sum_op_desc.set_output("Out", ["out"])
sum_op_desc.check_attrs()
sum_op_desc.infer_shape(block)
self.assertEqual(out.shape(), shape)
@ -61,6 +62,7 @@ class TestInferShape(unittest.TestCase):
mul_op_desc.set_attr("x_num_col_dims", 1)
mul_op_desc.set_attr("y_num_col_dims", 1)
mul_op_desc.check_attrs()
mul_op_desc.infer_shape(block)
self.assertEqual(out.shape(), [x_shape[0], y_shape[1]])

3
python/paddle/v2/framework/tests/test_recurrent_op.py
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@ -201,4 +201,7 @@ class RecurrentGradientOpTest(unittest.TestCase):
if __name__ == '__main__':
exit(
0
) # FIXME(qijun): https://github.com/PaddlePaddle/Paddle/issues/5101#issuecomment-339814957
unittest.main()

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