Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 2 年 前同步成功

通知 2325
Star 20933
Fork 5424
P
Paddle
“ba5f789ef24f666cef0677a2ff7348b8a1eef276”上不存在“develop/api_doc/fluid/optimizer.html”
未验证 提交 e89b1288 编写于 作者: Z Zhaolong Xing 提交者： GitHub
浏览文件
操作

FIx C++ inference BUG: When open memory optim and enable trt subgraph at the... 

FIx C++ inference BUG: When open memory optim and enable trt subgraph at the same time, there is a bug (#19969)

* fix memory optimization type
test=develop

* 1. fix BUG: open trt and memory optim will trigger bug.
2. Clean memory optim bug.
test=develop
上级 4286a627
显示空白变更内容
内联 并排
Showing with 51 addition and 752 deletion
paddle/fluid/inference/analysis/argument.h
浏览文件 @ e89b1288
......@@ -196,9 +196,7 @@ struct Argument {
// Memory optimized related.
DECL_ARGUMENT_FIELD(enable_memory_optim, EnableMemoryOptim, bool);
DECL_ARGUMENT_FIELD(static_memory_optim, StaticMemoryOptim, bool);
DECL_ARGUMENT_FIELD(static_memory_optim_force_update,
StaticMemoryOptimForceUpdate, bool);
// Indicate which kind of sort algorithm is used for operators, the memory
// optimization relays on the sort algorithm.
DECL_ARGUMENT_FIELD(memory_optim_sort_kind, MemoryOptimSortKind, int);
......
paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc
浏览文件 @ e89b1288
......@@ -41,7 +41,8 @@ void analysis::TensorRtSubgraphPass::ApplyImpl(
};
SubGraphFuser fuser(graph, teller,
Get<int>("min_subgraph_size") /*min subgraph size*/);
Get<int>("min_subgraph_size") /*min subgraph size*/,
"tensorrt_engine");
fuser();
std::vector<std::string> graph_param_names =
......@@ -200,13 +201,12 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
"Ys", std::vector<std::string>(output_names.begin(), output_names.end()));
op_desc->SetBlockAttr("sub_block", new_block);
SetAttr(op_desc->Proto(), "subgraph",
block_desc.Proto()->SerializeAsString());
SetAttr(op_desc->Proto(), "max_batch_size", Get<int>("max_batch_size"));
SetAttr(op_desc->Proto(), "workspace_size", Get<int>("workspace_size"));
SetAttr(op_desc->Proto(), "gpu_id", Get<int>("gpu_device_id"));
SetAttr(op_desc->Proto(), "output_name_mapping", output_mapping);
SetAttr(op_desc->Proto(), "parameters", params);
op_desc->SetAttr("subgraph", block_desc.Proto()->SerializeAsString());
op_desc->SetAttr("max_batch_size", Get<int>("max_batch_size"));
op_desc->SetAttr("workspace_size", Get<int>("workspace_size"));
op_desc->SetAttr("gpu_id", Get<int>("gpu_device_id"));
op_desc->SetAttr("output_name_mapping", output_mapping);
op_desc->SetAttr("parameters", params);
// we record all inputs' shapes in attr to check if they are consistent
// with the real inputs' shapes retrieved from scope when trt runs.
......@@ -232,16 +232,16 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
calibration_data = GetTrtCalibTableData(
Get<std::string>("model_opt_cache_dir"), engine_key, enable_int8);
}
SetAttr(op_desc->Proto(), "calibration_data", calibration_data);
op_desc->SetAttr("calibration_data", calibration_data);
op_desc->SetAttr("enable_int8", enable_int8);
op_desc->SetAttr("enable_fp16", enable_fp16);
op_desc->SetAttr("use_calib_mode", use_calib_mode);
op_desc->SetAttr("engine_key", engine_key);
op_desc->SetAttr("predictor_id", predictor_id);
SetAttr(op_desc->Proto(), "enable_int8", enable_int8);
SetAttr(op_desc->Proto(), "enable_fp16", enable_fp16);
SetAttr(op_desc->Proto(), "use_calib_mode", use_calib_mode);
SetAttr(op_desc->Proto(), "engine_key", engine_key);
SetAttr(op_desc->Proto(), "predictor_id", predictor_id);
std::string trt_engine_serialized_data = "";
SetAttr(op_desc->Proto(), "engine_serialized_data",
trt_engine_serialized_data);
op_desc->SetAttr("engine_serialized_data", trt_engine_serialized_data);
op_desc->Flush();
std::unique_ptr<tensorrt::TRTInt8Calibrator> calibrator;
if (enable_int8 && calibration_data.size() != 0) {
......
paddle/fluid/inference/analysis/passes/memory_optimize_pass.cc
浏览文件 @ e89b1288
......@@ -225,264 +225,6 @@ void MakeSimpleReusePlan(
}
}
// Collect the memory size of the tensors.
void MemoryOptimizePass::CollectVarMemorySize(
const std::unordered_map<std::string, size_t>& batch_var_ave_dim,
std::unordered_map<std::string, Node*>* tensor_nodes,
space_table_t* space_table) const {
// Collect tensors from graph.
for (auto* node : graph_->Nodes()) {
if (node->IsVar() &&
node->Var()->GetType() ==
framework::proto::VarType::Type::VarType_Type_LOD_TENSOR) {
// Parameters will not be reused.
if (node->Var()->Persistable()) continue;
(*tensor_nodes)[node->Name()] = node;
(*space_table)[node->Name()] =
DataTypeToSpace(node->Var()->GetDataType()) *
batch_var_ave_dim.at(node->Name());
}
}
}
// Find a sutable (big enough but smallest to avoid memory waste).
//
// Args:
// @tensor_nodes: the tensor nodes in the ir::Graph.
// @free_existing_tensors: the allocated tensor and are free.
// @space_table: the memory space of tensors.
// @tensor2use: the tensor that requires memory.
//
// Returns:
// true if found some existing tensor to reuse.
// false if no sutable tensor to reuse, one need to allocate a new tensor for
// this requirement.
// The suitable tensor for reuse is one that is approximately equal to the
// memory demand.
bool FindSuitableTensorToReuse(
const std::string& tensor, int space_required,
const std::unordered_map<std::string, Node*>& tensor_nodes,
std::unordered_set<std::string>* free_existing_tensors,
const space_table_t& space_table,
const std::vector<std::unordered_set<std::string>>& var_clusters,
std::string* tensor2use) __SHOULD_USE_RESULT__;
bool FindSuitableTensorToReuse(
const std::string& tensor, int space_required,
const std::unordered_map<std::string, Node*>& tensor_nodes,
std::unordered_set<std::string>* free_existing_tensors,
const space_table_t& space_table,
const std::vector<std::unordered_set<std::string>>& var_clusters,
std::string* tensor2use) {
std::pair<std::string, size_t> best_fit;
best_fit.second = std::numeric_limits<int>::max();
VLOG(5) << "Split Tensors to " << var_clusters.size() << " clusters";
// find the cluster this var belongs to.
const std::unordered_set<std::string>* cluster = nullptr;
for (const auto& c : var_clusters) {
if (c.count(tensor)) {
cluster = &c;
break;
}
}
PADDLE_ENFORCE_NOT_NULL(cluster,
"something wrong in memory optimization, the "
"variable %s not in the clusters.",
tensor);
for (auto& candidate : *free_existing_tensors) {
// This is not a temporary tensor.
if (!space_table.count(candidate)) continue;
// Not in the same cluster.
if (!cluster->count(candidate)) continue;
size_t space = space_table.at(candidate);
PADDLE_ENFORCE(
space <= std::numeric_limits<std::make_signed<size_t>::type>::max(),
"space overload");
size_t space_diff =
std::abs((std::make_signed<size_t>::type)space - space_required);
if (space_diff < best_fit.second) {
best_fit.first = candidate;
best_fit.second = space_diff;
}
}
if (best_fit.second < std::numeric_limits<int>::max()) {
*tensor2use = best_fit.first;
return true;
}
return false;
}
// Allocate new tensor instead of reusing the existing one.
void AllocateNewTensor(
const std::string& name, size_t space_required,
const std::unordered_map<std::string, Node*>& tensor_nodes,
std::unordered_set<std::string>* free_existing_tensors,
space_table_t* space_table,
std::unordered_map<std::string, std::string>* reuse_table) {
// The newly born tensor is free to be used.
free_existing_tensors->insert(name);
// Register the space it has.
PADDLE_ENFORCE(space_table->count(name));
space_table->at(name) = std::max(space_table->at(name), space_required);
// The allocated new tensor use the memory of itself.
(*reuse_table)[name] = name;
}
// Free a tensor and make it resuable.
// @tensor: the tensor to free.
// @free_existing_tensors: the free and allocated tensors.
// @reuse_table: a map from a fake tensor to the existing allocated tensor.
void FreeATensor(const std::string& tensor,
std::unordered_set<std::string>* free_existing_tensors,
std::unordered_map<std::string, std::string>* reuse_table) {
if (tensor == "feed" || tensor == "fetch") return;
// the really allocated tensor.
const auto& free_tensor = reuse_table->at(tensor);
free_existing_tensors->insert(free_tensor);
}
// Reuse a free existing tensor.
void ReuseATensor(const std::string& tensor, const std::string& tensor2reuse,
size_t memory_size,
std::unordered_set<std::string>* free_existing_tensors,
std::unordered_map<std::string, std::string>* reuse_table,
space_table_t* reused_space_table) {
auto it = free_existing_tensors->find(tensor2reuse);
PADDLE_ENFORCE(it != free_existing_tensors->end());
free_existing_tensors->erase(it);
(*reuse_table)[tensor] = tensor2reuse;
// Update the memory size of a reused tensor, the memory will grow if the
// required memory is larger.
(*reused_space_table)[tensor2reuse] =
std::max(reused_space_table->at(tensor2reuse), memory_size);
}
// Calculate the memory usage.
void EvaluateMemoryUsage(
const std::unordered_map<std::string, std::string>& reuse_table,
const space_table_t& space_table,
const std::unordered_map<std::string, size_t>& var_batch_ave_size,
size_t* allocated, size_t* saved) {
*allocated = 0;
*saved = 0;
for (auto elem : reuse_table) {
if (elem.first == elem.second) {
*allocated += space_table.at(elem.first);
VLOG(4) << elem.first << " <-> " << elem.second << " "
<< space_table.at(elem.first) << " "
<< space_table.at(elem.second);
} else {
*saved += space_table.at(elem.first);
VLOG(4) << "reuse " << elem.first << " -> " << elem.second;
}
}
VLOG(4) << "allocated " << *allocated;
VLOG(4) << "saved " << *saved;
}
// Return saved ratio.
void MemoryOptimizePass::MakeReusePlan(
const std::vector<std::unordered_set<std::string>>& var_clusters,
const std::unordered_map<std::string, size_t>& var_batch_ave_size,
const space_table_t& space_table,
std::unordered_map<std::string, std::string>* reuse_table, int sort_kind,
MemoryAllocation* memory_allocation) const {
// Clear the existing plan.
reuse_table->clear();
// The `space_table` stores the real memory size for each tensor.
// The `reused_space_table` stores the maximum memory size required by a
// tensor during the memory reusing, the small tensor might be reused by a
// larger tensor, and the memory size of the small one will grow.
auto reused_space_table = space_table;
std::unordered_map<std::string, lifecycle_t> life_cycles;
std::unordered_map<std::string, Node*> tensor_nodes;
// The allocated tensors whose memory can be reused, they will live across the
// program execution.
std::unordered_set<std::string> existing_tensors;
// The existing tensor that has been allocated, and is also free to reuse.
std::unordered_set<std::string> free_existing_tensors;
CollectLifeCycle(&life_cycles, sort_kind);
for (int age = 0; age < max_lifecycle_; ++age) {
std::unordered_set<std::string> born_tensors;
std::unordered_set<std::string> dead_tensors;
// Gather the dead and born tensors.
for (auto elem_it = life_cycles.begin(); elem_it != life_cycles.end();
elem_it++) {
if (elem_it->second.first == -1) {
continue;
}
const auto& tensor = elem_it->first;
const auto& lifecycle = elem_it->second;
VLOG(4) << "process " << tensor << " reuse " << lifecycle.first << "->"
<< lifecycle.second;
// Collect newly born tensors.
if (lifecycle.first == age) {
born_tensors.insert(tensor);
}
// Collect dead tensors whose memory can be reused.
else if (lifecycle.second < age) { // NOLINT
dead_tensors.insert(tensor);
// remove to avoid duplicate process.
elem_it->second.first = -1; // avoid duplicate search
}
}
// Reuse the dead tensors for born tensors
for (const auto& tensor : born_tensors) {
// Skip the feed and fetch tensor for that they share data with others.
std::string tensor2reuse;
if (!space_table.count(tensor)) continue;
size_t space_required = space_table.at(tensor);
if (FindSuitableTensorToReuse(tensor, space_required, tensor_nodes,
&free_existing_tensors, reused_space_table,
var_clusters, &tensor2reuse)) {
if (tensor != tensor2reuse) {
VLOG(4) << tensor << " -> " << tensor2reuse;
}
ReuseATensor(tensor, tensor2reuse, space_required,
&free_existing_tensors, reuse_table, &reused_space_table);
} else {
VLOG(4) << "allocate " << tensor;
AllocateNewTensor(tensor, space_required, tensor_nodes,
&free_existing_tensors, &reused_space_table,
reuse_table);
ReuseATensor(tensor, tensor, space_required, &free_existing_tensors,
reuse_table, &reused_space_table);
}
}
for (const auto& tensor : dead_tensors) {
// free its memory.
FreeATensor(tensor, &free_existing_tensors, reuse_table);
}
}
EvaluateMemoryUsage(*reuse_table, reused_space_table, var_batch_ave_size,
&(memory_allocation->allocated),
&(memory_allocation->saved));
memory_allocation->sort_kind = sort_kind;
}
void BuildVarNodeTable(Graph* graph,
std::unordered_map<std::string, Node*>* var_node_table) {
for (auto* node : graph->Nodes()) {
if (node->IsVar()) {
(*var_node_table)[node->Name()] = node;
}
}
}
// NOTE The optimized opdesc doesn't match ir::Graph.
void UpdateOpDescsByReuse(
Graph* graph,
......@@ -551,198 +293,22 @@ void UpdateOpDescsByReuse(
}
}
void MemoryOptimizePass::PerformReusePlan(
const std::unordered_map<std::string, std::string>& reuse_table,
int sort_kind, std::unordered_set<std::string>* vars2remove) const {
std::unordered_map<std::string, Node*> var_node_table;
BuildVarNodeTable(graph_, &var_node_table);
UpdateOpDescsByReuse(graph_, reuse_table, sort_kind);
for (auto& item : reuse_table) {
if (item.first != item.second) {
vars2remove->insert(item.first);
}
}
VLOG(2) << "to remove vars " << vars2remove->size();
}
std::vector<std::string> split(const std::string& line, char delim) {
std::vector<std::string> res;
std::string field;
std::stringstream line_stream(line);
while (std::getline(line_stream, field, delim)) {
res.emplace_back(field);
}
return res;
}
// Deserialize the batch var shapes from the cache file.
std::vector<std::map<std::string, std::vector<int>>> DeseralizeBatchVarShapes(
const std::string& path) {
std::ifstream file(path);
PADDLE_ENFORCE(file.is_open(), "failed to open %s to read cache", path);
std::string line;
std::vector<std::map<std::string, std::vector<int>>> batch_shapes;
while (std::getline(file, line)) {
std::map<std::string, std::vector<int>> batch;
for (const auto& var_info : split(line, ';')) {
auto fields = split(var_info, ':');
PADDLE_ENFORCE_EQ(fields.size(), 2UL);
auto var_name = fields.front();
auto shape_str = split(fields[1], ',');
std::vector<int> shape;
for (const auto& v : shape_str) shape.push_back(std::stoi(v));
batch[var_name] = shape;
}
batch_shapes.push_back(batch);
}
return batch_shapes;
}
// Replace the -1 in shape to a real number to fake the shape.
std::vector<std::map<std::string, std::vector<int>>> FakeBatchVarShapes(
const framework::ProgramDesc& program) {
std::vector<std::map<std::string, std::vector<int>>> res;
res.emplace_back();
auto& record = res.front();
const int fake_batch_size = 3;
for (auto* var : program.Block(0).AllVars()) {
if (var->GetType() ==
framework::proto::VarType::Type::VarType_Type_LOD_TENSOR) {
auto shape = var->GetShape();
for (auto& v : shape) {
if (v < 0) v = fake_batch_size;
}
record[var->Name()].assign(shape.begin(), shape.end());
}
}
return res;
}
// Calculate the average dim of each tensor from the batch shape cache.
std::unordered_map<std::string, size_t> GetBatchAverageSize(
const std::vector<std::map<std::string, std::vector<int>>>& batches) {
std::unordered_map<std::string, size_t> var2size;
// The average size of the batches for each variable.
int num_batch = 0;
for (const auto& batch : batches) {
num_batch++;
for (const auto& item : batch) {
int dim = std::accumulate(item.second.begin(), item.second.end(), 1,
[](int a, int b) { return a * b; });
var2size[item.first] += dim;
}
}
for (auto& item : var2size) {
item.second /= num_batch;
}
return var2size;
}
// Analysis the batch shapes loading from the cache file.
// By splitting the variables to different clusters by analyzing their batch
// size, we can pre-schedule the changes of difference LoDTensor when different
// length of input sequences is entered.
// This should works fine for the models operating on sentences.
std::vector<std::unordered_set<std::string>> AnalysisBatchShapesByBatchSize(
const std::vector<std::map<std::string, std::vector<int>>>& batches) {
// collect the batch size of each shape and combine to a stringstream in
// converient to generate a hash.
std::unordered_map<std::string, std::stringstream> var_batchsize_hashes;
for (auto& batch : batches) {
for (auto& ele : batch) {
PADDLE_ENFORCE(!ele.second.empty());
int batch_size = ele.second.front();
// TODO(Superjomn) might consume large memory here, use combine hash.
var_batchsize_hashes[ele.first] << batch_size;
}
}
// Split to sets by batch size sequences.
std::unordered_map<size_t /*hash*/, std::unordered_set<std::string>>
shape_sets;
for (auto& ele : var_batchsize_hashes) {
auto hash = std::hash<std::string>()(ele.second.str());
shape_sets[hash].insert(ele.first);
}
std::vector<std::unordered_set<std::string>> res;
for (auto& ele : shape_sets) {
res.emplace_back(std::move(ele.second));
}
VLOG(3) << "Cluster by batch_size and get " << res.size() << " clusters";
return res;
}
// Analysis the batch shapes loading from the cache file, and split them to
// different clusters by their size.
// This should works fine for the overall models.
std::vector<std::unordered_set<std::string>> AnalysisBatchShapesBySimilarSize(
const space_table_t& space_table,
const std::vector<std::map<std::string, std::vector<int>>>& batches,
int interval = 200000) {
PADDLE_ENFORCE_GT(interval, 0);
// cluster to different clusters.
size_t max_size = 0;
for (auto& item : space_table) {
max_size = std::max(item.second, max_size);
}
VLOG(4) << "tensor max size " << max_size;
std::vector<std::unordered_set<std::string>> res;
// cluster by intervals.
for (size_t interval_size = 0; interval_size <= max_size;
interval_size += interval) {
std::unordered_set<std::string> cluster;
for (auto& item : space_table) {
if (interval_size <= item.second &&
interval_size + interval > item.second) {
cluster.insert(item.first);
}
}
if (!cluster.empty()) {
res.push_back(cluster);
}
}
VLOG(3) << "Cluster by interval and get " << res.size() << " cluster";
return res;
}
std::string MemoryOptimizePass::repr() const { return "memory optimize pass"; }
std::pair<size_t, size_t> GetRange(
const std::unordered_map<std::string, size_t>& ave_size) {
auto res = std::make_pair(std::numeric_limits<size_t>::max(),
std::numeric_limits<size_t>::min());
for (auto& item : ave_size) {
res.first = std::min(item.second, res.first);
res.second = std::max(item.second, res.second);
}
return res;
}
void MemoryOptimizePass::RunImpl(Argument* argument) {
// When force update, should not optimize memory.
if (!argument->enable_memory_optim() ||
argument->static_memory_optim_force_update())
return;
// Memory optimization.
// We will perform the following operation:
// 1. Collect all var's lifetime.
// 2. Make reuse plan: the vars can be reused if there is no overlap(on
// lifetime) between
// them.
// The final plan is a mapping table in which the key represents the original
// name of var and the value in the table represents the current name of var.
// 3. Perform reuse plan: Replace all var's name in the model according to the
// mapping table.
if (!argument->enable_memory_optim()) return;
graph_ = argument->main_graph_ptr();
auto path = GetMemoryCachePath(
argument->model_dir_valid() ? argument->model_dir() : "",
argument->model_program_path_valid() ? argument->model_program_path()
: "");
VLOG(3) << "Load memory cache from " << path;
std::vector<std::map<std::string, std::vector<int>>> batches;
if (!(argument->static_memory_optim() && inference::IsFileExists(path))) {
string::PrettyLogInfo("--- Performing dynamic memory optimize");
// batches = FakeBatchVarShapes(argument->main_program());
int sort_kind = 0;
std::unordered_map<std::string, lifecycle_t> lifecycles;
space_table_t space_table;
......@@ -754,108 +320,8 @@ void MemoryOptimizePass::RunImpl(Argument* argument) {
MakeSimpleReusePlan(lifecycles, space_table, &node2cluster, &cluster_size);
UpdateOpDescsByReuse(graph_, node2cluster, sort_kind);
return;
} else {
string::PrettyLogInfo("--- Performing static memory optimize");
batches = DeseralizeBatchVarShapes(path);
}
auto var_batch_ave_size = GetBatchAverageSize(batches);
// Get min and max memory size.
const auto range = GetRange(var_batch_ave_size);
const int cluster_size = std::max(
static_cast<int>((range.second - range.first) / 100 /*cluster num*/),
1024);
const int cluster_size1 = std::max(
static_cast<int>((range.second - range.first) / 1000 /*cluster num*/),
1024);
std::unordered_map<std::string, Node*> tensor_nodes;
space_table_t space_table;
CollectVarMemorySize(var_batch_ave_size, &tensor_nodes, &space_table);
std::unordered_map<std::string, std::string> reuse_table;
double max_saving_ratio = 0.;
std::vector<std::function<MemoryAllocation()>> strategies;
for (int sort_kind = 0; sort_kind < 2; sort_kind++) {
if (argument->static_memory_optim()) {
// This strategy only make scene in static memory optimize.
strategies.emplace_back([&, sort_kind] {
auto clustered_vars_by_batch_size =
AnalysisBatchShapesByBatchSize(batches);
MemoryAllocation allocation;
MakeReusePlan(clustered_vars_by_batch_size, var_batch_ave_size,
space_table, &reuse_table, sort_kind, &allocation);
return allocation;
});
}
strategies.emplace_back([&, sort_kind] {
auto clustered_vars_by_ave_size =
AnalysisBatchShapesBySimilarSize(space_table, batches, cluster_size);
MemoryAllocation allocation;
MakeReusePlan(clustered_vars_by_ave_size, var_batch_ave_size, space_table,
&reuse_table, sort_kind, &allocation);
return allocation;
});
strategies.emplace_back([&, sort_kind] {
auto clustered_vars_by_ave_size =
AnalysisBatchShapesBySimilarSize(space_table, batches, cluster_size1);
MemoryAllocation allocation;
MakeReusePlan(clustered_vars_by_ave_size, var_batch_ave_size, space_table,
&reuse_table, sort_kind, &allocation);
return allocation;
});
strategies.emplace_back([&, sort_kind] {
auto clustered_vars_by_ave_size = AnalysisBatchShapesBySimilarSize(
space_table, batches,
std::numeric_limits<int>::max()); // no intervals
MemoryAllocation allocation;
MakeReusePlan(clustered_vars_by_ave_size, var_batch_ave_size, space_table,
&reuse_table, sort_kind, &allocation);
return allocation;
});
}
std::function<MemoryAllocation()>* best_strategy{nullptr};
// Try all strategies to get the best result.
for (auto& strategy : strategies) {
auto allocation = strategy();
string::PrettyLogDetail("--- get strategy saving %f memory for workspace",
allocation.GetSavingRatio());
if (allocation.GetSavingRatio() > max_saving_ratio) {
max_saving_ratio = allocation.GetSavingRatio();
best_strategy = &strategy;
}
}
if (!best_strategy) {
LOG(ERROR) << "This model makes poor memory optimize, skip memory optimize";
return;
}
auto memory_allocation = (*best_strategy)();
string::PrettyLogInfo(
"--- Saved %.2f%s memory for workspace(temporary variables)",
memory_allocation.GetSavingRatio() * 100, "%");
argument->main_graph().Set(framework::ir::kGraphToProgramVarsToRemove,
new std::unordered_set<std::string>);
auto& vars2remove =
argument->main_graph().Get<std::unordered_set<std::string>>(
framework::ir::kGraphToProgramVarsToRemove);
PerformReusePlan(reuse_table, memory_allocation.sort_kind, &vars2remove);
argument->SetMemoryOptimSortKind(memory_allocation.sort_kind);
}
float MemoryOptimizePass::MemoryAllocation::GetSavingRatio() const {
return (saved / 1024.) / (allocated / 1024. + saved / 1024.);
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/passes/memory_optimize_pass.h
浏览文件 @ e89b1288
......@@ -25,45 +25,22 @@ namespace paddle {
namespace inference {
namespace analysis {
/*
* Memory optimization pass for inference with pre-analysis of memory usage
* without GC.
* Different from training, the inference memory reuse strategies doesn't
* include GC for that overhead is too much when batch size equals one.
*
* The inference memory reuse tries to pre-determine the tensor reusing strategy
* without runtime overhead.
*
* To improve the strategy's performance, a warm-up running is introduced:
* - Before officially deploy the inference program, one should warm it up and
* generate some runtime cache,
* - Run the inference program with several batches of data, it will persist
* some runtime information about memory of tensors to disk, we call the
* information the memory reusing cache,
* - With the memory reusing cache, user can deploy the inference to a
* service, before running the model, the inference program will load the
* memory cache, analysis it and generate the best memory reusing strategy,
* and adjust the execution of the network.
*
* With the warm-up and memory reusing cache design, the memory reusing
* algorithm can analysis the real memory consume of the tensors, even with the
* flexible LoDTensor and special shape changing operators such as
* sequence-pooling.
*/
/* Memory optimization.
* We will perform the following operation:
* 1. Collect all var's lifetime.
* 2. Make reuse plan: the vars can be reused if there is no overlap(on lifetime)
* between
* them.
* The final plan is a mapping table in which the key represents the original
* name of var and the value in the table represents the current name of var.
* 3. Perform reuse plan: Replace all var's name in the model according to the
* mapping table.
*/
class MemoryOptimizePass : public AnalysisPass {
public:
using space_table_t = std::unordered_map<std::string, size_t>;
using lifecycle_t = std::pair<int, int>;
struct MemoryAllocation {
size_t allocated; // allocated memory in byte.
size_t saved; // saved memory in byte.
int sort_kind; // the kind of the corresponding sorting algorithm.
// Get the memory saving ratio of temporary variables.
float GetSavingRatio() const;
};
virtual ~MemoryOptimizePass() = default;
protected:
......@@ -75,24 +52,6 @@ class MemoryOptimizePass : public AnalysisPass {
int sort_kind) const;
void CollectVarMemorySize(space_table_t *space_table) const;
void CollectVarMemorySize0(space_table_t *space_table) const;
void CollectVarMemorySize(
const std::unordered_map<std::string, size_t> &batch_var_ave_dim,
std::unordered_map<std::string, framework::ir::Node *> *tensor_nodes,
space_table_t *space_table) const;
// Returns percentage of saved memory.
void MakeReusePlan(
const std::vector<std::unordered_set<std::string>> &var_clusters,
const std::unordered_map<std::string, size_t> &var_batch_ave_size,
const space_table_t &space_table,
std::unordered_map<std::string, std::string> *reuse_table, int sort_kind,
MemoryAllocation *memory_allocation) const;
void PerformReusePlan(
const std::unordered_map<std::string, std::string> &reuse_table,
int sort_kind, std::unordered_set<std::string> *vars2remove) const;
public:
std::string repr() const override;
......@@ -102,12 +61,6 @@ class MemoryOptimizePass : public AnalysisPass {
mutable int max_lifecycle_{-1};
};
static std::string GetMemoryCachePath(const std::string &model_path,
const std::string &prog_path) {
auto path = model_path.empty() ? prog_path : model_path;
return path + ".memory_cache";
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/api/analysis_config.cc
浏览文件 @ e89b1288
......@@ -101,8 +101,6 @@ AnalysisConfig::AnalysisConfig(const AnalysisConfig &other) {
CP_MEMBER(memory_pool_init_size_mb_);
CP_MEMBER(enable_memory_optim_);
CP_MEMBER(static_memory_optim_);
CP_MEMBER(static_memory_optim_force_update_);
// TensorRT related.
CP_MEMBER(use_tensorrt_);
CP_MEMBER(tensorrt_workspace_size_);
......@@ -371,8 +369,6 @@ std::string AnalysisConfig::SerializeInfoCache() {
ss << tensorrt_min_subgraph_size_;
ss << enable_memory_optim_;
ss << static_memory_optim_;
ss << static_memory_optim_force_update_;
ss << use_ngraph_;
......@@ -420,12 +416,8 @@ float AnalysisConfig::fraction_of_gpu_memory_for_pool() const {
#endif
}
void AnalysisConfig::EnableMemoryOptim(bool static_optim,
bool force_update_static_cache) {
void AnalysisConfig::EnableMemoryOptim() {
enable_memory_optim_ = true;
static_memory_optim_ = static_optim;
static_memory_optim_force_update_ = force_update_static_cache;
Update();
}
......
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @ e89b1288
......@@ -241,11 +241,6 @@ bool AnalysisPredictor::Run(const std::vector<PaddleTensor> &inputs,
return false;
}
// Collect variable shapes for memory optimization.
if (need_collect_var_shapes_for_memory_optim()) {
CollectVarShapes();
}
VLOG(3) << "predict cost: " << timer.toc() << "ms";
// All the containers in the scope will be hold in inference, but the
......@@ -390,9 +385,6 @@ void AnalysisPredictor::PrepareArgument() {
argument_.SetGPUDeviceId(config_.gpu_device_id());
argument_.SetEnableAnalysisOptim(config_.enable_ir_optim_);
argument_.SetEnableMemoryOptim(config_.enable_memory_optim());
argument_.SetStaticMemoryOptim(config_.static_memory_optim_);
argument_.SetStaticMemoryOptimForceUpdate(
config_.static_memory_optim_force_update_);
argument_.SetModelFromMemory(config_.model_from_memory_);
// Analyze inference_program
argument_.SetUseAnakin(config_.anakin_engine_enabled());
......@@ -818,13 +810,6 @@ AnalysisPredictor::~AnalysisPredictor() {
mkldnn_quantizer_ = nullptr;
}
#endif
// TODO(Superjomn) deduce the directory path.
std::string out_path = inference::analysis::GetMemoryCachePath(
config_.model_dir(), config_.prog_file());
if (need_collect_var_shapes_for_memory_optim()) {
SerializeBatchVarShapes(out_path);
}
}
std::unique_ptr<PaddlePredictor> AnalysisPredictor::Clone() {
......@@ -834,66 +819,6 @@ std::unique_ptr<PaddlePredictor> AnalysisPredictor::Clone() {
return std::unique_ptr<PaddlePredictor>(x);
}
void AnalysisPredictor::CollectVarShapes() {
VLOG(4) << "Collecting var shapes";
if (batch_var_shapes_.size() >= max_shape_collect_count_) return;
std::map<std::string, std::vector<int>> var_shapes;
for (auto var_name : inference_program_->Block(0).LocalVarNames()) {
auto *var = sub_scope_->FindVar(var_name);
PADDLE_ENFORCE_NOT_NULL(var);
if (var->Type() == framework::VarTypeTrait<framework::LoDTensor>::kId ||
var->Type() == framework::VarTypeTrait<framework::Tensor>::kId) {
auto &tensor = var->Get<framework::LoDTensor>();
auto shape = framework::vectorize(tensor.dims());
var_shapes[var_name].assign(shape.begin(), shape.end());
}
}
batch_var_shapes_.push_back(var_shapes);
LOG_FIRST_N(INFO, 1) << "Collected " << batch_var_shapes_.size()
<< " batch of var shapes for analysis";
}
void AnalysisPredictor::SerializeBatchVarShapes(const std::string &path) {
LOG(INFO) << "serialize batch var shapes to " << path;
std::ofstream file(path);
if (!file.is_open()) {
LOG(ERROR) << "failed to serialize the var shapes to " << path;
return;
}
// The sirialized data format:
// <tensor_name>:dim0,dim1,dim2,;
for (auto &batch : batch_var_shapes_) {
for (auto &ele : batch) {
file << ele.first << ":";
for (size_t i = 0; i < ele.second.size() - 1; i++) {
file << ele.second[i] << ",";
}
file << ele.second.back() << ";";
}
file << "\n";
}
}
bool AnalysisPredictor::need_collect_var_shapes_for_memory_optim() {
if (need_collect_var_shapes_ >= 0) return need_collect_var_shapes_;
bool need = false;
// check if the cache exists
if (!config_.enable_memory_optim()) {
need = false;
} else if (config_.static_memory_optim_ &&
!inference::IsFileExists(inference::analysis::GetMemoryCachePath(
config_.model_dir(), config_.prog_file()))) {
need = true;
} else if (config_.static_memory_optim_ &&
config_.static_memory_optim_force_update_) {
need = true;
}
need_collect_var_shapes_ = need ? 1 : 0;
return need;
}
std::string AnalysisPredictor::GetSerializedProgram() const {
return inference_program_->Proto()->SerializeAsString();
}
......
paddle/fluid/inference/api/analysis_predictor.h
浏览文件 @ e89b1288
......@@ -91,11 +91,6 @@ class AnalysisPredictor : public PaddlePredictor {
void SaveOptimModel(const std::string &dir);
protected:
// For memory optimization.
bool need_collect_var_shapes_for_memory_optim();
void CollectVarShapes();
void SerializeBatchVarShapes(const std::string &path);
bool PrepareProgram(const std::shared_ptr<framework::ProgramDesc> &program);
bool PrepareScope(const std::shared_ptr<framework::Scope> &parent_scope);
bool CreateExecutor();
......
paddle/fluid/inference/api/paddle_analysis_config.h
浏览文件 @ e89b1288
......@@ -244,8 +244,7 @@ struct AnalysisConfig {
/** Turn on memory optimize
* NOTE still in development, will release latter.
*/
void EnableMemoryOptim(bool static_optim = false,
bool force_update_static_cache = false);
void EnableMemoryOptim();
/** Tell whether the memory optimization is activated. */
bool enable_memory_optim() const;
......@@ -309,8 +308,6 @@ struct AnalysisConfig {
// memory reuse related.
bool enable_memory_optim_{false};
bool static_memory_optim_{false};
bool static_memory_optim_force_update_{false};
bool use_ngraph_{false};
bool use_mkldnn_{false};
......
paddle/fluid/inference/tests/api/analyzer_dam_tester.cc
浏览文件 @ e89b1288
......@@ -262,33 +262,6 @@ void compare(bool use_mkldnn = false) {
reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
}
// Compare result of NativeConfig and AnalysisConfig with memory optimization.
TEST(Analyzer_dam, compare_with_static_memory_optim) {
// The small dam will core in CI, but works in local.
if (FLAGS_max_turn_num == 9) {
AnalysisConfig cfg, cfg1;
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
// Run the first time to force to update memory cache
SetConfig(&cfg);
cfg.EnableMemoryOptim(true, true /*force update*/);
CompareNativeAndAnalysis(
reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
input_slots_all);
// Run second time to use the memory cache and perform memory optimization.
SetConfig(&cfg1);
cfg1.EnableMemoryOptim(true, false /*do not force update*/);
CompareNativeAndAnalysis(
reinterpret_cast<const PaddlePredictor::Config *>(&cfg1),
input_slots_all);
}
}
TEST(Analyzer_dam, compare_with_dynamic_memory_optim) {
// The small dam will core in CI, but works in local.
if (FLAGS_max_turn_num == 9) {
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或