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未验证 提交 bd7f28bb 编写于 作者: R Ruibiao Chen 提交者： GitHub
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Multiplex Workqueue for InterpreterCore (#43660) 

* Multiplex Workqueue for InterpreterCore

* Delete ResetWorkQueueOptions

* Update code format
上级 66a28e13
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Showing with 508 addition and 370 deletion
paddle/fluid/framework/new_executor/CMakeLists.txt
浏览文件 @ bd7f28bb
......@@ -110,7 +110,8 @@ if(WITH_GPU
sgd_op
squared_l2_norm_op
memcpy_h2d_op
memcpy_d2h_op)
memcpy_d2h_op
fetch_v2_op)
# All deps of the operators above, part of GLOB_OPERATOR_DEPS.
set(OP_DEPS generator softmax selected_rows_functor jit_kernel_helper
......
paddle/fluid/framework/new_executor/interpretercore.h
浏览文件 @ bd7f28bb
......@@ -34,36 +34,44 @@
namespace paddle {
namespace framework {
using AtomicVectorSizeT = std::vector<std::unique_ptr<std::atomic<size_t>>>;
class InterpreterCore {
public:
InterpreterCore(const platform::Place& place, const BlockDesc& block,
InterpreterCore(const platform::Place& place,
const BlockDesc& block,
const std::set<std::string>& skip_gc_vars,
VariableScope* global_scope);
~InterpreterCore();
interpreter::CostInfo DryRun(
const std::vector<std::string>& feed_names,
const std::vector<framework::LoDTensor>& feed_tensors);
paddle::framework::FetchList Run(
const std::vector<std::string>& feed_names,
const std::vector<framework::LoDTensor>& feed_tensors);
paddle::framework::FetchList Run(const std::vector<std::string>& feed_names);
interpreter::CostInfo DryRun(
const std::vector<std::string>& feed_names,
const std::vector<framework::LoDTensor>& feed_tensors);
void ShareWorkQueueFrom(std::shared_ptr<InterpreterCore> src);
void SetCopyProgram(std::shared_ptr<ProgramDesc> prog);
private:
void Convert(std::vector<paddle::framework::OpFuncNode>* op_func_nodes);
bool BuildInplaceCheckVarIsOnlyInput(size_t var_index);
std::shared_ptr<interpreter::AsyncWorkQueue> GetWorkQueue();
void BuildAndCacheInstructionCtx(Instruction* instr_node);
void BuildInplace();
bool BuildInplaceCheckVarIsOnlyInput(size_t var_index);
void BuildOperatorDependences();
void ClearLoDTensorArrayInLocalScope();
void Convert(std::vector<paddle::framework::OpFuncNode>* op_func_nodes);
void RunInstruction(const Instruction& instr_node);
......@@ -90,12 +98,8 @@ class InterpreterCore {
void BuildSkipShareLoDInfo();
void BuildOperatorDependences();
void SetFeedVarsInplaceSkip(const std::vector<std::string>& feed_names);
void ClearLoDTensorArrayInLocalScope();
bool is_build_;
const platform::Place& place_;
......@@ -123,7 +127,7 @@ class InterpreterCore {
StreamAnalyzer stream_analyzer_;
EventsWaiter main_thread_blocker_;
std::unique_ptr<interpreter::AsyncWorkQueue> async_work_queue_;
std::shared_ptr<interpreter::AsyncWorkQueue> async_work_queue_;
details::ExceptionHolder exception_holder_;
std::shared_ptr<EventsWaiter::EventNotifier> exception_notifier_{nullptr};
std::shared_ptr<EventsWaiter::EventNotifier> completion_notifier_{nullptr};
......@@ -132,10 +136,14 @@ class InterpreterCore {
std::vector<paddle::platform::DeviceEvent> gc_event_;
bool create_local_scope_{true};
Scope* local_scope_{nullptr}; // not owned
std::future<std::unique_ptr<AtomicVectorSizeT>> atomic_deps_;
std::future<std::unique_ptr<AtomicVectorSizeT>> atomic_var_ref_;
};
std::shared_ptr<InterpreterCore> CreateInterpreterCore(
const platform::Place& place, const ProgramDesc& prog,
const platform::Place& place,
const ProgramDesc& prog,
VariableScope* global_scope,
const std::vector<std::string>& fetch_names = {},
const std::set<std::string>& skip_gc_vars = {});
......
paddle/fluid/framework/new_executor/interpretercore_util.cc
浏览文件 @ bd7f28bb
......@@ -32,12 +32,14 @@
// Program, while "serial_run" ensures that all Ops are scheduled in a singal
// thread. In standalone executor, "sequential_run" is also "serial_run", while
// "serial_run" is not necessarily "sequential_run".
PADDLE_DEFINE_EXPORTED_bool(new_executor_sequential_run, false,
PADDLE_DEFINE_EXPORTED_bool(new_executor_sequential_run,
false,
"Enable sequential execution for standalone "
"executor, only applied to GPU OPs.");
PADDLE_DEFINE_EXPORTED_bool(
new_executor_serial_run, false,
new_executor_serial_run,
false,
"Enable serial execution for standalone executor, used for debug.");
DECLARE_bool(use_mkldnn);
......@@ -46,8 +48,47 @@ namespace paddle {
namespace framework {
namespace interpreter {
using VariableIdMap = std::map<std::string, std::vector<int>>;
constexpr size_t kPrepareWorkQueueIdx = 2;
const std::vector<WorkQueueOptions> ConstructWorkQueueOptions(
size_t host_num_threads, size_t device_num_threads, EventsWaiter* waiter) {
std::vector<WorkQueueOptions> group_options;
// for execute host Kernel
group_options.emplace_back(/*name*/ "HostTasks",
/*num_threads*/ host_num_threads,
/*allow_spinning*/ true,
/*always_spinning*/ false,
/*track_task*/ false,
/*detached*/ true,
/*events_waiter*/ waiter);
// for launch device Kernel
group_options.emplace_back(/*name*/ "DeviceKernelLaunch",
/*num_threads*/ device_num_threads,
/*allow_spinning*/ true,
/*always_spinning*/ true,
/*track_task*/ false,
/*detached*/ true,
/*events_waiter*/ waiter);
// for prepare deps and others
group_options.emplace_back(/*name*/ "Prepare",
/*num_threads*/ 1,
/*allow_spinning*/ true,
/*always_spinning*/ false,
/*track_task*/ false,
/*detached*/ true,
/*events_waiter*/ waiter);
return group_options;
}
AsyncWorkQueue::AsyncWorkQueue(size_t host_num_threads,
size_t device_num_threads,
EventsWaiter* waiter)
: host_num_thread_(host_num_threads) {
queue_group_ = CreateWorkQueueGroup(
ConstructWorkQueueOptions(host_num_threads, device_num_threads, waiter));
}
void AsyncWorkQueue::AddTask(const OpFuncType& op_func_type,
std::function<void()> fn) {
VLOG(4) << "Add task: " << static_cast<size_t>(op_func_type) << " ";
......@@ -60,36 +101,42 @@ void AsyncWorkQueue::AddTask(const OpFuncType& op_func_type,
}
}
using VariableIdMap = std::map<std::string, std::vector<int>>;
std::future<std::unique_ptr<AtomicVectorSizeT>>
AsyncWorkQueue::PrepareAtomicDeps(const std::vector<size_t>& dependecy_count) {
VLOG(4) << "PrepareAtomicDeps";
return queue_group_->AddAwaitableTask(
kPrepareWorkQueueIdx, interpreter::PrepareAtomicDeps, dependecy_count);
}
std::future<std::unique_ptr<AtomicVectorSizeT>>
AsyncWorkQueue::PrepareAtomicVarRef(
const std::vector<VariableMetaInfo>& vec_meta_info) {
VLOG(4) << "PrepareAtomicVarRef";
return queue_group_->AddAwaitableTask(
kPrepareWorkQueueIdx, interpreter::PrepareAtomicVarRef, vec_meta_info);
}
void AsyncWorkQueue::PrepareAtomicDeps(
std::unique_ptr<AtomicVectorSizeT> PrepareAtomicDeps(
const std::vector<size_t>& dependecy_count) {
VLOG(4) << "PrepareAtomicDeps";
atomic_deps_ =
queue_group_->AddAwaitableTask(kPrepareWorkQueueIdx, [&dependecy_count] {
auto op_deps = std::make_unique<std::vector<std::atomic<size_t>>>(
dependecy_count.size());
for (size_t i = 0; i < dependecy_count.size(); ++i) {
(*op_deps)[i] = dependecy_count[i];
}
VLOG(4) << "AtomicDeps:" << op_deps.get() << " " << op_deps->size();
return op_deps;
});
auto op_deps = std::make_unique<AtomicVectorSizeT>(dependecy_count.size());
for (size_t i = 0; i < dependecy_count.size(); ++i) {
(*op_deps)[i] = dependecy_count[i];
}
VLOG(4) << "AtomicDeps:" << op_deps.get() << " " << op_deps->size();
return op_deps;
}
void AsyncWorkQueue::PrepareAtomicVarRef(
std::unique_ptr<AtomicVectorSizeT> PrepareAtomicVarRef(
const std::vector<VariableMetaInfo>& vec_meta_info) {
VLOG(4) << "PrepareAtomicVarRef";
atomic_var_ref_ =
queue_group_->AddAwaitableTask(kPrepareWorkQueueIdx, [&vec_meta_info] {
auto var_ref = std::make_unique<std::vector<std::atomic<size_t>>>(
vec_meta_info.size());
for (size_t i = 0; i < vec_meta_info.size(); ++i) {
(*var_ref)[i] = vec_meta_info[i].var_ref_count_;
}
VLOG(4) << "AtomicVarRef:" << var_ref.get() << " " << var_ref->size();
return var_ref;
});
auto var_ref = std::make_unique<AtomicVectorSizeT>(vec_meta_info.size());
for (size_t i = 0; i < vec_meta_info.size(); ++i) {
(*var_ref)[i] = vec_meta_info[i].var_ref_count_;
}
VLOG(4) << "AtomicVarRef:" << var_ref.get() << " " << var_ref->size();
return var_ref;
}
bool var_can_be_deleted(const std::string& name, const BlockDesc& block) {
......@@ -160,7 +207,8 @@ get_unused_vars(const BlockDesc& block,
}
void build_variable_scope(const framework::BlockDesc& block,
VariableScope* var_scope, bool use_local_scope) {
VariableScope* var_scope,
bool use_local_scope) {
VLOG(3) << "Creating Variables";
auto inner_scope = var_scope->GetMutableScope();
......@@ -229,7 +277,8 @@ void create_all_ops(const framework::BlockDesc& block,
}
std::tuple<VariableValueMap, VariableIdMap> build_variable_map(
const VariableNameMap& var_name_map, VariableScope* var_scope,
const VariableNameMap& var_name_map,
VariableScope* var_scope,
bool enforce_exist = true) {
VariableValueMap name2var;
VariableIdMap name2id;
......@@ -293,7 +342,8 @@ void apply_device_guard(const OperatorBase* op_base,
void deal_operator_base(const platform::Place& place,
const VariableScope* var_scope,
std::shared_ptr<OperatorBase> op_base,
OpFuncNode* op_func_node, Scope* local_scope) {
OpFuncNode* op_func_node,
Scope* local_scope) {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto* dev_ctx = pool.Get(place);
// input, output is prepared. set the other attributes.
......@@ -325,7 +375,8 @@ void build_op_func_list(const platform::Place& place,
const framework::BlockDesc& block,
const std::set<std::string>& skip_gc_vars,
std::vector<OpFuncNode>* vec_func_list,
VariableScope* var_scope, bool use_local_scope) {
VariableScope* var_scope,
bool use_local_scope) {
Scope* local_scope = use_local_scope ? var_scope->GetMutableLocalScope()
: var_scope->GetMutableScope();
auto& all_op_kernels = OperatorWithKernel::AllOpKernels();
......@@ -428,9 +479,14 @@ void build_op_func_list(const platform::Place& place,
// NOTE(zhiqiu): op_func_node->operator_base_ maybe changed in
// ApplyDataTransform
ApplyDataTransform(expected_kernel_key, place, &ins_map_temp,
&outs_map_temp, var_scope, &op_func_node,
vec_func_list, use_local_scope);
ApplyDataTransform(expected_kernel_key,
place,
&ins_map_temp,
&outs_map_temp,
var_scope,
&op_func_node,
vec_func_list,
use_local_scope);
op_with_kernel = const_cast<framework::OperatorWithKernel*>(
static_cast<const framework::OperatorWithKernel*>(
op_func_node.operator_base_.get()));
......@@ -463,8 +519,8 @@ void build_op_func_list(const platform::Place& place,
op_with_kernel->Info().infer_shape_(&infer_shape_ctx);
}
auto exec_ctx = ExecutionContext(*op_with_kernel, *runtime_scope,
*dev_ctx, runtime_context);
auto exec_ctx = ExecutionContext(
*op_with_kernel, *runtime_scope, *dev_ctx, runtime_context);
auto run_phi_kernel = false;
if (phi::KernelFactory::Instance().HasCompatiblePhiKernel(
......@@ -498,25 +554,27 @@ void build_op_func_list(const platform::Place& place,
<< " : expected_kernel_key : " << expected_kernel_key;
if (run_phi_kernel) {
phi::KernelContext pt_kernel_context;
op_with_kernel->BuildPhiKernelContext(runtime_context, dev_ctx,
&pt_kernel_context);
op_with_kernel->BuildPhiKernelContext(
runtime_context, dev_ctx, &pt_kernel_context);
op_func_node.pt_kernel_ = op_with_kernel->PhiKernel();
(*op_func_node.pt_kernel_)(&pt_kernel_context);
} else {
auto kernels_iter = all_op_kernels.find(op->Type());
PADDLE_ENFORCE_NE(
kernels_iter, all_op_kernels.end(),
kernels_iter,
all_op_kernels.end(),
platform::errors::Unavailable(
"There are no kernels which are registered in the %s operator.",
op->Type()));
OpKernelMap& kernels = kernels_iter->second;
auto kernel_iter = kernels.find(expected_kernel_key);
PADDLE_ENFORCE_NE(
kernel_iter, kernels.end(),
platform::errors::NotFound(
"Operator (%s) does not have kernel for %s.", op->Type(),
KernelTypeToString(expected_kernel_key)));
PADDLE_ENFORCE_NE(kernel_iter,
kernels.end(),
platform::errors::NotFound(
"Operator (%s) does not have kernel for %s.",
op->Type(),
KernelTypeToString(expected_kernel_key)));
// TODO(zhiqiu): add fallback logic
op_func_node.kernel_func_ = OpKernelComputeFunc(kernel_iter->second);
op_func_node.kernel_func_(exec_ctx);
......@@ -525,9 +583,13 @@ void build_op_func_list(const platform::Place& place,
// post-process grad_op.outputs if need cast complex grad into real grad.
// NOTE(Aurelius84): insert a transfer_dtype_op inplacely to cast it.
if (framework::IsComplexType(expected_kernel_key.data_type_)) {
interpreter::HandleComplexGradToRealGrad(
op_func_node, place, outputs_names, &runtime_context.outputs,
var_scope, vec_func_list, local_scope);
interpreter::HandleComplexGradToRealGrad(op_func_node,
place,
outputs_names,
&runtime_context.outputs,
var_scope,
vec_func_list,
local_scope);
}
if (!op_func_node.inplace_back_map.empty()) {
auto& m = op_func_node.inplace_back_map;
......@@ -583,7 +645,8 @@ void build_op_func_list(const platform::Place& place,
} else {
PADDLE_THROW(platform::errors::Unimplemented(
"Type %s of variable %s is not supported eager deletion.",
framework::ToTypeName(var->Type()), var_name));
framework::ToTypeName(var->Type()),
var_name));
}
}
delete garbages; // free mem
......@@ -612,8 +675,8 @@ void add_fetch(const std::vector<std::string>& fetch_names,
std::vector<size_t> merge_vector(const std::vector<size_t>& first,
const std::vector<size_t>& second) {
std::vector<size_t> out(first.size() + second.size());
std::merge(first.begin(), first.end(), second.begin(), second.end(),
out.begin());
std::merge(
first.begin(), first.end(), second.begin(), second.end(), out.begin());
std::vector<size_t>::iterator it;
it = std::unique(out.begin(), out.end());
......@@ -625,7 +688,8 @@ std::vector<size_t> merge_vector(const std::vector<size_t>& first,
void update_var_min_rw_op(const std::map<int, std::set<int>>& op2dependences,
std::map<int, std::list<int>>* var2min_rw_op,
int cur_op, int rw_var) {
int cur_op,
int rw_var) {
// rw_var is inputs or outputs of cur_op
// this function update the var2min_rw_op set .
if (var2min_rw_op->find(rw_var) == var2min_rw_op->end()) {
......@@ -637,7 +701,8 @@ void update_var_min_rw_op(const std::map<int, std::set<int>>& op2dependences,
var2min_rw_op->at(rw_var).push_back(cur_op);
}
void AddDownstreamOp(int prior_op_idx, int posterior_op_idx,
void AddDownstreamOp(int prior_op_idx,
int posterior_op_idx,
std::map<int, std::list<int>>* op_downstream_map) {
if (op_downstream_map->find(prior_op_idx) == op_downstream_map->end()) {
op_downstream_map->emplace(std::make_pair(prior_op_idx, std::list<int>()));
......@@ -645,7 +710,8 @@ void AddDownstreamOp(int prior_op_idx, int posterior_op_idx,
op_downstream_map->at(prior_op_idx).push_back(posterior_op_idx);
}
void AddDownstreamOp(int prior_op_idx, int posterior_op_idx,
void AddDownstreamOp(int prior_op_idx,
int posterior_op_idx,
std::map<int, std::list<int>>* op_downstream_map,
const std::vector<std::vector<bool>>& op_happens_before) {
if (op_downstream_map->find(prior_op_idx) != op_downstream_map->end()) {
......@@ -675,7 +741,8 @@ const std::string StringizeDownstreamMap(
std::ostringstream oss;
for (auto pair : downstream_map) {
oss << pair.first << " -> ";
std::copy(pair.second.begin(), pair.second.end(),
std::copy(pair.second.begin(),
pair.second.end(),
std::ostream_iterator<int>(oss, " "));
oss << std::endl;
}
......@@ -717,8 +784,8 @@ void ShrinkDownstreamMap(std::map<int, std::list<int>>* downstream_map,
op_happens_before->resize(op_num);
for (size_t i = 0; i < op_num; ++i) {
(*op_happens_before)[i].resize(op_num);
std::fill((*op_happens_before)[i].begin(), (*op_happens_before)[i].end(),
false);
std::fill(
(*op_happens_before)[i].begin(), (*op_happens_before)[i].end(), false);
}
// bfs to get all next ops
......@@ -735,11 +802,15 @@ void ShrinkDownstreamMap(std::map<int, std::list<int>>* downstream_map,
}
for (auto next : downstream_map->at(op)) {
if (!visited[next]) {
PADDLE_ENFORCE_EQ((*op_happens_before)[next][op_idx], false,
PADDLE_ENFORCE_EQ((*op_happens_before)[next][op_idx],
false,
paddle::platform::errors::AlreadyExists(
"There exists circle in graph, expected "
"%d->%d, but already got %d->%d",
op_idx, next, next, op_idx));
op_idx,
next,
next,
op_idx));
(*op_happens_before)[op_idx][next] = true;
VLOG(8) << "happens before: " << op_idx << " " << next;
q.push(next);
......@@ -892,8 +963,8 @@ std::map<int, std::list<int>> build_op_downstream_map(
for (size_t op_idx = 0; op_idx < op_num; ++op_idx) {
if (random_op_set.count(vec_instruction[op_idx].OpBase()->Type())) {
if (dependence_op_idx != -1) {
AddDownstreamOp(dependence_op_idx, op_idx, &op_downstream_map,
*op_happens_before);
AddDownstreamOp(
dependence_op_idx, op_idx, &op_downstream_map, *op_happens_before);
}
dependence_op_idx = op_idx;
}
......@@ -919,8 +990,8 @@ std::map<int, std::list<int>> build_op_downstream_map(
for (size_t op_idx = 0; op_idx < op_num; ++op_idx) {
if (is_comm_op(vec_instruction[op_idx].OpBase()->Type())) {
if (dependence_op_idx != -1) {
AddDownstreamOp(dependence_op_idx, op_idx, &op_downstream_map,
*op_happens_before);
AddDownstreamOp(
dependence_op_idx, op_idx, &op_downstream_map, *op_happens_before);
VLOG(4) << "Add depend from "
<< vec_instruction[dependence_op_idx].OpBase()->Type() << " to "
<< vec_instruction[op_idx].OpBase()->Type();
......@@ -948,8 +1019,8 @@ std::map<int, std::list<int>> build_op_downstream_map(
VLOG(4) << "Add depend from "
<< vec_instruction[dependence_op_idx].OpBase()->Type() << " to "
<< vec_instruction[op_idx].OpBase()->Type();
AddDownstreamOp(dependence_op_idx, op_idx, &op_downstream_map,
*op_happens_before);
AddDownstreamOp(
dependence_op_idx, op_idx, &op_downstream_map, *op_happens_before);
}
}
}
......@@ -1002,10 +1073,13 @@ std::map<int, std::list<int>> build_op_downstream_map(
// first_read_fused_out_op)
// add depend: them->first_read_fused_out_op
for (auto j = op_idx + 1;
j < static_cast<size_t>(first_read_fused_out_op); ++j) {
j < static_cast<size_t>(first_read_fused_out_op);
++j) {
for (auto var_id : outputs) {
if (is_write(vec_instruction[j], var_id)) {
AddDownstreamOp(j, first_read_fused_out_op, &op_downstream_map,
AddDownstreamOp(j,
first_read_fused_out_op,
&op_downstream_map,
*op_happens_before);
VLOG(4) << j << " -> " << first_read_fused_out_op;
VLOG(4)
......@@ -1055,7 +1129,9 @@ std::map<int, std::list<int>> build_op_downstream_map(
for (size_t op_idx = 0; op_idx < op_num; ++op_idx) {
if (!IsCpuOp(vec_instruction[op_idx])) {
if (dependence_op_idx != -1) {
AddDownstreamOp(dependence_op_idx, op_idx, &op_downstream_map,
AddDownstreamOp(dependence_op_idx,
op_idx,
&op_downstream_map,
*op_happens_before);
VLOG(4) << "Add depend from "
<< vec_instruction[dependence_op_idx].OpBase()->Type() << "("
......
paddle/fluid/framework/new_executor/interpretercore_util.h
浏览文件 @ bd7f28bb
......@@ -44,49 +44,23 @@
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/init.h"
using AtomicVectorSizeT = std::vector<std::atomic<size_t>>;
namespace paddle {
namespace framework {
namespace interpreter {
using AtomicVectorSizeT =
std::future<std::unique_ptr<std::vector<std::atomic<size_t>>>>;
class AsyncWorkQueue {
public:
AsyncWorkQueue(size_t host_num_threads, size_t deivce_num_threads,
EventsWaiter* waiter)
: host_num_thread_(host_num_threads) {
std::vector<WorkQueueOptions> group_options;
// for execute host Kernel
group_options.emplace_back(/*name*/ "HostTasks",
/*num_threads*/ host_num_threads,
/*allow_spinning*/ true,
/*always_spinning*/ false,
/*track_task*/ false,
/*detached*/ true,
/*events_waiter*/ waiter);
// for launch device Kernel
group_options.emplace_back(/*name*/ "DeviceKernelLaunch",
/*num_threads*/ deivce_num_threads,
/*allow_spinning*/ true,
/*always_spinning*/ true,
/*track_task*/ false,
/*detached*/ true,
/*events_waiter*/ waiter);
// for prepare deps and others
group_options.emplace_back(/*name*/ "Prepare",
/*num_threads*/ 1,
/*allow_spinning*/ true,
/*always_spinning*/ false,
/*track_task*/ false,
/*detached*/ true,
/*events_waiter*/ waiter);
queue_group_ = CreateWorkQueueGroup(group_options);
}
void PrepareAtomicDeps(const std::vector<size_t>& dependecy_count);
void PrepareAtomicVarRef(const std::vector<VariableMetaInfo>& vec_meta_info);
AsyncWorkQueue(size_t host_num_threads,
size_t deivce_num_threads,
EventsWaiter* waiter);
std::future<std::unique_ptr<AtomicVectorSizeT>> PrepareAtomicDeps(
const std::vector<size_t>& dependecy_count);
std::future<std::unique_ptr<AtomicVectorSizeT>> PrepareAtomicVarRef(
const std::vector<VariableMetaInfo>& vec_meta_info);
// void WaitEmpty() { queue_group_->WaitQueueGroupEmpty(); }
......@@ -94,20 +68,16 @@ class AsyncWorkQueue {
void Cancel() { queue_group_->Cancel(); }
std::unique_ptr<std::vector<std::atomic<size_t>>> AtomicDeps() {
return atomic_deps_.get();
}
std::unique_ptr<std::vector<std::atomic<size_t>>> AtomicVarRef() {
return atomic_var_ref_.get();
}
private:
size_t host_num_thread_;
std::unique_ptr<WorkQueueGroup> queue_group_;
AtomicVectorSizeT atomic_deps_;
AtomicVectorSizeT atomic_var_ref_;
};
std::unique_ptr<AtomicVectorSizeT> PrepareAtomicDeps(
const std::vector<size_t>& dependecy_count);
std::unique_ptr<AtomicVectorSizeT> PrepareAtomicVarRef(
const std::vector<VariableMetaInfo>& vec_meta_info);
void build_variable_scope(const framework::BlockDesc& block,
VariableScope* var_scope,
bool use_local_scope = true);
......@@ -116,7 +86,8 @@ void build_op_func_list(const platform::Place& place,
const framework::BlockDesc& block,
const std::set<std::string>& skip_gc_vars,
std::vector<OpFuncNode>* vec_func_list,
VariableScope* var_scope, bool use_local_scope = true);
VariableScope* var_scope,
bool use_local_scope = true);
std::map<int, std::list<int>> build_op_downstream_map(
const std::vector<Instruction>& vec_instruction,
......
paddle/fluid/framework/new_executor/standalone_executor_test.cc
浏览文件 @ bd7f28bb
......@@ -12,15 +12,14 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/new_executor/standalone_executor.h"
#include <gtest/gtest.h>
#include <chrono>
#include <iostream>
#include <string>
// #include "gperftools/profiler.h"
#include "paddle/fluid/framework/new_executor/standalone_executor.h"
#include "paddle/phi/core/kernel_registry.h"
USE_OP_ITSELF(fill_constant);
......@@ -61,6 +60,7 @@ USE_OP_ITSELF(sgd);
USE_OP(squared_l2_norm);
USE_OP_ITSELF(memcpy_h2d);
USE_OP_ITSELF(memcpy_d2h);
USE_OP_ITSELF(fetch_v2);
PD_DECLARE_KERNEL(full, GPU, ALL_LAYOUT);
PD_DECLARE_KERNEL(uniform_random_raw, GPU, ALL_LAYOUT);
......@@ -72,6 +72,7 @@ PD_DECLARE_KERNEL(concat_grad, GPU, ALL_LAYOUT);
PD_DECLARE_KERNEL(matmul, GPU, ALL_LAYOUT);
PD_DECLARE_KERNEL(add_raw, KPS, ALL_LAYOUT);
PD_DECLARE_KERNEL(add, KPS, ALL_LAYOUT);
PD_DECLARE_KERNEL(add, CPU, ALL_LAYOUT);
PD_DECLARE_KERNEL(multiply, KPS, ALL_LAYOUT);
PD_DECLARE_KERNEL(multiply_grad, GPU, ALL_LAYOUT);
PD_DECLARE_KERNEL(divide, KPS, ALL_LAYOUT);
......@@ -99,8 +100,6 @@ PD_DECLARE_KERNEL(cross_entropy_with_softmax, GPU, ALL_LAYOUT);
PD_DECLARE_KERNEL(cross_entropy_with_softmax_grad, GPU, ALL_LAYOUT);
PD_DECLARE_KERNEL(sqrt, GPU, ALL_LAYOUT);
DECLARE_double(eager_delete_tensor_gb);
namespace paddle {
namespace framework {
......@@ -115,15 +114,11 @@ ProgramDesc load_from_file(const std::string& file_name) {
return program_desc;
}
TEST(StandaloneExecutor, run) {
FLAGS_eager_delete_tensor_gb = 0.1;
int64_t batch_size = 20;
auto place = platform::CUDAPlace(0);
auto test_prog = load_from_file("lm_startup_program");
auto main_prog = load_from_file("lm_main_program");
ProgramDesc GetLmMainProgram() {
ProgramDesc main_prog = load_from_file("lm_main_program");
auto& global_block = main_prog.Block(0);
int64_t batch_size = 20;
auto& op1 = global_block.AllOps()[1];
auto shape1 = BOOST_GET_CONST(std::vector<int64_t>, op1->GetAttr("shape"));
......@@ -139,6 +134,13 @@ TEST(StandaloneExecutor, run) {
auto shape3 = BOOST_GET_CONST(std::vector<int64_t>, op3->GetAttr("shape"));
shape3[0] = batch_size;
op3->SetAttr("shape", shape3);
return main_prog;
}
TEST(StandaloneExecutor, run) {
auto place = platform::CUDAPlace(0);
ProgramDesc test_prog = load_from_file("lm_startup_program");
ProgramDesc main_prog = GetLmMainProgram();
Scope scope;
StandaloneExecutor exec(place, test_prog, main_prog, &scope);
......@@ -159,31 +161,10 @@ TEST(StandaloneExecutor, run) {
std::cout << "time cost " << diff.count() << std::endl;
}
TEST(StandaloneExecutor, skip_gc_vars) {
FLAGS_eager_delete_tensor_gb = 0;
int64_t batch_size = 20;
TEST(InterpreterCore, skip_gc_vars) {
auto place = platform::CUDAPlace(0);
auto startup_prog = load_from_file("lm_startup_program");
auto main_prog = load_from_file("lm_main_program");
auto& global_block = main_prog.Block(0);
auto& op1 = global_block.AllOps()[1];
auto shape1 = BOOST_GET_CONST(std::vector<int64_t>, op1->GetAttr("shape"));
shape1[0] = batch_size * 20;
op1->SetAttr("shape", shape1);
auto& op2 = global_block.AllOps()[2];
auto shape2 = BOOST_GET_CONST(std::vector<int64_t>, op2->GetAttr("shape"));
shape2[0] = batch_size;
op2->SetAttr("shape", shape2);
auto& op3 = global_block.AllOps()[3];
auto shape3 = BOOST_GET_CONST(std::vector<int64_t>, op3->GetAttr("shape"));
shape3[0] = batch_size;
op3->SetAttr("shape", shape3);
ProgramDesc startup_prog = load_from_file("lm_startup_program");
ProgramDesc main_prog = GetLmMainProgram();
Scope scope;
......@@ -192,20 +173,24 @@ TEST(StandaloneExecutor, skip_gc_vars) {
CreateInterpreterCore(place, startup_prog, &startup_scope);
startup_core->Run({}, {});
std::set<std::string> skip_gc_vars = {"uniform_0.tmp_0", "transpose_0.tmp_0",
"embedding_0.tmp_0", "slice_0.tmp_0",
std::set<std::string> skip_gc_vars = {"uniform_0.tmp_0",
"transpose_0.tmp_0",
"embedding_0.tmp_0",
"slice_0.tmp_0",
"split_1.tmp_2"};
std::set<std::string> gc_vars = {"uniform_1.tmp_0", "matmul_0.tmp_0",
"split_0.tmp_0", "elementwise_add_0.tmp_0",
std::set<std::string> gc_vars = {"uniform_1.tmp_0",
"matmul_0.tmp_0",
"split_0.tmp_0",
"elementwise_add_0.tmp_0",
"tmp_0"};
auto check_gc_result = [](VariableScope& scope, std::set<std::string>& vars,
bool is_skip_gc) {
for (const std::string& var_name : vars) {
ASSERT_EQ(
scope.FindVar(var_name)->GetMutable<LoDTensor>()->IsInitialized(),
is_skip_gc);
}
};
auto check_gc_result =
[](VariableScope& scope, std::set<std::string>& vars, bool is_skip_gc) {
for (const std::string& var_name : vars) {
ASSERT_EQ(
scope.FindVar(var_name)->GetMutable<LoDTensor>()->IsInitialized(),
is_skip_gc);
}
};
VariableScope main_scope(&scope);
std::shared_ptr<InterpreterCore> main_core =
......@@ -220,5 +205,68 @@ TEST(StandaloneExecutor, skip_gc_vars) {
check_gc_result(main_scope, gc_vars, false);
}
void TestShareWorkQueue(const ProgramDesc& prog,
const std::vector<std::string>& feed_names,
const std::vector<LoDTensor>& feed_tensors,
const std::vector<std::string>& fetch_names,
const std::vector<float>& fetch_results) {
const platform::CPUPlace place = platform::CPUPlace();
Scope scope;
VariableScope variable_scope(&scope);
std::shared_ptr<InterpreterCore> core1 =
CreateInterpreterCore(place, prog, &variable_scope, fetch_names);
std::shared_ptr<InterpreterCore> core2 =
CreateInterpreterCore(place, prog, &variable_scope, fetch_names);
core2->ShareWorkQueueFrom(core1);
auto run_and_check = [&feed_names, &feed_tensors, &fetch_results](
std::shared_ptr<InterpreterCore> core) {
FetchList fetch_list = core->Run(feed_names, feed_tensors);
for (size_t i = 0; i < fetch_list.size(); ++i) {
const float* fetch_data =
BOOST_GET_CONST(LoDTensor, fetch_list[i]).data<float>();
ASSERT_FLOAT_EQ(*fetch_data, fetch_results.at(i));
}
};
run_and_check(core1);
run_and_check(core2);
run_and_check(core1);
run_and_check(core2);
}
TEST(InterpreterCore, workqueue_multiplexing) {
ProgramDesc program;
BlockDesc* main_block = program.MutableBlock(0);
VarDesc* var_a = main_block->Var("a");
VarDesc* var_b = main_block->Var("b");
VarDesc* var_c = main_block->Var("c");
var_a->SetType(proto::VarType::LOD_TENSOR);
var_b->SetType(proto::VarType::LOD_TENSOR);
var_c->SetType(proto::VarType::LOD_TENSOR);
OpDesc* add = main_block->AppendOp();
add->SetType("elementwise_add");
add->SetInput("X", {"a"});
add->SetInput("Y", {"b"});
add->SetOutput("Out", {"c"});
float data_a[] = {0, 1, 2, 3};
float data_b[] = {0.0, 0.1, 0.2, 0.3};
phi::DDim dims = phi::make_ddim({2, 2});
const platform::CPUPlace place = platform::CPUPlace();
LoDTensor tensor_a = LoDTensor();
LoDTensor tensor_b = LoDTensor();
std::copy_n(data_a, 4, tensor_a.mutable_data<float>(dims, place));
std::copy_n(data_b, 4, tensor_b.mutable_data<float>(dims, place));
TestShareWorkQueue(
program, {"a", "b"}, {tensor_a, tensor_b}, {"c"}, {0.0, 1.1, 2.2, 3.3});
}
} // namespace framework
} // namespace paddle
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