File size: 9,500 Bytes
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"cases": [
{
"name": "f32_values",
"inputs": {
"x": {
"dtype": "float32",
"shape": [6],
"data": { "kind": "values", "values": [-4.0, -1.0, 0.0, 0.5, 1.0, 4.0] }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [6], "tolerance": 0.000001 } }
},
{
"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.Atan",
"notes": "For tiny finite inputs atan(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.Atan",
"notes": "Unaligned scalar-path subnormal inputs have valid finite Atan 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": [-4.0, -1.0, 0.0, 0.5, 1.0, 4.0] }
}
},
"outputs": { "y": { "dtype": "float16", "shape": [2, 3] } },
"tolerance": 0.002
},
{
"name": "special_values_infinity_nan",
"inputs": {
"x": {
"dtype": "float32",
"shape": [5],
"data": { "kind": "values", "values": ["-Infinity", -1.0, 0.0, "Infinity", "NaN"] }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [5], "tolerance": 0.000001, "allowNaN": true } }
},
{
"name": "ort_float_wide_range",
"provenance": {
"source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc",
"test": "MathOpTest.Atan"
},
"inputs": {
"x": { "dtype": "float32", "shape": [5], "data": { "kind": "values", "values": [-10.0, -5.0, 0.0, 5.0, 10.0] } }
},
"outputs": {
"y": {
"dtype": "float32",
"shape": [5],
"tolerance": 0.00001,
"data": {
"kind": "values",
"values": [-1.4711276292800903, -1.3734008073806763, 0.0, 1.3734008073806763, 1.4711276292800903]
}
}
}
},
{
"name": "onnx_backend_example",
"provenance": {
"source": "cmake/external/onnx/onnx/backend/test/data/node/test_atan_example",
"test": "test_atan_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_atan",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_atan" },
"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_atan_example",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_atan_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": "vec4_f16_lanes",
"inputs": {
"x": {
"dtype": "float16",
"shape": [16],
"data": {
"kind": "values",
"values": [-8.0, -4.0, -2.0, -1.0, -0.5, -0.25, 0.0, 0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 50.0, 100.0]
}
}
},
"outputs": { "y": { "dtype": "float16", "shape": [16], "tolerance": 0.001, "relTolerance": 0.002 } }
},
{
"name": "empty_input_zero_dim",
"inputs": { "x": { "dtype": "float32", "shape": [0], "data": { "kind": "values", "values": [] } } },
"outputs": { "y": { "dtype": "float32", "shape": [0], "tolerance": 0 } }
},
{
"name": "f16_vec4_realistic_finite",
"provenance": {
"notes": "A 64-element float16 vector exercises packed vec4 evaluation because its element count is divisible by four. The ordinary finite values cover both signs and a range of magnitudes."
},
"inputs": {
"x": {
"dtype": "float16",
"shape": [64],
"data": {
"kind": "values",
"values": [-6.0, -5.0, -4.0, -3.5, -3.0, -2.5, -2.0, -1.75, -1.5, -1.25, -1.0, -0.875, -0.75, -0.625, -0.5, -0.375, -0.25, -0.125, -0.0625, -0.03125, 0.0, 0.03125, 0.0625, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0, 1.25, 1.5, 1.75, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 12.0, 16.0, 20.0, 24.0, 32.0, -7.0, -8.0, -9.0, -10.0, -12.0, -16.0, -20.0, -24.0, -32.0, 0.1875, -0.1875, 0.4375, -0.4375, 1.125]
}
}
},
"outputs": { "y": { "dtype": "float16", "shape": [64], "tolerance": 0.001, "relTolerance": 0.002 } }
},
{
"name": "f16_scalar_path_non_multiple_of_4",
"provenance": {
"notes": "A 15-element float16 vector is not divisible by four and therefore exercises scalar elementwise evaluation over ordinary finite values."
},
"inputs": {
"x": {
"dtype": "float16",
"shape": [15],
"data": {
"kind": "values",
"values": [-3.0, -2.0, -1.5, -1.0, -0.5, -0.25, -0.125, 0.0, 0.125, 0.25, 0.5, 1.0, 1.5, 2.0, 3.0]
}
}
},
"outputs": { "y": { "dtype": "float16", "shape": [15], "tolerance": 0.001, "relTolerance": 0.002 } }
},
{
"name": "f32_scalar_path_non_multiple_of_4",
"provenance": {
"notes": "A seven-element float32 vector is not divisible by four and therefore exercises scalar elementwise evaluation over ordinary finite values."
},
"inputs": {
"x": {
"dtype": "float32",
"shape": [7],
"data": { "kind": "values", "values": [-2.5, -1.0, -0.25, 0.0, 0.25, 1.0, 2.5] }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [7], "tolerance": 0.000001 } }
},
{
"name": "f16_2d_featuremap_vec4",
"provenance": {
"notes": "A rank-2 [4, 8] float16 tensor has a flat element count divisible by four, exercising vec4 packing across a nontrivial leading dimension."
},
"inputs": {
"x": {
"dtype": "float16",
"shape": [4, 8],
"data": {
"kind": "values",
"values": [-4.0, -3.0, -2.0, -1.0, -0.5, 0.0, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0, -5.0, -1.5, 1.5, -2.5, 2.5, -0.75, 0.75, -0.125, 0.125, -8.0, 8.0, -10.0, 10.0, -0.0625, 0.0625, 6.0, -6.0, 7.0, -7.0, 0.25]
}
}
},
"outputs": { "y": { "dtype": "float16", "shape": [4, 8], "tolerance": 0.001, "relTolerance": 0.002 } }
}
]
}
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