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[GPU] Update loop inst ids instead of getting from prim (#24234) 

### Details:
 - *Update loop inst ids instead of getting from prim*

### Tickets:
 - *137277*
This commit is contained in:
Kelvin Choi 2024-05-29 09:04:56 +09:00 committed by GitHub
parent 5f03621cfa
commit cbeac47383
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
3 changed files with 233 additions and 22 deletions
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32
src/plugins/intel_gpu/src/graph/impls/common/loop.cpp
View File

@ -130,17 +130,17 @@ struct loop_impl : typed_primitive_impl<loop> {
}
body_network->set_shape_predictor(outer_network.get_shape_predictor());
OPENVINO_ASSERT(!primitive->num_iteration_id.empty(), "loop operation should have num_iteration_id");
OPENVINO_ASSERT(!instance.get_num_iterations_id().empty(), "loop operation should have num_iteration_id");
// shortcut of execution_condition memory in body network
memory::ptr body_execution_condition_mem = nullptr;
if (!primitive->body_execution_condition_id.empty()) {
body_execution_condition_mem = body_network->get_primitive(primitive->body_execution_condition_id)->output_memory_ptr();
if (!instance.get_condition_id().empty()) {
body_execution_condition_mem = body_network->get_primitive(instance.get_condition_id())->output_memory_ptr();
}
// shortcut of current_iteration memory in body network
if (!primitive->body_current_iteration_id.empty()) {
memory::ptr body_current_iteration_mem = body_network->get_primitive(primitive->body_current_iteration_id)->output_memory_ptr();
if (!instance.get_current_iteration_id().empty()) {
memory::ptr body_current_iteration_mem = body_network->get_primitive(instance.get_current_iteration_id())->output_memory_ptr();
write_scalar_value(body_current_iteration_mem, body_network->get_stream(), 0);
}
@ -149,11 +149,11 @@ struct loop_impl : typed_primitive_impl<loop> {
// read trip_count from outer network
int64_t trip_count = -1;
if (!primitive->trip_count_id.empty()) {
memory::ptr trip_count_mem = outer_network.get_primitive(primitive->trip_count_id)->output_memory_ptr();
if (!instance.get_trip_count_id().empty()) {
memory::ptr trip_count_mem = outer_network.get_primitive(instance.get_trip_count_id())->output_memory_ptr();
trip_count = read_scalar_value(std::move(trip_count_mem), stream);
} else {
OPENVINO_ASSERT(!primitive->body_execution_condition_id.empty()
OPENVINO_ASSERT(!instance.get_condition_id().empty()
|| num_iterations > 0 || primitive->max_num_iterations > 0,
"num_iterations should be positive when trip_count_id is not existed");
// If trip_count_id is not existed, the original ngraph operation is TensorIterator.
@ -166,11 +166,11 @@ struct loop_impl : typed_primitive_impl<loop> {
// read initial execution condition from outer network
int64_t execution_condition = 1;
if (!primitive->first_execution_condition_id.empty()) {
if (!instance.get_initial_execution_id().empty()) {
// Wait for completion of the execution_condition of outer_network
if (outer_network.has_event(primitive->first_execution_condition_id))
outer_network.get_primitive_event(primitive->first_execution_condition_id)->wait();
memory::ptr first_execution_condition_mem = outer_network.get_primitive(primitive->first_execution_condition_id)->output_memory_ptr();
if (outer_network.has_event(instance.get_initial_execution_id()))
outer_network.get_primitive_event(instance.get_initial_execution_id())->wait();
memory::ptr first_execution_condition_mem = outer_network.get_primitive(instance.get_initial_execution_id())->output_memory_ptr();
execution_condition = read_scalar_value(first_execution_condition_mem, stream);
}
GPU_DEBUG_LOG << "execution_condition: " << execution_condition << std::endl;
@ -178,7 +178,7 @@ struct loop_impl : typed_primitive_impl<loop> {
// When execution_condition is false or trip_count is zero, return execute_impl without any body_network execution.
if (!execution_condition || trip_count == 0) {
// Update num_iterations (actual number of iterations)
memory::ptr num_actual_iterations_mem = outer_network.get_primitive(primitive->num_iteration_id)->output_memory_ptr();
memory::ptr num_actual_iterations_mem = outer_network.get_primitive(instance.get_num_iterations_id())->output_memory_ptr();
write_scalar_value(num_actual_iterations_mem, stream, current_iteration_idx);
instance.update_output_layout();
@ -255,7 +255,7 @@ struct loop_impl : typed_primitive_impl<loop> {
// execution condition is the result of body network execution
if (body_execution_condition_mem != nullptr) {
auto execution_id = primitive->body_execution_condition_id;
auto execution_id = instance.get_condition_id();
if (body_network->has_event(execution_id)) {
auto ev = body_network->get_primitive_event(execution_id);
if (ev) ev->wait();
@ -275,9 +275,9 @@ struct loop_impl : typed_primitive_impl<loop> {
// Update actual num iteration
// update num_iterations (actual number of iterations)
memory::ptr num_actual_iterations_mem = outer_network.get_primitive(primitive->num_iteration_id)->output_memory_ptr();
memory::ptr num_actual_iterations_mem = outer_network.get_primitive(instance.get_num_iterations_id())->output_memory_ptr();
write_scalar_value(num_actual_iterations_mem, stream, current_iteration_idx);
GPU_DEBUG_LOG << "current_iteration_idx(" << primitive->num_iteration_id << ", "
GPU_DEBUG_LOG << "current_iteration_idx(" << instance.get_num_iterations_id() << ", "
<< num_actual_iterations_mem << ") : " << current_iteration_idx << std::endl;
if (is_dynamic)

47
src/plugins/intel_gpu/src/graph/include/loop_inst.h
View File

@ -22,6 +22,12 @@ struct typed_program_node<loop> : public typed_program_node_base<loop> {
private:
using parent = typed_program_node_base<loop>;
primitive_id trip_count_id;
primitive_id initial_execution_id;
primitive_id current_iteration_id;
primitive_id execution_condition_id;
primitive_id num_iterations_id;
std::vector<loop::io_primitive_map>& input_primitive_maps;
std::vector<loop::io_primitive_map>& output_primitive_maps;
std::vector<loop::backedge_mapping>& back_edges;
@ -31,21 +37,32 @@ public:
parent(prim, prog),
input_primitive_maps(prim->input_primitive_maps),
output_primitive_maps(prim->output_primitive_maps),
back_edges(prim->back_edges) {}
back_edges(prim->back_edges) {
set_primitive_ids(prim);
}
program::ptr get_body_program() const { return get_primitive()->body_program; }
const primitive_id& get_trip_count_id() const { return get_primitive()->trip_count_id; }
const primitive_id& get_initial_execution_id() const { return get_primitive()->first_execution_condition_id; }
const primitive_id& get_current_iteration_id() const { return get_primitive()->body_current_iteration_id; }
const primitive_id& get_execution_condition_id() const { return get_primitive()->body_execution_condition_id; }
const primitive_id& get_num_iterations_id() const { return get_primitive()->num_iteration_id; }
const primitive_id& get_trip_count_id() const { return trip_count_id; }
const primitive_id& get_initial_execution_id() const { return initial_execution_id; }
const primitive_id& get_current_iteration_id() const { return current_iteration_id; }
const primitive_id& get_execution_condition_id() const { return execution_condition_id; }
const primitive_id& get_num_iterations_id() const { return num_iterations_id; }
const int32_t get_max_num_iteration() const { return get_primitive()->max_num_iterations; }
const std::vector<loop::io_primitive_map>& get_input_primitive_maps() const { return input_primitive_maps; }
const std::vector<loop::io_primitive_map>& get_output_primitive_maps() const { return output_primitive_maps; }
const std::vector<loop::backedge_mapping>& get_back_edges() const { return back_edges;}
void set_primitive_ids(std::shared_ptr<loop> prim) {
trip_count_id = prim->trip_count_id;
initial_execution_id = prim->first_execution_condition_id;
current_iteration_id = prim->body_current_iteration_id;
execution_condition_id = prim->body_execution_condition_id;
num_iterations_id = prim->num_iteration_id;
}
void update_primitive_map(const primitive_id& prevID, const primitive_id& newID, bool external_id = true) {
if (external_id) {
for (auto& pm : input_primitive_maps) {
@ -78,6 +95,18 @@ public:
}
}
}
// Update ids
if (get_trip_count_id() == prevID)
trip_count_id = newID;
if (get_initial_execution_id() == prevID)
initial_execution_id = newID;
if (get_current_iteration_id() == prevID)
current_iteration_id = newID;
if (get_execution_condition_id() == prevID)
execution_condition_id = newID;
if (get_num_iterations_id() == prevID)
num_iterations_id = newID;
}
// current_iteration is necessary to calculate output layout in dynamic shape
@ -329,6 +358,12 @@ public:
std::vector<event::ptr> preprocess_memory_for_body_network(int64_t current_iteration_idx);
std::vector<event::ptr> postprocess_memory_for_body_network(int64_t current_iteration_idx);
primitive_id get_trip_count_id() { return _trip_count_id; }
primitive_id get_initial_execution_id() { return _initial_execution_id; }
primitive_id get_current_iteration_id() { return _current_iteration_id; }
primitive_id get_condition_id() { return _condition_id; }
primitive_id get_num_iterations_id() { return _num_iterations_id; }
private:
network::ptr body_network;
memory::ptr get_external_memory(const primitive_id& external_id, size_t mem_idx = 0) const;

176
src/plugins/intel_gpu/tests/unit/test_cases/loop_gpu_test.cpp
View File

@ -787,3 +787,179 @@ TEST(loop_gpu, support_loop_w_dynamic_input_w_various_shapes) {
std::vector<float>(),
2, 3);
}
static void test_loop_gpu_wo_trip_count_update_primitive_id(ov::PartialShape body_input_layout,
std::vector<ov::PartialShape> whole_layouts,
std::vector<std::vector<float>> input_data_list,
std::vector<float> expected_output_data,
size_t axis,
size_t exit_value,
bool is_caching_test = false) {
auto& engine = get_test_engine();
auto b_input_layout = cldnn::layout{ body_input_layout, data_types::f32, format::bfyx };
ov::PartialShape sliced_input_shape = body_input_layout;
sliced_input_shape[axis] = 1;
auto sliced_input_layout = cldnn::layout{ sliced_input_shape, data_types::f32, format::bfyx };
auto const_layout = cldnn::layout{ {}, data_types::i64, format::bfyx };
auto e_initial_condition_mem = engine.allocate_memory(const_layout);
auto e_num_iteration_mem = engine.allocate_memory(const_layout);
auto b_exit_value_mem = engine.allocate_memory(const_layout);
auto b_index_inc_mem = engine.allocate_memory(const_layout);
// initialize input buffers
set_values(e_initial_condition_mem, {1});
set_values(b_exit_value_mem, {exit_value});
set_values(b_index_inc_mem, {1});
set_values(e_num_iteration_mem, {0});
primitive_id body_current_iteration_id = "b_index";
primitive_id body_execution_condition_id = "b_cond_exit_value";
cldnn::topology body(
input_layout(body_current_iteration_id, const_layout),
input_layout("b_add_data", sliced_input_layout),
input_layout("b_mul_data", sliced_input_layout),
data("b_exit_value", b_exit_value_mem),
data("b_index_inc", b_index_inc_mem),
eltwise("b_index_update", input_info(body_current_iteration_id), input_info("b_index_inc"), eltwise_mode::sum),
reorder("b_index_cast", input_info("b_index_update"),
cldnn::format::any, data_types::f32, {}, cldnn::reorder_mean_mode::subtract, cldnn::padding(), true),
eltwise(body_execution_condition_id, input_info("b_index"), input_info("b_exit_value"), eltwise_mode::lt),
eltwise("b_add", input_info("b_add_data"), input_info("b_index_cast"), eltwise_mode::sum),
eltwise("b_mul", input_info("b_mul_data"), input_info("b_index_cast"), eltwise_mode::prod));
primitive_id trip_count_id = "";
primitive_id actual_iteration_count_id = "actual_iteration_count";
primitive_id initial_mean = "initial_mean";
primitive_id initial_condition_id = "initial_condition";
primitive_id initial_condition_id_elt = "initial_condition_elt";
primitive_id initial_condition_id_reorder = "initial_condition_reorder";
primitive_id initial_condition_id_reorder2 = "initial_condition_reorder2";
int64_t num_iterations = -1;
std::vector<loop::io_primitive_map> input_primitive_maps {
loop::io_primitive_map("input", "b_add_data", axis),
loop::io_primitive_map("input", "b_mul_data", axis),
loop::io_primitive_map(actual_iteration_count_id, body_current_iteration_id) };
std::vector<loop::io_primitive_map> output_primitive_maps {
loop::io_primitive_map(cldnn::input_info("loop", 0), cldnn::input_info("b_add", 0), axis),
loop::io_primitive_map(cldnn::input_info("loop", 1), cldnn::input_info("b_mul", 0), axis) };
std::vector<loop::backedge_mapping> back_edges {
loop::backedge_mapping("b_index_update", body_current_iteration_id) };
auto body_program = build_program(engine, body, body_execution_condition_id, output_primitive_maps, back_edges, true);
auto const_shape = engine.allocate_memory({ov::PartialShape{4}, data_types::i32, format::bfyx});
std::vector<int32_t> body_input_layouts;
for (size_t i = 0; i < body_input_layout.size(); i++) {
if (body_input_layout[i].is_dynamic())
body_input_layouts.push_back(-1);
else
body_input_layouts.push_back(body_input_layout[i].get_length());
}
set_values<int32_t>(const_shape, body_input_layouts);
const std::vector<float> values_to_subtract = {0.f};
cldnn::topology topology(
input_layout("input_origin", b_input_layout),
input_layout(initial_condition_id, e_initial_condition_mem->get_layout()),
mutable_data(actual_iteration_count_id, e_num_iteration_mem),
reorder(initial_condition_id_reorder, input_info(initial_condition_id), cldnn::format::any, data_types::f32, values_to_subtract),
reorder(initial_condition_id_reorder2, input_info(initial_condition_id_reorder), cldnn::format::any, data_types::i32), // should be fused to test updating input id of loop
shape_of("shape_of_input", input_info("input_origin"), data_types::i32),
reduce("reduced_shape", input_info("shape_of_input"), reduce_mode::prod, {0}, true),
reshape("reshape1", input_info("input_origin"), input_info("reduced_shape"), false, ov::PartialShape::dynamic(1)),
data("const", const_shape),
reshape("input", input_info("reshape1"), input_info("const"), false, ov::PartialShape::dynamic(4)),
loop("loop", { input_info(actual_iteration_count_id), input_info(initial_condition_id_reorder2), input_info("input") }, body_program,
trip_count_id, initial_condition_id_reorder2, actual_iteration_count_id,
input_primitive_maps, output_primitive_maps, back_edges,
num_iterations, body_current_iteration_id, body_execution_condition_id, 2),
eltwise("out_sum", input_info("loop", 0), input_info("loop", 1), eltwise_mode::sum));
ExecutionConfig config = get_test_default_config(engine);
config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
cldnn::network::ptr network = get_network(engine, topology, config, get_test_stream_ptr(), is_caching_test);
for (size_t i = 0 ; i < whole_layouts.size(); i++) {
auto whole_layout = whole_layouts[i];
auto input_data = input_data_list[i];
// initialize input buffers
set_values(e_initial_condition_mem, {1});
set_values(b_exit_value_mem, {exit_value});
set_values(b_index_inc_mem, {1});
set_values(e_num_iteration_mem, {0});
auto e_input_layout = cldnn::layout{ whole_layout, data_types::f32, format::bfyx };
auto e_input_mem = engine.allocate_memory(e_input_layout); // b,f,x,y
auto expected_output_layout = whole_layout;
set_values(e_input_mem, input_data);
network->set_input_data("input_origin", e_input_mem);
network->set_input_data(initial_condition_id, e_initial_condition_mem);
auto outputs = network->execute();
ASSERT_EQ(outputs.size(), 1);
auto expected_num_iterations = (exit_value + 1);
expected_output_layout[axis] = expected_num_iterations;
auto e_output_layout = cldnn::layout{ expected_output_layout, data_types::f32, format::bfyx };
auto num_iter_mem = network->get_output_memory(actual_iteration_count_id);
if (num_iter_mem != nullptr) {
mem_lock<int64_t> num_iter_ptr{ num_iter_mem, get_test_stream() };
ASSERT_EQ(num_iter_ptr.data()[0], expected_num_iterations);
}
std::vector<float> expected(input_data.size());
if (expected_output_data.size() == 0) {
size_t unit = 1;
for (size_t k = axis; k < whole_layout.size(); k++) {
unit *= whole_layout[k].get_length();
}
for (size_t j = 0; j < input_data.size(); j++) {
auto val = static_cast<size_t>((j % unit) / 4) + 1;
expected[j] = static_cast<float>(input_data[j] + val) + static_cast<float>(input_data[j] * val);
}
} else {
expected = expected_output_data;
}
auto output_mem = outputs.begin()->second.get_memory();
auto output_layout = output_mem->get_layout();
ASSERT_EQ(output_layout.batch(), e_output_layout.batch());
ASSERT_EQ(output_layout.feature(), e_output_layout.feature());
ASSERT_EQ(output_layout.spatial(0), e_output_layout.spatial(0));
ASSERT_EQ(output_layout.spatial(1), e_output_layout.spatial(1));
// value check
{
mem_lock<float> output_ptr{ output_mem, get_test_stream() };
for (size_t i = 0, iend = output_layout.count(); i < iend; ++i) {
ASSERT_FLOAT_EQ(output_ptr[i], expected.at(i));
}
}
}
}
TEST(loop_gpu, support_loop_w_dynamic_input_update_primitive_id) {
test_loop_gpu_wo_trip_count_update_primitive_id(
{ 1, -1, 4, 4 },
{{ 1, 1, 4, 4 }}, // axis value should be iter_num = (exit_value + 1)
{input_data_4_4, input_data_2_4_4},
std::vector<float>(),
2, 3);
}