ai.onnx.Concat / build /webgpu /test.json
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{
"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": "rank6_axis2_two_inputs",
"attrs": { "axis": 2 },
"inputs": {
"a": {
"dtype": "float32",
"shape": [1, 2, 1, 1, 1, 2],
"data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29 }
},
"b": {
"dtype": "float32",
"shape": [1, 2, 2, 1, 1, 2],
"data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.07 }
}
},
"outputs": { "c": { "dtype": "float32", "shape": [1, 2, 3, 1, 1, 2], "tolerance": 0.000001 } }
},
{
"name": "uint32_axis_minus_one",
"attrs": { "axis": -1 },
"inputs": {
"a": { "dtype": "uint32", "shape": [2, 1], "data": { "kind": "values", "values": [7, 11] } },
"b": { "dtype": "uint32", "shape": [2, 2], "data": { "kind": "values", "values": [13, 17, 19, 23] } }
},
"outputs": { "c": { "dtype": "uint32", "shape": [2, 3], "tolerance": 0 } }
},
{
"name": "ort_1d_three_inputs",
"provenance": {
"source": "onnxruntime/test/providers/cpu/tensor/concat_op_test.cc",
"test": "ConcatOpTest.Concat1D_1"
},
"attrs": { "axis": 0 },
"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_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", "values": [11.0, 21.0, 31.0, 41.0] } },
"b": {
"dtype": "float32",
"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": "float32", "shape": [4, 1], "data": { "kind": "values", "values": [14.0, 24.0, 34.0, 44.0] } }
},
"outputs": { "c": { "dtype": "float32", "shape": [4, 4], "tolerance": 0.000001 } }
},
{
"name": "ort_3d_axis0_three_inputs",
"provenance": {
"source": "onnxruntime/test/providers/cpu/tensor/concat_op_test.cc",
"test": "ConcatOpTest.Concat3D_1"
},
"attrs": { "axis": 0 },
"inputs": {
"a": {
"dtype": "float32",
"shape": [1, 3, 3],
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{
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{
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{
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"test": "ConcatOpTest.Concat1D_exceed_maxStorageBuffersPerShaderStage",
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{
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{
"name": "onnx_backend_concat_2d_axis_negative_2",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_concat_2d_axis_negative_2" },
"attrs": { "axis": -2 },
"inputs": {
"a": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } },
"b": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [5.0, 6.0, 7.0, 8.0] } }
},
"outputs": { "c": { "dtype": "float32", "shape": [4, 2], "tolerance": 0 } }
},
{
"name": "onnx_backend_concat_3d_axis_0",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_concat_3d_axis_0" },
"attrs": { "axis": 0 },
"inputs": {
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"shape": [2, 2, 2],
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] }
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"b": {
"dtype": "float32",
"shape": [2, 2, 2],
"data": { "kind": "values", "values": [9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0] }
}
},
"outputs": { "c": { "dtype": "float32", "shape": [4, 2, 2], "tolerance": 0 } }
},
{
"name": "onnx_backend_concat_3d_axis_1",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_concat_3d_axis_1" },
"attrs": { "axis": 1 },
"inputs": {
"a": {
"dtype": "float32",
"shape": [2, 2, 2],
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] }
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"b": {
"dtype": "float32",
"shape": [2, 2, 2],
"data": { "kind": "values", "values": [9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0] }
}
},
"outputs": { "c": { "dtype": "float32", "shape": [2, 4, 2], "tolerance": 0 } }
},
{
"name": "onnx_backend_concat_3d_axis_2",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_concat_3d_axis_2" },
"attrs": { "axis": 2 },
"inputs": {
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"dtype": "float32",
"shape": [2, 2, 2],
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] }
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"b": {
"dtype": "float32",
"shape": [2, 2, 2],
"data": { "kind": "values", "values": [9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0] }
}
},
"outputs": { "c": { "dtype": "float32", "shape": [2, 2, 4], "tolerance": 0 } }
},
{
"name": "onnx_backend_concat_3d_axis_negative_1",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_concat_3d_axis_negative_1" },
"attrs": { "axis": -1 },
"inputs": {
"a": {
"dtype": "float32",
"shape": [2, 2, 2],
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] }
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"b": {
"dtype": "float32",
"shape": [2, 2, 2],
"data": { "kind": "values", "values": [9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0] }
}
},
"outputs": { "c": { "dtype": "float32", "shape": [2, 2, 4], "tolerance": 0 } }
},
{
"name": "onnx_backend_concat_3d_axis_negative_2",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_concat_3d_axis_negative_2" },
"attrs": { "axis": -2 },
"inputs": {
"a": {
"dtype": "float32",
"shape": [2, 2, 2],
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] }
},
"b": {
"dtype": "float32",
"shape": [2, 2, 2],
"data": { "kind": "values", "values": [9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0] }
}
},
"outputs": { "c": { "dtype": "float32", "shape": [2, 4, 2], "tolerance": 0 } }
},
{
"name": "onnx_backend_concat_3d_axis_negative_3",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_concat_3d_axis_negative_3" },
"attrs": { "axis": -3 },
"inputs": {
"a": {
"dtype": "float32",
"shape": [2, 2, 2],
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] }
},
"b": {
"dtype": "float32",
"shape": [2, 2, 2],
"data": { "kind": "values", "values": [9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0] }
}
},
"outputs": { "c": { "dtype": "float32", "shape": [4, 2, 2], "tolerance": 0 } }
},
{
"name": "ort_int8_1d_negative_axis_edge_values",
"provenance": {
"source": "onnxruntime/test/providers/cpu/tensor/concat_op_test.cc",
"test": "ConcatOpTest.Concat1D_int32_negative_axis",
"notes": "Same negative-axis concat pattern with logical int8 edge values."
},
"attrs": { "axis": -1 },
"inputs": {
"a": { "dtype": "int8", "shape": [3], "data": { "kind": "values", "values": [-128, -1, 0] } },
"b": { "dtype": "int8", "shape": [3], "data": { "kind": "values", "values": [1, 2, 127] } }
},
"outputs": { "c": { "dtype": "int8", "shape": [6], "tolerance": 0 } }
},
{
"name": "uint8_3d_axis1_edge_values",
"provenance": {
"source": "onnxruntime/test/providers/cpu/tensor/concat_op_test.cc",
"test": "ConcatOpTest.Concat3D_1",
"notes": "Axis-1 3D concat with logical uint8 edge values."
},
"attrs": { "axis": 1 },
"inputs": {
"a": { "dtype": "uint8", "shape": [1, 2, 2], "data": { "kind": "values", "values": [0, 1, 2, 3] } },
"b": { "dtype": "uint8", "shape": [1, 1, 2], "data": { "kind": "values", "values": [254, 255] } }
},
"outputs": { "c": { "dtype": "uint8", "shape": [1, 3, 2], "tolerance": 0 } }
},
{
"name": "block_vec4_axis0_two_f32",
"attrs": { "axis": 0 },
"inputs": {
"a": {
"dtype": "float32",
"shape": [2, 6],
"data": { "kind": "fillFloat32", "sinStep": 0.21, "cosStep": 0.13 }
},
"b": {
"dtype": "float32",
"shape": [4, 6],
"data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.29 }
}
},
"outputs": { "c": { "dtype": "float32", "shape": [6, 6], "tolerance": 0.000001 } }
},
{
"name": "block_vec4_axis1_two_f32_rank3",
"attrs": { "axis": 1 },
"inputs": {
"a": {
"dtype": "float32",
"shape": [2, 2, 4],
"data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23 }
},
"b": {
"dtype": "float32",
"shape": [2, 3, 4],
"data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.07 }
}
},
"outputs": { "c": { "dtype": "float32", "shape": [2, 5, 4], "tolerance": 0.000001 } }
},
{
"name": "block_vec4_negative_axis_two_f32",
"attrs": { "axis": -2 },
"inputs": {
"a": {
"dtype": "float32",
"shape": [3, 2, 2],
"data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.19 }
},
"b": {
"dtype": "float32",
"shape": [3, 4, 2],
"data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.11 }
}
},
"outputs": { "c": { "dtype": "float32", "shape": [3, 6, 2], "tolerance": 0.000001 } }
},
{
"name": "block_vec4_axis1_three_f16",
"attrs": { "axis": 1 },
"inputs": {
"a": {
"dtype": "float16",
"shape": [2, 2, 2],
"data": { "kind": "fillFloat32", "sinStep": 0.21, "cosStep": 0.13 }
},
"b": {
"dtype": "float16",
"shape": [2, 4, 2],
"data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.29 }
},
"d": {
"dtype": "float16",
"shape": [2, 2, 2],
"data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.23 }
}
},
"outputs": { "c": { "dtype": "float16", "shape": [2, 8, 2], "tolerance": 0.001 } }
},
{
"name": "block_vec4_axis0_four_u32_rank1",
"attrs": { "axis": 0 },
"inputs": {
"a": { "dtype": "uint32", "shape": [4], "data": { "kind": "cycle", "values": [3, 9, 1, 7] } },
"b": { "dtype": "uint32", "shape": [8], "data": { "kind": "cycle", "values": [5, 2, 11, 4, 8] } },
"d": { "dtype": "uint32", "shape": [4], "data": { "kind": "cycle", "values": [6, 0, 13, 2] } },
"e": { "dtype": "uint32", "shape": [12], "data": { "kind": "cycle", "values": [1, 15, 3, 10, 12, 9, 2] } }
},
"outputs": { "c": { "dtype": "uint32", "shape": [28], "tolerance": 0 } }
},
{
"name": "block_vec4_axis0_two_f32_large",
"attrs": { "axis": 0 },
"inputs": {
"a": {
"dtype": "float32",
"shape": [64, 128],
"data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.17 }
},
"b": {
"dtype": "float32",
"shape": [32, 128],
"data": { "kind": "fillFloat32", "sinStep": 0.29, "cosStep": 0.11 }
}
},
"outputs": { "c": { "dtype": "float32", "shape": [96, 128], "tolerance": 0.000001 } }
},
{
"name": "ort_1d_five_inputs_empty_middle_variadic",
"provenance": {
"source": "onnxruntime/test/providers/cpu/tensor/concat_op_test.cc",
"test": "ConcatOpTest.Concat1D_exceed_maxStorageBuffersPerShaderStage",
"notes": "Exercises variadic concatenation with five inputs, including an empty middle input."
},
"attrs": { "axis": 0 },
"inputs": {
"a": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [1.0, 2.0] } },
"b": { "dtype": "float32", "shape": [0], "data": { "kind": "values", "values": [] } },
"d": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [3.0] } },
"e": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [4.0, 5.0] } },
"f": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [6.0] } }
},
"outputs": {
"c": {
"dtype": "float32",
"shape": [6],
"tolerance": 0,
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] }
}
}
},
{
"name": "ort_1d_six_inputs_variadic",
"provenance": {
"source": "onnxruntime/test/providers/cpu/tensor/concat_op_test.cc",
"test": "ConcatOpTest.ConcatN_variadic",
"notes": "Exercises the 6-input variadic Concat variant (compact float32 projection)."
},
"attrs": { "axis": 0 },
"inputs": {
"a": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } },
"b": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [2.0] } },
"d": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [3.0] } },
"e": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [4.0] } },
"f": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [5.0] } },
"g": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [6.0] } }
},
"outputs": {
"c": {
"dtype": "float32",
"shape": [6],
"tolerance": 0,
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] }
}
}
},
{
"name": "ort_1d_seven_inputs_variadic",
"provenance": {
"source": "onnxruntime/test/providers/cpu/tensor/concat_op_test.cc",
"test": "ConcatOpTest.ConcatN_variadic",
"notes": "Exercises the 7-input variadic Concat variant (compact float32 projection)."
},
"attrs": { "axis": 0 },
"inputs": {
"a": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } },
"b": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [2.0] } },
"d": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [3.0] } },
"e": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [4.0] } },
"f": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [5.0] } },
"g": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [6.0] } },
"h": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [7.0] } }
},
"outputs": {
"c": {
"dtype": "float32",
"shape": [7],
"tolerance": 0,
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0] }
}
}
},
{
"name": "ort_1d_eight_inputs_variadic",
"provenance": {
"source": "onnxruntime/test/providers/cpu/tensor/concat_op_test.cc",
"test": "ConcatOpTest.ConcatN_variadic",
"notes": "Exercises the 8-input variadic Concat variant (compact float32 projection)."
},
"attrs": { "axis": 0 },
"inputs": {
"a": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } },
"b": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [2.0] } },
"d": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [3.0] } },
"e": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [4.0] } },
"f": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [5.0] } },
"g": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [6.0] } },
"h": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [7.0] } },
"i": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [8.0] } }
},
"outputs": {
"c": {
"dtype": "float32",
"shape": [8],
"tolerance": 0,
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] }
}
}
},
{
"name": "rank7_axis_minus1_two_inputs",
"attrs": { "axis": -1 },
"provenance": {
"source": "onnx spec / onnxruntime cpu Concat (no rank cap)",
"notes": "Concatenates rank-7 tensors along the normalized last axis."
},
"inputs": {
"a": {
"dtype": "float32",
"shape": [2, 1, 1, 1, 1, 1, 2],
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] }
},
"b": {
"dtype": "float32",
"shape": [2, 1, 1, 1, 1, 1, 2],
"data": { "kind": "values", "values": [10.0, 11.0, 12.0, 13.0] }
}
},
"outputs": { "c": { "dtype": "float32", "shape": [2, 1, 1, 1, 1, 1, 4], "tolerance": 0 } }
},
{
"name": "axis_last_odd_widths_two_f32",
"attrs": { "axis": -1 },
"inputs": {
"a": {
"dtype": "float32",
"shape": [2, 3],
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] }
},
"b": {
"dtype": "float32",
"shape": [2, 5],
"data": { "kind": "values", "values": [11.0, 12.0, 13.0, 14.0, 15.0, 21.0, 22.0, 23.0, 24.0, 25.0] }
}
},
"outputs": { "c": { "dtype": "float32", "shape": [2, 8], "tolerance": 0.000001 } }
},
{
"name": "axis1_odd_widths_two_int32",
"attrs": { "axis": 1 },
"inputs": {
"a": { "dtype": "int32", "shape": [4, 7], "data": { "kind": "cycle", "values": [1, 2, 3, 4, 5, 6, 7, 8, 9] } },
"b": {
"dtype": "int32",
"shape": [4, 9],
"data": { "kind": "cycle", "values": [101, 102, 103, 104, 105, 106, 107] }
}
},
"outputs": { "c": { "dtype": "int32", "shape": [4, 16], "tolerance": 0 } }
},
{
"name": "empty_a_nonempty_b_axis0_block_bypass_scalar",
"attrs": { "axis": 0 },
"inputs": {
"a": { "dtype": "float32", "shape": [0, 4], "data": { "kind": "values", "values": [] } },
"b": {
"dtype": "float32",
"shape": [3, 4],
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0] }
}
},
"outputs": {
"c": {
"dtype": "float32",
"shape": [3, 4],
"tolerance": 0,
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0] }
}
}
},
{
"name": "block_vec4_empty_middle_input_three_axis0",
"attrs": { "axis": 0 },
"inputs": {
"a": {
"dtype": "float32",
"shape": [2, 4],
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] }
},
"b": { "dtype": "float32", "shape": [0, 4], "data": { "kind": "values", "values": [] } },
"d": {
"dtype": "float32",
"shape": [2, 4],
"data": { "kind": "values", "values": [11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0] }
}
},
"outputs": {
"c": {
"dtype": "float32",
"shape": [4, 4],
"tolerance": 0,
"data": {
"kind": "values",
"values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0]
}
}
}
},
{
"name": "dispatch_fold_axis0_large_dim0_lastrow",
"attrs": { "axis": 0 },
"inputs": {
"a": {
"dtype": "float32",
"shape": [33000, 4],
"data": { "kind": "fillFloat32", "sinStep": 0.11, "scale": 3.0, "offset": 1.0 }
},
"b": {
"dtype": "float32",
"shape": [33000, 4],
"data": { "kind": "fillFloat32", "cosStep": 0.07, "scale": 2.0, "offset": -1.0 }
}
},
"outputs": { "c": { "dtype": "float32", "shape": [66000, 4], "tolerance": 0 } }
},
{
"name": "negative_axis_block_vec4_three_f16",
"requires": { "features": ["shader-f16"] },
"attrs": { "axis": -2 },
"inputs": {
"a": {
"dtype": "float16",
"shape": [2, 2, 2],
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] }
},
"b": {
"dtype": "float16",
"shape": [2, 1, 2],
"data": { "kind": "values", "values": [11.0, 12.0, 13.0, 14.0] }
},
"d": {
"dtype": "float16",
"shape": [2, 1, 2],
"data": { "kind": "values", "values": [21.0, 22.0, 23.0, 24.0] }
}
},
"outputs": { "c": { "dtype": "float16", "shape": [2, 4, 2], "tolerance": 0.001 } }
},
{
"name": "uint8_axis1_odd_widths_scalar_boundary",
"attrs": { "axis": 1 },
"inputs": {
"a": { "dtype": "uint8", "shape": [2, 3], "data": { "kind": "values", "values": [0, 1, 2, 253, 254, 255] } },
"b": {
"dtype": "uint8",
"shape": [2, 5],
"data": { "kind": "values", "values": [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] }
}
},
"outputs": {
"c": {
"dtype": "uint8",
"shape": [2, 8],
"tolerance": 0,
"data": { "kind": "values", "values": [0, 1, 2, 10, 20, 30, 40, 50, 253, 254, 255, 60, 70, 80, 90, 100] }
}
}
},
{
"name": "last_axis_cooperative_vec4_rank3_f32",
"provenance": {
"notes": "Exercises the general cooperative last-axis route on a non-model-specific rank-3 geometry."
},
"attrs": { "axis": -1 },
"inputs": {
"a": {
"dtype": "float32",
"shape": [2, 3, 256],
"data": { "kind": "fillFloat32", "sinStep": 0.013, "cosStep": 0.007 }
},
"b": {
"dtype": "float32",
"shape": [2, 3, 128],
"data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.011 }
}
},
"outputs": { "c": { "dtype": "float32", "shape": [2, 3, 384], "tolerance": 0 } }
},
{
"name": "nine_input_block_vec4_axis1_two_rows",
"provenance": {
"notes": "Exercises the generalized nine-input vec4 block-copy route with a row boundary. Expected output comes from the TypeScript Concat reference."
},
"attrs": { "axis": 1 },
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"d": { "dtype": "float32", "shape": [2, 4], "data": { "kind": "fillFloat32", "sinStep": 0.13, "offset": 3.0 } },
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"f": { "dtype": "float32", "shape": [2, 4], "data": { "kind": "fillFloat32", "sinStep": 0.15, "offset": 5.0 } },
"g": { "dtype": "float32", "shape": [2, 4], "data": { "kind": "fillFloat32", "sinStep": 0.16, "offset": 6.0 } },
"h": { "dtype": "float32", "shape": [2, 4], "data": { "kind": "fillFloat32", "sinStep": 0.17, "offset": 7.0 } },
"i": { "dtype": "float32", "shape": [2, 4], "data": { "kind": "fillFloat32", "sinStep": 0.18, "offset": 8.0 } },
"j": { "dtype": "float32", "shape": [2, 4], "data": { "kind": "fillFloat32", "sinStep": 0.19, "offset": 9.0 } }
},
"outputs": { "c": { "dtype": "float32", "shape": [2, 36], "tolerance": 0 } }
},
{
"name": "rank8_axis_minus1_two_inputs",
"attrs": { "axis": -1 },
"inputs": {
"a": {
"dtype": "float32",
"shape": [2, 1, 1, 1, 1, 1, 2, 2],
"data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] }
},
"b": {
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"data": { "kind": "values", "values": [10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0] }
}
},
"outputs": { "c": { "dtype": "float32", "shape": [2, 1, 1, 1, 1, 1, 2, 4], "tolerance": 0 } }
}
]
}