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未验证 提交 10225d22 编写于 作者: Z zhangkaihuo 提交者： GitHub
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[cherry-pick]Sparse static graph (#46838) 

cherry-pick : #46322, #46245
Sparse API 支持静态图
上级 976af0da
隐藏空白更改
内联 并排
Showing with 937 addition and 74 deletion
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 10225d22
......@@ -190,7 +190,7 @@ cc_test(
cc_library(
var_type_traits
SRCS var_type_traits.cc
DEPS framework_proto scope tensor_array)
DEPS framework_proto scope tensor_array sparse_coo_tensor)
if(WITH_GPU)
target_link_libraries(var_type_traits dynload_cuda)
endif()
......@@ -1138,7 +1138,8 @@ cc_library(
phi
phi_api_utils
op_info
shape_inference)
shape_inference
sparse_coo_tensor)
cc_test(
infershape_utils_test
SRCS infershape_utils_test.cc
......
paddle/fluid/framework/feed_fetch_type.h
浏览文件 @ 10225d22
......@@ -22,10 +22,11 @@ limitations under the License. */
namespace paddle {
namespace framework {
using FeedType = paddle::variant<LoDTensor, Strings>;
using FeedType = paddle::variant<LoDTensor, Strings, phi::SparseCooTensor>;
using FeedList = std::vector<FeedType>;
using FetchType = paddle::variant<LoDTensor, LoDTensorArray, framework::Vocab>;
using FetchType = paddle::
variant<LoDTensor, LoDTensorArray, framework::Vocab, phi::SparseCooTensor>;
using FetchList = std::vector<FetchType>;
using FetchUnmergedList = std::vector<std::vector<FetchType>>;
......@@ -52,6 +53,13 @@ inline bool data_is_string_tensor(const FeedType &data) {
return false;
}
inline bool data_is_sparse_coo_tensor(const FetchType &data) {
if (data.type() == typeid(phi::SparseCooTensor)) {
return true;
}
return false;
}
static const char kFeedOpType[] = "feed";
static const char kFetchOpType[] = "fetch";
......
paddle/fluid/framework/framework.proto
浏览文件 @ 10225d22
......@@ -154,6 +154,8 @@ message VarType {
FEED_LIST = 28;
// The data type of phi::StringTensor
PSTRING = 29;
// the data type of phi::SparseCooTensor
SPARSE_COO = 30;
}
required Type type = 1;
......@@ -186,6 +188,7 @@ message VarType {
optional TensorDesc string = 8;
optional TensorDesc strings = 9;
optional TensorDesc vocab = 10;
optional TensorDesc sparse_coo = 11;
}
message VarDesc {
......
paddle/fluid/framework/infershape_utils.cc
浏览文件 @ 10225d22
......@@ -101,6 +101,11 @@ class InferShapeArgumentMappingContext : public phi::ArgumentMappingContext {
});
}
bool IsSparseCooTensorInput(const std::string& name) const override {
auto var_type = ctx_.GetInputVarType(name);
return var_type == proto::VarType::SPARSE_COO;
}
bool IsDenseTensorOutput(const std::string& name) const override {
auto var_types = ctx_.GetOutputsVarType(name);
return std::all_of(var_types.begin(),
......@@ -145,6 +150,26 @@ int64_t CompatMetaTensor::numel() const {
}
}
bool CompatMetaTensor::is_dense() const {
if (is_runtime_) {
auto* var = PADDLE_GET_CONST(Variable*, var_);
return var->IsType<phi::DenseTensor>();
} else {
auto* var = PADDLE_GET_CONST(VarDesc*, var_);
return var->GetType() == proto::VarType::LOD_TENSOR;
}
}
bool CompatMetaTensor::is_tensor_array() const {
if (is_runtime_) {
auto* var = PADDLE_GET_CONST(Variable*, var_);
return var->IsType<framework::LoDTensorArray>();
} else {
auto* var = PADDLE_GET_CONST(VarDesc*, var_);
return var->GetType() == proto::VarType::LOD_TENSOR_ARRAY;
}
}
DDim CompatMetaTensor::dims() const {
ValidCheck(*this);
if (is_runtime_) {
......@@ -153,6 +178,8 @@ DDim CompatMetaTensor::dims() const {
return var->Get<phi::DenseTensor>().dims();
} else if (var->IsType<phi::SelectedRows>()) {
return var->Get<phi::SelectedRows>().dims();
} else if (var->IsType<phi::SparseCooTensor>()) {
return var->Get<phi::SparseCooTensor>().dims();
} else if (var->IsType<framework::LoDTensorArray>()) {
// use tensor array size as dims
auto& tensor_array = var->Get<framework::LoDTensorArray>();
......@@ -178,6 +205,8 @@ phi::DataType CompatMetaTensor::dtype() const {
return var->Get<phi::DenseTensor>().dtype();
} else if (var->IsType<phi::SelectedRows>()) {
return var->Get<phi::SelectedRows>().dtype();
} else if (var->IsType<phi::SparseCooTensor>()) {
return var->Get<phi::SparseCooTensor>().dtype();
} else if (var->IsType<framework::LoDTensorArray>()) {
// NOTE(chenweihang): do nothing
// Unsupported get dtype from LoDTensorArray now
......@@ -200,6 +229,8 @@ DataLayout CompatMetaTensor::layout() const {
return var->Get<phi::DenseTensor>().layout();
} else if (var->IsType<phi::SelectedRows>()) {
return var->Get<phi::SelectedRows>().layout();
} else if (var->IsType<phi::SparseCooTensor>()) {
return var->Get<phi::SparseCooTensor>().layout();
} else if (var->IsType<framework::LoDTensorArray>()) {
// NOTE(chenweihang): do nothing
// Unsupported get layout from LoDTensorArray now
......@@ -226,6 +257,9 @@ void CompatMetaTensor::set_dims(const DDim& dims) {
} else if (var->IsType<phi::SelectedRows>()) {
auto* tensor = var->GetMutable<phi::SelectedRows>()->mutable_value();
phi::DenseTensorUtils::GetMutableMeta(tensor)->dims = dims;
} else if (var->IsType<phi::SparseCooTensor>()) {
auto* tensor = var->GetMutable<phi::SparseCooTensor>();
phi::DenseTensorUtils::GetMutableMeta(tensor)->dims = dims;
} else if (var->IsType<framework::LoDTensorArray>()) {
auto* tensor_array = var->GetMutable<framework::LoDTensorArray>();
// Note: Here I want enforce `tensor_array->size() == 0UL`, because
......@@ -257,6 +291,9 @@ void CompatMetaTensor::set_dtype(phi::DataType dtype) {
} else if (var->IsType<phi::SelectedRows>()) {
auto* tensor = var->GetMutable<phi::SelectedRows>()->mutable_value();
phi::DenseTensorUtils::GetMutableMeta(tensor)->dtype = dtype;
} else if (var->IsType<phi::SparseCooTensor>()) {
auto* tensor = var->GetMutable<phi::SparseCooTensor>();
phi::DenseTensorUtils::GetMutableMeta(tensor)->dtype = dtype;
} else if (var->IsType<framework::LoDTensorArray>()) {
// NOTE(chenweihang): do nothing
// Unsupported set dtype for LoDTensorArray now
......@@ -280,6 +317,9 @@ void CompatMetaTensor::set_layout(DataLayout layout) {
} else if (var->IsType<phi::SelectedRows>()) {
auto* tensor = var->GetMutable<phi::SelectedRows>()->mutable_value();
phi::DenseTensorUtils::GetMutableMeta(tensor)->layout = layout;
} else if (var->IsType<phi::SparseCooTensor>()) {
auto* tensor = var->GetMutable<phi::SparseCooTensor>();
phi::DenseTensorUtils::GetMutableMeta(tensor)->layout = layout;
} else if (var->IsType<framework::LoDTensorArray>()) {
// NOTE(chenweihang): do nothing
// Unsupported set dtype for LoDTensorArray now
......@@ -299,7 +339,7 @@ void CompatMetaTensor::share_lod(const MetaTensor& meta_tensor) {
ValidCheck(meta_tensor);
if (is_runtime_) {
auto* var = PADDLE_GET(Variable*, var_);
if (var->IsType<phi::DenseTensor>()) {
if (var->IsType<phi::DenseTensor>() && meta_tensor.is_dense()) {
auto* tensor = var->GetMutable<phi::DenseTensor>();
phi::DenseTensorUtils::GetMutableMeta(tensor)->lod =
static_cast<const CompatMetaTensor&>(meta_tensor).GetRuntimeLoD();
......@@ -309,6 +349,10 @@ void CompatMetaTensor::share_lod(const MetaTensor& meta_tensor) {
}
} else {
auto* var = PADDLE_GET(VarDesc*, var_);
if (!meta_tensor.is_dense() && !meta_tensor.is_tensor_array()) {
VLOG(3) << "input metatensor is not LoDTensor or LoDTensorArray.";
return;
}
var->SetLoDLevel(
static_cast<const CompatMetaTensor&>(meta_tensor).GetCompileTimeLoD());
}
......
paddle/fluid/framework/infershape_utils.h
浏览文件 @ 10225d22
......@@ -59,6 +59,9 @@ class CompatMetaTensor : public phi::MetaTensor {
bool initialized() const override { return initialized_; };
bool is_tensor_array() const;
bool is_dense() const;
operator unspecified_bool_type() const override {
return initialized_ ? unspecified_bool_true : 0;
}
......
paddle/fluid/framework/operator.cc
浏览文件 @ 10225d22
......@@ -2382,6 +2382,17 @@ void OperatorWithKernel::ParseInputDataType(
t = &var->Get<LoDTensor>();
} else if (var->IsType<phi::SelectedRows>()) {
t = &(var->Get<phi::SelectedRows>().value());
} else if (var->IsType<phi::SparseCooTensor>()) {
const phi::SparseCooTensor* sp_t = &(var->Get<phi::SparseCooTensor>());
PADDLE_ENFORCE_EQ(
sp_t->initialized(),
true,
platform::errors::InvalidArgument("The %s Op's Input Variable `%s` "
"contains uninitialized Tensor.",
Type(),
name));
*data_type = paddle::framework::TransToProtoVarType(sp_t->dtype());
return;
} else if (var->IsType<LoDTensorArray>()) {
auto t_arr = &var->Get<LoDTensorArray>();
for (size_t j = 0; j < t_arr->size(); j++) {
......@@ -2419,6 +2430,29 @@ void OperatorWithKernel::ParseMultiInputDataType(
t = &var->Get<LoDTensor>();
} else if (var->IsType<phi::SelectedRows>()) {
t = &(var->Get<phi::SelectedRows>().value());
} else if (var->IsType<phi::SparseCooTensor>()) {
const phi::SparseCooTensor* sp_t = &(var->Get<phi::SparseCooTensor>());
PADDLE_ENFORCE_EQ(
sp_t->initialized(),
true,
platform::errors::InvalidArgument("The %s Op's Input Variable `%s` "
"contains uninitialized Tensor.",
Type(),
name));
proto::VarType::Type tmp =
paddle::framework::TransToProtoVarType(sp_t->dtype());
PADDLE_ENFORCE(tmp == *data_type || *data_type == default_data_type,
platform::errors::InvalidArgument(
"The DataType of %s Op's duplicable or different "
"slot Variable %s must be "
"consistent or reigster GetExpectedKernelType. The "
"current variable type is (%s), but the "
"previous variable type is (%s).",
Type(),
name,
DataTypeToString(tmp),
DataTypeToString(*data_type)));
*data_type = tmp;
} else if (var->IsType<LoDTensorArray>()) {
auto t_arr = &var->Get<LoDTensorArray>();
for (size_t j = 0; j < t_arr->size(); j++) {
......@@ -2663,6 +2697,9 @@ void OperatorWithKernel::BuildPhiKernelContext(
} else if (var->IsType<phi::SelectedRows>()) {
tensor_in = &(var->Get<phi::SelectedRows>());
phi_kernel_context->EmplaceBackInputWithoutSetRange(tensor_in);
} else if (var->IsType<phi::SparseCooTensor>()) {
tensor_in = &(var->Get<phi::SparseCooTensor>());
phi_kernel_context->EmplaceBackInputWithoutSetRange(tensor_in);
} else if (var->IsType<framework::LoDTensorArray>()) {
need_prepare_phi_data_ = true;
tensor_in = &(var->Get<framework::LoDTensorArray>());
......@@ -2708,6 +2745,9 @@ void OperatorWithKernel::BuildPhiKernelContext(
} else if (var->template IsType<phi::SelectedRows>()) {
tensor_out = var->template GetMutable<phi::SelectedRows>();
phi_kernel_context->EmplaceBackOutputWithoutSetRange(tensor_out);
} else if (var->template IsType<phi::SparseCooTensor>()) {
tensor_out = var->template GetMutable<phi::SparseCooTensor>();
phi_kernel_context->EmplaceBackOutputWithoutSetRange(tensor_out);
} else if (var->template IsType<framework::LoDTensorArray>()) {
tensor_out = var->template GetMutable<framework::LoDTensorArray>();
// Note: If the input LoDTensorArray size is 0, the output
......
paddle/fluid/framework/operator.h
浏览文件 @ 10225d22
......@@ -524,6 +524,11 @@ class ExecutionArgumentMappingContext : public phi::ArgumentMappingContext {
});
}
bool IsSparseCooTensorInput(const std::string& name) const override {
const auto* var = ctx_.InputVar(name);
return var->IsType<phi::SparseCooTensor>();
}
bool IsDenseTensorOutput(const std::string& name) const override {
auto vars = ctx_.MultiOutputVar(name);
return std::all_of(vars.begin(), vars.end(), [](const Variable* var) {
......
paddle/fluid/framework/tensor.h
浏览文件 @ 10225d22
......@@ -17,6 +17,7 @@ limitations under the License. */
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/mixed_vector.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/sparse_coo_tensor.h"
namespace paddle {
namespace framework {
......
paddle/fluid/framework/var_desc.cc
浏览文件 @ 10225d22
......@@ -237,6 +237,8 @@ const proto::VarType::TensorDesc &VarDesc::tensor_desc() const {
return desc_.type().strings();
case proto::VarType::VOCAB:
return desc_.type().vocab();
case proto::VarType::SPARSE_COO:
return desc_.type().sparse_coo();
default:
PADDLE_THROW(platform::errors::Unavailable(
"Getting 'tensor_desc' is not supported by the %s type variable.",
......@@ -284,6 +286,8 @@ proto::VarType::TensorDesc *VarDesc::mutable_tensor_desc() {
return desc_.mutable_type()->mutable_strings();
case proto::VarType::VOCAB:
return desc_.mutable_type()->mutable_vocab();
case proto::VarType::SPARSE_COO:
return desc_.mutable_type()->mutable_sparse_coo();
default:
PADDLE_THROW(
platform::errors::Unavailable("Getting 'mutable_tensor_desc' is not "
......
paddle/fluid/framework/var_type.h
浏览文件 @ 10225d22
......@@ -33,6 +33,7 @@ inline proto::VarType::Type ToVarType(int type) {
switch (type) {
case proto::VarType::LOD_TENSOR:
case proto::VarType::SELECTED_ROWS:
case proto::VarType::SPARSE_COO:
case proto::VarType::LOD_RANK_TABLE:
case proto::VarType::LOD_TENSOR_ARRAY:
case proto::VarType::FETCH_LIST:
......@@ -59,6 +60,9 @@ inline void VisitVarType(const framework::Variable& var, Visitor visitor) {
case proto::VarType::SELECTED_ROWS:
visitor(var.Get<phi::SelectedRows>());
return;
case proto::VarType::SPARSE_COO:
visitor(var.Get<phi::SparseCooTensor>());
return;
case proto::VarType::READER:
visitor(var.Get<ReaderHolder>());
return;
......
paddle/fluid/framework/var_type_traits.h
浏览文件 @ 10225d22
......@@ -54,6 +54,7 @@
namespace phi {
class DenseTensor;
class SelectedRows;
class SparseCooTensor;
} // namespace phi
// Users should add forward declarations here
......@@ -180,6 +181,7 @@ struct VarTypeRegistryImpl {
using VarTypeRegistry = detail::VarTypeRegistryImpl<
Tensor,
phi::SelectedRows,
phi::SparseCooTensor,
std::vector<Scope *>,
LoDRankTable,
Strings,
......@@ -252,6 +254,7 @@ REG_PROTO_VAR_TYPE_TRAIT(float, proto::VarType::FP32);
REG_PROTO_VAR_TYPE_TRAIT(Vocab, proto::VarType::VOCAB);
REG_PROTO_VAR_TYPE_TRAIT(String, proto::VarType::STRING);
REG_PROTO_VAR_TYPE_TRAIT(Strings, proto::VarType::STRINGS);
REG_PROTO_VAR_TYPE_TRAIT(phi::SparseCooTensor, proto::VarType::SPARSE_COO);
/** End of variable type registration */
......
paddle/fluid/framework/variable_helper.cc
浏览文件 @ 10225d22
......@@ -52,6 +52,8 @@ void InitializeVariable(Variable *var, proto::VarType::Type var_type) {
var->GetMutable<ReaderHolder>();
} else if (var_type == proto::VarType::RAW) {
// GetMutable will be called in operator
} else if (var_type == proto::VarType::SPARSE_COO) {
var->GetMutable<phi::SparseCooTensor>();
} else {
PADDLE_THROW(platform::errors::Unavailable(
"Variable type %d is not in "
......
paddle/fluid/inference/tensorrt/plugin_arg_mapping_context.cc
浏览文件 @ 10225d22
......@@ -104,6 +104,10 @@ bool PluginArgumentMappingContext::IsSelectedRowsInput(
const std::string& name) const {
return false;
}
bool PluginArgumentMappingContext::IsSparseCooTensorInput(
const std::string& name) const {
return false;
}
bool PluginArgumentMappingContext::IsDenseTensorVectorInput(
const std::string& name) const {
return false;
......
paddle/fluid/inference/tensorrt/plugin_arg_mapping_context.h
浏览文件 @ 10225d22
......@@ -46,6 +46,8 @@ class PluginArgumentMappingContext : public ::phi::ArgumentMappingContext {
bool IsSelectedRowsInput(const std::string& name) const override;
bool IsSparseCooTensorInput(const std::string& name) const override;
bool IsDenseTensorVectorInput(const std::string& name) const override;
bool IsDenseTensorOutput(const std::string& name) const override;
......
paddle/fluid/operators/controlflow/feed_op.cc
浏览文件 @ 10225d22
......@@ -11,6 +11,7 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/phi/core/tensor_utils.h"
namespace paddle {
namespace framework {
......@@ -61,6 +62,22 @@ class FeedVariableVisitor {
*out_str = in_str;
}
void operator()(const phi::SparseCooTensor &in_tensor) const {
phi::SparseCooTensor *out_tensor =
out_var_->GetMutable<phi::SparseCooTensor>();
if (platform::is_same_place(in_tensor.place(), place_)) {
*out_tensor = in_tensor;
} else {
platform::DeviceContext *context =
platform::DeviceContextPool::Instance().Get(place_);
phi::DenseTensor indices, values;
framework::TensorCopy(in_tensor.indices(), place_, *context, &indices);
framework::TensorCopy(in_tensor.values(), place_, *context, &values);
out_tensor->SetMember(indices, values, in_tensor.meta());
}
}
private:
framework::Variable *out_var_;
const platform::Place &place_;
......
paddle/fluid/operators/controlflow/fetch_op.cc
浏览文件 @ 10225d22
......@@ -123,6 +123,9 @@ class FetchOp : public framework::OperatorBase {
auto &src_item = fetch_var->Get<framework::Vocab>();
auto *dst_item = &(PADDLE_GET(framework::Vocab, fetch_list->at(col)));
*dst_item = src_item;
} else if (fetch_var->IsType<phi::SparseCooTensor>()) {
auto &src_item = fetch_var->Get<phi::SparseCooTensor>();
fetch_list->at(col) = src_item;
} else {
auto &src_item = fetch_var->Get<framework::LoDTensorArray>();
framework::LoDTensorArray tmp(src_item.size());
......
paddle/fluid/operators/controlflow/fetch_v2_op.cc
浏览文件 @ 10225d22
......@@ -98,6 +98,12 @@ class FetchV2Op : public framework::OperatorWithKernel {
return framework::OpKernelType(framework::proto::VarType::FP32,
platform::CPUPlace());
}
} else if (fetch_var->IsType<phi::SparseCooTensor>()) {
auto &src_item = fetch_var->Get<phi::SparseCooTensor>();
if (!src_item.initialized()) {
return framework::OpKernelType(framework::proto::VarType::FP32,
platform::CPUPlace());
}
} else {
auto &src_item = fetch_var->Get<framework::LoDTensorArray>();
if (src_item.empty() || !src_item[0].IsInitialized()) {
......@@ -163,6 +169,12 @@ class FetchV2Kernel {
dst_item->ShareDataWith(src_item);
dst_item->set_lod(src_item.lod());
}
} else if (fetch_var->IsType<phi::SparseCooTensor>()) {
auto &src_item = fetch_var->Get<phi::SparseCooTensor>();
if (!src_item.initialized()) {
return;
}
fetch_list->at(col) = src_item;
} else {
auto &src_item = fetch_var->Get<framework::LoDTensorArray>();
framework::LoDTensorArray tmp(src_item.size());
......
paddle/fluid/operators/sparse_manual_op.cc 0 → 100644
浏览文件 @ 10225d22
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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 <memory>
#include <string>
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/binary.h"
#include "paddle/phi/infermeta/sparse/binary.h"
#include "paddle/phi/infermeta/sparse/unary.h"
#include "paddle/phi/infermeta/unary.h"
namespace paddle {
namespace operators {
class SparseSparseCooTensorOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("values", "(Tensor), input 0 of sparse_coo_tensor op.");
AddInput("indices", "(Tensor), input 1 of sparse_coo_tensor op.");
AddOutput("out", "(Tensor), output 0 of sparse_coo_tensor op.");
AddAttr<std::vector<int>>(
"dense_shape", "(vector<int>), attribute 0 for sparse_coo_tensor op.");
AddComment(R"DOC(
TODO: Documentation of sparse_coo_tensor op.
)DOC");
}
};
class SparseSparseCooTensorOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
};
DECLARE_INFER_SHAPE_FUNCTOR(
sparse_sparse_coo_tensor,
SparseSparseCooTensorInferShapeFunctor,
PD_INFER_META(phi::sparse::SparseCooTensorInferMeta));
class SparseValuesOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("x", "(Tensor), input 0 of sparse_values op.");
AddOutput("out", "(Tensor), output 0 of sparse_values op.");
AddComment(R"DOC(
TODO: Documentation of sparse_values op.
)DOC");
}
};
class SparseValuesOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
};
DECLARE_INFER_SHAPE_FUNCTOR(sparse_values,
SparseValuesInferShapeFunctor,
PD_INFER_META(phi::sparse::ValuesInferMeta));
class SparseIndicesOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("x", "(Tensor), input 0 of sparse_indices op.");
AddOutput("out", "(Tensor), output 0 of sparse_indices op.");
AddComment(R"DOC(
TODO: Documentation of sparse_indices op.
)DOC");
}
};
class SparseIndicesOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
};
DECLARE_INFER_SHAPE_FUNCTOR(sparse_indices,
SparseIndicesInferShapeFunctor,
PD_INFER_META(phi::sparse::IndicesInferMeta));
class SparseToDenseOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("x", "(Tensor), input 0 of sparse_to_dense op.");
AddOutput("out", "(Tensor), output 0 of sparse_to_dense op.");
AddComment(R"DOC(
TODO: Documentation of sparse_to_dense op.
)DOC");
}
};
class SparseToDenseOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
};
DECLARE_INFER_SHAPE_FUNCTOR(sparse_to_dense,
SparseToDenseInferShapeFunctor,
PD_INFER_META(phi::UnchangedInferMeta));
class SparseReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("x", "(Tensor), input 0 of sparse_relu op.");
AddOutput("out", "(Tensor), output 0 of sparse_relu op.");
AddComment(R"DOC(
TODO: Documentation of sparse_relu op.
)DOC");
}
};
class SparseReluOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
};
DECLARE_INFER_SHAPE_FUNCTOR(sparse_relu,
SparseReluInferShapeFunctor,
PD_INFER_META(phi::UnchangedInferMeta));
class SparseConv3dOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("x", "(Tensor), input 0 of sparse_conv3d op.");
AddInput("kernel", "(Tensor), input 1 of sparse_conv3d op.");
AddOutput("out", "(Tensor), output 0 of sparse_conv3d op.");
AddOutput("rulebook", "(Tensor), output 1 of sparse_conv3d op.");
AddOutput("counter", "(Tensor), output 2 of sparse_conv3d op.");
AddAttr<std::vector<int>>(
"paddings", "(vector<int>), attribute 0 for sparse_conv3d op.");
AddAttr<std::vector<int>>(
"dilations", "(vector<int>), attribute 1 for sparse_conv3d op.");
AddAttr<std::vector<int>>(
"strides", "(vector<int>), attribute 2 for sparse_conv3d op.");
AddAttr<int>("groups", "(int), attribute 3 for sparse_conv3d op.");
AddAttr<bool>("subm", "(bool), attribute 4 for conv3d_coo op.");
AddAttr<std::string>("key", "(string), attribute 5 for sparse_conv3d op.")
.SetDefault("");
AddComment(R"DOC(
TODO: Documentation of sparse_conv3d op.
)DOC");
}
};
class SparseConv3dOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
};
DECLARE_INFER_SHAPE_FUNCTOR(sparse_conv3d,
SparseConv3dInferShapeFunctor,
PD_INFER_META(phi::sparse::Conv3dInferMeta));
class SparseAddOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("x", "(Tensor), input 0 of sparse_add op.");
AddInput("y", "(Tensor), input 1 of sparse_add op.");
AddOutput("out", "(Tensor), output 0 of sparse_add op.");
AddComment(R"DOC(
TODO: Documentation of sparse_add op.
)DOC");
}
};
class SparseAddOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
};
DECLARE_INFER_SHAPE_FUNCTOR(sparse_add,
SparseAddInferShapeFunctor,
PD_INFER_META(phi::UnchangedInferMeta));
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(sparse_sparse_coo_tensor,
ops::SparseSparseCooTensorOp,
ops::SparseSparseCooTensorOpMaker,
ops::SparseSparseCooTensorInferShapeFunctor);
REGISTER_OPERATOR(sparse_values,
ops::SparseValuesOp,
ops::SparseValuesOpMaker,
ops::SparseValuesInferShapeFunctor);
REGISTER_OPERATOR(sparse_indices,
ops::SparseIndicesOp,
ops::SparseIndicesOpMaker,
ops::SparseIndicesInferShapeFunctor);
REGISTER_OPERATOR(sparse_to_dense,
ops::SparseToDenseOp,
ops::SparseToDenseOpMaker,
ops::SparseToDenseInferShapeFunctor);
REGISTER_OPERATOR(sparse_relu,
ops::SparseReluOp,
ops::SparseReluOpMaker,
ops::SparseReluInferShapeFunctor);
REGISTER_OPERATOR(sparse_conv3d,
ops::SparseConv3dOp,
ops::SparseConv3dOpMaker,
ops::SparseConv3dInferShapeFunctor);
REGISTER_OPERATOR(sparse_add,
ops::SparseAddOp,
ops::SparseAddOpMaker,
ops::SparseAddInferShapeFunctor);
paddle/fluid/pybind/protobuf.cc
浏览文件 @ 10225d22
......@@ -275,7 +275,8 @@ void BindVarDsec(pybind11::module *m) {
.value("RAW", pd::proto::VarType::RAW)
.value("STRING", pd::proto::VarType::STRING)
.value("STRINGS", pd::proto::VarType::STRINGS)
.value("VOCAB", pd::proto::VarType::VOCAB);
.value("VOCAB", pd::proto::VarType::VOCAB)
.value("SPARSE_COO", pd::proto::VarType::SPARSE_COO);
}
void BindOpDesc(pybind11::module *m) {
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 10225d22
......@@ -1918,6 +1918,9 @@ All parameter, weight, gradient are variables in Paddle.
if (data_is_lod_tensor(self[i])) {
auto &data = PADDLE_GET(LoDTensor, self[i]);
res[i] = py::cast(std::move(data));
} else if (data_is_sparse_coo_tensor(self[i])) {
auto &data = PADDLE_GET(phi::SparseCooTensor, self[i]);
res[i] = py::cast(std::move(data));
} else {
auto &data = PADDLE_GET(LoDTensorArray, self[i]);
py::list tmp(data.size());
......
paddle/fluid/pybind/tensor.cc
浏览文件 @ 10225d22
......@@ -1105,6 +1105,20 @@ void BindTensor(pybind11::module &m) { // NOLINT
std::copy(rows.begin(), rows.end(), std::back_inserter(new_rows));
return new_rows;
});
py::class_<phi::SparseCooTensor>(m, "SparseCooTensor")
.def("__init__",
[](phi::SparseCooTensor &instance) {
new (&instance) phi::SparseCooTensor();
})
.def("numel",
[](const phi::SparseCooTensor &self) -> int64_t {
return self.numel();
})
.def("indices",
[](const phi::SparseCooTensor &self) -> framework::Tensor {
return self.indices();
});
}
} // namespace pybind
......
paddle/phi/api/lib/api_gen_utils.cc
浏览文件 @ 10225d22
......@@ -230,6 +230,9 @@ phi::SelectedRows* SetSelectedRowsKernelOutput(Tensor* out) {
}
phi::TensorBase* SetSparseKernelOutput(Tensor* out, TensorType type) {
if (!out) {
return nullptr;
}
if (!out->initialized()) {
if (type == TensorType::SPARSE_COO) {
auto sparse_tensor = std::make_shared<phi::SparseCooTensor>(
......
paddle/phi/api/yaml/sparse_backward.yaml
浏览文件 @ 10225d22
......@@ -36,11 +36,12 @@
args : (Tensor x, Tensor y, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
func : GeneralBinaryGradInferMeta
param : [x, y]
kernel :
func : add_coo_coo_grad{sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo},
add_csr_csr_grad{sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr}
add_csr_csr_grad{sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr},
add_coo_dense_grad{sparse_coo, dense, sparse_coo -> sparse_coo, dense}
- backward_op : addmm_grad
forward : addmm(Tensor input, Tensor x, Tensor y, float alpha=1.0, float beta=1.0) -> Tensor(out)
......@@ -104,7 +105,7 @@
args : (Tensor x, Tensor out_grad, DataType value_dtype)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
func : UnchangedInferMeta
param: [x]
kernel :
func : cast_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
......@@ -126,7 +127,7 @@
args : (Tensor x, Tensor y, Tensor out, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
func : GeneralBinaryGradInferMeta
param : [x, y]
kernel :
func : divide_coo_coo_grad{sparse_coo, sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo},
......@@ -209,7 +210,7 @@
args : (Tensor x, Tensor y, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
func : GeneralBinaryGradInferMeta
param : [x, y]
kernel :
func : multiply_coo_coo_grad{sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo},
......@@ -337,7 +338,7 @@
args : (Tensor x, Tensor y, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
func : GeneralBinaryGradInferMeta
param : [x, y]
kernel :
func : subtract_coo_coo_grad{sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo},
......@@ -399,7 +400,7 @@
args: (Tensor query, Tensor key, Tensor value, Tensor softmax, Tensor out_grad)
output : Tensor(query_grad), Tensor(key_grad), Tensor(value_grad)
infer_meta :
func : sparse::FusedAttentionGradInferMeta
func : sparse::FusedAttentionGradInferMeta
kernel :
func : fused_attention_csr_grad{dense, dense, dense, sparse_csr, dense -> dense, dense, dense}
layout : softmax
......
paddle/phi/api/yaml/sparse_ops.yaml
浏览文件 @ 10225d22
......@@ -35,10 +35,11 @@
args : (Tensor x, Tensor y)
output : Tensor(out)
infer_meta :
func : ElementwiseInferMeta
func : ElementwiseInferMeta
kernel :
func : add_coo_coo{sparse_coo, sparse_coo -> sparse_coo},
add_csr_csr{sparse_csr, sparse_csr -> sparse_csr}
add_coo_dense{sparse_coo, dense -> sparse_coo},
layout : x
backward : add_grad
......@@ -114,7 +115,7 @@
args : (Tensor x, Tensor y)
output : Tensor(out)
infer_meta :
func : ElementwiseInferMeta
func : ElementwiseInferMeta
kernel :
func : divide_coo_coo{sparse_coo, sparse_coo -> sparse_coo},
divide_csr_csr{sparse_csr, sparse_csr -> sparse_csr}
......
paddle/phi/core/compat/arg_map_context.h
浏览文件 @ 10225d22
......@@ -108,6 +108,7 @@ class ArgumentMappingContext {
virtual bool IsDenseTensorInput(const std::string& name) const = 0;
virtual bool IsDenseTensorInputs(const std::string& name) const = 0;
virtual bool IsSelectedRowsInput(const std::string& name) const = 0;
virtual bool IsSparseCooTensorInput(const std::string& name) const = 0;
// For compatibility with LoDTensorArray
virtual bool IsDenseTensorVectorInput(const std::string& name) const = 0;
......
paddle/phi/core/meta_tensor.cc
浏览文件 @ 10225d22
......@@ -164,6 +164,9 @@ void MetaTensor::share_meta(const MetaTensor& meta_tensor) {
}
}
bool MetaTensor::is_dense() const { return DenseTensor::classof(tensor_); }
bool MetaTensor::is_tensor_array() const { return false; }
void MetaTensor::share_dims(const MetaTensor& meta_tensor) {
ValidCheck(*this);
bool is_dense_tensor = phi::DenseTensor::classof(tensor_);
......
paddle/phi/core/meta_tensor.h
浏览文件 @ 10225d22
......@@ -68,6 +68,12 @@ class MetaTensor {
virtual bool initialized() const;
virtual bool is_dense() const;
// TODO(YuanRisheng) This API is for compatible with
// Fluid
// and it will be deleted in the future.
virtual bool is_tensor_array() const;
virtual operator unspecified_bool_type() const {
return tensor_ == nullptr ? 0 : unspecified_bool_true;
}
......
paddle/phi/kernels/sparse/cpu/elementwise_grad_kernel.cc
浏览文件 @ 10225d22
......@@ -415,3 +415,14 @@ PD_REGISTER_KERNEL(divide_coo_coo_grad,
kernel->InputAt(2).SetDataLayout(phi::DataLayout::SPARSE_COO);
kernel->InputAt(3).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(add_coo_dense_grad,
CPU,
ALL_LAYOUT,
phi::sparse::ElementWiseAddDenseGradKernel,
float,
double,
int,
int64_t) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
paddle/phi/kernels/sparse/cpu/elementwise_kernel.cc
浏览文件 @ 10225d22
......@@ -16,6 +16,7 @@ limitations under the License. */
#include "paddle/phi/core/enforce.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/tensor_meta.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/core/visit_type.h"
#include "paddle/phi/kernels/elementwise_add_kernel.h"
#include "paddle/phi/kernels/elementwise_kernel.h"
......@@ -156,6 +157,21 @@ void ElementWiseCooKernelImpl(const Context& dev_ctx,
"shape = [%s], Y's shape = [%s].",
x.dims(),
y.dims()));
// temporary policy: for broadcast add
// TODO(zhangkaihuo): implement a correct function
const bool is_add = std::is_same<Functor, funcs::AddFunctor<T>>::value;
if (is_add && x.indices().numel() == y.indices().numel()) {
int compare_indices = memcmp(x.indices().data<IntT>(),
y.indices().data<IntT>(),
sizeof(IntT) * x.indices().numel());
if (compare_indices == 0) {
EmptyLikeCooKernel<T, Context>(dev_ctx, x, out);
phi::AddKernel<T, Context>(
dev_ctx, x.values(), y.values(), out->mutable_values());
return;
}
}
int64_t element_size = 1;
for (auto j = 1; j < x.values().dims().size(); ++j) {
element_size *= x.values().dims()[j];
......@@ -435,3 +451,14 @@ PD_REGISTER_KERNEL(divide_coo_coo,
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
kernel->InputAt(1).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(add_coo_dense,
CPU,
ALL_LAYOUT,
phi::sparse::ElementWiseAddDenseKernel,
float,
double,
int,
int64_t) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
paddle/phi/kernels/sparse/elementwise_grad_kernel.h
浏览文件 @ 10225d22
......@@ -14,6 +14,9 @@ limitations under the License. */
#pragma once
#include "paddle/phi/kernels/elementwise_add_grad_kernel.h"
#include "paddle/phi/kernels/sparse/empty_kernel.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/sparse_coo_tensor.h"
#include "paddle/phi/core/sparse_csr_tensor.h"
......@@ -119,5 +122,27 @@ std::vector<SparseCooTensor> ElementWiseDivideCooGrad(
return std::vector<SparseCooTensor>{dx, dy};
}
template <typename T, typename Context>
void ElementWiseAddDenseGradKernel(const Context& dev_ctx,
const SparseCooTensor& x,
const DenseTensor& y,
const SparseCooTensor& dout,
SparseCooTensor* dx,
DenseTensor* dy) {
DenseTensor* x_values_grad = nullptr;
DenseTensor* y_grad = nullptr;
if (dx) {
EmptyLikeCooKernel<T, Context>(dev_ctx, x, dx);
x_values_grad = dx->mutable_values();
}
if (dy) {
*dy = phi::EmptyLike<T>(dev_ctx, y);
y_grad = dy;
}
phi::AddGradKernel<T, Context>(
dev_ctx, x.values(), y, dout.values(), -1, x_values_grad, y_grad);
}
} // namespace sparse
} // namespace phi
paddle/phi/kernels/sparse/elementwise_kernel.h
浏览文件 @ 10225d22
......@@ -14,6 +14,10 @@ limitations under the License. */
#pragma once
#include "paddle/phi/kernels/elementwise_add_kernel.h"
#include "paddle/phi/kernels/sparse/elementwise_kernel.h"
#include "paddle/phi/kernels/sparse/empty_kernel.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/sparse_coo_tensor.h"
#include "paddle/phi/core/sparse_csr_tensor.h"
......@@ -78,5 +82,21 @@ DEFINE_ELEMENTWISE_KERNEL_FUNC(Subtract)
DEFINE_ELEMENTWISE_KERNEL_FUNC(Multiply)
DEFINE_ELEMENTWISE_KERNEL_FUNC(Divide)
template <typename T, typename Context>
void ElementWiseAddDenseKernel(const Context& dev_ctx,
const SparseCooTensor& x,
const DenseTensor& y,
SparseCooTensor* out) {
// TODO(zhangkaiuo): to support universal sparse + dense
if (y.dims().size() == 1 && y.dims()[0] == x.dims()[x.dims().size() - 1]) {
EmptyLikeCooKernel<T, Context>(dev_ctx, x, out);
phi::AddKernel<T, Context>(dev_ctx, x.values(), y, out->mutable_values());
out->SetIndicesDict(x.GetIndicesDict());
} else {
PADDLE_THROW(
errors::Unimplemented("Not support Sparse + Dense in GPU mode"));
}
}
} // namespace sparse
} // namespace phi
paddle/phi/kernels/sparse/gpu/conv.cu.h
浏览文件 @ 10225d22
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <thrust/remove.h>
#include <thrust/sort.h>
#include <thrust/unique.h>
#include "paddle/phi/kernels/sparse/conv_kernel.h"
......@@ -186,8 +187,7 @@ __global__ void UniqueKernel(const IntT* in_indexs,
if (i < rulebook_len) {
// atomicOr only support int
int index = static_cast<int>(in_indexs[i]);
int change_index = index == 0 ? -1 : index;
int flag = atomicOr(out_index_table + index, change_index);
int flag = atomicOr(out_index_table + index, 1);
if (flag == 0) {
int j = atomicAdd(&count, 1);
cache[j] = index;
......@@ -772,6 +772,7 @@ int ProductRuleBook(const Context& dev_ctx,
phi::backends::gpu::GpuMemsetAsync(
out_index_table_ptr, 0, sizeof(int) * table_size, dev_ctx.stream());
phi::backends::gpu::GpuMemsetAsync(
unique_key_ptr, 0, sizeof(int), dev_ctx.stream());
......@@ -785,6 +786,7 @@ int ProductRuleBook(const Context& dev_ctx,
out_index_table_ptr,
out_index_ptr,
unique_key_ptr);
int out_nnz = 0;
phi::backends::gpu::GpuMemcpyAsync(&out_nnz,
unique_key_ptr,
......@@ -792,6 +794,13 @@ int ProductRuleBook(const Context& dev_ctx,
gpuMemcpyDeviceToHost,
dev_ctx.stream());
dev_ctx.Wait();
#ifdef PADDLE_WITH_HIP
thrust::sort(thrust::hip::par.on(dev_ctx.stream()),
#else
thrust::sort(thrust::cuda::par.on(dev_ctx.stream()),
#endif
out_index_ptr,
out_index_ptr + out_nnz);
const int64_t sparse_dim = 4;
phi::DenseTensor out_indices =
......
paddle/phi/kernels/sparse/gpu/elementwise_grad_kernel.cu
浏览文件 @ 10225d22
......@@ -15,6 +15,9 @@ limitations under the License. */
#include "paddle/phi/kernels/sparse/elementwise_grad_kernel.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/empty_kernel.h"
#include "paddle/phi/kernels/funcs/elementwise_grad_base.h"
#include "paddle/phi/kernels/funcs/reduce_function.h"
#include "paddle/phi/kernels/sparse/empty_kernel.h"
namespace phi {
......@@ -54,3 +57,15 @@ PD_REGISTER_KERNEL(add_coo_coo_grad,
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
kernel->InputAt(1).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(add_coo_dense_grad,
GPU,
ALL_LAYOUT,
phi::sparse::ElementWiseAddDenseGradKernel,
float,
double,
int,
int64_t,
phi::dtype::float16) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
paddle/phi/kernels/sparse/gpu/elementwise_kernel.cu
浏览文件 @ 10225d22
......@@ -31,6 +31,7 @@ void ElementWiseAddCooGPUKernel(const GPUContext& dev_ctx,
const SparseCooTensor& x,
const SparseCooTensor& y,
SparseCooTensor* out) {
// TODO(zhangkaiuo): to support universal sparse + sparse
const auto& x_indices = x.indices();
const auto& y_indices = y.indices();
PADDLE_ENFORCE_EQ(
......@@ -57,6 +58,7 @@ void ElementWiseAddCooGPUKernel(const GPUContext& dev_ctx,
EmptyLikeCooKernel<T, GPUContext>(dev_ctx, x, out);
phi::AddKernel<T, GPUContext>(
dev_ctx, x.values(), y.values(), out->mutable_values());
out->SetIndicesDict(x.GetIndicesDict());
}
template <typename T, typename Context>
......@@ -86,3 +88,15 @@ PD_REGISTER_KERNEL(add_coo_coo,
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
kernel->InputAt(1).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(add_coo_dense,
GPU,
ALL_LAYOUT,
phi::sparse::ElementWiseAddDenseKernel,
float,
double,
int,
int64_t,
phi::dtype::float16) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
paddle/phi/ops/compat/sparse_manual_op_sig.cc 0 → 100644
浏览文件 @ 10225d22
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// 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/phi/core/compat/op_utils.h"
namespace phi {
// TODO(zhangkaihuo): add csr op
KernelSignature SparseSparseCooTensorOpArgumentMapping(
const ArgumentMappingContext& ctx) {
return KernelSignature(
"sparse_coo_tensor", {"values", "indices"}, {"dense_shape"}, {"out"});
}
KernelSignature SparseValuesOpArgumentMapping(
const ArgumentMappingContext& ctx) {
if (ctx.IsSparseCooTensorInput("x")) {
return KernelSignature("values_coo", {"x"}, {}, {"out"});
} else {
return KernelSignature("unregistered", {}, {}, {});
}
}
KernelSignature SparseIndicesOpArgumentMapping(
const ArgumentMappingContext& ctx) {
if (ctx.IsSparseCooTensorInput("x")) {
return KernelSignature("indices_coo", {"x"}, {}, {"out"});
} else {
return KernelSignature("unregistered", {}, {}, {});
}
}
KernelSignature SparseToDenseOpArgumentMapping(
const ArgumentMappingContext& ctx) {
if (ctx.IsSparseCooTensorInput("x")) {
return KernelSignature("coo_to_dense", {"x"}, {}, {"out"});
} else {
return KernelSignature("unregistered", {}, {}, {});
}
}
KernelSignature SparseReluOpArgumentMapping(const ArgumentMappingContext& ctx) {
if (ctx.IsSparseCooTensorInput("x")) {
return KernelSignature("relu_coo", {"x"}, {}, {"out"});
} else {
return KernelSignature("unregistered", {}, {}, {});
}
}
KernelSignature SparseConv3dOpArgumentMapping(
const ArgumentMappingContext& ctx) {
if (ctx.IsSparseCooTensorInput("x")) {
return KernelSignature(
"conv3d_coo",
{"x", "kernel"},
{"paddings", "dilations", "strides", "groups", "subm", "key"},
{"out", "rulebook", "counter"});
} else {
return KernelSignature("unregistered", {}, {}, {});
}
}
KernelSignature SparseAddOpArgumentMapping(const ArgumentMappingContext& ctx) {
if (ctx.IsSparseCooTensorInput("x") && ctx.IsSparseCooTensorInput("y")) {
return KernelSignature("add_coo_coo", {"x", "y"}, {}, {"out"});
} else if (ctx.IsSparseCooTensorInput("x") && ctx.IsDenseTensorInput("y")) {
return KernelSignature("add_coo_dense", {"x", "y"}, {}, {"out"});
} else {
return KernelSignature("unregistered", {}, {}, {});
}
}
} // namespace phi
PD_REGISTER_BASE_KERNEL_NAME(sparse_sparse_coo_tensor, sparse_coo_tensor);
PD_REGISTER_ARG_MAPPING_FN(sparse_sparse_coo_tensor,
phi::SparseSparseCooTensorOpArgumentMapping);
PD_REGISTER_BASE_KERNEL_NAME(sparse_values, values_coo);
PD_REGISTER_ARG_MAPPING_FN(sparse_values, phi::SparseValuesOpArgumentMapping);
PD_REGISTER_BASE_KERNEL_NAME(sparse_indices, indices_coo);
PD_REGISTER_ARG_MAPPING_FN(sparse_indices, phi::SparseIndicesOpArgumentMapping);
PD_REGISTER_BASE_KERNEL_NAME(sparse_to_dense, coo_to_dense);
PD_REGISTER_ARG_MAPPING_FN(sparse_to_dense,
phi::SparseToDenseOpArgumentMapping);
PD_REGISTER_BASE_KERNEL_NAME(sparse_relu, relu_coo);
PD_REGISTER_ARG_MAPPING_FN(sparse_relu, phi::SparseReluOpArgumentMapping);
PD_REGISTER_BASE_KERNEL_NAME(sparse_conv3d, conv3d_coo);
PD_REGISTER_ARG_MAPPING_FN(sparse_conv3d, phi::SparseConv3dOpArgumentMapping);
PD_REGISTER_BASE_KERNEL_NAME(sparse_add, add_coo_coo);
PD_REGISTER_ARG_MAPPING_FN(sparse_add, phi::SparseAddOpArgumentMapping);
paddle/phi/tests/ops/test_op_signature.h
浏览文件 @ 10225d22
......@@ -82,6 +82,10 @@ class TestArgumentMappingContext : public phi::ArgumentMappingContext {
return false;
}
bool IsSparseCooTensorInput(const std::string& name) const override {
return false;
}
bool IsDenseTensorOutput(const std::string& name) const override {
return dense_tensor_outputs.count(name) > 0;
}
......
python/paddle/fluid/framework.py
浏览文件 @ 10225d22
......@@ -1373,6 +1373,9 @@ class Variable(object):
type = core.VarDesc.VarType.STRINGS
lod_level = None
if type == core.VarDesc.VarType.SPARSE_COO:
lod_level = None
self.belong_to_optimizer = belong_to_optimizer
self.error_clip = error_clip
......
python/paddle/fluid/layer_helper_base.py
浏览文件 @ 10225d22
......@@ -410,6 +410,30 @@ class LayerHelperBase(object):
persistable=False,
stop_gradient=stop_gradient)
def create_sparse_variable_for_type_inference(self,
dtype,
stop_gradient=False,
shape=None):
"""Create a temporary sparse variable that should be type inferred layer.
Note:
The default type will be set to SPARSE_COO. However, when
the var is used as operator output, its type will be updated
based on operator's `VarTypeInference` implementation in
infer_var_type.
"""
# set global dtype
if not dtype:
dtype = self.__dtype
return self.main_program.current_block().create_var(
name=unique_name.generate_with_ignorable_key(".".join(
[self.name, 'tmp'])),
dtype=dtype,
shape=shape,
type=core.VarDesc.VarType.SPARSE_COO,
persistable=False,
stop_gradient=stop_gradient)
def create_variable(self, *args, **kwargs):
"""Create Variable for this layers.
Returns created Variable.
......
python/paddle/fluid/layers/math_op_patch.py
浏览文件 @ 10225d22
......@@ -80,6 +80,10 @@ def monkey_patch_variable():
tmp_name = unique_tmp_name()
return block.create_var(name=tmp_name, dtype=dtype)
def create_new_tmp_sparse_var(block, dtype, type):
tmp_name = unique_tmp_name()
return block.create_var(name=tmp_name, dtype=dtype, type=type)
def create_tensor(block, value, dtype, shape):
value = float(value)
var = create_new_tmp_var(block, dtype)
......@@ -433,6 +437,33 @@ def monkey_patch_variable():
__impl__.__name__ = method_name
return __impl__
def values(var):
block = current_block(var)
out = create_new_tmp_var(block, var.dtype)
block.append_op(type="sparse_values",
inputs={"x": [var]},
outputs={"out": [out]},
attrs={})
return out
def indices(var):
block = current_block(var)
out = create_new_tmp_var(block, var.dtype)
block.append_op(type="sparse_indices",
inputs={"x": [var]},
outputs={"out": [out]},
attrs={})
return out
def to_dense(var):
block = current_block(var)
out = create_new_tmp_var(block, var.dtype)
block.append_op(type="sparse_to_dense",
inputs={"x": [var]},
outputs={"out": [out]},
attrs={})
return out
variable_methods = [
# b=-a
('__neg__', _neg_),
......@@ -485,7 +516,10 @@ def monkey_patch_variable():
('__lt__', _binary_creator_('__lt__', 'less_than', False, None)),
('__le__', _binary_creator_('__le__', 'less_equal', False, None)),
('__gt__', _binary_creator_('__gt__', 'greater_than', False, None)),
('__ge__', _binary_creator_('__ge__', 'greater_equal', False, None))
('__ge__', _binary_creator_('__ge__', 'greater_equal', False, None)),
('values', values),
('indices', indices),
('to_dense', to_dense),
]
global _already_patch_variable
......
python/paddle/fluid/tests/unittests/test_sparse_conv_op.py
浏览文件 @ 10225d22
......@@ -19,6 +19,7 @@ import paddle
from paddle import _C_ops, _legacy_C_ops
from paddle.fluid import core
from paddle.fluid.framework import _test_eager_guard
import paddle.incubate.sparse as sparse
class TestSparseConv(unittest.TestCase):
......@@ -159,3 +160,66 @@ class TestSparseConv(unittest.TestCase):
sp_conv3d.bias.grad.numpy(),
atol=1e-5,
rtol=1e-5)
class TestStatic(unittest.TestCase):
def test(self):
paddle.enable_static()
indices = paddle.static.data(name='indices',
shape=[4, 4],
dtype='int32')
values = paddle.static.data(name='values',
shape=[4, 1],
dtype='float32')
dense_shape = [1, 1, 3, 4, 1]
sp_x = sparse.sparse_coo_tensor(indices, values, dense_shape)
weight_shape = [1, 3, 3, 1, 1]
weight = paddle.static.data(name='weight',
shape=weight_shape,
dtype='float32')
bias_shape = [1]
bias = paddle.static.data(name='bias',
shape=bias_shape,
dtype='float32')
out = sparse.nn.functional.conv3d(sp_x,
weight,
bias,
stride=1,
padding=0,
dilation=1,
groups=1,
data_format="NDHWC")
sp_out = sparse.nn.functional.relu(out)
out_indices = sp_out.indices()
out_values = sp_out.values()
out = sp_out.to_dense()
exe = paddle.static.Executor()
indices_data = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
values_data = [[1.0], [2.0], [3.0], [4.0]]
weight_data = np.array([[[[[1], [1], [1]], [[1], [1], [1]],
[[1], [1], [1]]]]]).astype('float32')
weight_data = weight_data.reshape(weight_shape)
bias_data = np.array([1]).astype('float32')
fetch = exe.run(feed={
'indices': indices_data,
'values': values_data,
'weight': weight_data,
'bias': bias_data
},
fetch_list=[out, out_indices, out_values],
return_numpy=True)
correct_out = np.array([[[[[5.0], [11.0]]]]]).astype('float64')
correct_out_values = [[5.0], [11.0]]
assert np.array_equal(correct_out, fetch[0])
assert np.array_equal(correct_out_values, fetch[2])
assert out_indices.dtype == paddle.int32
paddle.disable_static()
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_sparse_elementwise_op.py
浏览文件 @ 10225d22
......@@ -163,6 +163,32 @@ class TestSparseElementWiseAPI(unittest.TestCase):
np.testing.assert_allclose(sp_b.grad.values().numpy(),
values2.grad.numpy())
def test_add_bias(self):
indices_data = [[0, 1], [0, 3]]
values_data = [[1.0, 1.0], [2.0, 2.0]]
shape = [2, 4, 2]
sp_a = sparse.sparse_coo_tensor(indices_data,
values_data,
shape,
stop_gradient=False)
bias_values = [1.0, 2.0]
values1 = paddle.to_tensor(values_data, stop_gradient=False)
values2 = paddle.to_tensor(bias_values, stop_gradient=False)
values3 = paddle.to_tensor(bias_values, stop_gradient=False)
#c.values() = a.values() + b
sp_c = sparse.add(sp_a, values2)
sp_c.backward()
ref_c = values1 + values3
ref_c.backward()
np.testing.assert_allclose(sp_c.values().numpy(), ref_c.numpy())
np.testing.assert_allclose(sp_a.grad.values().numpy(),
values1.grad.numpy())
np.testing.assert_allclose(values2.grad.numpy(), values3.grad.numpy())
if __name__ == "__main__":
paddle.device.set_device('cpu')
......
python/paddle/incubate/sparse/binary.py
浏览文件 @ 10225d22
......@@ -14,6 +14,9 @@
from paddle import _C_ops, _legacy_C_ops
from paddle.fluid.framework import dygraph_only, core
from paddle import in_dynamic_mode
from paddle.fluid.layer_helper import LayerHelper
from .unary import cast
__all__ = []
......@@ -253,8 +256,20 @@ def add(x, y, name=None):
"""
if y.dtype != x.dtype:
y = _C_ops.sparse_cast(y, None, x.dtype)
return _C_ops.sparse_add(x, y)
y = cast(y, None, x.dtype)
if in_dynamic_mode():
return _C_ops.sparse_add(x, y)
else:
op_type = 'sparse_add'
inputs = {'x': x, 'y': y}
helper = LayerHelper(op_type)
out = helper.create_sparse_variable_for_type_inference(x.dtype)
helper.append_op(type=op_type,
inputs=inputs,
outputs={'out': out},
attrs={})
return out
@dygraph_only
......
python/paddle/incubate/sparse/creation.py
浏览文件 @ 10225d22
......@@ -18,6 +18,8 @@ from paddle.fluid.framework import core, dygraph_only
from paddle.fluid.framework import _current_expected_place, _get_paddle_place
from paddle.tensor import to_tensor, max
from paddle.fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype
from paddle import in_dynamic_mode
from paddle.fluid.layer_helper import LayerHelper
import numpy as np
......@@ -64,7 +66,6 @@ def _check_indices_dtype(dtype):
)
@dygraph_only
def sparse_coo_tensor(indices,
values,
shape=None,
......@@ -120,53 +121,68 @@ def sparse_coo_tensor(indices,
# values=[1., 2., 3.])
"""
place = _get_place(place)
if in_dynamic_mode():
place = _get_place(place)
if not isinstance(indices, core.eager.Tensor):
indices = to_tensor(indices,
dtype=None,
place=place,
stop_gradient=True)
if not isinstance(values, core.eager.Tensor):
values = to_tensor(values, dtype, place, stop_gradient)
if len(indices.shape) != 2:
raise ValueError("'indices' must be 2-D.")
if not isinstance(indices, core.eager.Tensor):
indices = to_tensor(indices,
dtype=None,
place=place,
stop_gradient=True)
if not isinstance(values, core.eager.Tensor):
values = to_tensor(values, dtype, place, stop_gradient)
if len(indices.shape) != 2:
raise ValueError("'indices' must be 2-D.")
nnz = indices.shape[1]
sparse_dim = indices.shape[0]
nnz = indices.shape[1]
sparse_dim = indices.shape[0]
_check_indices_dtype(indices.dtype)
_check_indices_dtype(indices.dtype)
if nnz != values.shape[0]:
raise ValueError(
"the indices and values must have same number of non-zero, but get {} and {}"
.format(nnz, values.shape[0]))
if nnz != values.shape[0]:
raise ValueError(
"the indices and values must have same number of non-zero, but get {} and {}"
.format(nnz, values.shape[0]))
dense_dim = len(values.shape) - 1
dense_dim = len(values.shape) - 1
if not indices.place._equals(place):
indices = indices._copy_to(place, False)
if not indices.place._equals(place):
indices = indices._copy_to(place, False)
if not values.place._equals(place):
values = values._copy_to(place, False)
values = _handle_dtype(values, dtype)
values.stop_gradient = stop_gradient
if not values.place._equals(place):
values = values._copy_to(place, False)
values = _handle_dtype(values, dtype)
values.stop_gradient = stop_gradient
min_shape = _infer_dense_shape(indices, values)
min_shape = _infer_dense_shape(indices, values)
if shape is None:
shape = min_shape
else:
if shape < min_shape:
raise ValueError(
"the minimun shape required is {}, but get {}".format(
min_shape, shape))
if len(shape) != sparse_dim + dense_dim:
raise ValueError(
"the number of dimensions(len(shape) must be sparse_dim({}) + dense_dim({}), but get {}"
.format(sparse_dim, dense_dim, len(shape)))
if shape is None:
shape = min_shape
else:
if shape < min_shape:
raise ValueError(
"the minimun shape required is {}, but get {}".format(
min_shape, shape))
if len(shape) != sparse_dim + dense_dim:
raise ValueError(
"the number of dimensions(len(shape) must be sparse_dim({}) + dense_dim({}), but get {}"
.format(sparse_dim, dense_dim, len(shape)))
return _C_ops.sparse_sparse_coo_tensor(values, indices, shape)
return _C_ops.sparse_sparse_coo_tensor(values, indices, shape)
else:
op_type = 'sparse_sparse_coo_tensor'
inputs = {'values': values, 'indices': indices}
if shape[0] is None:
shape[0] = -1
attrs = {'dense_shape': shape}
helper = LayerHelper(op_type)
out = helper.create_sparse_variable_for_type_inference(dtype)
helper.append_op(type=op_type,
inputs=inputs,
outputs={'out': out},
attrs=attrs)
return out
#TODO: need to support shape is None
......
python/paddle/incubate/sparse/nn/functional/activation.py
浏览文件 @ 10225d22
......@@ -16,9 +16,10 @@ __all__ = []
from paddle import _C_ops, _legacy_C_ops
from paddle.fluid.framework import dygraph_only
from paddle import in_dynamic_mode
from paddle.fluid.layer_helper import LayerHelper
@dygraph_only
def relu(x, name=None):
"""
sparse relu activation, requiring x to be a SparseCooTensor or SparseCsrTensor.
......@@ -45,7 +46,17 @@ def relu(x, name=None):
out = paddle.incubate.sparse.nn.functional.relu(sparse_x)
# [0., 0., 1.]
"""
return _C_ops.sparse_relu(x)
if in_dynamic_mode():
return _C_ops.sparse_relu(x)
else:
op_type = 'sparse_relu'
helper = LayerHelper(op_type)
out = helper.create_sparse_variable_for_type_inference(x.dtype)
helper.append_op(type=op_type,
inputs={'x': x},
outputs={'out': out},
attrs={})
return out
@dygraph_only
......
python/paddle/incubate/sparse/nn/functional/conv.py
浏览文件 @ 10225d22
......@@ -18,7 +18,9 @@ from paddle import _C_ops, _legacy_C_ops, in_dynamic_mode
from paddle.fluid.layers.utils import convert_to_list
from paddle.fluid.layers.nn import elementwise_add
from ...creation import sparse_coo_tensor
from ...binary import add
from paddle.nn.functional.conv import _update_padding_nd
from paddle.fluid.layer_helper import LayerHelper
def _conv3d(x,
......@@ -32,7 +34,6 @@ def _conv3d(x,
key=None,
data_format="NDHWC",
name=None):
assert in_dynamic_mode(), "Currently, only support dynamic mode"
assert groups == 1, "Currently, only support groups=1"
dims = 3
......@@ -61,20 +62,41 @@ def _conv3d(x,
padding, padding_algorithm = _update_padding_nd(padding, channel_last, dims)
stride = convert_to_list(stride, dims, 'stride')
dilation = convert_to_list(dilation, dims, 'dilation')
op_type = "conv3d"
pre_bias = _C_ops.sparse_conv3d(x, weight, padding, dilation, stride,
groups, subm,
key if key is not None else "")
if bias is not None:
values = pre_bias.values()
add_bias = elementwise_add(values, bias, axis=1)
return sparse_coo_tensor(pre_bias.indices(),
add_bias,
shape=pre_bias.shape,
stop_gradient=pre_bias.stop_gradient)
if in_dynamic_mode():
pre_bias = _C_ops.sparse_conv3d(x, weight, padding, dilation, stride,
groups, subm,
key if key is not None else "")
if bias is not None:
return add(pre_bias, bias)
else:
return pre_bias
else:
return pre_bias
inputs = {'x': x, 'kernel': weight}
attrs = {
'paddings': padding,
'dilations': dilation,
'strides': stride,
'groups': groups,
'subm': subm,
'key': key
}
op_type = 'sparse_conv3d'
helper = LayerHelper(op_type, **locals())
rulebook = helper.create_variable_for_type_inference(dtype='int32',
stop_gradient=True)
counter = helper.create_variable_for_type_inference(dtype='int32',
stop_gradient=True)
pre_bias = helper.create_sparse_variable_for_type_inference(x.dtype)
outputs = {"out": pre_bias, "rulebook": rulebook, "counter": counter}
helper.append_op(type=op_type,
inputs=inputs,
outputs=outputs,
attrs=attrs)
if bias is not None:
return add(pre_bias, bias)
else:
return pre_bias
def conv3d(x,
......
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