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Paddle
提交 98ffde41 编写于 作者: S shippingwang
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into develop

上级 08d8a622 ac8208b6
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Showing with 1026 addition and 94 deletion
.gitignore
浏览文件 @ 98ffde41
......@@ -25,5 +25,6 @@ third_party/
bazel-*
third_party/
build_*
# clion workspace.
cmake-build-*
CMakeLists.txt
浏览文件 @ 98ffde41
......@@ -72,7 +72,7 @@ option(WITH_INFERENCE "Compile fluid inference library" ON)
option(WITH_INFERENCE_API_TEST "Test fluid inference high-level api interface" OFF)
option(WITH_SYSTEM_BLAS "Use system blas library" OFF)
option(PY_VERSION "Compile PaddlePaddle with python3 support" ${PY_VERSION})
option(WITH_FAST_MATH "Make use of fast math library" OFF)
option(WITH_FAST_MATH "Make use of fast math library, might affect the precision to some extent" ON)
# PY_VERSION
if(NOT PY_VERSION)
......
paddle/fluid/API.spec
浏览文件 @ 98ffde41
......@@ -198,6 +198,9 @@ paddle.fluid.layers.argsort ArgSpec(args=['input', 'axis', 'name'], varargs=None
paddle.fluid.layers.ones ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.zeros ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.reverse ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.has_inf ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.has_nan ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.isfinite ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.While.__init__ ArgSpec(args=['self', 'cond', 'is_test', 'name'], varargs=None, keywords=None, defaults=(False, None))
paddle.fluid.layers.While.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.Switch.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
......
paddle/fluid/framework/data_type.h
浏览文件 @ 98ffde41
......@@ -17,7 +17,6 @@ limitations under the License. */
#include <typeindex>
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/float16.h"
namespace paddle {
......
paddle/fluid/framework/op_desc.cc
浏览文件 @ 98ffde41
......@@ -50,6 +50,27 @@ class CompileTimeInferShapeContext : public InferShapeContext {
const std::vector<std::string> &Outputs(
const std::string &name) const override;
void ShareDim(const std::string &in, const std::string &out, size_t i = 0,
size_t j = 0) override {
PADDLE_ENFORCE_LT(i, Inputs(in).size());
PADDLE_ENFORCE_LT(j, Outputs(out).size());
const std::string &input_n = Inputs(in)[i];
const std::string &output_n = Outputs(out)[j];
PADDLE_ENFORCE(input_n != framework::kEmptyVarName, "The %s[%d] is @EMPTY@",
in, i);
PADDLE_ENFORCE(output_n != framework::kEmptyVarName,
"The %s[%d] is @EMPTY@", out, j);
auto *in_var = block_.FindVarRecursive(input_n);
auto *out_var = block_.FindVarRecursive(output_n);
PADDLE_ENFORCE(in_var->GetType() == out_var->GetType(),
"The type of %s and %s is not the same.", input_n, output_n);
SetDim(output_n, GetDim(input_n));
}
void ShareLoD(const std::string &in, const std::string &out, size_t i = 0,
size_t j = 0) const override {
PADDLE_ENFORCE_LT(i, Inputs(in).size());
......
paddle/fluid/framework/operator.cc
浏览文件 @ 98ffde41
......@@ -542,6 +542,36 @@ class RuntimeInferShapeContext : public InferShapeContext {
return op_.Outputs(name);
}
void ShareDim(const std::string& in, const std::string& out, size_t i = 0,
size_t j = 0) override {
PADDLE_ENFORCE_LT(i, Inputs(in).size());
PADDLE_ENFORCE_LT(j, Outputs(out).size());
const std::string& input_n = Inputs(in)[i];
const std::string& output_n = Outputs(out)[j];
Variable* in_var = scope_.FindVar(input_n);
Variable* out_var = scope_.FindVar(output_n);
PADDLE_ENFORCE(in_var->Type() == out_var->Type(),
"The type of %s and %s is not the same.", output_n,
GetDim(input_n));
if (in_var->IsType<framework::SelectedRows>()) {
auto& in_sele_rows = in_var->Get<framework::SelectedRows>();
auto out_sele_rows = out_var->GetMutable<framework::SelectedRows>();
out_sele_rows->mutable_value()->Resize(in_sele_rows.value().dims());
out_sele_rows->set_rows(in_sele_rows.rows());
out_sele_rows->set_height(in_sele_rows.height());
} else if (in_var->IsType<framework::LoDTensor>()) {
auto& in_lod_tensor = in_var->Get<framework::LoDTensor>();
auto* out_lod_tensor = out_var->GetMutable<framework::LoDTensor>();
out_lod_tensor->Resize(in_lod_tensor.dims());
} else {
PADDLE_THROW(
"Currently, the input type of ShareDim only can be LoDTensor "
"or SelectedRows.");
}
}
void ShareLoD(const std::string& in, const std::string& out, size_t i = 0,
size_t j = 0) const override {
const std::vector<std::string>& inputs = Inputs(in);
......
paddle/fluid/framework/shape_inference.h
浏览文件 @ 98ffde41
......@@ -56,6 +56,9 @@ class InferShapeContext {
virtual const std::vector<std::string> &Outputs(
const std::string &name) const = 0;
virtual void ShareDim(const std::string &in, const std::string &out,
size_t i = 0, size_t j = 0) = 0;
virtual void ShareLoD(const std::string &in, const std::string &out,
size_t i = 0, size_t j = 0) const = 0;
......
paddle/fluid/framework/tensor_util.cc
浏览文件 @ 98ffde41
......@@ -165,10 +165,12 @@ inline void AnyImpl(Predicate predicate, const framework::Tensor& tensor,
}
template <typename Predicate>
struct AnyVisitor : public boost::static_visitor<bool> {
class AnyVisitor : public boost::static_visitor<bool> {
private:
const framework::Tensor& tensor_;
Predicate predicate_;
public:
AnyVisitor(const framework::Tensor& tensor, Predicate predicate)
: tensor_(tensor), predicate_(std::move(predicate)) {}
......@@ -206,6 +208,27 @@ struct AnyVisitor : public boost::static_visitor<bool> {
}
};
template <typename Predicate>
class AnyOutVisitor : public boost::static_visitor<> {
private:
const framework::Tensor& tensor_;
mutable framework::Tensor* out_;
Predicate predicate_;
public:
AnyOutVisitor(const framework::Tensor& tensor, Predicate predicate,
framework::Tensor* out)
: tensor_(tensor), out_(out), predicate_(std::move(predicate)) {}
template <typename Place>
void operator()(const Place& place) const {
auto* ctx = platform::DeviceContextPool::Instance().GetByPlace(place);
out_->Resize({1});
out_->mutable_data<bool>(place);
AnyImpl(predicate_, tensor_, *ctx, out_);
}
};
template <typename Predicate>
inline bool Any(const framework::Tensor& tensor, Predicate predicate) {
AnyVisitor<Predicate> visitor(tensor, predicate);
......@@ -213,6 +236,14 @@ inline bool Any(const framework::Tensor& tensor, Predicate predicate) {
return platform::VisitPlace(place, visitor);
}
template <typename Predicate>
inline void Any(const framework::Tensor& tensor, Predicate predicate,
framework::Tensor* out) {
AnyOutVisitor<Predicate> visitor(tensor, predicate, out);
auto place = tensor.place();
platform::VisitPlace(place, visitor);
}
struct ContainsNANPredicate {
template <typename T>
auto operator()(const T& eigen_vec) const
......@@ -227,6 +258,12 @@ bool TensorContainsNAN(const framework::Tensor& tensor) {
return Any(tensor, predicate);
}
void TensorContainsNAN(const framework::Tensor& tensor,
framework::Tensor* out) {
ContainsNANPredicate predicate;
Any(tensor, predicate, out);
}
struct ContainsInfPredicate {
template <typename T>
auto operator()(const T& eigen_vec) const
......@@ -241,6 +278,71 @@ bool TensorContainsInf(const framework::Tensor& tensor) {
return Any(tensor, predicate);
}
void TensorContainsInf(const framework::Tensor& tensor,
framework::Tensor* out) {
ContainsInfPredicate predicate;
Any(tensor, predicate, out);
}
// NOTE(dzhwinter):
// Isfinite need a AllVisitor to loop through all the elements.
// We choose two cuda call instead of one allvisitor. The AllVisitor
// should be implemented if the performance hurts.
bool TensorIsfinite(const framework::Tensor& tensor) {
ContainsInfPredicate pred_inf;
ContainsNANPredicate pred_nan;
return !Any(tensor, pred_inf) && !Any(tensor, pred_nan);
}
#ifdef PADDLE_WITH_CUDA
template <typename T>
static inline void __global__ BothFalse(const T* cmp, T* out) {
out[0] = (!cmp[0]) && (!out[0]);
}
#endif
struct BothFalseVisitor : public boost::static_visitor<> {
const framework::Tensor& in_;
mutable framework::Tensor* out_;
BothFalseVisitor(const framework::Tensor& in, framework::Tensor* out)
: in_(in), out_(out) {}
template <typename Place>
void operator()(const Place& place) const {
VisitorImpl(place);
}
void VisitorImpl(const platform::CUDAPlace& gpu) const {
#ifdef PADDLE_WITH_CUDA
auto* ctx = platform::DeviceContextPool::Instance().GetByPlace(gpu);
BothFalse<bool><<<1, 1, 0, ctx->stream()>>>(in_.data<bool>(),
out_->mutable_data<bool>(gpu));
#endif
}
void VisitorImpl(const platform::CPUPlace& cpu) const {
bool lhs = !in_.data<bool>()[0];
bool rhs = !out_->mutable_data<bool>(cpu)[0];
out_->mutable_data<bool>(cpu)[0] = lhs && rhs;
}
void VisitorImpl(
const platform::CUDAPinnedPlace& cpu /* equals to cpu*/) const {
bool lhs = !in_.data<bool>()[0];
bool rhs = !out_->mutable_data<bool>(cpu)[0];
out_->mutable_data<bool>(cpu)[0] = lhs && rhs;
}
};
void TensorIsfinite(const framework::Tensor& tensor, framework::Tensor* out) {
framework::Tensor tmp;
TensorContainsInf(tensor, &tmp);
TensorContainsNAN(tensor, out);
BothFalseVisitor visitor(tmp, out);
auto place = tensor.place();
platform::VisitPlace(place, visitor);
}
void TensorToStream(std::ostream& os, const Tensor& tensor,
const platform::DeviceContext& dev_ctx) {
{ // the 1st field, uint32_t version
......
paddle/fluid/framework/tensor_util.h
浏览文件 @ 98ffde41
......@@ -57,8 +57,15 @@ void TensorToVector(const Tensor& src, const platform::DeviceContext& ctx,
template <typename T>
void TesnorToVector(const Tensor& src, std::vector<T>* dst);
// copy the result bool to cpu
bool TensorContainsNAN(const framework::Tensor& tensor);
bool TensorContainsInf(const framework::Tensor& tensor);
bool TensorIsfinite(const framework::Tensor& tensor);
// store the result bool in gpu tensor, async operation. Faster than above ones.
void TensorContainsNAN(const framework::Tensor& tensor, framework::Tensor* out);
void TensorContainsInf(const framework::Tensor& tensor, framework::Tensor* out);
void TensorIsfinite(const framework::Tensor& tensor, framework::Tensor* out);
void TensorToStream(std::ostream& os, const Tensor& tensor,
const platform::DeviceContext& dev_ctx);
......
paddle/fluid/framework/tensor_util_test.cc
浏览文件 @ 98ffde41
......@@ -36,7 +36,7 @@ TEST(TensorCopy, Tensor) {
TensorCopy(src_tensor, *cpu_place, &dst_tensor);
const int* dst_ptr = dst_tensor.data<int>();
ASSERT_NE(src_ptr, dst_ptr);
EXPECT_NE(src_ptr, dst_ptr);
for (size_t i = 0; i < 9; ++i) {
EXPECT_EQ(src_ptr[i], dst_ptr[i]);
}
......@@ -47,7 +47,7 @@ TEST(TensorCopy, Tensor) {
TensorCopy(slice_tensor, *cpu_place, &dst_tensor);
const int* slice_ptr = slice_tensor.data<int>();
dst_ptr = dst_tensor.data<int>();
ASSERT_NE(dst_ptr, slice_ptr);
EXPECT_NE(dst_ptr, slice_ptr);
for (size_t i = 0; i < 3; ++i) {
EXPECT_EQ(dst_ptr[i], slice_ptr[i]);
}
......@@ -77,7 +77,7 @@ TEST(TensorCopy, Tensor) {
// Sync before Compare Tensors
gpu_ctx.Wait();
const int* dst_ptr = dst_tensor.data<int>();
ASSERT_NE(src_ptr, dst_ptr);
EXPECT_NE(src_ptr, dst_ptr);
for (size_t i = 0; i < 9; ++i) {
EXPECT_EQ(src_ptr[i], dst_ptr[i]);
}
......@@ -94,7 +94,7 @@ TEST(TensorCopy, Tensor) {
gpu_ctx.Wait();
const int* slice_ptr = slice_tensor.data<int>();
dst_ptr = dst_tensor.data<int>();
ASSERT_NE(dst_ptr, slice_ptr);
EXPECT_NE(dst_ptr, slice_ptr);
for (size_t i = 0; i < 3; ++i) {
EXPECT_EQ(dst_ptr[i], slice_ptr[i]);
}
......@@ -117,7 +117,7 @@ TEST(TensorFromVector, Tensor) {
// Compare Tensors
const int* cpu_ptr = cpu_tensor.data<int>();
const int* src_ptr = src_vec.data();
ASSERT_NE(src_ptr, cpu_ptr);
EXPECT_NE(src_ptr, cpu_ptr);
for (size_t i = 0; i < 9; ++i) {
EXPECT_EQ(src_ptr[i], cpu_ptr[i]);
}
......@@ -127,7 +127,7 @@ TEST(TensorFromVector, Tensor) {
paddle::framework::TensorFromVector<int>(src_vec, &cpu_tensor);
cpu_ptr = cpu_tensor.data<int>();
src_ptr = src_vec.data();
ASSERT_NE(src_ptr, cpu_ptr);
EXPECT_NE(src_ptr, cpu_ptr);
for (size_t i = 0; i < 5; ++i) {
EXPECT_EQ(src_ptr[i], cpu_ptr[i]);
}
......@@ -161,8 +161,8 @@ TEST(TensorFromVector, Tensor) {
const int* src_ptr = src_vec.data();
const int* cpu_ptr = cpu_tensor.data<int>();
const int* dst_ptr = dst_tensor.data<int>();
ASSERT_NE(src_ptr, cpu_ptr);
ASSERT_NE(src_ptr, dst_ptr);
EXPECT_NE(src_ptr, cpu_ptr);
EXPECT_NE(src_ptr, dst_ptr);
for (size_t i = 0; i < 9; ++i) {
EXPECT_EQ(src_ptr[i], cpu_ptr[i]);
EXPECT_EQ(src_ptr[i], dst_ptr[i]);
......@@ -181,8 +181,8 @@ TEST(TensorFromVector, Tensor) {
src_ptr = src_vec.data();
cpu_ptr = cpu_tensor.data<int>();
dst_ptr = dst_tensor.data<int>();
ASSERT_NE(src_ptr, cpu_ptr);
ASSERT_NE(src_ptr, dst_ptr);
EXPECT_NE(src_ptr, cpu_ptr);
EXPECT_NE(src_ptr, dst_ptr);
for (size_t i = 0; i < 5; ++i) {
EXPECT_EQ(src_ptr[i], cpu_ptr[i]);
EXPECT_EQ(src_ptr[i], dst_ptr[i]);
......@@ -235,9 +235,9 @@ TEST(TensorContainsNAN, CPU) {
buf[0] = 0.0;
buf[1] = NAN;
buf[2] = 0.0;
ASSERT_TRUE(paddle::framework::TensorContainsNAN(src));
EXPECT_TRUE(paddle::framework::TensorContainsNAN(src));
buf[1] = 0.0;
ASSERT_FALSE(paddle::framework::TensorContainsNAN(src));
EXPECT_FALSE(paddle::framework::TensorContainsNAN(src));
}
{
......@@ -248,9 +248,9 @@ TEST(TensorContainsNAN, CPU) {
buf[0] = 0.0;
buf[1].x = 0x7fff;
buf[2] = 0.0;
ASSERT_TRUE(paddle::framework::TensorContainsNAN(src));
EXPECT_TRUE(paddle::framework::TensorContainsNAN(src));
buf[1] = 0.0;
ASSERT_FALSE(paddle::framework::TensorContainsNAN(src));
EXPECT_FALSE(paddle::framework::TensorContainsNAN(src));
}
}
......@@ -261,9 +261,9 @@ TEST(TensorContainsInf, CPU) {
buf[0] = 1.0;
buf[1] = INFINITY;
buf[2] = 0.0;
ASSERT_TRUE(paddle::framework::TensorContainsInf(src));
EXPECT_TRUE(paddle::framework::TensorContainsInf(src));
buf[1] = 1.0;
ASSERT_FALSE(paddle::framework::TensorContainsInf(src));
EXPECT_FALSE(paddle::framework::TensorContainsInf(src));
}
{
......@@ -274,9 +274,55 @@ TEST(TensorContainsInf, CPU) {
buf[0] = 1.0;
buf[1].x = 0x7c00;
buf[2] = 0.0;
ASSERT_TRUE(paddle::framework::TensorContainsInf(src));
EXPECT_TRUE(paddle::framework::TensorContainsInf(src));
buf[1] = 1.0;
ASSERT_FALSE(paddle::framework::TensorContainsInf(src));
EXPECT_FALSE(paddle::framework::TensorContainsInf(src));
}
}
TEST(TensorIsfinite, CPU) {
{
paddle::framework::Tensor src, out;
double* buf = src.mutable_data<double>({3}, paddle::platform::CPUPlace());
buf[0] = 1.0;
buf[1] = INFINITY;
buf[2] = 0.0;
paddle::framework::TensorIsfinite(src, &out);
EXPECT_EQ(out.data<bool>()[0], false);
buf[1] = 1.0;
paddle::framework::TensorIsfinite(src, &out);
EXPECT_EQ(out.data<bool>()[0], true);
}
{
paddle::framework::Tensor src, out;
double* buf = src.mutable_data<double>({3}, paddle::platform::CPUPlace());
buf[0] = 1.0;
buf[1] = NAN;
buf[2] = 0.0;
paddle::framework::TensorIsfinite(src, &out);
EXPECT_EQ(out.data<bool>()[0], false);
buf[1] = 1.0;
paddle::framework::TensorIsfinite(src, &out);
EXPECT_EQ(out.data<bool>()[0], true);
}
{
paddle::framework::Tensor src, out;
paddle::platform::float16* buf =
src.mutable_data<paddle::platform::float16>(
{3}, paddle::platform::CPUPlace());
buf[0] = 1.0;
buf[1].x = 0x7c00;
buf[2] = 0.0;
paddle::framework::TensorIsfinite(src, &out);
EXPECT_EQ(out.data<bool>()[0], false);
buf[1] = 1.0;
paddle::framework::TensorIsfinite(src, &out);
EXPECT_EQ(out.data<bool>()[0], true);
buf[1].x = 0x7fff;
paddle::framework::TensorIsfinite(src, &out);
EXPECT_EQ(out.data<bool>()[0], false);
}
}
......@@ -299,9 +345,9 @@ TEST(Tensor, FromAndToStream) {
TensorFromStream(iss, &dst_tensor, cpu_ctx);
int* dst_ptr = dst_tensor.mutable_data<int>(platform::CPUPlace());
for (int i = 0; i < 5; ++i) {
ASSERT_EQ(dst_ptr[i], array[i]);
EXPECT_EQ(dst_ptr[i], array[i]);
}
ASSERT_EQ(dst_tensor.dims(), src_tensor.dims());
EXPECT_EQ(dst_tensor.dims(), src_tensor.dims());
delete place;
}
#ifdef PADDLE_WITH_CUDA
......@@ -323,7 +369,7 @@ TEST(Tensor, FromAndToStream) {
int* dst_ptr = dst_tensor.mutable_data<int>(platform::CPUPlace());
for (int i = 0; i < 6; ++i) {
ASSERT_EQ(dst_ptr[i], array[i]);
EXPECT_EQ(dst_ptr[i], array[i]);
}
delete gpu_place;
}
......
paddle/fluid/framework/tensor_util_test.cu
浏览文件 @ 98ffde41
......@@ -27,9 +27,9 @@ static __global__ void FillNAN(float* buf) {
}
static __global__ void FillInf(float* buf) {
buf[0] = 0.0;
buf[1] = INFINITY;
buf[2] = 0.5;
buf[0] = INFINITY;
buf[1] = 0.1;
buf[2] = 0.2;
}
static __global__ void FillNAN(platform::float16* buf) {
......@@ -44,6 +44,18 @@ static __global__ void FillInf(platform::float16* buf) {
buf[2] = 0.5;
}
static __global__ void FillFinite(float* buf) {
buf[0] = 0.0;
buf[1] = 0.1;
buf[2] = 0.2;
}
static __global__ void FillFinite(platform::float16* buf) {
buf[0] = 0.0;
buf[1] = 0.1;
buf[2] = 0.2;
}
TEST(TensorContainsNAN, GPU) {
paddle::platform::CUDAPlace gpu(0);
auto& pool = paddle::platform::DeviceContextPool::Instance();
......@@ -86,5 +98,163 @@ TEST(TensorContainsInf, GPU) {
}
}
TEST(TensorIsfinite, GPU) {
paddle::platform::CUDAPlace gpu(0);
using paddle::platform::float16;
auto& pool = paddle::platform::DeviceContextPool::Instance();
auto* cuda_ctx = pool.GetByPlace(gpu);
// contains inf
{
Tensor tensor;
float* buf = tensor.mutable_data<float>({3}, gpu);
FillInf<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
EXPECT_TRUE(!TensorIsfinite(tensor));
}
{
Tensor tensor;
float16* buf = tensor.mutable_data<float16>({3}, gpu);
FillInf<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
EXPECT_TRUE(!TensorIsfinite(tensor));
}
// contains nan
{
Tensor tensor;
float* buf = tensor.mutable_data<float>({3}, gpu);
FillNAN<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
EXPECT_TRUE(!TensorIsfinite(tensor));
}
{
Tensor tensor;
float16* buf = tensor.mutable_data<float16>({3}, gpu);
FillNAN<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
EXPECT_TRUE(!TensorIsfinite(tensor));
}
// all element are finite
{
Tensor tensor;
float* buf = tensor.mutable_data<float>({3}, gpu);
FillFinite<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
EXPECT_TRUE(TensorIsfinite(tensor));
}
{
Tensor tensor;
float16* buf = tensor.mutable_data<float16>({3}, gpu);
FillFinite<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
EXPECT_TRUE(TensorIsfinite(tensor));
}
}
TEST(TensorContainsInf, GPUWithoutWait) {
paddle::platform::CUDAPlace gpu(0);
auto& pool = paddle::platform::DeviceContextPool::Instance();
auto* cuda_ctx = pool.GetByPlace(gpu);
{
Tensor tensor, out;
float* buf = tensor.mutable_data<float>({3}, gpu);
FillInf<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
TensorContainsInf(tensor, &out);
platform::CPUPlace cpu;
Tensor tmp;
TensorCopy(out, cpu, *cuda_ctx, &tmp);
cuda_ctx->Wait();
ASSERT_EQ(tmp.data<bool>()[0], true);
}
{
Tensor tensor, out;
paddle::platform::float16* buf =
tensor.mutable_data<paddle::platform::float16>({3}, gpu);
FillInf<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
TensorContainsInf(tensor, &out);
platform::CPUPlace cpu;
Tensor tmp;
TensorCopy(out, cpu, *cuda_ctx, &tmp);
cuda_ctx->Wait();
ASSERT_EQ(tmp.data<bool>()[0], true);
}
}
TEST(TensorContainsNAN, GPUWithoutWait) {
paddle::platform::CUDAPlace gpu(0);
auto& pool = paddle::platform::DeviceContextPool::Instance();
auto* cuda_ctx = pool.GetByPlace(gpu);
{
Tensor tensor, out;
float* buf = tensor.mutable_data<float>({3}, gpu);
FillNAN<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
TensorContainsNAN(tensor, &out);
platform::CPUPlace cpu;
Tensor tmp;
TensorCopy(out, cpu, *cuda_ctx, &tmp);
cuda_ctx->Wait();
ASSERT_EQ(tmp.data<bool>()[0], true);
}
{
Tensor tensor, out;
paddle::platform::float16* buf =
tensor.mutable_data<paddle::platform::float16>({3}, gpu);
FillNAN<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
TensorContainsNAN(tensor, &out);
platform::CPUPlace cpu;
Tensor tmp;
TensorCopy(out, cpu, *cuda_ctx, &tmp);
cuda_ctx->Wait();
ASSERT_EQ(tmp.data<bool>()[0], true);
}
}
TEST(TensorIsfinite, GPUWithoutWait) {
paddle::platform::CUDAPlace gpu(0);
auto& pool = paddle::platform::DeviceContextPool::Instance();
auto* cuda_ctx = pool.GetByPlace(gpu);
{
Tensor tensor, out;
float* buf = tensor.mutable_data<float>({3}, gpu);
FillInf<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
TensorIsfinite(tensor, &out);
platform::CPUPlace cpu;
Tensor tmp;
TensorCopy(out, cpu, *cuda_ctx, &tmp);
cuda_ctx->Wait();
EXPECT_EQ(tmp.data<bool>()[0], false);
}
{
Tensor tensor, out;
float* buf = tensor.mutable_data<float>({3}, gpu);
FillNAN<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
TensorIsfinite(tensor, &out);
platform::CPUPlace cpu;
Tensor tmp;
TensorCopy(out, cpu, *cuda_ctx, &tmp);
cuda_ctx->Wait();
EXPECT_EQ(tmp.data<bool>()[0], false);
}
{
Tensor tensor, out;
float* buf = tensor.mutable_data<float>({3}, gpu);
FillFinite<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
TensorIsfinite(tensor, &out);
platform::CPUPlace cpu;
Tensor tmp;
TensorCopy(out, cpu, *cuda_ctx, &tmp);
cuda_ctx->Wait();
EXPECT_EQ(tmp.data<bool>()[0], true);
}
}
} // namespace framework
} // namespace paddle
paddle/fluid/inference/api/demo_ci/simple_on_word2vec.cc
浏览文件 @ 98ffde41
......@@ -22,8 +22,8 @@ limitations under the License. */
#include <algorithm>
#include <memory>
#include <thread> //NOLINT
#include "paddle/fluid/inference/paddle_inference_api.h"
#include "paddle/fluid/platform/enforce.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
DEFINE_bool(use_gpu, false, "Whether use gpu.");
......@@ -62,17 +62,17 @@ void Main(bool use_gpu) {
CHECK(predictor->Run(slots, &outputs));
//# 4. Get output.
PADDLE_ENFORCE(outputs.size(), 1UL);
CHECK_EQ(outputs.size(), 1UL);
// Check the output buffer size and result of each tid.
PADDLE_ENFORCE(outputs.front().data.length(), 33168UL);
CHECK_EQ(outputs.front().data.length(), 33168UL);
float result[5] = {0.00129761, 0.00151112, 0.000423564, 0.00108815,
0.000932706};
const size_t num_elements = outputs.front().data.length() / sizeof(float);
// The outputs' buffers are in CPU memory.
for (size_t i = 0; i < std::min(static_cast<size_t>(5), num_elements);
i++) {
PADDLE_ENFORCE(static_cast<float*>(outputs.front().data.data())[i],
result[i]);
CHECK_NEAR(static_cast<float*>(outputs.front().data.data())[i], result[i],
0.001);
}
}
}
......@@ -108,9 +108,9 @@ void MainThreads(int num_threads, bool use_gpu) {
CHECK(predictor->Run(inputs, &outputs));
// 4. Get output.
PADDLE_ENFORCE(outputs.size(), 1UL);
CHECK_EQ(outputs.size(), 1UL);
// Check the output buffer size and result of each tid.
PADDLE_ENFORCE(outputs.front().data.length(), 33168UL);
CHECK_EQ(outputs.front().data.length(), 33168UL);
float result[5] = {0.00129761, 0.00151112, 0.000423564, 0.00108815,
0.000932706};
const size_t num_elements =
......@@ -118,8 +118,8 @@ void MainThreads(int num_threads, bool use_gpu) {
// The outputs' buffers are in CPU memory.
for (size_t i = 0; i < std::min(static_cast<size_t>(5), num_elements);
i++) {
PADDLE_ENFORCE(static_cast<float*>(outputs.front().data.data())[i],
result[i]);
CHECK_NEAR(static_cast<float*>(outputs.front().data.data())[i],
result[i], 0.001);
}
}
});
......
paddle/fluid/inference/api/demo_ci/vis_demo.cc
浏览文件 @ 98ffde41
......@@ -17,11 +17,12 @@ limitations under the License. */
*/
#include <gflags/gflags.h>
#include <glog/logging.h> // use glog instead of PADDLE_ENFORCE to avoid importing other paddle header files.
#include <glog/logging.h> // use glog instead of CHECK to avoid importing other paddle header files.
#include <fstream>
#include <iostream>
// #include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/inference/demo_ci/utils.h"
#include "paddle/fluid/platform/enforce.h"
#ifdef PADDLE_WITH_CUDA
DECLARE_double(fraction_of_gpu_memory_to_use);
......@@ -78,18 +79,17 @@ void CheckOutput(const std::string& referfile, const PaddleTensor& output) {
size_t numel = output.data.length() / PaddleDtypeSize(output.dtype);
VLOG(3) << "predictor output numel " << numel;
VLOG(3) << "reference output numel " << refer.data.size();
PADDLE_ENFORCE_EQ(numel, refer.data.size());
CHECK_EQ(numel, refer.data.size());
switch (output.dtype) {
case PaddleDType::INT64: {
for (size_t i = 0; i < numel; ++i) {
PADDLE_ENFORCE_EQ(static_cast<int64_t*>(output.data.data())[i],
refer.data[i]);
CHECK_EQ(static_cast<int64_t*>(output.data.data())[i], refer.data[i]);
}
break;
}
case PaddleDType::FLOAT32:
for (size_t i = 0; i < numel; ++i) {
PADDLE_ENFORCE_LT(
CHECK_LT(
fabs(static_cast<float*>(output.data.data())[i] - refer.data[i]),
1e-5);
}
......
paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc
浏览文件 @ 98ffde41
......@@ -27,9 +27,6 @@ void SetConfig(AnalysisConfig *cfg) {
cfg->device = 0;
cfg->enable_ir_optim = true;
cfg->specify_input_name = true;
#ifdef PADDLE_WITH_MKLDNN
cfg->_use_mkldnn = true;
#endif
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ 98ffde41
......@@ -80,7 +80,7 @@ class ActivationOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareDim("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
}
......@@ -91,12 +91,26 @@ class ActivationOp : public framework::OperatorWithKernel {
}
};
class ActivationOpInferVarType : public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc& op_desc,
framework::BlockDesc* block) const override {
auto x_name = op_desc.Input("X")[0];
auto out_name = op_desc.Output("Out")[0];
auto& x = block->FindRecursiveOrCreateVar(x_name);
auto& out = block->FindRecursiveOrCreateVar(out_name);
out.SetType(x.GetType());
out.SetDataType(x.GetDataType());
}
};
class ActivationOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Out"));
ctx->ShareDim("Out", framework::GradVarName("X"));
ctx->ShareLoD("Out", framework::GradVarName("X"));
}
protected:
......@@ -525,12 +539,14 @@ namespace ops = paddle::operators;
#define REGISTER_INPLACE_ACTIVATION_OP(OP_NAME, KERNEL_TYPE) \
REGISTER_OPERATOR(KERNEL_TYPE, ::paddle::operators::ActivationOp, \
::paddle::operators::OP_NAME##OpMaker, \
::paddle::operators::ActivationOpInferVarType, \
::paddle::operators::OP_NAME##GradMaker); \
REGISTER_OPERATOR(KERNEL_TYPE##_grad, ::paddle::operators::ActivationOpGrad)
#define REGISTER_ACTIVATION_OP(OP_NAME, KERNEL_TYPE) \
REGISTER_OPERATOR(KERNEL_TYPE, ::paddle::operators::ActivationOp, \
::paddle::operators::OP_NAME##OpMaker, \
::paddle::operators::ActivationOpInferVarType, \
::paddle::framework::DefaultGradOpDescMaker<true>); \
REGISTER_OPERATOR(KERNEL_TYPE##_grad, ::paddle::operators::ActivationOpGrad)
......
paddle/fluid/operators/argsort_op.cc
浏览文件 @ 98ffde41
......@@ -42,8 +42,8 @@ class ArgsortOp : public framework::OperatorWithKernel {
"-rank(Input(X)) (%d).",
axis, num_dims);
ctx->SetOutputDim("Out", in_dims);
ctx->SetOutputDim("Indices", in_dims);
ctx->ShareDim("X", "Out");
ctx->ShareDim("X", "Indices");
ctx->ShareLoD("X", "Out");
ctx->ShareLoD("X", "Indices");
}
......
paddle/fluid/operators/conv_shift_op.cc
浏览文件 @ 98ffde41
......@@ -44,7 +44,7 @@ class ConvShiftOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_LE(y_dims[1], x_dims[1],
"The 2nd dimension of Input(Y) should be less than or "
"equal to the 2nd dimension of Input(X).");
ctx->SetOutputDim("Out", x_dims);
ctx->ShareDim("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
}
};
......
paddle/fluid/operators/cub_reduce.h
浏览文件 @ 98ffde41
......@@ -22,6 +22,7 @@
#include <cub/cub.cuh> // NOLINT
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/tensor_util.h"
namespace paddle {
namespace operators {
......@@ -293,7 +294,12 @@ void TensorReduce(const framework::Tensor& x, framework::Tensor* y,
}
auto x_data = x.data<Tx>();
auto y_data = y->mutable_data<Ty>(x.place());
if (reduce_num == 1) return;
if (reduce_num == 1) {
auto out_dims = y->dims();
framework::TensorCopy(x, y->place(), y);
y->Resize(out_dims);
return;
}
#define CUB_BLOCK_DIM_CASE(block_dim) \
case block_dim: { \
......
paddle/fluid/operators/elementwise_op.h
浏览文件 @ 98ffde41
......@@ -41,7 +41,8 @@ class ElementwiseOp : public framework::OperatorWithKernel {
auto y_dim = ctx->GetInputDim("Y");
PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
"Rank of first input must >= rank of second input.");
ctx->SetOutputDim("Out", x_dim);
ctx->ShareDim("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
}
......@@ -70,6 +71,7 @@ class ElementwiseOpInferVarType : public framework::VarTypeInference {
auto& x = block->FindRecursiveOrCreateVar(x_name);
auto& out = block->FindRecursiveOrCreateVar(out_name);
out.SetType(x.GetType());
out.SetDataType(x.GetDataType());
}
};
......@@ -157,10 +159,12 @@ class ElementwiseOpGrad : public framework::OperatorWithKernel {
auto x_grad_name = framework::GradVarName("X");
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
ctx->ShareDim("X", /*->*/ x_grad_name);
ctx->ShareLoD("X", /*->*/ x_grad_name);
}
if (ctx->HasOutput(y_grad_name)) {
ctx->SetOutputDim(y_grad_name, y_dims);
ctx->ShareDim("Y", /*->*/ y_grad_name);
ctx->ShareLoD("Y", /*->*/ y_grad_name);
}
}
......@@ -193,14 +197,15 @@ class ElementwiseOpExplicitGrad : public ElementwiseOpGrad {
auto x_grad_name = framework::GradVarName("X");
if (ctx->HasOutput(x_grad_name)) {
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
ctx->SetOutputDim(x_grad_name, out_dims);
ctx->ShareDim(framework::GradVarName("Out"), /*->*/ x_grad_name);
ctx->ShareLoD(framework::GradVarName("Out"), /*->*/ x_grad_name);
}
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(y_grad_name)) {
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
auto y_dims = ctx->GetInputDim("Y");
ctx->SetOutputDim(y_grad_name, y_dims);
ctx->ShareDim("Y", /*->*/ y_grad_name);
ctx->ShareLoD("Y", /*->*/ y_grad_name);
}
}
};
......
paddle/fluid/operators/fake_dequantize_op.cc
浏览文件 @ 98ffde41
......@@ -48,7 +48,8 @@ class FakeDequantizeMaxAbsOp : public framework::OperatorWithKernel {
"Input(X) of FakeDequantizeMaxAbsOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of FakeDequantizeMaxAbsOp should not be null.");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareDim("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
}
};
......
paddle/fluid/operators/isfinite_op.cc 0 → 100644
浏览文件 @ 98ffde41
// Copyright (c) 2018 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/fluid/operators/isfinite_op.h"
#include <string>
#include <vector>
namespace paddle {
namespace operators {
class OverflowOp : public framework::OperatorWithKernel {
public:
OverflowOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInputs("X"), "Inputs(X) should not be null");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of OverflowOp should not be null.");
ctx->SetOutputDim("Out", {1});
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
int dtype = -1;
auto *x_var = ctx.InputVar("X");
if (x_var->IsType<framework::LoDTensor>()) {
dtype = framework::ToDataType(x_var->Get<framework::LoDTensor>().type());
} else if (x_var->IsType<framework::SelectedRows>()) {
dtype = framework::ToDataType(
x_var->Get<framework::SelectedRows>().value().type());
} else {
PADDLE_THROW("Cannot find the input data type by all input data");
}
return framework::OpKernelType(framework::proto::VarType::Type(dtype),
ctx.GetPlace());
}
};
class OverflowOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor) The input tensors of overflow operator.");
AddOutput("Out",
"(Tensor) 1-dim tensor, contains a bool scalar. The output "
"tensor of overflow operator.");
AddComment(string::Sprintf(R"DOC(
Overflow operator.
$$Out = any(X)$$
If any X contains Inf or Nan, the Out will generate a indicator.
Out = Inf if any X contains Inf,
Out = Nan if any X contains Nan,
Out = 0 if no Inf/Nan detected.
If X contains both Inf/Nan, it will return the first indicator it meeted.
)DOC",
GetName(), GetComments()));
}
protected:
virtual std::string GetName() const = 0;
virtual std::string GetComments() const = 0;
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
#define REGISTER_OP_MAKER(op_type, comment) \
namespace paddle { \
namespace operators { \
class _##op_type##OverflowOpMaker \
: public ::paddle::operators::OverflowOpMaker { \
protected: \
std::string GetName() const { return #op_type; } \
std::string GetComments() const { return comment; } \
}; \
} \
} \
REGISTER_OPERATOR(op_type, ops::OverflowOp, \
ops::_##op_type##OverflowOpMaker, \
paddle::framework::EmptyGradOpMaker)
#define REGISTER_OVERFLOW_CPU_KERNEL(op_type, functor) \
REGISTER_OP_CPU_KERNEL( \
op_type, ops::OverflowKernel<paddle::platform::CPUDeviceContext, int, \
ops::functor>, \
ops::OverflowKernel<paddle::platform::CPUDeviceContext, float, \
ops::functor>, \
ops::OverflowKernel<paddle::platform::CPUDeviceContext, double, \
ops::functor>);
REGISTER_OP_MAKER(isinf, "isinf(X)");
REGISTER_OP_MAKER(isnan, "isnan(X)");
REGISTER_OP_MAKER(isfinite, "isfinite(X)");
FOR_EACH_KERNEL_FUNCTOR(REGISTER_OVERFLOW_CPU_KERNEL);
paddle/fluid/operators/isfinite_op.cu 0 → 100644
浏览文件 @ 98ffde41
// Copyright (c) 2018 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.
#define EIGEN_USE_GPU
#include "paddle/fluid/operators/isfinite_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
namespace plat = paddle::platform;
#define REGISTER_OVERFLOW_CUDA_KERNEL(op_type, functor) \
REGISTER_OP_CUDA_KERNEL( \
op_type, ops::OverflowKernel<paddle::platform::CUDADeviceContext, int, \
ops::functor>, \
ops::OverflowKernel<paddle::platform::CUDADeviceContext, float, \
ops::functor>, \
ops::OverflowKernel<paddle::platform::CUDADeviceContext, double, \
ops::functor>, \
ops::OverflowKernel<paddle::platform::CUDADeviceContext, plat::float16, \
ops::functor>);
FOR_EACH_KERNEL_FUNCTOR(REGISTER_OVERFLOW_CUDA_KERNEL);
paddle/fluid/operators/isfinite_op.h 0 → 100644
浏览文件 @ 98ffde41
// Copyright (c) 2018 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.
#pragma once
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/platform/float16.h"
#include "paddle/fluid/platform/transform.h"
namespace paddle {
namespace operators {
struct InfinityFunctor {
void operator()(const framework::Tensor& tensor, framework::Tensor* out) {
framework::TensorContainsInf(tensor, out);
}
};
struct NANFunctor {
void operator()(const framework::Tensor& tensor, framework::Tensor* out) {
framework::TensorContainsNAN(tensor, out);
}
};
struct IsfiniteFunctor {
void operator()(const framework::Tensor& tensor, framework::Tensor* out) {
framework::TensorIsfinite(tensor, out);
}
};
template <typename DeviceContext, typename T, typename Functor>
class OverflowKernel : public framework::OpKernel<T> {
public:
virtual void Compute(const framework::ExecutionContext& ctx) const {
auto* x = ctx.InputVar("X");
auto* out = ctx.Output<framework::Tensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
Functor functor;
if (x->IsType<framework::LoDTensor>()) {
auto* in = ctx.Input<framework::Tensor>("X");
functor(*in, out);
} else if (x->IsType<framework::SelectedRows>()) {
auto& in = ctx.Input<framework::SelectedRows>("X")->value();
functor(in, out);
} else {
PADDLE_THROW("Unsupported input type.");
}
}
};
} // namespace operators
} // namespace paddle
#define FOR_EACH_KERNEL_FUNCTOR(__macro) \
__macro(isinf, InfinityFunctor); \
__macro(isnan, NANFunctor); \
__macro(isfinite, IsfiniteFunctor);
paddle/fluid/operators/lookup_table_op.cc
浏览文件 @ 98ffde41
......@@ -137,6 +137,7 @@ class LookupTableOpGradVarTypeInference : public framework::VarTypeInference {
<< " is set to LoDTensor";
block->Var(out_var_name)->SetType(framework::proto::VarType::LOD_TENSOR);
}
block->Var(out_var_name)->SetDataType(block->Var("W")->GetDataType());
}
};
......
paddle/fluid/operators/prelu_op.cc
浏览文件 @ 98ffde41
......@@ -49,7 +49,7 @@ class PReluOp : public framework::OperatorWithKernel {
} else {
PADDLE_THROW("Unkown mode %s", mode);
}
ctx->SetOutputDim("Out", x_dim);
ctx->ShareDim("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
}
......
paddle/fluid/operators/rnn_memory_helper_op.cc
浏览文件 @ 98ffde41
......@@ -54,7 +54,7 @@ class RNNMemoryHelperOpShapeInference : public framework::InferShapeBase {
"Input(X) of rnn_memory_helper op should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output of rnn_memory_helper op should not be null.");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareDim("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
}
};
......
paddle/fluid/operators/sequence_conv_op.cc
浏览文件 @ 98ffde41
......@@ -90,8 +90,8 @@ class SequenceConvGradOp : public framework::OperatorWithKernel {
ctx->GetInputDim("PaddingData"));
}
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->ShareLoD("X", framework::GradVarName("X"));
ctx->ShareDim("X", /*->*/ framework::GradVarName("X"));
ctx->ShareLoD("X", /*->*/ framework::GradVarName("X"));
}
if (ctx->HasOutput(framework::GradVarName("Filter"))) {
ctx->SetOutputDim(framework::GradVarName("Filter"),
......
paddle/fluid/operators/sequence_pool_op.cc
浏览文件 @ 98ffde41
......@@ -102,8 +102,9 @@ class SequencePoolGradOp : public framework::OperatorWithKernel {
for (int64_t i = 1; i < og_dims.size(); ++i) {
PADDLE_ENFORCE_EQ(og_dims[i], x_dims[i], "The dimension mismatch.");
}
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
ctx->ShareLoD("X", framework::GradVarName("X"));
ctx->ShareDim("X", /*->*/ framework::GradVarName("X"));
ctx->ShareLoD("X", /*->*/ framework::GradVarName("X"));
}
protected:
......
paddle/fluid/operators/sequence_reshape_op.cc
浏览文件 @ 98ffde41
......@@ -92,7 +92,7 @@ class SequenceReshapeGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequenceReshapeGradOp should not be null.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->ShareDim("X", /*->*/ framework::GradVarName("X"));
ctx->ShareLoD("X", /*->*/ framework::GradVarName("X"));
}
};
......
paddle/fluid/operators/sequence_softmax_op.cc
浏览文件 @ 98ffde41
......@@ -27,7 +27,8 @@ class SequenceSoftmaxOp : public framework::OperatorWithKernel {
"Input(X) of SequenceSoftmaxOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SequenceSoftmaxOp should not be null.");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareDim("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
}
......
paddle/fluid/operators/shrink_rnn_memory_op.cc
浏览文件 @ 98ffde41
......@@ -151,9 +151,9 @@ class ShrinkRNNMemoryGradInferShape : public framework::InferShapeBase {
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("X"));
PADDLE_ENFORCE(context->HasOutput(framework::GradVarName("X")));
context->SetOutputDim(framework::GradVarName("X"),
context->GetInputDim("X"));
context->ShareLoD("X", framework::GradVarName("X"));
context->ShareDim("X", /*->*/ framework::GradVarName("X"));
context->ShareLoD("X", /*->*/ framework::GradVarName("X"));
}
};
......
paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc
浏览文件 @ 98ffde41
......@@ -40,7 +40,7 @@ class SigmoidCrossEntropyWithLogitsOp : public framework::OperatorWithKernel {
"The 2nd dimension of Input(X) and Input(Label) should "
"be equal.");
ctx->SetOutputDim("Out", x_dims);
ctx->ShareDim("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
}
};
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 98ffde41
......@@ -620,7 +620,23 @@ All parameter, weight, gradient are variables in Paddle.
// -- python binds for parallel executor.
py::class_<ParallelExecutor> pe(m, "ParallelExecutor");
py::class_<ExecutionStrategy> exec_strategy(pe, "ExecutionStrategy");
py::class_<ExecutionStrategy> exec_strategy(pe, "ExecutionStrategy", R"DOC(
ExecutionStrategy allows the user to more preciously control how to run
the program in ParallelExecutor by setting the property.
The available properties include:
use_cuda (bool): Whether to use CUDA or not. Default True.
num_threads (int): The number of threads that used to run the
operators in ParallelExecutor. If it is not set, it will be
set in ParallelExecutor according to the device count.
Default 0.
allow_op_delay (bool): Whether to delay the communication operators
to run. Default False.
num_iteration_per_drop_scope (int): how many iterations between
the two dropping local scopes. Default 100.
)DOC");
exec_strategy.def(py::init())
.def_property(
"num_threads",
......@@ -658,7 +674,25 @@ All parameter, weight, gradient are variables in Paddle.
: ExecutionStrategy::kDefault;
});
py::class_<BuildStrategy> build_strategy(pe, "BuildStrategy");
py::class_<BuildStrategy> build_strategy(pe, "BuildStrategy", R"DOC(
BuildStrategy allows the user to more preciously control how to
build the SSA Graph in ParallelExecutor by setting the property.
The available properties include:
reduce_strategy (str): There are two reduce strategies, 'AllReduce'
and 'Reduce'. If you want that all parameters will be optimized
on all devices, you can choose 'AllReduce'; if you choose
'Reduce', all parameters will be evenly allocated to different
devices for optimization, and then broadcast the optimized
parameter to other devices. Default 'AllReduce'.
gradient_scale_strategy (str): There are two ways of defining loss@grad,
'CoeffNumDevice' and 'Customized'. By default, ParallelExecutor
sets the loss@grad according to the number of devices. If you want
to customize loss@grad, you can choose 'Customized'.
Default 'CoeffNumDevice'.
debug_graphviz_path (str): Whether to write the SSA Graph to file in the
form of graphviz. It is useful for debugging. Default "".
)DOC");
py::enum_<BuildStrategy::ReduceStrategy>(build_strategy, "ReduceStrategy")
.value("Reduce", BuildStrategy::ReduceStrategy::kReduce)
......
paddle/scripts/paddle_build.sh
浏览文件 @ 98ffde41
......@@ -600,7 +600,7 @@ EOF
if [[ ${WITH_GPU} == "ON" ]]; then
NCCL_DEPS="apt-get install -y --allow-downgrades libnccl2=2.2.13-1+cuda${CUDA_MAJOR} libnccl-dev=2.2.13-1+cuda${CUDA_MAJOR} || true"
else
NCCL_DEPS=""
NCCL_DEPS="true"
fi
if [[ ${WITH_FLUID_ONLY:-OFF} == "OFF" ]]; then
......
python/CMakeLists.txt
浏览文件 @ 98ffde41
......@@ -60,7 +60,7 @@ add_custom_command(OUTPUT ${PADDLE_PYTHON_BUILD_DIR}/.timestamp
COMMAND env ${py_env} ${PYTHON_EXECUTABLE} setup.py bdist_wheel
COMMAND ${CMAKE_COMMAND} -E touch ${PADDLE_PYTHON_BUILD_DIR}/.timestamp
COMMAND ${CMAKE_COMMAND} -E remove_directory ${PADDLE_PYTHON_BUILD_DIR}/lib-python
COMMAND ${CMAKE_COMMAND} -E copy_directory ${PADDLE_PYTHON_BUILD_DIR}/lib* ${PADDLE_PYTHON_BUILD_DIR}/lib-python
COMMAND ${CMAKE_COMMAND} -E copy_directory ${PADDLE_PYTHON_BUILD_DIR}/lib.* ${PADDLE_PYTHON_BUILD_DIR}/lib-python
DEPENDS gen_proto_py copy_paddle_pybind ${FLUID_CORE} framework_py_proto profiler_py_proto ${PY_FILES} ${external_project_dependencies} ${COPY_PADDLE_MASTER})
set(paddle_python_deps ${PADDLE_PYTHON_BUILD_DIR}/.timestamp ${MKL_DEPENDS})
......
python/paddle/dataset/flowers.py
浏览文件 @ 98ffde41
......@@ -35,16 +35,15 @@ import itertools
import functools
from .common import download
import tarfile
import six
import scipy.io as scio
from paddle.dataset.image import *
from paddle.reader import *
from paddle import compat as cpt
import os
import numpy as np
from multiprocessing import cpu_count
import six
from six.moves import cPickle as pickle
from six.moves import zip
__all__ = ['train', 'test', 'valid']
DATA_URL = 'http://paddlemodels.cdn.bcebos.com/flowers/102flowers.tgz'
......@@ -126,9 +125,11 @@ def reader_creator(data_file,
batch = pickle.load(f)
else:
batch = pickle.load(f, encoding='bytes')
if six.PY3:
batch = cpt.to_text(batch)
data = batch['data']
labels = batch['label']
for sample, label in zip(data, batch['label']):
for sample, label in six.moves.zip(data, batch['label']):
yield sample, int(label) - 1
if not cycle:
break
......
python/paddle/fluid/layers/tensor.py
浏览文件 @ 98ffde41
......@@ -24,21 +24,10 @@ from .layer_function_generator import templatedoc
import numpy
__all__ = [
'create_tensor',
'create_parameter',
'create_global_var',
'cast',
'concat',
'sums',
'assign',
'fill_constant_batch_size_like',
'fill_constant',
'argmin',
'argmax',
'argsort',
'ones',
'zeros',
'reverse',
'create_tensor', 'create_parameter', 'create_global_var', 'cast', 'concat',
'sums', 'assign', 'fill_constant_batch_size_like', 'fill_constant',
'argmin', 'argmax', 'argsort', 'ones', 'zeros', 'reverse', 'has_inf',
'has_nan', 'isfinite'
]
......@@ -652,3 +641,52 @@ def load_combine(out, file_path):
inputs={},
output={"Out": out},
args={"file_path": file_path})
def has_inf(x):
"""
Test if any of x contains an infinity number
Args:
x(variable): The Tensor/LoDTensor to be checked.
Returns:
Variable: The tensor variable storing the output, only a bool value.
"""
helper = LayerHelper("isinf", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(type="isinf", inputs={"X": x}, outputs={"Out": out})
return out
def has_nan(x):
"""
Test if any of x contains a NAN
Args:
x(variable): The Tensor/LoDTensor to be checked.
Returns:
Variable: The tensor variable storing the output, only a bool value.
"""
helper = LayerHelper("isnan", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(type="isnan", inputs={"X": x}, outputs={"Out": out})
return out
def isfinite(x):
"""
Test if any of x contains an infinity/NAN number. If all the elements are finite,
returns true, else false.
Args:
x(variable): The Tensor/LoDTensor to be checked.
Returns:
Variable: The tensor variable storing the output, contains a bool value.
"""
helper = LayerHelper("isfinite", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(type="isfinite", inputs={"X": x}, outputs={"Out": out})
return out
python/paddle/fluid/lod_tensor.py
浏览文件 @ 98ffde41
......@@ -74,7 +74,7 @@ def create_lod_tensor(data, recursive_seq_lens, place):
assert [
new_recursive_seq_lens
] == recursive_seq_lens, "data and recursive_seq_lens do not match"
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = np.concatenate(data, axis=0)
flattened_data = flattened_data.reshape([len(flattened_data), 1])
return create_lod_tensor(flattened_data, recursive_seq_lens, place)
elif isinstance(data, np.ndarray):
......
python/paddle/fluid/tests/unittests/test_elementwise_mul_op.py
浏览文件 @ 98ffde41
......@@ -16,6 +16,8 @@ from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid.core as core
from paddle.fluid.op import Operator
class ElementwiseMulOp(OpTest):
......@@ -115,5 +117,56 @@ class TestElementwiseMulOp_broadcast_3(ElementwiseMulOp):
}
class TestElementWiseMulSelectedRows(OpTest):
def setUp(self):
self.rows = [0, 1, 2, 3, 4, 5, 6]
self.feature = 12
self.height = 100
self.input_shape = (len(self.rows), self.feature)
def prepare_input(self, scope, place):
self.input = {
"X": np.random.random(self.input_shape).astype("float32"),
"Y": np.random.random(self.input_shape).astype("float32")
}
def init_input(in_name):
x_selected_rows = scope.var(in_name).get_selected_rows()
x_selected_rows.set_height(self.height)
x_selected_rows.set_rows(self.rows)
x_array = self.input[in_name]
x_tensor = x_selected_rows.get_tensor()
x_tensor.set(x_array, place)
init_input("X")
init_input("Y")
def create_out_selected_row(self, scope):
return scope.var('Out').get_selected_rows()
def check_result(self, out_selected_rows):
assert out_selected_rows.height() == self.height
assert out_selected_rows.rows() == self.rows
out_tensor = np.array(out_selected_rows.get_tensor())
assert out_tensor.shape == self.input_shape
def check_with_place(self, place):
scope = core.Scope()
self.prepare_input(scope, place)
out_selected_rows = self.create_out_selected_row(scope)
out_selected_rows.set_height(0)
out_selected_rows.set_rows([])
elementwise_mul = Operator("elementwise_mul", X='X', Y='Y', Out='Out')
elementwise_mul.run(scope, place)
self.check_result(out_selected_rows)
def test_elewisemul_with_selected_rows_input(self):
places = [core.CPUPlace()]
for place in places:
self.check_with_place(place)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_isfinite_op.py 0 → 100644
浏览文件 @ 98ffde41
# Copyright (c) 2018 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.
import unittest
import numpy as np
from op_test import OpTest
class TestInf(OpTest):
def setUp(self):
self.op_type = "isinf"
self.dtype = np.float32
self.init_dtype()
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
x[0] = np.inf
x[-1] = np.inf
self.inputs = {'X': x}
self.outputs = {'Out': np.array(True).astype(self.dtype)}
def init_dtype(self):
pass
def test_output(self):
self.check_output()
class TestFP16Inf(TestInf):
def init_dtype(self):
self.dtype = np.float16
class TestNAN(OpTest):
def setUp(self):
self.op_type = "isnan"
self.dtype = np.float32
self.init_dtype()
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
x[0] = np.nan
x[-1] = np.nan
self.inputs = {'X': x}
self.outputs = {'Out': np.array(True).astype(self.dtype)}
def init_dtype(self):
pass
def test_output(self):
self.check_output()
class TestFP16NAN(TestNAN):
def init_dtype(self):
self.dtype = np.float16
class TestIsfinite(OpTest):
def setUp(self):
self.op_type = "isfinite"
self.dtype = np.float32
self.init_dtype()
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
x[0] = np.inf
x[-1] = np.nan
out = np.isinf(x) | np.isnan(x)
self.inputs = {'X': x}
self.outputs = {'Out': np.array(False).astype(self.dtype)}
def init_dtype(self):
pass
def test_output(self):
self.check_output()
class TestFP16Isfinite(TestIsfinite):
def init_dtype(self):
self.dtype = np.float16
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_reduce_op.py
浏览文件 @ 98ffde41
......@@ -243,5 +243,87 @@ class TestKeepDimReduceSumMultiAxises(OpTest):
self.check_grad(['X'], 'Out')
class TestReduceSumWithDimOne(OpTest):
def setUp(self):
self.op_type = "reduce_sum"
self.inputs = {'X': np.random.random((10, 1, 1)).astype("float64")}
self.attrs = {'dim': [1, 2], 'keep_dim': True}
self.outputs = {
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
keepdims=True)
}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestReduceSumWithNumelOne(OpTest):
def setUp(self):
self.op_type = "reduce_sum"
self.inputs = {'X': np.random.random((1, 1)).astype("float64")}
self.attrs = {'dim': [1], 'keep_dim': False}
self.outputs = {
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
keepdims=False)
}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestReduceMeanWithDimOne(OpTest):
def setUp(self):
self.op_type = "reduce_mean"
self.inputs = {'X': np.random.random((10, 1, 1)).astype("float64")}
self.attrs = {'dim': [1], 'keep_dim': False}
self.outputs = {
'Out': self.inputs['X'].mean(
axis=tuple(self.attrs['dim']), keepdims=False)
}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestReduceMeanWithNumelOne(OpTest):
def setUp(self):
self.op_type = "reduce_mean"
self.inputs = {'X': np.random.random((1, 1)).astype("float64")}
self.attrs = {'dim': [1], 'keep_dim': True}
self.outputs = {
'Out': self.inputs['X'].mean(
axis=tuple(self.attrs['dim']), keepdims=True)
}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestReduceAll(OpTest):
def setUp(self):
self.op_type = "reduce_sum"
self.inputs = {'X': np.random.random((1, 1, 1)).astype("float64")}
self.attrs = {'reduce_all': True, 'keep_dim': False}
self.outputs = {'Out': self.inputs['X'].sum()}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
if __name__ == '__main__':
unittest.main()
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