| { |
| "op": "ai.onnx.Atan", |
| "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": "Scalar-path companion: subnormal inputs are 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": "MANDATE A clean coverage: f16 at a multiple-of-4 numel exercises the same_layout_vec4 packed path (atan(vec4<f16>)) over an ordinary finite range a real model would feed (e.g. coordinate/angle decomposition). Existing f16 vec4 case is only 16 elements; this is a more realistic feature-map row and a denser finite sweep." |
| }, |
| "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": "MANDATE A clean coverage: f16 with numel%4 != 0 (15) fails the same_layout_vec4 when-gate (numel(X) % 4 == 0) and must route through the scalar elementwise variant. Verifies f16 atan on the scalar fallback over a normal finite range; the only other non-%4 f16 case is shape [2,3]=6 with a tiny 6-value sweep." |
| }, |
| "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": "MANDATE A clean coverage: f32 numel%4 != 0 (7) routes through the scalar elementwise variant (same_layout_vec4 requires numel%4==0). No existing f32 case with non-%4 numel exercises the scalar path on the float32 dtype; ordinary finite range." |
| }, |
| "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": "MANDATE A clean coverage: rank-2 f16 with a flat numel (4x8=32) that is a multiple of 4 -> same_layout_vec4. Realistic small feature-map tile; existing f16 rank-2 case ([2,3]) is non-%4 (scalar). Confirms vec4 packing is correct when the leading dim is >1." |
| }, |
| "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 } } |
| } |
| ] |
| } |
|
|
