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Commit b5414ba5 authored by fenglei.tian's avatar fenglei.tian
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parent 6204a154
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src/ngraph/runtime/gpu/gpu_external_function.cpp
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......@@ -165,118 +165,113 @@ namespace ngraph
{
namespace gpu
{
static const OpMap dispatcher{
{TI(ngraph::op::Add), &GPU_Emitter::emit<ngraph::op::Add>},
{TI(ngraph::op::Dot), &GPU_Emitter::emit<ngraph::op::Dot>},
{TI(ngraph::op::Multiply), &GPU_Emitter::emit<ngraph::op::Multiply>},
{TI(ngraph::op::Parameter), &GPU_Emitter::nop},
{TI(ngraph::op::Abs), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Concat), &GPU_Emitter::emit<ngraph::op::Concat>},
{TI(ngraph::op::Divide), &GPU_Emitter::emit<ngraph::op::Divide>},
{TI(ngraph::op::Equal), &GPU_Emitter::emit<ngraph::op::Equal>},
{TI(ngraph::op::GetOutputElement),
&GPU_Emitter::emit<ngraph::op::GetOutputElement>},
{TI(ngraph::op::Greater), &GPU_Emitter::emit<ngraph::op::Greater>},
{TI(ngraph::op::GreaterEq), &GPU_Emitter::emit<ngraph::op::GreaterEq>},
{TI(ngraph::op::Less), &GPU_Emitter::emit<ngraph::op::Less>},
{TI(ngraph::op::LessEq), &GPU_Emitter::emit<ngraph::op::LessEq>},
{TI(ngraph::op::Log), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Maximum), &GPU_Emitter::emit<ngraph::op::Maximum>},
{TI(ngraph::op::Minimum), &GPU_Emitter::emit<ngraph::op::Minimum>},
{TI(ngraph::op::Negative), &GPU_Emitter::emit<ngraph::op::Negative>},
{TI(ngraph::op::NotEqual), &GPU_Emitter::emit<ngraph::op::NotEqual>},
{TI(ngraph::op::Power), &GPU_Emitter::emit<ngraph::op::Power>},
{TI(ngraph::op::Select), &GPU_Emitter::emit<ngraph::op::Select>},
{TI(ngraph::op::Subtract), &GPU_Emitter::emit<ngraph::op::Subtract>},
{TI(ngraph::op::Broadcast), &GPU_Emitter::emit<ngraph::op::Broadcast>},
{TI(ngraph::op::Convert), &GPU_Emitter::emit<ngraph::op::Convert>},
{TI(ngraph::op::Constant), &GPU_Emitter::emit<ngraph::op::Constant>},
{TI(ngraph::op::Reshape), &GPU_Emitter::emit<ngraph::op::Reshape>},
{TI(ngraph::op::FunctionCall),
&GPU_Emitter::emit<ngraph::op::FunctionCall>},
{TI(ngraph::op::Reduce), &GPU_Emitter::emit<ngraph::op::Reduce>},
{TI(ngraph::op::Sign), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Slice), &GPU_Emitter::emit<ngraph::op::Slice>},
{TI(ngraph::op::Sum), &GPU_Emitter::emit<ngraph::op::Sum>},
{TI(ngraph::op::Exp), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Sin), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Sinh), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Cos), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Cosh), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Tan), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Tanh), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Asin), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Acos), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Atan), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::ReplaceSlice),
&GPU_Emitter::emit<ngraph::op::ReplaceSlice>},
{TI(ngraph::op::OneHot), &GPU_Emitter::emit<ngraph::op::OneHot>},
{TI(ngraph::op::Floor), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Ceiling), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Sqrt), &GPU_Emitter::emit<ngraph::op::Sqrt>},
{TI(ngraph::op::Convolution),
&GPU_Emitter::emit<ngraph::op::Convolution>},
{TI(ngraph::op::ConvolutionBackpropFilters),
&GPU_Emitter::emit<ngraph::op::ConvolutionBackpropFilters>},
{TI(ngraph::op::ConvolutionBackpropData),
&GPU_Emitter::emit<ngraph::op::ConvolutionBackpropData>},
{TI(ngraph::op::Not), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::MaxPool), &GPU_Emitter::emit<ngraph::op::MaxPool>},
{TI(ngraph::op::Reverse), &GPU_Emitter::emit<ngraph::op::Reverse>},
{TI(ngraph::op::Result), &GPU_Emitter::emit<ngraph::op::Result>},
{TI(ngraph::op::ReduceWindow),
&GPU_Emitter::emit<ngraph::op::ReduceWindow>},
{TI(ngraph::op::SelectAndScatter),
&GPU_Emitter::emit<ngraph::op::SelectAndScatter>},
{TI(ngraph::op::AvgPool), &GPU_Emitter::emit<ngraph::op::AvgPool>},
{TI(ngraph::op::AvgPoolBackprop),
&GPU_Emitter::emit<ngraph::op::AvgPoolBackprop>},
{TI(ngraph::op::Pad), &GPU_Emitter::emit<ngraph::op::Pad>},
{TI(ngraph::op::BatchNorm), &GPU_Emitter::emit<ngraph::op::BatchNorm>},
{TI(ngraph::op::BatchNormBackprop),
&GPU_Emitter::emit<ngraph::op::BatchNormBackprop>},
{TI(ngraph::op::MaxPoolBackprop),
&GPU_Emitter::emit<ngraph::op::MaxPoolBackprop>},
{TI(ngraph::op::Product), &GPU_Emitter::emit<ngraph::op::Product>},
{TI(ngraph::op::Max), &GPU_Emitter::emit<ngraph::op::Max>},
{TI(ngraph::op::Min), &GPU_Emitter::emit<ngraph::op::Min>},
{TI(ngraph::op::Relu), &GPU_Emitter::emit<ngraph::op::Relu>},
{TI(ngraph::op::ReluBackprop),
&GPU_Emitter::emit<ngraph::op::ReluBackprop>},
{TI(ngraph::op::Softmax), &GPU_Emitter::emit<ngraph::op::Softmax>},
};
GPU_ExternalFunction::GPU_ExternalFunction(
const shared_ptr<ngraph::Function>& function, bool release_function)
: ngraph::runtime::ExternalFunction(function, release_function)
, m_compiled_function(nullptr)
, m_emit_timing(std::getenv("NGRAPH_GPU_EMIT_TIMING") != nullptr)
{
}
static const OpMap dispatcher{
{TI(ngraph::op::Add), &GPU_Emitter::emit<ngraph::op::Add>},
{TI(ngraph::op::Dot), &GPU_Emitter::emit<ngraph::op::Dot>},
{TI(ngraph::op::Multiply), &GPU_Emitter::emit<ngraph::op::Multiply>},
{TI(ngraph::op::Parameter), &GPU_Emitter::nop},
{TI(ngraph::op::Abs), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Concat), &GPU_Emitter::emit<ngraph::op::Concat>},
{TI(ngraph::op::Divide), &GPU_Emitter::emit<ngraph::op::Divide>},
{TI(ngraph::op::Equal), &GPU_Emitter::emit<ngraph::op::Equal>},
{TI(ngraph::op::GetOutputElement),
&GPU_Emitter::emit<ngraph::op::GetOutputElement>},
{TI(ngraph::op::Greater), &GPU_Emitter::emit<ngraph::op::Greater>},
{TI(ngraph::op::GreaterEq), &GPU_Emitter::emit<ngraph::op::GreaterEq>},
{TI(ngraph::op::Less), &GPU_Emitter::emit<ngraph::op::Less>},
{TI(ngraph::op::LessEq), &GPU_Emitter::emit<ngraph::op::LessEq>},
{TI(ngraph::op::Log), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Maximum), &GPU_Emitter::emit<ngraph::op::Maximum>},
{TI(ngraph::op::Minimum), &GPU_Emitter::emit<ngraph::op::Minimum>},
{TI(ngraph::op::Negative), &GPU_Emitter::emit<ngraph::op::Negative>},
{TI(ngraph::op::NotEqual), &GPU_Emitter::emit<ngraph::op::NotEqual>},
{TI(ngraph::op::Power), &GPU_Emitter::emit<ngraph::op::Power>},
{TI(ngraph::op::Select), &GPU_Emitter::emit<ngraph::op::Select>},
{TI(ngraph::op::Subtract), &GPU_Emitter::emit<ngraph::op::Subtract>},
{TI(ngraph::op::Broadcast), &GPU_Emitter::emit<ngraph::op::Broadcast>},
{TI(ngraph::op::Convert), &GPU_Emitter::emit<ngraph::op::Convert>},
{TI(ngraph::op::Constant), &GPU_Emitter::emit<ngraph::op::Constant>},
{TI(ngraph::op::Reshape), &GPU_Emitter::emit<ngraph::op::Reshape>},
{TI(ngraph::op::FunctionCall), &GPU_Emitter::emit<ngraph::op::FunctionCall>},
{TI(ngraph::op::Reduce), &GPU_Emitter::emit<ngraph::op::Reduce>},
{TI(ngraph::op::Sign), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Slice), &GPU_Emitter::emit<ngraph::op::Slice>},
{TI(ngraph::op::Sum), &GPU_Emitter::emit<ngraph::op::Sum>},
{TI(ngraph::op::Exp), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Sin), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Sinh), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Cos), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Cosh), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Tan), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Tanh), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Asin), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Acos), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Atan), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::ReplaceSlice), &GPU_Emitter::emit<ngraph::op::ReplaceSlice>},
{TI(ngraph::op::OneHot), &GPU_Emitter::emit<ngraph::op::OneHot>},
{TI(ngraph::op::Floor), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Ceiling), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::Sqrt), &GPU_Emitter::emit<ngraph::op::Sqrt>},
{TI(ngraph::op::Convolution), &GPU_Emitter::emit<ngraph::op::Convolution>},
{TI(ngraph::op::ConvolutionBackpropFilters),
&GPU_Emitter::emit<ngraph::op::ConvolutionBackpropFilters>},
{TI(ngraph::op::ConvolutionBackpropData),
&GPU_Emitter::emit<ngraph::op::ConvolutionBackpropData>},
{TI(ngraph::op::Not), &GPU_Emitter::EmitUnaryElementwise},
{TI(ngraph::op::MaxPool), &GPU_Emitter::emit<ngraph::op::MaxPool>},
{TI(ngraph::op::Reverse), &GPU_Emitter::emit<ngraph::op::Reverse>},
{TI(ngraph::op::Result), &GPU_Emitter::emit<ngraph::op::Result>},
{TI(ngraph::op::ReduceWindow), &GPU_Emitter::emit<ngraph::op::ReduceWindow>},
{TI(ngraph::op::SelectAndScatter),
&GPU_Emitter::emit<ngraph::op::SelectAndScatter>},
{TI(ngraph::op::AvgPool), &GPU_Emitter::emit<ngraph::op::AvgPool>},
{TI(ngraph::op::AvgPoolBackprop), &GPU_Emitter::emit<ngraph::op::AvgPoolBackprop>},
{TI(ngraph::op::Pad), &GPU_Emitter::emit<ngraph::op::Pad>},
{TI(ngraph::op::BatchNorm), &GPU_Emitter::emit<ngraph::op::BatchNorm>},
{TI(ngraph::op::BatchNormBackprop),
&GPU_Emitter::emit<ngraph::op::BatchNormBackprop>},
{TI(ngraph::op::MaxPoolBackprop), &GPU_Emitter::emit<ngraph::op::MaxPoolBackprop>},
{TI(ngraph::op::Product), &GPU_Emitter::emit<ngraph::op::Product>},
{TI(ngraph::op::Max), &GPU_Emitter::emit<ngraph::op::Max>},
{TI(ngraph::op::Min), &GPU_Emitter::emit<ngraph::op::Min>},
{TI(ngraph::op::Relu), &GPU_Emitter::emit<ngraph::op::Relu>},
{TI(ngraph::op::ReluBackprop), &GPU_Emitter::emit<ngraph::op::ReluBackprop>},
{TI(ngraph::op::Softmax), &GPU_Emitter::emit<ngraph::op::Softmax>},
};
GPU_ExternalFunction::GPU_ExternalFunction(const shared_ptr<ngraph::Function>& function,
bool release_function)
: ngraph::runtime::ExternalFunction(function, release_function)
, m_compiled_function(nullptr)
, m_emit_timing(std::getenv("NGRAPH_GPU_EMIT_TIMING") != nullptr)
{
}
void GPU_ExternalFunction::compile()
{
if (m_is_compiled)
{
return;
}
void GPU_ExternalFunction::compile()
{
if (m_is_compiled)
{
return;
}
string function_name = m_function->get_name();
string dump_filename = file_util::path_join(s_output_dir, function_name + "_ops.txt");
string function_name = m_function->get_name();
string dump_filename =
file_util::path_join(s_output_dir, function_name + "_ops.txt");
pass::Manager pass_manager;
// pass_manager.register_pass<pass::TopologicalSort>();
// For now, just make everyone row-major.
pass_manager.register_pass<pass::AssignLayout<descriptor::layout::DenseTensorViewLayout>>();
pass_manager.register_pass<pass::Liveness>();
pass_manager.register_pass<pass::MemoryLayout>(64);
pass_manager.register_pass<pass::DumpSorted>(dump_filename);
pass_manager.run_passes(m_function);
pass::Manager pass_manager;
// pass_manager.register_pass<pass::TopologicalSort>();
// For now, just make everyone row-major.
pass_manager
.register_pass<pass::AssignLayout<descriptor::layout::DenseTensorViewLayout>>();
pass_manager.register_pass<pass::Liveness>();
pass_manager.register_pass<pass::MemoryLayout>(64);
pass_manager.register_pass<pass::DumpSorted>(dump_filename);
pass_manager.run_passes(m_function);
codegen::CodeWriter writer;
codegen::CodeWriter writer;
writer +=
R"(// Generated by the NGraph GPU backend
writer +=
R"(// Generated by the NGraph GPU backend
#include <cublas_v2.h>
#include <cuda.h>
#include <cuda_runtime.h>
......@@ -302,532 +297,560 @@ void GPU_ExternalFunction::compile()
#include "ngraph/util.hpp"
)";
string pch_header_source = writer.get_code();
string pch_header_source = writer.get_code();
writer += R"(
writer += R"(
using namespace ngraph;
using namespace std;
)";
if (m_emit_timing)
{
writer << "// Declare debug timers\n";
vector<string> names;
for (shared_ptr<Function> current_function : pass_manager.get_state().get_functions())
{
for (shared_ptr<Node> node : current_function->get_ordered_ops())
{
if (!node->is_parameter() && !node->is_constant())
if (m_emit_timing)
{
names.push_back(node->get_name());
writer << "// Declare debug timers\n";
vector<string> names;
for (shared_ptr<Function> current_function :
pass_manager.get_state().get_functions())
{
for (shared_ptr<Node> node : current_function->get_ordered_ops())
{
if (!node->is_parameter() && !node->is_constant())
{
names.push_back(node->get_name());
}
}
}
for (const string& s : names)
{
writer << "ngraph::stopwatch timer_" << s << ";\n";
}
writer << "extern \"C\" size_t get_debug_timer_count() { return "
<< names.size() << "; }\n";
writer << "extern \"C\" const char* get_debug_timer_name(size_t index)\n";
writer << "{\n";
writer.indent++;
writer << "const char* rc;\n";
writer << "switch(index)\n";
writer << "{\n";
for (size_t i = 0; i < names.size(); i++)
{
writer << "case " << i << ": rc = \"" << names[i] << "\"; break;\n";
}
writer << "default: rc = \"\";\n";
writer << "}\n";
writer << "return rc;\n";
writer.indent--;
writer << "}\n";
writer
<< "extern \"C\" const size_t get_debug_timer_microseconds(size_t index)\n";
writer << "{\n";
writer.indent++;
writer << "size_t rc;\n";
writer << "switch(index)\n";
writer << "{\n";
for (size_t i = 0; i < names.size(); i++)
{
writer << "case " << i << ": rc = timer_" << names[i]
<< ".get_total_microseconds(); break;\n";
}
writer << "default: rc = 0;\n";
writer << "}\n";
writer << "return rc;\n";
writer.indent--;
writer << "}\n";
writer
<< "extern \"C\" const size_t get_debug_timer_call_count(size_t index)\n";
writer << "{\n";
writer.indent++;
writer << "size_t rc;\n";
writer << "switch(index)\n";
writer << "{\n";
for (size_t i = 0; i < names.size(); i++)
{
writer << "case " << i << ": rc = timer_" << names[i]
<< ".get_call_count(); break;\n";
}
writer << "default: rc = 0;\n";
writer << "}\n";
writer << "return rc;\n";
writer.indent--;
writer << "}\n";
writer << "\n";
}
// // The "dso_handle" symbol is required by __cxa_atexit()
// // which is enabled because the JIT uses it as the default mechanism
// // to register cleanup handlers. We use it, and not atexit(), because
// // atexit() happens too late, when the JIT is no longer alive
writer << "void *__dso_handle = 0;\n\n";
writer << "// Declare all constants\n";
for (shared_ptr<Function> current_function :
pass_manager.get_state().get_functions())
{
for (shared_ptr<Node> node : current_function->get_ordered_ops())
{
const op::Constant* c = dynamic_cast<ngraph::op::Constant*>(node.get());
if (c)
{
shared_ptr<descriptor::TensorView> tv =
node->get_outputs()[0].get_tensor_view();
auto c_value_strings = c->get_value_strings();
writer << "static "
<< tv->get_tensor().get_element_type().c_type_string() << " "
<< tv->get_tensor().get_name() << "_cpu["
<< c_value_strings.size() << "] =\n";
writer << "{\n";
writer.indent++;
writer << emit_string_array(c_value_strings, 100 - writer.indent * 4);
writer.indent--;
writer << "\n};\n\n";
writer << "static "
<< tv->get_tensor().get_element_type().c_type_string() << " *"
<< tv->get_tensor().get_name() << ";\n";
m_variable_name_map[tv->get_tensor().get_name()] =
tv->get_tensor().get_name();
}
}
}
}
}
for (const string& s : names)
{
writer << "ngraph::stopwatch timer_" << s << ";\n";
}
writer << "extern \"C\" size_t get_debug_timer_count() { return " << names.size()
<< "; }\n";
writer << "extern \"C\" const char* get_debug_timer_name(size_t index)\n";
writer << "{\n";
writer.indent++;
writer << "const char* rc;\n";
writer << "switch(index)\n";
writer << "{\n";
for (size_t i = 0; i < names.size(); i++)
{
writer << "case " << i << ": rc = \"" << names[i] << "\"; break;\n";
}
writer << "default: rc = \"\";\n";
writer << "}\n";
writer << "return rc;\n";
writer.indent--;
writer << "}\n";
writer << "extern \"C\" const size_t get_debug_timer_microseconds(size_t index)\n";
writer << "{\n";
writer.indent++;
writer << "size_t rc;\n";
writer << "switch(index)\n";
writer << "{\n";
for (size_t i = 0; i < names.size(); i++)
{
writer << "case " << i << ": rc = timer_" << names[i]
<< ".get_total_microseconds(); break;\n";
}
writer << "default: rc = 0;\n";
writer << "}\n";
writer << "return rc;\n";
writer.indent--;
writer << "}\n";
writer << "extern \"C\" const size_t get_debug_timer_call_count(size_t index)\n";
writer << "{\n";
writer.indent++;
writer << "size_t rc;\n";
writer << "switch(index)\n";
writer << "{\n";
for (size_t i = 0; i < names.size(); i++)
{
writer << "case " << i << ": rc = timer_" << names[i] << ".get_call_count(); break;\n";
}
writer << "default: rc = 0;\n";
writer << "}\n";
writer << "return rc;\n";
writer.indent--;
writer << "}\n";
writer << "\n";
}
// // The "dso_handle" symbol is required by __cxa_atexit()
// // which is enabled because the JIT uses it as the default mechanism
// // to register cleanup handlers. We use it, and not atexit(), because
// // atexit() happens too late, when the JIT is no longer alive
writer << "void *__dso_handle = 0;\n\n";
writer << "// Declare all constants\n";
for (shared_ptr<Function> current_function : pass_manager.get_state().get_functions())
{
for (shared_ptr<Node> node : current_function->get_ordered_ops())
{
const op::Constant* c = dynamic_cast<ngraph::op::Constant*>(node.get());
if (c)
{
shared_ptr<descriptor::TensorView> tv = node->get_outputs()[0].get_tensor_view();
auto c_value_strings = c->get_value_strings();
writer << "static " << tv->get_tensor().get_element_type().c_type_string() << " "
<< tv->get_tensor().get_name() << "_cpu[" << c_value_strings.size()
<< "] =\n";
writer << "{\n";
writer.indent++;
writer << emit_string_array(c_value_strings, 100 - writer.indent * 4);
writer.indent--;
writer << "\n};\n\n";
writer << "static " << tv->get_tensor().get_element_type().c_type_string() << " *"
<< tv->get_tensor().get_name() << ";\n";
m_variable_name_map[tv->get_tensor().get_name()] = tv->get_tensor().get_name();
}
}
}
writer << "// Declare all functions\n";
for (shared_ptr<Function> f : pass_manager.get_state().get_functions())
{
writer << "extern \"C\" void " << f->get_name() << "(void** inputs, void** outputs, "
"cublasHandle_t& cublas_handle, "
"cudnnHandle_t& cudnn_handle);\n";
}
writer << "\n";
writer << "// Declare all functions\n";
for (shared_ptr<Function> f : pass_manager.get_state().get_functions())
{
writer << "extern \"C\" void " << f->get_name()
<< "(void** inputs, void** outputs, "
"cublasHandle_t& cublas_handle, "
"cudnnHandle_t& cudnn_handle);\n";
}
unordered_map<Node*, string> match_functions;
for (shared_ptr<Function> current_function : pass_manager.get_state().get_functions())
{
bool temporaries_used = false;
size_t worst_case_tmp_size = 0;
writer << "\n";
set<string> output_names;
for (shared_ptr<Node> op : current_function->get_results())
{
shared_ptr<descriptor::TensorView> tv = op->get_output_tensor_view();
output_names.insert(tv->get_tensor().get_name());
}
const list<shared_ptr<Node>>& tmp = current_function->get_ordered_ops();
if (tmp.size() < 2)
{
// Since we are comparing ops there must be at least two ops to proceed.
continue;
}
vector<shared_ptr<Node>> op_list{tmp.begin(), tmp.end()};
for (size_t i = 0; i < op_list.size() - 1; i++)
{
if (op_list[i]->is_constant() || op_list[i]->is_parameter())
{
continue;
}
if (contains_key(match_functions, op_list[i].get()))
{
continue;
}
string match_function_name;
for (size_t j = i + 1; j < op_list.size(); j++)
{
if (0) //op_list[i]->is_functionally_identical(*op_list[j]))
unordered_map<Node*, string> match_functions;
for (shared_ptr<Function> current_function :
pass_manager.get_state().get_functions())
{
if (match_function_name.empty())
bool temporaries_used = false;
size_t worst_case_tmp_size = 0;
set<string> output_names;
for (shared_ptr<Node> op : current_function->get_results())
{
match_function_name = "func_" + op_list[i]->get_name();
match_functions.insert({op_list[i].get(), match_function_name});
shared_ptr<descriptor::TensorView> tv = op->get_output_tensor_view();
output_names.insert(tv->get_tensor().get_name());
}
match_functions.insert({op_list[j].get(), match_function_name});
}
}
if (!match_function_name.empty())
{
writer << "static void " << match_function_name << "(";
writer.indent++;
// Work around a compiler warning (*node inside typeid may have effects
// with shared pointers, which is fine here but clang doesn't like it.)
auto& n = *op_list[i];
auto handler = dispatcher.find(type_index(typeid(n)));
vector<GPU_TensorViewWrapper> in;
size_t arg_index = 0;
set<string> arg_names;
for (const descriptor::Input& input : n.get_inputs())
{
const descriptor::Output& output = input.get_output();
shared_ptr<descriptor::TensorView> tv = output.get_tensor_view();
GPU_TensorViewWrapper tvw{tv, "_arg" + to_string(arg_index)};
if (!contains(arg_names, tvw.get_name()))
const list<shared_ptr<Node>>& tmp = current_function->get_ordered_ops();
if (tmp.size() < 2)
{
arg_names.insert(tvw.get_name());
if (arg_index++ > 0)
// Since we are comparing ops there must be at least two ops to proceed.
continue;
}
vector<shared_ptr<Node>> op_list{tmp.begin(), tmp.end()};
for (size_t i = 0; i < op_list.size() - 1; i++)
{
if (op_list[i]->is_constant() || op_list[i]->is_parameter())
{
continue;
}
if (contains_key(match_functions, op_list[i].get()))
{
continue;
}
string match_function_name;
for (size_t j = i + 1; j < op_list.size(); j++)
{
if (0) //op_list[i]->is_functionally_identical(*op_list[j]))
{
if (match_function_name.empty())
{
match_function_name = "func_" + op_list[i]->get_name();
match_functions.insert({op_list[i].get(), match_function_name});
}
match_functions.insert({op_list[j].get(), match_function_name});
}
}
if (!match_function_name.empty())
{
writer << ",";
writer << "static void " << match_function_name << "(";
writer.indent++;
// Work around a compiler warning (*node inside typeid may have effects
// with shared pointers, which is fine here but clang doesn't like it.)
auto& n = *op_list[i];
auto handler = dispatcher.find(type_index(typeid(n)));
vector<GPU_TensorViewWrapper> in;
size_t arg_index = 0;
set<string> arg_names;
for (const descriptor::Input& input : n.get_inputs())
{
const descriptor::Output& output = input.get_output();
shared_ptr<descriptor::TensorView> tv = output.get_tensor_view();
GPU_TensorViewWrapper tvw{tv, "_arg" + to_string(arg_index)};
if (!contains(arg_names, tvw.get_name()))
{
arg_names.insert(tvw.get_name());
if (arg_index++ > 0)
{
writer << ",";
}
writer << "\n";
writer << tvw.get_type() << "* " << tvw.get_name();
}
in.push_back(tvw);
}
vector<GPU_TensorViewWrapper> out;
for (const descriptor::Output& output : n.get_outputs())
{
shared_ptr<descriptor::TensorView> tv = output.get_tensor_view();
GPU_TensorViewWrapper tvw{tv, "_out" + to_string(arg_index)};
if (arg_index++ > 0)
{
writer << ",";
}
writer << "\n";
writer << tvw.get_type() << "* " << tvw.get_name();
out.push_back(tvw);
}
writer.indent--;
writer << "\n)\n";
writer << "{\n";
writer.indent++;
handler->second(this, writer, &n, in, out);
writer.indent--;
writer << "}\n";
}
writer << "\n";
writer << tvw.get_type() << "* " << tvw.get_name();
}
in.push_back(tvw);
}
vector<GPU_TensorViewWrapper> out;
for (const descriptor::Output& output : n.get_outputs())
for (shared_ptr<Function> current_function :
pass_manager.get_state().get_functions())
{
shared_ptr<descriptor::TensorView> tv = output.get_tensor_view();
GPU_TensorViewWrapper tvw{tv, "_out" + to_string(arg_index)};
if (arg_index++ > 0)
set<string> output_names;
for (shared_ptr<Node> op : current_function->get_results())
{
writer << ",";
shared_ptr<descriptor::TensorView> tv = op->get_output_tensor_view();
output_names.insert(tv->get_tensor().get_name());
}
set<descriptor::TensorView*> constants;
for (shared_ptr<Node> node : current_function->get_ordered_ops())
{
if (dynamic_cast<ngraph::op::Constant*>(node.get()))
{
shared_ptr<descriptor::TensorView> tv =
node->get_outputs()[0].get_tensor_view();
constants.insert(tv.get());
}
}
writer << "\n";
writer << tvw.get_type() << "* " << tvw.get_name();
out.push_back(tvw);
}
writer.indent--;
writer << "\n)\n";
writer << "{\n";
writer.indent++;
handler->second(this, writer, &n, in, out);
writer.indent--;
writer << "}\n";
}
}
}
for (shared_ptr<Function> current_function : pass_manager.get_state().get_functions())
{
set<string> output_names;
for (shared_ptr<Node> op : current_function->get_results())
{
shared_ptr<descriptor::TensorView> tv = op->get_output_tensor_view();
output_names.insert(tv->get_tensor().get_name());
}
set<descriptor::TensorView*> constants;
for (shared_ptr<Node> node : current_function->get_ordered_ops())
{
if (dynamic_cast<ngraph::op::Constant*>(node.get()))
{
shared_ptr<descriptor::TensorView> tv = node->get_outputs()[0].get_tensor_view();
constants.insert(tv.get());
}
}
writer << "extern \"C\" void " << current_function->get_name();
writer << "(void** inputs, void** outputs, cublasHandle_t& cublas_handle, cudnnHandle_t& "
"cudnn_handle)\n";
writer << "{\n";
writer.indent++;
for (shared_ptr<Function> current_function : pass_manager.get_state().get_functions())
{
for (shared_ptr<Node> node : current_function->get_ordered_ops())
{
const op::Constant* c = dynamic_cast<op::Constant*>(node.get());
if (c)
{
shared_ptr<descriptor::TensorView> tv =
node->get_outputs()[0].get_tensor_view();
writer << "if(" << tv->get_tensor().get_name() << " == NULL)\n";
writer << "extern \"C\" void " << current_function->get_name();
writer << "(void** inputs, void** outputs, cublasHandle_t& cublas_handle, "
"cudnnHandle_t& "
"cudnn_handle)\n";
writer << "{\n";
writer.indent++;
writer << "runtime::gpu::cuda_memcpyHtD(" << tv->get_tensor().get_name() << ", "
<< tv->get_tensor().get_name() << "_cpu, " << tv->get_tensor().size()
<< ");\n";
writer.indent--;
writer << "}\n";
}
}
}
bool temporaries_used = false;
size_t worst_case_tmp_size = 0;
for (shared_ptr<Node> node : current_function->get_ordered_ops())
{
if (node->liveness_new_list.size() > 0)
{
temporaries_used = true;
for (descriptor::Tensor* tensor : node->liveness_new_list)
{
worst_case_tmp_size += tensor->size();
}
}
}
if (temporaries_used)
{
size_t temp_pool_size = current_function->get_temporary_pool_size();
writer << "// Allocate the memory pool\n";
// TODO memory pool malloc.
writer << "void* pool_base_ptr = ngraph::runtime::gpu::create_gpu_buffer("
<< temp_pool_size << ");\n";
// Add temporaries to the variable name map
for (shared_ptr<Node> node : current_function->get_ordered_ops())
{
for (descriptor::Tensor* tensor : node->liveness_new_list)
{
stringstream ss;
ss << "((" << tensor->get_element_type().c_type_string()
<< "*)((char *)pool_base_ptr + " << tensor->get_pool_offset() << "))";
m_variable_name_map[tensor->get_name()] = ss.str();
}
}
}
// Add inputs to the variable name map
size_t arg_index = 0;
for (shared_ptr<ngraph::op::Parameter> param : current_function->get_parameters())
{
for (size_t i = 0; i < param->get_output_size(); ++i)
{
shared_ptr<descriptor::TensorView> tv = param->get_output_tensor_view(i);
const element::Type& et = tv->get_tensor_view_type()->get_element_type();
string type = et.c_type_string();
stringstream ss;
ss << "((" << type << "*)(inputs[" << arg_index << "]))";
m_variable_name_map[tv->get_tensor().get_name()] = ss.str();
arg_index++;
}
}
for (shared_ptr<Function> current_function :
pass_manager.get_state().get_functions())
{
for (shared_ptr<Node> node : current_function->get_ordered_ops())
{
const op::Constant* c = dynamic_cast<op::Constant*>(node.get());
if (c)
{
shared_ptr<descriptor::TensorView> tv =
node->get_outputs()[0].get_tensor_view();
writer << "if(" << tv->get_tensor().get_name() << " == NULL)\n";
writer << "{\n";
writer.indent++;
writer << "runtime::gpu::cuda_memcpyHtD("
<< tv->get_tensor().get_name() << ", "
<< tv->get_tensor().get_name() << "_cpu, "
<< tv->get_tensor().size() << ");\n";
writer.indent--;
writer << "}\n";
}
}
}
bool temporaries_used = false;
size_t worst_case_tmp_size = 0;
for (shared_ptr<Node> node : current_function->get_ordered_ops())
{
if (node->liveness_new_list.size() > 0)
{
temporaries_used = true;
for (descriptor::Tensor* tensor : node->liveness_new_list)
{
worst_case_tmp_size += tensor->size();
}
}
}
if (temporaries_used)
{
size_t temp_pool_size = current_function->get_temporary_pool_size();
writer << "// Allocate the memory pool\n";
// TODO memory pool malloc.
writer << "void* pool_base_ptr = ngraph::runtime::gpu::create_gpu_buffer("
<< temp_pool_size << ");\n";
// Add temporaries to the variable name map
for (shared_ptr<Node> node : current_function->get_ordered_ops())
{
for (descriptor::Tensor* tensor : node->liveness_new_list)
{
stringstream ss;
ss << "((" << tensor->get_element_type().c_type_string()
<< "*)((char *)pool_base_ptr + " << tensor->get_pool_offset()
<< "))";
m_variable_name_map[tensor->get_name()] = ss.str();
}
}
}
// create output alias map
size_t output_index = 0;
unordered_map<descriptor::TensorView*, vector<size_t>> output_alias_map;
vector<size_t> aliases;
for (size_t i = 0; i < current_function->get_output_size(); ++i)
{
shared_ptr<Node> op = current_function->get_output_op(i);
shared_ptr<descriptor::TensorView> otv = op->get_output_tensor_view();
vector<size_t>& al = output_alias_map[otv.get()];
al.push_back(output_index);
if (al.size() > 1)
{
aliases.push_back(output_index);
}
output_index++;
}
// Add inputs to the variable name map
size_t arg_index = 0;
for (shared_ptr<ngraph::op::Parameter> param :
current_function->get_parameters())
{
for (size_t i = 0; i < param->get_output_size(); ++i)
{
shared_ptr<descriptor::TensorView> tv =
param->get_output_tensor_view(i);
const element::Type& et =
tv->get_tensor_view_type()->get_element_type();
string type = et.c_type_string();
stringstream ss;
ss << "((" << type << "*)(inputs[" << arg_index << "]))";
m_variable_name_map[tv->get_tensor().get_name()] = ss.str();
arg_index++;
}
}
// Add outputs to the variable name map
output_index = 0;
for (size_t i = 0; i < current_function->get_output_size(); ++i)
{
shared_ptr<Node> op = current_function->get_output_op(i);
shared_ptr<descriptor::TensorView> tv = op->get_output_tensor_view();
const element::Type& et = tv->get_tensor_view_type()->get_element_type();
bool parameter_as_output = false;
for (shared_ptr<ngraph::op::Parameter> param : current_function->get_parameters())
{
for (const descriptor::Output& pout : param->get_outputs())
{
shared_ptr<descriptor::TensorView> ptv = pout.get_tensor_view();
if (tv == ptv)
// create output alias map
size_t output_index = 0;
unordered_map<descriptor::TensorView*, vector<size_t>> output_alias_map;
vector<size_t> aliases;
for (size_t i = 0; i < current_function->get_output_size(); ++i)
{
parameter_as_output = true;
writer << "ngraph::runtime::gpu::cuda_memcpyDtD(reinterpret_cast<"
<< et.c_type_string() << "*>(outputs[" << output_index << "]), "
<< m_variable_name_map[ptv->get_tensor().get_name()] << ", "
<< ptv->get_tensor().size() << ");\n";
break;
shared_ptr<Node> op = current_function->get_output_op(i);
shared_ptr<descriptor::TensorView> otv = op->get_output_tensor_view();
vector<size_t>& al = output_alias_map[otv.get()];
al.push_back(output_index);
if (al.size() > 1)
{
aliases.push_back(output_index);
}
output_index++;
}
// Add outputs to the variable name map
output_index = 0;
for (size_t i = 0; i < current_function->get_output_size(); ++i)
{
shared_ptr<Node> op = current_function->get_output_op(i);
shared_ptr<descriptor::TensorView> tv = op->get_output_tensor_view();
const element::Type& et = tv->get_tensor_view_type()->get_element_type();
bool parameter_as_output = false;
for (shared_ptr<ngraph::op::Parameter> param :
current_function->get_parameters())
{
for (const descriptor::Output& pout : param->get_outputs())
{
shared_ptr<descriptor::TensorView> ptv = pout.get_tensor_view();
if (tv == ptv)
{
parameter_as_output = true;
writer
<< "ngraph::runtime::gpu::cuda_memcpyDtD(reinterpret_cast<"
<< et.c_type_string() << "*>(outputs[" << output_index
<< "]), "
<< m_variable_name_map[ptv->get_tensor().get_name()] << ", "
<< ptv->get_tensor().size() << ");\n";
break;
}
}
}
if (!parameter_as_output && !contains(aliases, output_index))
{
if (contains(constants, tv.get()))
{
writer << "ngraph::runtime::gpu::cuda_memcpyHtD(outputs["
<< output_index << "], " << tv->get_tensor().get_name()
<< ", " << tv->get_tensor().size() << ");\n";
}
else
{
string type = et.c_type_string();
stringstream ss;
ss << "((" << type << "*)(outputs[" << output_index << "]))";
m_variable_name_map[tv->get_tensor().get_name()] = ss.str();
}
}
output_index++;
}
for (shared_ptr<Node> node : current_function->get_ordered_ops())
{
auto& n =
*node; // Work around a compiler warning (*node inside typeid may have effects
// with shared pointers, which is fine here but clang doesn't like it.)
auto handler = dispatcher.find(type_index(typeid(n)));
if (handler == dispatcher.end())
{
throw ngraph_error("Unhandled op during code generation : " +
node->description());
}
vector<GPU_TensorViewWrapper> in;
for (const descriptor::Input& input : node->get_inputs())
{
const descriptor::Output& output = input.get_output();
shared_ptr<descriptor::TensorView> tv = output.get_tensor_view();
in.push_back(GPU_TensorViewWrapper(
tv, m_variable_name_map[tv->get_tensor().get_name()]));
}
vector<GPU_TensorViewWrapper> out;
for (const descriptor::Output& output : node->get_outputs())
{
shared_ptr<descriptor::TensorView> tv = output.get_tensor_view();
out.push_back(GPU_TensorViewWrapper(
tv, m_variable_name_map[tv->get_tensor().get_name()]));
}
// Emit operation prologue
if (!node->is_parameter() && !node->is_constant())
{
if (m_emit_timing)
{
emit_debug_function_entry(writer, node.get(), in, out);
}
}
// Emit operation body
string func_name;
auto it = match_functions.find(node.get());
if (it != match_functions.end())
{
func_name = it->second;
}
if (func_name.empty())
{
handler->second(this, writer, node.get(), in, out);
}
else
{
vector<string> names;
for (const GPU_TensorViewWrapper& tv : in)
{
names.push_back(tv.get_name());
}
for (const GPU_TensorViewWrapper& tv : out)
{
names.push_back(tv.get_name());
}
writer << func_name << "(" << join(names) << ");\n";
}
// Emit operation epilogue
if (!node->is_parameter() && !node->is_constant())
{
if (m_emit_timing)
{
emit_debug_function_exit(writer, node.get(), in, out);
}
}
}
writer.indent--;
// End generated function
writer += "}\n\n";
}
}
if (!parameter_as_output && !contains(aliases, output_index))
{
if (contains(constants, tv.get()))
// TODO: Cleanup and make this a utility function
file_util::make_directory(s_output_dir);
string filename =
file_util::path_join(s_output_dir, function_name + "_codegen.cpp");
ofstream out(filename);
string code = writer.get_code();
out << code;
out.close();
m_compiler.reset(new codegen::Compiler());
m_execution_engine.reset(new codegen::ExecutionEngine());
m_compiler->set_precompiled_header_source(pch_header_source);
auto codegen_module = m_compiler->compile(code);
if (codegen_module == nullptr)
{
writer << "ngraph::runtime::gpu::cuda_memcpyHtD(outputs[" << output_index
<< "], " << tv->get_tensor().get_name() << ", "
<< tv->get_tensor().size() << ");\n";
throw runtime_error("function failed to compile");
}
else
m_execution_engine->add_module(codegen_module);
m_execution_engine->finalize();
m_compiled_function =
m_execution_engine->find_function<EntryPoint_t>(function_name);
assert(m_compiled_function);
m_is_compiled = true;
if (m_release_function)
{
string type = et.c_type_string();
stringstream ss;
ss << "((" << type << "*)(outputs[" << output_index << "]))";
m_variable_name_map[tv->get_tensor().get_name()] = ss.str();
release_function();
}
}
output_index++;
}
for (shared_ptr<Node> node : current_function->get_ordered_ops())
{
auto& n = *node; // Work around a compiler warning (*node inside typeid may have effects
// with shared pointers, which is fine here but clang doesn't like it.)
auto handler = dispatcher.find(type_index(typeid(n)));
if (handler == dispatcher.end())
void GPU_ExternalFunction::handle_output_alias(
codegen::CodeWriter& writer,
const Node& node,
const unordered_map<descriptor::TensorView*, vector<size_t>>& output_alias_map)
{
throw ngraph_error("Unhandled op during code generation : " + node->description());
}
vector<GPU_TensorViewWrapper> in;
for (const descriptor::Input& input : node->get_inputs())
{
const descriptor::Output& output = input.get_output();
shared_ptr<descriptor::TensorView> tv = output.get_tensor_view();
in.push_back(
GPU_TensorViewWrapper(tv, m_variable_name_map[tv->get_tensor().get_name()]));
}
vector<GPU_TensorViewWrapper> out;
for (const descriptor::Output& output : node->get_outputs())
{
shared_ptr<descriptor::TensorView> tv = output.get_tensor_view();
out.push_back(
GPU_TensorViewWrapper(tv, m_variable_name_map[tv->get_tensor().get_name()]));
}
// Emit operation prologue
if (!node->is_parameter() && !node->is_constant())
{
if (m_emit_timing)
for (const descriptor::Output& output : node.get_outputs())
{
emit_debug_function_entry(writer, node.get(), in, out);
shared_ptr<descriptor::TensorView> otv = output.get_tensor_view();
auto it = output_alias_map.find(otv.get());
if (it != output_alias_map.end())
{
const vector<size_t>& outputs = it->second;
if (outputs.size() > 1)
{
writer << "{ // handle output alias for previous op\n";
writer.indent++;
for (size_t i = 1; i < outputs.size(); i++)
{
writer << "ngraph::runtime::gpu::cuda_memcpyDtD(static_cast<void*>("
"outputs["
<< outputs[i] << "]), static_cast<void*>(outputs["
<< outputs[0] << "]), " << otv->get_tensor().size()
<< ");\n";
}
writer.indent--;
writer << "}\n";
}
}
}
}
// Emit operation body
string func_name;
auto it = match_functions.find(node.get());
if (it != match_functions.end())
{
func_name = it->second;
}
if (func_name.empty())
{
handler->second(this, writer, node.get(), in, out);
}
else
shared_ptr<ngraph::runtime::CallFrame> GPU_ExternalFunction::make_call_frame()
{
vector<string> names;
for (const GPU_TensorViewWrapper& tv : in)
if (!m_is_compiled)
{
names.push_back(tv.get_name());
compile();
}
for (const GPU_TensorViewWrapper& tv : out)
{
names.push_back(tv.get_name());
}
writer << func_name << "(" << join(names) << ");\n";
return make_shared<GPU_CallFrame>(shared_from_this(), m_compiled_function);
}
// Emit operation epilogue
if (!node->is_parameter() && !node->is_constant())
void GPU_ExternalFunction::emit_debug_function_entry(
codegen::CodeWriter& writer,
Node* node,
const std::vector<GPU_TensorViewWrapper>& in,
const std::vector<GPU_TensorViewWrapper>& out)
{
if (m_emit_timing)
{
emit_debug_function_exit(writer, node.get(), in, out);
}
writer << "timer_" << node->get_name() << ".start();\n";
}
}
writer.indent--;
// End generated function
writer += "}\n\n";
}
// TODO: Cleanup and make this a utility function
file_util::make_directory(s_output_dir);
string filename = file_util::path_join(s_output_dir, function_name + "_codegen.cpp");
ofstream out(filename);
string code = writer.get_code();
out << code;
out.close();
m_compiler.reset(new codegen::Compiler());
m_execution_engine.reset(new codegen::ExecutionEngine());
m_compiler->set_precompiled_header_source(pch_header_source);
auto codegen_module = m_compiler->compile(code);
if (codegen_module == nullptr)
{
throw runtime_error("function failed to compile");
}
m_execution_engine->add_module(codegen_module);
m_execution_engine->finalize();
m_compiled_function = m_execution_engine->find_function<EntryPoint_t>(function_name);
assert(m_compiled_function);
m_is_compiled = true;
if (m_release_function)
{
release_function();
}
}
void GPU_ExternalFunction::handle_output_alias(
codegen::CodeWriter& writer,
const Node& node,
const unordered_map<descriptor::TensorView*, vector<size_t>>& output_alias_map)
{
for (const descriptor::Output& output : node.get_outputs())
{
shared_ptr<descriptor::TensorView> otv = output.get_tensor_view();
auto it = output_alias_map.find(otv.get());
if (it != output_alias_map.end())
{
const vector<size_t>& outputs = it->second;
if (outputs.size() > 1)
void GPU_ExternalFunction::emit_debug_function_exit(
codegen::CodeWriter& writer,
Node* node,
const std::vector<GPU_TensorViewWrapper>& in,
const std::vector<GPU_TensorViewWrapper>& out)
{
writer << "{ // handle output alias for previous op\n";
writer.indent++;
for (size_t i = 1; i < outputs.size(); i++)
{
writer << "ngraph::runtime::gpu::cuda_memcpyDtD(static_cast<void*>(outputs["
<< outputs[i] << "]), static_cast<void*>(outputs[" << outputs[0]
<< "]), " << otv->get_tensor().size() << ");\n";
}
writer.indent--;
writer << "}\n";
writer << "timer_" << node->get_name() << ".stop();\n";
}
}
}
}
shared_ptr<ngraph::runtime::CallFrame> GPU_ExternalFunction::make_call_frame()
{
if (!m_is_compiled)
{
compile();
}
return make_shared<GPU_CallFrame>(shared_from_this(),
m_compiled_function);
}
void GPU_ExternalFunction::emit_debug_function_entry(
codegen::CodeWriter& writer,
Node* node,
const std::vector<GPU_TensorViewWrapper>& in,
const std::vector<GPU_TensorViewWrapper>& out)
{
writer << "timer_" << node->get_name() << ".start();\n";
}
void GPU_ExternalFunction::emit_debug_function_exit(
codegen::CodeWriter& writer,
Node* node,
const std::vector<GPU_TensorViewWrapper>& in,
const std::vector<GPU_TensorViewWrapper>& out)
{
writer << "timer_" << node->get_name() << ".stop();\n";
}
}
}
}
\ No newline at end of file
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