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dist_model.cc
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/
dist_model.cc
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// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <glog/logging.h>
#include <chrono> // NOLINT
#include "paddle/fluid/distributed/fleet_executor/dist_model.h"
#include "paddle/fluid/distributed/fleet_executor/fleet_executor.h"
#include "paddle/fluid/distributed/fleet_executor/task_node.h"
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/tensor.h"
namespace paddle {
namespace distributed {
namespace {
bool IsPersistable(const framework::VarDesc *var) {
if (var->Persistable() &&
var->GetType() != framework::proto::VarType::FEED_MINIBATCH &&
var->GetType() != framework::proto::VarType::FETCH_LIST &&
var->GetType() != framework::proto::VarType::RAW) {
return true;
}
return false;
}
bool LoadDataFromDistModelTensor(const DistModelTensor &input_data,
framework::LoDTensor *input_tensor,
const platform::Place &place) {
VLOG(3) << "Loading data from DistModelTensor for " << input_data.name;
framework::DDim dims = phi::make_ddim(input_data.shape);
void *input_tensor_ptr;
if (input_data.dtype == DistModelDataType::INT64) {
input_tensor_ptr = input_tensor->mutable_data<int64_t>(dims, place);
} else if (input_data.dtype == DistModelDataType::FLOAT32) {
input_tensor_ptr = input_tensor->mutable_data<float>(dims, place);
} else if (input_data.dtype == DistModelDataType::INT32) {
input_tensor_ptr = input_tensor->mutable_data<int32_t>(dims, place);
} else if (input_data.dtype == DistModelDataType::FLOAT16) {
input_tensor_ptr = input_tensor->mutable_data<float16>(dims, place);
} else {
LOG(ERROR) << "unsupported feed type " << input_data.dtype;
return false;
}
PADDLE_ENFORCE_NOT_NULL(
input_tensor_ptr,
paddle::platform::errors::Fatal(
"LoDTensor creation failed. DistModel loaded data failed."));
PADDLE_ENFORCE_NOT_NULL(input_data.data.data(),
paddle::platform::errors::InvalidArgument(
"DistModelTensor contains no data."));
if (platform::is_cpu_place(place)) {
VLOG(3) << "Loading data for CPU.";
std::memcpy(static_cast<void *>(input_tensor_ptr), input_data.data.data(),
input_data.data.length());
} else if (platform::is_gpu_place(place)) {
VLOG(3) << "Loading data for GPU.";
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto *dev_ctx =
dynamic_cast<const platform::CUDADeviceContext *>(pool.Get(place));
auto gpu_place = place;
memory::Copy(gpu_place, static_cast<void *>(input_tensor_ptr),
platform::CPUPlace(), input_data.data.data(),
input_data.data.length(), dev_ctx->stream());
#else
PADDLE_THROW(paddle::platform::errors::Fatal(
"Paddle wasn't compiled with CUDA, but place is GPU."));
#endif
} else {
PADDLE_THROW(paddle::platform::errors::InvalidArgument(
"DistModel only supports CPU and GPU."));
}
framework::LoD dst_lod;
for (auto &src_lod : input_data.lod) {
dst_lod.emplace_back(src_lod);
}
input_tensor->set_lod(dst_lod);
return true;
}
std::string DistModelDTypeToString(DistModelDataType dtype) {
switch (dtype) {
case DistModelDataType::FLOAT32:
return "float32";
case DistModelDataType::FLOAT16:
return "float16";
case DistModelDataType::INT64:
return "int64";
case DistModelDataType::INT32:
return "int32";
case DistModelDataType::INT8:
return "int8";
}
return "NOT SUPPORT DTYPE";
}
class DistModelTimer {
public:
void tic() { tic_time = std::chrono::high_resolution_clock::now(); }
double toc() {
std::chrono::high_resolution_clock::time_point toc_time =
std::chrono::high_resolution_clock::now();
std::chrono::duration<double> time_elapse =
std::chrono::duration_cast<std::chrono::duration<double>>(toc_time -
tic_time);
double time_elapse_in_ms =
static_cast<double>(time_elapse.count()) * 1000.0;
return time_elapse_in_ms;
}
private:
std::chrono::high_resolution_clock::time_point tic_time;
};
} // namespace
bool DistModel::Init() {
carrier_id_ = "inference";
bool init_method = (!config_.model_dir.empty() || config_.program_desc);
PADDLE_ENFORCE_EQ(init_method, true,
platform::errors::InvalidArgument(
"One of model dir or program desc must be provided to "
"dist model inference."));
if (config_.program_desc) {
PADDLE_ENFORCE_NOT_NULL(
config_.scope, platform::errors::InvalidArgument(
"Scope must be provided to dist model inference if "
"program desc has been provided."));
}
if (!PreparePlace()) {
return false;
}
if (!config_.program_desc) {
if (config_.scope) {
LOG(WARNING) << "The provided scope will be ignored if model dir has "
"also been provided.";
}
if (!PrepareScope()) {
return false;
}
if (!PrepareProgram()) {
return false;
}
} else {
program_.reset(config_.program_desc);
scope_.reset(config_.scope);
}
if (!PrepareFeedAndFetch()) {
return false;
}
if (config_.nranks > 1 && !CommInit()) {
return false;
}
if (!PrepareFleetExe()) {
return false;
}
return true;
}
bool DistModel::PreparePlace() {
if (config_.place == "GPU") {
place_ = paddle::platform::CUDAPlace(config_.device_id);
} else if (config_.place == "CPU") {
place_ = paddle::platform::CPUPlace();
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"Place must be choosen from GPU or CPU, but got %s.", config_.place));
}
return true;
}
bool DistModel::CommInit() {
std::unique_ptr<framework::ProgramDesc> comm_init_program(
new framework::ProgramDesc());
framework::BlockDesc *comm_init_block = comm_init_program->MutableBlock(0);
std::vector<int64_t> &ring_ids =
config_.rank_to_ring_ids_[config_.local_rank];
int64_t order = 0;
std::string var_name_base = "comm_init_";
for (int64_t ring_id : ring_ids) {
VLOG(3) << "Init comm for ring id: " << ring_id;
int64_t ranks_in_group = config_.ring_id_to_ranks_[ring_id].size();
int64_t rank_in_group = 0;
std::vector<int64_t> &ranks = config_.ring_id_to_ranks_[ring_id];
for (int64_t rank : ranks) {
if (config_.local_rank == rank) {
break;
}
rank_in_group += 1;
}
std::vector<std::string> peer_endpoints;
for (int64_t rank : ranks) {
if (config_.local_rank == rank) {
continue;
}
peer_endpoints.emplace_back(config_.trainer_endpoints[rank]);
}
InsertCommOp(var_name_base + std::to_string(order), ranks_in_group,
rank_in_group, peer_endpoints, comm_init_block, ring_id);
order += 1;
}
framework::NaiveExecutor e(place_);
e.CreateVariables(*comm_init_program, 0, true, scope_.get());
e.Prepare(scope_.get(), *comm_init_program, 0, false);
e.Run();
VLOG(3) << "Comm init successful.";
return true;
}
void DistModel::InsertCommOp(std::string tmp_var_name, int nranks, int rank,
const std::vector<std::string> &peer_endpoints,
framework::BlockDesc *block, int ring_id) {
/*
* tmp_var_name: the var name for var comm_id
* nranks: number of total ranks
* rank: the rank of local rank in the comm group
* peer_endpoints: peer's endpoints
* block: the block where to insert the comm ops
* ring_id: the ring_id to be inited
*/
std::string &endpoint = config_.current_endpoint;
std::stringstream ss;
ss << "Init comm with tmp var: " << tmp_var_name
<< ". The ring id is: " << ring_id << ". The group has: " << nranks
<< " ranks. Current rank in the group is: " << rank
<< ". The endpoint is: " << endpoint << ". Peer endpoints are: ";
for (auto ep : peer_endpoints) {
ss << ep << ", ";
}
VLOG(3) << ss.str();
if (config_.place == "GPU") {
framework::VarDesc *new_var = block->Var(tmp_var_name);
new_var->SetType(framework::proto::VarType::RAW);
new_var->SetPersistable(true);
framework::OpDesc *gen_nccl_id_op = block->AppendOp();
gen_nccl_id_op->SetType("c_gen_nccl_id");
gen_nccl_id_op->SetOutput("Out", {tmp_var_name});
gen_nccl_id_op->SetAttr("rank", rank);
gen_nccl_id_op->SetAttr("endpoint", config_.current_endpoint);
gen_nccl_id_op->SetAttr("other_endpoints", peer_endpoints);
gen_nccl_id_op->SetAttr("ring_id", ring_id);
gen_nccl_id_op->SetAttr("op_role",
static_cast<int>(framework::OpRole::kForward));
gen_nccl_id_op->CheckAttrs();
framework::OpDesc *comm_init_op = block->AppendOp();
comm_init_op->SetType("c_comm_init");
comm_init_op->SetInput("X", {tmp_var_name});
comm_init_op->SetAttr("rank", rank);
comm_init_op->SetAttr("nranks", nranks);
comm_init_op->SetAttr("ring_id", ring_id);
comm_init_op->SetAttr("op_role",
static_cast<int>(framework::OpRole::kForward));
comm_init_op->CheckAttrs();
} else {
LOG(WARNING) << "DistModelInf doesn't init comm.";
// TODO(fleet exe dev): comm init for more devices
}
}
bool DistModel::PrepareScope() {
scope_.reset(new framework::Scope());
return true;
}
bool DistModel::PrepareProgram() {
if (!LoadProgram()) {
return false;
}
if (!LoadParameters()) {
return false;
}
return true;
}
bool DistModel::LoadProgram() {
VLOG(3) << "Loading program from " << config_.model_dir;
PADDLE_ENFORCE_NE(config_.model_dir, "", platform::errors::InvalidArgument(
"Model dir must be provided."));
std::string model_path = config_.model_dir + ".pdmodel";
framework::proto::ProgramDesc program_proto;
std::string pb_content;
// Read binary
std::ifstream fin(model_path, std::ios::in | std::ios::binary);
PADDLE_ENFORCE_EQ(
static_cast<bool>(fin.is_open()), true,
platform::errors::NotFound(
"Cannot open file %s, please confirm whether the file is normal.",
model_path));
fin.seekg(0, std::ios::end);
pb_content.resize(fin.tellg());
fin.seekg(0, std::ios::beg);
fin.read(&(pb_content.at(0)), pb_content.size());
fin.close();
program_proto.ParseFromString(pb_content);
VLOG(5) << pb_content;
program_.reset(new framework::ProgramDesc(program_proto));
return true;
}
bool DistModel::LoadParameters() {
VLOG(3) << "Loading parameters from " << config_.model_dir;
PADDLE_ENFORCE_NOT_NULL(program_.get(),
platform::errors::PreconditionNotMet(
"The program should be loaded first."));
const auto &global_block = program_->MutableBlock(0);
// create a temporary program to load parameters.
std::unique_ptr<framework::ProgramDesc> load_program(
new framework::ProgramDesc());
framework::BlockDesc *load_block = load_program->MutableBlock(0);
std::vector<std::string> params;
for (auto *var : global_block->AllVars()) {
if (IsPersistable(var)) {
VLOG(3) << "persistable variable's name: " << var->Name();
framework::VarDesc *new_var = load_block->Var(var->Name());
new_var->SetShape(var->GetShape());
new_var->SetDataType(var->GetDataType());
new_var->SetType(var->GetType());
new_var->SetLoDLevel(var->GetLoDLevel());
new_var->SetPersistable(true);
params.push_back(new_var->Name());
// NOTE: if the params are stored in different files, 'load' op should be
// added here
}
}
std::string param_path = config_.model_dir + ".pdiparams";
// sort paramlist to have consistent ordering
std::sort(params.begin(), params.end());
// append just the load_combine op
framework::OpDesc *op = load_block->AppendOp();
op->SetType("load_combine");
op->SetOutput("Out", params);
op->SetAttr("file_path", {param_path});
op->CheckAttrs();
framework::NaiveExecutor e(place_);
// Create all persistable variables in root scope to load them from ckpt.
// Other non-persistable variables will be created in the micro scope
// managed by fleet executor.
e.CreateVariables(*program_, 0, true, scope_.get());
e.Prepare(scope_.get(), *load_program, 0, false);
e.Run();
VLOG(3) << "After loading there are " << scope_->LocalVarNames().size()
<< " vars.";
return true;
}
bool DistModel::PrepareFleetExe() {
task_node_.reset(new TaskNode(program_.get(), config_.local_rank));
// With auto cut, there is no concept of pp, no need to add dependency.
task_node_->SetType("Compute");
task_node_->Init();
executor_desc_ = FleetExecutorDesc();
executor_desc_.set_cur_rank(config_.local_rank);
std::unordered_map<int64_t, int64_t> id_to_rank;
for (int i = 0; i < config_.nranks; ++i) {
RankInfo *rank_info = executor_desc_.add_cluster_info();
rank_info->set_rank(i);
rank_info->set_ip_port(config_.trainer_endpoints[i]);
id_to_rank.insert({i, i});
}
fleet_exe.reset(new FleetExecutor(executor_desc_));
fleet_exe->Init(carrier_id_, *(program_.get()), scope_.get(), place_, 1,
{task_node_.get()}, id_to_rank);
return true;
}
bool DistModel::PrepareFeedAndFetch() {
for (auto *op : program_->Block(0).AllOps()) {
if (op->Type() == "feed") {
VLOG(3) << "feed op with feed var: " << op->Output("Out")[0];
int idx = BOOST_GET_CONST(int, op->GetAttr("col"));
if (feeds_.size() <= static_cast<size_t>(idx)) {
feeds_.resize(idx + 1);
}
feeds_[idx] = op;
std::string var_name = op->Output("Out")[0];
feed_names_[var_name] = idx;
idx_to_feeds_[idx] = var_name;
framework::VarDesc *real_var = program_->Block(0).FindVar(var_name);
if (!real_var) {
LOG(ERROR)
<< "The output of feed ops [" << var_name
<< "] cannot be found in the program. Check the inference program.";
return false;
}
if (real_var->GetDataType() == framework::proto::VarType::FP32) {
feeds_to_dtype_.insert({var_name, DistModelDataType::FLOAT32});
} else if (real_var->GetDataType() == framework::proto::VarType::INT32) {
feeds_to_dtype_.insert({var_name, DistModelDataType::INT32});
} else if (real_var->GetDataType() == framework::proto::VarType::INT64) {
feeds_to_dtype_.insert({var_name, DistModelDataType::INT64});
} else if (real_var->GetDataType() == framework::proto::VarType::FP16) {
feeds_to_dtype_.insert({var_name, DistModelDataType::FLOAT16});
} else {
LOG(ERROR) << "Don't support feed var dtype for: "
<< real_var->GetDataType();
return false;
}
} else if (op->Type() == "fetch") {
VLOG(3) << "fetch op with fetch var: " << op->Input("X")[0];
int idx = BOOST_GET_CONST(int, op->GetAttr("col"));
if (fetches_.size() <= static_cast<size_t>(idx)) {
fetches_.resize(idx + 1);
}
fetches_[idx] = op;
idx_to_fetches_[idx] = op->Input("X")[0];
}
}
if (feeds_.size() == 0) {
LOG(ERROR) << "No feed ops in the inf program, please check the program.";
return false;
}
if (fetches_.size() == 0) {
LOG(ERROR) << "No fetch op in the inf program, please check the program.";
return false;
}
return true;
}
bool DistModel::FeedData(const std::vector<DistModelTensor> &input_data,
framework::Scope *scope) {
VLOG(3) << "DistModel is feeding data.";
if (input_data.size() != feeds_.size()) {
LOG(ERROR) << "Should provide " << feeds_.size() << " feeds, but got "
<< input_data.size() << " data.";
return false;
}
feed_tensors_.resize(feeds_.size());
for (size_t i = 0; i < input_data.size(); ++i) {
// feed each data separately
framework::LoDTensor *input_tensor = &(feed_tensors_[i]);
if (!LoadDataFromDistModelTensor(input_data[i], input_tensor, place_)) {
LOG(ERROR) << "Fail to load data from tensor " << input_data[i].name;
return false;
}
std::string target_name = input_data[i].name;
if (feed_names_.find(target_name) == feed_names_.end()) {
LOG(ERROR) << "The input name [" << target_name
<< "] cannot be found in the program."
<< " DistModel loads data failed.";
return false;
}
if (input_data[i].dtype != feeds_to_dtype_[target_name]) {
LOG(ERROR) << "Feed var [" << target_name << "] expected dtype is: "
<< DistModelDTypeToString(feeds_to_dtype_[target_name])
<< ". But received dtype is: "
<< DistModelDTypeToString(input_data[i].dtype) << ".";
return false;
}
int feed_idx = feed_names_[target_name];
framework::SetFeedVariable(scope, *input_tensor, "feed", feed_idx);
}
return true;
}
bool DistModel::FetchResults(std::vector<DistModelTensor> *output_data,
framework::Scope *scope) {
VLOG(3) << "DistModel is fetch results.";
output_data->resize(fetches_.size());
for (size_t i = 0; i < fetches_.size(); ++i) {
int idx = BOOST_GET_CONST(int, fetches_[i]->GetAttr("col"));
VLOG(3) << "Fetching data for [" << idx_to_fetches_[idx] << "]";
PADDLE_ENFORCE_EQ(
static_cast<size_t>(idx), i,
platform::errors::InvalidArgument(
"Fetch op's col attr(%d) should be equal to the index(%d)", idx,
i));
framework::FetchType &fetch_var =
framework::GetFetchVariable(*scope, "fetch", idx);
auto &fetch = BOOST_GET(framework::LoDTensor, fetch_var);
auto type = framework::TransToProtoVarType(fetch.dtype());
auto output = &(output_data->at(i));
output->name = idx_to_fetches_[idx];
bool rst = false;
if (type == framework::proto::VarType::FP32) {
rst = FetchResult<float>(fetch, output);
output->dtype = DistModelDataType::FLOAT32;
} else if (type == framework::proto::VarType::INT64) {
rst = FetchResult<int64_t>(fetch, output);
output->dtype = DistModelDataType::INT64;
} else if (type == framework::proto::VarType::INT32) {
rst = FetchResult<int32_t>(fetch, output);
output->dtype = DistModelDataType::INT32;
} else if (type == framework::proto::VarType::FP16) {
rst = FetchResult<float16>(fetch, output);
output->dtype = DistModelDataType::FLOAT16;
} else {
LOG(ERROR) << "DistModel meets unknown fetch data type. DistModel only "
"supports float32, float16, int64 and int32 fetch type "
"for now.";
}
if (!rst) {
LOG(ERROR) << "DistModel fails to fetch result " << idx_to_fetches_[idx];
return false;
}
}
return true;
}
template <typename T>
bool DistModel::FetchResult(const framework::LoDTensor &fetch,
DistModelTensor *output_data) {
auto shape = phi::vectorize(fetch.dims());
output_data->shape.assign(shape.begin(), shape.end());
const T *data = fetch.data<T>();
int64_t num_elems = fetch.numel();
output_data->data.Resize(num_elems * sizeof(T));
// The output of fetch op is always on the cpu, no need switch on place
memcpy(output_data->data.data(), data, num_elems * sizeof(T));
output_data->lod.clear();
for (auto &level : fetch.lod()) {
output_data->lod.emplace_back(level.begin(), level.end());
}
return true;
}
bool DistModel::Run(const std::vector<DistModelTensor> &input_data,
std::vector<DistModelTensor> *output_data) {
VLOG(3) << "DistModel run for once.";
DistModelTimer timer;
timer.tic();
double feed_elapse;
double fleet_exe_elapse;
double fetch_elapse;
if (!FeedData(input_data, scope_.get())) {
LOG(ERROR) << "DistModel failed at feeding data.";
return false;
}
if (config_.enable_timer) {
feed_elapse = timer.toc();
LOG(INFO) << "Finish loading data, cost " << feed_elapse << "ms.";
} else {
VLOG(3) << "Finish loading data.";
}
fleet_exe->Run(carrier_id_);
if (config_.enable_timer) {
fleet_exe_elapse = timer.toc();
LOG(INFO) << "Finish FleetExe running, cost "
<< fleet_exe_elapse - feed_elapse << "ms.";
} else {
VLOG(3) << "Finish FleetExe running.";
}
if (!FetchResults(output_data, scope_.get())) {
LOG(ERROR) << "DistModel failed at fetching result.";
return false;
}
if (config_.enable_timer) {
fetch_elapse = timer.toc();
LOG(INFO) << "Finish fetching data, cost "
<< fetch_elapse - fleet_exe_elapse << "ms.";
LOG(INFO) << "DistModel finish inf, cost " << fetch_elapse << "ms";
} else {
VLOG(3) << "Finish fetching data.";
VLOG(3) << "DistModel finish inf.";
}
return true;
}
} // namespace distributed
} // namespace paddle