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提交 dd0a4c35 编写于 作者: Y Yang Yu
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Merge branch 'feature/check_nan_executor' into feature/rnn_gradient_check

上级 d2cb2841 5139e6c7
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Showing with 442 addition and 184 deletion
paddle/framework/CMakeLists.txt
浏览文件 @ dd0a4c35
......@@ -6,13 +6,17 @@ cc_test(ddim_test SRCS ddim_test.cc DEPS ddim)
nv_test(dim_test SRCS dim_test.cu DEPS ddim)
if (WITH_GPU)
nv_library(tensor SRCS tensor.cc tensor_util.cu DEPS ddim place paddle_memory device_context)
nv_library(tensor SRCS tensor.cc tensor_util.cu DEPS ddim place paddle_memory device_context framework_proto)
else()
cc_library(tensor SRCS tensor.cc tensor_util.cc DEPS ddim place paddle_memory device_context)
cc_library(tensor SRCS tensor.cc tensor_util.cc DEPS ddim place paddle_memory device_context framework_proto)
endif ()
cc_test(tensor_test SRCS tensor_test.cc DEPS tensor)
cc_test(tensor_util_test SRCS tensor_util_test.cc DEPS tensor)
if (WITH_GPU)
nv_test(tensor_util_test SRCS tensor_util_test.cc tensor_util_test.cu DEPS tensor)
else()
cc_test(tensor_util_test SRCS tensor_util_test.cc DEPS tensor)
endif()
cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
......
paddle/framework/lod_tensor.cc
浏览文件 @ dd0a4c35
......@@ -189,62 +189,16 @@ void AppendLoD(LoD *lod, const LoD &lod_length) {
void SerializeToStream(std::ostream &os, const LoDTensor &tensor,
const platform::DeviceContext &dev_ctx) {
// TODO(typhoonzero): serialize to ostream
{ // the 1st field, uint32_t version
{ // the 1st field, uint32_t version for LoDTensor
constexpr uint32_t version = 0;
os.write(reinterpret_cast<const char *>(&version), sizeof(version));
}
{ // the 2nd field, tensor description
// int32_t size
// void* protobuf message
proto::TensorDesc desc;
desc.set_data_type(framework::ToDataType(tensor.type()));
auto dims = framework::vectorize(tensor.dims());
auto *pb_dims = desc.mutable_dims();
pb_dims->Resize(static_cast<int>(dims.size()), 0);
std::copy(dims.begin(), dims.end(), pb_dims->begin());
int32_t size = desc.ByteSize();
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
auto out = desc.SerializeAsString();
os.write(out.data(), size);
}
{ // the 3rd field, tensor data
uint64_t size = tensor.memory_size();
auto *data_ptr = tensor.data<void>();
PADDLE_ENFORCE(size < std::numeric_limits<std::streamsize>::max(),
"Index overflow when writing tensor");
if (platform::is_gpu_place(tensor.place())) {
#ifdef PADDLE_WITH_CUDA
constexpr size_t kBufSize = 1024 * 1024 * 64; // 64MB
std::unique_ptr<char[]> buf(new char[kBufSize]);
auto &gpu_dev_ctx =
static_cast<const platform::CUDADeviceContext &>(dev_ctx);
platform::CPUPlace cpu;
uintptr_t data = reinterpret_cast<uintptr_t>(data_ptr);
while (size != 0) {
size_t size_to_write = std::min(kBufSize, static_cast<size_t>(size));
memory::Copy(cpu, buf.get(),
boost::get<platform::CUDAPlace>(tensor.place()),
reinterpret_cast<const void *>(data), size_to_write,
gpu_dev_ctx.stream());
gpu_dev_ctx.Wait();
os.write(buf.get(), size_to_write);
data += size_to_write;
size -= size_to_write;
}
#else
PADDLE_THROW("Unexpected branch");
#endif
} else {
os.write(static_cast<const char *>(data_ptr),
static_cast<std::streamsize>(size));
}
}
{ // the 4th field, lod information
// uint64_t lod_level
// uint64_t lod_level_1 size in byte.
// int* lod_level_1 data
// ...
{
// the 2st field, LoD information
// uint64_t lod_level
// uint64_t lod_level_1 size in byte.
// int* lod_level_1 data
// ...
auto lod = tensor.lod();
uint64_t size = lod.size();
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
......@@ -256,49 +210,19 @@ void SerializeToStream(std::ostream &os, const LoDTensor &tensor,
static_cast<std::streamsize>(size));
}
}
// the 3st field, Tensor
SerializeToStream(os, static_cast<Tensor>(tensor), dev_ctx);
}
void DeserializeFromStream(std::istream &is, LoDTensor *tensor) {
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
proto::TensorDesc desc;
{ // int32_t size
// proto buffer
int32_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char *>(buf.get()), size);
PADDLE_ENFORCE(desc.ParseFromArray(buf.get(), size),
"Cannot parse tensor desc");
}
{ // read tensor
std::vector<int64_t> dims;
dims.reserve(static_cast<size_t>(desc.dims().size()));
std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
tensor->Resize(framework::make_ddim(dims));
void *buf;
platform::Place cpu = platform::CPUPlace();
switch (desc.data_type()) {
case proto::FP32:
buf = tensor->mutable_data<float>(cpu);
break;
case proto::FP64:
buf = tensor->mutable_data<double>(cpu);
break;
case proto::INT32:
buf = tensor->mutable_data<int>(cpu);
break;
case proto::INT64:
buf = tensor->mutable_data<int64_t>(cpu);
break;
default:
PADDLE_THROW("DataType %d not supported", desc.data_type());
}
is.read(static_cast<char *>(buf), tensor->memory_size());
}
{ // read lod
{
// the 1st field, unit32_t version for SelectedRows
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
}
{
// the 2st field, LoD information
uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
auto &lod = *tensor->mutable_lod();
......@@ -312,6 +236,8 @@ void DeserializeFromStream(std::istream &is, LoDTensor *tensor) {
lod[i] = tmp;
}
}
// the 3st filed, Tensor
DeserializeFromStream(is, static_cast<Tensor *>(tensor));
}
} // namespace framework
......
paddle/framework/lod_tensor_test.cc
浏览文件 @ dd0a4c35
......@@ -126,6 +126,20 @@ TEST_F(LoDTensorTester, ShrinkInLevel) {
EXPECT_NE(t1.data<float>(), lod_tensor_.data<float>());
}
TEST_F(LoDTensorTester, SerializeAndDeserialize) {
LoDTensor dst_tensor;
platform::CPUDeviceContext cpu_ctx((platform::CPUPlace()));
std::ostringstream oss;
SerializeToStream(oss, lod_tensor_, cpu_ctx);
std::istringstream iss(oss.str());
DeserializeFromStream(iss, &dst_tensor);
float* dst_ptr = dst_tensor.mutable_data<float>(platform::CPUPlace());
for (int i = 0; i < kLodTensorSize; ++i) {
EXPECT_EQ(dst_ptr[i], i);
}
EXPECT_EQ(dst_tensor.lod(), lod_tensor_.lod());
}
TEST(LodExpand, test) {
LoD lod{{0, 2}};
LoDTensor tensor;
......
paddle/framework/selected_rows.cc
浏览文件 @ dd0a4c35
......@@ -12,5 +12,58 @@ limitations under the License. */
#include "paddle/framework/selected_rows.h"
namespace paddle {
namespace framework {} // namespace framework
namespace framework {
void SerializeToStream(std::ostream& os, const SelectedRows& selected_rows,
const platform::DeviceContext& dev_ctx) {
{ // the 1st field, uint32_t version
constexpr uint32_t version = 0;
os.write(reinterpret_cast<const char*>(&version), sizeof(version));
}
{
// the 2st field, rows information
auto& rows = selected_rows.rows();
uint64_t size = rows.size();
os.write(reinterpret_cast<const char*>(&size), sizeof(size));
for (uint64_t i = 0; i < size; ++i) {
os.write(reinterpret_cast<const char*>(&rows[i]), sizeof(rows[i]));
}
}
{
// the 3st field, the height of SelectedRows
int64_t height = selected_rows.height();
os.write(reinterpret_cast<const char*>(&height), sizeof(height));
}
// the 4st field, Tensor data
SerializeToStream(os, selected_rows.value(), dev_ctx);
}
void DeserializeFromStream(std::istream& is, SelectedRows* selected_rows) {
auto tensor = *selected_rows->mutable_value();
{
// the 1st field, unit32_t version for SelectedRows
uint32_t version;
is.read(reinterpret_cast<char*>(&version), sizeof(version));
PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
}
{
// the 2st field, rows information
uint64_t size;
is.read(reinterpret_cast<char*>(&size), sizeof(size));
auto& rows = *selected_rows->mutable_rows();
rows.resize(size);
for (uint64_t i = 0; i < size; ++i) {
is.read(reinterpret_cast<char*>(&rows[i]), sizeof(int64_t));
}
}
{
// the 3st field, the height of the SelectedRows
int64_t height;
is.read(reinterpret_cast<char*>(&height), sizeof(int64_t));
selected_rows->set_height(height);
}
// the 4st field, tensor which contains the data
DeserializeFromStream(is, &tensor);
}
} // namespace framework
} // namespace paddle
paddle/framework/selected_rows.h
浏览文件 @ dd0a4c35
......@@ -59,5 +59,14 @@ class SelectedRows {
int64_t height_;
};
/*
* Serialize/Desiralize SelectedRows to std::ostream
* You can pass ofstream or ostringstream to serilize to file
* or to a in memory string. GPU tensor will be copied to CPU.
*/
void SerializeToStream(std::ostream& os, const SelectedRows& selected_rows,
const platform::DeviceContext& dev_ctx);
void DeserializeFromStream(std::istream& is, SelectedRows* selected_rows);
} // namespace framework
} // namespace paddle
paddle/framework/selected_rows_test.cc
浏览文件 @ dd0a4c35
......@@ -43,5 +43,19 @@ TEST_F(SelectedRowsTester, complete_dims) {
ASSERT_EQ(selected_rows_->GetCompleteDims(), make_ddim({10, 100}));
}
TEST_F(SelectedRowsTester, SerializeAndDeseralize) {
SelectedRows dst_tensor;
platform::CPUDeviceContext cpu_ctx(place_);
std::ostringstream oss;
SerializeToStream(oss, *selected_rows_, cpu_ctx);
std::istringstream iss(oss.str());
DeserializeFromStream(iss, &dst_tensor);
ASSERT_EQ(selected_rows_->rows(), dst_tensor.rows());
ASSERT_EQ(selected_rows_->height(), dst_tensor.height());
}
} // namespace framework
} // namespace paddle
paddle/framework/tensor_test.cc
浏览文件 @ dd0a4c35
......@@ -15,12 +15,13 @@
#include <gtest/gtest.h>
#include <string>
namespace framework = paddle::framework;
namespace platform = paddle::platform;
TEST(Tensor, Dims) {
using namespace paddle::framework;
using namespace paddle::platform;
Tensor tt;
framework::Tensor tt;
tt.Resize({2, 3, 4});
DDim dims = tt.dims();
framework::DDim dims = tt.dims();
ASSERT_EQ(arity(dims), 3);
for (int i = 0; i < 3; ++i) {
EXPECT_EQ(i + 2, dims[i]);
......@@ -28,12 +29,12 @@ TEST(Tensor, Dims) {
}
TEST(Tensor, DataAssert) {
paddle::framework::Tensor src_tensor;
framework::Tensor src_tensor;
bool caught = false;
try {
src_tensor.data<double>();
} catch (paddle::platform::EnforceNotMet err) {
} catch (platform::EnforceNotMet err) {
caught = true;
std::string msg =
"holder_ should not be null\nTensor holds no memory. Call "
......@@ -50,61 +51,65 @@ TEST(Tensor, DataAssert) {
because Memory::Alloc() and Memory::Free() have not been ready.
*/
TEST(Tensor, MutableData) {
using namespace paddle::framework;
using namespace paddle::platform;
{
Tensor src_tensor;
framework::Tensor src_tensor;
float* p1 = nullptr;
float* p2 = nullptr;
// initialization
p1 = src_tensor.mutable_data<float>(make_ddim({1, 2, 3}), CPUPlace());
p1 = src_tensor.mutable_data<float>(framework::make_ddim({1, 2, 3}),
platform::CPUPlace());
EXPECT_NE(p1, nullptr);
// set src_tensor a new dim with large size
// momery is supposed to be re-allocated
p2 = src_tensor.mutable_data<float>(make_ddim({3, 4}), CPUPlace());
p2 = src_tensor.mutable_data<float>(framework::make_ddim({3, 4}),
platform::CPUPlace());
EXPECT_NE(p2, nullptr);
EXPECT_NE(p1, p2);
// set src_tensor a new dim with same size
// momery block is supposed to be unchanged
p1 = src_tensor.mutable_data<float>(make_ddim({2, 2, 3}), CPUPlace());
p1 = src_tensor.mutable_data<float>(framework::make_ddim({2, 2, 3}),
platform::CPUPlace());
EXPECT_EQ(p1, p2);
// set src_tensor a new dim with smaller size
// momery block is supposed to be unchanged
p2 = src_tensor.mutable_data<float>(make_ddim({2, 2}), CPUPlace());
p2 = src_tensor.mutable_data<float>(framework::make_ddim({2, 2}),
platform::CPUPlace());
EXPECT_EQ(p1, p2);
}
#ifdef PADDLE_WITH_CUDA
{
Tensor src_tensor;
framework::Tensor src_tensor;
float* p1 = nullptr;
float* p2 = nullptr;
// initialization
p1 = src_tensor.mutable_data<float>(make_ddim({1, 2, 3}), CUDAPlace());
p1 = src_tensor.mutable_data<float>(framework::make_ddim({1, 2, 3}),
platform::CUDAPlace());
EXPECT_NE(p1, nullptr);
// set src_tensor a new dim with large size
// momery is supposed to be re-allocated
p2 = src_tensor.mutable_data<float>(make_ddim({3, 4}), CUDAPlace());
p2 = src_tensor.mutable_data<float>(framework::make_ddim({3, 4}),
platform::CUDAPlace());
EXPECT_NE(p2, nullptr);
EXPECT_NE(p1, p2);
// set src_tensor a new dim with same size
// momery block is supposed to be unchanged
p1 = src_tensor.mutable_data<float>(make_ddim({2, 2, 3}), CUDAPlace());
p1 = src_tensor.mutable_data<float>(framework::make_ddim({2, 2, 3}),
platform::CUDAPlace());
EXPECT_EQ(p1, p2);
// set src_tensor a new dim with smaller size
// momery block is supposed to be unchanged
p2 = src_tensor.mutable_data<float>(make_ddim({2, 2}), CUDAPlace());
p2 = src_tensor.mutable_data<float>(framework::make_ddim({2, 2}),
platform::CUDAPlace());
EXPECT_EQ(p1, p2);
}
#endif
}
TEST(Tensor, ShareDataWith) {
using namespace paddle::framework;
using namespace paddle::platform;
{
Tensor src_tensor;
Tensor dst_tensor;
framework::Tensor src_tensor;
framework::Tensor dst_tensor;
// Try to share data form uninitialized tensor
bool caught = false;
try {
......@@ -121,16 +126,18 @@ TEST(Tensor, ShareDataWith) {
}
ASSERT_TRUE(caught);
src_tensor.mutable_data<int>(make_ddim({2, 3, 4}), CPUPlace());
src_tensor.mutable_data<int>(framework::make_ddim({2, 3, 4}),
platform::CPUPlace());
dst_tensor.ShareDataWith(src_tensor);
ASSERT_EQ(src_tensor.data<int>(), dst_tensor.data<int>());
}
#ifdef PADDLE_WITH_CUDA
{
Tensor src_tensor;
Tensor dst_tensor;
src_tensor.mutable_data<int>(make_ddim({2, 3, 4}), CUDAPlace());
framework::Tensor src_tensor;
framework::Tensor dst_tensor;
src_tensor.mutable_data<int>(framework::make_ddim({2, 3, 4}),
platform::CUDAPlace());
dst_tensor.ShareDataWith(src_tensor);
ASSERT_EQ(src_tensor.data<int>(), dst_tensor.data<int>());
}
......@@ -138,13 +145,12 @@ TEST(Tensor, ShareDataWith) {
}
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();
framework::Tensor src_tensor;
src_tensor.mutable_data<int>(framework::make_ddim({5, 3, 4}),
platform::CPUPlace());
framework::Tensor slice_tensor = src_tensor.Slice(1, 3);
framework::DDim slice_dims = slice_tensor.dims();
ASSERT_EQ(arity(slice_dims), 3);
EXPECT_EQ(slice_dims[0], 2);
EXPECT_EQ(slice_dims[1], 3);
......@@ -153,11 +159,12 @@ TEST(Tensor, Slice) {
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()));
src_tensor.mutable_data<int>(src_tensor.dims(), platform::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()));
uintptr_t slice_mutable_data_address =
reinterpret_cast<uintptr_t>(slice_tensor.mutable_data<int>(
slice_tensor.dims(), platform::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);
......@@ -165,22 +172,25 @@ TEST(Tensor, Slice) {
#ifdef PADDLE_WITH_CUDA
{
Tensor src_tensor;
src_tensor.mutable_data<double>(make_ddim({6, 9}), CUDAPlace());
Tensor slice_tensor = src_tensor.Slice(2, 6);
DDim slice_dims = slice_tensor.dims();
framework::Tensor src_tensor;
src_tensor.mutable_data<double>(framework::make_ddim({6, 9}),
platform::CUDAPlace());
framework::Tensor slice_tensor = src_tensor.Slice(2, 6);
framework::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(), CUDAPlace()));
uintptr_t src_mutable_data_address =
reinterpret_cast<uintptr_t>(src_tensor.mutable_data<double>(
src_tensor.dims(), platform::CUDAPlace()));
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(), CUDAPlace()));
uintptr_t slice_mutable_data_address =
reinterpret_cast<uintptr_t>(slice_tensor.mutable_data<double>(
slice_tensor.dims(), platform::CUDAPlace()));
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);
......@@ -189,23 +199,19 @@ TEST(Tensor, Slice) {
}
TEST(Tensor, ReshapeToMatrix) {
using namespace paddle::framework;
using namespace paddle::platform;
Tensor src;
int* src_ptr = src.mutable_data<int>({2, 3, 4, 9}, CPUPlace());
framework::Tensor src;
int* src_ptr = src.mutable_data<int>({2, 3, 4, 9}, platform::CPUPlace());
for (int i = 0; i < 2 * 3 * 4 * 9; ++i) {
src_ptr[i] = i;
}
Tensor res = ReshapeToMatrix(src, 2);
framework::Tensor res = framework::ReshapeToMatrix(src, 2);
ASSERT_EQ(res.dims()[0], 2 * 3);
ASSERT_EQ(res.dims()[1], 4 * 9);
}
TEST(Tensor, Layout) {
using namespace paddle::framework;
using namespace paddle::platform;
Tensor src;
ASSERT_EQ(src.layout(), DataLayout::kNHWC);
src.set_layout(DataLayout::kAnyLayout);
ASSERT_EQ(src.layout(), DataLayout::kAnyLayout);
framework::Tensor src;
ASSERT_EQ(src.layout(), framework::DataLayout::kNHWC);
src.set_layout(framework::DataLayout::kAnyLayout);
ASSERT_EQ(src.layout(), framework::DataLayout::kAnyLayout);
}
paddle/framework/tensor_util.cc
浏览文件 @ dd0a4c35
......@@ -31,6 +31,7 @@ struct AnyDTypeVisitor {
void operator()() const {
auto t = EigenVector<T>::Flatten(tensor_);
auto o = EigenScalar<bool>::From(*out_);
// return any of predicate_(t) is true.
o.device(*ctx_.eigen_device()) = predicate_(t).any();
}
};
......@@ -66,9 +67,10 @@ struct AnyVisitor : public boost::static_visitor<bool> {
framework::Tensor tmp;
tmp.Resize({1});
tmp.mutable_data<bool>(cpu);
platform::DeviceContextPool::Instance().Get(gpu)->Wait();
CopyFrom(out, cpu, &tmp);
platform::DeviceContextPool::Instance().Get(gpu)->Wait();
auto gpuctx = platform::DeviceContextPool::Instance().Get(gpu);
gpuctx->Wait();
CopyFrom(out, cpu, *gpuctx, &tmp);
gpuctx->Wait();
return GetResult(tmp, cpu);
}
......@@ -89,6 +91,7 @@ struct HasNANPredicate {
template <typename T>
auto operator()(const T& eigen_vec) const
-> decltype(std::declval<T>().isnan()) {
// Cast eigen_vector to vector of bool. true if is inf.
return eigen_vec.isnan();
}
};
......@@ -102,6 +105,7 @@ struct HasInfPredicate {
template <typename T>
auto operator()(const T& eigen_vec) const
-> decltype(std::declval<T>().isinf()) {
// Cast eigen_vector to vector of bool. true if is inf.
return eigen_vec.isinf();
}
};
......
paddle/framework/tensor_util.h
浏览文件 @ dd0a4c35
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include "paddle/framework/data_type.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
......@@ -208,8 +209,109 @@ inline void CopyToVector(const Tensor& src, std::vector<T>* dst) {
src_ptr, size);
}
extern bool HasNAN(const framework::Tensor& tensor);
extern bool HasInf(const framework::Tensor& tensor);
// Returns true if a tensor contains NAN, i.e., Not A Number.
bool HasNAN(const framework::Tensor& tensor);
// Returns true if a tensor contains Inf, i.e., Infinity.
bool HasInf(const framework::Tensor& tensor);
inline void SerializeToStream(std::ostream& os, const Tensor& tensor,
const platform::DeviceContext& dev_ctx) {
// TODO(typhoonzero): serialize to ostream
{ // the 1st field, uint32_t version
constexpr uint32_t version = 0;
os.write(reinterpret_cast<const char*>(&version), sizeof(version));
}
{ // the 2nd field, tensor description
// int32_t size
// void* protobuf message
proto::TensorDesc desc;
desc.set_data_type(framework::ToDataType(tensor.type()));
auto dims = framework::vectorize(tensor.dims());
auto* pb_dims = desc.mutable_dims();
pb_dims->Resize(static_cast<int>(dims.size()), 0);
std::copy(dims.begin(), dims.end(), pb_dims->begin());
int32_t size = desc.ByteSize();
os.write(reinterpret_cast<const char*>(&size), sizeof(size));
auto out = desc.SerializeAsString();
os.write(out.data(), size);
}
{ // the 3rd field, tensor data
uint64_t size = tensor.memory_size();
auto* data_ptr = tensor.data<void>();
PADDLE_ENFORCE(size < std::numeric_limits<std::streamsize>::max(),
"Index overflow when writing tensor");
if (platform::is_gpu_place(tensor.place())) {
#ifdef PADDLE_WITH_CUDA
constexpr size_t kBufSize = 1024 * 1024 * 64; // 64MB
std::unique_ptr<char[]> buf(new char[kBufSize]);
auto& gpu_dev_ctx =
static_cast<const platform::CUDADeviceContext&>(dev_ctx);
platform::CPUPlace cpu;
uintptr_t data = reinterpret_cast<uintptr_t>(data_ptr);
while (size != 0) {
size_t size_to_write = std::min(kBufSize, static_cast<size_t>(size));
memory::Copy(cpu, buf.get(),
boost::get<platform::CUDAPlace>(tensor.place()),
reinterpret_cast<const void*>(data), size_to_write,
gpu_dev_ctx.stream());
gpu_dev_ctx.Wait();
os.write(buf.get(), size_to_write);
data += size_to_write;
size -= size_to_write;
}
#else
PADDLE_THROW("Unexpected branch");
#endif
} else {
os.write(static_cast<const char*>(data_ptr),
static_cast<std::streamsize>(size));
}
}
}
inline void DeserializeFromStream(std::istream& is, Tensor* tensor) {
uint32_t version;
is.read(reinterpret_cast<char*>(&version), sizeof(version));
PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
proto::TensorDesc desc;
{ // int32_t size
// proto buffer
int32_t size;
is.read(reinterpret_cast<char*>(&size), sizeof(size));
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char*>(buf.get()), size);
PADDLE_ENFORCE(desc.ParseFromArray(buf.get(), size),
"Cannot parse tensor desc");
}
{ // read tensor
std::vector<int64_t> dims;
dims.reserve(static_cast<size_t>(desc.dims().size()));
std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
tensor->Resize(framework::make_ddim(dims));
void* buf;
platform::Place cpu = platform::CPUPlace();
// TODO(Yancey1989): use VisiterDataType instead of DataType switch
switch (desc.data_type()) {
case proto::FP32:
buf = tensor->mutable_data<float>(cpu);
break;
case proto::FP64:
buf = tensor->mutable_data<double>(cpu);
break;
case proto::INT32:
buf = tensor->mutable_data<int>(cpu);
break;
case proto::INT64:
buf = tensor->mutable_data<int64_t>(cpu);
break;
default:
PADDLE_THROW("DataType %d not supported", desc.data_type());
}
is.read(static_cast<char*>(buf), tensor->memory_size());
}
}
} // namespace framework
} // namespace paddle
paddle/framework/tensor_util_test.cc
浏览文件 @ dd0a4c35
......@@ -231,7 +231,7 @@ TEST(CopyToVector, Tensor) {
#endif
}
TEST(IsNAN, CPU) {
TEST(HasNAN, CPU) {
using namespace paddle::framework;
using namespace paddle::platform;
Tensor src;
......@@ -243,7 +243,7 @@ TEST(IsNAN, CPU) {
ASSERT_TRUE(HasNAN(src));
}
TEST(IsInf, CPU) {
TEST(HasInf, CPU) {
using namespace paddle::framework;
using namespace paddle::platform;
Tensor src;
......@@ -254,5 +254,55 @@ TEST(IsInf, CPU) {
ASSERT_TRUE(HasInf(src));
}
TEST(Tensor, SerializeAndDeserialize) {
framework::Tensor src_tensor;
int array[6] = {1, 2, 3, 4, 5, 6};
src_tensor.Resize({2, 3});
int* src_ptr = src_tensor.mutable_data<int>(platform::CPUPlace());
for (int i = 0; i < 6; ++i) {
src_ptr[i] = array[i];
}
{
framework::Tensor dst_tensor;
auto place = new platform::CPUPlace();
platform::CPUDeviceContext cpu_ctx(*place);
std::ostringstream oss;
SerializeToStream(oss, src_tensor, cpu_ctx);
std::istringstream iss(oss.str());
DeserializeFromStream(iss, &dst_tensor);
int* dst_ptr = dst_tensor.mutable_data<int>(platform::CPUPlace());
for (int i = 0; i < 5; ++i) {
ASSERT_EQ(dst_ptr[i], array[i]);
}
delete place;
}
#ifdef PADDLE_WITH_CUDA
{
Tensor gpu_tensor;
gpu_tensor.Resize({2, 3});
Tensor dst_tensor;
auto gpu_place = new platform::CUDAPlace();
platform::CUDADeviceContext gpu_ctx(*gpu_place);
CopyFrom(src_tensor, *gpu_place, gpu_ctx, &gpu_tensor);
std::ostringstream oss;
SerializeToStream(oss, gpu_tensor, gpu_ctx);
std::istringstream iss(oss.str());
DeserializeFromStream(iss, &dst_tensor);
int* dst_ptr = dst_tensor.mutable_data<int>(platform::CPUPlace());
for (int i = 0; i < 6; ++i) {
ASSERT_EQ(dst_ptr[i], array[i]);
}
delete gpu_place;
}
#endif
}
} // namespace framework
} // namespace paddle
paddle/framework/tensor_util_test.cu 0 → 100644
浏览文件 @ dd0a4c35
/* Copyright (c) 2016 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 "gtest/gtest.h"
#include "paddle/framework/tensor_util.h"
#include "paddle/platform/device_context.h"
#include "paddle/platform/place.h"
namespace paddle {
namespace framework {
static __global__ void FillNAN(float* buf) {
buf[0] = 0.0;
buf[1] = 0.1;
buf[2] = NAN;
}
static __global__ void FillInf(float* buf) {
buf[0] = 0.0;
buf[1] = INFINITY;
buf[2] = 0.5;
}
TEST(HasNAN, GPU) {
Tensor tensor;
platform::CUDAPlace gpu(0);
auto& pool = platform::DeviceContextPool::Instance();
auto* cuda_ctx = pool.GetByPlace(gpu);
float* buf = tensor.mutable_data<float>({3}, gpu);
FillNAN<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
ASSERT_TRUE(HasNAN(tensor));
}
TEST(HasInf, GPU) {
Tensor tensor;
platform::CUDAPlace gpu(0);
auto& pool = platform::DeviceContextPool::Instance();
auto* cuda_ctx = pool.GetByPlace(gpu);
float* buf = tensor.mutable_data<float>({3}, gpu);
FillInf<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
ASSERT_TRUE(HasInf(tensor));
}
} // namespace framework
} // namespace paddle
paddle/framework/threadpool.h
浏览文件 @ dd0a4c35
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <condition_variable>
#include <functional>
#include <future>
#include <mutex>
#include <queue>
#include <thread>
......@@ -25,10 +26,11 @@ limitations under the License. */
namespace paddle {
namespace framework {
typedef std::function<void()> Task;
class ThreadPool {
public:
typedef std::packaged_task<void()> Task;
typedef std::function<void()> Fun;
/**
* @brief Get a instance of threadpool, the thread number will
* be specified as the number of hardware thread contexts
......@@ -61,13 +63,18 @@ class ThreadPool {
/**
* @brief Push a function to the queue, and will be scheduled and
* executed if a thread is available.
* @param[in] Task will be pushed to the task queue.
* @param[in] Task, will be pushed to the task queue.
* @return std::future<void>, we could wait for the task finished by
* f.wait().
*/
void Run(const Task& fn) {
std::future<void> Run(const Fun& fn) {
std::unique_lock<std::mutex> lock(mutex_);
tasks_.push(fn);
Task task(std::bind(fn));
std::future<void> f = task.get_future();
tasks_.push(std::move(task));
lock.unlock();
scheduled_.notify_one();
return f;
}
/**
......@@ -110,7 +117,7 @@ class ThreadPool {
break;
}
// pop a task from the task queue
auto task = tasks_.front();
auto task = std::move(tasks_.front());
tasks_.pop();
--available_;
......
paddle/framework/threadpool_test.cc
浏览文件 @ dd0a4c35
......@@ -20,16 +20,21 @@ limitations under the License. */
namespace framework = paddle::framework;
void do_sum(framework::ThreadPool* pool, std::atomic<int>& sum, int cnt) {
std::vector<std::future<void>> fs;
for (int i = 0; i < cnt; ++i) {
pool->Run([&sum]() { sum.fetch_add(1); });
auto f = pool->Run([&sum]() { sum.fetch_add(1); });
fs.push_back(std::move(f));
}
for (auto& f : fs) {
f.wait();
}
}
TEST(ThreadPool, ConcurrentInit) {
framework::ThreadPool* pool;
int concurrent_cnt = 50;
int n = 50;
std::vector<std::thread> threads;
for (int i = 0; i < concurrent_cnt; ++i) {
for (int i = 0; i < n; ++i) {
std::thread t([&pool]() { pool = framework::ThreadPool::GetInstance(); });
threads.push_back(std::move(t));
}
......@@ -38,13 +43,13 @@ TEST(ThreadPool, ConcurrentInit) {
}
}
TEST(ThreadPool, ConcurrentStart) {
TEST(ThreadPool, ConcurrentRun) {
framework::ThreadPool* pool = framework::ThreadPool::GetInstance();
std::atomic<int> sum(0);
std::vector<std::thread> threads;
int concurrent_cnt = 50;
int n = 50;
// sum = (n * (n + 1)) / 2
for (int i = 1; i <= concurrent_cnt; ++i) {
for (int i = 1; i <= n; ++i) {
std::thread t(do_sum, pool, std::ref(sum), i);
threads.push_back(std::move(t));
}
......@@ -52,5 +57,5 @@ TEST(ThreadPool, ConcurrentStart) {
t.join();
}
pool->Wait();
EXPECT_EQ(sum, ((concurrent_cnt + 1) * concurrent_cnt) / 2);
EXPECT_EQ(sum, ((n + 1) * n) / 2);
}
paddle/operators/conv_op.cc
浏览文件 @ dd0a4c35
......@@ -31,8 +31,6 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
int groups = ctx->Attrs().Get<int>("groups");
std::vector<int> dilations = ctx->Attrs().Get<std::vector<int>>("dilations");
int input_channels = in_dims[1];
int output_channels = filter_dims[0];
PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 5,
"Conv intput should be 4-D or 5-D tensor.");
......@@ -45,9 +43,13 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE_EQ(
paddings.size(), strides.size(),
"Conv paddings dimension and Conv strides dimension should be the same.");
int input_channels = in_dims[1];
PADDLE_ENFORCE_EQ(input_channels, filter_dims[1] * groups,
"The number of input channels should be equal to filter "
"channels * groups.");
int output_channels = filter_dims[0];
PADDLE_ENFORCE_EQ(
output_channels % groups, 0,
"The number of output channels should be divided by groups.");
......
paddle/operators/load_op.cc
浏览文件 @ dd0a4c35
......@@ -38,7 +38,7 @@ class LoadOp : public framework::OperatorBase {
out_var_name);
auto *tensor = out_var->GetMutable<framework::LoDTensor>();
framework::DeserializeFromStream(fin, tensor);
DeserializeFromStream(fin, tensor);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
......
paddle/operators/math/CMakeLists.txt
浏览文件 @ dd0a4c35
......@@ -9,9 +9,9 @@ if(WITH_GPU)
nv_library(cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS device_context)
nv_library(pooling SRCS pooling.cc pooling.cu DEPS device_context)
nv_library(sequence_pooling SRCS sequence_pooling.cc sequence_pooling.cu DEPS device_context math_function)
nv_library(vol2col SRCS vol2col.cc vol2col.cu DEPS device_context)
nv_library(vol2col SRCS vol2col.cc vol2col.cu DEPS device_context tensor)
nv_library(context_project SRCS context_project.cc context_project.cu DEPS device_context math_function)
nv_library(sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context)
nv_library(sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context tensor)
nv_library(lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions)
nv_library(maxouting SRCS maxouting.cc maxouting.cu DEPS device_context)
nv_library(unpooling SRCS unpooling.cc unpooling.cu DEPS device_context)
......@@ -23,9 +23,9 @@ else()
cc_library(cross_entropy SRCS cross_entropy.cc DEPS device_context)
cc_library(pooling SRCS pooling.cc DEPS device_context)
cc_library(sequence_pooling SRCS sequence_pooling.cc DEPS device_context math_function)
cc_library(vol2col SRCS vol2col.cc DEPS device_context)
cc_library(vol2col SRCS vol2col.cc DEPS device_context tensor)
cc_library(context_project SRCS context_project.cc DEPS device_context math_function)
cc_library(sequence2batch SRCS sequence2batch.cc DEPS device_context)
cc_library(sequence2batch SRCS sequence2batch.cc DEPS device_context tensor)
cc_library(lstm_compute SRCS lstm_compute.cc DEPS device_context activation_functions)
cc_library(maxouting SRCS maxouting.cc DEPS device_context)
cc_library(unpooling SRCS unpooling.cc DEPS device_context)
......
paddle/platform/for_range.h
浏览文件 @ dd0a4c35
......@@ -62,7 +62,7 @@ struct ForRange<CUDADeviceContext> {
template <typename Function>
inline void operator()(Function func) const {
constexpr size_t num_threads = 1024;
constexpr int num_threads = 1024;
int block_size = limit_ <= num_threads ? limit_ : num_threads;
int grid_size = (limit_ + num_threads - 1) / num_threads;
......
paddle/pybind/CMakeLists.txt
浏览文件 @ dd0a4c35
......@@ -3,6 +3,7 @@ if(WITH_PYTHON)
SRCS pybind.cc exception.cc protobuf.cc const_value.cc
DEPS pybind python backward proto_desc paddle_memory executor prune init
${GLOB_OP_LIB})
target_link_libraries(paddle_pybind rt)
endif(WITH_PYTHON)
if(WITH_DOC)
......
paddle/scripts/docker/build.sh
浏览文件 @ dd0a4c35
......@@ -178,7 +178,7 @@ EOF
# run paddle version to install python packages first
RUN apt-get update &&\
${NCCL_DEPS}\
apt-get install -y wget python-pip dmidecode && pip install -U pip && \
apt-get install -y wget python-pip dmidecode python-tk && pip install -U pip && \
pip install /*.whl; apt-get install -f -y && \
apt-get clean -y && \
rm -f /*.whl && \
......
python/paddle/v2/dataset/flowers.py
浏览文件 @ dd0a4c35
......@@ -44,7 +44,7 @@ __all__ = ['train', 'test', 'valid']
DATA_URL = 'http://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz'
LABEL_URL = 'http://www.robots.ox.ac.uk/~vgg/data/flowers/102/imagelabels.mat'
SETID_URL = 'http://www.robots.ox.ac.uk/~vgg/data/flowers/102/setid.mat'
DATA_MD5 = '52808999861908f626f3c1f4e79d11fa'
DATA_MD5 = '33bfc11892f1e405ca193ae9a9f2a118'
LABEL_MD5 = 'e0620be6f572b9609742df49c70aed4d'
SETID_MD5 = 'a5357ecc9cb78c4bef273ce3793fc85c'
# In official 'readme', tstid is the flag of test data
......
python/paddle/v2/fluid/data_feeder.py
浏览文件 @ dd0a4c35
......@@ -3,7 +3,7 @@ import core
import numpy
import six.moves as six
from framework import Variable
from framework import Variable, default_main_program
__all__ = ['DataFeeder']
......@@ -53,12 +53,16 @@ class DataToLoDTensorConverter(object):
class DataFeeder(object):
def __init__(self, feed_list, place):
def __init__(self, feed_list, place, program=None):
self.feed_dtypes = []
self.feed_names = []
self.feed_shapes = []
self.feed_lod_level = []
if program is None:
program = default_main_program()
for each_var in feed_list:
if isinstance(each_var, basestring):
each_var = program.block(0).var(each_var)
if not isinstance(each_var, Variable):
raise TypeError("Feed list should contain a list of variable")
self.feed_dtypes.append(each_var.dtype)
......
python/paddle/v2/fluid/io.py
浏览文件 @ dd0a4c35
......@@ -188,7 +188,7 @@ def save_inference_model(dirname,
raise ValueError("'feed_var_names' should be a list of str.")
if isinstance(target_vars, Variable):
feeded_var_names = [feeded_var_names]
target_vars = [target_vars]
else:
if not (bool(target_vars) and all(
isinstance(var, Variable) for var in target_vars)):
......
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