| { |
| "op": "ai.onnx.Abs", |
| "fixtureArrays": { |
| "onnx_backend_input_x": [1.764052391052246, 0.40015721321105957, 0.978738009929657, 2.2408931255340576, 1.8675580024719238, -0.9772778749465942, 0.9500884413719177, -0.15135720372200012, -0.10321885347366333, 0.4105985164642334, 0.14404356479644775, 1.4542734622955322, 0.7610377073287964, 0.12167501449584961, 0.44386324286460876, 0.3336743414402008, 1.4940791130065918, -0.2051582634449005, 0.3130677044391632, -0.8540957570075989, -2.5529897212982178, 0.653618574142456, 0.8644362092018127, -0.7421650290489197, 2.269754648208618, -1.4543657302856445, 0.04575851559638977, -0.18718385696411133, 1.5327792167663574, 1.4693588018417358, 0.154947429895401, 0.37816253304481506, -0.8877857327461243, -1.980796456336975, -0.34791216254234314, 0.15634897351264954, 1.2302906513214111, 1.202379822731018, -0.38732680678367615, -0.302302747964859, -1.0485529899597168, -1.420017957687378, -1.7062702178955078, 1.950775384902954, -0.5096521973609924, -0.4380742907524109, -1.2527953386306763, 0.7774903774261475, -1.6138978004455566, -0.21274028718471527, -0.8954665660858154, 0.38690251111984253, -0.5108051300048828, -1.18063223361969, -0.02818222902715206, 0.4283318817615509, 0.06651721894741058, 0.30247190594673157, -0.6343221068382263, -0.3627411723136902] |
| }, |
| "cases": [ |
| { |
| "name": "f32_values", |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [6], |
| "data": { "kind": "values", "values": [-3.0, -0.25, 0.0, 0.5, 2.0, -7.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [6] } } |
| }, |
| { |
| "name": "f16_values", |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [2, 3], |
| "data": { "kind": "values", "values": [-1.0, 2.0, -3.0, 4.0, -5.0, 6.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 3] } }, |
| "tolerance": 0.001 |
| }, |
| { |
| "name": "rank0_negative_scalar", |
| "inputs": { "x": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [-7.25] } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [] } } |
| }, |
| { |
| "name": "int32_exact_above_float24", |
| "inputs": { |
| "x": { |
| "dtype": "int32", |
| "shape": [4], |
| "data": { "kind": "values", "values": [-16777217, 16777217, -123456789, 0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "int32", "shape": [4] } } |
| }, |
| { |
| "name": "ort_int32_min_value_overflow_edge", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.Abs_int32", |
| "notes": "Extends ORT's int32 Abs coverage with INT32_MIN, whose mathematical absolute value is not representable in int32 storage." |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "int32", |
| "shape": [4], |
| "data": { "kind": "values", "values": [-2147483648, -2147483647, -1, 0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "int32", "shape": [4], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_int8_min_value_overflow_edge", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.Abs_int8", |
| "notes": "Extends ORT's int8 Abs coverage with INT8_MIN, whose mathematical absolute value is not representable in int8 storage." |
| }, |
| "inputs": { "x": { "dtype": "int8", "shape": [4], "data": { "kind": "values", "values": [-128, -127, -1, 0] } } }, |
| "outputs": { "y": { "dtype": "int8", "shape": [4], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_int16_min_value_overflow_edge_gpu_gap", |
| "skipGpu": { |
| "category": "todo", |
| "reason": "int16 is widened to i32 on WebGPU, but the current Abs kernel does not narrow the result back to signed 16-bit; abs(-32768) therefore produces 32768 instead of the required wrapped int16 result." |
| }, |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.Abs_int32", |
| "notes": "Signed integer Abs minimum-value edge adapted to ONNX-valid int16; ORT CPU returns the wrapped INT16_MIN value and current WebGPU Abs variants do not admit int16 tensors." |
| }, |
| "inputs": { |
| "x": { "dtype": "int16", "shape": [4], "data": { "kind": "values", "values": [-32768, -32767, -1, 0] } } |
| }, |
| "outputs": { "y": { "dtype": "int16", "shape": [4], "tolerance": 0 } } |
| }, |
| { |
| "name": "uint32_large_identity", |
| "inputs": { |
| "x": { |
| "dtype": "uint32", |
| "shape": [4], |
| "data": { "kind": "values", "values": [0, 16777217, 4000000001, 4294967295] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "uint32", "shape": [4] } } |
| }, |
| { |
| "name": "f32_subnormal_magnitude_vec4", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.Abs", |
| "notes": "Finite signed subnormal float32 inputs are valid; Abs should preserve their magnitudes instead of flushing them to zero." |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [4], |
| "data": { "kind": "values", "values": [-1e-40, 1e-40, -2e-40, 2e-40] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [4], "tolerance": 0 } } |
| }, |
| { |
| "name": "f32_subnormal_magnitude_scalar", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.Abs", |
| "notes": "Scalar-path companion for signed subnormal Abs magnitude preservation." |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [-1e-40, 0.0, 1e-40] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_float_2x2", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.Abs" |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [1.0, -2.0, 0.0, -10.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_int8_values", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.Abs_int8" |
| }, |
| "inputs": { "x": { "dtype": "int8", "shape": [4], "data": { "kind": "values", "values": [1, 2, -1, -5] } } }, |
| "outputs": { "y": { "dtype": "int8", "shape": [4], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_int32_values", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.Abs_int32" |
| }, |
| "inputs": { "x": { "dtype": "int32", "shape": [4], "data": { "kind": "values", "values": [1, 2, -1, -5] } } }, |
| "outputs": { "y": { "dtype": "int32", "shape": [4], "tolerance": 0 } } |
| }, |
| { |
| "name": "onnx_backend_rank3_float32", |
| "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_abs" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [3, 4, 5], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_input_x" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3, 4, 5], "tolerance": 0 } } |
| }, |
| { |
| "name": "onnx_backend_abs", |
| "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_abs" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [3, 4, 5], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_input_x" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3, 4, 5], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "vec4_f16_lanes", |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [16], |
| "data": { |
| "kind": "values", |
| "values": [-6.0, -4.0, -3.0, -2.0, -1.5, -1.0, -0.5, -0.25, 0.0, 0.25, 0.5, 1.0, 1.5, 2.0, 4.0, 6.0] |
| } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [16], "tolerance": 0 } } |
| }, |
| { |
| "name": "empty_input_zero_dim", |
| "inputs": { "x": { "dtype": "float32", "shape": [0], "data": { "kind": "values", "values": [] } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [0], "tolerance": 0 } } |
| }, |
| { |
| "name": "uint8_values", |
| "inputs": { |
| "x": { "dtype": "uint8", "shape": [6], "data": { "kind": "values", "values": [0, 1, 127, 128, 255, 3] } } |
| }, |
| "outputs": { "y": { "dtype": "uint8", "shape": [6] } } |
| }, |
| { |
| "name": "rank0_f32_scalar", |
| "inputs": { "x": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [-2.5] } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [] } } |
| } |
| ] |
| } |
|
|
