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Ngraph to ov (#21185)

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Vitaliy Urusovskij 2023-11-22 09:44:17 +04:00 committed by GitHub
parent 0137fd7008
commit b66488e4ff
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GPG Key ID: 4AEE18F83AFDEB23
6 changed files with 32 additions and 32 deletions
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2
src/plugins/intel_gpu/tests/unit/CMakeLists.txt
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@ -17,7 +17,7 @@ file(GLOB_RECURSE SOURCES_MAIN
"${CMAKE_CURRENT_SOURCE_DIR}/*.h"
"${CMAKE_CURRENT_SOURCE_DIR}/*.hpp"
"${CMAKE_CURRENT_SOURCE_DIR}/*.cpp"
# ngraph graph transformation
# openvino graph transformation
"${CMAKE_HOME_DIRECTORY}/src/plugins/intel_gpu/src/plugin/transformations/*.hpp"
"${CMAKE_HOME_DIRECTORY}/src/plugins/intel_gpu/src/plugin/transformations/*.cpp"
)

2
src/plugins/intel_gpu/tests/unit/shape_infer/broadcast_si_test.cpp
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@ -25,7 +25,7 @@ struct broadcast_test_params {
layout data_layout;
layout target_shape_layout;
ov::Shape target_shape_data;
ngraph::AxisSet axes_mapping_data;
ov::AxisSet axes_mapping_data;
ov::op::BroadcastModeSpec mode;
layout expected_layout;
};

4
src/plugins/intel_gpu/tests/unit/test_cases/gemm_gpu_test.cpp
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@ -175,7 +175,7 @@ class GemmGPUTestRandom : public GemmGPUTest {
void generated_inputs() {
for (size_t i = 0; i < shapes.size(); ++i) {
size_t size = ngraph::shape_size(shapes[i]);
size_t size = ov::shape_size(shapes[i]);
input_data[i] = rg.generate_random_1d<float>(size, -1, 1, 10);
}
}
@ -189,7 +189,7 @@ class GemmGPUTestRandom : public GemmGPUTest {
input1_shape = (*input_it)->get_output_layout().get_shape();
layout output_layout = prog_nodes[0]->get_output_layout();
output_shape = output_layout.get_shape();
out_data.resize(ngraph::shape_size(output_shape));
out_data.resize(ov::shape_size(output_shape));
calculate_output_data();
}

2
src/plugins/intel_gpu/tests/unit/test_cases/scatter_elements_update_gpu_test.cpp
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@ -611,7 +611,7 @@ INSTANTIATE_TEST_SUITE_P(scatter_elements_update_gpu_max_reduction_use_init,
PrintToStringParamName());
// Disabled due to bug in reference implementation - see function reduction_neutral_value()
// in core/reference/include/ngraph/runtime/reference/scatter_elements_update.hpp.
// in core/reference/include/openvino/reference/scatter_elements_update.hpp.
// For MAX reduction it returns numeric_limits<T>::min() which is minimal *positive*, not truly minimal value of type T,
// which causes wrong result on negative input/update values.
// Enable when/if reference implementation is fixed.

8
src/plugins/intel_gpu/tests/unit/transformations/convert_pooling_to_reduce_test.cpp
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@ -7,9 +7,9 @@
#include <string>
#include <memory>
#include <ngraph/function.hpp>
#include <ngraph/opsets/opset9.hpp>
#include <ngraph/pass/manager.hpp>
#include <openvino/core/model.hpp>
#include <openvino/opsets/opset9.hpp>
#include <openvino/pass/manager.hpp>
#include "plugin/transformations/convert_pooling_to_reduce.hpp"
using namespace testing;
@ -37,7 +37,7 @@ static std::shared_ptr<ov::Model> CreateFunction(const ov::Shape& input_shape,
}
TEST(TransformationTests, ConvertAvgPoolToReduce) {
ngraph::pass::Manager manager;
ov::pass::Manager manager;
manager.set_per_pass_validation(false);
manager.register_pass<ov::intel_gpu::ConvertAvgPoolingToReduce>();

46
src/plugins/intel_gpu/tests/unit/transformations/decompose_reduce_for_false_keepdims_test.cpp
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@ -5,9 +5,9 @@
#include <gtest/gtest.h>
#include <memory>
#include <ngraph/function.hpp>
#include <ngraph/opsets/opset10.hpp>
#include <ngraph/pass/manager.hpp>
#include <openvino/core/model.hpp>
#include <openvino/opsets/opset10.hpp>
#include <openvino/pass/manager.hpp>
#include <plugin/transformations/decompose_reduce_for_false_keepdims.hpp>
#include <string>
#include <transformations/init_node_info.hpp>
@ -15,7 +15,7 @@
#include <tuple>
#include "intel_gpu/primitives/reduce.hpp"
#include "ngraph/type/element_type.hpp"
#include "openvino/core/type/element_type.hpp"
#include "openvino/core/descriptor/tensor.hpp"
#include "test_utils.h"
@ -23,7 +23,7 @@ using namespace testing;
using namespace ::tests;
using namespace cldnn;
using InputShape = ngraph::PartialShape;
using InputShape = ov::PartialShape;
using KeepDims = bool;
using ReduceAxes = std::vector<int64_t>;
using ReduceType = cldnn::reduce_mode;
@ -35,7 +35,7 @@ class ReduceDecomposeTests
public testing::WithParamInterface<
std::tuple<ReduceType, InputShape, ReduceAxes, KeepDims, NeedDecompose, ReshapeShape>> {
public:
std::shared_ptr<ngraph::Function> fc;
std::shared_ptr<ov::Model> fc;
bool need_decompose;
ReshapeShape result_shape;
@ -50,42 +50,42 @@ public:
fc = get_transformed_function(input_shape, axes, reduce_type, keep_dims);
}
static std::shared_ptr<ngraph::Function> get_transformed_function(const ngraph::PartialShape& input_shape,
static std::shared_ptr<ov::Model> get_transformed_function(const ov::PartialShape& input_shape,
const std::vector<int64_t>& axes,
const ReduceType& reduce_type,
const bool keep_dim) {
auto param = std::make_shared<ngraph::opset10::Parameter>(ngraph::element::f32, input_shape);
auto param = std::make_shared<ov::opset10::Parameter>(ov::element::f32, input_shape);
if (reduce_type == reduce_mode::logical_or || reduce_type == reduce_mode::logical_and)
param = std::make_shared<ngraph::opset10::Parameter>(ngraph::element::boolean, input_shape);
param = std::make_shared<ov::opset10::Parameter>(ov::element::boolean, input_shape);
ngraph::Output<ngraph::Node> input = param->output(0);
ov::Output<ov::Node> input = param->output(0);
auto axes_const = ngraph::opset10::Constant::create(ngraph::element::i64, ngraph::Shape{axes.size()}, axes);
auto axes_const = ov::opset10::Constant::create(ov::element::i64, ov::Shape{axes.size()}, axes);
if (reduce_type == reduce_mode::sum)
input = std::make_shared<ngraph::opset10::ReduceSum>(input, axes_const, keep_dim);
input = std::make_shared<ov::opset10::ReduceSum>(input, axes_const, keep_dim);
else if (reduce_type == reduce_mode::mean)
input = std::make_shared<ngraph::opset10::ReduceMean>(input, axes_const, keep_dim);
input = std::make_shared<ov::opset10::ReduceMean>(input, axes_const, keep_dim);
else if (reduce_type == reduce_mode::min)
input = std::make_shared<ngraph::opset10::ReduceMin>(input, axes_const, keep_dim);
input = std::make_shared<ov::opset10::ReduceMin>(input, axes_const, keep_dim);
else if (reduce_type == reduce_mode::max)
input = std::make_shared<ngraph::opset10::ReduceMax>(input, axes_const, keep_dim);
input = std::make_shared<ov::opset10::ReduceMax>(input, axes_const, keep_dim);
else if (reduce_type == reduce_mode::prod)
input = std::make_shared<ngraph::opset10::ReduceProd>(input, axes_const, keep_dim);
input = std::make_shared<ov::opset10::ReduceProd>(input, axes_const, keep_dim);
else if (reduce_type == reduce_mode::logical_or)
input = std::make_shared<ngraph::opset10::ReduceLogicalOr>(input, axes_const, keep_dim);
input = std::make_shared<ov::opset10::ReduceLogicalOr>(input, axes_const, keep_dim);
else if (reduce_type == reduce_mode::logical_and)
input = std::make_shared<ngraph::opset10::ReduceLogicalAnd>(input, axes_const, keep_dim);
input = std::make_shared<ov::opset10::ReduceLogicalAnd>(input, axes_const, keep_dim);
else
throw std::runtime_error("Invalid reduce type for this test-case.");
return std::make_shared<ngraph::Function>(ngraph::NodeVector{input.get_node_shared_ptr()},
ngraph::ParameterVector{param});
return std::make_shared<ov::Model>(ov::NodeVector{input.get_node_shared_ptr()},
ov::ParameterVector{param});
}
};
TEST_P(ReduceDecomposeTests, CompareFunctions) {
ngraph::pass::Manager m;
ov::pass::Manager m;
m.set_per_pass_validation(false);
m.register_pass<ov::pass::InitNodeInfo>();
m.register_pass<ov::intel_gpu::DecomposeReduceForFalseKeepDims>();
@ -208,8 +208,8 @@ INSTANTIATE_TEST_SUITE_P(ReduceDecomposeForTrueKeepdims,
TEST(DecomposeReduceForFalseKeepDims, Negative) {
auto f =
ReduceDecomposeTests::get_transformed_function(ngraph::PartialShape::dynamic(), {3}, reduce_mode::max, true);
ngraph::pass::Manager manager;
ReduceDecomposeTests::get_transformed_function(ov::PartialShape::dynamic(), {3}, reduce_mode::max, true);
ov::pass::Manager manager;
manager.register_pass<ov::intel_gpu::DecomposeReduceForFalseKeepDims>();
ASSERT_NO_THROW(manager.run_passes(f));
}