From e168c9b1c3de117d9b720441fa4059c2a8ec696f Mon Sep 17 00:00:00 2001 From: Wilson Seok Date: Tue, 15 Feb 2022 02:48:18 -0800 Subject: [PATCH] Add slt in template plugin/tensor iterator (#9692) * Remove fp16 of Convert layer test from skip_tests.config.cpp as it works now * update repo * add initial op reference code of TensorIterator with LSTM body function * add GRU/RNN case in setup * add all other test cases * add visitor api test * remove unnecessary header files * fix clang-format issue * fix copyright year and remove ngraph_helper namespace * rename ti.cpp to tensor_iterator.cpp in core unit test * apply suggestions --- .../op_reference/tensor_iterator.cpp | 2386 ++++++++++++++++- src/core/tests/CMakeLists.txt | 3 +- .../type_prop/{ti.cpp => tensor_iterator.cpp} | 0 .../tests/visitors/op/tensor_iterator.cpp | 138 + 4 files changed, 2497 insertions(+), 30 deletions(-) rename src/core/tests/type_prop/{ti.cpp => tensor_iterator.cpp} (100%) create mode 100644 src/core/tests/visitors/op/tensor_iterator.cpp diff --git a/docs/template_plugin/tests/functional/op_reference/tensor_iterator.cpp b/docs/template_plugin/tests/functional/op_reference/tensor_iterator.cpp index 8d55ae01a01..b9a57e35ad7 100644 --- a/docs/template_plugin/tests/functional/op_reference/tensor_iterator.cpp +++ b/docs/template_plugin/tests/functional/op_reference/tensor_iterator.cpp @@ -10,16 +10,21 @@ #include "base_reference_test.hpp" #include "functional_test_utils/skip_tests_config.hpp" +#include "ngraph_functions/builders.hpp" + +namespace { struct TIFunctionalBase { - virtual std::shared_ptr create_function(const std::vector& ti_inputs, - const std::vector& results) = 0; + virtual std::shared_ptr create_function(const std::vector &ti_inputs, + const std::vector &results) = 0; + TIFunctionalBase() = default; + virtual ~TIFunctionalBase() = default; }; struct TIDynamicInputs : public TIFunctionalBase { - std::shared_ptr create_function(const std::vector& ti_inputs, - const std::vector& results) override { + std::shared_ptr create_function(const std::vector &ti_inputs, + const std::vector &results) override { auto X = std::make_shared(ov::element::f32, ov::PartialShape::dynamic()); auto Y = std::make_shared(ov::element::f32, ov::PartialShape::dynamic()); auto M = std::make_shared(ov::element::f32, ov::PartialShape::dynamic()); @@ -53,14 +58,14 @@ struct TIDynamicInputs : public TIFunctionalBase { }; struct TensorIteratorParams { - TensorIteratorParams(const std::shared_ptr& functional, - const std::vector& ti_inputs, - const std::vector& expected_results, - const std::string& test_case_name) - : function(functional), - inputs(ti_inputs), - expected_results(expected_results), - test_case_name(test_case_name) {} + TensorIteratorParams(const std::shared_ptr &functional, + const std::vector &ti_inputs, + const std::vector &expected_results, + const std::string &test_case_name) + : function(functional), + inputs(ti_inputs), + expected_results(expected_results), + test_case_name(test_case_name) {} std::shared_ptr function; std::vector inputs; @@ -69,7 +74,7 @@ struct TensorIteratorParams { }; class ReferenceTILayerTest : public testing::TestWithParam, - public reference_tests::CommonReferenceTest { + public reference_tests::CommonReferenceTest { public: void SetUp() override { SKIP_IF_CURRENT_TEST_IS_DISABLED() @@ -77,14 +82,15 @@ public: function = params.function->create_function(params.inputs, params.expected_results); inputData.reserve(params.inputs.size()); refOutData.reserve(params.expected_results.size()); - for (auto& input_tensor : params.inputs) { + for (auto &input_tensor: params.inputs) { inputData.push_back(input_tensor.data); } - for (auto& expected_tensor : params.expected_results) { + for (auto &expected_tensor: params.expected_results) { refOutData.push_back(expected_tensor.data); } } - static std::string getTestCaseName(const testing::TestParamInfo& obj) { + + static std::string getTestCaseName(const testing::TestParamInfo &obj) { auto param = obj.param; return param.test_case_name; } @@ -95,16 +101,2338 @@ TEST_P(ReferenceTILayerTest, TensorIteratorWithHardcodedRefs) { } INSTANTIATE_TEST_SUITE_P( - smoke_TensorIterator_With_Hardcoded_Refs, - ReferenceTILayerTest, - ::testing::Values( - TensorIteratorParams( - std::make_shared(), - std::vector{ - reference_tests::Tensor(ov::element::f32, ov::Shape{1, 2}, std::vector{2, 3}), - reference_tests::Tensor(ov::element::f32, ov::Shape{2, 1}, std::vector{4, 5}), - reference_tests::Tensor(ov::element::f32, ov::Shape{1, 1}, std::vector{5})}, - std::vector{ - reference_tests::Tensor(ov::element::f32, ov::Shape{1, 1}, std::vector{240})}, - "tensor_iterator_dynamic_inputs")), - ReferenceTILayerTest::getTestCaseName); + smoke_TensorIterator_With_Hardcoded_Refs, + ReferenceTILayerTest, + ::testing::Values( + TensorIteratorParams( + std::make_shared(), + std::vector{ + reference_tests::Tensor(ov::element::f32, ov::Shape{1, 2}, std::vector{2, 3}), + reference_tests::Tensor(ov::element::f32, ov::Shape{2, 1}, std::vector{4, 5}), + reference_tests::Tensor(ov::element::f32, ov::Shape{1, 1}, std::vector{5})}, + std::vector{ + reference_tests::Tensor(ov::element::f32, ov::Shape{1, 1}, std::vector{240})}, + "tensor_iterator_dynamic_inputs")), + ReferenceTILayerTest::getTestCaseName); + +enum TensorIteratorBodyType { + RNN, + GRU, + LSTM, +}; + +struct TensorIteratorStaticParams; + +struct TIStaticFunctionalBase { + virtual std::shared_ptr create_function(const TensorIteratorStaticParams& params) = 0; + TIStaticFunctionalBase() = default; + virtual ~TIStaticFunctionalBase() = default; +}; + +struct TensorIteratorStaticParams { + template + TensorIteratorStaticParams( + const std::shared_ptr& functional, + const size_t batchSize, const size_t inputSize, const size_t hiddenSize, const size_t seqLength, + const float clip, const ov::op::RecurrentSequenceDirection& direction, + const TensorIteratorBodyType& body_type, + const ov::element::Type_t& iType, + const std::vector& XValues, const std::vector& H_tValues, const std::vector& C_tValues, const std::vector& S_tValues, + const std::vector& WValues, const std::vector& RValues, const std::vector& BValues, + const std::vector& YValues, const std::vector& HoValues, const std::vector& CoValues, + const std::string& testcaseName = "") : + function(functional), + batchSize(batchSize), inputSize(inputSize), hiddenSize(hiddenSize), seqLength(seqLength), + clip(clip), body_type(body_type), direction(direction), iType(iType), oType(iType), + testcaseName(testcaseName) { + switch (body_type) { + case TensorIteratorBodyType::LSTM: { + ov::Shape XShape = ov::Shape{batchSize, seqLength, inputSize}; + ov::Shape H_tShape = ov::Shape{batchSize, hiddenSize}; + ov::Shape C_tShape = ov::Shape{batchSize, hiddenSize}; + ov::Shape S_tShape = ov::Shape{batchSize}; + ov::Shape WShape = ov::Shape{4 * hiddenSize, inputSize}; + ov::Shape RShape = ov::Shape{4 * hiddenSize, hiddenSize}; + ov::Shape BShape = ov::Shape{4 * hiddenSize}; + ov::Shape YShape = ov::Shape{batchSize, seqLength, hiddenSize}; + ov::Shape HoShape = ov::Shape{batchSize, hiddenSize}; + ov::Shape CoShape = ov::Shape{batchSize, hiddenSize}; + + X = reference_tests::Tensor(XShape, iType, XValues); + H_t = reference_tests::Tensor(H_tShape, iType, H_tValues); + C_t = reference_tests::Tensor(C_tShape, iType, C_tValues); + S_t = reference_tests::Tensor(S_tShape, ov::element::Type_t::i64, S_tValues); + W = reference_tests::Tensor(WShape, iType, WValues); + R = reference_tests::Tensor(RShape, iType, RValues); + B = reference_tests::Tensor(BShape, iType, BValues); + Y = reference_tests::Tensor(YShape, oType, YValues); + Ho = reference_tests::Tensor(HoShape, oType, HoValues); + Co = reference_tests::Tensor(CoShape, oType, CoValues); + break; + } + case TensorIteratorBodyType::GRU: { + ov::Shape XShape = ov::Shape{batchSize, seqLength, inputSize}; + ov::Shape H_tShape = ov::Shape{batchSize, hiddenSize}; + ov::Shape S_tShape = ov::Shape{batchSize}; + ov::Shape WShape = ov::Shape{3 * hiddenSize, inputSize}; + ov::Shape RShape = ov::Shape{3 * hiddenSize, hiddenSize}; + ov::Shape BShape = ov::Shape{3 * hiddenSize}; + ov::Shape YShape = ov::Shape{batchSize, seqLength, hiddenSize}; + ov::Shape HoShape = ov::Shape{batchSize, hiddenSize}; + + X = reference_tests::Tensor(XShape, iType, XValues); + H_t = reference_tests::Tensor(H_tShape, iType, H_tValues); + S_t = reference_tests::Tensor(S_tShape, ov::element::Type_t::i64, S_tValues); + W = reference_tests::Tensor(WShape, iType, WValues); + R = reference_tests::Tensor(RShape, iType, RValues); + B = reference_tests::Tensor(BShape, iType, BValues); + Y = reference_tests::Tensor(YShape, oType, YValues); + Ho = reference_tests::Tensor(HoShape, oType, HoValues); + break; + } + case TensorIteratorBodyType::RNN: { + ov::Shape XShape = ov::Shape{batchSize, seqLength, inputSize}; + ov::Shape H_tShape = ov::Shape{batchSize, hiddenSize}; + ov::Shape S_tShape = ov::Shape{batchSize}; + ov::Shape WShape = ov::Shape{hiddenSize, inputSize}; + ov::Shape RShape = ov::Shape{hiddenSize, hiddenSize}; + ov::Shape BShape = ov::Shape{hiddenSize}; + ov::Shape YShape = ov::Shape{batchSize, seqLength, hiddenSize}; + ov::Shape HoShape = ov::Shape{batchSize, hiddenSize}; + + X = reference_tests::Tensor(XShape, iType, XValues); + H_t = reference_tests::Tensor(H_tShape, iType, H_tValues); + S_t = reference_tests::Tensor(S_tShape, ov::element::Type_t::i64, S_tValues); + W = reference_tests::Tensor(WShape, iType, WValues); + R = reference_tests::Tensor(RShape, iType, RValues); + B = reference_tests::Tensor(BShape, iType, BValues); + Y = reference_tests::Tensor(YShape, oType, YValues); + Ho = reference_tests::Tensor(HoShape, oType, HoValues); + break; + } + } + } + + std::shared_ptr function; + + size_t batchSize; + size_t inputSize; + size_t hiddenSize; + size_t seqLength; + size_t sequenceAxis = 1; + float clip; + TensorIteratorBodyType body_type; + ov::op::RecurrentSequenceDirection direction; + + ov::element::Type_t iType; + ov::element::Type_t oType; + + reference_tests::Tensor X; + reference_tests::Tensor H_t; + reference_tests::Tensor C_t; + reference_tests::Tensor S_t; + reference_tests::Tensor W; + reference_tests::Tensor R; + reference_tests::Tensor B; + reference_tests::Tensor Y; + reference_tests::Tensor Ho; + reference_tests::Tensor Co; + std::string testcaseName; +}; + +struct TIStaticInputs : public TIStaticFunctionalBase { + std::shared_ptr create_function(const TensorIteratorStaticParams& params) override { + std::vector inputShapes; + std::shared_ptr function; + auto tensor_iterator = std::make_shared(); + + // Each case consist of 3 steps: + // 1. Create TensorIterator body. + // 2. Set PortMap + // 3. Create outer function + auto axis = std::make_shared(ov::element::i64, ov::Shape{1}, + std::vector{static_cast(params.sequenceAxis)}); + switch (params.body_type) { + case TensorIteratorBodyType::LSTM: { + inputShapes = { + {{params.batchSize, params.seqLength, params.inputSize}, // X + {params.batchSize, params.hiddenSize}, // H_i + {params.batchSize, params.hiddenSize}, // C_i + {4 * params.hiddenSize, params.inputSize}, // W + {4 * params.hiddenSize, params.inputSize}, // R + {4 * params.inputSize}}, // B + }; + if (params.sequenceAxis == 0) { + // swap params.batchSize and params.seqLength + std::swap(inputShapes[0][0], inputShapes[0][1]); + } + auto outer_params = ngraph::builder::makeParams(params.iType, {inputShapes[0], inputShapes[1], inputShapes[2]}); + + // 1. Create TensorIterator body. + inputShapes[0][params.sequenceAxis] = 1; // sliced dimension + auto body_params = ngraph::builder::makeParams(params.iType, {inputShapes[0], inputShapes[1], inputShapes[2]}); + auto squeeze = std::make_shared(body_params[0], axis); + ov::OutputVector out_vector = {squeeze, body_params[1], body_params[2]}; + + auto W = std::make_shared(params.W.type, params.W.shape, params.W.data.data()); + auto R = std::make_shared(params.R.type, params.R.shape, params.R.data.data()); + auto B = std::make_shared(params.B.type, params.B.shape, params.B.data.data()); + auto lstm_cell = std::make_shared(out_vector[0], + out_vector[1], + out_vector[2], + W, + R, + B, + params.hiddenSize, + std::vector{"sigmoid", "tanh", "tanh"}, + std::vector{}, + std::vector{}, + params.clip); + + auto unsqueeze = std::make_shared(lstm_cell->output(0), axis); + ov::ResultVector results{std::make_shared(unsqueeze), + std::make_shared(lstm_cell->output(0)), + std::make_shared(lstm_cell->output(1))}; + auto body = std::make_shared(results, body_params, "lstm_cell"); + tensor_iterator->set_function(body); + + // 2. Set PortMap + if (params.direction == ov::op::RecurrentSequenceDirection::FORWARD) { + tensor_iterator->set_sliced_input(body_params[0], outer_params[0], 0, 1, 1, -1, params.sequenceAxis); + tensor_iterator->get_concatenated_slices(results[0], 0, 1, 1, -1, params.sequenceAxis); + } else if (params.direction == ov::op::RecurrentSequenceDirection::REVERSE) { + tensor_iterator->set_sliced_input(body_params[0], outer_params[0], -1, -1, 1, 0, params.sequenceAxis); + tensor_iterator->get_concatenated_slices(results[0], -1, -1, 1, 0, params.sequenceAxis); + } else { + NGRAPH_CHECK(false, "Bidirectional case is not supported."); + } + + tensor_iterator->set_merged_input(body_params[1], outer_params[1], results[1]); + tensor_iterator->set_merged_input(body_params[2], outer_params[2], results[2]); + tensor_iterator->get_iter_value(results[1]); + tensor_iterator->get_iter_value(results[2]); + + // 3. Outer function + function = std::make_shared(ngraph::OutputVector{tensor_iterator->output(0), tensor_iterator->output(1), + tensor_iterator->output(2)}, outer_params); + break; + } + case TensorIteratorBodyType::GRU: { + inputShapes = { + {{params.batchSize, params.seqLength, params.inputSize}, // X + {params.batchSize, params.inputSize}, // H_i + {3 * params.hiddenSize, params.inputSize}, // W + {3 * params.hiddenSize, params.hiddenSize}, // R + {3 * params.hiddenSize}}, // B + }; + if (params.sequenceAxis == 0) { + // swap params.batchSize and params.seqLength + std::swap(inputShapes[0][0], inputShapes[0][1]); + } + auto outer_params = ngraph::builder::makeParams(params.iType, {inputShapes[0], inputShapes[1]}); + + // 1. Create TensorIterator body. + inputShapes[0][params.sequenceAxis] = 1; // sliced dimension + auto body_params = ngraph::builder::makeParams(params.iType, {inputShapes[0], inputShapes[1]}); + auto squeeze = std::make_shared(body_params[0], axis); + ngraph::OutputVector out_vector = {squeeze, body_params[1]}; + + auto W = std::make_shared(params.W.type, params.W.shape, params.W.data.data()); + auto R = std::make_shared(params.R.type, params.R.shape, params.R.data.data()); + auto B = std::make_shared(params.B.type, params.B.shape, params.B.data.data()); + auto gru_cell = std::make_shared(out_vector[0], + out_vector[1], + W, + R, + B, + params.hiddenSize, + std::vector{"sigmoid", "tanh"}, + std::vector{}, + std::vector{}, + params.clip, + false); + + auto unsqueeze = std::make_shared(gru_cell->output(0), axis); + ngraph::ResultVector results{std::make_shared(gru_cell->output(0)), + std::make_shared(unsqueeze)}; + auto body = std::make_shared(results, body_params, "gru_cell"); + tensor_iterator->set_function(body); + + // 2. Set PortMap + if (params.direction == ov::op::RecurrentSequenceDirection::FORWARD) { + tensor_iterator->set_sliced_input(body_params[0], outer_params[0], 0, 1, 1, -1, params.sequenceAxis); + tensor_iterator->get_concatenated_slices(results[1], 0, 1, 1, -1, params.sequenceAxis); + } else if (params.direction == ov::op::RecurrentSequenceDirection::REVERSE) { + tensor_iterator->set_sliced_input(body_params[0], outer_params[0], -1, -1, 1, 0, params.sequenceAxis); + tensor_iterator->get_concatenated_slices(results[1], -1, -1, 1, 0, params.sequenceAxis); + } else { + NGRAPH_CHECK(false, "Bidirectional case is not supported."); + } + + tensor_iterator->set_merged_input(body_params[1], outer_params[1], results[0]); + tensor_iterator->get_iter_value(results[0]); + + // 3. Outer function + function = std::make_shared(ngraph::OutputVector{tensor_iterator->output(0), tensor_iterator->output(1)}, outer_params); + break; + } + case TensorIteratorBodyType::RNN: { + inputShapes = { + {{params.batchSize, params.seqLength, params.inputSize}, // X + {params.batchSize, params.inputSize}, // H_i + {params.hiddenSize, params.inputSize}, // W + {params.hiddenSize, params.hiddenSize}, // R + {params.hiddenSize}}, // B + }; + if (params.sequenceAxis == 0) { + // swap params.batchSize and params.seqLength + std::swap(inputShapes[0][0], inputShapes[0][1]); + } + auto outer_params = ngraph::builder::makeParams(params.iType, {inputShapes[0], inputShapes[1]}); + + // 1. Create TensorIterator body. + inputShapes[0][params.sequenceAxis] = 1; // sliced dimension + auto body_params = ngraph::builder::makeParams(params.iType, {inputShapes[0], inputShapes[1]}); + auto squeeze = std::make_shared(body_params[0], axis); + ngraph::OutputVector out_vector = {squeeze, body_params[1]}; + + auto W = std::make_shared(params.W.type, params.W.shape, params.W.data.data()); + auto R = std::make_shared(params.R.type, params.R.shape, params.R.data.data()); + auto B = std::make_shared(params.B.type, params.B.shape, params.B.data.data()); + auto rnn_cell = std::make_shared(out_vector[0], + out_vector[1], + W, + R, + B, + params.hiddenSize, + std::vector{"tanh"}, + std::vector{}, + std::vector{}, + params.clip); + + auto unsqueeze = std::make_shared(rnn_cell->output(0), axis); + ngraph::ResultVector results{std::make_shared(rnn_cell), + std::make_shared(unsqueeze)}; + auto body = std::make_shared(results, body_params, "rnn_cell"); + tensor_iterator->set_function(body); + + // 2. Set PortMap + if (params.direction == ov::op::RecurrentSequenceDirection::FORWARD) { + tensor_iterator->set_sliced_input(body_params[0], outer_params[0], 0, 1, 1, -1, params.sequenceAxis); + tensor_iterator->get_concatenated_slices(results[1], 0, 1, 1, -1, params.sequenceAxis); + } else if (params.direction == ov::op::RecurrentSequenceDirection::REVERSE) { + tensor_iterator->set_sliced_input(body_params[0], outer_params[0], -1, -1, 1, 0, params.sequenceAxis); + tensor_iterator->get_concatenated_slices(results[1], -1, -1, 1, 0, params.sequenceAxis); + } else { + NGRAPH_CHECK(false, "Bidirectional case is not supported."); + } + + tensor_iterator->set_merged_input(body_params[1], outer_params[1], results[0]); + tensor_iterator->get_iter_value(results[0]); + + // 3. Outer function + function = std::make_shared(ngraph::OutputVector{tensor_iterator->output(0), tensor_iterator->output(1)}, outer_params); + break; + } + } + return function; + } +}; + +class ReferenceTILayerStaticTest : public testing::TestWithParam, + public reference_tests::CommonReferenceTest { +public: + void SetUp() override { + SKIP_IF_CURRENT_TEST_IS_DISABLED() + auto params = GetParam(); + function = params.function->create_function(params); + if (params.body_type == TensorIteratorBodyType::LSTM) { + inputData = {params.X.data, params.H_t.data, params.C_t.data}; + refOutData = {params.Y.data, params.Ho.data, params.Co.data}; + } else { + inputData = {params.X.data, params.H_t.data}; + refOutData = {params.Y.data, params.Ho.data}; + } + } + + static std::string getTestCaseName(const testing::TestParamInfo& obj) { + auto param = obj.param; + std::ostringstream result; + result << "bSize=" << param.batchSize; + result << "_iSize=" << param.inputSize; + result << "_hSize=" << param.hiddenSize; + result << "_sLength=" << param.seqLength; + result << "_clip=" << param.clip; + result << "_body=" << param.body_type; + result << "_xType=" << param.X.type; + result << "_xShape=" << param.X.shape; + if (param.testcaseName != "") { + result << "_direction=" << param.direction; + result << "_" << param.testcaseName; + } else { + result << "_direction=" << param.direction; + } + return result.str(); + } +}; + +TEST_P(ReferenceTILayerStaticTest, CompareWithRefs) { + Exec(); +} + +template +std::vector generateParams() { + using T = typename ov::element_type_traits::value_type; + + std::vector params { + TensorIteratorStaticParams( + std::make_shared(), + 5, 10, 10, 10, + 0.7f, ov::op::RecurrentSequenceDirection::FORWARD, TensorIteratorBodyType::LSTM, + ET, + std::vector{ + 1, 9.97466, 9.39302, 2.15312, 9.99136, 3.1248, 4.56923, 4.4912, 7.02771, 9.41985, + 8.6168, 3.81946, 5.72093, 4.99108, 3.0662, 5.80973, 9.22566, 5.11484, 4.87629, 9.45215, + 8.0055, 7.44373, 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7.875, 5.8125, 1.40625, 6.96875, 2.25, 5.625, 8.125, 9.5, 1.26562, 6.25, + 8.9375, 9.125, 5.875, 2.23438, 5.03125, 2.25, 9, 8.25, 4.375, 4.5625, + 5.84375, 2.48438, 6.875, 9.375, 4.25, 4.125, 6.125, 7.75, 6.75, 7.53125, + 2.125, 8.9375, 7.1875, 6.625, 6.8125, 7.75, 4.1875, 4.125, 7.875, 3.42188, + 4.1875, 9.0625, 7.75, 4.84375, 8.875, 9.625, 1.10156, 6.96875, 5.46875, 6.59375, + 1.66406, 2.03125, 8.0625, 9.5, 1.57812, 5.0625, 4.1875, 6.1875, 9.5, 4.6875, + 4.40625, 3.125, 7.875, 9.125, 7.9375, 6.15625, 3.71875, 1.02344, 7.9375, 6.5625, + 2.375, 3.9375, 6.1875, 5.75, 1.07812, 9, 7.375, 4.1875, 5.25, 9.125, + 7.875, 6.625, 5.1875, 1.14062, 3.40625, 9.375, 8.5, 7.1875, 5.9375, 10, + 1.625, 2.54688, 5.25, 2.21875, 7.6875, 9.375, 2.71875, 7.25, 5.1875, 1.59375, + 3.0625, 7.8125, 5.5625, 7.78125, 2.875, 9.25, 1.4375, 7.375, 5.65625, 2.125, + 2.54688, 1.17188, 4.5625, 1.23438, 1.96875, 1.25, 5.5625, 3.21875, 1.92188, 8.75, + 3.59375, 5.84375, 3.07812, 5.96875, 9.6875, 8.5625, 3.5, 2.125, 3.09375, 3.5, + 1.82031, 6.25, 6.125, 9.75, 4.75, 6.0625, 4.3125, 1.16406, 8.3125, 8.1875, + 3.59375, 3.09375, 7.4375, 8.25, 6.5, 4.5, 4.8125, 8.75, 7.75, 7.71875, + 4.84375, 6, 4.84375, 2.21875, 4.25, 1.53906, 2.375, 2.09375, 9.375, 1.39844, + 9.25, 1.96875, 8, 3.03125, 6.5625, 7.40625, 1.32031, 6.03125, 6.875, 10}, + std::vector{ + 1, 4.75, 10, 7.46875, 9.375, 1, 2.15625, 3.71875, 10, 2.3125, + 3.125, 1.82812, 4.5625, 2.67188, 4.5, 4.125, 7, 4.5625, 9.375, 5.84375, + 8.625, 4.75, 3.8125, 7.15625, 5.71875, 2.84375, 5, 8.875, 3.0625, 10}, + std::vector{ + 0.605469, 0.671875, 0.761719, 0.714844, 0.753906, 0.605469, 0.625, 0.652344, 0.761719, 0.625, + 0.609375, 0.707031, 0.839844, 0.777344, 0.832031, 0.609375, 0.640625, 0.679688, 0.839844, 0.640625, + 0.613281, 0.761719, 0.964844, 0.867188, 0.945312, 0.613281, 0.660156, 0.722656, 0.964844, 0.664062, + 0.621094, 0.839844, 1.14844, 1, 1.11719, 0.621094, 0.691406, 0.785156, 1.14844, 0.699219, + 0.632812, 0.964844, 1.42188, 1.20312, 1.375, 0.632812, 0.738281, 0.878906, 1.42188, 0.753906, + 0.648438, 1.14844, 1.82812, 1.5, 1.75, 0.648438, 0.808594, 1.01562, 1.82812, 0.832031, + 0.675781, 1.42188, 2.4375, 1.95312, 2.3125, 0.675781, 0.910156, 1.21875, 2.4375, 0.945312, + 0.714844, 1.82812, 3.375, 2.625, 3.1875, 0.714844, 1.0625, 1.51562, 3.375, 1.11719, + 0.777344, 2.4375, 4.75, 3.65625, 4.5, 0.777344, 1.29688, 1.96875, 4.75, 1.375, + 0.867188, 3.375, 6.875, 5.1875, 6.4375, 0.867188, 1.64062, 2.65625, 6.875, 1.75, + 0.640625, 0.621094, 0.664062, 0.632812, 0.664062, 0.660156, 0.707031, 0.664062, 0.753906, 0.691406, + 0.664062, 0.632812, 0.699219, 0.652344, 0.699219, 0.691406, 0.761719, 0.699219, 0.832031, 0.738281, + 0.695312, 0.648438, 0.753906, 0.679688, 0.753906, 0.738281, 0.847656, 0.753906, 0.945312, 0.808594, + 0.746094, 0.671875, 0.832031, 0.722656, 0.832031, 0.808594, 0.972656, 0.832031, 1.11719, 0.910156, + 0.816406, 0.707031, 0.945312, 0.785156, 0.945312, 0.910156, 1.15625, 0.945312, 1.375, 1.0625, + 0.929688, 0.761719, 1.11719, 0.878906, 1.11719, 1.0625, 1.4375, 1.11719, 1.75, 1.29688, + 1.09375, 0.839844, 1.375, 1.01562, 1.375, 1.29688, 1.84375, 1.375, 2.3125, 1.64062, + 1.34375, 0.964844, 1.76562, 1.21875, 1.75, 1.64062, 2.46875, 1.76562, 3.1875, 2.15625, + 1.71875, 1.14844, 2.34375, 1.53125, 2.3125, 2.15625, 3.4375, 2.34375, 4.5, 2.9375, + 2.28125, 1.42188, 3.25, 1.98438, 3.1875, 2.9375, 4.875, 3.25, 6.4375, 4.09375, + 0.738281, 0.671875, 0.652344, 0.714844, 0.6875, 0.636719, 0.675781, 0.746094, 0.640625, 0.609375, + 0.808594, 0.707031, 0.679688, 0.769531, 0.730469, 0.65625, 0.714844, 0.816406, 0.664062, 0.617188, + 0.917969, 0.761719, 0.722656, 0.855469, 0.800781, 0.6875, 0.769531, 0.925781, 0.695312, 0.625, + 1.07812, 0.839844, 0.785156, 0.988281, 0.902344, 0.730469, 0.855469, 1.08594, 0.746094, 0.636719, + 1.3125, 0.964844, 0.878906, 1.17969, 1.05469, 0.800781, 0.988281, 1.32812, 0.816406, 0.65625, + 1.65625, 1.14844, 1.01562, 1.46875, 1.28125, 0.898438, 1.17969, 1.6875, 0.925781, 0.6875, + 2.1875, 1.42188, 1.21875, 1.89062, 1.625, 1.04688, 1.46875, 2.21875, 1.08594, 0.730469, + 2.96875, 1.82812, 1.53125, 2.53125, 2.125, 1.26562, 1.89062, 3.03125, 1.32812, 0.792969, + 4.15625, 2.4375, 2, 3.5, 2.875, 1.59375, 2.53125, 4.25, 1.6875, 0.886719, + 5.9375, 3.375, 2.71875, 4.9375, 4, 2.09375, 3.53125, 6.125, 2.21875, 1.03125, + 0.6875, 0.707031, 0.746094, 0.671875, 0.675781, 0.691406, 0.671875, 0.625, 0.753906, 0.636719, + 0.730469, 0.761719, 0.816406, 0.707031, 0.714844, 0.738281, 0.707031, 0.640625, 0.832031, 0.65625, + 0.800781, 0.847656, 0.929688, 0.761719, 0.777344, 0.808594, 0.761719, 0.664062, 0.945312, 0.6875, + 0.902344, 0.972656, 1.09375, 0.839844, 0.867188, 0.917969, 0.847656, 0.695312, 1.11719, 0.730469, + 1.05469, 1.15625, 1.34375, 0.964844, 1, 1.07812, 0.972656, 0.746094, 1.375, 0.792969, + 1.28125, 1.4375, 1.71875, 1.14844, 1.20312, 1.3125, 1.15625, 0.816406, 1.75, 0.886719, + 1.625, 1.84375, 2.28125, 1.42188, 1.5, 1.65625, 1.4375, 0.929688, 2.3125, 1.03125, + 2.125, 2.46875, 3.15625, 1.82812, 1.9375, 2.1875, 1.84375, 1.09375, 3.1875, 1.25, + 2.90625, 3.4375, 4.4375, 2.4375, 2.59375, 3, 2.46875, 1.34375, 4.5, 1.57812, + 4.0625, 4.875, 6.375, 3.375, 3.625, 4.1875, 3.4375, 1.70312, 6.5, 2.0625, + 0.730469, 0.730469, 0.714844, 0.753906, 0.730469, 0.652344, 0.621094, 0.714844, 0.6875, 0.761719, + 0.792969, 0.800781, 0.777344, 0.832031, 0.800781, 0.679688, 0.632812, 0.769531, 0.730469, 0.839844, + 0.886719, 0.898438, 0.867188, 0.949219, 0.902344, 0.722656, 0.648438, 0.855469, 0.800781, 0.964844, + 1.03125, 1.04688, 1, 1.125, 1.05469, 0.785156, 0.671875, 0.988281, 0.898438, 1.14844, + 1.25, 1.26562, 1.20312, 1.39062, 1.28125, 0.878906, 0.707031, 1.17969, 1.04688, 1.42188, + 1.57812, 1.59375, 1.5, 1.78125, 1.625, 1.01562, 0.761719, 1.46875, 1.26562, 1.82812, + 2.0625, 2.09375, 1.9375, 2.375, 2.125, 1.21875, 0.839844, 1.89062, 1.59375, 2.4375, + 2.78125, 2.84375, 2.59375, 3.28125, 2.875, 1.53125, 0.964844, 2.53125, 2.09375, 3.375, + 3.875, 3.96875, 3.625, 4.625, 4, 2, 1.14844, 3.53125, 2.84375, 4.75, + 5.53125, 5.65625, 5.125, 6.65625, 5.6875, 2.71875, 1.42188, 5, 3.96875, 6.875}, + std::vector{ + 0.605469, 0.671875, 0.761719, 0.714844, 0.753906, 0.605469, 0.625, 0.652344, 0.761719, 0.625, + 0.640625, 0.621094, 0.664062, 0.632812, 0.664062, 0.660156, 0.707031, 0.664062, 0.753906, 0.691406, + 0.738281, 0.671875, 0.652344, 0.714844, 0.6875, 0.636719, 0.675781, 0.746094, 0.640625, 0.609375, + 0.6875, 0.707031, 0.746094, 0.671875, 0.675781, 0.691406, 0.671875, 0.625, 0.753906, 0.636719, + 0.730469, 0.730469, 0.714844, 0.753906, 0.730469, 0.652344, 0.621094, 0.714844, 0.6875, 0.761719}, + std::vector{0}), + }; + return params; +} + +std::vector generateCombinedParams() { + const std::vector> generatedParams { + generateParams(), + generateParams(), + generateParams(), + generateParamsBF16(), + }; + std::vector combinedParams; + + for (const auto& params : generatedParams) { + combinedParams.insert(combinedParams.end(), params.begin(), params.end()); + } + return combinedParams; +} + +INSTANTIATE_TEST_SUITE_P(smoke_TensorIterator_With_Hardcoded_Refs, ReferenceTILayerStaticTest, + testing::ValuesIn(generateCombinedParams()), ReferenceTILayerStaticTest::getTestCaseName); +} // namespace diff --git a/src/core/tests/CMakeLists.txt b/src/core/tests/CMakeLists.txt index db1421004cb..3f8f8c98dbe 100644 --- a/src/core/tests/CMakeLists.txt +++ b/src/core/tests/CMakeLists.txt @@ -243,7 +243,7 @@ set(SRC type_prop/swish.cpp type_prop/tan.cpp type_prop/tanh.cpp - type_prop/ti.cpp + type_prop/tensor_iterator.cpp type_prop/tile.cpp type_prop/top_k.cpp type_prop/transpose.cpp @@ -405,6 +405,7 @@ set(SRC visitors/op/swish.cpp visitors/op/tan.cpp visitors/op/tanh.cpp + visitors/op/tensor_iterator.cpp visitors/op/topk.cpp visitors/op/transpose.cpp visitors/op/unsqueeze.cpp diff --git a/src/core/tests/type_prop/ti.cpp b/src/core/tests/type_prop/tensor_iterator.cpp similarity index 100% rename from src/core/tests/type_prop/ti.cpp rename to src/core/tests/type_prop/tensor_iterator.cpp diff --git a/src/core/tests/visitors/op/tensor_iterator.cpp b/src/core/tests/visitors/op/tensor_iterator.cpp new file mode 100644 index 00000000000..f6ec19d0d82 --- /dev/null +++ b/src/core/tests/visitors/op/tensor_iterator.cpp @@ -0,0 +1,138 @@ +// Copyright (C) 2018-2022 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "gtest/gtest.h" +#include "ngraph/builder/reshape.hpp" +#include "ngraph/ngraph.hpp" +#include "ngraph/op/util/attr_types.hpp" +#include "ngraph/opsets/opset1.hpp" +#include "ngraph/opsets/opset5.hpp" +#include "util/visitor.hpp" + +using namespace std; +using namespace ngraph; +using ngraph::test::NodeBuilder; +using ngraph::test::ValueMap; + +TEST(attributes, tensor_iterator_lstm) { + // That which we iterate over + const size_t N = 32; // Batch size + const size_t L = 10; // Sequence length + const size_t I = 8; // Input size + const size_t H = 32; // Hidden size + + NodeBuilder::get_ops().register_factory(); + + auto SENT = make_shared(element::f32, Shape{N, L, I}); + + auto H_init = make_shared(element::f32, Shape{N, 1, H}); + auto C_init = make_shared(element::f32, Shape{N, 1, H}); + + auto W = make_shared(element::f32, Shape{4 * H, I}); + auto R = make_shared(element::f32, Shape{4 * H, H}); + auto H_t = make_shared(element::f32, Shape{N, 1, H}); + auto C_t = make_shared(element::f32, Shape{N, 1, H}); + + // Body + auto X = make_shared(element::f32, Shape{N, 1, I}); + auto W_body = make_shared(element::f32, Shape{4 * H, I}); + auto R_body = make_shared(element::f32, Shape{4 * H, H}); + auto LSTM_cell = make_shared(builder::opset1::reshape(X, Shape{N, I}), + builder::opset1::reshape(H_t, Shape{N, H}), + builder::opset1::reshape(C_t, Shape{N, H}), + W_body, + R_body, + H); + auto H_o = builder::opset1::reshape(LSTM_cell->output(0), Shape{N, 1, H}); + auto C_o = builder::opset1::reshape(LSTM_cell->output(1), Shape{N, 1, H}); + auto body = make_shared(OutputVector{H_o, C_o}, ParameterVector{X, H_t, C_t, W_body, R_body}); + + auto tensor_iterator = make_shared(); + tensor_iterator->set_body(body); + // start=0, stride=1, part_size=1, end=39, axis=1 + tensor_iterator->set_sliced_input(X, SENT, 0, 1, 1, -1, 1); + // H_t is Hinit on the first iteration, Ho after that + tensor_iterator->set_merged_input(H_t, H_init, H_o); + tensor_iterator->set_merged_input(C_t, C_init, C_o); + tensor_iterator->set_invariant_input(W_body, W); + tensor_iterator->set_invariant_input(R_body, R); + + NodeBuilder builder(tensor_iterator); + const auto g_tensor_iterator = ov::as_type_ptr(builder.create()); + + EXPECT_EQ(g_tensor_iterator->get_body(), tensor_iterator->get_body()); + EXPECT_EQ(g_tensor_iterator->get_input_descriptions(), tensor_iterator->get_input_descriptions()); + EXPECT_EQ(g_tensor_iterator->get_output_descriptions(), tensor_iterator->get_output_descriptions()); +} + +TEST(attributes, tensor_iterator_2_slice_inputs_part_size_2) { + NodeBuilder::get_ops().register_factory(); + // That which we iterate over + auto X = make_shared(element::f32, Shape{32, 40, 10}); + auto Y = make_shared(element::f32, Shape{32, 40, 10}); + auto M = make_shared(element::f32, Shape{32, 2, 10}); + + // Set up the cell body, a function from (Xi, Yi) -> (Zo) + // Body parameters + auto Xi = make_shared(element::f32, Shape{32, 2, 10}); + auto Yi = make_shared(element::f32, Shape{32, 2, 10}); + auto M_body = make_shared(element::f32, Shape{32, 2, 10}); + + // Body + auto Zo = std::make_shared(std::make_shared(Xi, Yi), M_body); + auto body = make_shared(OutputVector{Zo}, ParameterVector{Xi, Yi, M_body}); + + auto tensor_iterator = make_shared(); + + tensor_iterator->set_body(body); + // The Xi are the elements of Xseq + // start=0, stride=2, part_size=2, end=39, axis=1 + tensor_iterator->set_sliced_input(Xi, X, 0, 2, 2, 39, 1); + // The Yi are the elements of Yseq + // start=0, stride=2, part_size=2, end=-1, axis=1 + tensor_iterator->set_sliced_input(Yi, Y, 0, 2, 2, -1, 1); + tensor_iterator->set_invariant_input(M_body, M); + + NodeBuilder builder(tensor_iterator); + const auto g_tensor_iterator = ov::as_type_ptr(builder.create()); + + EXPECT_EQ(g_tensor_iterator->get_body(), tensor_iterator->get_body()); + EXPECT_EQ(g_tensor_iterator->get_input_descriptions(), tensor_iterator->get_input_descriptions()); + EXPECT_EQ(g_tensor_iterator->get_output_descriptions(), tensor_iterator->get_output_descriptions()); +} + +TEST(attributes, tensor_iterator_2_slice_inputs_part_size_2_dynamic) { + NodeBuilder::get_ops().register_factory(); + // That which we iterate over + auto X = make_shared(element::f32, Shape{32, 40, 10}); + auto Y = make_shared(element::f32, Shape{32, 40, 10}); + auto M = make_shared(element::f32, Shape{32, 2, 10}); + + // Set up the cell body, a function from (Xi, Yi) -> (Zo) + // Body parameters + auto Xi = make_shared(element::f32, PartialShape::dynamic()); + auto Yi = make_shared(element::f32, PartialShape::dynamic()); + auto M_body = make_shared(element::f32, PartialShape::dynamic()); + + // Body + auto Zo = std::make_shared(std::make_shared(Xi, Yi), M_body); + auto body = make_shared(OutputVector{Zo}, ParameterVector{Xi, Yi, M_body}); + + auto tensor_iterator = make_shared(); + tensor_iterator->set_body(body); + // The Xi are the elements of Xseq + // start=0, stride=2, part_size=2, end=38, axis=1 + tensor_iterator->set_sliced_input(Xi, X, 0, 2, 2, 38, 1); + // The Yi are the elements of Yseq + // start=0, stride=2, part_size=2, end=-2, axis=1 + tensor_iterator->set_sliced_input(Yi, Y, 0, 2, 2, -2, 1); + tensor_iterator->set_invariant_input(M_body, M); + + NodeBuilder builder(tensor_iterator); + const auto g_tensor_iterator = ov::as_type_ptr(builder.create()); + + EXPECT_EQ(g_tensor_iterator->get_body(), tensor_iterator->get_body()); + EXPECT_EQ(g_tensor_iterator->get_input_descriptions(), tensor_iterator->get_input_descriptions()); + EXPECT_EQ(g_tensor_iterator->get_output_descriptions(), tensor_iterator->get_output_descriptions()); +}