| { |
| "op": "ai.onnx.Where", |
| "cases": [ |
| { |
| "name": "int16_same_shape_boundaries", |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [4], "data": { "kind": "values", "values": [1, 0, 1, 0] } }, |
| "x": { "dtype": "int16", "shape": [4], "data": { "kind": "values", "values": [-32768, -1, 32767, 0] } }, |
| "y": { "dtype": "int16", "shape": [4], "data": { "kind": "values", "values": [32767, -32768, 1, -32767] } } |
| }, |
| "outputs": { |
| "output": { |
| "dtype": "int16", |
| "shape": [4], |
| "tolerance": 0, |
| "data": { "kind": "values", "values": [-32768, -32768, 32767, -32767] } |
| } |
| } |
| }, |
| { |
| "name": "f32_subnormal_select_preserves_data", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/where_op_test.cc", |
| "test": "WhereOpTest.BasicNumeric", |
| "notes": "Where is a selector, not an arithmetic op: selected finite subnormal payloads must pass through unchanged." |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [4], "data": { "kind": "values", "values": [1, 0, 1, 0] } }, |
| "x": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [1e-40, 10.0, -1e-40, 20.0] } }, |
| "y": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [30.0, -1e-40, 40.0, 1e-40] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [4], "tolerance": 0 } } |
| }, |
| { |
| "name": "rank2_broadcast", |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [2, 1], "data": { "kind": "values", "values": [1, 0] } }, |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 3], |
| "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29 } |
| }, |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 3], |
| "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.07 } |
| } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank4_nonzero_condition_broadcast", |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [1, 1, 3, 1], "data": { "kind": "values", "values": [1, 0, 1] } }, |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 1, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23 } |
| }, |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 2, 1, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.07 } |
| } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 2, 3, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "zero_size_noop", |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [0, 1], "data": { "kind": "values", "values": [] } }, |
| "x": { "dtype": "float32", "shape": [0, 3], "data": { "kind": "values", "values": [] } }, |
| "y": { "dtype": "float32", "shape": [1, 3], "data": { "kind": "values", "values": [10.0, 20.0, 30.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [0, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "scalar_condition_broadcast", |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [], "data": { "kind": "values", "values": [1] } }, |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 3], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] } |
| }, |
| "y": { |
| "dtype": "float32", |
| "shape": [2, 3], |
| "data": { "kind": "values", "values": [-1.0, -2.0, -3.0, -4.0, -5.0, -6.0] } |
| } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank0_all_scalars_false_condition", |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [], "data": { "kind": "values", "values": [0] } }, |
| "x": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [123.5] } }, |
| "y": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [-7.25] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "nan_in_unselected_branch_does_not_leak", |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [4], "data": { "kind": "values", "values": [1, 0, 1, 0] } }, |
| "x": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [1.0, "NaN", "Infinity", 4.0] } }, |
| "y": { |
| "dtype": "float32", |
| "shape": [4], |
| "data": { "kind": "values", "values": ["NaN", 2.0, 3.0, "-Infinity"] } |
| } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [4], "tolerance": 0, "allowNaN": false } } |
| }, |
| { |
| "name": "ort_broadcast_dim_with_zero", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/where_op_test.cc", |
| "test": "WhereOpTest.BroadcastDimWithZero" |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [3], "data": { "kind": "values", "values": [1, 0, 1] } }, |
| "x": { "dtype": "float32", "shape": [1, 3], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } }, |
| "y": { "dtype": "float32", "shape": [0, 1], "data": { "kind": "values", "values": [] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [0, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_basic_numeric_float32", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/where_op_test.cc", |
| "test": "WhereOpTest.BasicNumeric" |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [2, 2], "data": { "kind": "values", "values": [0, 1, 1, 0] } }, |
| "x": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } }, |
| "y": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [5.0, 6.0, 7.0, 8.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_basic_numeric_float16_adapted", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/where_op_test.cc", |
| "test": "WhereOpTest.BasicNumeric", |
| "notes": "ORT covers float and double; this framework also checks the same branch pattern for float16." |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [2, 2], "data": { "kind": "values", "values": [0, 1, 1, 0] } }, |
| "x": { "dtype": "float16", "shape": [2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } }, |
| "y": { "dtype": "float16", "shape": [2, 2], "data": { "kind": "values", "values": [5.0, 6.0, 7.0, 8.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float16", "shape": [2, 2], "tolerance": 0.001 } } |
| }, |
| { |
| "name": "ort_broadcast_pattern_condition_last_dim", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/where_op_test.cc", |
| "test": "WhereOpTest.Broadcast" |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [1, 1, 3], "data": { "kind": "values", "values": [1, 0, 1] } }, |
| "x": { "dtype": "float32", "shape": [1, 3, 1], "data": { "kind": "values", "values": [1.0, 1.0, 1.0] } }, |
| "y": { "dtype": "float32", "shape": [3, 1, 1], "data": { "kind": "values", "values": [0.0, 0.0, 0.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [3, 3, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_broadcast_pattern_condition_first_dim", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/where_op_test.cc", |
| "test": "WhereOpTest.Broadcast" |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [3, 1, 1], "data": { "kind": "values", "values": [1, 0, 1] } }, |
| "x": { "dtype": "float32", "shape": [1, 1, 3], "data": { "kind": "values", "values": [1.0, 1.0, 1.0] } }, |
| "y": { "dtype": "float32", "shape": [1, 3, 1], "data": { "kind": "values", "values": [0.0, 0.0, 0.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [3, 3, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_broadcast_with_scalar_float32", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/where_op_test.cc", |
| "test": "WhereOpTest.BroadcastWithScalar", |
| "notes": "ORT uses int64 values; this framework covers the same scalar broadcast shape behavior with float32 data." |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [3], "data": { "kind": "values", "values": [1, 0, 1] } }, |
| "x": { "dtype": "float32", "shape": [1, 3], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } }, |
| "y": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [1.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [1, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_broadcast_with_scalar_int32", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/where_op_test.cc", |
| "test": "WhereOpTest.BroadcastWithScalar", |
| "notes": "ORT uses int64 values; this framework covers the same scalar broadcast shape behavior with int32 data." |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [3], "data": { "kind": "values", "values": [1, 0, 1] } }, |
| "x": { "dtype": "int32", "shape": [1, 3], "data": { "kind": "values", "values": [1, 2, 3] } }, |
| "y": { "dtype": "int32", "shape": [], "data": { "kind": "values", "values": [1] } } |
| }, |
| "outputs": { "output": { "dtype": "int32", "shape": [1, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "onnx_backend_where_example", |
| "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_where_example" }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [2, 2], "data": { "kind": "values", "values": [1, 0, 1, 1] } }, |
| "x": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } }, |
| "y": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [9.0, 8.0, 7.0, 6.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "onnx_backend_where_long_example", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_where_long_example", |
| "notes": "The ONNX int64 X/Y payloads are adapted to supported int32; the condition remains bool." |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [2, 2], "data": { "kind": "values", "values": [1, 0, 1, 1] } }, |
| "x": { "dtype": "int32", "shape": [2, 2], "data": { "kind": "values", "values": [1, 2, 3, 4] } }, |
| "y": { "dtype": "int32", "shape": [2, 2], "data": { "kind": "values", "values": [9, 8, 7, 6] } } |
| }, |
| "outputs": { "output": { "dtype": "int32", "shape": [2, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_basic_numeric_int8_edge_values", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/where_op_test.cc", |
| "test": "WhereOpTest.BasicNumeric", |
| "notes": "Same branch pattern as ORT's numeric test, using ONNX-valid int8 edge values." |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [2, 2], "data": { "kind": "values", "values": [0, 1, 1, 0] } }, |
| "x": { "dtype": "int8", "shape": [2, 2], "data": { "kind": "values", "values": [-128, -1, 0, 127] } }, |
| "y": { "dtype": "int8", "shape": [2, 2], "data": { "kind": "values", "values": [127, 0, -1, -128] } } |
| }, |
| "outputs": { "output": { "dtype": "int8", "shape": [2, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_basic_numeric_uint8_edge_values", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/where_op_test.cc", |
| "test": "WhereOpTest.BasicNumeric", |
| "notes": "Same branch pattern as ORT's numeric test, using ONNX-valid uint8 edge values." |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [2, 2], "data": { "kind": "values", "values": [0, 1, 1, 0] } }, |
| "x": { "dtype": "uint8", "shape": [2, 2], "data": { "kind": "values", "values": [0, 1, 254, 255] } }, |
| "y": { "dtype": "uint8", "shape": [2, 2], "data": { "kind": "values", "values": [255, 254, 1, 0] } } |
| }, |
| "outputs": { "output": { "dtype": "uint8", "shape": [2, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "scalar_condition_true_vec4", |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [], "data": { "kind": "values", "values": [1] } }, |
| "x": { |
| "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] } |
| }, |
| "y": { |
| "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] } |
| } |
| }, |
| "outputs": { |
| "output": { |
| "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] }, |
| "tolerance": 0.000001 |
| } |
| } |
| }, |
| { |
| "name": "scalar_condition_false_vec4", |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [1], "data": { "kind": "values", "values": [0] } }, |
| "x": { |
| "dtype": "float32", |
| "shape": [8], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| }, |
| "y": { |
| "dtype": "float32", |
| "shape": [8], |
| "data": { "kind": "values", "values": [-1.5, -2.5, -3.5, -4.5, -5.5, -6.5, -7.5, -8.5] } |
| } |
| }, |
| "outputs": { |
| "output": { |
| "dtype": "float32", |
| "shape": [8], |
| "data": { "kind": "values", "values": [-1.5, -2.5, -3.5, -4.5, -5.5, -6.5, -7.5, -8.5] }, |
| "tolerance": 0.000001 |
| } |
| } |
| }, |
| { |
| "name": "scalar_condition_true_vec4_4096", |
| "provenance": { |
| "notes": "Compact sibling for the scalar-condition Where benchmark; preserves a shape-[1] condition broadcasting over a vec4-aligned f32 payload." |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [1], "data": { "kind": "values", "values": [1] } }, |
| "x": { |
| "dtype": "float32", |
| "shape": [4096], |
| "data": { "kind": "fillFloat32", "sinStep": 0.019, "cosStep": 0.031, "scale": 1.0 } |
| }, |
| "y": { |
| "dtype": "float32", |
| "shape": [4096], |
| "data": { "kind": "fillFloat32", "sinStep": 0.023, "cosStep": 0.037, "scale": 1.0 } |
| } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [4096], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_bool_exact_condition_basic_numeric", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/where_op_test.cc", |
| "test": "WhereOpTest.BasicNumeric" |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [2, 2], "data": { "kind": "values", "values": [0, 1, 1, 0] } }, |
| "x": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } }, |
| "y": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [5.0, 6.0, 7.0, 8.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_bool_exact_condition_zero_dim_broadcast", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/where_op_test.cc", |
| "test": "WhereOpTest.BroadcastDimWithZero", |
| "notes": "Uses int32 payloads in place of ORT's int64 payloads; the zero-dimension broadcast behavior is identical." |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [3], "data": { "kind": "values", "values": [1, 0, 1] } }, |
| "x": { "dtype": "int32", "shape": [1, 3], "data": { "kind": "values", "values": [1, 2, 3] } }, |
| "y": { "dtype": "int32", "shape": [0, 1], "data": { "kind": "values", "values": [] } } |
| }, |
| "outputs": { "output": { "dtype": "int32", "shape": [0, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_bool_exact_condition_scalar_y_broadcast", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/where_op_test.cc", |
| "test": "WhereOpTest.BroadcastWithScalar", |
| "notes": "Uses int32 payloads in place of ORT's int64 payloads; preserves bool condition and scalar Y broadcasting." |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [3], "data": { "kind": "values", "values": [1, 0, 1] } }, |
| "x": { "dtype": "int32", "shape": [1, 3], "data": { "kind": "values", "values": [1, 2, 3] } }, |
| "y": { "dtype": "int32", "shape": [], "data": { "kind": "values", "values": [1] } } |
| }, |
| "outputs": { |
| "output": { |
| "dtype": "int32", |
| "shape": [1, 3], |
| "tolerance": 0, |
| "data": { "kind": "values", "values": [1, 1, 3] } |
| } |
| } |
| }, |
| { |
| "name": "rank6_mixed_axis_broadcast_all_inputs", |
| "inputs": { |
| "condition": { |
| "dtype": "bool", |
| "shape": [2, 1, 3, 1, 1, 1], |
| "data": { "kind": "cycle", "values": [1, 0, 1, 0, 1, 1] } |
| }, |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 4, 1, 1, 5, 1], |
| "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.11 } |
| }, |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 1, 2, 1, 3], |
| "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.31 } |
| } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 4, 3, 2, 5, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "broadcast_fold_over_16M_condition_outer", |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [8224, 1], "data": { "kind": "cycle", "values": [1, 0] } }, |
| "x": { |
| "dtype": "float32", |
| "shape": [8224, 2048], |
| "data": { "kind": "fillFloat32", "sinStep": 0.001, "cosStep": 0.002 } |
| }, |
| "y": { |
| "dtype": "float32", |
| "shape": [8224, 2048], |
| "data": { "kind": "fillFloat32", "sinStep": 0.003, "cosStep": 0.004 } |
| } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [8224, 2048], "tolerance": 0 } } |
| }, |
| { |
| "name": "broadcast_innermost_dim1_x_expands_y_full", |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [3, 5], "data": { "kind": "cycle", "values": [1, 0, 1, 0, 1] } }, |
| "x": { "dtype": "float32", "shape": [3, 1], "data": { "kind": "values", "values": [10.0, 20.0, 30.0] } }, |
| "y": { |
| "dtype": "float32", |
| "shape": [3, 5], |
| "data": { "kind": "fillFloat32", "sinStep": 0.29, "cosStep": 0.13 } |
| } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [3, 5], "tolerance": 0 } } |
| }, |
| { |
| "name": "rank7_broadcast_scalar_tail", |
| "inputs": { |
| "condition": { |
| "dtype": "bool", |
| "shape": [1, 1, 1, 1, 1, 1, 3], |
| "data": { "kind": "values", "values": [1, 0, 1] } |
| }, |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 1, 2, 1, 2, 1, 3], |
| "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23 } |
| }, |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 2, 1, 2, 1, 2, 1], |
| "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.07 } |
| } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 2, 2, 2, 2, 2, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "broadcast_inner_vec4_mixed_splat", |
| "provenance": { |
| "source": "onnxruntime js/web/lib/wasm/jsep webgpu where op", |
| "test": "vec4 outputs under broadcast", |
| "notes": "The output inner axis is a multiple of 4, so each input either loads 4 contiguous elements (last axis matches) or splats one (last axis broadcast)." |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [3, 1, 1], "data": { "kind": "cycle", "values": [1, 0, 1] } }, |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 3, 1, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.29, "scale": 0.5 } |
| }, |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 5, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.5 } |
| } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 3, 5, 8], "tolerance": 0 } } |
| }, |
| { |
| "name": "broadcast_inner_vec4_scalar_y", |
| "provenance": { |
| "source": "onnxruntime js/web/lib/wasm/jsep webgpu where op", |
| "test": "vec4 outputs under broadcast", |
| "notes": "The output inner axis is a multiple of 4, so each input either loads 4 contiguous elements (last axis matches) or splats one (last axis broadcast)." |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [1, 1, 12, 1], "data": { "kind": "cycle", "values": [1, 0, 0, 1] } }, |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 1, 12, 12], |
| "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.31, "scale": 0.5 } |
| }, |
| "y": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [-7.5] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 1, 12, 12], "tolerance": 0 } } |
| }, |
| { |
| "name": "broadcast_inner_vec4_splat_x", |
| "provenance": { |
| "notes": "The vec4 broadcast route loads each operand either as four contiguous elements or as one splat value. Here x is the lower-rank splat operand, independently checking its left-hand broadcast addressing." |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [12], "data": { "kind": "cycle", "values": [1, 0, 0, 1] } }, |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 1, 12, 1], |
| "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.31, "scale": 0.5 } |
| }, |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 12, 12], |
| "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.5 } |
| } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 1, 12, 12], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "broadcast_inner_vec4_cond_contiguous", |
| "provenance": { |
| "source": "onnxruntime js/web/lib/wasm/jsep webgpu where op", |
| "test": "vec4 outputs under broadcast", |
| "notes": "The output inner axis is a multiple of 4, so each input either loads 4 contiguous elements (last axis matches) or splats one (last axis broadcast)." |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [8], "data": { "kind": "cycle", "values": [1, 0] } }, |
| "x": { |
| "dtype": "float32", |
| "shape": [4, 1, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.27, "scale": 0.5 } |
| }, |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 6, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.37, "scale": 0.5 } |
| } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [4, 6, 8], "tolerance": 0 } } |
| }, |
| { |
| "name": "rank8_broadcast_alternating", |
| "attrs": {}, |
| "inputs": { |
| "condition": { |
| "dtype": "bool", |
| "shape": [1, 1, 1, 1, 1, 1, 1, 3], |
| "data": { "kind": "values", "values": [1, 0, 1] } |
| }, |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 1, 2, 1, 2, 1, 2, 3], |
| "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23 } |
| }, |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 2, 1, 2, 1, 2, 1, 1], |
| "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.07 } |
| } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 2, 2, 2, 2, 2, 2, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "onnx_standard_uint32_payload", |
| "provenance": { |
| "notes": "ONNX-standard uint32 payload coverage with a bool condition and multidirectional broadcasting." |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [2, 1], "data": { "kind": "values", "values": [1, 0] } }, |
| "x": { |
| "dtype": "uint32", |
| "shape": [2, 3], |
| "data": { "kind": "values", "values": [0, 2147483648, 4294967295, 1, 2, 3] } |
| }, |
| "y": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [9, 8, 7] } } |
| }, |
| "outputs": { "output": { "dtype": "uint32", "shape": [2, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "onnx_standard_bool_payload", |
| "provenance": { |
| "notes": "ONNX-standard bool payload coverage with independent condition, X, and Y broadcasting." |
| }, |
| "inputs": { |
| "condition": { "dtype": "bool", "shape": [2, 1], "data": { "kind": "values", "values": [1, 0] } }, |
| "x": { "dtype": "bool", "shape": [1, 3], "data": { "kind": "values", "values": [0, 1, 0] } }, |
| "y": { "dtype": "bool", "shape": [], "data": { "kind": "values", "values": [1] } } |
| }, |
| "outputs": { "output": { "dtype": "bool", "shape": [2, 3], "tolerance": 0 } } |
| } |
| ] |
| } |
|
|
