| { |
| "op": "ai.onnx.Expand", |
| "cases": [ |
| { |
| "name": "int16_broadcast_boundaries", |
| "inputs": { |
| "input": { "dtype": "int16", "shape": [1, 4], "data": { "kind": "values", "values": [-32768, -1, 0, 32767] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [2, 4] } } |
| }, |
| "outputs": { |
| "output": { |
| "dtype": "int16", |
| "shape": [2, 4], |
| "tolerance": 0, |
| "data": { "kind": "values", "values": [-32768, -1, 0, 32767, -32768, -1, 0, 32767] } |
| } |
| } |
| }, |
| { |
| "name": "dispatch_cliff_scalar_splat", |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [7.0] } }, |
| "shape": { "dtype": "uint32", "shape": [1], "data": { "kind": "values", "values": [16776963] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [16776963], "tolerance": 0 } } |
| }, |
| { |
| "name": "scalar_to_3x3", |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [2.5] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [3, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_float_scalar_to_3x3", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_3x3" |
| }, |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [3, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "row_vector_to_3x3", |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [1, 3], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [3, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_float_vector_to_rows", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_3x1" |
| }, |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [3, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "col_vector_to_3x3", |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [3, 1], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [3, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_float_col_vector_to_cols", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_1x3" |
| }, |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [3, 1], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [1, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [3, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank3_broadcast", |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 3, 1], |
| "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29 } |
| }, |
| "shape": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [2, 3, 4] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 3, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "uint32_bool_like_broadcast", |
| "inputs": { |
| "input": { "dtype": "uint32", "shape": [1, 3, 1], "data": { "kind": "values", "values": [1, 0, 2] } }, |
| "shape": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [2, 3, 4] } } |
| }, |
| "outputs": { "output": { "dtype": "uint32", "shape": [2, 3, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "rank5_broadcast", |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [2, 1, 2, 1, 1], |
| "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23 } |
| }, |
| "shape": { "dtype": "uint32", "shape": [5], "data": { "kind": "values", "values": [2, 3, 2, 4, 2] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 3, 2, 4, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank7_broadcast", |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 2, 1, 3, 1, 1, 2], |
| "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.19 } |
| }, |
| "shape": { "dtype": "uint32", "shape": [7], "data": { "kind": "values", "values": [2, 2, 4, 3, 2, 3, 2] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 2, 4, 3, 2, 3, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "true_scalar_to_rank3", |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [-2.5] } }, |
| "shape": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [2, 2, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 2, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "scalar_to_scalar_empty_shape", |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [6.5] } }, |
| "shape": { "dtype": "uint32", "shape": [0], "data": { "kind": "values", "values": [] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "broadcast_to_zero_width_rows", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_3x1", |
| "notes": "Valid empty-output broadcast: input shape [1,0] expands to [3,0]. No elements are written, but generated stride math must still compile." |
| }, |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [1, 0], "data": { "kind": "values", "values": [] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 0] } } |
| }, |
| "outputs": { |
| "output": { "dtype": "float32", "shape": [3, 0], "tolerance": 0, "data": { "kind": "values", "values": [] } } |
| } |
| }, |
| { |
| "name": "broadcast_to_zero_middle_dim_scalar", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_3x1x3x1_int64", |
| "notes": "Scalar-template companion for zero-sized broadcast output: the final dimension is not vec4-aligned, so the generic Expand shader must handle zero strides." |
| }, |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [1, 0, 5], "data": { "kind": "values", "values": [] } }, |
| "shape": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [3, 0, 5] } } |
| }, |
| "outputs": { |
| "output": { "dtype": "float32", "shape": [3, 0, 5], "tolerance": 0, "data": { "kind": "values", "values": [] } } |
| } |
| }, |
| { |
| "name": "ort_int32_scalar_to_3x3", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_3x3_int32" |
| }, |
| "inputs": { |
| "input": { "dtype": "int32", "shape": [1], "data": { "kind": "values", "values": [1] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "int32", "shape": [3, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_int32_vector_to_rows", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_3x1_int32" |
| }, |
| "inputs": { |
| "input": { "dtype": "int32", "shape": [3], "data": { "kind": "values", "values": [1, 2, 3] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "int32", "shape": [3, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_int32_rank4_singletons", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_3x1x3x1_int64", |
| "notes": "Same rank-4 singleton broadcast pattern adapted to int32 storage." |
| }, |
| "inputs": { |
| "input": { |
| "dtype": "int32", |
| "shape": [1, 3, 1, 3], |
| "data": { "kind": "values", "values": [1, 2, 3, 4, 5, 6, 7, 8, 9] } |
| }, |
| "shape": { "dtype": "uint32", "shape": [4], "data": { "kind": "values", "values": [3, 3, 3, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "int32", "shape": [3, 3, 3, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_f16_scalar_to_3x3", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_3x3_float16" |
| }, |
| "inputs": { |
| "input": { "dtype": "float16", "shape": [1], "data": { "kind": "values", "values": [1.0] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "float16", "shape": [3, 3], "tolerance": 0.001 } } |
| }, |
| { |
| "name": "ort_f16_vector_to_rows", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_3x1_float16" |
| }, |
| "inputs": { |
| "input": { "dtype": "float16", "shape": [3], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "float16", "shape": [3, 3], "tolerance": 0.001 } } |
| }, |
| { |
| "name": "ort_f16_col_vector_to_cols", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_1x3_float16" |
| }, |
| "inputs": { |
| "input": { "dtype": "float16", "shape": [3, 1], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [1, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "float16", "shape": [3, 3], "tolerance": 0.001 } } |
| }, |
| { |
| "name": "ort_uint32_bool_scalar_to_3x3", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_3x3_bool", |
| "notes": "Adapts ORT's bool tensor to logical uint32 values because this framework stores bool-like tensors as u32 slots." |
| }, |
| "inputs": { |
| "input": { "dtype": "uint32", "shape": [1], "data": { "kind": "values", "values": [1] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "uint32", "shape": [3, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_uint32_bool_col_to_four_cols", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_1x4_bool", |
| "notes": "Adapts ORT's bool tensor to logical uint32 values because this framework stores bool-like tensors as u32 slots." |
| }, |
| "inputs": { |
| "input": { "dtype": "uint32", "shape": [3, 1], "data": { "kind": "values", "values": [0, 1, 0] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [1, 4] } } |
| }, |
| "outputs": { "output": { "dtype": "uint32", "shape": [3, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_shape_dim_one_does_not_shrink_rank5", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_2x2x1x2x1_float" |
| }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [2, 2, 1, 2, 1], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| }, |
| "shape": { "dtype": "uint32", "shape": [5], "data": { "kind": "values", "values": [1, 2, 2, 2, 2] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 2, 2, 2, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_shape_dim_one_does_not_shrink_middle", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_3x1x8_float" |
| }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [3, 2, 1], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] } |
| }, |
| "shape": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [3, 1, 8] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [3, 2, 8], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_int32_col_vector_to_cols", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_1x3_int32" |
| }, |
| "inputs": { |
| "input": { "dtype": "int32", "shape": [3, 1], "data": { "kind": "values", "values": [1, 2, 3] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [1, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "int32", "shape": [3, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_uint32_bool_col_to_cols", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_1x3_bool", |
| "notes": "Adapts ORT's bool tensor to logical uint32 values because this framework stores bool-like tensors as u32 slots." |
| }, |
| "inputs": { |
| "input": { "dtype": "uint32", "shape": [3, 1], "data": { "kind": "values", "values": [0, 1, 0] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [1, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "uint32", "shape": [3, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_uint32_bool_row_to_rows", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_4x1_bool", |
| "notes": "Adapts ORT's bool tensor to logical uint32 values because this framework stores bool-like tensors as u32 slots." |
| }, |
| "inputs": { |
| "input": { "dtype": "uint32", "shape": [1, 4], "data": { "kind": "values", "values": [0, 1, 0, 0] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [4, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "uint32", "shape": [4, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_scalar_float_empty_shape", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_scalar_float" |
| }, |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [3.0] } }, |
| "shape": { "dtype": "uint32", "shape": [0], "data": { "kind": "values", "values": [] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "onnx_backend_expand_dim_changed", |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [3, 1], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } }, |
| "shape": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [2, 1, 6] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 3, 6] } }, |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_expand_dim_changed", |
| "notes": "ONNX int64 metadata/index tensors use framework int32/uint32 slots where representable." |
| } |
| }, |
| { |
| "name": "onnx_backend_expand_dim_unchanged", |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [3, 1], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 4] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [3, 4] } }, |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_expand_dim_unchanged", |
| "notes": "ONNX int64 metadata/index tensors use framework int32/uint32 slots where representable." |
| } |
| }, |
| { |
| "name": "ort_scalar_float_to_scalar", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_scalar_float", |
| "notes": "ORT shape is int64; this framework stores the empty shape tensor in uint32 slots." |
| }, |
| "inputs": { |
| "input": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [3.0] } }, |
| "shape": { "dtype": "uint32", "shape": [0], "data": { "kind": "values", "values": [] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_scalar_int32_to_rank3", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_scalar_int32", |
| "notes": "ORT shape is int64; this framework stores representable shape values in uint32 slots." |
| }, |
| "inputs": { |
| "input": { "dtype": "int32", "shape": [], "data": { "kind": "values", "values": [9] } }, |
| "shape": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [2, 3, 4] } } |
| }, |
| "outputs": { "output": { "dtype": "int32", "shape": [2, 3, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_bool_pattern_uint32_row_expand", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_3x1_bool", |
| "notes": "ORT uses bool payloads; this framework represents logical bool-like values as uint32 slots." |
| }, |
| "inputs": { |
| "input": { "dtype": "uint32", "shape": [3], "data": { "kind": "values", "values": [0, 1, 0] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "uint32", "shape": [3, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_int8_vector_to_rows_edge_values", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_3x1_int32", |
| "notes": "Same row-broadcast shape as ORT's int32 case, using ONNX-valid signed byte edge values." |
| }, |
| "inputs": { |
| "input": { "dtype": "int8", "shape": [3], "data": { "kind": "values", "values": [-128, -1, 127] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 1] } } |
| }, |
| "outputs": { "output": { "dtype": "int8", "shape": [3, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_uint8_col_vector_to_cols_edge_values", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_1x3_int32", |
| "notes": "Same column-broadcast shape as ORT's int32 case, using ONNX-valid unsigned byte edge values." |
| }, |
| "inputs": { |
| "input": { "dtype": "uint8", "shape": [3, 1], "data": { "kind": "values", "values": [0, 128, 255] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [1, 3] } } |
| }, |
| "outputs": { "output": { "dtype": "uint8", "shape": [3, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_bool_scalar_to_3x3", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_3x3_bool" |
| }, |
| "inputs": { |
| "input": { "dtype": "bool", "shape": [1], "data": { "kind": "values", "values": [1] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 3] } } |
| }, |
| "outputs": { |
| "output": { |
| "dtype": "bool", |
| "shape": [3, 3], |
| "tolerance": 0, |
| "data": { "kind": "values", "values": [1, 1, 1, 1, 1, 1, 1, 1, 1] } |
| } |
| } |
| }, |
| { |
| "name": "ort_bool_vector_to_rows", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_3x1_bool" |
| }, |
| "inputs": { |
| "input": { "dtype": "bool", "shape": [3], "data": { "kind": "values", "values": [0, 1, 0] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 1] } } |
| }, |
| "outputs": { |
| "output": { |
| "dtype": "bool", |
| "shape": [3, 3], |
| "tolerance": 0, |
| "data": { "kind": "values", "values": [0, 1, 0, 0, 1, 0, 0, 1, 0] } |
| } |
| } |
| }, |
| { |
| "name": "ort_bool_col_to_cols", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_1x3_bool", |
| "notes": "The requested shape has a leading dimension of 1; ONNX Expand keeps the input's leading dimension of 3 rather than shrinking it." |
| }, |
| "inputs": { |
| "input": { "dtype": "bool", "shape": [3, 1], "data": { "kind": "values", "values": [0, 1, 0] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [1, 3] } } |
| }, |
| "outputs": { |
| "output": { |
| "dtype": "bool", |
| "shape": [3, 3], |
| "tolerance": 0, |
| "data": { "kind": "values", "values": [0, 0, 0, 1, 1, 1, 0, 0, 0] } |
| } |
| } |
| }, |
| { |
| "name": "ort_bool_row_to_four_rows", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/expand_test.cc", |
| "test": "ExpandOpTest.Expand_4x1_bool" |
| }, |
| "inputs": { |
| "input": { "dtype": "bool", "shape": [1, 4], "data": { "kind": "values", "values": [0, 1, 0, 0] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [4, 1] } } |
| }, |
| "outputs": { |
| "output": { |
| "dtype": "bool", |
| "shape": [4, 4], |
| "tolerance": 0, |
| "data": { "kind": "values", "values": [0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0] } |
| } |
| } |
| }, |
| { |
| "name": "int8_vec4_path_edge_values", |
| "inputs": { |
| "input": { "dtype": "int8", "shape": [1, 4], "data": { "kind": "values", "values": [-128, -1, 0, 127] } }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 4] } } |
| }, |
| "outputs": { "output": { "dtype": "int8", "shape": [3, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "uint8_vec4_path_broadcast_row", |
| "inputs": { |
| "input": { |
| "dtype": "uint8", |
| "shape": [1, 8], |
| "data": { "kind": "values", "values": [0, 64, 128, 192, 255, 1, 127, 254] } |
| }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [4, 8] } } |
| }, |
| "outputs": { "output": { "dtype": "uint8", "shape": [4, 8], "tolerance": 0 } } |
| }, |
| { |
| "name": "rank7_vec4_last_dim_divisible", |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [2, 1, 3, 1, 2, 1, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.19 } |
| }, |
| "shape": { "dtype": "uint32", "shape": [7], "data": { "kind": "values", "values": [2, 3, 3, 2, 2, 2, 4] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 3, 3, 2, 2, 2, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank8_broadcast", |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 2, 1, 3, 1, 1, 2, 1], |
| "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.19 } |
| }, |
| "shape": { "dtype": "uint32", "shape": [8], "data": { "kind": "values", "values": [2, 2, 4, 3, 2, 3, 2, 2] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 2, 4, 3, 2, 3, 2, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "f16_vec4_row_broadcast", |
| "provenance": { |
| "source": "authored for render coverage", |
| "test": "expand-vec4 `usesF16` arm", |
| "notes": "A float16 output whose innermost dimension is four exercises vec4 broadcasting and half-precision storage. Expand only copies exactly representable values, so the expected output is exact at tolerance zero." |
| }, |
| "inputs": { |
| "input": { |
| "dtype": "float16", |
| "shape": [1, 8], |
| "data": { "kind": "values", "values": [1.0, -2.0, 3.5, -4.25, 8.0, -16.5, 0.125, 64.0] } |
| }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [3, 8] } } |
| }, |
| "outputs": { "output": { "dtype": "float16", "shape": [3, 8], "tolerance": 0 } } |
| }, |
| { |
| "name": "same_shape_identity_vec4", |
| "provenance": { |
| "source": "authored for render coverage", |
| "test": "expand-vec4 `x_same` arm", |
| "notes": "The requested shape equals the input shape, so expand-vec4 takes the x_same branch and emits the identity offset `return out_index;` instead of including expand-broadcast-offset. No other case has an input shape identical to the output shape while the innermost dim is divisible by 4; every other vec4-eligible case broadcasts at least one axis. All eight values are distinct so a wrong offset in the identity path shows up per position." |
| }, |
| "inputs": { |
| "input": { |
| "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] } |
| }, |
| "shape": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [2, 4] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 4], "tolerance": 0 } } |
| } |
| ] |
| } |
|
|
