提交 ca23d861 编写于 作者: Q qijun

merge baidu/develop

......@@ -15,7 +15,6 @@ if(Boost_FOUND)
add_subdirectory(memory)
add_subdirectory(platform)
add_subdirectory(framework)
add_subdirectory(operators)
add_subdirectory(pybind)
endif()
......
......@@ -266,29 +266,6 @@ HOSTDEVICE inline bool contained(const Dim<1>& idx, const Dim<1>& size) {
return ((0 <= idx.head) && (idx.head < size.head));
}
/**
* \brief Check if a size and a stride create a Fortran order contiguous
* block of memory.
*/
template <int i>
HOST bool contiguous(const Dim<i>& size, const Dim<i>& stride, int mul = 1) {
if (product(size) == 0) return true;
int contiguous_stride = get<0>(size) == 1 ? 0 : mul;
return (get<0>(stride) == contiguous_stride &&
contiguous(size.tail, stride.tail, mul * get<0>(size)));
}
///\cond HIDDEN
// Base case of contiguous, check the nth stride is the size of
// the prefix multiply of n-1 dims.
template <>
inline bool contiguous(const Dim<1>& size, const Dim<1>& stride, int mul) {
if (get<0>(size) == 0) return true;
int contiguous_stride = get<0>(size) == 1 ? 0 : mul;
return get<0>(stride) == contiguous_stride;
}
///\endcond
/**
* \brief Compute exclusive prefix-multiply of a Dim.
*/
......@@ -306,31 +283,6 @@ HOSTDEVICE inline Dim<1> ex_prefix_mul(const Dim<1>& src, int mul) {
}
///\endcond
/**
* \brief Calculate strides of a contiguous array of the given size
*
* Sets the stride for any dimension with an extent of 1 to 0.
* \param size Dim object containing the size of the array.
* \param base The base stride to use.
* \return Dim object the same size as \p size with the strides.
*/
template <int i>
HOSTDEVICE Dim<i> contiguous_strides(const Dim<i>& size, int base = 1) {
int stride = size.head == 1 ? 0 : base;
return Dim<i>(stride, contiguous_strides(size.tail, base * size.head));
}
///\cond HIDDEN
// Base case of contiguous_strides
template <>
HOSTDEVICE inline Dim<1> contiguous_strides(const Dim<1>& size, int base) {
int stride = size.head == 1 ? 0 : base;
return Dim<1>(stride);
}
///\endcond
/**
* Add two dimensions together
*/
......
......@@ -58,24 +58,6 @@ TEST(Dim, Equality) {
EXPECT_EQ(paddle::framework::get<1>(c), 3);
EXPECT_EQ(paddle::framework::get<2>(c), 12);
// contiguous_strides
c = paddle::framework::contiguous_strides(paddle::framework::Dim<3>(10, 1, 10));
EXPECT_EQ(paddle::framework::get<0>(c), 1);
EXPECT_EQ(paddle::framework::get<1>(c), 0);
EXPECT_EQ(paddle::framework::get<2>(c), 10);
c = paddle::framework::contiguous_strides(paddle::framework::Dim<3>(10, 10, 1));
EXPECT_EQ(paddle::framework::get<0>(c), 1);
EXPECT_EQ(paddle::framework::get<1>(c), 10);
EXPECT_EQ(paddle::framework::get<2>(c), 0);
c = paddle::framework::contiguous_strides(paddle::framework::Dim<3>(1, 10, 10));
EXPECT_EQ(paddle::framework::get<0>(c), 0);
EXPECT_EQ(paddle::framework::get<1>(c), 1);
EXPECT_EQ(paddle::framework::get<2>(c), 10);
c = paddle::framework::contiguous_strides(paddle::framework::Dim<3>(2, 3, 4));
EXPECT_EQ(paddle::framework::get<0>(c), 1);
EXPECT_EQ(paddle::framework::get<1>(c), 2);
EXPECT_EQ(paddle::framework::get<2>(c), 6);
// generate from an index
auto size = paddle::framework::make_dim(4, 5, 2);
c = paddle::framework::Dim<3>(14, size);
......@@ -101,16 +83,6 @@ TEST(Dim, Bool) {
EXPECT_TRUE(a == a);
EXPECT_FALSE(a == b);
EXPECT_TRUE(a == c);
// contiguous check
int x = 4, y = 5, z = 2;
paddle::framework::Dim<3> sizef(x, y, z);
paddle::framework::Dim<3> stridea(1, x, x*y);
paddle::framework::Dim<3> strideb(2, 2*x, 2*x*y);
paddle::framework::Dim<3> stridec(1, x, 2*x*y);
EXPECT_TRUE(paddle::framework::contiguous(sizef, stridea));
EXPECT_FALSE(paddle::framework::contiguous(sizef, strideb));
EXPECT_FALSE(paddle::framework::contiguous(sizef, stridec));
}
TEST(Dim, Print) {
......
#include "paddle/framework/op_registry.h"
#include <gtest/gtest.h>
#include "paddle/framework/operator.h"
#include "paddle/operators/demo_op.h"
using namespace paddle::framework;
namespace paddle {
namespace framework {
class CosineOp : public OperatorWithKernel {
class CosineOp : public OperatorBase {
public:
void Run(const OpRunContext* context) const override {
printf("%s\n", DebugString().c_str());
}
void Run(const std::shared_ptr<Scope>& scope,
const platform::DeviceContext& dev_ctx) const override {}
void InferShape(const std::shared_ptr<Scope>& scope) const override {}
};
class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
......@@ -30,12 +28,13 @@ class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
REGISTER_OP(CosineOp, CosineOpProtoAndCheckerMaker, cos_sim)
class MyTestOp : public OperatorWithKernel {
class MyTestOp : public OperatorBase {
public:
void InferShape(const std::shared_ptr<Scope>& scope) const override {}
void Run(const std::shared_ptr<Scope>& scope,
const platform::DeviceContext& dev_ctx) const override {}
public:
void Run(const OpRunContext* ctx) const override {
printf("%s\n", DebugString().c_str());
printf("test_attr = %d\n", ctx->op_->GetAttr<int>("test_attr"));
}
};
class MyTestOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
......@@ -73,8 +72,8 @@ TEST(OpRegistry, CreateOp) {
paddle::framework::OperatorBase* op =
paddle::framework::OpRegistry::CreateOp(op_desc);
auto scope = std::make_shared<Scope>();
auto dev_ctx = DeviceContext();
op->Run(scope, &dev_ctx);
paddle::platform::CPUDeviceContext dev_ctx;
op->Run(scope, dev_ctx);
float scale_get = op->GetAttr<float>("scale");
ASSERT_EQ(scale_get, scale);
}
......@@ -116,8 +115,8 @@ TEST(OpRegistry, DefaultValue) {
paddle::framework::OperatorBase* op =
paddle::framework::OpRegistry::CreateOp(op_desc);
auto scope = std::make_shared<Scope>();
auto dev_ctx = DeviceContext();
op->Run(scope, &dev_ctx);
paddle::platform::CPUDeviceContext dev_ctx;
op->Run(scope, dev_ctx);
ASSERT_EQ(op->GetAttr<float>("scale"), 1.0);
}
......@@ -169,9 +168,9 @@ TEST(OpRegistry, CustomChecker) {
attr->set_i(4);
paddle::framework::OperatorBase* op =
paddle::framework::OpRegistry::CreateOp(op_desc);
auto dev_ctx = DeviceContext();
paddle::platform::CPUDeviceContext dev_ctx;
auto scope = std::make_shared<Scope>();
op->Run(scope, &dev_ctx);
op->Run(scope, dev_ctx);
int test_attr = op->GetAttr<int>("test_attr");
ASSERT_EQ(test_attr, 4);
}
......
......@@ -39,13 +39,5 @@ std::string OperatorBase::DebugString() const {
return ss.str();
}
const Variable* OpRunContext::Input(int index) const {
return scope_->GetVariable(op_->inputs_[index]);
}
Variable* OpRunContext::Output(int index) const {
return scope_->GetVariable(op_->outputs_[index]);
}
} // namespace framework
} // namespace paddle
\ No newline at end of file
......@@ -14,44 +14,22 @@ limitations under the License. */
#pragma once
#include <paddle/framework/attr_checker.h>
#include <paddle/framework/op_desc.pb.h>
#include <paddle/framework/scope.h>
#include <paddle/platform/device_context.h>
#include <paddle/platform/place.h>
#include <paddle/utils/Error.h>
#include <boost/variant.hpp>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/framework/attr_checker.h"
#include "paddle/framework/op_desc.pb.h"
#include "paddle/framework/scope.h"
#include "paddle/utils/Error.h"
namespace paddle {
namespace framework {
class OperatorBase;
class DeviceContext {};
/**
* OpRunContext is the only parameter of Operator's Run function.
* Run will get input/output variables, state such as momentum and
* device resource such as CUDA stream, cublas handle, etc. from
* OpRunContext. User should construct it before run the Operator.
*/
class OpRunContext {
public:
OpRunContext(const OperatorBase* op, const std::shared_ptr<Scope> scope,
const DeviceContext* device_context)
: op_(op), scope_(scope), device_context_(device_context) {}
const Variable* Input(int index) const;
Variable* Output(int index) const;
public:
const OperatorBase* op_;
const std::shared_ptr<Scope> scope_;
const DeviceContext* device_context_;
};
/**
* OperatorBase has the basic element that Net will call to do computation.
* Only CreateOperator from OpRegistry will new Operator directly. User
......@@ -77,7 +55,10 @@ class OperatorBase {
/// Net will call this function to Run an op.
virtual void Run(const std::shared_ptr<Scope>& scope,
const DeviceContext* dev_ctx) const = 0;
const platform::DeviceContext& dev_ctx) const = 0;
protected:
std::string Type() const { return desc_.type(); }
public:
OpDesc desc_;
......@@ -86,22 +67,84 @@ class OperatorBase {
AttributeMap attrs_;
};
class OpKernel {
public:
/**
* KernelContext is the only parameter of Kernel Run function.
* Run will get input/output variables, state such as momentum and
* device resource such as CUDA stream, cublas handle, etc. from
* KernelContext. User should construct it before run the Operator.
*/
class KernelContext {
public:
KernelContext(const OperatorBase* op, const std::shared_ptr<Scope>& scope,
const platform::DeviceContext& device_context)
: op_(*op), scope_(scope), device_context_(device_context) {}
const Variable* Input(int index) const {
return scope_->GetVariable(op_.inputs_[index]);
}
Variable* Output(int index) const {
return scope_->GetVariable(op_.outputs_[index]);
}
const OperatorBase& op_;
const std::shared_ptr<Scope>& scope_;
const platform::DeviceContext& device_context_;
};
virtual void Compute(const KernelContext& context) const = 0;
virtual ~OpKernel() {}
};
class OperatorWithKernel : public OperatorBase {
public:
virtual ~OperatorWithKernel() {}
struct OpKernelKey {
platform::Place place_;
OpKernelKey() = default;
OpKernelKey(const platform::DeviceContext& dev_ctx) {
place_ = dev_ctx.GetPlace();
}
virtual void InferShape(const std::shared_ptr<Scope>& scope) const {}
bool operator==(const OpKernelKey& o) const { return place_ == o.place_; }
};
struct OpKernelHash {
std::hash<bool> hash_;
size_t operator()(const OpKernelKey& key) const {
return hash_(platform::is_gpu_place(key.place_));
}
};
using OpKernelMap =
std::unordered_map<OpKernelKey, std::unique_ptr<OpKernel>, OpKernelHash>;
void Run(const std::shared_ptr<Scope>& scope,
const DeviceContext* dev_ctx) const {
OpRunContext op_ctx(this, scope, dev_ctx);
Run(&op_ctx);
const platform::DeviceContext& dev_ctx) const final {
auto& opKernel = AllOpKernels().at(Type()).at(OpKernelKey(dev_ctx));
opKernel->Compute(OpKernel::KernelContext(this, scope, dev_ctx));
}
/// when implement an Op, your should implement this function.
/// this function should be moved to OpKernel later
virtual void Run(const OpRunContext* context) const = 0;
static std::unordered_map<std::string /* op_type */, OpKernelMap>&
AllOpKernels() {
static std::unordered_map<std::string, OpKernelMap> g_all_op_kernels;
return g_all_op_kernels;
};
};
} // namespace framework
} // namespace paddle
#define REGISTER_OP_KERNEL(type, PlaceType, KernelType) \
struct __op_kernel_register__##type##__ { \
__op_kernel_register__##type##__() { \
::paddle::framework::OperatorWithKernel::OpKernelKey key; \
key.place_ = PlaceType(); \
::paddle::framework::OperatorWithKernel::AllOpKernels()[#type][key] \
.reset(new KernelType()); \
} \
}; \
static __op_kernel_register__##type##__ __reg_kernel_##type##__
......@@ -19,17 +19,15 @@ limitations under the License. */
namespace paddle {
namespace framework {
class OperatorTest : public OperatorWithKernel {
class OperatorTest : public OperatorBase {
public:
void Run(const OpRunContext* ctx) const override {
float scale = ctx->op_->GetAttr<float>("scale");
PADDLE_ENFORCE(ctx->Input(0) == nullptr, "Input(0) should not initialized");
PADDLE_ENFORCE(ctx->Output(0) == nullptr,
"Output(1) should not initialized");
auto output1 = ctx->scope_->CreateVariable("output1");
PADDLE_ENFORCE(output1 != nullptr, "should create output1 from scope");
printf("get attr %s = %f\n", "scale", scale);
printf("%s\n", DebugString().c_str());
void InferShape(const std::shared_ptr<Scope>& scope) const override {}
void Run(const std::shared_ptr<Scope>& scope,
const platform::DeviceContext& dev_ctx) const override {
float scale = GetAttr<float>("scale");
ASSERT_NEAR(scale, 3.14, 1e-5);
ASSERT_EQ(scope->GetVariable(inputs_[0]), nullptr);
ASSERT_NE(scope->GetVariable(outputs_[0]), nullptr);
}
};
......@@ -49,31 +47,26 @@ class OperatorTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
REGISTER_OP(OperatorTest, OperatorTestProtoAndCheckerMaker, test_operator)
TEST(OperatorBase, DebugString) {
TEST(OperatorBase, all) {
OpDesc op_desc;
op_desc.set_type("test_operator");
std::vector<std::string> inputs = {"IN1", "IN2"};
for (auto& input : inputs) {
op_desc.add_inputs(input);
}
std::vector<std::string> outputs = {"OUT1", "OUT2"};
for (auto& output : outputs) {
op_desc.add_outputs(output);
}
*op_desc.mutable_inputs()->Add() = "IN1";
*op_desc.mutable_outputs()->Add() = "OUT1";
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
float scale = 3.14;
attr->set_f(scale);
DeviceContext device_context;
platform::CPUDeviceContext device_context;
auto scope = std::make_shared<Scope>();
OperatorBase* op = paddle::framework::OpRegistry::CreateOp(op_desc);
ASSERT_EQ(op->inputs_, inputs);
ASSERT_EQ(op->outputs_, outputs);
ASSERT_EQ(op->GetAttr<float>("scale"), scale);
op->Run(scope, &device_context);
scope->CreateVariable("OUT1");
op->Run(scope, device_context);
std::cout << op->DebugString() << std::endl;
delete op;
}
} // namespace framework
......
......@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <cstdint>
#include <memory>
#include <type_traits>
#include "paddle/framework/ddim.h"
......@@ -26,31 +27,65 @@ namespace framework {
class Tensor {
public:
Tensor() : offset_(0) {}
explicit Tensor(const DDim& dims) : dims_(dims), offset_(0) {}
template <typename T>
const T* data() const {
PADDLE_ENFORCE(holder_ != nullptr,
"Tensor::data must be called after Tensor::mutable_data.");
return static_cast<const T*>(holder_->Ptr());
PADDLE_ENFORCE(
holder_ != nullptr,
"Tenosr has not been initialized. Call Tensor::mutable_data first.");
return reinterpret_cast<const T*>(
reinterpret_cast<uintptr_t>(holder_->Ptr()) + offset_);
}
template <typename T, // must be POD types
typename std::enable_if<std::is_pod<T>::value>::type* = nullptr>
T* mutable_data(DDim dims, paddle::platform::Place place) {
dims_ = dims;
if (holder_ == nullptr ||
!(holder_->Place() ==
place) /* some versions of boost::variant don't have operator!= */
|| holder_->Size() < product(dims) * sizeof(T)) {
|| holder_->Size() < product(dims) * sizeof(T) + offset_) {
holder_.reset(new PlaceholderImpl<T>(place, product(dims) * sizeof(T)));
offset_ = 0;
}
return static_cast<T*>(holder_->Ptr());
return reinterpret_cast<T*>(reinterpret_cast<uintptr_t>(holder_->Ptr()) +
offset_);
}
template <typename T, // must be POD types
typename std::enable_if<std::is_pod<T>::value>::type* = nullptr>
T* mutable_data(DDim dims) {
return mutable_data<T>(dims, paddle::platform::get_place());
void ShareDataFrom(const Tensor& src) {
PADDLE_ENFORCE(src.holder_ != nullptr,
"Can not share data from an uninitialized tensor.");
holder_ = src.holder_;
dims_ = src.dims_;
offset_ = src.offset_;
}
Tensor Slice(const int& begin_idx, const int& end_idx) const {
PADDLE_ENFORCE(holder_ != nullptr,
"The sliced tenosr has not been initialized.");
PADDLE_ENFORCE(begin_idx >= 0 && end_idx <= dims_[0],
"Slice index is less than zero or out of bound.");
PADDLE_ENFORCE(begin_idx < end_idx,
"Begin index must be less than end index.");
PADDLE_ENFORCE(dims_[0] != 1, "Can not slice a tensor with dims_[0] = 1.");
std::vector<int> d = vectorize(dims_);
int base = 1;
for (size_t i = 1; i < d.size(); ++i) {
base *= d[i];
}
Tensor dst;
dst.holder_ = holder_;
dst.dims_ = dims_;
dst.dims_[0] = end_idx - begin_idx;
dst.offset_ = offset_ + begin_idx * base * holder_->TypeSize();
return dst;
}
DDim dims() const { return dims_; }
private:
// Placeholder hides type T, so it doesn't appear as a template
// parameter of Variable.
......@@ -59,6 +94,7 @@ class Tensor {
virtual void* Ptr() const = 0;
virtual paddle::platform::Place Place() const = 0;
virtual size_t Size() const = 0;
virtual size_t TypeSize() const = 0;
};
template <typename T>
......@@ -85,6 +121,7 @@ class Tensor {
virtual void* Ptr() const { return static_cast<void*>(ptr_.get()); }
virtual size_t Size() const { return size_; }
virtual paddle::platform::Place Place() const { return place_; }
virtual size_t TypeSize() const { return sizeof(T); }
std::unique_ptr<T, Deleter> ptr_;
paddle::platform::Place place_; // record the place of ptr_.
......@@ -92,6 +129,8 @@ class Tensor {
};
std::shared_ptr<Placeholder> holder_; // holds the memory block if allocated.
DDim dims_;
size_t offset_; // marks the begin of tensor data area.
};
} // namespace framework
......
......@@ -15,15 +15,27 @@
#include <gtest/gtest.h>
#include <string>
TEST(Tensor, ASSERT) {
paddle::framework::Tensor cpu_tensor;
TEST(Tensor, Dims) {
using namespace paddle::framework;
using namespace paddle::platform;
Tensor tt(make_ddim({2, 3, 4}));
DDim dims = tt.dims();
ASSERT_EQ(arity(dims), 3);
for (int i = 0; i < 3; ++i) {
EXPECT_EQ(i + 2, dims[i]);
}
}
TEST(Tensor, DataAssert) {
paddle::framework::Tensor src_tensor;
bool caught = false;
try {
const double* p __attribute__((unused)) = cpu_tensor.data<double>();
src_tensor.data<double>();
} catch (paddle::framework::EnforceNotMet err) {
caught = true;
std::string msg = "Tensor::data must be called after Tensor::mutable_data.";
std::string msg =
"Tenosr has not been initialized. Call Tensor::mutable_data first.";
const char* what = err.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
......@@ -32,54 +44,138 @@ TEST(Tensor, ASSERT) {
ASSERT_TRUE(caught);
}
/* mutable_data() is not tested at present
/* following tests are not available at present
because Memory::Alloc() and Memory::Free() have not been ready.
TEST(Tensor, MutableData) {
using namespace paddle::framework;
using namespace paddle::platform;
{
Tensor cpu_tensor;
Tensor src_tensor;
float* p1 = nullptr;
float* p2 = nullptr;
// initialization
p1 = cpu_tensor.mutable_data<float>(make_ddim({1, 2, 3}), CPUPlace());
p1 = src_tensor.mutable_data<float>(make_ddim({1, 2, 3}), CPUPlace());
EXPECT_NE(p1, nullptr);
// set cpu_tensor a new dim with large size
// set src_tensor a new dim with large size
// momery is supposed to be re-allocated
p2 = cpu_tensor.mutable_data<float>(make_ddim({3, 4}));
p2 = src_tensor.mutable_data<float>(make_ddim({3, 4}), CPUPlace());
EXPECT_NE(p2, nullptr);
EXPECT_NE(p1, p2);
// set cpu_tensor a new dim with same size
// set src_tensor a new dim with same size
// momery block is supposed to be unchanged
p1 = cpu_tensor.mutable_data<float>(make_ddim({2, 2, 3}));
p1 = src_tensor.mutable_data<float>(make_ddim({2, 2, 3}), CPUPlace());
EXPECT_EQ(p1, p2);
// set cpu_tensor a new dim with smaller size
// set src_tensor a new dim with smaller size
// momery block is supposed to be unchanged
p2 = cpu_tensor.mutable_data<float>(make_ddim({2, 2}));
p2 = src_tensor.mutable_data<float>(make_ddim({2, 2}), CPUPlace());
EXPECT_EQ(p1, p2);
}
{
Tensor gpu_tensor;
Tensor src_tensor;
float* p1 = nullptr;
float* p2 = nullptr;
// initialization
p1 = gpu_tensor.mutable_data<float>(make_ddim({1, 2, 3}), GPUPlace());
p1 = src_tensor.mutable_data<float>(make_ddim({1, 2, 3}), GPUPlace());
EXPECT_NE(p1, nullptr);
// set gpu_tensor a new dim with large size
// set src_tensor a new dim with large size
// momery is supposed to be re-allocated
p2 = gpu_tensor.mutable_data<float>(make_ddim({3, 4}));
p2 = src_tensor.mutable_data<float>(make_ddim({3, 4}), GPUPlace());
EXPECT_NE(p2, nullptr);
EXPECT_NE(p1, p2);
// set gpu_tensor a new dim with same size
// set src_tensor a new dim with same size
// momery block is supposed to be unchanged
p1 = gpu_tensor.mutable_data<float>(make_ddim({2, 2, 3}));
p1 = src_tensor.mutable_data<float>(make_ddim({2, 2, 3}), GPUPlace());
EXPECT_EQ(p1, p2);
// set gpu_tensor a new dim with smaller size
// set src_tensor a new dim with smaller size
// momery block is supposed to be unchanged
p2 = gpu_tensor.mutable_data<float>(make_ddim({2, 2}));
p2 = src_tensor.mutable_data<float>(make_ddim({2, 2}), GPUPlace());
EXPECT_EQ(p1, p2);
}
}
TEST(Tensor, ShareDataFrom) {
using namespace paddle::framework;
using namespace paddle::platform;
{
Tensor src_tensor;
Tensor dst_tensor;
// Try to share data form uninitialized tensor
bool caught = false;
try {
dst_tensor.ShareDataFrom(src_tensor);
} catch (EnforceNotMet err) {
caught = true;
std::string msg = "Can not share data from an uninitialized tensor.";
const char* what = err.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
}
}
ASSERT_TRUE(caught);
src_tensor.mutable_data<int>(make_ddim({2, 3, 4}), CPUPlace());
dst_tensor.ShareDataFrom(src_tensor);
ASSERT_EQ(src_tensor.data<int>(), dst_tensor.data<int>());
}
{
Tensor src_tensor;
Tensor dst_tensor;
src_tensor.mutable_data<int>(make_ddim({2, 3, 4}), GPUPlace());
dst_tensor.ShareDataFrom(src_tensor);
ASSERT_EQ(src_tensor.data<int>(), dst_tensor.data<int>());
}
}
TEST(Tensor, Slice) {
using namespace paddle::framework;
using namespace paddle::platform;
{
Tensor src_tensor;
src_tensor.mutable_data<int>(make_ddim({5, 3, 4}), CPUPlace());
Tensor slice_tensor = src_tensor.Slice(1, 3);
DDim slice_dims = slice_tensor.dims();
ASSERT_EQ(arity(slice_dims), 3);
EXPECT_EQ(slice_dims[0], 2);
EXPECT_EQ(slice_dims[1], 3);
EXPECT_EQ(slice_dims[2], 4);
uintptr_t src_data_address =
reinterpret_cast<uintptr_t>(src_tensor.data<int>());
uintptr_t src_mutable_data_address = reinterpret_cast<uintptr_t>(
src_tensor.mutable_data<int>(src_tensor.dims(), CPUPlace()));
uintptr_t slice_data_address =
reinterpret_cast<uintptr_t>(slice_tensor.data<int>());
uintptr_t slice_mutable_data_address = reinterpret_cast<uintptr_t>(
slice_tensor.mutable_data<int>(slice_tensor.dims(), CPUPlace()));
EXPECT_EQ(src_data_address, src_mutable_data_address);
EXPECT_EQ(slice_data_address, slice_mutable_data_address);
EXPECT_EQ(src_data_address + 3 * 4 * 1 * sizeof(int), slice_data_address);
}
{
Tensor src_tensor;
src_tensor.mutable_data<double>(make_ddim({6, 9}), GPUPlace());
Tensor slice_tensor = src_tensor.Slice(2, 6);
DDim slice_dims = slice_tensor.dims();
ASSERT_EQ(arity(slice_dims), 2);
EXPECT_EQ(slice_dims[0], 4);
EXPECT_EQ(slice_dims[1], 9);
uintptr_t src_data_address =
reinterpret_cast<uintptr_t>(src_tensor.data<double>());
uintptr_t src_mutable_data_address = reinterpret_cast<uintptr_t>(
src_tensor.mutable_data<double>(src_tensor.dims(), GPUPlace()));
uintptr_t slice_data_address =
reinterpret_cast<uintptr_t>(slice_tensor.data<double>());
uintptr_t slice_mutable_data_address = reinterpret_cast<uintptr_t>(
slice_tensor.mutable_data<double>(slice_tensor.dims(), GPUPlace()));
EXPECT_EQ(src_data_address, src_mutable_data_address);
EXPECT_EQ(slice_data_address, slice_mutable_data_address);
EXPECT_EQ(src_data_address + 9 * 2 * sizeof(double), slice_data_address);
}
}
*/
\ No newline at end of file
---
Language: Cpp
BasedOnStyle: Google
Standard: Cpp11
...
#pragma once
#include "paddle/framework/op_registry.h"
using namespace paddle::framework;
namespace paddle {
namespace operators {
class CosineOp : public OperatorWithKernel {
public:
void Run(const OpRunContext *context) const override {
printf("%s\n", DebugString().c_str());
}
};
class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
public:
CosineOpProtoAndCheckerMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("input", "input of cosine op");
AddOutput("output", "output of cosine op");
AddAttr<float>("scale", "scale of cosine op")
.SetDefault(1.0)
.LargerThan(0.0);
AddType("cos");
AddComment("This is cos op");
}
};
REGISTER_OP(CosineOp, CosineOpProtoAndCheckerMaker, cos_sim)
class MyTestOp : public OperatorWithKernel {
public:
void Run(const OpRunContext *context) const override {
printf("%s\n", DebugString().c_str());
}
};
class MyTestOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
public:
MyTestOpProtoAndCheckerMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("input", "input of cosine op");
AddOutput("output", "output of cosine op");
auto my_checker = [](int i) {
PADDLE_ENFORCE(i % 2 == 0, "'test_attr' must be even!");
};
AddAttr<int>("test_attr", "a simple test attribute")
.AddCustomChecker(my_checker);
AddType("my_test_op");
AddComment("This is my_test op");
}
};
REGISTER_OP(MyTestOp, MyTestOpProtoAndCheckerMaker, my_test_op)
} // namespace operators
} // namespace operators
......@@ -23,15 +23,13 @@ limitations under the License. */
#include "paddle/platform/place.h"
#include "unsupported/Eigen/CXX11/Tensor"
using DEVICE_GPU = Eigen::GpuDevice;
namespace paddle {
namespace platform {
class CUDADeviceContext;
template <>
DEVICE_GPU DeviceContext::get_eigen_device<DEVICE_GPU>() {
Eigen::GpuDevice DeviceContext::get_eigen_device<Eigen::GpuDevice>() {
return static_cast<CUDADeviceContext*>(this)->eigen_handle();
}
......@@ -59,6 +57,11 @@ class CUDADeviceContext : public DeviceContext {
eigen_device_ = new Eigen::GpuDevice(eigen_stream_);
}
Place GetPlace() const override {
Place retv = GPUPlace();
return retv;
}
void Wait() {
paddle::platform::throw_on_error(cudaStreamSynchronize(stream_),
"cudaStreamSynchronize failed");
......
......@@ -13,10 +13,10 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/enforce.h"
#include "paddle/platform/place.h"
#include "unsupported/Eigen/CXX11/Tensor"
using DEVICE_CPU = Eigen::DefaultDevice;
namespace paddle {
namespace platform {
......@@ -28,10 +28,12 @@ class DeviceContext {
template <typename DeviceType>
DeviceType get_eigen_device();
virtual Place GetPlace() const = 0;
};
template <>
DEVICE_CPU DeviceContext::get_eigen_device<DEVICE_CPU>() {
Eigen::DefaultDevice DeviceContext::get_eigen_device<Eigen::DefaultDevice>() {
return static_cast<CPUDeviceContext*>(this)->eigen_handle();
}
......@@ -44,9 +46,13 @@ class CPUDeviceContext : public DeviceContext {
return *eigen_handle_;
}
Place GetPlace() const override {
Place retv = CPUPlace();
return retv;
}
private:
Eigen::DefaultDevice* eigen_handle_{nullptr};
};
} // namespace platform
} // namespace paddle
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