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coalesce_grad_tensor_pass.cc
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coalesce_grad_tensor_pass.cc
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// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/coalesce_grad_tensor_pass.h"
#include <algorithm>
#include <string>
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
namespace paddle {
namespace framework {
class ProgramDesc;
class VarDesc;
} // namespace framework
} // namespace paddle
DEFINE_double(fuse_parameter_memory_size, -1.0, // MBytes
"fuse_parameter_memory_size is up limited memory size(MB)"
"of one group parameters' gradient which is the input "
"of communication calling(e.g NCCLAllReduce). "
"The default value is 0, it means that "
"not set group according to memory_size.");
DEFINE_int32(
fuse_parameter_groups_size, 1,
"fuse_parameter_groups_size is the up limited size of one group "
"parameters' gradient. "
"The default value is a experimental result. If the "
"fuse_parameter_groups_size is 1, it means that the groups size is "
"the number of parameters' gradient. If the fuse_parameter_groups_size is "
"-1, it means that there are only one group. The default value is 3, it is "
"an experimental value.");
namespace paddle {
namespace framework {
namespace ir {
// unit of the FLAGS_fuse_parameter_memory_size.
static constexpr double kMB = 1048576.0;
// SetFuseParameterGroupsSize and SetFuseParameterMemorySize are used in unit
// test, because it is invalid that seting 'FLAGS_fuse_parameter_memory_size'
// and 'FLAGS_fuse_parameter_groups_size' in unit test.
void SetFuseParameterGroupsSize(int group_size) {
FLAGS_fuse_parameter_groups_size = group_size;
}
int GetFuseParameterGroupsSize() { return FLAGS_fuse_parameter_groups_size; }
void SetFuseParameterMemorySize(double memory_size) {
FLAGS_fuse_parameter_memory_size = memory_size;
}
double GetFuseParameterMemorySize() { return FLAGS_fuse_parameter_memory_size; }
class CoalesceGradTensorPass : public ir::Pass {
protected:
void ApplyImpl(ir::Graph *graph) const {
if (Get<size_t>(details::kNRanks) <= 1) {
VLOG(6) << "The number of place is" << Get<size_t>(details::kNRanks)
<< ", there doesn't need apply FuseAllReduceOpPass.";
return;
}
ir::Graph &result = *graph;
details::ParamsAndGrads params_grads;
RecordParamsAndGrads(result, ¶ms_grads);
ResetAttribute<details::ParamsAndGrads>(details::kParamsAndDenseGrads,
&result);
ResetAttribute<details::ParamsAndGrads>(details::kParamsAndSparseGrads,
&result);
ResetAttribute<details::GroupParamsAndGrads>(
details::kGroupParamsAndDenseGrads, &result);
VLOG(10) << "The number of params and grads is:" << params_grads.size();
if (params_grads.size() == 0) {
return;
}
auto &p_g_dense_grad =
result.Get<details::ParamsAndGrads>(details::kParamsAndDenseGrads);
auto &p_g_sparse_grad =
result.Get<details::ParamsAndGrads>(details::kParamsAndSparseGrads);
auto vars_info = GetVarInfo(result);
for (auto ¶m_grad : params_grads) {
if (IsLoDTensorType(GetTypeOfVar(vars_info, param_grad.second))) {
p_g_dense_grad.emplace_back(param_grad);
} else {
p_g_sparse_grad.emplace_back(param_grad);
}
}
VLOG(10) << "Dense grads: " << p_g_dense_grad.size()
<< ", Sparse grads: " << p_g_sparse_grad.size();
if (p_g_dense_grad.size() == 0) {
return;
}
auto num_of_p_g_dense_grad = p_g_dense_grad.size();
auto &group_params_grads = result.Get<details::GroupParamsAndGrads>(
details::kGroupParamsAndDenseGrads);
// Note: the order of p_g_dense_grad may be changed by
// SetGroupParamsAndGrads.
SetGroupParamsAndGrads(vars_info, p_g_dense_grad, &group_params_grads);
p_g_dense_grad.clear();
p_g_dense_grad.reserve(num_of_p_g_dense_grad);
for (auto &group_p_g : group_params_grads) {
p_g_dense_grad.insert(p_g_dense_grad.end(), group_p_g.begin(),
group_p_g.end());
}
PADDLE_ENFORCE_EQ(p_g_dense_grad.size(), num_of_p_g_dense_grad,
platform::errors::InvalidArgument(
"The number of dense grads is not consistent with "
"previous. Previous(%d), now(%d).",
p_g_dense_grad.size(), num_of_p_g_dense_grad));
auto &pinned_var_set =
graph->GetOrInit<details::PinnedVars>(details::kPinnedVars);
if (IsUnifiedDtype(p_g_dense_grad, vars_info)) {
RecordGradients(p_g_dense_grad, vars_info, &pinned_var_set);
CoalesceTensors(vars_info, p_g_dense_grad, &result);
} else {
for (auto &sub_param_grad : group_params_grads) {
RecordGradients(p_g_dense_grad, vars_info, &pinned_var_set);
PADDLE_ENFORCE_EQ(
IsUnifiedDtype(sub_param_grad, vars_info), true,
platform::errors::InvalidArgument("All gradient variable in "
"kGroupParamsAndDenseGrads, must "
"have same type."));
CoalesceTensors(vars_info, sub_param_grad, &result);
}
}
}
void RecordGradients(
const std::vector<std::pair<std::string, std::string>> &sub_param_grad,
const std::unordered_map<std::string, std::vector<ir::Node *>> &vars_info,
std::unordered_set<std::string> *pinned_var_set) const {
// The Gradients should not be reused during memory optimization.
for (auto &p_g : sub_param_grad) {
auto iter = vars_info.find(p_g.second);
PADDLE_ENFORCE_EQ(iter != vars_info.end(), true,
platform::errors::NotFound(
"Parameter@Grad %s is not found.", p_g.second));
PADDLE_ENFORCE_EQ(
!iter->second.empty(), true,
platform::errors::InvalidArgument(
"Parameter@Grad %s's var node is empty.", p_g.second));
for (auto it : iter->second) {
PADDLE_ENFORCE_NOT_NULL(
it->Var(),
platform::errors::InvalidArgument(
"A node of Parameter@Grad %s does not hold variable.",
p_g.second));
pinned_var_set->insert(it->Var()->Name());
}
PADDLE_ENFORCE_EQ(IsLoDTensorType(GetTypeOfVar(vars_info, p_g.second)),
true,
platform::errors::InvalidArgument(
"Parameter@Grad %s is not LoDTensor.", p_g.second));
}
}
bool IsUnifiedDtype(
const details::ParamsAndGrads ¶ms_grads,
const std::unordered_map<std::string, std::vector<ir::Node *>> &vars_info)
const {
if (params_grads.empty()) return true;
auto dtype = GetDtypeOfVar(vars_info, params_grads.front().second);
for (auto p_g : params_grads) {
auto next_dtype = GetDtypeOfVar(vars_info, p_g.second);
if (next_dtype != dtype) {
return false;
}
}
return true;
}
void CoalesceTensors(
const std::unordered_map<std::string, std::vector<ir::Node *>> &vars_info,
const details::ParamsAndGrads ¶ms_grads, Graph *result) const {
// Create a FusedVarsSet to avoid duplicating names for fused_var in other
// pass.
if (!result->Has(details::kFusedVars)) {
result->Set(details::kFusedVars, new details::FusedVars);
}
// the kFusedGrads is used be fuse_optimizer_op_pass.
if (!result->Has(details::kFusedGrads)) {
result->Set(details::kFusedGrads, new details::FusedGrads);
}
if (!result->Has(details::kStartupProgramDescs)) {
result->Set(details::kStartupProgramDescs, new details::ProgramDescs);
}
if (!result->Has(details::kProgramDescs)) {
result->Set(details::kProgramDescs, new details::ProgramDescs);
}
auto type = GetTypeOfVar(vars_info, params_grads.front().second);
bool persistable = false;
for (auto &p_g : params_grads) {
if (IsPersistableVar(vars_info, p_g.second)) {
// NOTE. If one of the grads is persistable, then the fused_grad_var
// should be set to persistable.
persistable = true;
break;
}
}
// the fused_var_name should be unique, so it appends
// params_grads.begin()->second.
auto fused_grad_var_name = std::string(details::kFusedVarNamePrefix) +
"@GRAD@" + params_grads.begin()->second;
// what a pity, visual c++ unsupport {.type_ = type}
details::VariableInfo var_info;
var_info.name_ = fused_grad_var_name;
var_info.type_ = type;
var_info.persistable_ = persistable;
auto &fused_var_set = result->Get<details::FusedVars>(details::kFusedVars);
PADDLE_ENFORCE_EQ(
fused_var_set.count(fused_grad_var_name), 0,
platform::errors::AlreadyExists("Var(%s) is duplicate in FusedVars.",
fused_grad_var_name));
fused_var_set.insert({fused_grad_var_name, var_info});
result->Get<details::FusedGrads>(details::kFusedGrads)
.emplace_back(fused_grad_var_name);
InitFusedVarsAndAllocSpaceForVars(vars_info, fused_grad_var_name,
params_grads, result);
}
template <typename AttrType>
void ResetAttribute(const std::string &attr_name, ir::Graph *graph) const {
if (graph->Has(attr_name)) {
VLOG(10) << attr_name << " is reset.";
graph->Erase(attr_name);
}
graph->Set(attr_name, new AttrType);
}
void SetGroupParamsAndGrads(
const std::unordered_map<std::string, std::vector<ir::Node *>> &vars_info,
const details::ParamsAndGrads ¶ms_grads,
details::GroupParamsAndGrads *group_params_grads) const {
if (GetFuseParameterMemorySize() == 0) {
group_params_grads->resize(1);
auto &result_param_grads = (*group_params_grads)[0];
result_param_grads = params_grads;
std::sort(result_param_grads.begin(), result_param_grads.end());
} else {
SetGroupAccordingToLayers(vars_info, params_grads, group_params_grads);
SetGroupAccordingToMemorySize(vars_info, group_params_grads);
}
if (!IsUnifiedDtype(params_grads, vars_info)) {
ReGroupByDtype(vars_info, group_params_grads);
}
}
void SetGroupAccordingToLayers(
const std::unordered_map<std::string, std::vector<ir::Node *>> &vars_info,
const details::ParamsAndGrads ¶ms_grads,
details::GroupParamsAndGrads *group_params_grads) const {
std::map<std::string, size_t> var_idx;
for (size_t i = 0; i < params_grads.size(); ++i) {
auto pos = params_grads[i].first.find_first_of(".");
std::string var_key;
if (pos == std::string::npos) {
var_key = params_grads[i].first;
} else {
var_key = params_grads[i].first.substr(0, pos);
}
size_t idx = 0;
auto var_idx_iter = var_idx.find(var_key);
if (var_idx_iter != var_idx.end()) {
idx = var_idx_iter->second;
} else {
group_params_grads->emplace_back();
idx = group_params_grads->size() - 1;
var_idx[var_key] = idx;
}
auto &local_group_params_grads = group_params_grads->at(idx);
local_group_params_grads.emplace_back(
std::make_pair(params_grads[i].first, params_grads[i].second));
}
if (VLOG_IS_ON(10)) {
VLOG(10) << "SetGroupAccordingToLayers: ";
PrintGroupInfo(vars_info, group_params_grads);
}
}
void PrintGroupInfo(
const std::unordered_map<std::string, std::vector<ir::Node *>> &vars_info,
details::GroupParamsAndGrads *group_params_grads) const {
for (size_t i = 0; i < group_params_grads->size(); ++i) {
VLOG(10) << "group " << i;
std::stringstream out;
size_t gps_size = 0;
for (auto &p_g : group_params_grads->at(i)) {
auto var_desc = GetVarDescFromVarsInfo(vars_info, p_g.first);
auto shape = var_desc->GetShape();
size_t size = framework::SizeOfType(var_desc->GetDataType());
std::for_each(shape.begin(), shape.end(),
[&size](const int64_t &n) { size *= n; });
gps_size += size;
out << string::Sprintf("(%s(%d), %s)", p_g.first, size, p_g.second);
}
auto dtype =
GetDtypeOfVar(vars_info, group_params_grads->at(i).front().second);
VLOG(10) << out.str()
<< ", group size:" << group_params_grads->at(i).size()
<< ", group memory size:" << static_cast<double>(gps_size) / kMB
<< "(MB), dtype:" << dtype;
}
}
void SetGroupAccordingToMemorySize(
const std::unordered_map<std::string, std::vector<ir::Node *>> &vars_info,
details::GroupParamsAndGrads *group_params_grads) const {
const double group_memory_size = GetFuseParameterMemorySize();
if (group_memory_size <= 0.0) {
return;
}
details::GroupParamsAndGrads local_group_params_grads;
size_t j = 0;
while (j < group_params_grads->size()) {
local_group_params_grads.emplace_back();
auto &group_p_g = local_group_params_grads.back();
size_t local_group_memory_size = 0;
while (j < group_params_grads->size()) {
for (auto &p_g_iter : group_params_grads->at(j)) {
auto var_desc = GetVarDescFromVarsInfo(vars_info, p_g_iter.second);
size_t size = framework::SizeOfType(var_desc->GetDataType());
auto shape = var_desc->GetShape();
std::for_each(shape.begin(), shape.end(),
[&size](const int64_t &n) { size *= n; });
local_group_memory_size += size;
}
group_p_g.insert(group_p_g.end(), group_params_grads->at(j).begin(),
group_params_grads->at(j).end());
++j;
if (j >= group_params_grads->size()) {
break;
}
if (GetFuseParameterGroupsSize() > 1 &&
group_p_g.size() >
static_cast<size_t>(GetFuseParameterGroupsSize())) {
break;
}
if (static_cast<double>(local_group_memory_size) / kMB >=
group_memory_size) {
break;
}
}
}
std::swap(*group_params_grads, local_group_params_grads);
if (VLOG_IS_ON(10)) {
VLOG(10) << string::Sprintf(
"SetGroupAccordingToMemorySize(memory_size: %f MB):",
GetFuseParameterMemorySize());
PrintGroupInfo(vars_info, group_params_grads);
}
}
void ReGroupByDtype(
const std::unordered_map<std::string, std::vector<ir::Node *>> &vars_info,
details::GroupParamsAndGrads *group_params_grads) const {
details::GroupParamsAndGrads new_group_params_grads;
for (auto &group_p_g : *group_params_grads) {
std::map<proto::VarType::Type, size_t> type_idx;
details::GroupParamsAndGrads local_group_params_grads;
for (auto &p_g : group_p_g) {
auto dtype = GetDtypeOfVar(vars_info, p_g.second);
size_t idx = 0;
auto var_idx_iter = type_idx.find(dtype);
if (var_idx_iter != type_idx.end()) {
idx = var_idx_iter->second;
} else {
local_group_params_grads.emplace_back();
idx = local_group_params_grads.size() - 1;
type_idx[dtype] = idx;
}
auto &local = local_group_params_grads.at(idx);
local.emplace_back(p_g);
}
VLOG(10) << "local_group_params_grads size:"
<< local_group_params_grads.size();
new_group_params_grads.insert(new_group_params_grads.end(),
local_group_params_grads.begin(),
local_group_params_grads.end());
}
std::swap(*group_params_grads, new_group_params_grads);
if (VLOG_IS_ON(10)) {
VLOG(10) << string::Sprintf("ReGroupByDtype(memory_size: %f MB, %u):",
GetFuseParameterMemorySize(),
GetFuseParameterGroupsSize());
PrintGroupInfo(vars_info, group_params_grads);
}
}
proto::VarType::Type GetDtypeOfVar(
const std::unordered_map<std::string, std::vector<ir::Node *>> &vars_info,
const std::string &name) const {
auto var_desc = GetVarDescFromVarsInfo(vars_info, name);
return var_desc->GetDataType();
}
proto::VarType::Type GetTypeOfVar(
const std::unordered_map<std::string, std::vector<ir::Node *>> &vars_info,
const std::string &name) const {
auto var_desc = GetVarDescFromVarsInfo(vars_info, name);
return var_desc->GetType();
}
bool IsPersistableVar(
const std::unordered_map<std::string, std::vector<ir::Node *>> &vars_info,
const std::string &name) const {
auto var_desc = GetVarDescFromVarsInfo(vars_info, name);
return var_desc->Persistable();
}
private:
bool IsLoDTensorType(const proto::VarType::Type &type) const {
// Current only support LOD_TENSOR.
return type == proto::VarType::LOD_TENSOR;
}
std::unordered_map<std::string, std::vector<Node *>> GetVarInfo(
const Graph &result) const {
std::unordered_map<std::string, std::vector<Node *>> vars;
for (Node *node : result.Nodes()) {
if (node->IsVar() && node->Var()) {
// Note: The graph may have the same name node. For example, parameter
// is the input of operator and it also is the output of optimizer;
vars[node->Var()->Name()].emplace_back(node);
}
}
return vars;
}
const VarDesc *GetVarDescFromVarsInfo(
const std::unordered_map<std::string, std::vector<Node *>> &vars_info,
const std::string &var_name) const {
auto grad_iter = vars_info.find(var_name);
PADDLE_ENFORCE_EQ(
grad_iter != vars_info.end(), true,
platform::errors::NotFound("Variable %s is not found.", var_name));
PADDLE_ENFORCE_EQ(!grad_iter->second.empty(), true,
platform::errors::InvalidArgument(
"Variable %s's node is empty.", var_name));
PADDLE_ENFORCE_NOT_NULL(
grad_iter->second.front()->Var(),
platform::errors::InvalidArgument(
"A node of %s does not hold variable.", var_name));
return grad_iter->second.front()->Var();
}
void RecordParamsAndGrads(const ir::Graph &graph,
details::ParamsAndGrads *params_grads) const {
std::vector<ir::Node *> topo_nodes = ir::TopologySortOperations(graph);
for (auto &node : topo_nodes) {
auto &op_desc = *(node->Op());
bool is_bk_op = details::IsOpRole(op_desc, OpRole::kBackward);
if (!is_bk_op) continue;
// Currently, we assume that once gradient is generated, it can be
// broadcast, and each gradient is only broadcast once.
auto backward_vars = details::GetOpRoleVarsOrEmpty(op_desc);
for (size_t i = 0; i < backward_vars.size(); i += 2) {
VLOG(10) << "Trainable parameter: " << backward_vars[i]
<< ", gradient: " << backward_vars[i + 1];
params_grads->emplace_back(std::make_pair(
backward_vars[i] /*param*/, backward_vars[i + 1] /*grad*/));
}
}
}
void InitFusedVarsAndAllocSpaceForVars(
const std::unordered_map<std::string, std::vector<ir::Node *>> &vars_info,
const std::string &fused_var_name,
const details::ParamsAndGrads ¶ms_grads, ir::Graph *result) const {
// Alloc continuous space for vars.
std::vector<std::string> grads_name;
std::vector<std::string> params_name;
grads_name.reserve(params_grads.size());
params_name.reserve(params_grads.size());
auto dtype = GetDtypeOfVar(vars_info, params_grads.front().second);
for (auto &p_g : params_grads) {
params_name.emplace_back(p_g.first);
grads_name.emplace_back(p_g.second);
auto next_dtype = GetDtypeOfVar(vars_info, p_g.second);
PADDLE_ENFORCE_EQ(
next_dtype, dtype,
platform::errors::InvalidArgument(
"All Parameter@Grad should have same dtype, but "
"there are two different type: %s, %s.",
DataTypeToString(next_dtype), DataTypeToString(dtype)));
}
bool any_persistable = false;
bool all_persistable = true;
for (auto &p_g : params_grads) {
if (IsPersistableVar(vars_info, p_g.second)) {
any_persistable = true;
} else {
all_persistable = false;
}
}
if (all_persistable) {
// All grads are persistable, only need to be executed once at the
// beginning.
result->Get<details::ProgramDescs>(details::kStartupProgramDescs)
.emplace_back();
ProgramDesc &program_desc =
result->Get<details::ProgramDescs>(details::kStartupProgramDescs)
.back();
auto *global_block = program_desc.MutableBlock(0);
AppendAllocSpaceForVarsOp(params_name, grads_name, fused_var_name, dtype,
all_persistable, global_block);
} else {
// NOTE. In scope_buffered_ssa_graph_executor, after each execution of
// DropScope(), non persistable vars will be Erase or Clear. So
// coalesce_tensor op needs to be executed again after the execution
// of DropScope().
result->Get<details::ProgramDescs>(details::kProgramDescs).emplace_back();
ProgramDesc &program_desc =
result->Get<details::ProgramDescs>(details::kProgramDescs).back();
auto *global_block = program_desc.MutableBlock(0);
AppendAllocSpaceForVarsOp(params_name, grads_name, fused_var_name, dtype,
any_persistable, global_block);
}
}
void AppendAllocSpaceForVarsOp(const std::vector<std::string> ¶ms_name,
const std::vector<std::string> &grads_name,
const std::string &fused_var_name,
const proto::VarType::Type &dtype,
bool persistable,
BlockDesc *global_block) const {
auto op_desc = global_block->AppendOp();
op_desc->SetType("coalesce_tensor");
op_desc->SetInput("Input", params_name);
op_desc->SetOutput("Output", grads_name);
op_desc->SetOutput("FusedOutput", {fused_var_name});
op_desc->SetAttr("dtype", static_cast<int>(dtype));
op_desc->SetAttr("persist_output", persistable);
}
};
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(coalesce_grad_tensor_pass,
paddle::framework::ir::CoalesceGradTensorPass)
.RequirePassAttr(paddle::framework::details::kNRanks);