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未验证 提交 728c0624 编写于 作者: X xiongkun 提交者： GitHub
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change Vector to std::vector and provide MixVector class as a helper … (#39559) 

* change Vector to std::vector and provide MixVector class as a helper wrapper class

* solve the multi-gpu hang problem

* remove the duplicate template instantialize

* Copy vector to cpu

* add CopyToCPU

* xxx

* final version: fix the problem of all reduce

* remove mixvector dependence

* fix

* merge

* fix code

* fix by CI
上级 d56a0a1b
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Showing with 792 addition and 766 deletion
paddle/fluid/framework/lod_tensor_test.cu
浏览文件 @ 728c0624
......@@ -31,15 +31,17 @@ TEST(LoD, data) {
lod.push_back(std::vector<size_t>({0, 1, 6, 8, 10, 11}));
auto& v = lod[0];
paddle::framework::MixVector<size_t> mix_vector_v(&v);
paddle::platform::CUDAPlace gpu(0);
#ifdef PADDLE_WITH_HIP
hipLaunchKernelGGL(test, dim3(1), dim3(1), 0, 0, v.CUDAMutableData(gpu),
v.size());
hipLaunchKernelGGL(test, dim3(1), dim3(1), 0, 0,
mix_vector_v.CUDAMutableData(gpu), v.size());
hipDeviceSynchronize();
#else
test<<<1, 1>>>(v.CUDAMutableData(gpu), v.size());
test<<<1, 1>>>(mix_vector_v.CUDAMutableData(gpu), v.size());
cudaDeviceSynchronize();
#endif
mix_vector_v.CopyToCPU();
for (size_t i = 0; i < v.size(); ++i) {
EXPECT_EQ(v[i], i * 2);
}
......@@ -62,15 +64,17 @@ TEST(LoDTensor, LoDInGPU) {
EXPECT_EQ(lod_tensor.lod_element(0, 4).first, 8UL);
auto lod = lod_tensor.lod();
paddle::framework::MixVector<size_t> mix_vector(&(lod[0]));
#ifdef PADDLE_WITH_HIP
hipLaunchKernelGGL(test, dim3(1), dim3(8), 0, 0,
lod[0].CUDAMutableData(place), lod[0].size());
mix_vector.CUDAMutableData(place), lod[0].size());
hipDeviceSynchronize();
#else
test<<<1, 8>>>(lod[0].CUDAMutableData(place), lod[0].size());
test<<<1, 8>>>(mix_vector.CUDAMutableData(place), lod[0].size());
cudaDeviceSynchronize();
#endif
mix_vector.CopyToCPU();
for (size_t i = 0; i < src_lod[0].size(); ++i) {
EXPECT_EQ(lod[0].data()[i], src_lod[0].data()[i] * 2);
......
paddle/fluid/framework/mixed_vector.cc
浏览文件 @ 728c0624
......@@ -64,19 +64,20 @@ void CopyCPUDataToCUDAHelper(std::vector<T> *cpu_,
auto stream = dev_ctx->stream();
paddle::memory::Copy(OptionalCUDAPlace(*gpu_).get(), dst,
platform::CPUPlace(), src, *gpu_memory_size_, stream);
dev_ctx->Wait();
#endif
}
#define INSTANTIATE_VECTOR_FOR_TYPE(__TYPE__) \
template <> \
void Vector<__TYPE__>::VectorData::CopyToCPU() const { \
CopyToCPUHelper<__TYPE__>(&cpu_, &gpu_, &gpu_memory_size_); \
} \
\
template <> \
void Vector<__TYPE__>::VectorData::CopyCPUDataToCUDA( \
const platform::Place &place) const { \
CopyCPUDataToCUDAHelper<__TYPE__>(&cpu_, &gpu_, &gpu_memory_size_, place); \
#define INSTANTIATE_VECTOR_FOR_TYPE(__TYPE__) \
template <> \
void MixVector<__TYPE__>::VectorData::CopyToCPU() const { \
CopyToCPUHelper<__TYPE__>(cpu_, &gpu_, &gpu_memory_size_); \
} \
\
template <> \
void MixVector<__TYPE__>::VectorData::CopyCPUDataToCUDA( \
const platform::Place &place) const { \
CopyCPUDataToCUDAHelper<__TYPE__>(cpu_, &gpu_, &gpu_memory_size_, place); \
}
INSTANTIATE_VECTOR_FOR_TYPE(size_t)
......
paddle/fluid/framework/mixed_vector.h
浏览文件 @ 728c0624
......@@ -22,7 +22,6 @@ limitations under the License. */
#include <vector>
#include "glog/logging.h"
#include "paddle/fluid/framework/details/cow_ptr.h"
#include "paddle/fluid/memory/allocation/allocator.h"
#include "paddle/utils/none.h"
#include "paddle/utils/optional.h"
......@@ -30,6 +29,9 @@ limitations under the License. */
namespace paddle {
namespace framework {
template <class T>
using Vector = std::vector<T>;
inline paddle::optional<platform::CUDAPlace> OptionalCUDAPlace(
const paddle::memory::allocation::AllocationPtr &gpu_) {
return gpu_ == nullptr ? paddle::none
......@@ -39,7 +41,7 @@ inline paddle::optional<platform::CUDAPlace> OptionalCUDAPlace(
// Vector<T> implements the std::vector interface, and can get Data or
// MutableData from any place. The data will be synced implicitly inside.
template <typename T>
class Vector {
class MixVector {
public:
using value_type = T;
using iterator = typename std::vector<T>::iterator;
......@@ -49,82 +51,68 @@ class Vector {
// The actual class to implement vector logic
class VectorData {
public:
VectorData() : flag_(kDataInCPU) {}
VectorData(size_t count, const T &value)
: cpu_(count, value), flag_(kDataInCPU) {}
VectorData(std::initializer_list<T> init) : cpu_(init), flag_(kDataInCPU) {}
template <typename U>
explicit VectorData(const std::vector<U> &dat)
: cpu_(dat), flag_(kDataInCPU) {}
explicit VectorData(std::vector<U> *dat) : cpu_(dat), flag_(kDataInCPU) {}
~VectorData() {}
VectorData(const VectorData &o) {
o.ImmutableCPU();
cpu_ = o.cpu_;
flag_ = kDataInCPU;
}
VectorData(const VectorData &o) = delete;
VectorData &operator=(const VectorData &o) {
o.ImmutableCPU();
cpu_ = o.cpu_;
flag_ = kDataInCPU;
return *this;
}
VectorData &operator=(const VectorData &o) = delete;
T &operator[](size_t i) {
MutableCPU();
return cpu_[i];
return (*cpu_)[i];
}
const T &operator[](size_t i) const {
ImmutableCPU();
return cpu_[i];
return (*cpu_)[i];
}
size_t size() const { return cpu_.size(); }
size_t size() const { return (*cpu_).size(); }
iterator begin() {
MutableCPU();
return cpu_.begin();
return (*cpu_).begin();
}
iterator end() {
MutableCPU();
return cpu_.end();
return (*cpu_).end();
}
T &front() {
MutableCPU();
return cpu_.front();
return (*cpu_).front();
}
T &back() {
MutableCPU();
return cpu_.back();
return (*cpu_).back();
}
const_iterator begin() const {
ImmutableCPU();
return cpu_.begin();
return (*cpu_).begin();
}
const_iterator end() const {
ImmutableCPU();
return cpu_.end();
return (*cpu_).end();
}
const T &back() const {
ImmutableCPU();
return cpu_.back();
return (*cpu_).back();
}
T *data() { return &(*this)[0]; }
T *data() { return cpu_->data(); }
const T *data() const { return &(*this)[0]; }
const T *data() const { return cpu_->data(); }
const T &front() const {
ImmutableCPU();
return cpu_.front();
return (*cpu_).front();
}
// assign this from iterator.
......@@ -132,14 +120,14 @@ class Vector {
template <typename Iter>
void assign(Iter begin, Iter end) {
MutableCPU();
cpu_.assign(begin, end);
(*cpu_).assign(begin, end);
}
// push_back. If the previous capacity is not enough, the memory will
// double.
void push_back(T elem) {
MutableCPU();
cpu_.push_back(elem);
(*cpu_).push_back(elem);
}
// extend a vector by iterator.
......@@ -147,14 +135,14 @@ class Vector {
template <typename It>
void Extend(It begin, It end) {
MutableCPU();
auto out_it = std::back_inserter<std::vector<T>>(this->cpu_);
auto out_it = std::back_inserter<std::vector<T>>(*(this->cpu_));
std::copy(begin, end, out_it);
}
// resize the vector
void resize(size_t size) {
MutableCPU();
cpu_.resize(size);
(*cpu_).resize(size);
}
// get cuda ptr. immutable
......@@ -176,26 +164,16 @@ class Vector {
// clear
void clear() {
cpu_.clear();
(*cpu_).clear();
flag_ = kDirty | kDataInCPU;
}
size_t capacity() const { return cpu_.capacity(); }
// reserve data
void reserve(size_t size) const { cpu_.reserve(size); }
std::vector<T> *get_vector() { return cpu_; }
// implicit cast operator. Vector can be cast to std::vector implicitly.
operator std::vector<T>() const {
ImmutableCPU();
return cpu_;
}
size_t capacity() const { return (*cpu_).capacity(); }
bool operator==(const VectorData &other) const {
ImmutableCPU();
other.ImmutableCPU();
return cpu_ == other.cpu_;
}
// reserve data
void reserve(size_t size) const { (*cpu_).reserve(size); }
std::mutex &Mutex() const { return mtx_; }
......@@ -203,6 +181,13 @@ class Vector {
return OptionalCUDAPlace(gpu_);
}
void MutableCPU() {
if (IsInCUDA() && IsDirty()) {
CopyToCPU();
}
flag_ = kDirty | kDataInCPU;
}
private:
enum DataFlag {
kDataInCPU = 0x01,
......@@ -213,13 +198,6 @@ class Vector {
void CopyToCPU() const;
void MutableCPU() {
if (IsInCUDA() && IsDirty()) {
CopyToCPU();
}
flag_ = kDirty | kDataInCPU;
}
void ImmutableCUDA(platform::Place place) const {
if (IsDirty()) {
if (IsInCPU()) {
......@@ -269,7 +247,7 @@ class Vector {
bool IsInCPU() const { return flag_ & kDataInCPU; }
mutable std::vector<T> cpu_;
std::vector<T> *cpu_;
mutable paddle::memory::allocation::AllocationPtr gpu_;
mutable size_t gpu_memory_size_{0};
mutable int flag_;
......@@ -278,89 +256,77 @@ class Vector {
};
public:
// Default ctor. Create empty Vector
Vector() : m_(new VectorData()) {}
// Fill vector with value. The vector size is `count`.
explicit Vector(size_t count, const T &value = T())
: m_(new VectorData(count, value)) {}
// Ctor with init_list
Vector(std::initializer_list<T> init) : m_(new VectorData(init)) {}
// implicit cast from std::vector.
template <typename U>
Vector(const std::vector<U> &dat) : m_(new VectorData(dat)) { // NOLINT
MixVector(const std::vector<U> *dat) { // NOLINT
m_.reset(new VectorData(const_cast<std::vector<U> *>(dat)));
}
// Copy ctor
Vector(const Vector<T> &other) { m_ = other.m_; }
MixVector(const MixVector<T> &other) = delete;
// Copy operator
Vector<T> &operator=(const Vector<T> &other) {
m_ = other.m_;
return *this;
}
MixVector<T> &operator=(const MixVector<T> &other) = delete;
// Move ctor
Vector(Vector<T> &&other) { m_ = std::move(other.m_); }
MixVector(MixVector<T> &&other) = delete;
// CPU data access method. Mutable.
T &operator[](size_t i) { return (*m_.MutableData())[i]; }
T &operator[](size_t i) { return (*m_)[i]; }
// CPU data access method. Immutable.
const T &operator[](size_t i) const { return m_.Data()[i]; }
const T &operator[](size_t i) const { return (*m_)[i]; }
// std::vector iterator methods. Based on CPU data access method
size_t size() const { return m_.Data().size(); }
size_t size() const { return m_->size(); }
iterator begin() { return m_.MutableData()->begin(); }
iterator begin() { return m_->begin(); }
iterator end() { return m_.MutableData()->end(); }
iterator end() { return m_->end(); }
T &front() { return m_.MutableData()->front(); }
T &front() { return m_->front(); }
T &back() { return m_.MutableData()->back(); }
T &back() { return m_->back(); }
const_iterator begin() const { return m_.Data().begin(); }
const_iterator begin() const { return m_->begin(); }
const_iterator end() const { return m_.Data().end(); }
const_iterator end() const { return m_->end(); }
const_iterator cbegin() const { return begin(); }
const_iterator cend() const { return end(); }
const T &back() const { return m_.Data().back(); }
const T &back() const { return m_->back(); }
T *data() { return m_.MutableData()->data(); }
T *data() { return m_->data(); }
const T *data() const { return m_.Data().data(); }
const T *data() const { return m_->data(); }
const T &front() const { return m_.Data().front(); }
const T &front() const { return m_->front(); }
// end of std::vector iterator methods
// assign this from iterator.
// NOTE: the iterator must support `end-begin`
template <typename Iter>
void assign(Iter begin, Iter end) {
m_.MutableData()->assign(begin, end);
m_->assign(begin, end);
}
// push_back. If the previous capacity is not enough, the memory will
// double.
void push_back(T elem) { m_.MutableData()->push_back(elem); }
void push_back(T elem) { m_->push_back(elem); }
// extend a vector by iterator.
// NOTE: the iterator must support end-begin
template <typename It>
void Extend(It begin, It end) {
m_.MutableData()->Extend(begin, end);
m_->Extend(begin, end);
}
// resize the vector
void resize(size_t size) {
if (m_.Data().size() != size) {
m_.MutableData()->resize(size);
if (m_->size() != size) {
m_->resize(size);
}
}
......@@ -368,15 +334,15 @@ class Vector {
const T *CUDAData(platform::Place place) const {
{
platform::CUDAPlace p(place.GetDeviceId());
auto &mtx = m_.Data().Mutex();
auto &mtx = m_->Mutex();
std::lock_guard<std::mutex> guard(mtx);
auto cuda_place = m_.Data().CUDAPlace();
auto cuda_place = m_->CUDAPlace();
if (cuda_place == paddle::none || cuda_place == p) {
return m_.Data().CUDAData(place);
return m_->CUDAData(place);
}
}
// If m_ contains CUDAData in a different place. Detach manually.
m_.Detach();
m_->MutableCPU();
m_.reset(new VectorData(m_->get_vector()));
return CUDAData(place);
}
......@@ -384,25 +350,25 @@ class Vector {
T *CUDAMutableData(platform::Place place) {
{
platform::CUDAPlace p(place.GetDeviceId());
auto &mtx = m_.Data().Mutex();
auto &mtx = m_->Mutex();
std::lock_guard<std::mutex> guard(mtx);
auto cuda_place = m_.Data().CUDAPlace();
auto cuda_place = m_->CUDAPlace();
if (cuda_place == paddle::none || cuda_place == p) {
return m_.MutableData()->CUDAMutableData(place);
return m_->CUDAMutableData(place);
}
}
// If m_ contains CUDAData in a different place. Detach manually.
m_.Detach();
m_->MutableCPU();
m_.reset(new VectorData(m_->get_vector()));
return CUDAMutableData(place);
}
// clear
void clear() { m_.MutableData()->clear(); }
void clear() { m_->clear(); }
size_t capacity() const { return m_.Data().capacity(); }
size_t capacity() const { return m_->capacity(); }
// reserve data
void reserve(size_t size) { m_.Data().reserve(size); }
void reserve(size_t size) { m_->reserve(size); }
// the unify method to access CPU or CUDA data. immutable.
const T *Data(platform::Place place) const {
......@@ -422,26 +388,12 @@ class Vector {
}
}
// implicit cast operator. Vector can be cast to std::vector implicitly.
operator std::vector<T>() const { return m_.Data(); }
bool operator==(const Vector<T> &other) const {
if (size() != other.size()) return false;
auto it1 = cbegin();
auto it2 = other.cbegin();
for (; it1 < cend(); ++it1, ++it2) {
if (*it1 != *it2) {
return false;
}
}
return true;
}
void CopyToCPU() { m_->MutableCPU(); }
const void *Handle() const { return &m_.Data(); }
const void *Handle() const { return m_.get(); }
private:
// Vector is an COW object.
mutable details::COWPtr<VectorData> m_;
mutable std::unique_ptr<VectorData> m_;
};
}; // namespace framework
......
paddle/fluid/framework/mixed_vector_test.cu
浏览文件 @ 728c0624
......@@ -28,7 +28,7 @@
#include "paddle/fluid/platform/device_context.h"
template <typename T>
using vec = paddle::framework::Vector<T>;
using vec = paddle::framework::MixVector<T>;
using gpuStream_t = paddle::gpuStream_t;
static __global__ void multiply_10(int* ptr) {
......@@ -44,10 +44,11 @@ gpuStream_t GetCUDAStream(paddle::platform::CUDAPlace place) {
}
TEST(mixed_vector, GPU_VECTOR) {
vec<int> tmp;
std::vector<int> x;
for (int i = 0; i < 10; ++i) {
tmp.push_back(i);
x.push_back(i);
}
vec<int> tmp(&x);
ASSERT_EQ(tmp.size(), 10UL);
paddle::platform::CUDAPlace gpu(0);
......@@ -70,10 +71,11 @@ TEST(mixed_vector, MultiGPU) {
return;
}
vec<int> tmp;
std::vector<int> x;
for (int i = 0; i < 10; ++i) {
tmp.push_back(i);
x.push_back(i);
}
vec<int> tmp(&x);
ASSERT_EQ(tmp.size(), 10UL);
paddle::platform::CUDAPlace gpu0(0);
paddle::platform::SetDeviceId(0);
......
paddle/fluid/framework/tensor.h
浏览文件 @ 728c0624
......@@ -30,6 +30,7 @@ limitations under the License. */
#include "paddle/phi/core/ddim.h"
#include "paddle/phi/core/stream.h"
#include "paddle/fluid/framework/mixed_vector.h"
#include "paddle/phi/core/dense_tensor.h"
namespace paddle {
......
paddle/fluid/framework/tensor_util.cc
浏览文件 @ 728c0624
......@@ -1455,22 +1455,10 @@ std::ostream& print_tensor<paddle::platform::complex<double>>(
}
std::ostream& operator<<(std::ostream& os, const LoD& lod) {
os << "{";
for (auto& v : lod) {
os << "{";
bool is_first = true;
for (auto& i : v) {
if (is_first) {
os << i;
is_first = false;
} else {
os << ", " << i;
}
}
os << "}";
}
os << "}";
// NOTE(xiongkun):
// https://stackoverflow.com/questions/5195512/namespaces-and-operator-resolution
// if we don't redefine, the operator << of pten / framework LoD is not found.
paddle::string::operator<<(os, lod);
return os;
}
......@@ -1479,6 +1467,11 @@ std::ostream& operator<<(std::ostream& os, const LoD& lod) {
namespace phi {
std::ostream& operator<<(std::ostream& os, const LoD& lod) {
paddle::string::operator<<(os, lod);
return os;
}
std::ostream& operator<<(std::ostream& os, const phi::DenseTensor& t) {
if (t.lod().size() > 0) {
os << " - lod: " << t.lod() << "\n";
......
paddle/fluid/imperative/all_reduce.cc
浏览文件 @ 728c0624
......@@ -90,6 +90,7 @@ static void AllReduce(const phi::SelectedRows &src, phi::SelectedRows *dst,
platform::DeviceContextPool::Instance().Get(place));
bool use_calc_stream = (dev_ctx->stream() == stream);
VLOG(4) << "Is use calculate stream: " << use_calc_stream;
// 1. Gather rows number from all workers. Here use ncclAllGather to do this,
// but we can use other ways to implement is in the future
......@@ -97,7 +98,9 @@ static void AllReduce(const phi::SelectedRows &src, phi::SelectedRows *dst,
framework::Vector<int64_t> rows_num_vector(strategy.nranks_);
rows_num_vector[strategy.local_rank_] = static_cast<int64_t>(src_rows.size());
// CUDAMutableData use CalStream
auto *gpu_rows_num_ptr = rows_num_vector.CUDAMutableData(place);
paddle::framework::MixVector<int64_t> mixv_rows_num_vector(&rows_num_vector);
auto *gpu_rows_num_ptr = mixv_rows_num_vector.CUDAMutableData(place);
VLOG(4) << "start dev_ctx->wait";
if (!use_calc_stream) {
dev_ctx->Wait();
}
......@@ -109,6 +112,7 @@ static void AllReduce(const phi::SelectedRows &src, phi::SelectedRows *dst,
platform::GpuStreamSync(stream);
}
mixv_rows_num_vector.CopyToCPU();
const auto *cpu_rows_num_ptr = rows_num_vector.data();
auto rows_num =
std::accumulate(cpu_rows_num_ptr, cpu_rows_num_ptr + strategy.nranks_,
......@@ -121,8 +125,10 @@ static void AllReduce(const phi::SelectedRows &src, phi::SelectedRows *dst,
auto *dst_rows = dst->mutable_rows();
dst_rows->resize(rows_num);
auto *dst_rows_ptr = dst_rows->CUDAMutableData(place);
const auto *src_rows_ptr = src_rows.CUDAData(place);
paddle::framework::MixVector<int64_t> mixv_dst_rows(dst_rows);
auto *dst_rows_ptr = mixv_dst_rows.CUDAMutableData(place);
paddle::framework::MixVector<int64_t> mixv_src_rows(&src_rows);
const auto *src_rows_ptr = mixv_src_rows.CUDAData(place);
auto *dst_tensor = dst->mutable_value();
auto dims = src_tensor.dims();
......@@ -150,24 +156,28 @@ static void AllReduce(const phi::SelectedRows &src, phi::SelectedRows *dst,
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclAllGather(
src_tensor_ptr, dst_tensor_ptr, value_sendcount, nccl_dtype,
comm->comm(), stream));
return;
}
for (int i = 0; i < strategy.nranks_; ++i) {
if (cpu_rows_num_ptr[i] > 0) {
// 2. Broadcast the rows of SelectedRows
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclBroadcast(
src_rows_ptr, dst_rows_ptr + row_offset, cpu_rows_num_ptr[i],
ncclInt64, i, comm->comm(), stream));
// 3. Broadcast the tensor data of SelectedRows
auto *dst_tensor_ptr_i = reinterpret_cast<uint8_t *>(dst_tensor_ptr) +
row_offset * feature_size * sizeof_dtype;
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclBroadcast(
src_tensor_ptr, dst_tensor_ptr_i, cpu_rows_num_ptr[i] * feature_size,
nccl_dtype, i, comm->comm(), stream));
row_offset += cpu_rows_num_ptr[i];
} else {
for (int i = 0; i < strategy.nranks_; ++i) {
if (cpu_rows_num_ptr[i] > 0) {
// 2. Broadcast the rows of SelectedRows
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclBroadcast(
src_rows_ptr, dst_rows_ptr + row_offset, cpu_rows_num_ptr[i],
ncclInt64, i, comm->comm(), stream));
// 3. Broadcast the tensor data of SelectedRows
auto *dst_tensor_ptr_i = reinterpret_cast<uint8_t *>(dst_tensor_ptr) +
row_offset * feature_size * sizeof_dtype;
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclBroadcast(
src_tensor_ptr, dst_tensor_ptr_i,
cpu_rows_num_ptr[i] * feature_size, nccl_dtype, i, comm->comm(),
stream));
row_offset += cpu_rows_num_ptr[i];
}
}
}
if (!use_calc_stream) {
platform::GpuStreamSync(stream);
}
mixv_dst_rows.CopyToCPU();
VLOG(3) << "Original SelectedRows rows: "
<< string::join_strings(src_rows, ',');
VLOG(3) << "Result SelectedRows rows: "
......
paddle/fluid/imperative/gloo_context.cc
浏览文件 @ 728c0624
......@@ -143,7 +143,7 @@ void GLOOParallelContext::AllReduce(const phi::SelectedRows &src,
auto dtype = framework::TransToProtoVarType(src_tensor.dtype());
// 1. Gather rows number from all workers. Here use ncclAllGather to do this,
// but we can use other ways to implement is in the future
const auto &src_rows = src.rows();
auto &src_rows = src.rows();
auto gloo_wrapper = framework::GlooWrapper::GetInstance();
size_t local_row_num = src_rows.size();
std::vector<size_t> rows_num_vector =
......@@ -157,8 +157,10 @@ void GLOOParallelContext::AllReduce(const phi::SelectedRows &src,
<< ", height: " << src.height();
auto *dst_rows = dst->mutable_rows();
dst_rows->resize(rows_num);
auto *dst_rows_ptr = dst_rows->MutableData(place);
const int64_t *src_rows_ptr = src_rows.Data(place);
paddle::framework::MixVector<int64_t> mixv_dst_rows(dst_rows);
auto *dst_rows_ptr = mixv_dst_rows.MutableData(place);
paddle::framework::MixVector<int64_t> mixv_src_rows(&src_rows);
const int64_t *src_rows_ptr = mixv_src_rows.Data(place);
auto *dst_tensor = dst->mutable_value();
auto dims = src_tensor.dims();
......
paddle/fluid/inference/lite/tensor_utils.cc
浏览文件 @ 728c0624
......@@ -38,8 +38,6 @@ void SetLoD(DstLoD* dst, const SrcLoD& src) {
dst->emplace_back(v);
}
}
template void SetLoD<paddle::lite::LoD, framework::LoD>(
paddle::lite::LoD* dst, const framework::LoD& src);
template void SetLoD<framework::LoD, paddle::lite::LoD>(
framework::LoD* dst, const paddle::lite::LoD& src);
......
paddle/fluid/operators/ctc_align_op.cu
浏览文件 @ 728c0624
......@@ -110,10 +110,12 @@ class CTCAlignOpCUDAKernel : public framework::OpKernel<T> {
// merge elements and delete blank
T* output_data = output->mutable_data<T>({num_tokens, 1}, ctx.GetPlace());
paddle::framework::MixVector<size_t> mixv_input_lod(&input_lod[level]);
MergeAndDelCudaKernel<T><<<1, 1, 0, stream>>>(
num_tokens, tokens, num_seq,
input_lod[level].CUDAMutableData(ctx.GetPlace()), blank,
merge_repeated, dev_out_lod0_ptr, output_data);
mixv_input_lod.CUDAMutableData(ctx.GetPlace()), blank, merge_repeated,
dev_out_lod0_ptr, output_data);
mixv_input_lod.CopyToCPU();
// set output lod
std::vector<size_t> host_out_lod0(dev_out_lod0.begin(),
......
paddle/fluid/operators/cvm_op.cu
浏览文件 @ 728c0624
......@@ -149,11 +149,12 @@ class CVMGradCUDAKernel : public framework::OpKernel<T> {
batch_size, lod[lod.size() - 1],
platform::errors::PreconditionNotMet(
"Output(X@GRAD)'s dim[0] must be equal to last element of lod"));
paddle::framework::MixVector<size_t> mixv_lod(&lod);
CvmGradComputeKernel<<<(dx_numel + PADDLE_CUDA_NUM_THREADS - 1) /
PADDLE_CUDA_NUM_THREADS,
PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
use_cvm, item_size, cvm_data, dout_data, dx_data, true,
lod.CUDAData(context.GetPlace()), lod.size(), dx_numel);
mixv_lod.CUDAData(context.GetPlace()), lod.size(), dx_numel);
}
}
};
......
paddle/fluid/operators/detection/box_clip_op.cu
浏览文件 @ 728c0624
......@@ -57,9 +57,11 @@ class GPUBoxClipKernel : public framework::OpKernel<T> {
auto stream = dev_ctx.stream();
const size_t batch_size = lod.back().size() - 1;
T *output_data = output->mutable_data<T>(dev_ctx.GetPlace());
paddle::framework::MixVector<size_t> mix_vector(&abs_offset_lod[0]);
GPUBoxClip<T, 512><<<batch_size, 512, 0, stream>>>(
input->data<T>(), abs_offset_lod[0].CUDAMutableData(dev_ctx.GetPlace()),
input->data<T>(), mix_vector.CUDAMutableData(dev_ctx.GetPlace()),
bbox_width, im_info->data<T>(), output_data);
mix_vector.CopyToCPU();
}
};
......
paddle/fluid/operators/detection/target_assign_op.h
浏览文件 @ 728c0624
......@@ -108,7 +108,8 @@ class TargetAssignKernel : public framework::OpKernel<T> {
auto x_lod = x->lod().back();
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
size_t* x_lod_data = x_lod.MutableData(ctx.GetPlace());
paddle::framework::MixVector<size_t> mixv_x_lod(&x_lod);
size_t* x_lod_data = mixv_x_lod.MutableData(ctx.GetPlace());
#else
size_t* x_lod_data = x_lod.data();
#endif
......@@ -116,6 +117,9 @@ class TargetAssignKernel : public framework::OpKernel<T> {
TargetAssignFunctor<T, WT> functor(x_data, match_idx_data, x_lod_data,
mismatch_value, n, m, p, k, out_data,
out_wt_data);
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
mixv_x_lod.CopyToCPU();
#endif
auto& device_ctx = ctx.template device_context<DeviceContext>();
platform::ForRange<DeviceContext> for_range(device_ctx, n * m);
......@@ -130,13 +134,17 @@ class TargetAssignKernel : public framework::OpKernel<T> {
const int* neg_idx_data = neg_indices->data<int>();
auto neg_lod = neg_indices->lod().back();
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
size_t* neg_lod_data = neg_lod.MutableData(ctx.GetPlace());
paddle::framework::MixVector<size_t> mixv_neg_lod(&neg_lod);
size_t* neg_lod_data = mixv_neg_lod.MutableData(ctx.GetPlace());
#else
size_t* neg_lod_data = neg_lod.data();
#endif
NegTargetAssignFunctor<DeviceContext, T, WT> neg_trg_functor;
neg_trg_functor(device_ctx, neg_idx_data, neg_lod_data, n, m, k,
mismatch_value, out_data, out_wt_data);
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
mixv_neg_lod.CopyToCPU();
#endif
}
}
};
......
paddle/fluid/operators/fused/mkldnn/multi_gru_mkldnn_op.cc
浏览文件 @ 728c0624
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <iostream>
#include <memory>
#include "dnnl.hpp"
#include "paddle/fluid/framework/mixed_vector.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/fused/multi_gru_op.h"
#include "paddle/fluid/platform/errors.h"
......
paddle/fluid/operators/lookup_table_op.cu
浏览文件 @ 728c0624
......@@ -164,8 +164,10 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
auto gpu_place = context.GetPlace();
// TODO(yuyang18): Strange code here.
memory::Copy(gpu_place, new_rows.CUDAMutableData(context.GetPlace()),
paddle::framework::MixVector<int64_t> mixv_new_rows(&new_rows);
memory::Copy(gpu_place, mixv_new_rows.CUDAMutableData(context.GetPlace()),
gpu_place, ids_data, ids_num * sizeof(int64_t), stream);
mixv_new_rows.CopyToCPU();
d_table->set_rows(new_rows);
auto *d_table_value = d_table->mutable_value();
......
paddle/fluid/operators/lookup_table_v2_op.cu
浏览文件 @ 728c0624
......@@ -152,14 +152,16 @@ struct LookupTableV2GradCUDAFunctor {
new_rows.resize(ids_num);
auto gpu_place = context_.GetPlace();
paddle::framework::MixVector<int64_t> mixv_new_rows(&new_rows);
if (!std::is_same<IdT, int64_t>::value) {
InputTypeConvert<<<grids, threads, 0, stream>>>(
ids_data, ids_num, new_rows.MutableData(gpu_place));
ids_data, ids_num, mixv_new_rows.MutableData(gpu_place));
} else {
memory::Copy(gpu_place, new_rows.CUDAMutableData(gpu_place), gpu_place,
ids_data, ids_num * sizeof(int64_t), stream);
memory::Copy(gpu_place, mixv_new_rows.CUDAMutableData(gpu_place),
gpu_place, ids_data, ids_num * sizeof(int64_t), stream);
}
mixv_new_rows.CopyToCPU();
d_table->set_rows(new_rows);
auto *d_table_value = d_table->mutable_value();
......
paddle/fluid/operators/math/beam_search.cu
浏览文件 @ 728c0624
......@@ -357,8 +357,9 @@ class BeamSearchFunctor<platform::CUDADeviceContext, T> {
framework::LoD selected_lod(2);
selected_lod[0].assign(abs_lod[level].begin(), abs_lod[level].end());
selected_lod[1].resize(scores->dims()[0] + 1);
size_t* selected_offsets =
selected_lod[1].CUDAMutableData(context.GetPlace());
paddle::framework::MixVector<size_t> mix_vector(&selected_lod[1]);
paddle::framework::MixVector<size_t> mixv_abs(&abs_lod[level]);
size_t* selected_offsets = mix_vector.CUDAMutableData(context.GetPlace());
if (num_seqs == 1) {
const int seq_length = static_cast<int>(abs_lod[level][1]);
......@@ -377,7 +378,7 @@ class BeamSearchFunctor<platform::CUDADeviceContext, T> {
is_accumulated, num_used_threads));
}
} else if (num_seqs <= 4) {
const size_t* seq_offsets = abs_lod[level].CUDAData(context.GetPlace());
const size_t* seq_offsets = mixv_abs.CUDAData(context.GetPlace());
// Use only 1 block
const int kMaxThreadsPerSeq = 32;
const int kMaxSeqs = 4;
......@@ -400,6 +401,7 @@ class BeamSearchFunctor<platform::CUDADeviceContext, T> {
}
context.Wait();
mix_vector.CopyToCPU();
if (!framework::CheckLoD(selected_lod)) {
PADDLE_THROW(platform::errors::InvalidArgument(
"lod %s is not right in"
......
paddle/fluid/operators/math/selected_rows_functor.cc
浏览文件 @ 728c0624
......@@ -170,7 +170,8 @@ struct SelectedRowsAddTo<platform::CPUDeviceContext, T> {
auto* in2_value = input2->mutable_value();
// concat rows
in2_rows.Extend(in1_rows.begin(), in1_rows.end());
paddle::framework::MixVector<int64_t> mixv_in2_rows(&in2_rows);
mixv_in2_rows.Extend(in1_rows.begin(), in1_rows.end());
auto in1_place = input1.place();
PADDLE_ENFORCE_EQ(platform::is_cpu_place(in1_place), true,
......
paddle/fluid/operators/math/selected_rows_functor.cu
浏览文件 @ 728c0624
......@@ -161,9 +161,10 @@ struct SelectedRowsAddTensor<platform::CUDADeviceContext, T> {
const int block_size = 256;
dim3 threads(block_size, 1);
dim3 grid(in1_rows.size(), 1);
paddle::framework::MixVector<int64_t> mixv_in1_rows(&in1_rows);
SelectedRowsAddTensorKernel<
T, block_size><<<grid, threads, 0, context.stream()>>>(
in1_data, in1_rows.CUDAData(context.GetPlace()), out_data,
in1_data, mixv_in1_rows.CUDAData(context.GetPlace()), out_data,
in1_row_numel);
auto out_eigen = framework::EigenVector<T>::Flatten(*output);
......@@ -198,8 +199,9 @@ struct SelectedRowsAddTo<platform::CUDADeviceContext, T> {
auto* in2_value = input2->mutable_value();
// concat rows
paddle::framework::MixVector<int64_t> mixv_in2_rows(&in2_rows);
if (in1_rows.size()) {
in2_rows.Extend(in1_rows.begin(), in1_rows.end());
mixv_in2_rows.Extend(in1_rows.begin(), in1_rows.end());
}
auto in1_place = input1.place();
......@@ -274,9 +276,10 @@ struct SelectedRowsAddToTensor<platform::CUDADeviceContext, T> {
const int block_size = 256;
dim3 threads(block_size, 1);
dim3 grid(in1_rows.size(), 1);
paddle::framework::MixVector<int64_t> mixv_in1_rows(&in1_rows);
SelectedRowsAddToTensorKernel<
T, block_size><<<grid, threads, 0, context.stream()>>>(
in1_data, in1_rows.CUDAData(context.GetPlace()), in2_data,
in1_data, mixv_in1_rows.CUDAData(context.GetPlace()), in2_data,
in1_row_numel);
}
};
......@@ -356,10 +359,13 @@ struct MergeAdd<platform::CUDADeviceContext, T> {
dim3 threads(block_size, 1);
dim3 grid1(input_rows.size(), 1);
paddle::framework::MixVector<int64_t> mix_vector_input(&input_rows);
paddle::framework::MixVector<int64_t> mix_vector_out(out.mutable_rows());
MergeAddKernel<T, 256><<<grid1, threads, 0, context.stream()>>>(
input_data, input_rows.CUDAData(context.GetPlace()), out_data,
out.mutable_rows()->CUDAMutableData(context.GetPlace()),
out.rows().size(), input_width);
input_data, mix_vector_input.CUDAData(context.GetPlace()), out_data,
mix_vector_out.CUDAMutableData(context.GetPlace()), out.rows().size(),
input_width);
mix_vector_out.CopyToCPU();
}
void operator()(const platform::CUDADeviceContext& context,
......@@ -423,10 +429,13 @@ struct MergeAdd<platform::CUDADeviceContext, T> {
auto& input_rows = input->rows();
dim3 grid1(input_rows.size(), 1);
paddle::framework::MixVector<int64_t> mix_vector_input(&input_rows);
paddle::framework::MixVector<int64_t> mix_vector_out(out.mutable_rows());
MergeAddKernel<T, 256><<<grid1, threads, 0, context.stream()>>>(
input_data, input_rows.CUDAData(context.GetPlace()), out_data,
out.mutable_rows()->CUDAMutableData(context.GetPlace()),
out.rows().size(), input_width);
input_data, mix_vector_input.CUDAData(context.GetPlace()), out_data,
mix_vector_out.CUDAMutableData(context.GetPlace()), out.rows().size(),
input_width);
mix_vector_out.CopyToCPU();
}
}
};
......
paddle/fluid/operators/math/sequence2batch.cu
浏览文件 @ 728c0624
......@@ -72,8 +72,9 @@ class CopyMatrixRowsFunctor<platform::CUDADeviceContext, T> {
dim3 threads(128, 8);
dim3 grid(8, 1);
auto stream = context.stream();
paddle::framework::MixVector<size_t> mix_index_lod(&index_lod);
CopyMatrixRowsKernel<T, 128, 8, 8><<<grid, threads, 0, stream>>>(
src_data, dst_data, index_lod.CUDAData(context.GetPlace()), height,
src_data, dst_data, mix_index_lod.CUDAData(context.GetPlace()), height,
width, is_src_index);
}
};
......
paddle/fluid/operators/math/sequence_padding.cu
浏览文件 @ 728c0624
......@@ -59,7 +59,7 @@ class PaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
int lod_level = 0, bool norm_by_times = false,
const PadLayout layout = kBatchLengthWidth) {
auto seq_lod = seq_tensor.lod();
const auto seq_offsets = framework::ToAbsOffset(seq_lod)[lod_level];
auto seq_offsets = framework::ToAbsOffset(seq_lod)[lod_level];
const auto& seq_tensor_dims = seq_tensor.dims();
const auto& pad_tensor_dims = pad_tensor->dims();
int max_seq_len = MaximumSequenceLength(seq_offsets);
......@@ -104,10 +104,11 @@ class PaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
T* pad_data = pad_tensor->data<T>();
const T* pad_value_data = pad_value.data<T>();
paddle::framework::MixVector<size_t> mix_vector_seq_offsets(&seq_offsets);
SequencePaddingKernel<T, kSeqToPad><<<grid, threads, 0, context.stream()>>>(
pad_data, seq_data, pad_value_data, pad_value.numel() == 1,
seq_offsets.CUDAData(context.GetPlace()), seq_num, pad_seq_len,
step_width, norm_by_times, layout);
mix_vector_seq_offsets.CUDAData(context.GetPlace()), seq_num,
pad_seq_len, step_width, norm_by_times, layout);
}
};
......@@ -157,9 +158,10 @@ class UnpaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
const T* pad_data = pad_tensor.data<T>();
T* seq_data = seq_tensor->data<T>();
paddle::framework::MixVector<size_t> mixv_seq_offsets(&seq_offsets);
SequencePaddingKernel<T, kPadToSeq><<<grid, threads, 0, context.stream()>>>(
seq_data, pad_data, nullptr, false,
seq_offsets.CUDAData(context.GetPlace()), seq_num, pad_seq_len,
mixv_seq_offsets.CUDAData(context.GetPlace()), seq_num, pad_seq_len,
step_width, norm_by_times, layout);
}
};
......
paddle/fluid/operators/math/sequence_pooling.cu
浏览文件 @ 728c0624
......@@ -168,41 +168,42 @@ class SequencePoolFunctor<platform::CUDADeviceContext, T> {
const size_t item_dim = output->numel() / output->dims()[0];
dim3 threads(1024, 1);
dim3 grid(std::max(static_cast<int>(lod.size()) - 1, 1), 1);
paddle::framework::MixVector<size_t> mix_vector(&lod);
if (pooltype == "MAX") {
sequence_pool_kernel<
T, MaxPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>(
MaxPoolFunctor<T>(), input.data<T>(), pad_value,
lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
mix_vector.CUDAData(context.GetPlace()), lod.size(), item_dim,
output->mutable_data<T>(context.GetPlace()), index->data<int>());
} else if (pooltype == "AVERAGE") {
sequence_pool_kernel<
T, AvgPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>(
AvgPoolFunctor<T>(), input.data<T>(), pad_value,
lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
mix_vector.CUDAData(context.GetPlace()), lod.size(), item_dim,
output->mutable_data<T>(context.GetPlace()), nullptr);
} else if (pooltype == "SUM") {
sequence_pool_kernel<
T, SumPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>(
SumPoolFunctor<T>(), input.data<T>(), pad_value,
lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
mix_vector.CUDAData(context.GetPlace()), lod.size(), item_dim,
output->mutable_data<T>(context.GetPlace()), nullptr);
} else if (pooltype == "SQRT") {
sequence_pool_kernel<
T, SqrtPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>(
SqrtPoolFunctor<T>(), input.data<T>(), pad_value,
lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
mix_vector.CUDAData(context.GetPlace()), lod.size(), item_dim,
output->mutable_data<T>(context.GetPlace()), nullptr);
} else if (pooltype == "LAST") {
sequence_pool_kernel<
T, LastPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>(
LastPoolFunctor<T>(), input.data<T>(), pad_value,
lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
mix_vector.CUDAData(context.GetPlace()), lod.size(), item_dim,
output->mutable_data<T>(context.GetPlace()), nullptr);
} else if (pooltype == "FIRST") {
sequence_pool_kernel<
T, FirstPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>(
FirstPoolFunctor<T>(), input.data<T>(), pad_value,
lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
mix_vector.CUDAData(context.GetPlace()), lod.size(), item_dim,
output->mutable_data<T>(context.GetPlace()), nullptr);
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
......@@ -335,41 +336,42 @@ class SequencePoolGradFunctor<platform::CUDADeviceContext, T> {
const size_t item_dim = in_grad->numel() / in_grad->dims()[0];
dim3 threads(1024, 1);
dim3 grid(std::max(static_cast<int>(lod.size()) - 1, 1), 1);
paddle::framework::MixVector<size_t> mix_vector(&lod);
if (pooltype == "MAX") {
sequence_pool_grad_kernel<
T, MaxPoolGradFunctor<T>><<<grid, threads, 0, context.stream()>>>(
MaxPoolGradFunctor<T>(), out_grad.data<T>(),
lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
mix_vector.CUDAData(context.GetPlace()), lod.size(), item_dim,
in_grad->mutable_data<T>(context.GetPlace()), index->data<int>());
} else if (pooltype == "AVERAGE") {
sequence_pool_grad_kernel<
T, AvgPoolGradFunctor<T>><<<grid, threads, 0, context.stream()>>>(
AvgPoolGradFunctor<T>(), out_grad.data<T>(),
lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
mix_vector.CUDAData(context.GetPlace()), lod.size(), item_dim,
in_grad->mutable_data<T>(context.GetPlace()), nullptr);
} else if (pooltype == "SUM") {
sequence_pool_grad_kernel<
T, SumPoolGradFunctor<T>><<<grid, threads, 0, context.stream()>>>(
SumPoolGradFunctor<T>(), out_grad.data<T>(),
lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
mix_vector.CUDAData(context.GetPlace()), lod.size(), item_dim,
in_grad->mutable_data<T>(context.GetPlace()), nullptr);
} else if (pooltype == "SQRT") {
sequence_pool_grad_kernel<
T, SqrtPoolGradFunctor<T>><<<grid, threads, 0, context.stream()>>>(
SqrtPoolGradFunctor<T>(), out_grad.data<T>(),
lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
mix_vector.CUDAData(context.GetPlace()), lod.size(), item_dim,
in_grad->mutable_data<T>(context.GetPlace()), nullptr);
} else if (pooltype == "LAST") {
sequence_pool_grad_kernel<
T, LastPoolGradFunctor<T>><<<grid, threads, 0, context.stream()>>>(
LastPoolGradFunctor<T>(), out_grad.data<T>(),
lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
mix_vector.CUDAData(context.GetPlace()), lod.size(), item_dim,
in_grad->mutable_data<T>(context.GetPlace()), nullptr);
} else if (pooltype == "FIRST") {
sequence_pool_grad_kernel<
T, FirstPoolGradFunctor<T>><<<grid, threads, 0, context.stream()>>>(
FirstPoolGradFunctor<T>(), out_grad.data<T>(),
lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
mix_vector.CUDAData(context.GetPlace()), lod.size(), item_dim,
in_grad->mutable_data<T>(context.GetPlace()), nullptr);
} else {
......
paddle/fluid/operators/math/sequence_scale.cu
浏览文件 @ 728c0624
......@@ -41,21 +41,23 @@ class ScaleLoDTensorFunctor<platform::CUDADeviceContext, T> {
auto lod = seq->lod();
const size_t num_seq = lod[level].size() - 1;
const size_t seq_width = seq->numel() / seq->dims()[0];
framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);
auto abs_offset_lod = framework::ToAbsOffset(lod);
T* seq_data = seq->mutable_data<T>(context.GetPlace());
paddle::framework::MixVector<size_t> mix_vector(&(abs_offset_lod[level]));
#ifdef PADDLE_WITH_HIP
hipLaunchKernelGGL(
HIP_KERNEL_NAME(SequenceScaleKernel<T, PADDLE_CUDA_NUM_THREADS>),
dim3(num_seq), dim3(PADDLE_CUDA_NUM_THREADS), 0, context.stream(),
seq_data, abs_offset_lod[level].CUDAMutableData(context.GetPlace()),
scales, seq_width);
seq_data, mix_vector.CUDAMutableData(context.GetPlace()), scales,
seq_width);
#else
SequenceScaleKernel<T, PADDLE_CUDA_NUM_THREADS><<<
num_seq, PADDLE_CUDA_NUM_THREADS, 0, context.stream()>>>(
seq_data, abs_offset_lod[level].CUDAMutableData(context.GetPlace()),
scales, seq_width);
seq_data, mix_vector.CUDAMutableData(context.GetPlace()), scales,
seq_width);
#endif
mix_vector.CopyToCPU();
}
};
......
paddle/fluid/operators/optimizers/adagrad_op.cu
浏览文件 @ 728c0624
......@@ -96,12 +96,14 @@ struct SparseAdagradFunctor<platform::CUDADeviceContext, T> {
const int block_size = 256;
dim3 threads(block_size, 1);
dim3 grid2(1, merge_rows.size());
paddle::framework::MixVector<int64_t> mixv_merge_rows(&merge_rows);
SparseAdagradFunctorKernel<
T, 256><<<grid2, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
grad_merge_data, merge_rows.CUDAMutableData(context.GetPlace()), lr,
param_data, moment_data, grad_width, epsilon);
grad_merge_data, mixv_merge_rows.CUDAMutableData(context.GetPlace()),
lr, param_data, moment_data, grad_width, epsilon);
mixv_merge_rows.CopyToCPU();
}
};
......
paddle/fluid/operators/optimizers/adam_op.cu
浏览文件 @ 728c0624
......@@ -345,7 +345,10 @@ class AdamOpCUDAKernel : public framework::OpKernel<T> {
auto& grad_merge = *grad_merge_ptr;
auto& grad_tensor = grad_merge.value();
const T* grad_data = grad_tensor.template data<T>();
const int64_t* rows = grad_merge.rows().Data(ctx.GetPlace());
auto* grad_merge_rows = &grad_merge.rows();
paddle::framework::MixVector<int64_t> mixv_grad_merge_rows(
grad_merge_rows);
const int64_t* rows = mixv_grad_merge_rows.Data(ctx.GetPlace());
auto row_numel = grad_tensor.numel() / grad_merge.rows().size();
if (beta1_pow->place() == platform::CPUPlace() &&
......
paddle/fluid/operators/optimizers/adam_op.h
浏览文件 @ 728c0624
......@@ -592,7 +592,10 @@ class AdamOpKernel : public framework::OpKernel<T> {
auto& grad_merge = *grad_merge_ptr;
auto& grad_tensor = grad_merge.value();
const T* grad_data = grad_tensor.template data<T>();
const int64_t* rows = grad_merge.rows().Data(ctx.GetPlace());
auto* grad_merge_rows = &grad_merge.rows();
paddle::framework::MixVector<int64_t> mixv_grad_merge_rows(
grad_merge_rows);
const int64_t* rows = mixv_grad_merge_rows.Data(ctx.GetPlace());
auto row_numel = grad_tensor.numel() / grad_merge.rows().size();
SparseAdamFunctor<T, CPUAdam> functor(
......
paddle/fluid/operators/optimizers/adamw_op.cu
浏览文件 @ 728c0624
......@@ -368,7 +368,10 @@ class AdamWOpCUDAKernel : public framework::OpKernel<T> {
auto& grad_merge = *grad_merge_ptr;
auto& grad_tensor = grad_merge.value();
const T* grad_data = grad_tensor.template data<T>();
const int64_t* rows = grad_merge.rows().Data(ctx.GetPlace());
auto* grad_merge_rows = &grad_merge.rows();
paddle::framework::MixVector<int64_t> mixv_grad_merge_rows(
grad_merge_rows);
const int64_t* rows = mixv_grad_merge_rows.Data(ctx.GetPlace());
auto row_numel = grad_tensor.numel() / grad_merge.rows().size();
if (beta1_pow->place() == platform::CPUPlace() &&
......
paddle/fluid/operators/optimizers/ftrl_op.h
浏览文件 @ 728c0624
......@@ -189,7 +189,9 @@ class FTRLOpKernel : public framework::OpKernel<T> {
merge_func(ctx.template device_context<DeviceContext>(), *grad,
merged_grad);
const int64_t* rows = merged_grad->rows().Data(ctx.GetPlace());
auto* merged_rows = merged_grad->mutable_rows();
paddle::framework::MixVector<int64_t> mixv_merged_rows(merged_rows);
const int64_t* rows = mixv_merged_rows.Data(ctx.GetPlace());
auto row_numel = static_cast<int64_t>(merged_grad->value().dims()[1]);
auto row_height = static_cast<int64_t>(merged_grad->rows().size());
......
paddle/fluid/operators/optimizers/lamb_op.h
浏览文件 @ 728c0624
......@@ -594,7 +594,10 @@ class LambOpKernel : public framework::OpKernel<T> {
auto& grad_merge = *grad_merge_ptr;
auto& grad_tensor = grad_merge.value();
const T* grad_data = grad_tensor.template data<T>();
const int64_t* rows = grad_merge.rows().Data(ctx.GetPlace());
auto* grad_merge_rows = &grad_merge.rows();
paddle::framework::MixVector<int64_t> mixv_grad_merge_rows(
grad_merge_rows);
const int64_t* rows = mixv_grad_merge_rows.Data(ctx.GetPlace());
auto row_numel = grad_tensor.numel() / grad_merge.rows().size();
if (platform::is_gpu_place(ctx.GetPlace()) &&
beta1_pow.place() == platform::CPUPlace() &&
......
paddle/fluid/operators/optimizers/momentum_op.h
浏览文件 @ 728c0624
......@@ -561,7 +561,10 @@ class MomentumOpKernel : public framework::OpKernel<T> {
merge_func(ctx.template device_context<DeviceContext>(), *grad,
merged_grad);
const int64_t* rows = merged_grad->rows().Data(ctx.GetPlace());
auto* grad_merge_rows = merged_grad->mutable_rows();
paddle::framework::MixVector<int64_t> mixv_grad_merge_rows(
grad_merge_rows);
const int64_t* rows = mixv_grad_merge_rows.Data(ctx.GetPlace());
int64_t row_numel =
merged_grad->value().numel() / merged_grad->rows().size();
platform::ForRange<DeviceContext> for_range(
......
paddle/fluid/operators/optimizers/rmsprop_op.h
浏览文件 @ 728c0624
......@@ -227,7 +227,10 @@ class RmspropOpKernel : public framework::OpKernel<T> {
merge_func(dev_ctx, grad, merged_grad);
platform::ForRange<DeviceContext> for_range(dev_ctx, limit);
const int64_t *rows = merged_grad->rows().Data(ctx.GetPlace());
auto &grad_merge_rows = merged_grad->rows();
paddle::framework::MixVector<int64_t> mixv_grad_merge_rows(
&grad_merge_rows);
const int64_t *rows = mixv_grad_merge_rows.Data(ctx.GetPlace());
auto &merged_tensor = merged_grad->value();
int64_t row_count = merged_grad->rows().size();
......
paddle/fluid/operators/optimizers/sgd_op.cu
浏览文件 @ 728c0624
......@@ -148,11 +148,11 @@ class SGDOpKernel<platform::CUDADeviceContext, T>
int thread_x = kThreadsPerBlock;
int max_threads = ctx.cuda_device_context().GetMaxPhysicalThreadCount();
int max_blocks = std::max(max_threads / kThreadsPerBlock, 1);
paddle::framework::MixVector<int64_t> mixv_in_rows(&in_rows);
SparseSGDFunctorKernel<<<max_blocks, thread_x, 0,
ctx.cuda_device_context().stream()>>>(
in_data, in_rows.CUDAData(ctx.GetPlace()), learning_rate->data<T>(),
out_data, in_row_numel, in_rows.size());
in_data, mixv_in_rows.CUDAData(ctx.GetPlace()),
learning_rate->data<T>(), out_data, in_row_numel, in_rows.size());
} else {
PADDLE_ENFORCE_EQ(false, true,
......
paddle/fluid/operators/row_conv_op.cu
浏览文件 @ 728c0624
......@@ -336,7 +336,8 @@ class RowConvKernel<platform::CUDADeviceContext, T>
int num_sequence = batch_indices.size() - 1;
int future_context = Filter->dims()[0];
size_t *idx = batch_indices.CUDAMutableData(context.GetPlace());
paddle::framework::MixVector<size_t> mix_vector(&batch_indices);
size_t *idx = mix_vector.CUDAMutableData(context.GetPlace());
auto stream = context.cuda_device_context().stream();
if (future_context <= 32) {
......@@ -352,6 +353,7 @@ class RowConvKernel<platform::CUDADeviceContext, T>
RowConvForward<T><<<grid_dim, block_dim, 0, stream>>>(
in, weight, num_sequence, input_dim, future_context, idx, out);
}
mix_vector.CopyToCPU();
}
};
......@@ -392,7 +394,8 @@ class RowConvGradKernel<platform::CUDADeviceContext, T>
// int input_dim = X->dims()[1];
int num_sequence = batch_indices.size() - 1;
int future_context = Filter->dims()[0];
size_t *idx = batch_indices.CUDAMutableData(context.GetPlace());
paddle::framework::MixVector<size_t> mixv_batch_indices(&batch_indices);
size_t *idx = mixv_batch_indices.CUDAMutableData(context.GetPlace());
auto &device_ctx = context.cuda_device_context();
phi::funcs::SetConstant<platform::CUDADeviceContext, T> zero;
......@@ -444,6 +447,7 @@ class RowConvGradKernel<platform::CUDADeviceContext, T>
dout, weights, num_sequence, input_dim, future_context, idx, din);
}
}
mixv_batch_indices.CopyToCPU();
}
};
} // namespace operators
......
paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cu
浏览文件 @ 728c0624
......@@ -71,7 +71,8 @@ class SequenceEnumerateOpCUDAKernel : public framework::OpKernel<T> {
out->Resize({in_dims[0], win_size});
auto out_data = out->mutable_data<T>(context.GetPlace());
// Copy LoD to GPU
const size_t* dev_in_lod_ptr = lod0.CUDAData(context.GetPlace());
paddle::framework::MixVector<size_t> mixv_lod0(&lod0);
const size_t* dev_in_lod_ptr = mixv_lod0.CUDAData(context.GetPlace());
// Calc output tensor
CalcOutPut<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
......
paddle/fluid/operators/sequence_ops/sequence_erase_op.cu
浏览文件 @ 728c0624
......@@ -88,7 +88,8 @@ class SequenceEraseOpCUDAKernel : public framework::OpKernel<T> {
// Copy LoD to GPU
auto last_lod = lod[lod.size() - 1];
auto lod_len = last_lod.size();
const size_t* dev_in_lod_ptr = last_lod.CUDAData(ctx.GetPlace());
paddle::framework::MixVector<size_t> mixv_last_lod(&last_lod);
const size_t* dev_in_lod_ptr = mixv_last_lod.CUDAData(ctx.GetPlace());
// Calc output LoD
thrust::device_vector<size_t> dev_out_lod(lod_len);
size_t* dev_out_lod_ptr = thrust::raw_pointer_cast(dev_out_lod.data());
......
paddle/fluid/operators/sequence_ops/sequence_expand_as_op.cu
浏览文件 @ 728c0624
......@@ -81,8 +81,9 @@ struct SequenceExpandAsFunctor<platform::CUDADeviceContext, T> {
dim3 block_size(thread_x);
dim3 grid_size(block_x);
paddle::framework::MixVector<size_t> mixv_ref_lod(&ref_lod);
sequence_expand_as_kernel<<<grid_size, block_size, 0, context.stream()>>>(
x.data<T>(), ref_lod.CUDAData(context.GetPlace()), height, width,
x.data<T>(), mixv_ref_lod.CUDAData(context.GetPlace()), height, width,
out->mutable_data<T>(context.GetPlace()));
}
};
......@@ -107,10 +108,11 @@ struct SequenceExpandAsGradFunctor<platform::CUDADeviceContext, T> {
dim3 block_size(thread_x);
dim3 grid_size(block_x);
paddle::framework::MixVector<size_t> mixv_ref_lod(&ref_lod);
sequence_expand_as_grad_kernel<<<grid_size, block_size, 0,
context.stream()>>>(
dout.data<T>(), ref_lod.CUDAData(context.GetPlace()), height, width,
dx->mutable_data<T>(context.GetPlace()));
dout.data<T>(), mixv_ref_lod.CUDAData(context.GetPlace()), height,
width, dx->mutable_data<T>(context.GetPlace()));
}
};
......
paddle/fluid/operators/sequence_ops/sequence_expand_op.cu
浏览文件 @ 728c0624
......@@ -157,7 +157,9 @@ struct SequenceExpandFunctor<platform::CUDADeviceContext, T> {
out_offset[2 * x_lod_size + i] = ref_lod[i];
}
const size_t* out_offset_data = out_offset.CUDAData(context.GetPlace());
paddle::framework::MixVector<size_t> mixv_out_offset(&out_offset);
const size_t* out_offset_data =
mixv_out_offset.CUDAData(context.GetPlace());
const size_t* x_lod_data = out_offset_data + x_lod_size;
const size_t* ref_lod_data = out_offset_data + 2 * x_lod_size;
......@@ -193,11 +195,14 @@ struct SequenceExpandGradFunctor<platform::CUDADeviceContext, T> {
int block_x = static_cast<int>(ref_lod.size());
dim3 block_size(thread_x, thread_y, thread_z);
dim3 grid_size(block_x, 1);
paddle::framework::MixVector<size_t> mixv_ref_lod(&ref_lod);
paddle::framework::MixVector<size_t> mixv_x_lod(&x_lod);
paddle::framework::MixVector<size_t> mixv_out_offset(&out_offset);
sequence_expand_grad_kernel<<<grid_size, block_size, 0, context.stream()>>>(
dout.data<T>(), ref_lod.CUDAData(context.GetPlace()),
x_lod.CUDAData(context.GetPlace()),
out_offset.CUDAData(context.GetPlace()), ref_lod.size(), x_item_length,
dx->mutable_data<T>(context.GetPlace()));
dout.data<T>(), mixv_ref_lod.CUDAData(context.GetPlace()),
mixv_x_lod.CUDAData(context.GetPlace()),
mixv_out_offset.CUDAData(context.GetPlace()), ref_lod.size(),
x_item_length, dx->mutable_data<T>(context.GetPlace()));
}
};
......
paddle/fluid/operators/sequence_ops/sequence_reverse_op.h
浏览文件 @ 728c0624
......@@ -132,7 +132,9 @@ class SequenceReverseOpKernel : public framework::OpKernel<T> {
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
if (platform::is_gpu_place(ctx.GetPlace())) {
lod = x.lod()[0].CUDAData(ctx.GetPlace());
auto xlod = x.lod()[0];
paddle::framework::MixVector<size_t> mixv_xlod(&xlod);
lod = mixv_xlod.CUDAData(ctx.GetPlace());
} else {
#endif
lod = x.lod()[0].data();
......
paddle/fluid/operators/sequence_ops/sequence_softmax_op.cu
浏览文件 @ 728c0624
......@@ -133,9 +133,10 @@ struct SequenceSoftmaxFunctor<platform::CUDADeviceContext, T> {
dim3 block_size(thread_x);
dim3 grid_size(max_blocks);
paddle::framework::MixVector<size_t> mixv_ref_lod(&ref_lod);
sequence_softmax_kernel<
T, kThreadsPerBlock><<<grid_size, block_size, 0, context.stream()>>>(
x.data<T>(), ref_lod.CUDAData(context.GetPlace()), height,
x.data<T>(), mixv_ref_lod.CUDAData(context.GetPlace()), height,
out->mutable_data<T>(context.GetPlace()));
}
};
......@@ -156,10 +157,12 @@ struct SequenceSoftmaxGradFunctor<platform::CUDADeviceContext, T> {
dim3 block_size(thread_x);
dim3 grid_size(max_blocks);
paddle::framework::MixVector<size_t> mixv_ref_lod(&ref_lod);
sequence_softmax_grad_kernel<
T, kThreadsPerBlock><<<grid_size, block_size, 0, context.stream()>>>(
dout.data<T>(), out.data<T>(), ref_lod.CUDAData(context.GetPlace()),
height, dx->mutable_data<T>(context.GetPlace()));
dout.data<T>(), out.data<T>(),
mixv_ref_lod.CUDAData(context.GetPlace()), height,
dx->mutable_data<T>(context.GetPlace()));
}
};
......
paddle/phi/api/ext/dispatch.h
浏览文件 @ 728c0624
......@@ -292,7 +292,7 @@ namespace paddle {
paddle::experimental::complex128, \
__VA_ARGS__) \
default: \
PADDLE_THROW(paddle::platform::errors::InvalidArgument( \
PADDLE_THROW(phi::errors::InvalidArgument( \
"Invalid enum data type `%d`.", static_cast<int>(__dtype__))); \
} \
}()
......
paddle/phi/api/lib/utils/storage.cc
浏览文件 @ 728c0624
......@@ -19,7 +19,7 @@ namespace experimental {
ExternalStorage::ExternalStorage(void* ptr,
size_t size,
const paddle::platform::Place& place)
const phi::Place& place)
: phi::Storage(std::make_shared<phi::Allocation>(ptr, size, place)),
size_(size) {}
......@@ -29,11 +29,11 @@ ExternalStorage::ExternalStorage(const phi::intrusive_ptr<phi::Storage>& root,
: Storage(std::make_shared<phi::Allocation>(
static_cast<uint8_t*>(root->data()) + delta, size, root->place())),
size_(size) {
PADDLE_ENFORCE_LE(static_cast<size_t>(delta + size),
root->size(),
paddle::platform::errors::InvalidArgument(
"The size of the external storage does "
"not meet the metadata requirements."));
PADDLE_ENFORCE_LE(
static_cast<size_t>(delta + size),
root->size(),
phi::errors::InvalidArgument("The size of the external storage does "
"not meet the metadata requirements."));
}
} // namespace experimental
......
paddle/phi/api/lib/utils/storage.h
浏览文件 @ 728c0624
......@@ -30,7 +30,7 @@ class ExternalStorage : public phi::Storage {
static const char* name() { return "ExternalStorage"; }
void Realloc(size_t n) override {
PADDLE_THROW(paddle::platform::errors::Unavailable(
PADDLE_THROW(phi::errors::Unavailable(
"The external shared storage cannot be reallocated."));
}
......@@ -55,7 +55,7 @@ class ExternalStorage : public phi::Storage {
const phi::Place& place() const override {
PADDLE_ENFORCE_NOT_NULL(
data_,
paddle::platform::errors::Unavailable(
phi::errors::Unavailable(
"Unable to visit place as data_ has not been initialized yet."));
return data_->place();
}
......
paddle/phi/backends/dynload/cudnn.cc
浏览文件 @ 728c0624
......@@ -54,7 +54,7 @@ bool HasCUDNN() {
void EnforceCUDNNLoaded(const char* fn_name) {
PADDLE_ENFORCE_NOT_NULL(
cudnn_dso_handle,
paddle::platform::errors::PreconditionNotMet(
phi::errors::PreconditionNotMet(
"Cannot load cudnn shared library. Cannot invoke method %s.",
fn_name));
}
......
paddle/phi/backends/dynload/cufft.cc
浏览文件 @ 728c0624
......@@ -33,7 +33,7 @@ bool HasCUFFT() {
void EnforceCUFFTLoaded(const char* fn_name) {
PADDLE_ENFORCE_NOT_NULL(
cufft_dso_handle,
paddle::platform::errors::PreconditionNotMet(
phi::errors::PreconditionNotMet(
"Cannot load cufft shared library. Cannot invoke method %s.",
fn_name));
}
......
paddle/phi/backends/dynload/dynamic_loader.cc
浏览文件 @ 728c0624
......@@ -24,7 +24,7 @@ limitations under the License. */
#include <windows.h>
#endif
// TODO(wilber): The pten computing library requires a component to manage flags
// TODO(wilber): The phi computing library requires a component to manage flags
// (maybe not use gflags).
#include "gflags/gflags.h"
#include "glog/logging.h"
......@@ -299,8 +299,8 @@ static inline void* GetDsoHandleFromSearchPath(
#endif // !_WIN32
if (throw_on_error) {
// NOTE: Special error report case, no need to change its format
PADDLE_THROW(paddle::platform::errors::PreconditionNotMet(
error_msg, dso_name, errorno));
PADDLE_THROW(
phi::errors::PreconditionNotMet(error_msg, dso_name, errorno));
} else {
LOG(WARNING) << paddle::string::Sprintf(error_msg, dso_name, errorno);
}
......@@ -547,14 +547,11 @@ void* GetOpDsoHandle(const std::string& dso_name) {
void* GetNvtxDsoHandle() {
#if defined(__APPLE__) || defined(__OSX__)
PADDLE_THROW(
paddle::platform::errors::Unimplemented("Nvtx do not support Apple."));
PADDLE_THROW(phi::errors::Unimplemented("Nvtx do not support Apple."));
#elif defined(_WIN32)
PADDLE_THROW(
paddle::platform::errors::Unimplemented("Nvtx do not support Windows."));
PADDLE_THROW(phi::errors::Unimplemented("Nvtx do not support Windows."));
#elif !defined(PADDLE_WITH_CUDA)
PADDLE_THROW(paddle::platform::errors::Unimplemented(
"Nvtx do not support without CUDA."));
PADDLE_THROW(phi::errors::Unimplemented("Nvtx do not support without CUDA."));
#else
return GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libnvToolsExt.so");
#endif
......
paddle/phi/backends/dynload/miopen.cc
浏览文件 @ 728c0624
......@@ -58,7 +58,7 @@ bool HasCUDNN() {
void EnforceCUDNNLoaded(const char* fn_name) {
PADDLE_ENFORCE_NOT_NULL(
miopen_dso_handle,
paddle::platform::errors::PreconditionNotMet(
phi::errors::PreconditionNotMet(
"Cannot load miopen shared library. Cannot invoke method %s.",
fn_name));
}
......
paddle/phi/backends/dynload/tensorrt.h
浏览文件 @ 728c0624
......@@ -54,21 +54,21 @@ extern void* tensorrt_plugin_dso_handle;
}; \
extern DynLoad__##__name __name
#define DECLARE_DYNAMIC_LOAD_TENSORRT_NON_POINTER_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> DECLARE_TYPE(__name, args...) { \
std::call_once(tensorrt_dso_flag, []() { \
tensorrt_dso_handle = phi::dynload::GetTensorRtHandle(); \
}); \
static void* p_##__name = dlsym(tensorrt_dso_handle, #__name); \
PADDLE_ENFORCE_NOT_NULL(p_##__name, \
paddle::platform::errors::Unavailable( \
"Load tensorrt api %s failed", #__name)); \
using tensorrt_func = decltype(&::__name); \
return reinterpret_cast<tensorrt_func>(p_##__name)(args...); \
} \
}; \
#define DECLARE_DYNAMIC_LOAD_TENSORRT_NON_POINTER_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> DECLARE_TYPE(__name, args...) { \
std::call_once(tensorrt_dso_flag, []() { \
tensorrt_dso_handle = phi::dynload::GetTensorRtHandle(); \
}); \
static void* p_##__name = dlsym(tensorrt_dso_handle, #__name); \
PADDLE_ENFORCE_NOT_NULL( \
p_##__name, \
phi::errors::Unavailable("Load tensorrt api %s failed", #__name)); \
using tensorrt_func = decltype(&::__name); \
return reinterpret_cast<tensorrt_func>(p_##__name)(args...); \
} \
}; \
extern DynLoad__##__name __name
#define DECLARE_DYNAMIC_LOAD_TENSORRT_PLUGIN_WRAP(__name) \
......@@ -80,7 +80,7 @@ extern void* tensorrt_plugin_dso_handle;
}); \
static void* p_##__name = dlsym(tensorrt_plugin_dso_handle, #__name); \
PADDLE_ENFORCE_NOT_NULL(p_##__name, \
paddle::platform::errors::Unavailable( \
phi::errors::Unavailable( \
"Load tensorrt plugin %s failed", #__name)); \
using tensorrt_plugin_func = decltype(&::__name); \
return reinterpret_cast<tensorrt_plugin_func>(p_##__name)(args...); \
......
paddle/phi/backends/gpu/cuda/cuda_info.cc
浏览文件 @ 728c0624
......@@ -14,7 +14,7 @@
#include "paddle/phi/backends/gpu/gpu_info.h"
// TODO(pten): remove fluid headers.
// TODO(phi): remove fluid headers.
#include "paddle/fluid/platform/enforce.h"
static std::once_flag g_device_props_size_init_flag;
......@@ -74,13 +74,13 @@ int GetGPUDeviceCount() {
}
int GetGPUComputeCapability(int id) {
PADDLE_ENFORCE_LT(id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
int major, minor;
auto major_error_code =
cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, id);
......@@ -93,26 +93,26 @@ int GetGPUComputeCapability(int id) {
}
int GetGPURuntimeVersion(int id) {
PADDLE_ENFORCE_LT(id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
int runtime_version = 0;
PADDLE_ENFORCE_GPU_SUCCESS(cudaRuntimeGetVersion(&runtime_version));
return runtime_version;
}
int GetGPUDriverVersion(int id) {
PADDLE_ENFORCE_LT(id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
int driver_version = 0;
PADDLE_ENFORCE_GPU_SUCCESS(cudaDriverGetVersion(&driver_version));
return driver_version;
......@@ -125,13 +125,13 @@ bool TensorCoreAvailable() {
}
int GetGPUMultiProcessors(int id) {
PADDLE_ENFORCE_LT(id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
int count;
PADDLE_ENFORCE_GPU_SUCCESS(
cudaDeviceGetAttribute(&count, cudaDevAttrMultiProcessorCount, id));
......@@ -139,13 +139,13 @@ int GetGPUMultiProcessors(int id) {
}
int GetGPUMaxThreadsPerMultiProcessor(int id) {
PADDLE_ENFORCE_LT(id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
int count;
PADDLE_ENFORCE_GPU_SUCCESS(cudaDeviceGetAttribute(
&count, cudaDevAttrMaxThreadsPerMultiProcessor, id));
......@@ -154,13 +154,13 @@ int GetGPUMaxThreadsPerMultiProcessor(int id) {
}
int GetGPUMaxThreadsPerBlock(int id) {
PADDLE_ENFORCE_LT(id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
int count;
PADDLE_ENFORCE_GPU_SUCCESS(
cudaDeviceGetAttribute(&count, cudaDevAttrMaxThreadsPerBlock, id));
......@@ -174,13 +174,13 @@ int GetCurrentDeviceId() {
}
std::array<int, 3> GetGpuMaxGridDimSize(int id) {
PADDLE_ENFORCE_LT(id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
std::array<int, 3> ret;
int size;
auto error_code_x = cudaDeviceGetAttribute(&size, cudaDevAttrMaxGridDimX, id);
......@@ -213,7 +213,7 @@ const gpuDeviceProp &GetDeviceProperties(int id) {
}
if (id < 0 || id >= static_cast<int>(g_device_props.size())) {
PADDLE_THROW(paddle::platform::errors::OutOfRange(
PADDLE_THROW(phi::errors::OutOfRange(
"The device id %d is out of range [0, %d), where %d is the number of "
"devices on this machine. Because the device id should be greater than "
"or equal to zero and smaller than the number of gpus. Please input "
......@@ -233,13 +233,13 @@ const gpuDeviceProp &GetDeviceProperties(int id) {
void SetDeviceId(int id) {
// TODO(qijun): find a better way to cache the cuda device count
PADDLE_ENFORCE_LT(id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_RETRY_CUDA_SUCCESS(cudaSetDevice(id));
}
......@@ -294,13 +294,13 @@ gpuError_t GpuGetLastError() { return cudaGetLastError(); }
// https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#um-requirements
// for more detail about managed memory requirements
bool IsGPUManagedMemorySupported(int dev_id) {
PADDLE_ENFORCE_LT(dev_id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
dev_id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
dev_id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
dev_id,
GetGPUDeviceCount()));
#if defined(__linux__) || defined(_WIN32)
int ManagedMemoryAttr;
PADDLE_ENFORCE_GPU_SUCCESS(cudaDeviceGetAttribute(
......@@ -312,13 +312,13 @@ bool IsGPUManagedMemorySupported(int dev_id) {
}
bool IsGPUManagedMemoryOversubscriptionSupported(int dev_id) {
PADDLE_ENFORCE_LT(dev_id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
dev_id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
dev_id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
dev_id,
GetGPUDeviceCount()));
#ifdef __linux__
return IsGPUManagedMemorySupported(dev_id) &&
GetGPUComputeCapability(dev_id) >= 60;
......
paddle/phi/backends/gpu/gpu_launch_config.h
浏览文件 @ 728c0624
......@@ -100,12 +100,12 @@ struct GpuLaunchConfig {
inline GpuLaunchConfig GetGpuLaunchConfig1D(const phi::GPUContext& context,
int64_t numel,
int vec_size = 1) {
PADDLE_ENFORCE_GT(numel,
0,
paddle::platform::errors::InvalidArgument(
"element quantity should be greater than 0,"
" but received value is: %d.",
numel));
PADDLE_ENFORCE_GT(
numel,
0,
phi::errors::InvalidArgument("element quantity should be greater than 0,"
" but received value is: %d.",
numel));
// Get compute_capability
const int capability = context.GetComputeCapability();
/* If thread number per block is 64/128/256/512, cuda performs better.*/
......@@ -142,18 +142,18 @@ inline GpuLaunchConfig GetGpuLaunchConfig1D(const phi::GPUContext& context,
inline GpuLaunchConfig GetGpuLaunchConfig2D(const phi::GPUContext& context,
int x_dim,
int y_dim) {
PADDLE_ENFORCE_GT(x_dim,
0,
paddle::platform::errors::InvalidArgument(
"x dim number should greater than 0,"
" but received value is: %d",
x_dim));
PADDLE_ENFORCE_GT(y_dim,
0,
paddle::platform::errors::InvalidArgument(
"y dim number should greater than 0,"
" but received value is: %d",
y_dim));
PADDLE_ENFORCE_GT(
x_dim,
0,
phi::errors::InvalidArgument("x dim number should greater than 0,"
" but received value is: %d",
x_dim));
PADDLE_ENFORCE_GT(
y_dim,
0,
phi::errors::InvalidArgument("y dim number should greater than 0,"
" but received value is: %d",
y_dim));
const int kThreadsPerBlock = 256;
int block_cols = (std::min)(x_dim, kThreadsPerBlock);
......
paddle/phi/backends/gpu/rocm/rocm_info.cc
浏览文件 @ 728c0624
......@@ -78,13 +78,13 @@ int GetGPUDeviceCount() {
}
int GetGPUComputeCapability(int id) {
PADDLE_ENFORCE_LT(id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
int major, minor;
auto major_error_code = hipDeviceGetAttribute(
&major, hipDeviceAttributeComputeCapabilityMajor, id);
......@@ -97,26 +97,26 @@ int GetGPUComputeCapability(int id) {
}
int GetGPURuntimeVersion(int id) {
PADDLE_ENFORCE_LT(id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
int runtime_version = 0;
PADDLE_ENFORCE_GPU_SUCCESS(hipRuntimeGetVersion(&runtime_version));
return runtime_version;
}
int GetGPUDriverVersion(int id) {
PADDLE_ENFORCE_LT(id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
int driver_version = 0;
PADDLE_ENFORCE_GPU_SUCCESS(hipDriverGetVersion(&driver_version));
return driver_version;
......@@ -125,13 +125,13 @@ int GetGPUDriverVersion(int id) {
bool TensorCoreAvailable() { return false; }
int GetGPUMultiProcessors(int id) {
PADDLE_ENFORCE_LT(id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
int count;
PADDLE_ENFORCE_GPU_SUCCESS(
hipDeviceGetAttribute(&count, hipDeviceAttributeMultiprocessorCount, id));
......@@ -139,13 +139,13 @@ int GetGPUMultiProcessors(int id) {
}
int GetGPUMaxThreadsPerMultiProcessor(int id) {
PADDLE_ENFORCE_LT(id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
int count;
PADDLE_ENFORCE_GPU_SUCCESS(hipDeviceGetAttribute(
&count, hipDeviceAttributeMaxThreadsPerMultiProcessor, id));
......@@ -154,13 +154,13 @@ int GetGPUMaxThreadsPerMultiProcessor(int id) {
}
int GetGPUMaxThreadsPerBlock(int id) {
PADDLE_ENFORCE_LT(id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
int count;
PADDLE_ENFORCE_GPU_SUCCESS(
hipDeviceGetAttribute(&count, hipDeviceAttributeMaxThreadsPerBlock, id));
......@@ -174,13 +174,13 @@ int GetCurrentDeviceId() {
}
std::array<int, 3> GetGpuMaxGridDimSize(int id) {
PADDLE_ENFORCE_LT(id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
std::array<int, 3> ret;
int size;
auto error_code_x =
......@@ -216,7 +216,7 @@ const gpuDeviceProp &GetDeviceProperties(int id) {
}
if (id < 0 || id >= static_cast<int>(g_device_props.size())) {
PADDLE_THROW(paddle::platform::errors::OutOfRange(
PADDLE_THROW(phi::errors::OutOfRange(
"The device id %d is out of range [0, %d), where %d is the number of "
"devices on this machine. Because the device id should be greater than "
"or equal to zero and smaller than the number of gpus. Please input "
......@@ -235,13 +235,13 @@ const gpuDeviceProp &GetDeviceProperties(int id) {
void SetDeviceId(int id) {
// TODO(qijun): find a better way to cache the cuda device count
PADDLE_ENFORCE_LT(id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
id,
GetGPUDeviceCount()));
PADDLE_RETRY_CUDA_SUCCESS(hipSetDevice(id));
}
......@@ -293,13 +293,13 @@ void GpuDeviceSync() { PADDLE_ENFORCE_GPU_SUCCESS(hipDeviceSynchronize()); }
gpuError_t GpuGetLastError() { return hipGetLastError(); }
bool IsGPUManagedMemorySupported(int dev_id) {
PADDLE_ENFORCE_LT(dev_id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
dev_id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
dev_id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
dev_id,
GetGPUDeviceCount()));
#if defined(__linux__) || defined(_WIN32)
int ManagedMemoryAttr;
PADDLE_ENFORCE_GPU_SUCCESS(hipDeviceGetAttribute(
......@@ -311,13 +311,13 @@ bool IsGPUManagedMemorySupported(int dev_id) {
}
bool IsGPUManagedMemoryOversubscriptionSupported(int dev_id) {
PADDLE_ENFORCE_LT(dev_id,
GetGPUDeviceCount(),
paddle::platform::errors::InvalidArgument(
"Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
dev_id,
GetGPUDeviceCount()));
PADDLE_ENFORCE_LT(
dev_id,
GetGPUDeviceCount(),
phi::errors::InvalidArgument("Device id must be less than GPU count, "
"but received id is: %d. GPU count is: %d.",
dev_id,
GetGPUDeviceCount()));
#ifdef __linux__
return IsGPUManagedMemorySupported(dev_id) &&
GetGPUComputeCapability(dev_id) >= 60;
......
paddle/phi/backends/xpu/enforce_xpu.h
浏览文件 @ 728c0624
......@@ -173,7 +173,7 @@ DEFINE_EXTERNAL_API_TYPE(BKCLResult_t, BKCL_SUCCESS);
::phi::backends::xpu::details::ExternalApiType< \
__XPU_STATUS_TYPE__>::kSuccess; \
if (UNLIKELY(__cond__ != __success_type__)) { \
auto __summary__ = paddle::platform::errors::External( \
auto __summary__ = phi::errors::External( \
::phi::backends::xpu::build_xpu_error_msg(__cond__)); \
__THROW_ERROR_INTERNAL__(__summary__); \
} \
......@@ -183,7 +183,7 @@ DEFINE_EXTERNAL_API_TYPE(BKCLResult_t, BKCL_SUCCESS);
do { \
auto __cond__ = (COND); \
if (UNLIKELY(__cond__ != baidu::xpu::api::Error_t::SUCCESS)) { \
auto __summary__ = paddle::platform::errors::External( \
auto __summary__ = phi::errors::External( \
::phi::backends::xpu::build_xpu_xdnn_error_msg(__cond__, MSG)); \
__THROW_ERROR_INTERNAL__(__summary__); \
} \
......@@ -192,7 +192,7 @@ DEFINE_EXTERNAL_API_TYPE(BKCLResult_t, BKCL_SUCCESS);
#define PADDLE_ENFORCE_XDNN_NOT_NULL(ptr) \
do { \
if (UNLIKELY(ptr == nullptr)) { \
auto __summary__ = paddle::platform::errors::External( \
auto __summary__ = phi::errors::External( \
::phi::backends::xpu::build_xpu_xdnn_error_msg( \
baidu::xpu::api::Error_t::NO_ENOUGH_WORKSPACE, \
"XPU memory is not enough")); \
......
paddle/phi/backends/xpu/xpu_info.cc
浏览文件 @ 728c0624
......@@ -100,7 +100,7 @@ void SetXPUDeviceId(int id) {
PADDLE_ENFORCE_LT(
id,
GetXPUDeviceCount(),
paddle::platform::errors::InvalidArgument("id must less than XPU count"));
phi::errors::InvalidArgument("id must less than XPU count"));
PADDLE_ENFORCE_XPU_SUCCESS(xpu_set_device(id));
}
......
paddle/phi/core/CMakeLists.txt
浏览文件 @ 728c0624
......@@ -13,8 +13,8 @@ cc_library(kernel_context SRCS kernel_context.cc DEPS pten_enforce pten_context)
cc_library(ddim SRCS ddim.cc DEPS pten_enforce)
cc_library(tensor_base SRCS tensor_base.cc allocator.cc DEPS pten_enforce)
cc_library(tensor_meta SRCS tensor_meta.cc DEPS pten_enforce mixed_vector)
cc_library(lod_utils SRCS lod_utils.cc DEPS pten_enforce mixed_vector)
cc_library(tensor_meta SRCS tensor_meta.cc DEPS pten_enforce)
cc_library(lod_utils SRCS lod_utils.cc DEPS pten_enforce)
cc_library(pten_device_context SRCS device_context.cc DEPS tensor_base)
cc_library(dense_tensor SRCS dense_tensor.cc dense_tensor_impl.cc DEPS fluid_convert_utils tensor_meta tensor_base)
......@@ -23,7 +23,7 @@ cc_library(sparse_csr_tensor SRCS sparse_csr_tensor.cc DEPS dense_tensor tensor_
cc_library(meta_tensor SRCS meta_tensor.cc DEPS tensor_base tensor_meta dense_tensor)
cc_library(infermeta_utils SRCS infermeta_utils.cc DEPS meta_tensor)
cc_library(selected_rows SRCS selected_rows_impl.cc DEPS dense_tensor mixed_vector pten_enforce ddim)
cc_library(selected_rows SRCS selected_rows_impl.cc DEPS dense_tensor pten_enforce ddim)
cc_library(pten_custom_kernel SRCS custom_kernel.cc DEPS kernel_factory convert_utils)
......
paddle/phi/core/ddim.h
浏览文件 @ 728c0624
......@@ -13,6 +13,7 @@
// limitations under the License.
#pragma once
#include <initializer_list>
#include <numeric>
#include <stdexcept>
#include <string>
#include <vector>
......
paddle/phi/core/dense_tensor.cc
浏览文件 @ 728c0624
......@@ -73,7 +73,7 @@ void* DenseTensor::AllocateFrom(Allocator* allocator,
size_t requested_size) {
PADDLE_ENFORCE_NOT_NULL(
allocator,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Required allocator shall not be nullptr, but received nullptr."));
if (this->dtype() != dtype) {
VLOG(10) << "change data type in mutbale_data, target dtype - " << dtype;
......@@ -81,13 +81,13 @@ void* DenseTensor::AllocateFrom(Allocator* allocator,
}
PADDLE_ENFORCE(
valid(),
paddle::platform::errors::PreconditionNotMet(
phi::errors::PreconditionNotMet(
"The meta data must be valid when call the mutable data function."));
size_t bytes = numel() * SizeOf(this->dtype());
if (requested_size) {
PADDLE_ENFORCE_GE(requested_size,
bytes,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The reserved size %d should be enough to meet the "
"volume required by metadata %d.",
requested_size,
......@@ -112,7 +112,7 @@ const T* DenseTensor::data() const {
check_memory_size();
PADDLE_ENFORCE(
(dtype() == paddle::experimental::CppTypeToDataType<T>::Type()),
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The type of data we are trying to retrieve does not match the "
"type of data currently contained in the container."));
return static_cast<const T*>(data());
......@@ -123,7 +123,7 @@ T* DenseTensor::data() {
check_memory_size();
PADDLE_ENFORCE(
(dtype() == paddle::experimental::CppTypeToDataType<T>::Type()),
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The type of data we are trying to retrieve does not match the "
"type of data currently contained in the container."));
return static_cast<T*>(data());
......@@ -133,7 +133,7 @@ void* DenseTensor::data() {
check_memory_size();
PADDLE_ENFORCE_NOT_NULL(
holder_,
paddle::platform::errors::PreconditionNotMet(
phi::errors::PreconditionNotMet(
"The storage must be valid when call the data function."));
return reinterpret_cast<void*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
meta_.offset);
......@@ -143,7 +143,7 @@ const void* DenseTensor::data() const {
check_memory_size();
PADDLE_ENFORCE_NOT_NULL(
holder_,
paddle::platform::errors::PreconditionNotMet(
phi::errors::PreconditionNotMet(
"The storage must be valid when call the data function."));
return reinterpret_cast<const void*>(
reinterpret_cast<uintptr_t>(holder_->ptr()) + meta_.offset);
......@@ -151,7 +151,7 @@ const void* DenseTensor::data() const {
void DenseTensor::set_meta(DenseTensorMeta&& meta) {
PADDLE_ENFORCE(!meta_.valid(),
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Only when the original attribute of Tensor is "
"incomplete, can it be reset."));
meta_ = std::move(meta);
......@@ -160,7 +160,7 @@ void DenseTensor::set_meta(DenseTensorMeta&& meta) {
void DenseTensor::set_meta(const DenseTensorMeta& meta) {
PADDLE_ENFORCE(
meta.valid(),
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Input meta is invalid, please check the meta attribute."));
meta_.dims = meta.dims;
meta_.dtype = meta.dtype;
......
paddle/phi/core/dense_tensor.inl
浏览文件 @ 728c0624
......@@ -54,22 +54,22 @@ DenseTensor(intrusive_ptr<Storage> storage, DenseTensorMeta&& meta);
inline bool IsInitialized() const { return holder_ != nullptr; }
template <typename T>
T* mutable_data(const paddle::platform::Place& place,
T* mutable_data(const phi::Place& place,
size_t requested_size = 0);
template <typename T>
T* mutable_data(const DDim& dims,
const paddle::platform::Place& place,
const phi::Place& place,
size_t requested_size = 0);
void* mutable_data(const paddle::platform::Place& place,
void* mutable_data(const phi::Place& place,
paddle::experimental::DataType type,
size_t requested_size = 0);
void* mutable_data(const paddle::platform::Place& place,
void* mutable_data(const phi::Place& place,
size_t requested_size = 0);
void* mutable_data(const paddle::platform::Place& place,
void* mutable_data(const phi::Place& place,
paddle::experimental::DataType type,
const phi::Stream& stream);
......
paddle/phi/core/infermeta_utils.h
浏览文件 @ 728c0624
......@@ -25,6 +25,7 @@ limitations under the License. */
#include "paddle/phi/core/macros.h"
#include "paddle/phi/core/meta_tensor.h"
#include "paddle/phi/core/type_defs.h"
#include "paddle/utils/any.h"
#include "paddle/utils/flat_hash_map.h"
#include "paddle/utils/small_vector.h"
......
paddle/phi/core/kernel_context.cc
浏览文件 @ 728c0624
......@@ -69,7 +69,7 @@ void KernelContext::AssignInputRange(std::pair<int, int>&& range, size_t idx) {
} else if (idx == input_range_.size()) {
input_range_.emplace_back(range);
} else {
PADDLE_THROW(paddle::platform::errors::PreconditionNotMet(
PADDLE_THROW(phi::errors::PreconditionNotMet(
"Invalid idx when trying to set InputRange, "
"index is `%d`, it is greater than the size(%d) of InputRange.",
idx,
......@@ -83,7 +83,7 @@ void KernelContext::AssignOutputRange(std::pair<int, int>&& range, size_t idx) {
} else if (idx == output_range_.size()) {
output_range_.emplace_back(range);
} else {
PADDLE_THROW(paddle::platform::errors::PreconditionNotMet(
PADDLE_THROW(phi::errors::PreconditionNotMet(
"Invalid idx when trying to set InputRange, "
"index is `%d`, it is greater than the size(%d) of InputRange.",
idx,
......
paddle/phi/core/lod_utils.h
浏览文件 @ 728c0624
......@@ -13,18 +13,11 @@
// limitations under the License.
#pragma once
// See Note [ Why still include the fluid headers? ]
#ifndef PADDLE_WITH_CUSTOM_KERNEL
#include "paddle/fluid/framework/mixed_vector.h"
#endif
#include <cstddef>
#include <vector>
namespace phi {
#ifndef PADDLE_WITH_CUSTOM_KERNEL
using LoD = std::vector<paddle::framework::Vector<size_t>>;
#else
using LoD = std::vector<std::vector<size_t>>;
#endif
using LoD = std::vector<std::vector<std::size_t>>;
void AppendLoD(LoD* lod, const LoD& lod_length);
......@@ -40,4 +33,4 @@ void AppendLoD(LoD* lod, const LoD& lod_length);
*/
LoD ConvertToLengthBasedLoD(const LoD& offset_lod);
} // namespace pten
} // namespace phi
paddle/phi/core/selected_rows.h
浏览文件 @ 728c0624
......@@ -55,25 +55,17 @@ class SelectedRows : public TensorBase,
void set_height(int64_t height) { impl_->set_height(height); }
const paddle::framework::Vector<int64_t>& rows() const {
return impl_->rows();
}
const std::vector<int64_t>& rows() const { return impl_->rows(); }
paddle::framework::Vector<int64_t>* mutable_rows() {
return impl_->mutable_rows();
}
void set_rows(const paddle::framework::Vector<int64_t>& rows) {
impl_->set_rows(rows);
}
std::vector<int64_t>* mutable_rows() { return impl_->mutable_rows(); }
void set_rows(const std::vector<int64_t>& rows) { impl_->set_rows(rows); }
/*
* @brief Get the index of key in rows
*
* @return -1 if the key does not exists.
*/
int64_t Index(int64_t key) const { return impl_->Index(key); }
/*
* @brief whether has the specified key in the table.
*
......
paddle/phi/core/selected_rows_impl.cc
浏览文件 @ 728c0624
......@@ -28,7 +28,7 @@ struct ReAllocateVisitor {
template <typename T>
void operator()() const {
phi::DenseTensor cpu_tensor;
paddle::platform::CPUPlace cpu;
phi::CPUPlace cpu;
T* ptr = cpu_tensor.mutable_data<T>(dims_, cpu);
const T* old_ptr =
tensor_->memory_size() == 0 ? nullptr : tensor_->data<T>();
......@@ -57,7 +57,7 @@ struct TensorCopyVisitor {
template <typename T>
void apply() const {
// TODO(Yancey1989): support other place
paddle::platform::CPUPlace cpu;
phi::CPUPlace cpu;
paddle::memory::Copy(cpu,
dst_->mutable_data<T>(cpu) + dst_offset_,
cpu,
......@@ -82,7 +82,7 @@ struct TensorFillVisitor {
template <typename T>
void apply() const {
// TODO(qiao): support other place
paddle::platform::CPUPlace cpu;
phi::CPUPlace cpu;
auto* tensor_data = dst_->mutable_data<T>(cpu);
auto* start = tensor_data + dst_offset_;
auto* end = start + size_;
......@@ -121,16 +121,16 @@ int64_t SelectedRowsImpl::AutoGrownIndex(int64_t key,
auto iter = id_to_index_.find(key);
if (iter == id_to_index_.end()) {
rwlock_->UNLock();
PADDLE_ENFORCE_EQ(auto_grown,
true,
paddle::platform::errors::NotFound(
"Input key(%lld) is not found.", key));
PADDLE_ENFORCE_EQ(
auto_grown,
true,
phi::errors::NotFound("Input key(%lld) is not found.", key));
rwlock_->WRLock();
auto map_size = id_to_index_.size();
auto vector_size = rows_.size();
if (map_size != vector_size) {
rwlock_->UNLock();
PADDLE_THROW(paddle::platform::errors::InvalidArgument(
PADDLE_THROW(phi::errors::InvalidArgument(
"Row map size(%zu) should be equal to rows size(%zu).",
map_size,
vector_size));
......@@ -140,7 +140,7 @@ int64_t SelectedRowsImpl::AutoGrownIndex(int64_t key,
int row_num = rows_.size();
if (row_num == value_->dims()[0]) {
rwlock_->UNLock();
PADDLE_THROW(paddle::platform::errors::InvalidArgument(
PADDLE_THROW(phi::errors::InvalidArgument(
"Selected rows is full, then length exceed the length of first "
"dimension (%d).",
row_num));
......@@ -187,7 +187,7 @@ void SelectedRowsImpl::Get(const phi::DenseTensor& ids,
PADDLE_ENFORCE_EQ(
value_width,
value->numel() / value->dims()[0],
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Output tensor should have the same shape with table "
"except the first dimmension, excepted value width not counting "
"the first dimension is %d, actual value width is %d.",
......
paddle/phi/core/selected_rows_impl.h
浏览文件 @ 728c0624
......@@ -27,8 +27,6 @@ limitations under the License. */
#include "paddle/phi/core/enforce.h"
#include "paddle/phi/core/utils/rw_lock.h"
// See Note [ Why still include the fluid headers? ]
#include "paddle/fluid/framework/mixed_vector.h"
namespace phi {
class SelectedRowsImpl {
/*
......@@ -68,13 +66,11 @@ class SelectedRowsImpl {
void set_height(int64_t height) { height_ = height; }
const paddle::framework::Vector<int64_t>& rows() const { return rows_; }
const std::vector<int64_t>& rows() const { return rows_; }
paddle::framework::Vector<int64_t>* mutable_rows() { return &rows_; }
std::vector<int64_t>* mutable_rows() { return &rows_; }
void set_rows(const paddle::framework::Vector<int64_t>& rows) {
rows_ = rows;
}
void set_rows(const std::vector<int64_t>& rows) { rows_ = rows; }
/*
* @brief Get the index of key in rows
......@@ -84,7 +80,7 @@ class SelectedRowsImpl {
int64_t Index(int64_t key) const {
auto it = std::find(rows_.begin(), rows_.end(), key);
if (it == rows_.end()) {
PADDLE_THROW(paddle::platform::errors::NotFound(
PADDLE_THROW(phi::errors::NotFound(
"Input id (%lld) is not in current rows table.", key));
}
return static_cast<int64_t>(std::distance(rows_.begin(), it));
......@@ -156,10 +152,7 @@ class SelectedRowsImpl {
/// \brief Returns the dims of the tensor.
/// \return The dims of the tensor.
const DDim& dims() const noexcept {
return value_->dims();
// return phi::make_ddim(dims);
}
const DDim& dims() const noexcept { return value_->dims(); }
/// \brief Returns the data type of the tensor.
/// \return The data type of the tensor.
......@@ -185,7 +178,7 @@ class SelectedRowsImpl {
// Notice: rows can be duplicate. We can have {0, 4, 7, 0, 5, 7, 9} here.
// SelectedRowsImpl are simply concated when adding together. Until a
// SelectedRowsImpl add a Tensor, will the duplicate rows be handled.
paddle::framework::Vector<int64_t> rows_;
std::vector<int64_t> rows_;
std::unordered_map<int64_t, int64_t>
id_to_index_; // should not be used when rows_ has duplicate member
std::unique_ptr<DenseTensor> value_{nullptr};
......
paddle/phi/core/sparse_coo_tensor.cc
浏览文件 @ 728c0624
......@@ -69,17 +69,17 @@ void SparseCooTensor::Resize(const DDim& dense_dims,
const int64_t non_zero_num) {
PADDLE_ENFORCE_GE(non_zero_num,
this->nnz(),
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"the non_zero_num must be greater than or equal to the "
"origin non_zero_num."));
PADDLE_ENFORCE_GE(sparse_dim,
1,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"the sparse_dim must be greater than or equal 1."));
PADDLE_ENFORCE_LE(
sparse_dim,
dense_dims.size(),
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"the sparse_dim must be less than or equal dense_dims."));
DDim indices_dims = phi::make_ddim({sparse_dim, non_zero_num});
......
paddle/phi/core/sparse_csr_tensor.cc
浏览文件 @ 728c0624
......@@ -20,7 +20,7 @@ inline void check_shape(const DDim& dims) {
bool valid = dims.size() == 2 || dims.size() == 3;
PADDLE_ENFORCE(valid,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"the SparseCsrTensor only support 2-D Tensor."));
}
#define Check(non_zero_crows, non_zero_cols, non_zero_elements, dims) \
......@@ -29,12 +29,12 @@ inline void check_shape(const DDim& dims) {
PADDLE_ENFORCE_EQ( \
non_zero_cols.place(), \
non_zero_crows.place(), \
paddle::platform::errors::InvalidArgument( \
phi::errors::InvalidArgument( \
"non_zero_crows and non_zero_cols must have the same place.")); \
PADDLE_ENFORCE_EQ( \
non_zero_cols.place(), \
non_zero_elements.place(), \
paddle::platform::errors::InvalidArgument( \
phi::errors::InvalidArgument( \
"non_zero_cols and non_zero_elements must have the same place.")); \
}
......@@ -77,7 +77,7 @@ void* SparseCsrTensor::AllocateFrom(Allocator* allocator,
void SparseCsrTensor::Resize(const DDim& dense_dims,
const int64_t non_zero_num) {
PADDLE_ENFORCE(this->initialized(),
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"the SparseCsrTensor must be initialized when call Resize "
"function."));
check_shape(dense_dims);
......
paddle/phi/core/tensor_meta.h
浏览文件 @ 728c0624
......@@ -20,6 +20,8 @@ limitations under the License. */
#include "paddle/phi/common/data_type.h"
#include "paddle/phi/common/layout.h"
#include "paddle/phi/core/ddim.h"
#include "paddle/utils/any.h"
#include "paddle/utils/optional.h"
// Note: mixed_vector include many header now, LoD will be
// used on CUDA device? Can we use small_vector here?
......@@ -31,11 +33,7 @@ limitations under the License. */
namespace phi {
using DDim = phi::DDim;
#ifndef PADDLE_WITH_CUSTOM_KERNEL
using LoD = std::vector<paddle::framework::Vector<size_t>>;
#else
using LoD = std::vector<std::vector<size_t>>;
#endif
/// \brief The meta data of dense tensor. Take the structure type
/// and use all default operations.
///
......
paddle/phi/infermeta/binary.cc
浏览文件 @ 728c0624
......@@ -23,7 +23,7 @@ void DotInferMeta(const MetaTensor& x, const MetaTensor& y, MetaTensor* out) {
auto x_rank = static_cast<size_t>(x_dims.size());
PADDLE_ENFORCE_EQ(true,
1 == x_rank || 2 == x_rank,
paddle::platform::errors::PreconditionNotMet(
phi::errors::PreconditionNotMet(
"ShapeError: The dimensions of input tensor X (%s) "
"should be 1 or 2",
x_dims.to_str()));
......@@ -32,7 +32,7 @@ void DotInferMeta(const MetaTensor& x, const MetaTensor& y, MetaTensor* out) {
PADDLE_ENFORCE_EQ(
true,
x_rank == static_cast<size_t>(y_dims.size()),
paddle::platform::errors::PreconditionNotMet(
phi::errors::PreconditionNotMet(
"ShapeError: The shape of input tensor Y: %s should match with "
"input tenosr X: %s",
y_dims.to_str(),
......@@ -47,7 +47,7 @@ void DotInferMeta(const MetaTensor& x, const MetaTensor& y, MetaTensor* out) {
PADDLE_ENFORCE_EQ(true,
shape_match,
paddle::platform::errors::PreconditionNotMet(
phi::errors::PreconditionNotMet(
"ShapeError: The shape of input tensor X: %s should "
"be exactly the same "
"with input tensor Y: %s",
......@@ -71,12 +71,12 @@ void MatmulInferMeta(const MetaTensor& x,
auto ndims_y = dims_y.size();
PADDLE_ENFORCE_GT(ndims_x,
0UL,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The Input(x) dims size must be greater than 0,"
" but reviced dims size is 0. "));
PADDLE_ENFORCE_GT(ndims_y,
0UL,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The Input(y) dims size must be greater than 0,"
" but reviced dims size is 0. "));
......@@ -150,7 +150,7 @@ void ElementwiseRawInferMeta(const MetaTensor& x,
if (x_dims.size() == y_dims.size()) {
PADDLE_ENFORCE_EQ((axis == -1) || (axis == 0),
true,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"axis should be -1 or 0 while the dimension of "
"tensor X (%s) is equal to the dimension of "
"tensor Y (%s), but received axis: %s",
......@@ -160,7 +160,7 @@ void ElementwiseRawInferMeta(const MetaTensor& x,
}
PADDLE_ENFORCE_EQ((axis >= (-1 * max_dim)) && (axis < max_dim),
true,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The axis range must be [%s, %s), but axis is %s. "
"Please set the axis again.",
-1 * max_dim,
......
paddle/phi/infermeta/multiary.cc
浏览文件 @ 728c0624
......@@ -24,7 +24,7 @@ void ConcatInferMeta(const std::vector<MetaTensor>& x,
MetaConfig config) {
PADDLE_ENFORCE_GE(x.size(),
0UL,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The size of input meta vector should be greater"
"than 0."));
......@@ -34,7 +34,7 @@ void ConcatInferMeta(const std::vector<MetaTensor>& x,
PADDLE_ENFORCE_EQ(
axis >= -rank && axis < rank,
true,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The axis is expected to be in range of [%d, %d), but got %d",
-rank,
rank,
......
paddle/phi/infermeta/unary.cc
浏览文件 @ 728c0624
......@@ -38,11 +38,11 @@ void FlattenInferMeta(const MetaTensor& x,
if (stop_axis < 0) {
stop_axis = stop_axis + in_dims_size;
}
PADDLE_ENFORCE_GE(stop_axis,
start_axis,
paddle::platform::errors::InvalidArgument(
"The stop_axis should be greater"
"than or equal to start_axis."));
PADDLE_ENFORCE_GE(
stop_axis,
start_axis,
phi::errors::InvalidArgument("The stop_axis should be greater"
"than or equal to start_axis."));
int64_t outer = 1;
std::vector<int32_t> out_shape;
......@@ -113,7 +113,7 @@ static phi::DDim ValidateShape(const std::vector<int64_t> shape,
PADDLE_ENFORCE_EQ(
unk_dim_idx,
-1,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Only one dimension value of 'shape' in ReshapeOp can "
"be -1. But received shape = [%s], shape[%d] is also -1.",
phi::make_ddim(shape),
......@@ -123,7 +123,7 @@ static phi::DDim ValidateShape(const std::vector<int64_t> shape,
PADDLE_ENFORCE_LT(
static_cast<int>(i),
in_dims.size(),
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The index of 0 in `shape` must be less than "
"the input tensor X's dimensions. "
"But received shape = [%s], shape[%d] = 0, X's shape = [%s], "
......@@ -136,7 +136,7 @@ static phi::DDim ValidateShape(const std::vector<int64_t> shape,
PADDLE_ENFORCE_GT(
shape[i],
0,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Each dimension value of 'shape' in ReshapeOp must not "
"be negative except one unknown dimension. "
"But received shape = [%s], shape[%d] = %d.",
......@@ -161,7 +161,7 @@ static phi::DDim ValidateShape(const std::vector<int64_t> shape,
PADDLE_ENFORCE_EQ(
output_shape[unk_dim_idx] * capacity,
-in_size,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The 'shape' attribute in ReshapeOp is invalid. "
"The input tensor X'size must be divisible by known "
"capacity of 'shape'. "
......@@ -179,7 +179,7 @@ static phi::DDim ValidateShape(const std::vector<int64_t> shape,
PADDLE_ENFORCE_EQ(
capacity,
in_size,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The 'shape' in ReshapeOp is invalid. "
"The input tensor X'size must be equal to the capacity of "
"'shape'. "
......@@ -199,7 +199,7 @@ static phi::DDim ValidateShape(const std::vector<int64_t> shape,
PADDLE_ENFORCE_LE(
capacity,
in_size,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The 'shape' in ReshapeOp is invalid. "
"The input tensor X's shape = [%s], X's capacity = %d."
"But the target shape of Out is [%s], the "
......@@ -364,7 +364,7 @@ void SplitInferMeta(const MetaTensor& x,
PADDLE_ENFORCE_EQ(
axis_value >= -rank && axis_value < rank,
true,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The axis is expected to be in range of [%d, %d), but got %d",
-rank,
rank,
......@@ -383,7 +383,7 @@ void SplitInferMeta(const MetaTensor& x,
PADDLE_ENFORCE_EQ(input_axis_dim % num,
0,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The input's size along the split dimension "
"must be evenly divisible by Attr(num_or_sections). "
"But received Attr(num_or_sections) "
......@@ -416,7 +416,7 @@ void SplitInferMeta(const MetaTensor& x,
if (config.is_runtime) {
PADDLE_ENFORCE_LE(num_of_unknow,
1,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Only one dimension value of Attr(num_or_sections) "
"in SplitOp can be -1. "
"But received Attr(num_or_sections) = [%s].",
......@@ -430,7 +430,7 @@ void SplitInferMeta(const MetaTensor& x,
PADDLE_ENFORCE_LT(
sum_of_section,
input_axis_dim,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Sum of Attr(num_or_sections) other than unknown section "
"must be less than the input's "
"size "
......@@ -447,7 +447,7 @@ void SplitInferMeta(const MetaTensor& x,
PADDLE_ENFORCE_EQ(
sum_of_section,
input_axis_dim,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Sum of Attr(num_or_sections) must be equal to the input's "
"size "
"along the split dimension. But received Attr(num_or_sections)"
......
paddle/phi/kernels/cpu/concat_kernel.cc
浏览文件 @ 728c0624
......@@ -54,7 +54,7 @@ void ConcatKernel(const Context& dev_ctx,
PADDLE_ENFORCE_EQ(
x[i].lod().size(),
lod_size_0,
paddle::platform::errors::Unimplemented(
phi::errors::Unimplemented(
"The lod level of all input LoDTensors should be same. "
"Maybe different lod level of input LoDTensors can concat,"
"it is not supported currently. The lod level of %dth input "
......
paddle/phi/kernels/cpu/elementwise.h
浏览文件 @ 728c0624
......@@ -127,7 +127,7 @@ struct SameDimsDivideFunctor<
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* z) {
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"If use SameDimsDivideFunctor, template args(T) must be floating "
"point. ");
}
......@@ -278,12 +278,10 @@ void CommonForwardBroadcastCPU(const DenseTensor& x,
std::vector<int> index_array(max_dim, 0);
const T* x_data = x.data<T>();
const T* y_data = y.data<T>();
PADDLE_ENFORCE_NOT_NULL(x_data,
paddle::platform::errors::InvalidArgument(
"The input X should not be empty."));
PADDLE_ENFORCE_NOT_NULL(y_data,
paddle::platform::errors::InvalidArgument(
"The input Y should not be empty."));
PADDLE_ENFORCE_NOT_NULL(
x_data, phi::errors::InvalidArgument("The input X should not be empty."));
PADDLE_ENFORCE_NOT_NULL(
y_data, phi::errors::InvalidArgument("The input Y should not be empty."));
OutType* out_data = ctx.Alloc<OutType>(z);
const int out_size = std::accumulate(
......@@ -317,12 +315,12 @@ void CommonElementwiseBroadcastForward(const CPUContext& dev_ctx,
PADDLE_ENFORCE_GE(
axis,
0,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Axis should be great than or equal to 0, but received axis is %d.",
axis));
PADDLE_ENFORCE_LT(axis,
max_dim,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Axis should be less than %d, but received axis is %d.",
max_dim,
axis));
......@@ -385,12 +383,12 @@ void ElementwiseCompute(const CPUContext& dev_ctx,
PADDLE_ENFORCE_GE(
axis,
0,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Axis should be great than or equal to 0, but received axis is %d.",
axis));
PADDLE_ENFORCE_LT(axis,
max_dim,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Axis should be less than %d, but received axis is %d.",
max_dim,
axis));
......@@ -630,12 +628,12 @@ void ElemwiseGradComputeWithBroadcast(const CPUContext& ctx,
PADDLE_ENFORCE_GE(
axis,
0,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Axis should be great than or equal to 0, but received axis is %d.",
axis));
PADDLE_ENFORCE_LT(axis,
max_dim,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Axis should be less than %d, but received axis is %d.",
max_dim,
axis));
......
paddle/phi/kernels/cpu/masked_select_kernel.cc
浏览文件 @ 728c0624
......@@ -48,7 +48,7 @@ void MaskedSelectKernel(const Context& dev_ctx,
DDim out_dim{out_size};
out->Resize(out_dim);
auto out_data = out->mutable_data<T>(paddle::platform::CPUPlace());
auto out_data = out->mutable_data<T>(phi::CPUPlace());
int index = 0;
for (int i = 0; i < mask_size; i++) {
......
paddle/phi/kernels/funcs/common_shape.h
浏览文件 @ 728c0624
......@@ -42,12 +42,12 @@ inline void GetBroadcastDimsArrays(const DDim &x_dims,
PADDLE_ENFORCE_GE(
axis,
0,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Axis should be great than or equal to 0, but received axis is %d.",
axis));
PADDLE_ENFORCE_LT(axis,
max_dim,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Axis should be less than %d, but received axis is %d.",
max_dim,
axis));
......@@ -72,7 +72,7 @@ inline void GetBroadcastDimsArrays(const DDim &x_dims,
x_dims_array[i] == y_dims_array[i] || x_dims_array[i] <= 1 ||
y_dims_array[i] <= 1,
true,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Broadcast dimension mismatch. Operands could "
"not be broadcast together with the shape of X = [%s] and "
"the shape of Y = [%s]. Received [%d] in X is not equal to "
......
paddle/phi/kernels/funcs/concat_funcs.h
浏览文件 @ 728c0624
......@@ -23,7 +23,7 @@ static inline int64_t ComputeAxis(int64_t axis, int64_t rank) {
PADDLE_ENFORCE_EQ(
axis >= -rank && axis < rank,
true,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The axis is expected to be in range of [%d, %d), but got %d",
-rank,
rank,
......@@ -42,17 +42,17 @@ static inline phi::DDim ComputeAndCheckShape(
auto out_dims = inputs_dims[0];
size_t in_zero_dims_size = out_dims.size();
for (size_t i = 1; i < n; i++) {
PADDLE_ENFORCE_EQ(inputs_dims[i].size(),
out_dims.size(),
paddle::platform::errors::InvalidArgument(
"The shape of input[0] and input[%d] "
"is expected to be equal."
"But received input[0]'s shape = "
"[%s], input[%d]'s shape = [%s].",
i,
inputs_dims[0],
i,
inputs_dims[i]));
PADDLE_ENFORCE_EQ(
inputs_dims[i].size(),
out_dims.size(),
phi::errors::InvalidArgument("The shape of input[0] and input[%d] "
"is expected to be equal."
"But received input[0]'s shape = "
"[%s], input[%d]'s shape = [%s].",
i,
inputs_dims[0],
i,
inputs_dims[i]));
for (size_t j = 0; j < in_zero_dims_size; j++) {
if (j == axis) {
if (is_runtime) {
......@@ -71,7 +71,7 @@ static inline phi::DDim ComputeAndCheckShape(
// check all shape in run time
PADDLE_ENFORCE_EQ(inputs_dims[0][j],
inputs_dims[i][j],
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The %d-th dimension of input[0] and input[%d] "
"is expected to be equal."
"But received input[0]'s shape = "
......@@ -92,4 +92,4 @@ static inline phi::DDim ComputeAndCheckShape(
}
} // namespace funcs
} // namespace pten
} // namespace phi
paddle/phi/kernels/funcs/eigen/common.h
浏览文件 @ 728c0624
......@@ -21,7 +21,7 @@ limitations under the License. */
namespace phi {
// EigenDim converts paddle::platform::DDim into Eigen::DSizes.
// EigenDim converts phi::DDim into Eigen::DSizes.
template <int D>
struct EigenDim {
using Type = Eigen::DSizes<Eigen::DenseIndex, D>;
......@@ -29,7 +29,7 @@ struct EigenDim {
static Type From(const DDim& dims) {
PADDLE_ENFORCE_EQ(arity(dims),
D,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Input dimension size should be equal to %d, but "
"received dimension size is %d.",
arity(dims),
......@@ -42,7 +42,7 @@ struct EigenDim {
}
};
// Interpret paddle::platform::Tensor as EigenTensor and EigenConstTensor.
// Interpret phi::Tensor as EigenTensor and EigenConstTensor.
template <typename T,
size_t D,
int MajorType = Eigen::RowMajor,
......@@ -86,7 +86,7 @@ struct EigenMatrix : public EigenTensor<T, 2, MajorType, IndexType> {
int rank = tensor.dims().size();
PADDLE_ENFORCE_EQ((num_col_dims > 0 && num_col_dims < rank),
true,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Input dimension number(num_col_dims) must be "
"between 0 and %d, but received number is %d.",
rank,
......@@ -100,7 +100,7 @@ struct EigenMatrix : public EigenTensor<T, 2, MajorType, IndexType> {
int rank = tensor.dims().size();
PADDLE_ENFORCE_EQ((num_col_dims > 0 && num_col_dims < rank),
true,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Input dimension number(num_col_dims) must be "
"between 0 and %d, but received number is %d.",
rank,
......
paddle/phi/kernels/funcs/elementwise_base.h
浏览文件 @ 728c0624
......@@ -343,7 +343,7 @@ inline void get_mid_dims(const DDim &x_dims,
if (x_dims[i + axis] != y_dims[i]) {
PADDLE_ENFORCE_EQ(y_dims[i] == 1 || x_dims[i + axis] == 1,
true,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Broadcast dimension mismatch. Operands "
"could not be broadcast together with the shape of "
"X = [%s] and the shape of Y = [%s]. Received [%d] "
......@@ -754,7 +754,7 @@ void ElementwiseKernel(const KPDevice &ctx,
const int kArity = Traits::arity;
PADDLE_ENFORCE_EQ(ins.size(),
kArity,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The number of inputs is expected to be equal to the "
"arity of functor. But recieved: the number of inputs "
"is %d, the arity of functor is %d.",
......@@ -762,7 +762,7 @@ void ElementwiseKernel(const KPDevice &ctx,
kArity));
PADDLE_ENFORCE_EQ(outs->size(),
NumOuts,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Number of outputs shall equal to number of functions, "
"but number of outputs is %d, of functions is %d.",
outs->size(),
......@@ -773,7 +773,7 @@ void ElementwiseKernel(const KPDevice &ctx,
PADDLE_ENFORCE_EQ(
(*outs)[i]->dims(),
(*outs)[0]->dims(),
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The shape of each output tensor shall be identical yet, "
"but %dth output tensor`s shape is not.",
i));
......@@ -796,7 +796,7 @@ void ElementwiseKernel(const KPDevice &ctx,
ctx, ins, outs, func);
break;
default: {
PADDLE_THROW(paddle::platform::errors::Unimplemented(
PADDLE_THROW(phi::errors::Unimplemented(
"Unsupported vectorized size: %d !", vec_size));
break;
}
......
paddle/phi/kernels/funcs/math_function.cc
浏览文件 @ 728c0624
......@@ -184,7 +184,7 @@ struct TensorSetConstantCPU {
: tensor_(tensor), value_(value) {}
template <typename T>
void apply() const {
auto cpu = paddle::platform::CPUPlace();
auto cpu = phi::CPUPlace();
auto* begin = tensor_->mutable_data<T>(cpu);
std::fill(begin, begin + tensor_->numel(), static_cast<T>(value_));
}
......@@ -197,8 +197,7 @@ void set_constant_with_place<paddle::platform::XPUPlace>(
const paddle::platform::DeviceContext& context,
paddle::framework::Tensor* tensor,
float value) {
PADDLE_THROW(
paddle::platform::errors::Unimplemented("XPUPlace is not supported"));
PADDLE_THROW(phi::errors::Unimplemented("XPUPlace is not supported"));
}
template <>
......@@ -206,8 +205,7 @@ void set_constant_with_place<paddle::platform::NPUPlace>(
const paddle::platform::DeviceContext& context,
paddle::framework::Tensor* tensor,
float value) {
PADDLE_THROW(
paddle::platform::errors::Unimplemented("NPUPlace is not supported"));
PADDLE_THROW(phi::errors::Unimplemented("NPUPlace is not supported"));
}
template <>
......@@ -215,8 +213,7 @@ void set_constant_with_place<paddle::platform::NPUPinnedPlace>(
const paddle::platform::DeviceContext& context,
paddle::framework::Tensor* tensor,
float value) {
PADDLE_THROW(paddle::platform::errors::Unimplemented(
"NPUPinnedPlace is not supported"));
PADDLE_THROW(phi::errors::Unimplemented("NPUPinnedPlace is not supported"));
}
template <>
......@@ -224,8 +221,7 @@ void set_constant_with_place<paddle::platform::IPUPlace>(
const paddle::platform::DeviceContext& context,
paddle::framework::Tensor* tensor,
float value) {
PADDLE_THROW(
paddle::platform::errors::Unimplemented("IPUPlace is not supported"));
PADDLE_THROW(phi::errors::Unimplemented("IPUPlace is not supported"));
}
template <>
......@@ -233,12 +229,11 @@ void set_constant_with_place<paddle::platform::CustomPlace>(
const paddle::platform::DeviceContext& context,
paddle::framework::Tensor* tensor,
float value) {
PADDLE_THROW(
paddle::platform::errors::Unimplemented("CustomPlace is not supported"));
PADDLE_THROW(phi::errors::Unimplemented("CustomPlace is not supported"));
}
template <>
void set_constant_with_place<paddle::platform::CPUPlace>(
void set_constant_with_place<phi::CPUPlace>(
const paddle::platform::DeviceContext& context,
paddle::framework::Tensor* tensor,
float value) {
......@@ -250,8 +245,7 @@ void set_constant_with_place<paddle::platform::MLUPlace>(
const paddle::platform::DeviceContext& context,
paddle::framework::Tensor* tensor,
float value) {
PADDLE_THROW(
paddle::platform::errors::Unimplemented("MLUPlace is not supported"));
PADDLE_THROW(phi::errors::Unimplemented("MLUPlace is not supported"));
}
template <>
......@@ -286,7 +280,7 @@ void set_constant(const paddle::platform::DeviceContext& context,
// tensor->place().apply_visitor(func);
paddle::platform::VisitPlace(tensor->place(), func);
#else
func(paddle::platform::CPUPlace());
func(phi::CPUPlace());
#endif
}
......@@ -302,7 +296,7 @@ struct RowwiseAdd<paddle::platform::CPUDeviceContext, T> {
PADDLE_ENFORCE_EQ(
vector.numel(),
size,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The input vector size"
" should be equal to the size of each row of input tensor."
" Expected vector size=%d, but received %d",
......@@ -312,7 +306,7 @@ struct RowwiseAdd<paddle::platform::CPUDeviceContext, T> {
const char* out_dims_cstr = out_dims.to_str().c_str();
PADDLE_ENFORCE_EQ(out_dims,
in_dims,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The output tensor shape should be same as the input"
" tensor shape. Expected output tensor shape: %s,"
" but received %s",
......
paddle/phi/kernels/funcs/math_function.cu
浏览文件 @ 728c0624
......@@ -257,7 +257,7 @@ struct RowwiseAdd<paddle::platform::CUDADeviceContext, T> {
PADDLE_ENFORCE_EQ(
vector.numel(),
size,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The input vector size"
" should be equal to the size of each row of input tensor."
" Expected vector size=%d, but received %d",
......@@ -268,7 +268,7 @@ struct RowwiseAdd<paddle::platform::CUDADeviceContext, T> {
PADDLE_ENFORCE_EQ(
out_dims,
in_dims,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The output tensor shape should be same as the input tensor"
" shape. Expected output tensor shape: %s,"
" but received %s",
......@@ -303,7 +303,7 @@ void ColwiseSum<paddle::platform::CUDADeviceContext, double>::operator()(
auto size = input.numel() / in_dims[0];
PADDLE_ENFORCE_EQ(vector->numel(),
size,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The size of input vector"
" should be equal to the size of input tensor column"
" dimension. Expected vector size=%d, but received %d",
......@@ -339,7 +339,7 @@ void RowwiseSum<paddle::platform::CUDADeviceContext, double>::operator()(
auto size = input.numel() / in_dims[0];
PADDLE_ENFORCE_EQ(vector->numel(),
in_dims[0],
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The size of input vector"
" should be equal to the size of input tensor row"
" dimension. Expected vector size=%d, but received %d",
......
paddle/phi/kernels/funcs/math_function.h
浏览文件 @ 728c0624
......@@ -115,7 +115,7 @@ struct TensorSetConstantXPU {
std::fill(data_cpu.get(), data_cpu.get() + numel, static_cast<T>(value_));
paddle::memory::Copy(place_,
begin,
paddle::platform::CPUPlace(),
phi::CPUPlace(),
static_cast<void*>(data_cpu.get()),
numel * sizeof(T));
}
......
paddle/phi/kernels/funcs/math_function_impl.h
浏览文件 @ 728c0624
......@@ -74,7 +74,7 @@ void ColwiseSum<DeviceContext, T>::operator()(
auto size = input.numel() / in_dims[0];
PADDLE_ENFORCE_EQ(out->numel(),
size,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The size of output tensor "
"should be equal to the size of input tensor column"
" dimension. Expected output size=%d, but received %d",
......@@ -102,7 +102,7 @@ class ColwiseSum<paddle::platform::CPUDeviceContext, T> {
PADDLE_ENFORCE_EQ(
out->numel(),
size,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The size of output tensor "
"should be equal to the size of input tensor column"
" dimension. Expected output size=%d, but received %d",
......@@ -130,15 +130,14 @@ void RowwiseMean<DeviceContext, T>::operator()(
const paddle::framework::Tensor& input,
paddle::framework::Tensor* out) {
auto in_dims = input.dims();
PADDLE_ENFORCE_EQ(
in_dims.size(),
2U,
paddle::platform::errors::InvalidArgument("The rank of input tensor "
"should be 2, but received %d",
in_dims.size()));
PADDLE_ENFORCE_EQ(in_dims.size(),
2U,
phi::errors::InvalidArgument("The rank of input tensor "
"should be 2, but received %d",
in_dims.size()));
PADDLE_ENFORCE_EQ(out->numel(),
in_dims[0],
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The size of output tensor "
"should be equal to the size of input tensor row"
" dimension. Expected output size=%d, but received %d",
......@@ -161,18 +160,18 @@ class RowwiseMean<paddle::platform::CPUDeviceContext, T> {
const paddle::framework::Tensor& input,
paddle::framework::Tensor* out) {
auto& in_dims = input.dims();
PADDLE_ENFORCE_EQ(in_dims.size(),
2U,
paddle::platform::errors::InvalidArgument(
"The rank of input tensor "
"should be 2, but received %d",
in_dims.size()));
PADDLE_ENFORCE_EQ(
in_dims.size(),
2U,
phi::errors::InvalidArgument("The rank of input tensor "
"should be 2, but received %d",
in_dims.size()));
auto height = in_dims[0];
auto size = in_dims[1];
PADDLE_ENFORCE_EQ(
out->numel(),
height,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The size of output tensor "
"should be equal to the size of input tensor row"
" dimension. Expected output size=%d, but received %d",
......@@ -198,15 +197,14 @@ void RowwiseSum<DeviceContext, T>::operator()(
const paddle::framework::Tensor& input,
paddle::framework::Tensor* out) {
auto in_dims = input.dims();
PADDLE_ENFORCE_EQ(
in_dims.size(),
2U,
paddle::platform::errors::InvalidArgument("The rank of input tensor "
"should be 2, but received %d",
in_dims.size()));
PADDLE_ENFORCE_EQ(in_dims.size(),
2U,
phi::errors::InvalidArgument("The rank of input tensor "
"should be 2, but received %d",
in_dims.size()));
PADDLE_ENFORCE_EQ(out->numel(),
in_dims[0],
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The size of output tensor "
"should be equal to the size of input tensor row"
" dimension. Expected output size=%d, but received %d",
......@@ -229,18 +227,18 @@ class RowwiseSum<paddle::platform::CPUDeviceContext, T> {
const paddle::framework::Tensor& input,
paddle::framework::Tensor* out) {
auto& in_dims = input.dims();
PADDLE_ENFORCE_EQ(in_dims.size(),
2U,
paddle::platform::errors::InvalidArgument(
"The rank of input tensor "
"should be 2, but received %d",
in_dims.size()));
PADDLE_ENFORCE_EQ(
in_dims.size(),
2U,
phi::errors::InvalidArgument("The rank of input tensor "
"should be 2, but received %d",
in_dims.size()));
auto height = in_dims[0];
auto size = in_dims[1];
PADDLE_ENFORCE_EQ(
out->numel(),
height,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The size of output tensor "
"should be equal to the size of input tensor row"
" dimension. Expected output size=%d, but received %d",
......
paddle/phi/kernels/gpu/concat_and_split.h
浏览文件 @ 728c0624
......@@ -16,7 +16,6 @@
#include <algorithm>
#include <vector>
#include "gflags/gflags.h"
#include "paddle/fluid/framework/mixed_vector.h"
#include "paddle/fluid/memory/malloc.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/platform/cuda_graph_with_memory_pool.h"
......@@ -329,7 +328,7 @@ void ConcatImpl(const Context& context,
inputs_data, in_num);
paddle::memory::Copy(context.GetPlace(),
tmp_dev_ins_data->ptr(),
paddle::platform::CPUPlace(),
phi::CPUPlace(),
restored,
in_num * sizeof(T*),
context.stream());
......@@ -376,7 +375,7 @@ void ConcatImpl(const Context& context,
inputs_col, inputs_col_num);
paddle::memory::Copy(context.GetPlace(),
tmp_dev_ins_col_data->ptr(),
paddle::platform::CPUPlace(),
phi::CPUPlace(),
restored,
inputs_col_num * sizeof(int64_t),
context.stream());
......@@ -488,7 +487,7 @@ void SplitImpl(const Context& context,
outputs_data, o_num);
paddle::memory::Copy(context.GetPlace(),
tmp_dev_outs_data->ptr(),
paddle::platform::CPUPlace(),
phi::CPUPlace(),
restored,
o_num * sizeof(T*),
context.stream());
......@@ -535,7 +534,7 @@ void SplitImpl(const Context& context,
outputs_cols, outputs_cols_num);
paddle::memory::Copy(context.GetPlace(),
tmp_dev_ins_col_data->ptr(),
paddle::platform::CPUPlace(),
phi::CPUPlace(),
restored,
outputs_cols_num * sizeof(int64_t),
context.stream());
......
paddle/phi/kernels/gpu/concat_kernel.cu
浏览文件 @ 728c0624
......@@ -54,7 +54,7 @@ void ConcatKernel(const Context& dev_ctx,
PADDLE_ENFORCE_EQ(
x[i].lod().size(),
lod_size_0,
paddle::platform::errors::Unimplemented(
phi::errors::Unimplemented(
"The lod level of all input LoDTensors should be same. "
"Maybe different lod level of input LoDTensors can concat,"
"it is not supported currently. The lod level of %dth input "
......
paddle/phi/kernels/gpu/copy_kernel.cu
浏览文件 @ 728c0624
......@@ -35,7 +35,7 @@ void Copy(const Context& dev_ctx,
auto dst_place = dst->place();
if (src_place == dst_place && paddle::platform::is_cpu_place(src_place)) {
PADDLE_THROW(paddle::platform::errors::InvalidArgument(
PADDLE_THROW(phi::errors::InvalidArgument(
"The src and dst tensor are all CPU tensor, you should call copy "
"function in CPU mode."));
}
......@@ -74,13 +74,13 @@ void Copy(const Context& dev_ctx,
PADDLE_ENFORCE_EQ(
paddle::platform::is_gpu_place(ctx_place),
true,
paddle::platform::errors::PreconditionNotMet(
phi::errors::PreconditionNotMet(
"Context place error, excepted GPUPlace, but actually %s.",
ctx_place));
auto ctx_gpu_place = ctx_place;
PADDLE_ENFORCE_EQ(src_gpu_place,
ctx_gpu_place,
paddle::platform::errors::Unavailable(
phi::errors::Unavailable(
"Source place and context place do not match, source "
"place is %s, context place is %s.",
src_gpu_place,
......@@ -98,13 +98,13 @@ void Copy(const Context& dev_ctx,
PADDLE_ENFORCE_EQ(
paddle::platform::is_gpu_place(ctx_place),
true,
paddle::platform::errors::PreconditionNotMet(
phi::errors::PreconditionNotMet(
"Context place error, excepted GPUPlace, but actually %s.",
ctx_place));
auto ctx_gpu_place = ctx_place;
PADDLE_ENFORCE_EQ(dst_gpu_place,
ctx_gpu_place,
paddle::platform::errors::Unavailable(
phi::errors::Unavailable(
"Destination place and context place do not match, "
"destination place is %s, context place is %s.",
dst_gpu_place,
......@@ -121,14 +121,14 @@ void Copy(const Context& dev_ctx,
auto ctx_place = dev_ctx.GetPlace();
PADDLE_ENFORCE_EQ(paddle::platform::is_gpu_place(ctx_place),
true,
paddle::platform::errors::PreconditionNotMet(
phi::errors::PreconditionNotMet(
"Device context place mismatch. When copying Tensor "
"data from GPU memory to CUDA Pinned memory, current "
"device context place should be GPU."));
auto ctx_gpu_place = ctx_place;
PADDLE_ENFORCE_EQ(src_gpu_place,
ctx_gpu_place,
paddle::platform::errors::PreconditionNotMet(
phi::errors::PreconditionNotMet(
"The source GPU device and current device context do "
"not match. The source GPU device number is %d, but "
"device context GPU number is %d.",
......@@ -146,14 +146,14 @@ void Copy(const Context& dev_ctx,
auto ctx_place = dev_ctx.GetPlace();
PADDLE_ENFORCE_EQ(paddle::platform::is_gpu_place(ctx_place),
true,
paddle::platform::errors::PreconditionNotMet(
phi::errors::PreconditionNotMet(
"Device context place mismatch. When copying Tensor "
"data from CUDA Pinned memory to GPU memory, current "
"device context place should be GPU."));
auto ctx_gpu_place = ctx_place;
PADDLE_ENFORCE_EQ(dst_gpu_place,
ctx_gpu_place,
paddle::platform::errors::PreconditionNotMet(
phi::errors::PreconditionNotMet(
"The target GPU device and current device context do "
"not match. The target GPU device number is %d, but "
"device context GPU number is %d.",
......@@ -172,7 +172,7 @@ void Copy(const Context& dev_ctx,
PADDLE_ENFORCE_EQ(
paddle::platform::is_gpu_place(ctx_place),
true,
paddle::platform::errors::PreconditionNotMet(
phi::errors::PreconditionNotMet(
"Context place error, excepted GPUPlace, but actually %s.",
ctx_place));
auto stream =
......@@ -195,12 +195,12 @@ void Copy(const Context& dev_ctx,
paddle::memory::Copy(
dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size, stream);
} else {
PADDLE_THROW(paddle::platform::errors::Unavailable(
PADDLE_THROW(phi::errors::Unavailable(
"Context place dose not match the source and destination place."));
}
}
} else {
PADDLE_THROW(paddle::platform::errors::InvalidArgument(
PADDLE_THROW(phi::errors::InvalidArgument(
"Place type error. Please check the place of src and dst Tensor."));
}
}
......
paddle/phi/kernels/gpu/elementwise.h
浏览文件 @ 728c0624
......@@ -714,7 +714,7 @@ void CommonGradBroadcastCUDA(const DenseTensor &x,
DX_OP dx_op,
DY_OP dy_op) {
const auto gplace = ctx.GetPlace();
auto cplace = paddle::platform::CPUPlace();
auto cplace = phi::CPUPlace();
const T *x_data = x.data<T>();
const T *y_data = y.data<T>();
const Tout *out_data = out.data<Tout>();
......@@ -1339,12 +1339,12 @@ void ElemwiseGradComputeWithBroadcast(const GPUContext &ctx,
PADDLE_ENFORCE_GE(
axis,
0,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Axis should be great than or equal to 0, but received axis is %d.",
axis));
PADDLE_ENFORCE_LT(axis,
max_dim,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Axis should be less than %d, but received axis is %d.",
max_dim,
axis));
......
paddle/phi/kernels/gpu/histogram_kernel.cu
浏览文件 @ 728c0624
......@@ -111,9 +111,9 @@ void HistogramKernel(const Context& dev_ctx,
DenseTensor input_min_cpu, input_max_cpu;
paddle::framework::TensorCopySync(
input_min_t, paddle::platform::CPUPlace(), &input_min_cpu);
input_min_t, phi::CPUPlace(), &input_min_cpu);
paddle::framework::TensorCopySync(
input_max_t, paddle::platform::CPUPlace(), &input_max_cpu);
input_max_t, phi::CPUPlace(), &input_max_cpu);
output_min = input_min_cpu.data<T>()[0];
output_max = input_max_cpu.data<T>()[0];
......
paddle/phi/kernels/impl/full_kernel_impl.h
浏览文件 @ 728c0624
......@@ -59,7 +59,7 @@ void FullLikeKernel(const Context& dev_ctx,
(common_type_value <=
static_cast<CommonType>(std::numeric_limits<T>::max())),
true,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"The filled value is out of range for target type, "
"current kernel type is %s, the range should between %f "
"and %f, but now value is %f.",
......
paddle/phi/kernels/impl/matmul_kernel_impl.h
浏览文件 @ 728c0624
......@@ -38,7 +38,7 @@ static void GetBroadcastFromDims(const int x_ndim,
PADDLE_ENFORCE_EQ(
x_bd_dims[i] == y_bd_dims[i] || x_bd_dims[i] <= 1 || y_bd_dims[i] <= 1,
true,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"Input(X) and Input(Y) has error dim."
"X_broadcast's shape[%s] must be equal to Y_broadcast's shape[%s],"
"or X_broadcast's shape[%s] <= 1, or Y_broadcast's shape[%s] <= 1,"
......@@ -110,7 +110,7 @@ void MatMulFunction(const Context& dev_ctx,
PADDLE_ENFORCE_EQ(
M,
N,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"X's numbers must be equal to Y's numbers,"
"when X/Y's dims =1. But received X has [%d] elements,"
"received Y has [%d] elements",
......@@ -135,27 +135,27 @@ void MatMulFunction(const Context& dev_ctx,
if (x_ndim == 1) {
const int N = X.numel();
if (trans_y) {
PADDLE_ENFORCE_EQ(y_dims[y_ndim - 1],
N,
paddle::platform::errors::InvalidArgument(
"Input(Y) has error dim."
"Y'dims[%d] must be equal to %d"
"But received Y'dims[%d] is %d",
y_ndim - 1,
N,
y_ndim - 1,
y_dims[y_ndim - 1]));
PADDLE_ENFORCE_EQ(
y_dims[y_ndim - 1],
N,
phi::errors::InvalidArgument("Input(Y) has error dim."
"Y'dims[%d] must be equal to %d"
"But received Y'dims[%d] is %d",
y_ndim - 1,
N,
y_ndim - 1,
y_dims[y_ndim - 1]));
} else {
PADDLE_ENFORCE_EQ(y_dims[y_ndim - 2],
N,
paddle::platform::errors::InvalidArgument(
"Input(Y) has error dim."
"Y'dims[%d] must be equal to %d"
"But received Y'dims[%d] is %d",
y_ndim - 2,
N,
y_ndim - 2,
y_dims[y_ndim - 2]));
PADDLE_ENFORCE_EQ(
y_dims[y_ndim - 2],
N,
phi::errors::InvalidArgument("Input(Y) has error dim."
"Y'dims[%d] must be equal to %d"
"But received Y'dims[%d] is %d",
y_ndim - 2,
N,
y_ndim - 2,
y_dims[y_ndim - 2]));
}
std::vector<std::int64_t> out_dims(y_ndim - 1);
if (trans_y) {
......@@ -213,27 +213,27 @@ void MatMulFunction(const Context& dev_ctx,
if (y_ndim == 1) {
const int N = Y.numel();
if (trans_x) {
PADDLE_ENFORCE_EQ(x_dims[x_ndim - 2],
N,
paddle::platform::errors::InvalidArgument(
"Input(X) has error dim."
"X'dims[%d] must be equal to %d"
"But received X'dims[%d] is %d",
x_ndim - 2,
N,
x_ndim - 2,
x_dims[x_ndim - 2]));
PADDLE_ENFORCE_EQ(
x_dims[x_ndim - 2],
N,
phi::errors::InvalidArgument("Input(X) has error dim."
"X'dims[%d] must be equal to %d"
"But received X'dims[%d] is %d",
x_ndim - 2,
N,
x_ndim - 2,
x_dims[x_ndim - 2]));
} else {
PADDLE_ENFORCE_EQ(x_dims[x_ndim - 1],
N,
paddle::platform::errors::InvalidArgument(
"Input(X) has error dim."
"X'dims[%d] must be equal to %d"
"But received X'dims[%d] is %d",
x_ndim - 1,
N,
x_ndim - 1,
x_dims[x_ndim - 1]));
PADDLE_ENFORCE_EQ(
x_dims[x_ndim - 1],
N,
phi::errors::InvalidArgument("Input(X) has error dim."
"X'dims[%d] must be equal to %d"
"But received X'dims[%d] is %d",
x_ndim - 1,
N,
x_ndim - 1,
x_dims[x_ndim - 1]));
}
std::vector<std::int64_t> out_dims(x_ndim - 1);
if (trans_x) {
......@@ -292,27 +292,27 @@ void MatMulFunction(const Context& dev_ctx,
const int M = trans_x ? x_dims[x_ndim - 1] : x_dims[x_ndim - 2];
const int K = trans_x ? x_dims[x_ndim - 2] : x_dims[x_ndim - 1];
if (trans_y) {
PADDLE_ENFORCE_EQ(y_dims[y_ndim - 1],
K,
paddle::platform::errors::InvalidArgument(
"Input(Y) has error dim."
"Y'dims[%d] must be equal to %d"
"But received Y'dims[%d] is %d",
y_ndim - 1,
K,
y_ndim - 1,
y_dims[y_ndim - 1]));
PADDLE_ENFORCE_EQ(
y_dims[y_ndim - 1],
K,
phi::errors::InvalidArgument("Input(Y) has error dim."
"Y'dims[%d] must be equal to %d"
"But received Y'dims[%d] is %d",
y_ndim - 1,
K,
y_ndim - 1,
y_dims[y_ndim - 1]));
} else {
PADDLE_ENFORCE_EQ(y_dims[y_ndim - 2],
K,
paddle::platform::errors::InvalidArgument(
"Input(Y) has error dim."
"Y'dims[%d] must be equal to %d"
"But received Y'dims[%d] is %d",
y_ndim - 2,
K,
y_ndim - 2,
y_dims[y_ndim - 2]));
PADDLE_ENFORCE_EQ(
y_dims[y_ndim - 2],
K,
phi::errors::InvalidArgument("Input(Y) has error dim."
"Y'dims[%d] must be equal to %d"
"But received Y'dims[%d] is %d",
y_ndim - 2,
K,
y_ndim - 2,
y_dims[y_ndim - 2]));
}
const int N = trans_y ? y_dims[y_ndim - 2] : y_dims[y_ndim - 1];
const int ndim = (std::max)(x_ndim, y_ndim);
......@@ -493,16 +493,16 @@ void MatmulKernel(const Context& dev_ctx,
bool transpose_x,
bool transpose_y,
DenseTensor* out) {
PADDLE_ENFORCE_NE(phi::product(x.dims()),
0,
paddle::platform::errors::InvalidArgument(
"The Input(X) dims size must not be equal 0,"
" but reviced dims size is 0. "));
PADDLE_ENFORCE_NE(phi::product(y.dims()),
0,
paddle::platform::errors::InvalidArgument(
"The Input(Y) dims size must not be equal 0,"
" but reviced dims size is 0. "));
PADDLE_ENFORCE_NE(
phi::product(x.dims()),
0,
phi::errors::InvalidArgument("The Input(X) dims size must not be equal 0,"
" but reviced dims size is 0. "));
PADDLE_ENFORCE_NE(
phi::product(y.dims()),
0,
phi::errors::InvalidArgument("The Input(Y) dims size must not be equal 0,"
" but reviced dims size is 0. "));
MatMulFunction<Context, T>(dev_ctx, x, y, out, transpose_x, transpose_y);
}
......
paddle/phi/kernels/sparse/cpu/sparse_utils_kernel.cc
浏览文件 @ 728c0624
......@@ -41,7 +41,7 @@ inline int64_t GetNonZeroNum(const DenseTensor& dense,
PADDLE_ENFORCE_GE(
dims.size(),
sparse_dim,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"sparse_dim(%d) should be less than or equal to dense.dim(%d)",
sparse_dim,
dims.size()));
......@@ -161,7 +161,7 @@ void SparseCooToCsrKernel(const Context& dev_ctx,
bool valid = x_dims.size() == 2 || x_dims.size() == 3;
PADDLE_ENFORCE_EQ(valid,
true,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"SparseCsrTensor only support 2-D or 3-D matrix"));
const int64_t non_zero_num = x.nnz();
if (non_zero_num <= 0) return;
......
paddle/phi/kernels/sparse/gpu/sparse_utils_kernel.cu
浏览文件 @ 728c0624
......@@ -379,7 +379,7 @@ void SparseCooToCsrKernel(const Context& dev_ctx,
bool valid = x_dims.size() == 2 || x_dims.size() == 3;
PADDLE_ENFORCE_EQ(valid,
true,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"SparseCsrTensor only support 2-D or 3-D matrix"));
const int64_t non_zero_num = x.nnz();
if (non_zero_num <= 0) return;
......
paddle/phi/kernels/sparse/sparse_utils_kernel.h
浏览文件 @ 728c0624
......@@ -97,7 +97,7 @@ void DenseToSparseCsrKernel(const Context& dev_ctx,
bool valid = x_dims.size() == 2 || x_dims.size() == 3;
PADDLE_ENFORCE_EQ(valid,
true,
paddle::platform::errors::InvalidArgument(
phi::errors::InvalidArgument(
"SparseCsrTensor only support 2-D or 3-D Tensor."));
const int64_t sparse_dim = x_dims.size() == 2 ? 2 : 3;
DenseTensor indices = phi::Empty<T, Context>(dev_ctx);
......
paddle/phi/kernels/xpu/copy_kernel.cc
浏览文件 @ 728c0624
......@@ -62,7 +62,7 @@ void Copy(const Context& dev_ctx,
}
paddle::memory::Copy(dst_place, dst_ptr, src_place, src_ptr, size);
} else {
PADDLE_THROW(paddle::platform::errors::Unimplemented(
PADDLE_THROW(phi::errors::Unimplemented(
"Copy from %s to %s is not supported.", src_place, dst_place));
}
}
......
paddle/phi/kernels/xpu/scale_kernel.cc
浏览文件 @ 728c0624
......@@ -32,13 +32,13 @@ void ScaleKernel(const Context& dev_ctx,
DenseTensor* out) {
out->mutable_data<T>(dev_ctx.GetPlace());
PADDLE_ENFORCE_EQ(x.dims(),
out->dims(),
paddle::platform::errors::InvalidArgument(
"In and out should have the same dim,"
" expected %s, but got %s.",
x.dims().to_str().c_str(),
out->dims().to_str().c_str()));
PADDLE_ENFORCE_EQ(
x.dims(),
out->dims(),
phi::errors::InvalidArgument("In and out should have the same dim,"
" expected %s, but got %s.",
x.dims().to_str().c_str(),
out->dims().to_str().c_str()));
using XPUType = typename XPUTypeTrait<T>::Type;
int r = xpu::scale(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x.data<T>()),
......@@ -50,7 +50,7 @@ void ScaleKernel(const Context& dev_ctx,
PADDLE_ENFORCE_EQ(
r,
XPU_SUCCESS,
paddle::platform::errors::External(
phi::errors::External(
"XPU scale kernel return wrong value[%d %s]", r, XPUAPIErrorMsg[r]));
}
......
paddle/phi/tests/core/allocator.h
浏览文件 @ 728c0624
......@@ -29,8 +29,7 @@ class FancyAllocator : public phi::Allocator {
AllocationPtr Allocate(size_t bytes_size) override {
void* data = ::operator new(bytes_size);
auto* allocation =
new phi::Allocation(data, bytes_size, paddle::platform::CPUPlace());
auto* allocation = new phi::Allocation(data, bytes_size, phi::CPUPlace());
return AllocationPtr(allocation, Delete);
}
};
......
paddle/phi/tests/core/test_dense_tensor.cc
浏览文件 @ 728c0624
......@@ -85,7 +85,7 @@ TEST(dense_tensor, ctor) {
r = r && (t.dims() == m.dims);
r = r && (t.dtype() == m.dtype);
r = r && (t.layout() == m.layout);
r = r && (t.place() == paddle::platform::CPUPlace());
r = r && (t.place() == phi::CPUPlace());
r = r && t.initialized();
r = r && t.IsSharedWith(t);
return r;
......
paddle/phi/tests/core/test_sparse_coo_tensor.cc
浏览文件 @ 728c0624
......@@ -53,7 +53,7 @@ TEST(sparse_coo_tensor, construct) {
CHECK(sparse.dims() == dense_dims);
CHECK(sparse.dtype() == DataType::FLOAT32);
CHECK(sparse.layout() == DataLayout::SPARSE_COO);
CHECK(sparse.place() == paddle::platform::CPUPlace());
CHECK(sparse.place() == phi::CPUPlace());
}
TEST(sparse_coo_tensor, other_function) {
......
paddle/utils/string/tinyformat/tinyformat.h
浏览文件 @ 728c0624
......@@ -133,6 +133,8 @@
#include <iostream>
#include <sstream>
#include "paddle/utils/string/to_string.h"
namespace paddle {
namespace string {
namespace tinyformat {
......
paddle/utils/string/to_string.h
浏览文件 @ 728c0624
......@@ -56,5 +56,26 @@ inline std::string to_string(const char* v) {
return std::string(v);
}
inline std::ostream& operator<<(std::ostream& os,
const std::vector<std::vector<size_t>>& lod) {
os << "{";
for (auto& v : lod) {
os << "{";
bool is_first = true;
for (auto& i : v) {
if (is_first) {
os << i;
is_first = false;
} else {
os << ", " << i;
}
}
os << "}";
}
os << "}";
return os;
}
} // namespace string
} // namespace paddle
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