File size: 9,917 Bytes
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"fixtureArrays": {
"onnx_backend_alpha2_rank4_positive_input_x": [0.8439682722091675, 0.5665143728256226, 0.058367349207401276, 0.029163669794797897, 0.12964272499084473, 0.5060197114944458, 0.7953830361366272, 0.9411345720291138, 0.9546573162078857, 0.17730942368507385, 0.46192094683647156, 0.264804482460022, 0.6746842265129089, 0.01665256917476654, 0.6247307658195496, 0.9240844249725342, 0.9722340703010559, 0.1196569874882698, 0.41356155276298523, 0.9129372835159302, 0.5933007597923279, 0.8192993402481079, 0.7862604260444641, 0.11799798905849457, 0.692484438419342, 0.5411941409111023, 0.07513222843408585]
},
"cases": [
{
"name": "f32_values",
"attrs": { "alpha": 0.75 },
"inputs": {
"x": {
"dtype": "float32",
"shape": [6],
"data": { "kind": "values", "values": [-3.0, -1.0, -0.25, 0.0, 0.5, 2.0] }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [6], "tolerance": 0.00001 } }
},
{
"name": "f32_non_finite_and_saturation_edges",
"attrs": { "alpha": 0.75 },
"inputs": {
"x": {
"dtype": "float32",
"shape": [7],
"data": { "kind": "values", "values": ["-Infinity", -1000.0, -2.0, 0.0, 0.0, "Infinity", "NaN"] }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [7], "tolerance": 0.00001, "allowNaN": true } }
},
{
"name": "f32_negative_subnormal_expm1_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/activation/activation_op_test.cc",
"test": "ActivationOpTest.Celu",
"notes": "For tiny negative inputs, alpha*(exp(x/alpha)-1) rounds to zero; returning the original subnormal is incorrect."
},
"attrs": { "alpha": 0.75 },
"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": 1e-43 } }
},
{
"name": "f32_negative_subnormal_expm1_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/activation/activation_op_test.cc",
"test": "ActivationOpTest.Celu",
"notes": "On an unaligned scalar path, tiny negative inputs round the expm1 branch to zero while positive subnormals pass through."
},
"attrs": { "alpha": 0.75 },
"inputs": {
"x": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [-1e-40, 0.0, 1e-40] } }
},
"outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 1e-43 } }
},
{
"name": "f32_near_zero_negative_expm1_cancellation",
"provenance": {
"source": "onnxruntime/test/providers/cpu/activation/activation_op_test.cc",
"test": "ActivationOpTest.Celu",
"notes": "Valid normal-range CELU cancellation edge: alpha * (exp(x / alpha) - 1) should preserve small negative values near zero."
},
"attrs": { "alpha": 0.75 },
"inputs": {
"x": {
"dtype": "float32",
"shape": [4],
"data": { "kind": "values", "values": [-1e-7, -5e-7, -0.000001, -0.00001] }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [4], "tolerance": 2e-8 } }
},
{
"name": "f32_near_zero_negative_expm1_cancellation_scalar",
"provenance": {
"source": "onnxruntime/test/providers/cpu/activation/activation_op_test.cc",
"test": "ActivationOpTest.Celu",
"notes": "Unaligned scalar-path inputs exercise cancellation near zero in the normal range."
},
"attrs": { "alpha": 0.75 },
"inputs": {
"x": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [-1e-7, -5e-7, -0.000001] } }
},
"outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 2e-8 } }
},
{
"name": "f32_custom_alpha_wider_range",
"attrs": { "alpha": 1.5 },
"inputs": {
"x": {
"dtype": "float32",
"shape": [7],
"data": { "kind": "values", "values": [-8.0, -2.0, -0.5, 0.0, 0.0, 0.5, 8.0] }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [7], "tolerance": 0.00001 } }
},
{
"name": "ort_negative_alpha_extreme_values",
"provenance": {
"source": "onnxruntime/test/providers/cpu/activation/activation_op_test.cc",
"test": "ActivationOpTest.Celu"
},
"attrs": { "alpha": -0.5 },
"inputs": {
"x": {
"dtype": "float32",
"shape": [14],
"data": {
"kind": "values",
"values": [-1.0, 0.0, 1.0, 100.0, -100.0, 1000.0, -1000.0, 1.17549435e-38, 1.17549435e-39, -1.17549435e-39, 3.4028234663852886e+38, -3.4028234663852886e+38, "Infinity", "-Infinity"]
}
}
},
"outputs": { "y": { "dtype": "float32", "shape": [14], "tolerance": 0.00001 } }
},
{
"name": "onnx_backend_alpha2_rank4_positive",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_celu", "test": "test_celu" },
"attrs": { "alpha": 2 },
"inputs": {
"x": {
"dtype": "float32",
"shape": [3, 3, 3, 1],
"data": {
"kind": "values",
"values": { "$ref": "#/fixtureArrays/onnx_backend_alpha2_rank4_positive_input_x" }
}
}
},
"outputs": { "y": { "dtype": "float32", "shape": [3, 3, 3, 1], "tolerance": 0.000001 } }
},
{
"name": "onnx_backend_celu",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_celu" },
"attrs": { "alpha": 2 },
"inputs": {
"x": {
"dtype": "float32",
"shape": [3, 3, 3, 1],
"data": {
"kind": "values",
"values": { "$ref": "#/fixtureArrays/onnx_backend_alpha2_rank4_positive_input_x" }
}
}
},
"outputs": { "y": { "dtype": "float32", "shape": [3, 3, 3, 1], "tolerance": 0.00001 } }
},
{
"name": "vec4_f32_lanes",
"attrs": { "alpha": 1.5 },
"inputs": {
"x": {
"dtype": "float32",
"shape": [8],
"data": { "kind": "values", "values": [-4.0, -2.0, -1.0, -0.5, 0.0, 0.5, 1.0, 3.0] }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [8], "tolerance": 0.00001 } }
},
{
"name": "empty_input_zero_dim",
"attrs": { "alpha": 0.75 },
"inputs": { "x": { "dtype": "float32", "shape": [0], "data": { "kind": "values", "values": [] } } },
"outputs": { "y": { "dtype": "float32", "shape": [0], "tolerance": 0 } }
},
{
"name": "expm1_safe_maclaurin_boundary_vec4",
"attrs": { "alpha": 0.75 },
"inputs": {
"x": {
"dtype": "float32",
"shape": [8],
"data": { "kind": "values", "values": [-0.094, -0.0938, -0.09375, -0.0937, 0.0, 0.0937, 0.09375, 0.094] }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [8], "tolerance": 0.00001 } }
},
{
"name": "negative_alpha_positive_input_passthrough",
"attrs": { "alpha": -0.5 },
"inputs": {
"x": {
"dtype": "float32",
"shape": [8],
"data": { "kind": "values", "values": [0.0, 0.0, 0.25, 0.5, 1.0, 2.0, 4.0, 8.0] }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [8], "tolerance": 0.00001 } }
},
{
"name": "alpha_one_identity_at_boundary_vec4",
"inputs": {
"x": {
"dtype": "float32",
"shape": [8],
"data": { "kind": "values", "values": [-1.0, -0.001, 0.0, 0.0, 0.001, 1.0, 2.0, 4.0] }
}
},
"outputs": {
"y": {
"dtype": "float32",
"shape": [8],
"data": {
"kind": "values",
"values": [-0.6321205588285578, -0.0009995001665833, 0.0, 0.0, 0.001, 1.0, 2.0, 4.0]
},
"tolerance": 0.00001
}
}
},
{
"name": "onnx28_f16_vec4",
"provenance": {
"source": "ONNX Operators.md Celu-28",
"notes": "Float16 CELU covers negative exponential, signed-zero boundary and positive pass-through values."
},
"attrs": { "alpha": 0.75 },
"inputs": {
"x": {
"dtype": "float16",
"shape": [8],
"data": { "kind": "values", "values": [-8.0, -2.0, -0.5, -0.01, 0.0, 0.5, 2.0, 8.0] }
}
},
"outputs": { "y": { "dtype": "float16", "shape": [8], "tolerance": 0.002 } }
},
{
"name": "onnx28_f16_scalar_tail",
"provenance": {
"source": "ONNX Operators.md Celu-28",
"notes": "A non-multiple-of-four element count exercises float16 CELU on the scalar tail as well as the vec4 bulk."
},
"attrs": { "alpha": 1.25 },
"inputs": {
"x": { "dtype": "float16", "shape": [3], "data": { "kind": "values", "values": [-1.5, -0.125, 3.0] } }
},
"outputs": { "y": { "dtype": "float16", "shape": [3], "tolerance": 0.002 } }
}
]
}
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