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Inference: careful not to repeatedly modify output when evaluating multiple different templates

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This commit is contained in:
Philippe Tillet
2016-10-10 01:30:27 -04:00
parent faa06d32b9
commit 112fdfa95a
2 changed files with 17 additions and 3 deletions
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1
lib/jit/generation/elementwise_1d.cpp
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@@ -124,6 +124,7 @@ void elementwise_1d::enqueue(driver::CommandQueue &, driver::Program const & pro
//Kernel //Kernel
std::string name = "elementwise_1d"; std::string name = "elementwise_1d";
name += suffix; name += suffix;
// std::cout << name << std::endl;
driver::Kernel kernel(program, name.c_str()); driver::Kernel kernel(program, name.c_str());
//NDRange //NDRange
driver::NDRange global(ls0_*ng_); driver::NDRange global(ls0_*ng_);

19
lib/runtime/profiles.cpp
View File

@@ -80,8 +80,9 @@ profiles::value_type::value_type(numeric_type dtype, std::shared_ptr<templates::
void profiles::value_type::execute(runtime::execution_handler const & expr) void profiles::value_type::execute(runtime::execution_handler const & expr)
{ {
static const int MAX_TEMPORARY_WORKSPACE = 1e6; static const int MAX_TEMPORARY_WORKSPACE = 1e6;
expression_tree const & tree = expr.x();
driver::Program const & program = init(expr); driver::Program const & program = init(expr);
std::vector<int_t> x = templates_[0]->input_sizes(expr.x()); std::vector<int_t> x = templates_[0]->input_sizes(tree);
//Cached //Cached
auto it = labels_.find(x); auto it = labels_.find(x);
@@ -91,6 +92,16 @@ void profiles::value_type::execute(runtime::execution_handler const & expr)
} }
//Not cached //Not cached
expression_tree::node const & root = tree[tree.root()];
expression_tree::node const & left = tree[root.binary_operator.lhs];
array_base* out = left.array.base;
auto read_out = [&](expression_tree::node const & x){
return x.type == DENSE_ARRAY_TYPE && (&x != &left) && x.array.base == out;
};
bool modify_output = std::find_if(tree.data().begin(), tree.data().end(), read_out) != tree.data().end();
std::unique_ptr<array> bkp;
if(modify_output)
bkp.reset(new array(*out));
tools::Timer tmr; tools::Timer tmr;
std::vector<double> times; std::vector<double> times;
std::vector<float> perf = predictor_->predict(x); std::vector<float> perf = predictor_->predict(x);
@@ -100,7 +111,7 @@ void profiles::value_type::execute(runtime::execution_handler const & expr)
bool valid_found = false; bool valid_found = false;
for(size_t k = 0 ; k < std::min<size_t>(5, idx.size()) || !valid_found ; k++){ for(size_t k = 0 ; k < std::min<size_t>(5, idx.size()) || !valid_found ; k++){
size_t i = idx[k]; size_t i = idx[k];
if(templates_[i]->temporary_workspace(expr.x()) > MAX_TEMPORARY_WORKSPACE){ if(templates_[i]->temporary_workspace(tree) > MAX_TEMPORARY_WORKSPACE){
times.push_back(INFINITY); times.push_back(INFINITY);
continue; continue;
} }
@@ -109,7 +120,7 @@ void profiles::value_type::execute(runtime::execution_handler const & expr)
std::vector<double> ctimes; std::vector<double> ctimes;
while(total_time < 1e-2){ while(total_time < 1e-2){
tmr.start(); tmr.start();
templates_[i]->enqueue(queue_, program, tools::to_string(i), runtime::execution_handler(expr.x())); templates_[i]->enqueue(queue_, program, tools::to_string(i), runtime::execution_handler(tree));
queue_.synchronize(); queue_.synchronize();
ctimes.push_back(1e-9*tmr.get().count()); ctimes.push_back(1e-9*tmr.get().count());
total_time += ctimes.back(); total_time += ctimes.back();
@@ -122,6 +133,8 @@ void profiles::value_type::execute(runtime::execution_handler const & expr)
} }
size_t i = idx[std::distance(times.begin(),std::min_element(times.begin(), times.end()))]; size_t i = idx[std::distance(times.begin(),std::min_element(times.begin(), times.end()))];
labels_.insert({x, i}); labels_.insert({x, i});
if(modify_output)
*out = *bkp;
templates_[i]->enqueue(queue_, program, tools::to_string(i), expr); templates_[i]->enqueue(queue_, program, tools::to_string(i), expr);
} }