{ "op": "ai.onnx.Concat", "fixtureArrays": { "ort_2d_nine_inputs_axis0_variadic_output_c": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18] }, "cases": [ { "name": "int16_max_arity_boundaries", "attrs": { "axis": 0 }, "inputs": { "a": { "dtype": "int16", "shape": [1], "data": { "kind": "values", "values": [-32768] } }, "b": { "dtype": "int16", "shape": [1], "data": { "kind": "values", "values": [-1] } }, "d": { "dtype": "int16", "shape": [1], "data": { "kind": "values", "values": [0] } }, "e": { "dtype": "int16", "shape": [1], "data": { "kind": "values", "values": [1] } }, "f": { "dtype": "int16", "shape": [1], "data": { "kind": "values", "values": [32767] } }, "g": { "dtype": "int16", "shape": [1], "data": { "kind": "values", "values": [-32767] } }, "h": { "dtype": "int16", "shape": [1], "data": { "kind": "values", "values": [32766] } }, "i": { "dtype": "int16", "shape": [1], "data": { "kind": "values", "values": [-2] } }, "j": { "dtype": "int16", "shape": [1], "data": { "kind": "values", "values": [2] } } }, "outputs": { "c": { "dtype": "int16", "shape": [9], "tolerance": 0, "data": { "kind": "values", "values": [-32768, -1, 0, 1, 32767, -32767, 32766, -2, 2] } } } }, { "name": "max_arity_float16_positions", "provenance": { "notes": "Synthetic nine-input float16 Concat contract fixture; every bounded input position contributes one distinguishable output element." }, "attrs": { "axis": 0 }, "inputs": { "a": { "dtype": "float16", "shape": [1], "data": { "kind": "values", "values": [1.0] } }, "b": { "dtype": "float16", "shape": [1], "data": { "kind": "values", "values": [2.0] } }, "d": { "dtype": "float16", "shape": [1], "data": { "kind": "values", "values": [3.0] } }, "e": { "dtype": "float16", "shape": [1], "data": { "kind": "values", "values": [4.0] } }, "f": { "dtype": "float16", "shape": [1], "data": { "kind": "values", "values": [5.0] } }, "g": { "dtype": "float16", "shape": [1], "data": { "kind": "values", "values": [6.0] } }, "h": { "dtype": "float16", "shape": [1], "data": { "kind": "values", "values": [7.0] } }, "i": { "dtype": "float16", "shape": [1], "data": { "kind": "values", "values": [8.0] } }, "j": { "dtype": "float16", "shape": [1], "data": { "kind": "values", "values": [9.0] } } }, "outputs": { "c": { "dtype": "float16", "shape": [9], "tolerance": 0, "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0] } } } }, { "name": "max_arity_int32_positions", "provenance": { "notes": "Synthetic nine-input int32 Concat contract fixture; every bounded input position contributes one distinguishable output element." }, "attrs": { "axis": 0 }, "inputs": { "a": { "dtype": "int32", "shape": [1], "data": { "kind": "values", "values": [1] } }, "b": { "dtype": "int32", "shape": [1], "data": { "kind": "values", "values": [2] } }, "d": { "dtype": "int32", "shape": [1], "data": { "kind": "values", "values": [3] } }, "e": { "dtype": "int32", "shape": [1], "data": { "kind": "values", "values": [4] } }, "f": { "dtype": "int32", "shape": [1], "data": { "kind": "values", "values": [5] } }, "g": { "dtype": "int32", "shape": [1], "data": { "kind": "values", "values": [6] } }, "h": { "dtype": "int32", "shape": [1], "data": { "kind": "values", "values": [7] } }, "i": { "dtype": "int32", "shape": [1], "data": { "kind": "values", "values": [8] } }, "j": { "dtype": "int32", "shape": [1], "data": { "kind": "values", "values": [9] } } }, "outputs": { "c": { "dtype": "int32", "shape": [9], "tolerance": 0, "data": { "kind": "values", "values": [1, 2, 3, 4, 5, 6, 7, 8, 9] } } } }, { "name": "max_arity_int8_positions", "provenance": { "notes": "Synthetic nine-input int8 Concat contract fixture; every bounded input position contributes one distinguishable output element." }, "attrs": { "axis": 0 }, "inputs": { "a": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [1] } }, "b": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [2] } }, "d": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [3] } }, "e": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [4] } }, "f": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [5] } }, "g": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [6] } }, "h": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [7] } }, "i": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [8] } }, "j": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [9] } } }, "outputs": { "c": { "dtype": "int8", "shape": [9], "tolerance": 0, "data": { "kind": "values", "values": [1, 2, 3, 4, 5, 6, 7, 8, 9] } } } }, { "name": "max_arity_uint32_positions", "provenance": { "notes": "Synthetic nine-input uint32 Concat contract fixture; every bounded input position contributes one distinguishable output element." }, "attrs": { "axis": 0 }, "inputs": { "a": { "dtype": "uint32", "shape": [1], "data": { "kind": "values", "values": [1] } }, "b": { "dtype": "uint32", "shape": [1], "data": { "kind": "values", "values": [2] } }, "d": { "dtype": "uint32", "shape": [1], "data": { "kind": "values", "values": [3] } }, "e": { "dtype": "uint32", "shape": [1], "data": { "kind": "values", "values": [4] } }, "f": { "dtype": "uint32", "shape": [1], "data": { "kind": "values", "values": [5] } }, "g": { "dtype": "uint32", "shape": [1], "data": { "kind": "values", "values": [6] } }, "h": { "dtype": "uint32", "shape": [1], "data": { "kind": "values", "values": [7] } }, "i": { "dtype": "uint32", "shape": [1], "data": { "kind": "values", "values": [8] } }, "j": { "dtype": "uint32", "shape": [1], "data": { "kind": "values", "values": [9] } } }, "outputs": { "c": { "dtype": "uint32", "shape": [9], "tolerance": 0, "data": { "kind": "values", "values": [1, 2, 3, 4, 5, 6, 7, 8, 9] } } } }, { "name": "max_arity_uint8_positions", "provenance": { "notes": "Synthetic nine-input uint8 Concat contract fixture; every bounded input position contributes one distinguishable output element." }, "attrs": { "axis": 0 }, "inputs": { "a": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [1] } }, "b": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [2] } }, "d": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [3] } }, "e": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [4] } }, "f": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [5] } }, "g": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [6] } }, "h": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [7] } }, "i": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [8] } }, "j": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [9] } } }, "outputs": { "c": { "dtype": "uint8", "shape": [9], "tolerance": 0, "data": { "kind": "values", "values": [1, 2, 3, 4, 5, 6, 7, 8, 9] } } } }, { "name": "max_arity_bool_positions", "provenance": { "notes": "Synthetic nine-input bool Concat contract fixture; every bounded input position contributes a unique non-constant bit pattern." }, "attrs": { "axis": 0 }, "inputs": { "a": { "dtype": "bool", "shape": [4], "data": { "kind": "values", "values": [1, 0, 0, 0] } }, "b": { "dtype": "bool", "shape": [4], "data": { "kind": "values", "values": [0, 1, 0, 0] } }, "d": { "dtype": "bool", "shape": [4], "data": { "kind": "values", "values": [0, 0, 1, 0] } }, "e": { "dtype": "bool", "shape": [4], "data": { "kind": "values", "values": [0, 0, 0, 1] } }, "f": { "dtype": "bool", "shape": [4], "data": { "kind": "values", "values": [1, 1, 0, 0] } }, "g": { "dtype": "bool", "shape": [4], "data": { "kind": "values", "values": [1, 0, 1, 0] } }, "h": { "dtype": "bool", "shape": [4], "data": { "kind": "values", "values": [1, 0, 0, 1] } }, "i": { "dtype": "bool", "shape": [4], "data": { "kind": "values", "values": [0, 1, 1, 0] } }, "j": { "dtype": "bool", "shape": [4], "data": { "kind": "values", "values": [0, 1, 0, 1] } } }, "outputs": { "c": { "dtype": "bool", "shape": [36], "tolerance": 0, "data": { "kind": "values", "values": [1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1] } } } }, { "name": "single_input_is_identity", "provenance": { "notes": "ONNX Concat is variadic with min_arity=1; this pins the legal single-input identity case." }, "attrs": { "axis": -1 }, "inputs": { "a": { "dtype": "float32", "shape": [2, 3], "data": { "kind": "values", "values": [-3.5, 0.0, 2.25, 8.0, -1.0, 4.5] } } }, "outputs": { "c": { "dtype": "float32", "shape": [2, 3], "tolerance": 0, "data": { "kind": "values", "values": [-3.5, 0.0, 2.25, 8.0, -1.0, 4.5] } } } }, { "name": "axis0", "attrs": { "axis": 0 }, "inputs": { "a": { "dtype": "float32", "shape": [2, 3], "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29 } }, "b": { "dtype": "float32", "shape": [1, 3], "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.07 } } }, "outputs": { "c": { "dtype": "float32", "shape": [3, 3], "tolerance": 0.000001 } } }, { "name": "axis1_rank3", "attrs": { "axis": 1 }, "inputs": { "a": { "dtype": "float32", "shape": [2, 2, 3], "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.23 } }, "b": { "dtype": "float32", "shape": [2, 1, 3], "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.11 } } }, "outputs": { "c": { "dtype": "float32", "shape": [2, 3, 3], "tolerance": 0.000001 } } }, { "name": "axis1_odd_width_two_inputs_vec4_sibling", "provenance": { "notes": "Compact axis-1 sibling for the aligned/unaligned Concat benchmarks; preserves odd input widths and vec4-sized row output without benchmark-scale rows." }, "attrs": { "axis": 1 }, "inputs": { "a": { "dtype": "float32", "shape": [4, 5], "data": { "kind": "fillFloat32", "sinStep": 0.071, "cosStep": 0.113, "scale": 0.5 } }, "b": { "dtype": "float32", "shape": [4, 3], "data": { "kind": "fillFloat32", "sinStep": 0.097, "cosStep": 0.041, "scale": 0.25 } } }, "outputs": { "c": { "dtype": "float32", "shape": [4, 8], "tolerance": 0.000001 } } }, { "name": "axis1_rank3_three_inputs", "attrs": { "axis": 1 }, "inputs": { "a": { "dtype": "float32", "shape": [2, 1, 2], "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29 } }, "b": { "dtype": "float32", "shape": [2, 2, 2], "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.23 } }, "d": { "dtype": "float32", "shape": [2, 1, 2], "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.07 } } }, "outputs": { "c": { "dtype": "float32", "shape": [2, 4, 2], "tolerance": 0.000001 } } }, { "name": "axis1_rank3_four_inputs", "attrs": { "axis": 1 }, "inputs": { "a": { "dtype": "float32", "shape": [1, 1, 2], "data": { "kind": "values", "values": [0.0, 1.0] } }, "b": { "dtype": "float32", "shape": [1, 2, 2], "data": { "kind": "values", "values": [10.0, 11.0, 20.0, 21.0] } }, "d": { "dtype": "float32", "shape": [1, 1, 2], "data": { "kind": "values", "values": [30.0, 31.0] } }, "e": { "dtype": "float32", "shape": [1, 3, 2], "data": { "kind": "values", "values": [40.0, 41.0, 50.0, 51.0, 60.0, 61.0] } } }, "outputs": { "c": { "dtype": "float32", "shape": [1, 7, 2], "tolerance": 0.000001 } } }, { "name": "ort_bool_1d_three_inputs", "attrs": { "axis": 0 }, "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/concat_op_test.cc", "test": "ConcatOpTest.Concat1D_int32", "notes": "Exercises one-dimensional, three-input concatenation with an ONNX-valid bool payload." }, "inputs": { "a": { "dtype": "bool", "shape": [1], "data": { "kind": "values", "values": [1] } }, "b": { "dtype": "bool", "shape": [2], "data": { "kind": "values", "values": [0, 1] } }, "d": { "dtype": "bool", "shape": [4], "data": { "kind": "values", "values": [1, 0, 0, 1] } } }, "outputs": { "c": { "dtype": "bool", "shape": [7], "tolerance": 0 } } }, { "name": 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"data": { "kind": "values", "values": [1.0] } }, "b": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [2.0, 3.0] } }, "d": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [4.0, 5.0, 6.0, 7.0] } } }, "outputs": { "c": { "dtype": "float32", "shape": [7], "tolerance": 0.000001 } } }, { "name": "ort_1d_three_inputs_negative_axis", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/concat_op_test.cc", "test": "ConcatOpTest.Concat1D_int32_negative_axis", "notes": "Same negative-axis rank-1 concat behavior adapted to float32." }, "attrs": { "axis": -1 }, "inputs": { "a": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } }, "b": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [2.0, 3.0] } }, "d": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [4.0, 5.0, 6.0, 7.0] } } }, "outputs": { "c": { "dtype": "float32", "shape": [7], "tolerance": 0.000001 } } }, { "name": "ort_2d_axis1_three_inputs_float16", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/concat_op_test.cc", "test": "ConcatOpTest.Concat2D_2" }, "attrs": { "axis": 1 }, "inputs": { "a": { "dtype": "float16", "shape": [4, 1], "data": { "kind": "values", "values": [11.0, 21.0, 31.0, 41.0] } }, "b": { "dtype": "float16", "shape": [4, 2], "data": { "kind": "values", "values": [12.0, 13.0, 22.0, 23.0, 32.0, 33.0, 42.0, 43.0] } }, "d": { "dtype": "float16", "shape": [4, 1], "data": { "kind": "values", "values": [14.0, 24.0, 34.0, 44.0] } } }, "outputs": { "c": { "dtype": "float16", "shape": [4, 4], "tolerance": 0.001 } } }, { "name": "ort_2d_axis0_three_inputs_float32", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/concat_op_test.cc", "test": "ConcatOpTest.Concat2D_1" }, "attrs": { "axis": 0 }, "inputs": { "a": { "dtype": "float32", "shape": [1, 4], "data": { "kind": "values", "values": [11.0, 12.0, 13.0, 14.0] } }, "b": { "dtype": "float32", "shape": [1, 4], "data": { "kind": "values", "values": [21.0, 22.0, 23.0, 24.0] } }, "d": { "dtype": "float32", "shape": [1, 4], "data": { "kind": "values", "values": [31.0, 32.0, 33.0, 34.0] } } }, "outputs": { "c": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.000001 } } }, { "name": "ort_2d_axis0_three_inputs_float16", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/concat_op_test.cc", "test": "ConcatOpTest.Concat2D_1", "notes": "Float16 instantiation of ORT's typed axis-0 2D concat case." }, "attrs": { "axis": 0 }, "inputs": { "a": { "dtype": "float16", "shape": [1, 4], "data": { "kind": "values", "values": [11.0, 12.0, 13.0, 14.0] } }, "b": { "dtype": "float16", "shape": [1, 4], "data": { "kind": "values", "values": [21.0, 22.0, 23.0, 24.0] } }, "d": { "dtype": "float16", "shape": [1, 4], "data": { "kind": "values", "values": [31.0, 32.0, 33.0, 34.0] } } }, "outputs": { "c": { "dtype": "float16", "shape": [3, 4], "tolerance": 0.001 } } }, { "name": "ort_2d_axis0_two_inputs_float32", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/concat_op_test.cc", "test": "ConcatOpTest.Concat2D_5", "notes": "Float32 projection of ORT's double two-input axis-0 concat case." }, "attrs": { "axis": 0 }, "inputs": { "a": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [111.0, 112.0, 121.0, 122.0] } }, "b": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [211.0, 212.0, 221.0, 222.0] } } }, "outputs": { "c": { "dtype": "float32", "shape": [4, 2], "tolerance": 0.000001 } } }, { "name": "ort_2d_axis1_dynamic_shape_projection", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/concat_op_test.cc", "test": "ConcatOpTest.Concat2D_4", "notes": "Projects ORT's symbolic-dimension case to concrete JSON tensor shapes while preserving the axis-1 concat inputs." }, "attrs": { "axis": 1 }, "inputs": { "a": { "dtype": "float32", "shape": [4, 1], "data": { "kind": "values", 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