File size: 8,685 Bytes
c255a81 618a3b5 c255a81 618a3b5 c255a81 618a3b5 c255a81 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | {
"cases": [
{
"name": "f32_values",
"inputs": {
"x": {
"dtype": "float32",
"shape": [6],
"data": { "kind": "values", "values": [-2.0, -1.0, 0.0, 0.5, 1.0, 2.0] }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [6], "tolerance": 0.00001 } }
},
{
"name": "f32_subnormal_identity_tail_gpu_gap",
"skipGpu": {
"category": "permanent",
"reason": "Portable WGSL floating-point semantics do not guarantee preservation of the subnormal values required by this fixture. Backend evidence: WebGPU/Metal flushes subnormals to zero (f32 and f16); the kernel cannot preserve denormal inputs/outputs bit-exactly."
},
"provenance": {
"source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc",
"test": "MathOpTest.Sinh",
"notes": "For tiny finite inputs sinh(x) rounds back to x in float32; zero-flushing erases the signed tail."
},
"inputs": {
"x": {
"dtype": "float32",
"shape": [4],
"data": { "kind": "values", "values": [-1e-39, -1e-40, 1e-40, 1e-39] }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [4], "tolerance": 0 } }
},
{
"name": "f32_subnormal_identity_tail_scalar_gpu_gap",
"skipGpu": {
"category": "permanent",
"reason": "Portable WGSL floating-point semantics do not guarantee preservation of the subnormal values required by this fixture. Backend evidence: WebGPU/Metal flushes subnormals to zero (f32 and f16); the kernel cannot preserve denormal inputs/outputs bit-exactly."
},
"provenance": {
"source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc",
"test": "MathOpTest.Sinh",
"notes": "On the scalar path, subnormal inputs produce valid finite Sinh outputs."
},
"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": "f16_values",
"inputs": {
"x": {
"dtype": "float16",
"shape": [2, 3],
"data": { "kind": "values", "values": [-2.0, -1.0, 0.0, 0.5, 1.0, 2.0] }
}
},
"outputs": { "y": { "dtype": "float16", "shape": [2, 3] } },
"tolerance": 0.003
},
{
"name": "overflow_and_special_values",
"inputs": {
"x": {
"dtype": "float32",
"shape": [8],
"data": { "kind": "values", "values": [-100.0, -20.0, 0.0, 20.0, 100.0, "Infinity", "-Infinity", "NaN"] }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [8], "allowNaN": true } }
},
{
"name": "f32_near_overflow_threshold_finite",
"provenance": {
"source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc",
"test": "MathOpTest.Sinh",
"notes": "sinh(x) remains finite beyond the exp(x) overflow threshold because the mathematical result is approximately exp(x) / 2."
},
"inputs": {
"x": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [88.75, 89.0, -88.75, -89.0] } }
},
"outputs": { "y": { "dtype": "float32", "shape": [4], "tolerance": 0, "relTolerance": 0.00001 } }
},
{
"name": "f32_near_overflow_threshold_finite_scalar",
"provenance": {
"source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc",
"test": "MathOpTest.Sinh",
"notes": "The scalar path exercises finite Sinh values above the exp(x) overflow threshold."
},
"inputs": {
"x": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [88.75, 89.0, -89.0] } }
},
"outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 0, "relTolerance": 0.00001 } }
},
{
"name": "f16_overflow_and_special_values",
"inputs": {
"x": {
"dtype": "float16",
"shape": [8],
"data": { "kind": "values", "values": [-20.0, -12.0, 0.0, 0.0, 12.0, 20.0, "Infinity", "NaN"] }
}
},
"outputs": { "y": { "dtype": "float16", "shape": [8], "tolerance": 0.003, "allowNaN": true } }
},
{
"name": "ort_float_small_range",
"provenance": {
"source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc",
"test": "MathOpTest.Sinh"
},
"inputs": {
"x": { "dtype": "float32", "shape": [5], "data": { "kind": "values", "values": [-1.0, -0.5, 0.0, 0.5, 1.0] } }
},
"outputs": {
"y": {
"dtype": "float32",
"shape": [5],
"tolerance": 0.00001,
"data": {
"kind": "values",
"values": [-1.175201177597046, -0.5210952758789062, 0.0, 0.5210952758789062, 1.175201177597046]
}
}
}
},
{
"name": "onnx_backend_example",
"provenance": {
"source": "cmake/external/onnx/onnx/backend/test/data/node/test_sinh_example",
"test": "test_sinh_example"
},
"inputs": { "x": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [-1.0, 0.0, 1.0] } } },
"outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 0.000001 } }
},
{
"name": "onnx_backend_sinh",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_sinh" },
"inputs": {
"x": {
"dtype": "float32",
"shape": [3, 4, 5],
"data": {
"kind": "values",
"values": [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]
}
}
},
"outputs": { "y": { "dtype": "float32", "shape": [3, 4, 5], "tolerance": 0.00001 } }
},
{
"name": "onnx_backend_sinh_example",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_sinh_example" },
"inputs": { "x": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [-1.0, 0.0, 1.0] } } },
"outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 0.00001 } }
},
{
"name": "empty_input_zero_dim",
"inputs": { "x": { "dtype": "float32", "shape": [0], "data": { "kind": "values", "values": [] } } },
"outputs": { "y": { "dtype": "float32", "shape": [0], "tolerance": 0 } }
},
{
"name": "empty_input_f16_zero_dim",
"inputs": { "x": { "dtype": "float16", "shape": [0], "data": { "kind": "values", "values": [] } } },
"outputs": { "y": { "dtype": "float16", "shape": [0], "tolerance": 0 } }
},
{
"name": "f16_nonmult4_scalar_path_range",
"inputs": {
"x": { "dtype": "float16", "shape": [5], "data": { "kind": "values", "values": [-2.0, -0.5, 0.0, 0.5, 2.0] } }
},
"outputs": { "y": { "dtype": "float16", "shape": [5], "tolerance": 0.005 } }
},
{
"name": "f32_scalar_tail_4097",
"provenance": { "notes": "An odd float32 element count exercises scalar Sinh with finite moderate inputs." },
"inputs": {
"x": {
"dtype": "float32",
"shape": [4097],
"data": { "kind": "fillFloat32", "sinStep": 0.011, "cosStep": 0.019, "scale": 0.5 }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [4097], "tolerance": 0.00001 } }
}
]
}
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