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未验证 提交 c202a613 编写于 作者: D danleifeng 提交者： GitHub
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【heterps】datafeed puttofeedvec performance (#40168) 

* perform SlotRecordInMemoryDataFeed feedvec;test=develop
上级 7b627dd8
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Showing with 793 addition and 22 deletion
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ c202a613
......@@ -295,7 +295,7 @@ if(WITH_DISTRIBUTE)
dist_multi_trainer.cc trainer_factory.cc trainer.cc data_feed_factory.cc
heterxpu_trainer.cc
data_feed.cc device_worker.cc hogwild_worker.cc hetercpu_worker.cc ps_gpu_worker.cc
ps_gpu_trainer.cc downpour_worker.cc downpour_worker_opt.cc
ps_gpu_trainer.cc downpour_worker.cc downpour_worker_opt.cc data_feed.cu
pull_dense_worker.cc section_worker.cc device_worker_factory.cc data_set.cc DEPS op_registry
device_context scope framework_proto trainer_desc_proto glog fs shell
fleet_wrapper heter_wrapper ps_gpu_wrapper box_wrapper metrics lodtensor_printer
......@@ -316,7 +316,7 @@ if(WITH_DISTRIBUTE)
dist_multi_trainer.cc trainer_factory.cc trainer.cc data_feed_factory.cc
heterxpu_trainer.cc heter_pipeline_trainer.cc
data_feed.cc device_worker.cc hogwild_worker.cc hetercpu_worker.cc
downpour_worker.cc downpour_lite_worker.cc downpour_worker_opt.cc
downpour_worker.cc downpour_lite_worker.cc downpour_worker_opt.cc data_feed.cu
pull_dense_worker.cc section_worker.cc heter_section_worker.cc device_worker_factory.cc data_set.cc DEPS op_registry
device_context scope framework_proto data_feed_proto heter_service_proto trainer_desc_proto glog
index_sampler index_wrapper sampler index_dataset_proto
......@@ -339,7 +339,7 @@ if(WITH_DISTRIBUTE)
dist_multi_trainer.cc trainer_factory.cc trainer.cc data_feed_factory.cc
heterxpu_trainer.cc
data_feed.cc device_worker.cc hogwild_worker.cc hetercpu_worker.cc ps_gpu_worker.cc
ps_gpu_trainer.cc downpour_worker.cc downpour_worker_opt.cc
ps_gpu_trainer.cc downpour_worker.cc downpour_worker_opt.cc data_feed.cu
pull_dense_worker.cc section_worker.cc device_worker_factory.cc data_set.cc DEPS op_registry
device_context scope framework_proto data_feed_proto heter_service_proto trainer_desc_proto glog
lod_rank_table fs shell fleet_wrapper heter_wrapper ps_gpu_wrapper box_wrapper metrics lodtensor_printer feed_fetch_method
......@@ -359,7 +359,7 @@ elseif(WITH_PSLIB)
dist_multi_trainer.cc trainer_factory.cc trainer.cc data_feed_factory.cc
heterxpu_trainer.cc
data_feed.cc device_worker.cc hogwild_worker.cc hetercpu_worker.cc ps_gpu_worker.cc
ps_gpu_trainer.cc downpour_worker.cc downpour_worker_opt.cc
ps_gpu_trainer.cc downpour_worker.cc downpour_worker_opt.cc data_feed.cu
pull_dense_worker.cc section_worker.cc device_worker_factory.cc data_set.cc DEPS op_registry
device_context scope framework_proto data_feed_proto heter_service_proto trainer_desc_proto glog
lod_rank_table fs shell fleet_wrapper heter_wrapper ps_gpu_wrapper box_wrapper lodtensor_printer feed_fetch_method
......@@ -369,7 +369,7 @@ else()
dist_multi_trainer.cc trainer_factory.cc trainer.cc data_feed_factory.cc
heterxpu_trainer.cc
data_feed.cc device_worker.cc hogwild_worker.cc hetercpu_worker.cc ps_gpu_worker.cc
ps_gpu_trainer.cc downpour_worker.cc downpour_worker_opt.cc
ps_gpu_trainer.cc downpour_worker.cc downpour_worker_opt.cc data_feed.cu
pull_dense_worker.cc section_worker.cc device_worker_factory.cc data_set.cc DEPS op_registry
device_context scope framework_proto data_feed_proto heter_service_proto trainer_desc_proto glog
lod_rank_table fs shell fleet_wrapper heter_wrapper ps_gpu_wrapper box_wrapper lodtensor_printer feed_fetch_method
......
paddle/fluid/framework/data_feed.cc
浏览文件 @ c202a613
......@@ -2394,9 +2394,6 @@ bool SlotRecordInMemoryDataFeed::ParseOneInstance(const std::string& line,
for (int j = 0; j < num; ++j) {
uint64_t feasign =
static_cast<uint64_t>(strtoull(endptr, &endptr, 10));
if (feasign == 0 && !used_slots_info_[info.used_idx].dense) {
continue;
}
slot_fea.push_back(feasign);
++uint64_total_slot_num;
}
......@@ -2419,8 +2416,21 @@ bool SlotRecordInMemoryDataFeed::ParseOneInstance(const std::string& line,
return (uint64_total_slot_num > 0);
}
void SlotRecordInMemoryDataFeed::AssignFeedVar(const Scope& scope) {
CheckInit();
for (int i = 0; i < use_slot_size_; ++i) {
feed_vec_[i] =
scope.FindVar(used_slots_info_[i].slot)->GetMutable<LoDTensor>();
}
}
void SlotRecordInMemoryDataFeed::PutToFeedVec(const SlotRecord* ins_vec,
int num) {
#if defined(PADDLE_WITH_CUDA) && defined(PADDLE_WITH_HETERPS)
paddle::platform::SetDeviceId(place_.GetDeviceId());
pack_->pack_instance(ins_vec, num);
BuildSlotBatchGPU(pack_->ins_num());
#else
for (int j = 0; j < use_slot_size_; ++j) {
auto& feed = feed_vec_[j];
if (feed == nullptr) {
......@@ -2497,6 +2507,7 @@ void SlotRecordInMemoryDataFeed::PutToFeedVec(const SlotRecord* ins_vec,
feed_vec_[j]->set_lod(data_lod);
}
}
#endif
}
void SlotRecordInMemoryDataFeed::ExpandSlotRecord(SlotRecord* rec) {
......@@ -2573,6 +2584,10 @@ bool SlotRecordInMemoryDataFeed::Start() {
this->offset_index_ = 0;
}
this->finish_start_ = true;
#if defined(PADDLE_WITH_CUDA) && defined(PADDLE_WITH_HETERPS)
CHECK(paddle::platform::is_gpu_place(this->place_));
pack_ = BatchGpuPackMgr().get(this->GetPlace(), used_slots_info_);
#endif
return true;
}
......@@ -2607,5 +2622,315 @@ int SlotRecordInMemoryDataFeed::Next() {
#endif
}
#if defined(PADDLE_WITH_CUDA) && defined(PADDLE_WITH_HETERPS)
void SlotRecordInMemoryDataFeed::BuildSlotBatchGPU(const int ins_num) {
int offset_cols_size = (ins_num + 1);
size_t slot_total_num = (use_slot_size_ * offset_cols_size);
pack_->resize_gpu_slot_offsets(slot_total_num * sizeof(size_t));
auto& value = pack_->value();
const UsedSlotGpuType* used_slot_gpu_types =
static_cast<const UsedSlotGpuType*>(pack_->get_gpu_slots());
FillSlotValueOffset(ins_num, use_slot_size_,
reinterpret_cast<size_t*>(pack_->gpu_slot_offsets()),
value.d_uint64_offset.data(), uint64_use_slot_size_,
value.d_float_offset.data(), float_use_slot_size_,
used_slot_gpu_types);
size_t* d_slot_offsets = reinterpret_cast<size_t*>(pack_->gpu_slot_offsets());
HostBuffer<size_t>& offsets = pack_->offsets();
offsets.resize(slot_total_num);
HostBuffer<void*>& h_tensor_ptrs = pack_->h_tensor_ptrs();
h_tensor_ptrs.resize(use_slot_size_);
// alloc gpu memory
pack_->resize_tensor();
LoDTensor& float_tensor = pack_->float_tensor();
LoDTensor& uint64_tensor = pack_->uint64_tensor();
int64_t float_offset = 0;
int64_t uint64_offset = 0;
// copy index
CUDA_CHECK(cudaMemcpy(offsets.data(), d_slot_offsets,
slot_total_num * sizeof(size_t),
cudaMemcpyDeviceToHost));
for (int j = 0; j < use_slot_size_; ++j) {
auto& feed = feed_vec_[j];
if (feed == nullptr) {
h_tensor_ptrs[j] = nullptr;
continue;
}
size_t* off_start_ptr = &offsets[j * offset_cols_size];
int total_instance = static_cast<int>(off_start_ptr[offset_cols_size - 1]);
CHECK(total_instance >= 0) << "slot idx:" << j
<< ", total instance:" << total_instance;
auto& info = used_slots_info_[j];
// fill slot value with default value 0
if (info.type[0] == 'f') { // float
if (total_instance > 0) {
feed->ShareDataWith(float_tensor.Slice(
static_cast<int64_t>(float_offset),
static_cast<int64_t>(float_offset + total_instance)));
feed->Resize({total_instance, 1});
float_offset += total_instance;
h_tensor_ptrs[j] = feed->mutable_data<float>(this->place_);
} else {
h_tensor_ptrs[j] =
feed->mutable_data<float>({total_instance, 1}, this->place_);
}
} else if (info.type[0] == 'u') { // uint64
if (total_instance > 0) {
feed->ShareDataWith(uint64_tensor.Slice(
static_cast<int64_t>(uint64_offset),
static_cast<int64_t>(uint64_offset + total_instance)));
feed->Resize({total_instance, 1});
uint64_offset += total_instance;
h_tensor_ptrs[j] = feed->mutable_data<int64_t>(this->place_);
} else {
h_tensor_ptrs[j] =
feed->mutable_data<int64_t>({total_instance, 1}, this->place_);
}
}
if (info.dense) {
if (info.inductive_shape_index != -1) {
info.local_shape[info.inductive_shape_index] =
total_instance / info.total_dims_without_inductive;
}
feed->Resize(phi::make_ddim(info.local_shape));
} else {
LoD& lod = (*feed->mutable_lod());
lod.resize(1);
lod[0].resize(offset_cols_size);
paddle::framework::MixVector<size_t> mixv_lod(&lod[0]);
memcpy(mixv_lod.MutableData(platform::CPUPlace()), off_start_ptr,
offset_cols_size * sizeof(size_t));
}
}
void** dest_gpu_p = reinterpret_cast<void**>(pack_->slot_buf_ptr());
CUDA_CHECK(cudaMemcpy(dest_gpu_p, h_tensor_ptrs.data(),
use_slot_size_ * sizeof(void*),
cudaMemcpyHostToDevice));
CopyForTensor(ins_num, use_slot_size_, dest_gpu_p,
(const size_t*)pack_->gpu_slot_offsets(),
(const uint64_t*)value.d_uint64_keys.data(),
(const int*)value.d_uint64_offset.data(),
(const int*)value.d_uint64_lens.data(), uint64_use_slot_size_,
(const float*)value.d_float_keys.data(),
(const int*)value.d_float_offset.data(),
(const int*)value.d_float_lens.data(), float_use_slot_size_,
used_slot_gpu_types);
}
MiniBatchGpuPack::MiniBatchGpuPack(const paddle::platform::Place& place,
const std::vector<UsedSlotInfo>& infos) {
place_ = place;
stream_ = dynamic_cast<platform::CUDADeviceContext*>(
platform::DeviceContextPool::Instance().Get(place))
->stream();
ins_num_ = 0;
pv_num_ = 0;
used_float_num_ = 0;
used_uint64_num_ = 0;
used_slot_size_ = static_cast<int>(infos.size());
for (int i = 0; i < used_slot_size_; ++i) {
auto& info = infos[i];
if (info.type[0] == 'u') {
gpu_used_slots_.push_back({1, info.slot_value_idx});
++used_uint64_num_;
} else {
gpu_used_slots_.push_back({0, info.slot_value_idx});
++used_float_num_;
}
}
copy_host2device(&gpu_slots_, gpu_used_slots_.data(), gpu_used_slots_.size());
slot_buf_ptr_ = memory::AllocShared(place_, used_slot_size_ * sizeof(void*));
int device_id = place_.GetDeviceId();
VLOG(3) << "begin get batch pack device id: " << device_id;
// sync
CUDA_CHECK(cudaStreamSynchronize(stream_));
}
MiniBatchGpuPack::~MiniBatchGpuPack() {}
void MiniBatchGpuPack::reset(const paddle::platform::Place& place) {
place_ = place;
stream_ = dynamic_cast<platform::CUDADeviceContext*>(
platform::DeviceContextPool::Instance().Get(place))
->stream();
ins_num_ = 0;
pv_num_ = 0;
}
void MiniBatchGpuPack::pack_all_data(const SlotRecord* ins_vec, int num) {
int uint64_total_num = 0;
int float_total_num = 0;
buf_.h_uint64_lens.resize(num + 1);
buf_.h_uint64_lens[0] = 0;
buf_.h_float_lens.resize(num + 1);
buf_.h_float_lens[0] = 0;
for (int i = 0; i < num; ++i) {
auto r = ins_vec[i];
uint64_total_num += r->slot_uint64_feasigns_.slot_values.size();
buf_.h_uint64_lens[i + 1] = uint64_total_num;
float_total_num += r->slot_float_feasigns_.slot_values.size();
buf_.h_float_lens[i + 1] = float_total_num;
}
int uint64_cols = (used_uint64_num_ + 1);
buf_.h_uint64_offset.resize(uint64_cols * num);
buf_.h_uint64_keys.resize(uint64_total_num);
int float_cols = (used_float_num_ + 1);
buf_.h_float_offset.resize(float_cols * num);
buf_.h_float_keys.resize(float_total_num);
size_t fea_num = 0;
uint64_total_num = 0;
float_total_num = 0;
for (int i = 0; i < num; ++i) {
auto r = ins_vec[i];
auto& uint64_feasigns = r->slot_uint64_feasigns_;
fea_num = uint64_feasigns.slot_values.size();
if (fea_num > 0) {
memcpy(&buf_.h_uint64_keys[uint64_total_num],
uint64_feasigns.slot_values.data(), fea_num * sizeof(uint64_t));
}
uint64_total_num += fea_num;
// copy uint64 offset
memcpy(&buf_.h_uint64_offset[i * uint64_cols],
uint64_feasigns.slot_offsets.data(), sizeof(int) * uint64_cols);
auto& float_feasigns = r->slot_float_feasigns_;
fea_num = float_feasigns.slot_values.size();
memcpy(&buf_.h_float_keys[float_total_num],
float_feasigns.slot_values.data(), fea_num * sizeof(float));
float_total_num += fea_num;
// copy float offset
memcpy(&buf_.h_float_offset[i * float_cols],
float_feasigns.slot_offsets.data(), sizeof(int) * float_cols);
}
CHECK(uint64_total_num == static_cast<int>(buf_.h_uint64_lens.back()))
<< "uint64 value length error";
CHECK(float_total_num == static_cast<int>(buf_.h_float_lens.back()))
<< "float value length error";
}
void MiniBatchGpuPack::pack_uint64_data(const SlotRecord* ins_vec, int num) {
int uint64_total_num = 0;
buf_.h_float_lens.clear();
buf_.h_float_keys.clear();
buf_.h_float_offset.clear();
buf_.h_uint64_lens.resize(num + 1);
buf_.h_uint64_lens[0] = 0;
for (int i = 0; i < num; ++i) {
auto r = ins_vec[i];
uint64_total_num += r->slot_uint64_feasigns_.slot_values.size();
buf_.h_uint64_lens[i + 1] = uint64_total_num;
}
int uint64_cols = (used_uint64_num_ + 1);
buf_.h_uint64_offset.resize(uint64_cols * num);
buf_.h_uint64_keys.resize(uint64_total_num);
size_t fea_num = 0;
uint64_total_num = 0;
for (int i = 0; i < num; ++i) {
auto r = ins_vec[i];
auto& uint64_feasigns = r->slot_uint64_feasigns_;
fea_num = uint64_feasigns.slot_values.size();
if (fea_num > 0) {
memcpy(&buf_.h_uint64_keys[uint64_total_num],
uint64_feasigns.slot_values.data(), fea_num * sizeof(uint64_t));
}
uint64_total_num += fea_num;
// copy uint64 offset
memcpy(&buf_.h_uint64_offset[i * uint64_cols],
uint64_feasigns.slot_offsets.data(), sizeof(int) * uint64_cols);
}
CHECK(uint64_total_num == static_cast<int>(buf_.h_uint64_lens.back()))
<< "uint64 value length error";
}
void MiniBatchGpuPack::pack_float_data(const SlotRecord* ins_vec, int num) {
int float_total_num = 0;
buf_.h_uint64_lens.clear();
buf_.h_uint64_offset.clear();
buf_.h_uint64_keys.clear();
buf_.h_float_lens.resize(num + 1);
buf_.h_float_lens[0] = 0;
for (int i = 0; i < num; ++i) {
auto r = ins_vec[i];
float_total_num += r->slot_float_feasigns_.slot_values.size();
buf_.h_float_lens[i + 1] = float_total_num;
}
int float_cols = (used_float_num_ + 1);
buf_.h_float_offset.resize(float_cols * num);
buf_.h_float_keys.resize(float_total_num);
size_t fea_num = 0;
float_total_num = 0;
for (int i = 0; i < num; ++i) {
auto r = ins_vec[i];
auto& float_feasigns = r->slot_float_feasigns_;
fea_num = float_feasigns.slot_values.size();
memcpy(&buf_.h_float_keys[float_total_num],
float_feasigns.slot_values.data(), fea_num * sizeof(float));
float_total_num += fea_num;
// copy float offset
memcpy(&buf_.h_float_offset[i * float_cols],
float_feasigns.slot_offsets.data(), sizeof(int) * float_cols);
}
CHECK(float_total_num == static_cast<int>(buf_.h_float_lens.back()))
<< "float value length error";
}
void MiniBatchGpuPack::pack_instance(const SlotRecord* ins_vec, int num) {
ins_num_ = num;
batch_ins_ = ins_vec;
CHECK(used_uint64_num_ > 0 || used_float_num_ > 0);
// uint64 and float
if (used_uint64_num_ > 0 && used_float_num_ > 0) {
pack_all_data(ins_vec, num);
} else if (used_uint64_num_ > 0) { // uint64
pack_uint64_data(ins_vec, num);
} else { // only float
pack_float_data(ins_vec, num);
}
// to gpu
transfer_to_gpu();
}
void MiniBatchGpuPack::transfer_to_gpu(void) {
copy_host2device(&value_.d_uint64_lens, buf_.h_uint64_lens);
copy_host2device(&value_.d_uint64_keys, buf_.h_uint64_keys);
copy_host2device(&value_.d_uint64_offset, buf_.h_uint64_offset);
copy_host2device(&value_.d_float_lens, buf_.h_float_lens);
copy_host2device(&value_.d_float_keys, buf_.h_float_keys);
copy_host2device(&value_.d_float_offset, buf_.h_float_offset);
CUDA_CHECK(cudaStreamSynchronize(stream_));
}
#endif
} // namespace framework
} // namespace paddle
paddle/fluid/framework/data_feed.cu 0 → 100644
浏览文件 @ c202a613
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#if defined _WIN32 || defined __APPLE__
#else
#define _LINUX
#endif
#if defined(PADDLE_WITH_CUDA) && defined(PADDLE_WITH_HETERPS)
#include "paddle/fluid/framework/data_feed.h"
namespace paddle {
namespace framework {
#define CUDA_KERNEL_LOOP(i, n) \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
i += blockDim.x * gridDim.x)
// CUDA: use 512 threads per block
const int CUDA_NUM_THREADS = 512;
// CUDA: number of blocks for threads.
inline int GET_BLOCKS(const int N) {
return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS;
}
// fill slot values
__global__ void FillSlotValueOffsetKernel(
const int ins_num, const int used_slot_num, size_t *slot_value_offsets,
const int *uint64_offsets, const int uint64_slot_size,
const int *float_offsets, const int float_slot_size,
const UsedSlotGpuType *used_slots) {
int col_num = ins_num + 1;
int uint64_cols = uint64_slot_size + 1;
int float_cols = float_slot_size + 1;
CUDA_KERNEL_LOOP(slot_idx, used_slot_num) {
int value_off = slot_idx * col_num;
slot_value_offsets[value_off] = 0;
auto &info = used_slots[slot_idx];
if (info.is_uint64_value) {
for (int k = 0; k < ins_num; ++k) {
int pos = k * uint64_cols + info.slot_value_idx;
int num = uint64_offsets[pos + 1] - uint64_offsets[pos];
PADDLE_ENFORCE(num >= 0, "The number of slot size must be ge 0.");
slot_value_offsets[value_off + k + 1] =
slot_value_offsets[value_off + k] + num;
}
} else {
for (int k = 0; k < ins_num; ++k) {
int pos = k * float_cols + info.slot_value_idx;
int num = float_offsets[pos + 1] - float_offsets[pos];
PADDLE_ENFORCE(num >= 0, "The number of slot size must be ge 0.");
slot_value_offsets[value_off + k + 1] =
slot_value_offsets[value_off + k] + num;
}
}
}
}
void SlotRecordInMemoryDataFeed::FillSlotValueOffset(
const int ins_num, const int used_slot_num, size_t *slot_value_offsets,
const int *uint64_offsets, const int uint64_slot_size,
const int *float_offsets, const int float_slot_size,
const UsedSlotGpuType *used_slots) {
auto stream =
dynamic_cast<platform::CUDADeviceContext *>(
paddle::platform::DeviceContextPool::Instance().Get(this->place_))
->stream();
FillSlotValueOffsetKernel<<<GET_BLOCKS(used_slot_num), CUDA_NUM_THREADS, 0,
stream>>>(
ins_num, used_slot_num, slot_value_offsets, uint64_offsets,
uint64_slot_size, float_offsets, float_slot_size, used_slots);
cudaStreamSynchronize(stream);
}
__global__ void CopyForTensorKernel(
const int used_slot_num, const int ins_num, void **dest,
const size_t *slot_value_offsets, const uint64_t *uint64_feas,
const int *uint64_offsets, const int *uint64_ins_lens,
const int uint64_slot_size, const float *float_feas,
const int *float_offsets, const int *float_ins_lens,
const int float_slot_size, const UsedSlotGpuType *used_slots) {
int col_num = ins_num + 1;
int uint64_cols = uint64_slot_size + 1;
int float_cols = float_slot_size + 1;
CUDA_KERNEL_LOOP(i, ins_num * used_slot_num) {
int slot_idx = i / ins_num;
int ins_idx = i % ins_num;
uint32_t value_offset = slot_value_offsets[slot_idx * col_num + ins_idx];
auto &info = used_slots[slot_idx];
if (info.is_uint64_value) {
uint64_t *up = reinterpret_cast<uint64_t *>(dest[slot_idx]);
int index = info.slot_value_idx + uint64_cols * ins_idx;
int old_off = uint64_offsets[index];
int num = uint64_offsets[index + 1] - old_off;
PADDLE_ENFORCE(num >= 0, "The number of slot size must be ge 0.");
int uint64_value_offset = uint64_ins_lens[ins_idx];
for (int k = 0; k < num; ++k) {
up[k + value_offset] = uint64_feas[k + old_off + uint64_value_offset];
}
} else {
float *fp = reinterpret_cast<float *>(dest[slot_idx]);
int index = info.slot_value_idx + float_cols * ins_idx;
int old_off = float_offsets[index];
int num = float_offsets[index + 1] - old_off;
PADDLE_ENFORCE(num >= 0, "The number of slot size must be ge 0.");
int float_value_offset = float_ins_lens[ins_idx];
for (int k = 0; k < num; ++k) {
fp[k + value_offset] = float_feas[k + old_off + float_value_offset];
}
}
}
}
void SlotRecordInMemoryDataFeed::CopyForTensor(
const int ins_num, const int used_slot_num, void **dest,
const size_t *slot_value_offsets, const uint64_t *uint64_feas,
const int *uint64_offsets, const int *uint64_ins_lens,
const int uint64_slot_size, const float *float_feas,
const int *float_offsets, const int *float_ins_lens,
const int float_slot_size, const UsedSlotGpuType *used_slots) {
auto stream =
dynamic_cast<platform::CUDADeviceContext *>(
paddle::platform::DeviceContextPool::Instance().Get(this->place_))
->stream();
CopyForTensorKernel<<<GET_BLOCKS(used_slot_num * ins_num), CUDA_NUM_THREADS,
0, stream>>>(
used_slot_num, ins_num, dest, slot_value_offsets, uint64_feas,
uint64_offsets, uint64_ins_lens, uint64_slot_size, float_feas,
float_offsets, float_ins_lens, float_slot_size, used_slots);
cudaStreamSynchronize(stream);
}
} // namespace framework
} // namespace paddle
#endif
paddle/fluid/framework/data_feed.h
浏览文件 @ c202a613
......@@ -41,6 +41,10 @@ limitations under the License. */
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/platform/timer.h"
#include "paddle/fluid/string/string_helper.h"
#if defined(PADDLE_WITH_CUDA)
#include "paddle/fluid/platform/cuda_device_guard.h"
#include "paddle/fluid/platform/device/gpu/gpu_info.h"
#endif
DECLARE_int32(record_pool_max_size);
DECLARE_int32(slotpool_thread_num);
......@@ -409,6 +413,266 @@ class CustomParser {
}
};
struct UsedSlotGpuType {
int is_uint64_value;
int slot_value_idx;
};
#if defined(PADDLE_WITH_CUDA) && defined(PADDLE_WITH_HETERPS)
#define CUDA_CHECK(val) CHECK(val == gpuSuccess)
template <typename T>
struct CudaBuffer {
T* cu_buffer;
uint64_t buf_size;
CudaBuffer<T>() {
cu_buffer = NULL;
buf_size = 0;
}
~CudaBuffer<T>() { free(); }
T* data() { return cu_buffer; }
uint64_t size() { return buf_size; }
void malloc(uint64_t size) {
buf_size = size;
CUDA_CHECK(
cudaMalloc(reinterpret_cast<void**>(&cu_buffer), size * sizeof(T)));
}
void free() {
if (cu_buffer != NULL) {
CUDA_CHECK(cudaFree(cu_buffer));
cu_buffer = NULL;
}
buf_size = 0;
}
void resize(uint64_t size) {
if (size <= buf_size) {
return;
}
free();
malloc(size);
}
};
template <typename T>
struct HostBuffer {
T* host_buffer;
size_t buf_size;
size_t data_len;
HostBuffer<T>() {
host_buffer = NULL;
buf_size = 0;
data_len = 0;
}
~HostBuffer<T>() { free(); }
T* data() { return host_buffer; }
const T* data() const { return host_buffer; }
size_t size() const { return data_len; }
void clear() { free(); }
T& back() { return host_buffer[data_len - 1]; }
T& operator[](size_t i) { return host_buffer[i]; }
const T& operator[](size_t i) const { return host_buffer[i]; }
void malloc(size_t len) {
buf_size = len;
CUDA_CHECK(cudaHostAlloc(reinterpret_cast<void**>(&host_buffer),
buf_size * sizeof(T), cudaHostAllocDefault));
CHECK(host_buffer != NULL);
}
void free() {
if (host_buffer != NULL) {
CUDA_CHECK(cudaFreeHost(host_buffer));
host_buffer = NULL;
}
buf_size = 0;
}
void resize(size_t size) {
if (size <= buf_size) {
data_len = size;
return;
}
data_len = size;
free();
malloc(size);
}
};
struct BatchCPUValue {
HostBuffer<int> h_uint64_lens;
HostBuffer<uint64_t> h_uint64_keys;
HostBuffer<int> h_uint64_offset;
HostBuffer<int> h_float_lens;
HostBuffer<float> h_float_keys;
HostBuffer<int> h_float_offset;
HostBuffer<int> h_rank;
HostBuffer<int> h_cmatch;
HostBuffer<int> h_ad_offset;
};
struct BatchGPUValue {
CudaBuffer<int> d_uint64_lens;
CudaBuffer<uint64_t> d_uint64_keys;
CudaBuffer<int> d_uint64_offset;
CudaBuffer<int> d_float_lens;
CudaBuffer<float> d_float_keys;
CudaBuffer<int> d_float_offset;
CudaBuffer<int> d_rank;
CudaBuffer<int> d_cmatch;
CudaBuffer<int> d_ad_offset;
};
class MiniBatchGpuPack {
public:
MiniBatchGpuPack(const paddle::platform::Place& place,
const std::vector<UsedSlotInfo>& infos);
~MiniBatchGpuPack();
void reset(const paddle::platform::Place& place);
void pack_instance(const SlotRecord* ins_vec, int num);
int ins_num() { return ins_num_; }
int pv_num() { return pv_num_; }
BatchGPUValue& value() { return value_; }
BatchCPUValue& cpu_value() { return buf_; }
UsedSlotGpuType* get_gpu_slots(void) {
return reinterpret_cast<UsedSlotGpuType*>(gpu_slots_.data());
}
SlotRecord* get_records(void) { return &ins_vec_[0]; }
// tensor gpu memory reused
void resize_tensor(void) {
if (used_float_num_ > 0) {
int float_total_len = buf_.h_float_lens.back();
if (float_total_len > 0) {
float_tensor_.mutable_data<float>({float_total_len, 1}, this->place_);
}
}
if (used_uint64_num_ > 0) {
int uint64_total_len = buf_.h_uint64_lens.back();
if (uint64_total_len > 0) {
uint64_tensor_.mutable_data<int64_t>({uint64_total_len, 1},
this->place_);
}
}
}
LoDTensor& float_tensor(void) { return float_tensor_; }
LoDTensor& uint64_tensor(void) { return uint64_tensor_; }
HostBuffer<size_t>& offsets(void) { return offsets_; }
HostBuffer<void*>& h_tensor_ptrs(void) { return h_tensor_ptrs_; }
void* gpu_slot_offsets(void) { return gpu_slot_offsets_->ptr(); }
void* slot_buf_ptr(void) { return slot_buf_ptr_->ptr(); }
void resize_gpu_slot_offsets(const size_t slot_total_bytes) {
if (gpu_slot_offsets_ == nullptr) {
gpu_slot_offsets_ = memory::AllocShared(place_, slot_total_bytes);
} else if (gpu_slot_offsets_->size() < slot_total_bytes) {
auto buf = memory::AllocShared(place_, slot_total_bytes);
gpu_slot_offsets_.swap(buf);
buf = nullptr;
}
}
const std::string& get_lineid(int idx) {
if (enable_pv_) {
return ins_vec_[idx]->ins_id_;
}
return batch_ins_[idx]->ins_id_;
}
private:
void transfer_to_gpu(void);
void pack_all_data(const SlotRecord* ins_vec, int num);
void pack_uint64_data(const SlotRecord* ins_vec, int num);
void pack_float_data(const SlotRecord* ins_vec, int num);
public:
template <typename T>
void copy_host2device(CudaBuffer<T>* buf, const T* val, size_t size) {
if (size == 0) {
return;
}
buf->resize(size);
CUDA_CHECK(cudaMemcpyAsync(buf->data(), val, size * sizeof(T),
cudaMemcpyHostToDevice, stream_));
}
template <typename T>
void copy_host2device(CudaBuffer<T>* buf, const HostBuffer<T>& val) {
copy_host2device(buf, val.data(), val.size());
}
private:
paddle::platform::Place place_;
cudaStream_t stream_;
BatchGPUValue value_;
BatchCPUValue buf_;
int ins_num_ = 0;
int pv_num_ = 0;
bool enable_pv_ = false;
int used_float_num_ = 0;
int used_uint64_num_ = 0;
int used_slot_size_ = 0;
CudaBuffer<UsedSlotGpuType> gpu_slots_;
std::vector<UsedSlotGpuType> gpu_used_slots_;
std::vector<SlotRecord> ins_vec_;
const SlotRecord* batch_ins_ = nullptr;
// uint64 tensor
LoDTensor uint64_tensor_;
// float tensor
LoDTensor float_tensor_;
// batch
HostBuffer<size_t> offsets_;
HostBuffer<void*> h_tensor_ptrs_;
std::shared_ptr<phi::Allocation> gpu_slot_offsets_ = nullptr;
std::shared_ptr<phi::Allocation> slot_buf_ptr_ = nullptr;
};
class MiniBatchGpuPackMgr {
static const int MAX_DEIVCE_NUM = 16;
public:
MiniBatchGpuPackMgr() {
for (int i = 0; i < MAX_DEIVCE_NUM; ++i) {
pack_list_[i] = nullptr;
}
}
~MiniBatchGpuPackMgr() {
for (int i = 0; i < MAX_DEIVCE_NUM; ++i) {
if (pack_list_[i] == nullptr) {
continue;
}
delete pack_list_[i];
pack_list_[i] = nullptr;
}
}
// one device one thread
MiniBatchGpuPack* get(const paddle::platform::Place& place,
const std::vector<UsedSlotInfo>& infos) {
int device_id = place.GetDeviceId();
if (pack_list_[device_id] == nullptr) {
pack_list_[device_id] = new MiniBatchGpuPack(place, infos);
} else {
pack_list_[device_id]->reset(place);
}
return pack_list_[device_id];
}
private:
MiniBatchGpuPack* pack_list_[MAX_DEIVCE_NUM];
};
// global mgr
inline MiniBatchGpuPackMgr& BatchGpuPackMgr() {
static MiniBatchGpuPackMgr mgr;
return mgr;
}
#endif
typedef paddle::framework::CustomParser* (*CreateParserObjectFunc)();
class DLManager {
......@@ -1126,7 +1390,13 @@ class MultiSlotInMemoryDataFeed : public InMemoryDataFeed<Record> {
class SlotRecordInMemoryDataFeed : public InMemoryDataFeed<SlotRecord> {
public:
SlotRecordInMemoryDataFeed() {}
virtual ~SlotRecordInMemoryDataFeed() {}
virtual ~SlotRecordInMemoryDataFeed() {
#if defined(PADDLE_WITH_CUDA) && defined(PADDLE_WITH_HETERPS)
if (pack_ != nullptr) {
pack_ = nullptr;
}
#endif
}
virtual void Init(const DataFeedDesc& data_feed_desc);
virtual void LoadIntoMemory();
void ExpandSlotRecord(SlotRecord* ins);
......@@ -1149,6 +1419,23 @@ class SlotRecordInMemoryDataFeed : public InMemoryDataFeed<SlotRecord> {
}
bool ParseOneInstance(const std::string& line, SlotRecord* rec);
virtual void PutToFeedVec(const SlotRecord* ins_vec, int num);
virtual void AssignFeedVar(const Scope& scope);
#if defined(PADDLE_WITH_CUDA) && defined(PADDLE_WITH_HETERPS)
void BuildSlotBatchGPU(const int ins_num);
void FillSlotValueOffset(const int ins_num, const int used_slot_num,
size_t* slot_value_offsets,
const int* uint64_offsets,
const int uint64_slot_size, const int* float_offsets,
const int float_slot_size,
const UsedSlotGpuType* used_slots);
void CopyForTensor(const int ins_num, const int used_slot_num, void** dest,
const size_t* slot_value_offsets,
const uint64_t* uint64_feas, const int* uint64_offsets,
const int* uint64_ins_lens, const int uint64_slot_size,
const float* float_feas, const int* float_offsets,
const int* float_ins_lens, const int float_slot_size,
const UsedSlotGpuType* used_slots);
#endif
float sample_rate_ = 1.0f;
int use_slot_size_ = 0;
int float_use_slot_size_ = 0;
......@@ -1157,6 +1444,10 @@ class SlotRecordInMemoryDataFeed : public InMemoryDataFeed<SlotRecord> {
std::vector<UsedSlotInfo> used_slots_info_;
size_t float_total_dims_size_ = 0;
std::vector<int> float_total_dims_without_inductives_;
#if defined(PADDLE_WITH_CUDA) && defined(PADDLE_WITH_HETERPS)
MiniBatchGpuPack* pack_ = nullptr;
#endif
};
class PaddleBoxDataFeed : public MultiSlotInMemoryDataFeed {
......
paddle/fluid/framework/fleet/ps_gpu_wrapper.cc
浏览文件 @ c202a613
......@@ -271,13 +271,13 @@ void PSGPUWrapper::PreBuildTask(std::shared_ptr<HeterContext> gpu_task) {
}
timeline.Pause();
VLOG(1) << "GpuPs task add keys cost " << timeline.ElapsedSec()
VLOG(0) << "GpuPs task add keys cost " << timeline.ElapsedSec()
<< " seconds.";
timeline.Start();
gpu_task->UniqueKeys();
timeline.Pause();
VLOG(1) << "GpuPs task unique cost " << timeline.ElapsedSec() << " seconds.";
VLOG(0) << "GpuPs task unique cost " << timeline.ElapsedSec() << " seconds.";
if (!multi_mf_dim_) {
for (int i = 0; i < thread_keys_shard_num_; i++) {
......@@ -667,7 +667,7 @@ void PSGPUWrapper::BuildGPUTask(std::shared_ptr<HeterContext> gpu_task) {
if (!multi_mf_dim_) {
for (int i = 0; i < device_num; i++) {
feature_keys_count[i] = gpu_task->device_keys_[i].size();
VLOG(1) << i << " card contains feasign nums: " << feature_keys_count[i];
VLOG(0) << i << " card contains feasign nums: " << feature_keys_count[i];
size_max = std::max(size_max, feature_keys_count[i]);
}
} else {
......@@ -675,7 +675,7 @@ void PSGPUWrapper::BuildGPUTask(std::shared_ptr<HeterContext> gpu_task) {
for (int j = 0; j < multi_mf_dim_; j++) {
feature_keys_count[i] += gpu_task->device_dim_ptr_[i][j].size();
}
VLOG(1) << i << " card with dynamic mf contains feasign nums: "
VLOG(0) << i << " card with dynamic mf contains feasign nums: "
<< feature_keys_count[i];
size_max = std::max(size_max, feature_keys_count[i]);
}
......@@ -685,7 +685,7 @@ void PSGPUWrapper::BuildGPUTask(std::shared_ptr<HeterContext> gpu_task) {
HeterPs_ = nullptr;
}
if (size_max <= 0) {
VLOG(1) << "Skip build gpu ps cause feasign nums = " << size_max;
VLOG(0) << "Skip build gpu ps cause feasign nums = " << size_max;
return;
}
std::vector<std::thread> threads(device_num);
......@@ -707,7 +707,7 @@ void PSGPUWrapper::BuildGPUTask(std::shared_ptr<HeterContext> gpu_task) {
t.join();
}
timeline.Pause();
VLOG(1) << "GpuPs build table total costs: " << timeline.ElapsedSec()
VLOG(0) << "GpuPs build table total costs: " << timeline.ElapsedSec()
<< " s.";
}
......@@ -749,7 +749,7 @@ void PSGPUWrapper::pre_build_thread() {
// build cpu ps data process
PreBuildTask(gpu_task);
timer.Pause();
VLOG(1) << "thread PreBuildTask end, cost time: " << timer.ElapsedSec()
VLOG(0) << "thread PreBuildTask end, cost time: " << timer.ElapsedSec()
<< "s";
buildcpu_ready_channel_->Put(gpu_task);
}
......@@ -768,13 +768,13 @@ void PSGPUWrapper::build_task() {
return;
}
VLOG(1) << "BuildPull start.";
VLOG(0) << "BuildPull start.";
platform::Timer timer;
timer.Start();
BuildPull(gpu_task);
BuildGPUTask(gpu_task);
timer.Pause();
VLOG(1) << "BuildPull + BuildGPUTask end, cost time: " << timer.ElapsedSec()
VLOG(0) << "BuildPull + BuildGPUTask end, cost time: " << timer.ElapsedSec()
<< "s";
current_task_ = gpu_task;
......
paddle/fluid/framework/ps_gpu_worker.cc
浏览文件 @ c202a613
......@@ -119,6 +119,7 @@ void PSGPUWorker::SetChannelWriter(ChannelObject<std::string>* queue) {
}
void PSGPUWorker::TrainFiles() {
VLOG(0) << "Begin to train files";
platform::SetNumThreads(1);
platform::Timer timeline;
timeline.Start();
......@@ -129,6 +130,8 @@ void PSGPUWorker::TrainFiles() {
device_reader_->Start();
int cur_batch;
int batch_cnt = 0;
platform::SetDeviceId(thread_id_);
while ((cur_batch = device_reader_->Next()) > 0) {
total_ins_num += cur_batch;
for (auto& op : ops_) {
......@@ -190,14 +193,14 @@ void PSGPUWorker::TrainFiles() {
writer_.Flush();
}
timeline.Pause();
VLOG(1) << "GpuPs worker " << thread_id_ << " train cost "
VLOG(0) << "GpuPs worker " << thread_id_ << " train cost "
<< timeline.ElapsedSec() << " seconds, ins_num: " << total_ins_num;
return;
}
void PSGPUWorker::TrainFilesWithProfiler() {
platform::SetNumThreads(1);
VLOG(1) << "Begin to train files with profiler";
VLOG(0) << "Begin to train files with profiler";
device_reader_->Start();
std::vector<double> op_total_time;
std::vector<std::string> op_name;
......@@ -225,6 +228,7 @@ void PSGPUWorker::TrainFilesWithProfiler() {
int total_ins_num = 0;
int cur_batch;
timeline.Start();
platform::SetDeviceId(thread_id_);
while ((cur_batch = device_reader_->Next()) > 0) {
total_ins_num += cur_batch;
timeline.Pause();
......@@ -260,13 +264,15 @@ void PSGPUWorker::TrainFilesWithProfiler() {
total_time += timeline.ElapsedSec();
timeline.Start();
}
VLOG(1) << "GpuPs worker " << thread_id_ << " train cost " << total_time
VLOG(0) << "GpuPs worker " << thread_id_ << " train cost " << total_time
<< " seconds, ins_num: " << total_ins_num;
for (size_t i = 0; i < op_name.size(); ++i) {
VLOG(1) << "card:" << thread_id_ << ", op: " << op_name[i]
VLOG(0) << "card:" << thread_id_ << ", op: " << op_name[i]
<< ", mean time: " << op_total_time[i] / total_ins_num
<< "s, totol time:" << op_total_time[i] << "sec";
}
VLOG(0) << "card: " << thread_id_ << " read time: " << read_time
<< ", percent: " << read_time / total_time * 100;
return;
}
......
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