From 86c3a14bc3c8c554b557ebb7912676f7041bd01d Mon Sep 17 00:00:00 2001 From: sbalandi Date: Tue, 30 Apr 2024 22:47:52 +0100 Subject: [PATCH] test --- .../single_layer_tests/classes/reduce.cpp | 14 +- .../custom/single_layer_tests/loop.cpp | 3 + .../src/index_add_scatter_elements_update.cpp | 6 +- .../skip_tests_config.cpp | 50 ++++ .../functional/behavior/infer_request.cpp | 2 +- .../skip_tests_config.cpp | 24 ++ .../base/utils/generate_inputs.hpp | 2 +- .../shared_test_classes/base/utils/ranges.hpp | 237 +++++------------- .../src/base/utils/generate_inputs.cpp | 14 ++ .../src/base/utils/ranges.cpp | 3 +- .../src/single_op/gru_sequence.cpp | 2 +- .../include/common_test_utils/data_utils.hpp | 2 +- .../common_test_utils/ov_tensor_utils.hpp | 16 +- .../common_test_utils/tests/CMakeLists.txt | 3 +- .../common_test_utils/tests/core_config.cpp | 15 ++ .../tests/generate_intepus.cpp | 4 +- .../tests/skip_tests_config.cpp | 12 + .../skip_configs/CPU/expected_failures_OP.csv | 3 +- 18 files changed, 219 insertions(+), 193 deletions(-) create mode 100644 src/tests/test_utils/common_test_utils/tests/core_config.cpp create mode 100644 src/tests/test_utils/common_test_utils/tests/skip_tests_config.cpp diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/reduce.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/reduce.cpp index 06dd6a56e50f93..403e2f2f808e96 100644 --- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/reduce.cpp +++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/reduce.cpp @@ -139,11 +139,15 @@ void ReduceCPULayerTest::SetUp() { function = makeNgraphFunction(netPrecision, params, reduce, "Reduce"); - if (ov::with_cpu_x86_avx512_core_amx()) { - if (netPrecision == ov::element::f32 && configuration.count(ov::hint::inference_precision.name()) && - configuration.at(ov::hint::inference_precision.name()) == ov::element::f16) { - abs_threshold = 5e-3; - } + if (netPrecision == ov::element::f32 && configuration.count(ov::hint::inference_precision.name()) && + (configuration.at(ov::hint::inference_precision.name()) == ov::element::f16 || + configuration.at(ov::hint::inference_precision.name()) == ov::element::bf16)) { + abs_threshold = 5e-3; + // if (ov::with_cpu_x86_avx512_core_amx()) { + // abs_threshold = 5e-3; + // } else { + // abs_threshold = 5e-2; + // } } } diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/loop.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/loop.cpp index ede3b3e2339086..c03984ccc9f676 100644 --- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/loop.cpp +++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/loop.cpp @@ -175,6 +175,7 @@ class LoopWhileLayerCPUTest : public LoopLayerCPUTest { bool exec_cond; std::vector shapes; std::vector types; + // ov::element::Type in_type; std::tie(trip_count_type, trip_count, exec_cond, shapes, types, inType) = this->GetParam(); targetDevice = ov::test::utils::DEVICE_CPU; @@ -248,6 +249,7 @@ class LoopForDiffShapesLayerCPUTest : public LoopLayerCPUTest { bool exec_cond; std::vector shapes; std::vector types; + ov::element::Type inType; std::tie(trip_count_type, trip_count, exec_cond, shapes, types, inType) = this->GetParam(); targetDevice = ov::test::utils::DEVICE_CPU; @@ -326,6 +328,7 @@ class LoopForConcatLayerCPUTest : public LoopLayerCPUTest { bool exec_cond; std::vector shapes; std::vector types; + // ov::element::Type in_type; std::tie(trip_count_type, trip_count, exec_cond, shapes, types, inType) = this->GetParam(); targetDevice = ov::test::utils::DEVICE_CPU; diff --git a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/index_add_scatter_elements_update.cpp b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/index_add_scatter_elements_update.cpp index 4aa422fa263b18..9934ceed3bd6b4 100644 --- a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/index_add_scatter_elements_update.cpp +++ b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/index_add_scatter_elements_update.cpp @@ -98,7 +98,7 @@ class IndexAddTest : public testing::WithParamInterface, if (ov::element::bf16 == data_type || ov::element::f16 == data_type) { configuration.insert({ov::hint::inference_precision.name(), data_type}); - inType = outType = data_type; + outType = data_type; abs_threshold = 0.01f; rel_threshold = 0.01f; } @@ -209,12 +209,14 @@ class IndexAddTest : public testing::WithParamInterface, // All index values are expected to be within bounds [-d, d - 1] along dimension d pointed by axis. auto d = dataShape[normalized_axis]; in_data.start_from = -1.0 * static_cast(d); - in_data.range = d-1; + // in_data.range = static_cast(d-1 - in_data.start_from); + in_data.range = d - 1; in_data.resolution = 1; tensor = shape_size(indicesShape) == 0 ? ov::Tensor(funcInput.get_element_type(), indicesShape) : ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), indicesShape, in_data); } else if (i == 2) { // "updates" in_data.start_from = -50; + // in_data.range = 100; in_data.range = 50; in_data.resolution = 1; tensor = shape_size(updateShape) == 0 ? ov::Tensor(funcInput.get_element_type(), updateShape) : diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/skip_tests_config.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/skip_tests_config.cpp index 85e8c2e10615b7..162331cf44f02d 100644 --- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/skip_tests_config.cpp +++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/skip_tests_config.cpp @@ -313,6 +313,28 @@ std::vector disabledTestPatterns() { R"(.*smoke_LoopForCommon/LoopLayerCPUTest.CompareWithRefs/.*trip_count=5_exec_cond=1_netType=i8.*)", R"(.*smoke_LoopForCommon/LoopLayerCPUTest.CompareWithRefs/Input0_IS=\[\?.1.\?\]_TS=\(10.1.10\)_\(1.1.1\)_\(1.1.1\)_\(5.1.3\)_Input1_IS=\[\?.\?.\?\]_TS=.*_Input2_IS=\[\?.1.\?\]_.*_types=0_0_1_trip_count_type=.*_trip_count=(1|5)_exec_cond=1_netType=i8.*)", R"(.*smoke_LoopForCommon/LoopLayerCPUTest.CompareWithRefs/Input0_IS=\[1..10.1.1..10\]_.*_Input1_IS=\[1..8.1.1..8\]_.*_Input2_IS=\[1..10.\?.1..10\]_TS=.*_types=0_0_1_trip_count_type=.*_trip_count=(1|5)_exec_cond=1_netType=i8.*)", + // R"(.*smoke_Reduce_MultiAxis_4D_fusing_CPU/ReduceCPULayerTest.CompareWithRefs/.*=VECTOR_type=(Max|ReduceL2|Mean)_.*_INFERENCE_PRECISION_HINT=bf16_.*_Fused=Multiply\(PerChannel\).Add\(PerChannel\).*)", + // R"(.*smoke_Reduce_MultiAxis_4D_fusing_CPU/ReduceCPULayerTest.CompareWithRefs/IS=.*_axes=\((0.2|0.3)\)_opType=VECTOR_type=Mean_.*_INFERENCE_PRECISION_HINT=bf16_.*_Fused=Multiply\(PerChannel\).Add\(PerChannel\).*)", + // R"(.*smoke_Reduce_OneAxis_fusing_CPU/ReduceCPULayerTest.CompareWithRefs/.*_INFERENCE_PRECISION_HINT=bf16_Fused=Multiply\(PerChannel\).Add\(PerChannel\).*)", + // R"(.*smoke_Reduce_OneAxis_CPU/ReduceCPULayerTest.CompareWithRefs/IS=.*_axes=\((1|3)\)_opType=.*_type=(ReduceL1|Sum)_.*_INFERENCE_PRECISION_HINT=bf16.*)", + // R"(.*smoke_Reduce_MultiAxis_4D_CPU/ReduceCPULayerTest.CompareWithRefs/IS=.*_axes=\((0.1|0.3|1.2|1.3|2.3|0.1.2|0.1.3|0.2.3|1.2.3)\)_opType=.*_type=(ReduceL1|Sum)_.*_INFERENCE_PRECISION_HINT=bf16.*)", + // R"(.*smoke_Reduce_MultiAxis_5D_fusing_CPU/ReduceCPULayerTest.CompareWithRefs/IS=.*_axes=\((2.4|0.2.4|0.1.2.3.4)\)_opType=.*_type=(ReduceL1|Mean|ReduceL2|Max)_KeepDims=true_netPRC=f32_.*_INFERENCE_PRECISION_HINT=bf16_.*_Fused=Multiply\(PerChannel\).Add\(PerChannel\).*)", + // R"(.*smoke_Reduce_MultiAxis_5D_fusing_CPU/ReduceCPULayerTest.CompareWithRefs/IS=.*_axes=\(1.2.4\)_opType=.*_type=(ReduceL2|Max)_KeepDims=true_netPRC=f32_.*_INFERENCE_PRECISION_HINT=bf16_.*_Fused=Multiply\(PerChannel\).Add\(PerChannel\).*)", + // R"(.*smoke_Reduce_SingleBatch_CPU/ReduceCPULayerTest.CompareWithRefs/.*_axes=\((1|3)\)_opType=.*_type=(ReduceL1|Sum)_KeepDims=true_netPRC=f32_.*_INFERENCE_PRECISION_HINT=bf16_.*)", + // R"(.*smoke_Reduce_MultiAxis_5D_CPU/ReduceCPULayerTest.CompareWithRefs/IS=.*_axes=\((2.4|0.2.4)\)_opType=.*_type=(ReduceL1|Sum)_KeepDims=true_netPRC=f32_.*_INFERENCE_PRECISION_HINT=bf16.*)", + // R"(.*smoke_Reduce_MultiAxis_4D_dynamic_CPU/ReduceCPULayerTest.CompareWithRefs/IS=.*_axes=\(0.1\)_opType=.*_type=(ReduceL1|Sum)_KeepDims=true_netPRC=f32_.*_INFERENCE_PRECISION_HINT=bf16.*)", + // R"(.*smoke_Reduce_NHWC_SmallChannel_CPU/ReduceCPULayerTest.CompareWithRefs/IS=.*_axes=\(2.3\)_opType=.*_type=(ReduceL1|Sum)_KeepDims=true_netPRC=f32_.*_INFERENCE_PRECISION_HINT=bf16.*)", + // R"(.*smoke_GatherTree/GatherTreeLayerTest.Inference/IS=\(20.(1|20).10\)_secondary_input_type=CONSTANT_netPRC=f32.*)", + // R"(.*smoke_FakeQuantizeLayerCPUTest_5D_jit/FakeQuantizeLayerCPUTest.CompareWithRefs/IS=\[\?.\?.\?.\?.\?\]_TS=\(\(4.16.6.7.8\)\)_\(\(1.16.1.1.1\)\)_.*_inPrec=f32_LOW_BOUNDS=-10_HIGH_BOUNDS=10_IL=\(-10\)_IH=\(-5\)_OL=\(5\)_OH=\(25\)_LEVELS=256_.*)", + // R"(.*smoke_basic/PermConvPermConcat.CompareWithRefs/IS=\(1.1.8.16\)_KS=\(1.3\)_OC=(32|64)_ET=f32_targetDevice=CPU.*)", + // R"(.*smoke_GatherTreeCPUStatic/GatherTreeLayerCPUTest.CompareWithRefs/IS=.*_TS=\(20.(1|20).10\)_secondaryInputType=CONSTANT_netPRC=f32_inPRC=undefined_outPRC=undefined_trgDev=CPU.*)", + // R"(.*smoke_GatherTreeCPUDynamicConstant/GatherTreeLayerCPUTest.CompareWithRefs/IS=.*_TS=\((7.1.10|2.1.7|20.1.10|20.20.15)\)_secondaryInputType=CONSTANT_netPRC=f32_inPRC=undefined_outPRC=undefined_trgDev=CPU.*)", + // R"(.*smoke_GroupConv_3D_Gemm_FP32/GroupConvolutionLayerCPUTest.CompareWithRefs/IS=\[\].*S\(1.1.1\).*O=6_G=3_AP=explicit_netPRC=f32_.*inFmts=(ndhwc|ncdhw)_outFmts=(ndhwc|ncdhw)_.*_Fused=FakeQuantize\(PerTensor\).Relu.*)", + // R"(.*smoke_GroupConv_3D_Gemm_FP32/GroupConvolutionLayerCPUTest.CompareWithRefs/IS=\[1..200.12.\?.1..200.\?\]_.*_S\(1.1.1\)_.*_O=6_G=3_AP=explicit_netPRC=f32_inPRC=undefined_outPRC=undefined_trgDev=CPU_inFmts=(ndhwc|ncdhw)_outFmts=(ndhwc|ncdhw)_primitive=jit_gemm_Fused=FakeQuantize\(PerTensor\).Relu.*)", + // R"(.*smoke_JIT_SSE42_DW_GroupConv/GroupConvolutionLayerCPUTest.CompareWithRefs/IS=.*_TS=\(\(2.8.129.129\)_\)_K\(3.3\)_S\(2.2\)_PB\(1.1\)_PE\(1.1\)_D=\(1.1\)_O=8_G=8_AP=explicit_netPRC=f32_.*_inFmts=(nChw8c|nhwc)_outFmts=(nChw8c|nhwc)_primitive=jit_sse42_dw_Fused=Add\(Parameters\).Elu.FakeQuantize\(PerTensor\).*)", + // R"(.*smoke_FC_3D_BF16/MatMulLayerCPUTest.CompareWithRefs/FullyConnected_IS=\[\?.\?\]_\[1.129.1\]_TS=\(\(1.129\)_\(2.129\)_\(1.129\)_\(2.129\)\)_\(\(1.129.1\)_\(1.129.1\)_\(1.129.1\)_\(1.129.1\)\)_transpose_a=1_transpose_b=1_secondaryInputType=CONSTANT_netPRC=(bf16|f32).*config=\(INFERENCE_PRECISION_HINT=bf16_\)_Fused=Multiply\(PerChannel\).*)", + // R"(.*smoke_MatMulCompressedWeights_non_default_dyn_quant_group_sizes/MatmulWeightsDecompression.CompareWithRefs/data_shape=\[\]_\(\[1,1,1728\]\)_weights_shape=\[1728,128\]_group_size=64_weights_precision=(u8|u4)_decompression_precision=f32_transpose_weights= + // 1_decompression_subtract=full_reshape_on_decompression=0_config=\(DYNAMIC_QUANTIZATION_GROUP_SIZE, 128:\).*)", }; #if defined(OPENVINO_ARCH_X86) @@ -366,6 +388,26 @@ std::vector disabledTestPatterns() { retVector.emplace_back(R"(smoke_VariableState/OVInferRequestVariableStateTest.*)"); // Issue: 141705 retVector.emplace_back(R"(.*smoke_arm_Deconv_2D_Planar_FP16/DeconvolutionLayerCPUTest.*INFERENCE_PRECISION_HINT=f16.*)"); + // fill_data_random fix + // retVector.emplace_back(R"(.*smoke_arm_Deconv_2D_Planar_FP16/DeconvolutionLayerCPUTest.*INFERENCE_PRECISION_HINT=f16.*)"); + // retVector.emplace_back(R"(.*smoke_CompareWithRefs_dynamic/EltwiseLayerTest.Inference/IS=.*1..10.200.*1..10.200.*_TS=.*2.200.*1.200.*2.200.*5.200.*_eltwise_op_type=Sum_secondary_input_type=PARAMETER_opType=VECTOR_model_type=i32_.*)"); + // retVector.emplace_back(R"(.*smoke_CompareWithRefs_static_check_collapsing/EltwiseLayerTest.Inference/IS=.*_TS=\(\(16.16.16.16\)_\(16.16.(16.1|1.16)\)_\)_eltwise_op_type=Sum_secondary_input_type=PARAMETER_opType=VECTOR_model_type=i32_.*)"); + // retVector.emplace_back(R"(.*smoke_CompareWithRefs_static_check_collapsing/EltwiseLayerTest.Inference/IS=.*_TS=\(\(16.16.16.(16|1)\)_\(16.16.16.1\)_\)_eltwise_op_type=SqDiff_secondary_input_type=.*_opType=VECTOR_model_type=i32.*)"); + // retVector.emplace_back(R"(.*smoke_CompareWithRefs_static_check_collapsing/EltwiseLayerTest.Inference/IS=.*_TS=\(\(16.16.(16|1).16\)_\(16.16.1.16\)_\)_eltwise_op_type=SqDiff_secondary_input_type=.*_opType=VECTOR_model_type=i32.*)"); + // retVector.emplace_back(R"(.*smoke_CompareWithRefs_static/EltwiseLayerTest.Inference/IS=.*_TS=\(\(16.16.16.(16|1).(16|1)\)_\)_eltwise_op_type=Sum_secondary_input_type=PARAMETER_opType=SCALAR_model_type=i32.*)"); + // retVector.emplace_back(R"(.*smoke_LSTMCellCommon/LSTMCellTest.Inference/decomposition0_batch=5_hidden_size=10_input_size=1_IS=\(5.1\)\(5.10\)\(5.10\)\(40.1\)\(40.10\)\(40\)_activations=\(relu.(sigmoid.tanh|relu.relu)\)_clip=0_WType=.*_RType=CONSTANT_BType=PARAMETER_modelType=f16.*)"); + // retVector.emplace_back(R"(.*smoke_LSTMCellCommon/LSTMCellTest.Inference/decomposition0_batch=5_hidden_size=10_input_size=30_IS=\(5.30\)\(5.10\)\(5.10\)\(40.30\)\(40.10\)\(40\)_activations=\(relu.(sigmoid.tanh|relu.relu)\)_clip=0_WType=.*_RType=CONSTANT_BType=PARAMETER_modelType=f16.*)"); + // retVector.emplace_back(R"(.*smoke_GRUCellCommon/GRUCellTest.Inference/decomposition1_batch=5_hidden_size=1_input_size=1_IS=\(5.1\)\(5.1\)\(3.1\)\(3.1\)\(4\)_activations=\(tanh.relu\)_clip=0_linear_before_reset=1_WType=.*_RType=.*_BType=CONSTANT_netPRC=f32_.*)"); + // retVector.emplace_back(R"(.*smoke_GRUCellCommon/GRUCellTest.Inference/decomposition1_batch=5_hidden_size=1_input_size=30_IS=\(5.30\)\(5.1\)\(3.30\)\(3.1\)\(4\)_activations=\(tanh.relu\)_clip=0_linear_before_reset=1_WType=.*_RType=.*_BType=CONSTANT_netPRC=f32_.*)"); + // retVector.emplace_back(R"(.*smoke_GRUCellCommon/GRUCellTest.Inference/decomposition1_batch=5_hidden_size=10_input_size=1_IS=\(5.1\)\(5.10\)\(30.1\)\(30.10\)\((40|30)\)_activations=\(tanh.relu\)_clip=0_linear_before_reset=(0|1)_WType=.*_RType=.*_BType=CONSTANT_netPRC=f32_.*)"); + // retVector.emplace_back(R"(.*smoke_GRUCellCommon/GRUCellTest.Inference/decomposition1_batch=5_hidden_size=10_input_size=30_IS=\(5.30\)\(5.10\)\(30.30\)\(30.10\)\(30\)_activations=\(tanh.relu\)_clip=0_linear_before_reset=0_WType=.*_RType=.*_BType=CONSTANT_netPRC=f32.*)"); + // retVector.emplace_back(R"(.*moke_Activation5D_dynamicMath_CPU/ActivationLayerCPUTest.CompareWithRefs/Log_IS=\(\[?.?\]\)_TS=\(1.50\)_\(5.128\)_\(3.64\)_AS=\(\)_ConstantsValue=\(\)_netPRC=f32_inPRC=f32_outPRC=f32_.*)"); + // retVector.emplace_back(R"(.*moke_Activation5D_dynamicMath_CPU/ActivationLayerCPUTest.CompareWithRefs/Log_IS=\(\[1..5.128\]\)_TS=\(1.128\)_\(3.128\)_\(5.128\)_AS=\(\)_ConstantsValue=\(\)_netPRC=f32_inPRC=f32_outPRC=f32_.*)"); + // retVector.emplace_back(R"(.*smoke_EltwiseChain_MergeConvert_int8/EltwiseChainTest.CompareWithRefs/IS=.*_TS=\(\(1.1.2.3\)_\(1.1.2.3\)_\(1.1.2.3\)_InPRC0=f16_InPRC1=f32_InPRC2=f32_Op0=Div_secondaryInputType=CONSTANT_WithQuant=0_Conversion=(i8|u8).*)"); + // retVector.emplace_back(R"(.*smoke_EltwiseChain_MergeConvert_int8/EltwiseChainTest.CompareWithRefs/IS=.*_TS=\(\(1.1.2.3\)_\(1.1.2.3\)_\(1.1.2.3\)_InPRC0=f32_InPRC1=f32_InPRC2=f32_Op0=Prod_secondaryInputType=CONSTANT_WithQuant=0_Conversion=(i8|u8).*)"); + // // to long + // retVector.emplace_back(R"(.*smoke_TensorIteratorCommonClip/TensorIteratorTest.Inference/.*_TensorIteratorBody=LSTM_.*_modelType=(f16|f32).*)"); + // retVector.emplace_back(R"(.*smoke_EltwiseChain_MergeConvert_int8/EltwiseChainTest.CompareWithRefs/IS=.*_TS=\(\(1.1.2.3\)_\(1.1.2.3\)_\(1.1.2.3\)_InPRC0=f16_InPRC1=f32_InPRC2=f32_Op0=(Prod|Sum)_secondaryInputType=CONSTANT_WithQuant=0_Conversion=(i8|u8)_.*)"); #endif #if defined(OPENVINO_ARCH_ARM) @@ -521,6 +563,14 @@ std::vector disabledTestPatterns() { // Issue: 141705 retVector.emplace_back(R"(.*smoke_Deconv_(2|3)D_NSPC_INT8_AMX/DeconvolutionLayerCPUTest.*)"); retVector.emplace_back(R"(.*smoke_Deconv_(2|3)D_NSPC_INT8_AMX/DeconvolutionLayerCPUTest.*)"); + // range + // retVector.emplace_back(R"(.*smoke_FC_3D_BF16/MatMulLayerCPUTest.CompareWithRefs/FullyConnected_IS=\[\?.\?\]_\[1.129.1\]_.*_netPRC=(f32|bf16)_.*config=\(INFERENCE_PRECISION_HINT=bf16_\)_Fused=Multiply\(PerChannel\)_primitive=jit_gemm.*)"); + // retVector.emplace_back(R"(.*smoke_MatMulCompressedWeights_non_default_dyn_quant_group_sizes/MatmulWeightsDecompression.CompareWithRefs/.*_\(\[1,1,1728\]\)_.*_precision=(u8|u4)_decompression_precision=f32_.*_subtract=full_reshape_on_decompression=0_config=\(DYNAMIC_QUANTIZATION_GROUP_SIZE.*128.*Fused=fusingBias.*)"); + // retVector.emplace_back(R"(.*smoke_LoopForDiffShapesConcat/LoopForDiffShapesLayerCPUTest.CompareWithRefs/Input0_IS=.*_TS=\(10.1.10\)_\(1.10.1\)_\(1.10.1\)_\(2.2.2\)_types=trip_count_type=PARAMETER_trip_count=(1|5)_exec_cond=1_netType=bf16.*)"); + // retVector.emplace_back(R"(.*smoke_LoopForDiffShapesConcat/LoopForDiffShapesLayerCPUTest.CompareWithRefs/Input0_IS=.*_TS=\(10.5.10\)_\(1.10.1\)_\(1.10.1\)_\(2.1.2\)_types=trip_count_type=PARAMETER_trip_count=(1|5)_exec_cond=1_netType=bf16.*)"); + // retVector.emplace_back(R"(.*smoke_LoopForConcat/LoopForConcatLayerCPUTest.CompareWithRefs/Input0_IS=.*_TS=\(10.5.10\)_\(1.10.1\)_\(1.10.1\)_\(2.1.2\)_types=trip_count_type=PARAMETER_trip_count=(1|5)_exec_cond=1_netType=bf16.*)"); + // retVector.emplace_back(R"(.*smoke_LoopForConcat/LoopForConcatLayerCPUTest.CompareWithRefs/Input0_IS=.*_TS=\(10.10.10\)_\(5.10.10\)_\(5.10.10\)_\(8.10.10\)_Input1_IS=\[\?.10.10\]_.*_types=trip_count_type=PARAMETER_trip_count=1_exec_cond=1_netType=bf16.*)"); + // retVector.emplace_back(R"(.*smoke_Deconv_(2|3)D_NSPC_INT8_AMX/DeconvolutionLayerCPUTest.*)"); } if (ov::with_cpu_x86_avx512_core_fp16()) { diff --git a/src/plugins/intel_gpu/tests/functional/behavior/infer_request.cpp b/src/plugins/intel_gpu/tests/functional/behavior/infer_request.cpp index 2a29775c6ebd87..db2b1636e5988d 100644 --- a/src/plugins/intel_gpu/tests/functional/behavior/infer_request.cpp +++ b/src/plugins/intel_gpu/tests/functional/behavior/infer_request.cpp @@ -262,7 +262,7 @@ TEST(VariablesTest, smoke_set_get_state_with_convert) { auto variables = request.query_state(); ASSERT_EQ(variables.size(), 1); auto variable = variables.front(); - ASSERT_EQ(variable.get_name(), "v0"); +ASSERT_EQ(variable.get_name(), "v0"); auto state_tensor = variable.get_state(); ASSERT_EQ(state_tensor.get_shape(), virable_shape); ASSERT_EQ(state_tensor.get_element_type(), et); diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/skip_tests_config.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/skip_tests_config.cpp index 2b2c2c92f71d29..105c6a951c1da5 100644 --- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/skip_tests_config.cpp +++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/skip_tests_config.cpp @@ -201,8 +201,32 @@ std::vector disabledTestPatterns() { R"(.*smoke_RDFT_5d_last_axis/RDFTLayerTest.Inference/IS=\(10.4.8.2.5\)_modelType=f32_Axes=\(0.1.2.3.4\)_SignalSize=\(\).*)", // Issue: 136862 R"(.*smoke_ConditionGPUTest_static/StaticConditionLayerGPUTest.CompareWithRefs/IS=\(3.6\)_netPRC=i8_ifCond=PARAM_targetDevice=GPU_.*)", + // // ranges + // R"(.*smoke_MaxPool8_ExplicitPad_(Floor|Ceil)Rounding/MaxPoolingV8LayerTest.Inference/IS=\(\[\]\)_TS=\{\(1.3.30.30\)\}_K\(3.3\)_S\(1.1\)_D\(1.1\)_PB\(0.0\)_PE\(0.0\)_IETi32_(A0|A2)_Rounding=(floor|cell)_AutoPad=explicit_modelType=f32_.*)", + // R"(.*smoke_ConvolutionBackpropData3D_ExplicitPadding_OutputPaddingDefined/ConvolutionBackpropDataLayerTest.Inference/IS=\(\[\]\)_TS=\{\(1.3.10.10.10\)\}_OS=\(\)_K\(3.3.3\)_S\(3.3.3\)_PB\(0.0.0\)_PE\(0.0.0\)_D=\(1.1.1\)_.*_O=5_AP=valid_netPRC=f16.*)", + // R"(.*smoke_ConvolutionBackpropData3D_AutoPadding_OutputPaddingDefined/ConvolutionBackpropDataLayerTest.Inference/IS=\(\[\]\)_TS=\{\(1.3.10.10.10\)\}_OS=\(\)_K\(3.3.3\)_S\(3.3.3\)_PB\(0.0.0\)_.*_D=\(1.1.1\)_.*_O=5_AP=explicit_netPRC=f16_.*)", + // R"(.*smoke_Multinomial/MultinomialLayerTestGPU.Inference/static_probs_shape=\[(1,32|2,28)\]_num_samples=(2|4)_inType=f16_convert_type=i64_.*_seed_g=0_seed_o=2_.*)", + // R"(.*smoke_ConvolutionBackpropData3D_AutoPadding_OutputPaddingDefined/ConvolutionBackpropDataLayerTest.Inference/IS=\(\[\]\)_TS=\{\(1.3.10.10.10\)\}_OS=\(\)_K\(3.3.3\)_S\(3.3.3\)_PB\(0.0.0\)_.*_D=\(1.1.1\)_.*_O=5_AP=explicit_netPRC=f16_.*)", + // R"(.*smoke_gathertree_constant_compareWithRefs_dynamic/GatherTreeLayerGPUTest.Inference/IS=.*_secondaryInputType=CONSTANT_netPRC=f32_.*)", + // R"(.*Basic_smoke/GatherTreeLayerTest.Inference/IS=\(5.1.10\)_secondary_input_type=PARAMETER_netPRC=f16_trgDev=GPU.*)", + // R"(.*Basic_smoke/GatherTreeLayerTest.Inference/IS=\(20.1.10\)_secondary_input_type=.*_netPRC=f16_trgDev=GPU.*)", + // R"(.*Basic_smoke/GatherTreeLayerTest.Inference/IS=\(20.1.10\)_secondary_input_type=CONSTANT_netPRC=f32_trgDev=GPU.*)", + // R"(.*Basic_smoke/GatherTreeLayerTest.Inference/IS=\(20.20.10\)_secondary_input_type=.*_netPRC=(f32|f16)_trgDev=GPU.*)", + // R"(.*smoke_AvgPool_ExplicitPad_CeilRounding/PoolingLayerTest.Inference/IS=\(\[\]\)_TS=\{\(1.3.30.30\)\}_AvgPool_.*_Rounding=ceil_AutoPad=explicit_modelType=f16.*)", + // R"(.*smoke_AvgPool_ExplicitPad_FloorRounding/PoolingLayerTest.Inference/IS=\(\[\]\)_TS=\{\(1.3.30.30\)\}_AvgPool_ExcludePad=.*_Rounding=floor_AutoPad=explicit_modelType=f16.*)", + // R"(.*smoke_MAX_and_AVGPool_ValidPad/PoolingLayerTest.Inference/IS=\(\[\]\)_TS=\{\(1.3.50.50\)\}_AvgPool_ExcludePad=0_K\(3.3\).*_PB\(0.0\)_.*_Rounding=floor_AutoPad=valid_modelType=f16.*)", + // R"(.*smoke_MaxPool8_ExplicitPad_FloorRounding/MaxPoolingV8LayerTest.Inference/IS=\(\[\]\)_TS=\{\(1.3.30.30\)\}_K\(3.5\)_S\(1.1\)_D\(1.1\)_PB\(0.0\)_PE\(0.0\)_IETi32_A(0|2)_Rounding=floor_AutoPad=explicit_modelType=f32.*)", + // R"(.*smoke_MaxPool8_ExplicitPad_CeilRounding/MaxPoolingV8LayerTest.Inference/IS=\(\[\]\)_TS=\{\(1.3.30.30\)\}_K\(3.*\)_S\(1.1\)_D\(1.1\)_PB\(0.0\)_PE\(0.0\)_IETi32_A(0|2)_Rounding=ceil_AutoPad=explicit_modelType=f32.*)", + // R"(.*smoke_TestsROIAlign_average/ROIAlignLayerTest.Inference/IS=\(\[\]\)_TS=\{\(3.8.16.16\)\}_coordShape=\(2.4\)_pooledH=2_pooledW=2_spatialScale=1_poolingRatio=2_poolingMode=avg_modelType=f32.*)", + // R"(.*Basic_smoke/GatherTreeLayerTest.Inference/IS=\(5.1.10\)_secondary_input_type=PARAMETER_netPRC=f16.*)", + // R"(.*Basic_smoke/GatherTreeLayerTest.Inference/IS=\(20.(20|1).10\)_secondary_input_type=CONSTANT_netPRC=(f16|f32).*)", + // R"(.*Basic_smoke/GatherTreeLayerTest.Inference/IS=\(20.1.10\)_secondary_input_type=PARAMETER_netPRC=f16.*)", + // R"(.*smoke_TestsROIAlign_avg_asym/ROIAlignV9LayerTest.Inference/IS=\(\[\]\)_TS=\{\(2.8.20.20\)\}_coordShape=\(2.4\)_pooledH=2_pooledW=2_spatialScale=1_poolingRatio=2_poolingMode=avg_ROIMode=asymmetric_modelType=f32.*)", + // R"(.*smoke_Grn_Basic/GrnLayerTest.Inference/IS=\(\[\]\)_TS=\{\((2.16.15.20|1.3.30.30)\)\}_modelType=f16_bias=(1.1|0.33).*)", #if defined(_WIN32) // by calc abs_threshold with expected value + // R"(.*Basic_smoke/GatherTreeLayerTest.Inference/IS=\(20.20.10\)_secondary_input_type=PARAMETER_netPRC=f16.*)", + // R"(.*Basic_smoke/GatherTreeLayerTest.Inference/IS=\(5.1.10\)_secondary_input_type=CONSTANT_netPRC=f16.*)", R"(.*smoke_RemoteTensor/OVRemoteTensorBatched_Test.NV12toBGR_buffer/(num_batch_4|num_batch_2).*)", R"(.*smoke_Check/ConstantResultSubgraphTest.Inference/SubgraphType=SINGLE_COMPONENT_IS=\[1,3,10,10\]_IT=i16_Device=GPU.*)", #endif diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/base/utils/generate_inputs.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/base/utils/generate_inputs.hpp index d2930be59d5eac..4a17fdf3d2ff7d 100644 --- a/src/tests/functional/shared_test_classes/include/shared_test_classes/base/utils/generate_inputs.hpp +++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/base/utils/generate_inputs.hpp @@ -7,7 +7,7 @@ #include "openvino/core/preprocess/color_format.hpp" #include "functional_test_utils/common_utils.hpp" -#include "shared_test_classes/base/utils/ranges.hpp" +#include "ranges.hpp" namespace ov { namespace test { diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/base/utils/ranges.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/base/utils/ranges.hpp index 342e5863d19d3f..db1b9d0079d2df 100644 --- a/src/tests/functional/shared_test_classes/include/shared_test_classes/base/utils/ranges.hpp +++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/base/utils/ranges.hpp @@ -10,6 +10,7 @@ #include #include "common_test_utils/ov_tensor_utils.hpp" +#include "common_test_utils/type_ranges.hpp" #include "openvino/core/node.hpp" #include "openvino/op/abs.hpp" #include "openvino/op/acos.hpp" @@ -112,177 +113,42 @@ #include "openvino/op/topk.hpp" #include "openvino/op/unsqueeze.hpp" #include "openvino/op/variadic_split.hpp" +#include "openvino/op/cum_sum.hpp" +#include "openvino/op/mvn.hpp" +#include "openvino/op/gru_cell.hpp" +#include "openvino/op/gru_sequence.hpp" +#include "openvino/op/if.hpp" +#include "openvino/op/tensor_iterator.hpp" +#include "openvino/op/group_normalization.hpp" +#include "openvino/op/reverse_sequence.hpp" +#include "openvino/op/gather_tree.hpp" +#include "openvino/op/deformable_psroi_pooling.hpp" +#include "openvino/op/softmax.hpp" +#include "openvino/op/psroi_pooling.hpp" +#include "ov_ops/augru_sequence.hpp" +#include "ov_ops/augru_cell.hpp" +#include "openvino/op/roll.hpp" +#include "openvino/op/lstm_cell.hpp" +#include "openvino/op/lstm_sequence.hpp" +#include "openvino/op/squared_difference.hpp" +#include "openvino/op/scaled_dot_product_attention.hpp" +#include "openvino/op/transpose.hpp" +#include "openvino/op/loop.hpp" +#include "openvino/op/squared_difference.hpp" +#include "openvino/op/avg_pool.hpp" +#include "openvino/op/ctc_loss.hpp" +#include "openvino/op/grid_sample.hpp" +#include "openvino/op/multinomial.hpp" +#include "openvino/op/embeddingbag_offsets_sum.hpp" +#include "openvino/op/generate_proposals.hpp" +#include "openvino/op/roi_pooling.hpp" +#include "openvino/op/shuffle_channels.hpp" +#include "openvino/op/slice.hpp" namespace ov { namespace test { namespace utils { -static ov::test::utils::InputGenerateData get_range_by_type(ov::element::Type temp_type) { - double min_start = 0 - (int32_t)round(testing::internal::Random::kMaxRange / 2); - uint32_t max_range = testing::internal::Random::kMaxRange - 1; - - ov::test::utils::InputGenerateData inData(min_start, max_range); -#define CASE_T(X) \ - case X: { \ - auto lowest = std::numeric_limits::value_type>::lowest(); \ - auto max = std::numeric_limits::value_type>::max(); \ - double tmp_range = static_cast(max) - static_cast(lowest); \ - if (tmp_range < testing::internal::Random::kMaxRange) { \ - inData.start_from = lowest; \ - inData.range = (uint32_t)round(tmp_range); \ - } else { \ - inData.range = testing::internal::Random::kMaxRange - 1; \ - inData.start_from = lowest > min_start ? static_cast(lowest) : min_start; \ - } \ - break; \ - } - - switch (temp_type) { - case (ov::element::Type_t::undefined): { - inData.start_from = min_start; - inData.range = max_range; - break; - } - case (ov::element::Type_t::dynamic): { - inData.start_from = min_start; - inData.range = max_range; - break; - } - case (ov::element::Type_t::boolean): { - inData.start_from = 0; - inData.range = 2; - break; - } - case (ov::element::Type_t::bf16): { - ov::bfloat16 lowest_tmp = std::numeric_limits::lowest(); - ov::bfloat16 max_tmp = std::numeric_limits::max(); - - double lowest = 0 - static_cast(lowest_tmp.to_bits()); - double max = max_tmp.to_bits(); - - double tmp_range = max - lowest; - if (tmp_range < testing::internal::Random::kMaxRange) { - inData.start_from = lowest; - inData.range = (uint32_t)round(tmp_range); - } else { - inData.start_from = lowest > min_start ? lowest : min_start; - inData.range = testing::internal::Random::kMaxRange - 1; - } - - break; - } - case ov::element::Type_t::f16: { - ov::float16 lowest_tmp = std::numeric_limits::lowest(); - ov::float16 max_tmp = std::numeric_limits::max(); - - double lowest = 0 - static_cast(lowest_tmp.to_bits()); - double max = max_tmp.to_bits(); - - double tmp_range = max - lowest; - if (tmp_range < testing::internal::Random::kMaxRange) { - inData.start_from = lowest; - inData.range = (uint32_t)round(tmp_range); - } else { - inData.start_from = lowest > min_start ? lowest : min_start; - inData.range = testing::internal::Random::kMaxRange - 1; - } - - break; - } - case ov::element::Type_t::f8e4m3: { - ov::float8_e4m3 lowest_tmp = std::numeric_limits::lowest(); - ov::float8_e4m3 max_tmp = std::numeric_limits::max(); - - double lowest = 0 - static_cast(lowest_tmp.to_bits()); - double max = max_tmp.to_bits(); - - double tmp_range = max - lowest; - if (tmp_range < testing::internal::Random::kMaxRange) { - inData.start_from = lowest; - inData.range = (uint32_t)round(tmp_range); - } else { - inData.start_from = lowest > min_start ? lowest : min_start; - inData.range = testing::internal::Random::kMaxRange - 1; - } - - break; - } - case ov::element::Type_t::f8e5m2: { - ov::float8_e5m2 lowest_tmp = std::numeric_limits::lowest(); - ov::float8_e5m2 max_tmp = std::numeric_limits::max(); - - double lowest = 0 - static_cast(lowest_tmp.to_bits()); - double max = max_tmp.to_bits(); - - double tmp_range = max - lowest; - if (tmp_range < testing::internal::Random::kMaxRange) { - inData.start_from = lowest; - inData.range = (uint32_t)round(tmp_range); - } else { - inData.start_from = lowest > min_start ? lowest : min_start; - inData.range = testing::internal::Random::kMaxRange - 1; - } - - break; - } - case ov::element::Type_t::string: { - auto lowest = std::numeric_limits::lowest(); - auto max = std::numeric_limits::max(); - - double tmp_range = static_cast(max) - static_cast(lowest); - if (tmp_range < testing::internal::Random::kMaxRange) { - inData.start_from = lowest; - inData.range = (uint32_t)round(tmp_range); - } else { - inData.start_from = lowest > min_start ? lowest : min_start; - inData.range = testing::internal::Random::kMaxRange - 1; - } - - break; - } - CASE_T(ov::element::Type_t::f32) - CASE_T(ov::element::Type_t::f64) - CASE_T(ov::element::Type_t::i4) - CASE_T(ov::element::Type_t::i8) - CASE_T(ov::element::Type_t::i16) - CASE_T(ov::element::Type_t::i32) - CASE_T(ov::element::Type_t::i64) - CASE_T(ov::element::Type_t::u1) - CASE_T(ov::element::Type_t::u2) - CASE_T(ov::element::Type_t::u3) - CASE_T(ov::element::Type_t::u4) - CASE_T(ov::element::Type_t::u6) - CASE_T(ov::element::Type_t::u8) - CASE_T(ov::element::Type_t::nf4) - CASE_T(ov::element::Type_t::u16) - CASE_T(ov::element::Type_t::u32) - CASE_T(ov::element::Type_t::u64) - break; - } - - return inData; -} - -struct RangeByType { - std::map data; - - RangeByType() { - for (auto& type : ov::element::Type::get_known_types()) { - data[*type] = get_range_by_type(*type); - } - } - - ov::test::utils::InputGenerateData get_range(ov::element::Type type) { - if (data.count(type) > 0) { - return data.at(type); - } else { - throw std::runtime_error("Couln't find Type in typeMap: " + type.to_string()); - } - } -}; - -static RangeByType rangeByType; - struct Range { std::vector int_port_ranges; std::vector real_port_ranges; @@ -390,6 +256,8 @@ static std::map inputRanges = { {ov::op::v5::Round::get_type_info_static(), Range({{0, 15}}, {{-10, 20, 4}})}, {ov::op::v7::Gelu::get_type_info_static(), Range({{0, 15}}, {{-1, 2, 32768}})}, {ov::op::v8::MaxPool::get_type_info_static(), Range({{0, 10, 1, 1}}, {{0, 10, 1, 1}})}, + {ov::op::v1::MaxPool::get_type_info_static(), Range({{0, 10, 1, 1}}, {{0, 10, 1, 1}})}, + {ov::op::v1::AvgPool::get_type_info_static(), Range({{0, 10, 1, 1}}, {{0, 10, 1, 1}})}, {ov::op::v9::SoftSign::get_type_info_static(), Range({{0, 15}}, {{-100, 200, 32768}})}, // new temp {ov::op::v1::Convolution::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, @@ -402,9 +270,10 @@ static std::map inputRanges = { {ov::op::v0::RegionYolo::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, {ov::op::v0::MatMul::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, {ov::op::v11::Interpolate::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v4::Interpolate::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, {ov::op::v0::LRN::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, {ov::op::v1::Pad::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, - {ov::op::v3::Broadcast::get_type_info_static(), Range({{0, 2000}}, {{0, 2000, 32768}})}, + {ov::op::v3::Broadcast::get_type_info_static(), Range({{0, 200}}, {{0, 2000, 32768}})}, {ov::op::v5::NonMaxSuppression::get_type_info_static(), Range({{0, 15}, {0, 1, 1000, 1, true}}, {{0, 8, 32}, {0, 1, 1000, 1, true}})}, {ov::op::v9::NonMaxSuppression::get_type_info_static(), Range({{0, 15}, {0, 1, 1000, 1, true}}, @@ -427,12 +296,12 @@ static std::map inputRanges = { {ov::op::v1::ReduceLogicalAnd::get_type_info_static(), Range({{0, 2}}, {{0, 2}})}, {ov::op::v1::ReduceLogicalOr::get_type_info_static(), Range({{0, 2}}, {{0, 2}})}, {ov::op::v1::Reshape::get_type_info_static(), Range({{-1000, 2000}, {0, 256, 1, 1, true}}, {{-100, 200, 32768}})}, - {ov::op::v4::Interpolate::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, {ov::op::v3::TopK::get_type_info_static(), Range({{-1000, 2000}, {0, 1000, 1, 1, true}}, {{-1000, 2000, 32768}})}, {ov::op::v11::TopK::get_type_info_static(), Range({{-1000, 2000}, {0, 1000, 1, 1, true}}, {{-1000, 2000, 32768}})}, {ov::op::v4::Range::get_type_info_static(), Range({{0, 15}, {1, 1000, 1, 1, true}}, {{-1000, 2000, 32768}, {1, 1000, 1, 1, true}})}, - {ov::op::v11::Interpolate::get_type_info_static(), Range({{0, 15}}, {{-1000, 2000, 32768}})}, + {ov::op::v3::ROIAlign::get_type_info_static(), Range({{0, 15}, {0, 1000, 1, 1, true}, {0, 1000, 1, 1, true}}, + {{-1000, 2000, 32768}, {0, 1000, 1, 1, true}, {0, 1000, 1, 1, true}})}, {ov::op::v9::ROIAlign::get_type_info_static(), Range({{0, 15}, {0, 1000, 1, 1, true}, {0, 1000, 1, 1, true}}, {{-1000, 2000, 32768}, {0, 1000, 1, 1, true}, {0, 1000, 1, 1, true}})}, {ov::op::v0::Convert::get_type_info_static(), Range({{0, 1000}}, {{-100, 200, 32768}})}, @@ -451,6 +320,36 @@ static std::map inputRanges = { {ov::op::v7::Einsum::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, {ov::op::v8::RandomUniform::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, {ov::op::v9::Eye::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v0::CumSum::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v0::MVN::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v6::MVN::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v3::GRUCell::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v5::GRUSequence::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v8::If::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v0::TensorIterator::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v12::GroupNormalization::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v0::ReverseSequence::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v1::GatherTree::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v1::DeformablePSROIPooling::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v1::Softmax::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v8::Softmax::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v0::PSROIPooling::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::internal::AUGRUSequence::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::internal::AUGRUCell::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v7::Roll::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v4::LSTMCell::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v13::ScaledDotProductAttention::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v1::Transpose::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v5::Loop::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v0::SquaredDifference::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v4::CTCLoss::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v9::GridSample::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v13::Multinomial::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v3::EmbeddingBagOffsetsSum::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v9::GenerateProposals::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v0::ROIPooling::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v0::ShuffleChannels::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, + {ov::op::v8::Slice::get_type_info_static(), Range({{0, 15}}, {{0, 8, 32}})}, }; class ModelRange { diff --git a/src/tests/functional/shared_test_classes/src/base/utils/generate_inputs.cpp b/src/tests/functional/shared_test_classes/src/base/utils/generate_inputs.cpp index 332b4dc43a2d06..171266db31a4b9 100644 --- a/src/tests/functional/shared_test_classes/src/base/utils/generate_inputs.cpp +++ b/src/tests/functional/shared_test_classes/src/base/utils/generate_inputs.cpp @@ -971,6 +971,20 @@ ov::Tensor generate(const std::shared_ptr& node, + size_t port, + const ov::element::Type& elemType, + const ov::Shape& targetShape, + std::shared_ptr inGenRangeData = nullptr) { + if (port == 1) { + auto seq_axis = node->get_sequence_axis(); + // range - [start_data, start_data + range) + InputGenerateData inGenData(1, seq_axis + 1); + return ov::test::utils::create_and_fill_tensor(elemType, targetShape, inGenData); + } + return generate(std::dynamic_pointer_cast(node), port, elemType, targetShape, inGenRangeData); +} + template ov::Tensor generateInput(const std::shared_ptr& node, size_t port, diff --git a/src/tests/functional/shared_test_classes/src/base/utils/ranges.cpp b/src/tests/functional/shared_test_classes/src/base/utils/ranges.cpp index 85a04402a2f142..48c6f70db877cd 100644 --- a/src/tests/functional/shared_test_classes/src/base/utils/ranges.cpp +++ b/src/tests/functional/shared_test_classes/src/base/utils/ranges.cpp @@ -44,7 +44,7 @@ ov::Tensor ModelRange::generate_input(std::shared_ptr node, size_t por void ModelRange::find_mode_ranges(const std::shared_ptr& model) { for (auto param : model->get_parameters()) { std::shared_ptr data = - std::make_shared(rangeByType.get_range(param->get_element_type())); + std::make_shared(ov::test::utils::rangeByType.get_range(param->get_element_type())); bool range_corrected = true; std::queue> queue; @@ -80,6 +80,7 @@ void ModelRange::find_mode_ranges(const std::shared_ptr& model) { } } } catch (const std::exception& ex) { + (void)ex; #ifndef NDEBUG std::cout << ex.what() << std::endl; #endif diff --git a/src/tests/functional/shared_test_classes/src/single_op/gru_sequence.cpp b/src/tests/functional/shared_test_classes/src/single_op/gru_sequence.cpp index fe4f387aadb575..47e58a8839034f 100644 --- a/src/tests/functional/shared_test_classes/src/single_op/gru_sequence.cpp +++ b/src/tests/functional/shared_test_classes/src/single_op/gru_sequence.cpp @@ -62,6 +62,7 @@ void GRUSequenceTest::SetUp() { ov::op::RecurrentSequenceDirection direction; InputLayerType wbr_type; ov::test::utils::SequenceTestsMode mode; + // ov::element::Type_t in_type = ov::element::undefined; std::tie(mode, shapes, activations, clip, linear_before_reset, direction, wbr_type, inType, targetDevice) = this->GetParam(); outType = inType; @@ -82,7 +83,6 @@ void GRUSequenceTest::SetUp() { ov::ParameterVector params{std::make_shared(inType, inputDynamicShapes[0]), std::make_shared(inType, inputDynamicShapes[1])}; - const auto& w_shape = ov::Shape{num_directions, 3 * hidden_size, input_size}; const auto& r_shape = ov::Shape{num_directions, 3 * hidden_size, hidden_size}; const auto& b_shape = ov::Shape{num_directions, (linear_before_reset ? 4 : 3) * hidden_size}; diff --git a/src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp b/src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp index 6a8b7fdee9a06f..d4df10444caac8 100644 --- a/src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp +++ b/src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp @@ -237,7 +237,7 @@ void inline fill_data_random(T* pointer, start_from = 0; } for (std::size_t i = 0; i < size; i++) { - pointer[i] = static_cast(start_from + (random.Generate(k_range) / k)); + pointer[i] = static_cast(start_from + static_cast(random.Generate(k_range)) / k); } } diff --git a/src/tests/test_utils/common_test_utils/include/common_test_utils/ov_tensor_utils.hpp b/src/tests/test_utils/common_test_utils/include/common_test_utils/ov_tensor_utils.hpp index fb26f18b25888f..8bc454f035cf44 100644 --- a/src/tests/test_utils/common_test_utils/include/common_test_utils/ov_tensor_utils.hpp +++ b/src/tests/test_utils/common_test_utils/include/common_test_utils/ov_tensor_utils.hpp @@ -78,12 +78,12 @@ struct InputGenerateData { // could not find range - -----start_new---range*res new-----start_curr-----range*res curr--- if (start_from > new_max) { success = false; -// #ifndef NDEBUG +#ifndef NDEBUG std::cout << " FAIL TO FIND RANGE: current->start_from > new_range->start_from + new_range->range " << " current->start_from: " << std::to_string(start_from) << " new_range->start_from: " << std::to_string(new_range->start_from) << " new_range max: " << std::to_string(new_max) << std::endl; -// #endif +#endif } else if (current_max > new_max) { range = (uint32_t)round(new_max - start_from); resolution = new_range->resolution > resolution ? new_range->resolution : resolution; @@ -95,12 +95,12 @@ struct InputGenerateData { // could not find range - -----start_curr---range*res curr-----start_new-----range*res new--- if (current_max < new_range->start_from) { success = false; -// #ifndef NDEBUG +#ifndef NDEBUG std::cout << " FAIL TO FIND RANGE: current->start_from + current->range < new_range->start_from " << " new_range start_from: " << std::to_string(new_range->start_from) << " current->start_from: " << std::to_string(start_from) << " current max: " << std::to_string(current_max) << std::endl; -// #endif +#endif } else if (current_max >= new_max) { start_from = new_range->start_from; range = new_range->range; @@ -136,12 +136,12 @@ struct InputGenerateData { // could not find range - -----start_new---range*res new-----start_curr-----range*res curr--- if (start_from > new_max) { success = false; -// #ifndef NDEBUG +#ifndef NDEBUG std::cout << " FAIL TO FIND RANGE: current->start_from > new_range->start_from + new_range->range " << " current->start_from: " << std::to_string(start_from) << " new_range->start_from: " << std::to_string(new_range.start_from) << " new_range max: " << std::to_string(new_max) << std::endl; -// #endif +#endif } else if (current_max > new_max) { range = (uint32_t)round(new_max - start_from); resolution = new_range.resolution > resolution ? new_range.resolution : resolution; @@ -153,12 +153,12 @@ struct InputGenerateData { // could not find range - -----start_curr---range*res curr-----start_new-----range*res new--- if (current_max < new_range.start_from) { success = false; -// #ifndef NDEBUG +#ifndef NDEBUG std::cout << " FAIL TO FIND RANGE: current->start_from + current->range < new_range->start_from " << " new_range start_from: " << std::to_string(new_range.start_from) << " current->start_from: " << std::to_string(start_from) << " current max: " << std::to_string(current_max) << std::endl; -// #endif +#endif } else if (current_max >= new_max) { start_from = new_range.start_from; range = new_range.range; diff --git a/src/tests/test_utils/common_test_utils/tests/CMakeLists.txt b/src/tests/test_utils/common_test_utils/tests/CMakeLists.txt index c994ed5f75cc3e..69f9d716b35ce5 100644 --- a/src/tests/test_utils/common_test_utils/tests/CMakeLists.txt +++ b/src/tests/test_utils/common_test_utils/tests/CMakeLists.txt @@ -10,8 +10,9 @@ ov_add_test_target( DEPENDENCIES LINK_LIBRARIES common_test_utils + func_test_utils sharedTestClasses ADD_CPPLINT LABELS OV UNIT -) +) \ No newline at end of file diff --git a/src/tests/test_utils/common_test_utils/tests/core_config.cpp b/src/tests/test_utils/common_test_utils/tests/core_config.cpp new file mode 100644 index 00000000000000..71fb033de4bcd1 --- /dev/null +++ b/src/tests/test_utils/common_test_utils/tests/core_config.cpp @@ -0,0 +1,15 @@ +// Copyright (C) 2018-2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "common_test_utils/file_utils.hpp" +#include "shared_test_classes/base/ov_subgraph.hpp" +#include "openvino/util/file_util.hpp" + +namespace ov { +namespace test { + +void core_configuration(ov::test::SubgraphBaseTest* test) {} + +} // namespace test +} // namespace ov \ No newline at end of file diff --git a/src/tests/test_utils/common_test_utils/tests/generate_intepus.cpp b/src/tests/test_utils/common_test_utils/tests/generate_intepus.cpp index 9368404acd22b9..ec4727e8ec3234 100644 --- a/src/tests/test_utils/common_test_utils/tests/generate_intepus.cpp +++ b/src/tests/test_utils/common_test_utils/tests/generate_intepus.cpp @@ -5,6 +5,7 @@ #include #include "openvino/op/util/op_types.hpp" +#include "common_test_utils/type_ranges.hpp" #include "shared_test_classes/base/utils/ranges.hpp" #include "shared_test_classes/base/utils/generate_inputs.hpp" @@ -14,6 +15,7 @@ #include "openvino/op/result.hpp" #include "openvino/op/reduce_mean.hpp" #include "openvino/op/floor_mod.hpp" +#include "openvino/op/reshape.hpp" using namespace testing; using namespace ov::util; @@ -236,7 +238,7 @@ TEST(RangesTests, intersection_range) { ASSERT_EQ(real_range->range, intersection_range_real.range); ASSERT_EQ(real_range->resolution, intersection_range_real.resolution); - ov::Tensor tensor1 = modelRange.generate_input(add, 0, Shape{1}); + ov::Tensor tensor1 = modelRange.generate_input(relu, 0, Shape{1}); auto data1 = tensor1.data(); for (size_t i = 0; i < shape_size(tensor1.get_shape()); ++i) { double value = data1[i]; diff --git a/src/tests/test_utils/common_test_utils/tests/skip_tests_config.cpp b/src/tests/test_utils/common_test_utils/tests/skip_tests_config.cpp new file mode 100644 index 00000000000000..dc54bec286395e --- /dev/null +++ b/src/tests/test_utils/common_test_utils/tests/skip_tests_config.cpp @@ -0,0 +1,12 @@ +// Copyright (C) 2018-2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "functional_test_utils/skip_tests_config.hpp" + +#include +#include + +std::vector disabledTestPatterns() { + return std::vector{}; +} diff --git a/src/tests/test_utils/functional_test_utils/layer_tests_summary/github/skip_configs/CPU/expected_failures_OP.csv b/src/tests/test_utils/functional_test_utils/layer_tests_summary/github/skip_configs/CPU/expected_failures_OP.csv index 084c11cef57813..2dc390ed536fc8 100644 --- a/src/tests/test_utils/functional_test_utils/layer_tests_summary/github/skip_configs/CPU/expected_failures_OP.csv +++ b/src/tests/test_utils/functional_test_utils/layer_tests_summary/github/skip_configs/CPU/expected_failures_OP.csv @@ -246,5 +246,4 @@ conformance_subgraph/ReadIRTest.Inference/Extractor=fused_names_Shape=static_IR= conformance_Loop/ReadIRTest.Inference/Op=Loop.5_Type=f32_Shape=static_IR=35c61b2251b78ad9f9804bd3f9e301e1f974c6dc138ce0466b8b940d106ddd72_Device=CPU_Config=(),9.92895e-06 conformance_subgraph/ReadIRTest.Inference/Extractor=fused_names_Shape=static_IR=7caba2dff8ab10660f66796a39d8d2a78f3e282f0629c2ecbee9b90c34e62aa0_Device=CPU_Config=(),2.1896e-06 conformance_subgraph/ReadIRTest.Inference/Extractor=fused_names_Shape=static_IR=2e06088cb191d8d26309843b1285b9ae4a1eb0722e1370875edde7fd2783851b_Device=CPU_Config=(),1.88776e-06 -conformance_subgraph/ReadIRTest.Inference/Extractor=repeat_pattern_Shape=static_IR=183e5203c7008618a9cfb2680265bb3f588f80c2493bf7fac92eb258e66da2cf_Device=CPU_Config=(),1.88776e-06 -conformance_subgraph/ReadIRTest.Inference/Extractor=repeat_pattern_Shape=static_IR=d9771ac46751569172412bbd4495eccdbac435f78a97f8fdfffa9215faa74544_Device=CPU_Config=(),1.88776e-06 \ No newline at end of file +conformance_subgraph/ReadIRTest.Inference/Extractor=repeat_pattern_Shape=static_IR=183e5203c7008618a9cfb2680265bb3f588f80c2493bf7fac92eb258e66da2cf_Device=CPU_Config=(),1.88776e-06 \ No newline at end of file