| { |
| "op": "ai.onnx.Tile", |
| "cases": [ |
| { |
| "name": "rank2", |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } }, |
| "repeats": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [2, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [4, 6] } } |
| }, |
| { |
| "name": "ort_float_2d_two_axes_full_repeat", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileFloatType", |
| "notes": "RunTest<float>({2, 2}, {2, 2}) from ORT's shared Tile wrapper." |
| }, |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [0.0, 1.0, 2.0, 3.0] } }, |
| "repeats": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [2, 2] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [4, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_float_2d_first_axis_repeat", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileFloatType", |
| "notes": "RunTest<float>({2, 3}, {2, 1}) from ORT's shared Tile wrapper." |
| }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [2, 3], |
| "data": { "kind": "values", "values": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0] } |
| }, |
| "repeats": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [2, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [4, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_f16_2d_two_axes_full_repeat", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileMLFloat16Type", |
| "notes": "RunTest<MLFloat16>({2, 2}, {2, 2}) from ORT's shared Tile wrapper." |
| }, |
| "inputs": { |
| "input": { "dtype": "float16", "shape": [2, 2], "data": { "kind": "values", "values": [0.0, 1.0, 2.0, 3.0] } }, |
| "repeats": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [2, 2] } } |
| }, |
| "outputs": { "output": { "dtype": "float16", "shape": [4, 4], "tolerance": 0.001 } } |
| }, |
| { |
| "name": "rank3_f16", |
| "inputs": { |
| "input": { "dtype": "float16", "shape": [1, 2, 2] }, |
| "repeats": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [2, 1, 2] } } |
| }, |
| "outputs": { "output": { "dtype": "float16", "shape": [2, 2, 4] } }, |
| "tolerance": 0.001 |
| }, |
| { |
| "name": "rank5_nonuniform_repeats", |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 2, 1, 2, 1], |
| "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.11 } |
| }, |
| "repeats": { "dtype": "uint32", "shape": [5], "data": { "kind": "values", "values": [2, 1, 3, 1, 4] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 2, 3, 2, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "large_output_from_scalar", |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [7.0] } }, |
| "repeats": { "dtype": "uint32", "shape": [1], "data": { "kind": "values", "values": [257] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [257], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank6_nonuniform_repeats", |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 2, 1, 2, 1, 2], |
| "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.19 } |
| }, |
| "repeats": { "dtype": "uint32", "shape": [6], "data": { "kind": "values", "values": [2, 1, 3, 1, 2, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 2, 3, 2, 2, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank0_scalar_empty_repeats_noop", |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [42.5] } }, |
| "repeats": { "dtype": "uint32", "shape": [0], "data": { "kind": "values", "values": [] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "zero_repeat_axis_empty_output", |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } }, |
| "repeats": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [0, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [0, 6], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_int32_1d_zero_repeats", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileInt32Type", |
| "notes": "RunTest<int32_t>({3}, {0})." |
| }, |
| "inputs": { |
| "input": { "dtype": "int32", "shape": [3], "data": { "kind": "values", "values": [0, 1, 2] } }, |
| "repeats": { "dtype": "uint32", "shape": [1], "data": { "kind": "values", "values": [0] } } |
| }, |
| "outputs": { "output": { "dtype": "int32", "shape": [0], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_int32_2d_two_axes", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileInt32Type", |
| "notes": "RunTest<int32_t>({2, 2}, {2, 2})." |
| }, |
| "inputs": { |
| "input": { "dtype": "int32", "shape": [2, 2], "data": { "kind": "values", "values": [0, 1, 2, 3] } }, |
| "repeats": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [2, 2] } } |
| }, |
| "outputs": { "output": { "dtype": "int32", "shape": [4, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_int16_2d_two_axes", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileInt16Type", |
| "notes": "RunTest<int16_t>({2, 2}, {2, 2})." |
| }, |
| "inputs": { |
| "input": { "dtype": "int16", "shape": [2, 2], "data": { "kind": "values", "values": [0, 1, 2, 3] } }, |
| "repeats": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [2, 2] } } |
| }, |
| "outputs": { "output": { "dtype": "int16", "shape": [4, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_uint32_3d_middle_repeat", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileUint32Type", |
| "notes": "RunTest<uint32_t>({2, 1, 3}, {1, 2, 1})." |
| }, |
| "inputs": { |
| "input": { "dtype": "uint32", "shape": [2, 1, 3], "data": { "kind": "values", "values": [0, 1, 2, 3, 4, 5] } }, |
| "repeats": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [1, 2, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "uint32", "shape": [2, 2, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_uint32_large_values_no_float_cast", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileUint32Type", |
| "notes": "Uses the ORT tile copy pattern with large uint32 values to guard against f32 round trips." |
| }, |
| "inputs": { |
| "input": { "dtype": "uint32", "shape": [1, 2], "data": { "kind": "values", "values": [16777217, 4000000000] } }, |
| "repeats": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 2] } } |
| }, |
| "outputs": { "output": { "dtype": "uint32", "shape": [3, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_int32_singleton_prefix_copy_like", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileInt32Type", |
| "notes": "RunTest<int32_t>({1, 1, 3}, {2, 2, 1})." |
| }, |
| "inputs": { |
| "input": { "dtype": "int32", "shape": [1, 1, 3], "data": { "kind": "values", "values": [-7, 0, 9] } }, |
| "repeats": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [2, 2, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "int32", "shape": [2, 2, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_int32_singleton_batch_copy_like", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileInt32Type", |
| "notes": "RunTest<int32_t>({1, 1, 3}, {3, 1, 1})." |
| }, |
| "inputs": { |
| "input": { "dtype": "int32", "shape": [1, 1, 3], "data": { "kind": "values", "values": [-7, 0, 9] } }, |
| "repeats": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [3, 1, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "int32", "shape": [3, 1, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_int32_batched_copy_like", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileInt32Type", |
| "notes": "RunTest<int32_t>({2, 1, 3}, {2, 2, 1})." |
| }, |
| "inputs": { |
| "input": { "dtype": "int32", "shape": [2, 1, 3], "data": { "kind": "values", "values": [0, 1, 2, 3, 4, 5] } }, |
| "repeats": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [2, 2, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "int32", "shape": [4, 2, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_1d_repeat3_f32", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileFloatType", |
| "notes": "RunTest<float>({3}, {3})." |
| }, |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [0.0, 1.0, 2.0] } }, |
| "repeats": { "dtype": "uint32", "shape": [1], "data": { "kind": "values", "values": [3] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [9], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_2d_second_axis_zero_repeat", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileFloatType", |
| "notes": "RunTest<float>({2, 2}, {2, 0})." |
| }, |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [0.0, 1.0, 2.0, 3.0] } }, |
| "repeats": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [2, 0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [4, 0], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_rank3_all_repeats_one_noop", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileFloatType", |
| "notes": "RunTest<float>({2, 1, 3}, {1, 1, 1})." |
| }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [2, 1, 3], |
| "data": { "kind": "values", "values": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0] } |
| }, |
| "repeats": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [1, 1, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 1, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_4d_repeated_middle_spatial", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileFloatType", |
| "notes": "RunTest<float>({1, 2, 3, 4}, {2, 1, 2, 1})." |
| }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 2, 3, 4], |
| "data": { |
| "kind": "values", |
| "values": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0] |
| } |
| }, |
| "repeats": { "dtype": "uint32", "shape": [4], "data": { "kind": "values", "values": [2, 1, 2, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 2, 6, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_rank5_float_nonuniform_repeats", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileFloatType", |
| "notes": "RunTest<float>({2, 3, 2, 3, 2}, {2, 1, 2, 1, 2})." |
| }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [2, 3, 2, 3, 2], |
| "data": { |
| "kind": "values", |
| "values": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0] |
| } |
| }, |
| "repeats": { "dtype": "uint32", "shape": [5], "data": { "kind": "values", "values": [2, 1, 2, 1, 2] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [4, 3, 4, 3, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "onnx_backend_tile_precomputed", |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [0.0, 1.0, 2.0, 3.0] } }, |
| "repeats": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [2, 2] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [4, 4] } }, |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_tile_precomputed", |
| "notes": "ONNX int64 metadata/index tensors use framework int32/uint32 slots where representable." |
| } |
| }, |
| { |
| "name": "onnx_backend_tile", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_tile", |
| "notes": "ONNX repeats are int64 metadata; this framework stores non-negative repeats as uint32 metadata slots." |
| }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [2, 3, 4, 5], |
| "data": { |
| "kind": "values", |
| "values": [0.54881352186203, 0.7151893377304077, 0.6027633547782898, 0.5448831915855408, 0.42365479469299316, 0.6458941102027893, 0.4375872015953064, 0.891772985458374, 0.9636627435684204, 0.3834415078163147, 0.7917250394821167, 0.5288949012756348, 0.5680445432662964, 0.9255966544151306, 0.07103605568408966, 0.08712930232286453, 0.020218396559357643, 0.832619845867157, 0.7781567573547363, 0.8700121641159058, 0.978618323802948, 0.7991585731506348, 0.4614793658256531, 0.7805292010307312, 0.11827442795038223, 0.6399210095405579, 0.14335328340530396, 0.9446688890457153, 0.5218483209609985, 0.4146619439125061, 0.26455560326576233, 0.7742336988449097, 0.4561503231525421, 0.568433940410614, 0.018789799883961678, 0.6176354885101318, 0.6120957136154175, 0.6169340014457703, 0.9437480568885803, 0.681820273399353, 0.35950788855552673, 0.43703195452690125, 0.6976311802864075, 0.0602254718542099, 0.6667667031288147, 0.670637845993042, 0.21038256585597992, 0.12892629206180573, 0.31542834639549255, 0.36371076107025146, 0.5701967477798462, 0.4386015236377716, 0.9883738160133362, 0.10204481333494186, 0.20887675881385803, 0.16130951046943665, 0.6531082987785339, 0.25329160690307617, 0.4663107693195343, 0.24442559480667114, 0.15896958112716675, 0.11037514358758926, 0.6563295722007751, 0.13818295300006866, 0.1965823620557785, 0.3687251806259155, 0.8209932446479797, 0.09710127860307693, 0.8379449248313904, 0.0960984081029892, 0.9764594435691833, 0.4686512053012848, 0.9767611026763916, 0.6048455238342285, 0.7392635941505432, 0.03918779268860817, 0.28280696272850037, 0.12019655853509903, 0.296140193939209, 0.11872772127389908, 0.3179831802845001, 0.414262980222702, 0.06414749473333359, 0.6924721002578735, 0.5666014552116394, 0.26538950204849243, 0.5232480764389038, 0.09394051134586334, 0.5759465098381042, 0.9292961955070496, 0.3185689449310303, 0.6674103736877441, 0.13179786503314972, 0.7163271903991699, 0.28940609097480774, 0.18319135904312134, 0.5865129232406616, 0.02010754682123661, 0.8289400339126587, 0.004695476032793522, 0.6778165102005005, 0.2700079679489136, 0.7351940274238586, 0.9621885418891907, 0.2487531453371048, 0.5761573314666748, 0.5920419096946716, 0.5722519159317017, 0.22308163344860077, 0.9527490139007568, 0.4471253752708435, 0.8464086651802063, 0.6994792819023132, 0.2974369525909424, 0.8137978315353394, 0.396505743265152, 0.8811032176017761, 0.5812729001045227, 0.8817353844642639, 0.6925315856933594] |
| } |
| }, |
| "repeats": { "dtype": "uint32", "shape": [4], "data": { "kind": "values", "values": [7, 6, 4, 2] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [14, 18, 16, 10], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_int8_2d_two_axes_edge_values", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileInt8Type", |
| "notes": "RunTest<int8_t>({2, 2}, {2, 2}) with signed-byte edge values." |
| }, |
| "inputs": { |
| "input": { "dtype": "int8", "shape": [2, 2], "data": { "kind": "values", "values": [-128, -1, 0, 127] } }, |
| "repeats": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [2, 2] } } |
| }, |
| "outputs": { "output": { "dtype": "int8", "shape": [4, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_uint8_3d_middle_repeat_edge_values", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileUint8Type", |
| "notes": "RunTest<uint8_t>({2, 1, 3}, {1, 2, 1}) with high logical-byte values." |
| }, |
| "inputs": { |
| "input": { |
| "dtype": "uint8", |
| "shape": [2, 1, 3], |
| "data": { "kind": "values", "values": [0, 1, 127, 128, 254, 255] } |
| }, |
| "repeats": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [1, 2, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "uint8", "shape": [2, 2, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "inner_vec4_rank3_f16", |
| "inputs": { |
| "input": { "dtype": "float16", "shape": [2, 3, 8] }, |
| "repeats": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [2, 2, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "float16", "shape": [4, 6, 8], "tolerance": 0.001 } } |
| }, |
| { |
| "name": "inner_vec4_u32_large_values", |
| "inputs": { |
| "input": { |
| "dtype": "uint32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [16777217, 4000000000, 1, 2, 3, 4, 5, 4294967295] } |
| }, |
| "repeats": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "uint32", "shape": [6, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_bool_2d_two_axes", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileBoolType", |
| "notes": "RunTestForBool Tile2D_2Axes." |
| }, |
| "inputs": { |
| "input": { "dtype": "bool", "shape": [2, 2], "data": { "kind": "values", "values": [1, 0, 1, 0] } }, |
| "repeats": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [2, 2] } } |
| }, |
| "outputs": { |
| "output": { |
| "dtype": "bool", |
| "shape": [4, 4], |
| "tolerance": 0, |
| "data": { "kind": "values", "values": [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0] } |
| } |
| } |
| }, |
| { |
| "name": "ort_bool_2d_zero_repeat_axis", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tile_op_test.cc", |
| "test": "TensorOpTest.TileBoolType", |
| "notes": "RunTestForBool Tile2DWithZeroRepeats." |
| }, |
| "inputs": { |
| "input": { "dtype": "bool", "shape": [2, 2], "data": { "kind": "values", "values": [1, 0, 1, 0] } }, |
| "repeats": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [2, 0] } } |
| }, |
| "outputs": { |
| "output": { "dtype": "bool", "shape": [4, 0], "data": { "kind": "values", "values": [] }, "tolerance": 0 } |
| } |
| }, |
| { |
| "name": "rank7_unit_input_alternating_repeats", |
| "provenance": { |
| "source": "ONNX Tile spec (no rank limit) + onnxruntime CPU Tile kernel", |
| "notes": "Spec-valid rank-7 Tile. ORT CPU computes it via its generic strided copy." |
| }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [2, 1, 2, 1, 2, 1, 2], |
| "data": { |
| "kind": "values", |
| "values": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0] |
| } |
| }, |
| "repeats": { "dtype": "uint32", "shape": [7], "data": { "kind": "values", "values": [1, 2, 1, 2, 1, 2, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 2, 2, 2, 2, 2, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "inner_vec4_rank4_f16_middle_repeats", |
| "inputs": { |
| "input": { |
| "dtype": "float16", |
| "shape": [2, 3, 2, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.13 } |
| }, |
| "repeats": { "dtype": "uint32", "shape": [4], "data": { "kind": "values", "values": [2, 1, 3, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "float16", "shape": [4, 3, 6, 8], "tolerance": 0.001 } } |
| }, |
| { |
| "name": "inner_vec4_rank7_lastdim_mul4_outer_repeats", |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [2, 1, 2, 1, 2, 1, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.23 } |
| }, |
| "repeats": { "dtype": "uint32", "shape": [7], "data": { "kind": "values", "values": [2, 1, 2, 1, 2, 1, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [4, 1, 4, 1, 4, 1, 8], "tolerance": 0 } } |
| }, |
| { |
| "name": "generic_rank6_lastaxis_repeat_broadcast_fold", |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [2, 1, 3, 1, 2, 5], |
| "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.29 } |
| }, |
| "repeats": { "dtype": "uint32", "shape": [6], "data": { "kind": "values", "values": [2, 3, 1, 2, 1, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [4, 3, 3, 2, 2, 15], "tolerance": 0 } } |
| }, |
| { |
| "name": "inner_vec4_int8_lastdim_mul4_outer_repeat", |
| "inputs": { |
| "input": { |
| "dtype": "int8", |
| "shape": [2, 8], |
| "data": { "kind": "values", "values": [-128, 127, 0, -1, 1, -2, 64, -64, 100, -100, 7, -7, 3, -3, 15, -15] } |
| }, |
| "repeats": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "int8", "shape": [6, 8], "tolerance": 0 } } |
| }, |
| { |
| "name": "rank8_unit_input_alternating_repeats", |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [2, 1, 2, 1, 2, 1, 2, 1], |
| "data": { "kind": "linspace", "start": 0.0, "end": 15.0 } |
| }, |
| "repeats": { "dtype": "uint32", "shape": [8], "data": { "kind": "values", "values": [1, 2, 1, 2, 1, 2, 1, 2] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 2, 2, 2, 2, 2, 2, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "empty_input_axis_generic", |
| "provenance": { |
| "notes": "An empty INPUT axis, as opposed to the zero-repeat cases which empty the OUTPUT while every input extent stays positive. The generic route divides the output coordinate by the input extent on each axis, and only this shape drives that extent to zero; the vec4 route cannot, because its guard requires a non-empty output." |
| }, |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [0, 3], "data": { "kind": "values", "values": [] } }, |
| "repeats": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [2, 2] } } |
| }, |
| "outputs": { |
| "output": { "dtype": "float32", "shape": [0, 6], "tolerance": 0, "data": { "kind": "values", "values": [] } } |
| } |
| } |
| ] |
| } |
|
|
