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"usageCase": "default",
"fixtureArrays": {
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},
"cases": [
{
"name": "default",
"inputs": {
"x": { "dtype": "float32", "shape": [5], "data": { "kind": "values", "values": [-4.0, -1.0, 0.0, 1.0, 4.0] } }
},
"outputs": { "y": { "dtype": "float32", "shape": [5] } }
},
{
"name": "nan_input_propagates",
"inputs": {
"x": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": ["NaN", -10.0, 0.0, 10.0] } }
},
"outputs": { "y": { "dtype": "float32", "shape": [4], "tolerance": 0.000001, "allowNaN": true } }
},
{
"name": "f32_beta_zero_subnormal_clamp_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.HardSigmoid",
"notes": "With beta=0 and alpha=1, positive subnormal inputs should pass through the lower clamp while negative subnormals clamp to zero."
},
"attrs": { "alpha": 1, "beta": 0 },
"inputs": {
"x": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [-1e-40, 0.0, 1e-40, 1e-39] } }
},
"outputs": { "y": { "dtype": "float32", "shape": [4], "tolerance": 0 } }
},
{
"name": "f32_beta_zero_subnormal_clamp_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.HardSigmoid",
"notes": "On an unaligned scalar path with beta=0 and alpha=1, positive subnormals pass through the lower clamp."
},
"attrs": { "alpha": 1, "beta": 0 },
"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": "f32_nonfinite_endpoints",
"provenance": {
"source": "onnxruntime/test/providers/cpu/activation/activation_op_test.cc",
"test": "ActivationOpTest.HardSigmoid",
"notes": "Additional ORT-validated endpoint coverage for explicit +/-Infinity and NaN behavior."
},
"attrs": { "alpha": 0.2, "beta": 0.5 },
"inputs": {
"x": {
"dtype": "float32",
"shape": [8],
"data": { "kind": "values", "values": ["-Infinity", -100.0, -3.0, 0.0, 3.0, 100.0, "Infinity", "NaN"] }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [8], "tolerance": 0.000001, "allowNaN": true } }
},
{
"name": "onnx_example_nondefault_alpha_beta",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_hardsigmoid_example" },
"attrs": { "alpha": 0.5, "beta": 0.6 },
"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": "f16_nondefault_clamp_boundaries",
"attrs": { "alpha": 0.5, "beta": 0.6 },
"inputs": {
"x": {
"dtype": "float16",
"shape": [6],
"data": { "kind": "values", "values": [-4.0, -1.2, -1.0, 0.8, 1.0, 4.0] }
}
},
"outputs": { "y": { "dtype": "float16", "shape": [6], "tolerance": 0.001 } }
},
{
"name": "ort_alpha_0_2_beta_0_5_activation_extremes",
"provenance": {
"source": "onnxruntime/test/providers/cpu/activation/activation_op_test.cc",
"test": "ActivationOpTest.HardSigmoid",
"notes": "ORT shared activation vector with alpha=0.2 and beta=0.5."
},
"attrs": { "alpha": 0.2, "beta": 0.5 },
"inputs": {
"x": {
"dtype": "float32",
"shape": [13],
"data": {
"kind": "values",
"values": [-1.0, 0.0, 1.0, 100.0, -100.0, 1000.0, -1000.0, 1.1754943508222875e-38, 1.1754943508222876e-39, -1.1754943508222876e-39, 3.4028234663852886e+38, -3.4028234663852886e+38, "Infinity"]
}
}
},
"outputs": { "y": { "dtype": "float32", "shape": [13], "tolerance": 0.000001 } }
},
{
"name": "ort_fp16_alpha_0_2_beta_0_5_activation_extremes",
"provenance": {
"source": "onnxruntime/test/providers/cpu/activation/activation_op_test.cc",
"test": "ActivationOpTest.HardSigmoid",
"notes": "Float16 projection of ORT's shared activation vector with alpha=0.2 and beta=0.5."
},
"attrs": { "alpha": 0.2, "beta": 0.5 },
"inputs": {
"x": {
"dtype": "float16",
"shape": [13],
"data": {
"kind": "values",
"values": [-1.0, 0.0, 1.0, 100.0, -100.0, 1000.0, -1000.0, 1.1754943508222875e-38, 1.1754943508222876e-39, -1.1754943508222876e-39, 65504.0, -65504.0, "Infinity"]
}
}
},
"outputs": { "y": { "dtype": "float16", "shape": [13], "tolerance": 0.001 } }
},
{
"name": "onnx_backend_hardsigmoid",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_hardsigmoid" },
"attrs": { "alpha": 0.5, "beta": 0.6000000238418579 },
"inputs": {
"x": {
"dtype": "float32",
"shape": [3, 4, 5],
"data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_hardsigmoid_input_x" } }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [3, 4, 5], "tolerance": 0.00001 } }
},
{
"name": "onnx_backend_hardsigmoid_default",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_hardsigmoid_default" },
"inputs": {
"x": {
"dtype": "float32",
"shape": [3, 4, 5],
"data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_hardsigmoid_input_x" } }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [3, 4, 5], "tolerance": 0.00001 } }
},
{
"name": "onnx_backend_hardsigmoid_example",
"provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_hardsigmoid_example" },
"attrs": { "alpha": 0.5, "beta": 0.6000000238418579 },
"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": [-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": 0.001, "relTolerance": 0.002 } }
},
{
"name": "vec4_f32_nan_passthrough",
"inputs": {
"x": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": ["NaN", -10.0, 0.25, 10.0] } }
},
"outputs": { "y": { "dtype": "float32", "shape": [4], "tolerance": 0.000001, "allowNaN": true } }
},
{
"name": "empty_input_zero_dim",
"inputs": { "x": { "dtype": "float32", "shape": [0], "data": { "kind": "values", "values": [] } } },
"outputs": { "y": { "dtype": "float32", "shape": [0], "tolerance": 0 } }
},
{
"name": "f32_exact_clamp_boundary_values",
"attrs": { "alpha": 0.2, "beta": 0.5 },
"inputs": {
"x": {
"dtype": "float32",
"shape": [6],
"data": { "kind": "values", "values": [-3.0, -2.5, -2.0, 2.0, 2.5, 3.0] }
}
},
"outputs": {
"y": {
"dtype": "float32",
"shape": [6],
"tolerance": 0,
"data": { "kind": "values", "values": [0.0, 0.0, 0.1, 0.9, 1.0, 1.0] }
}
}
},
{
"name": "alpha_zero_constant_beta",
"provenance": {
"notes": "With alpha=0, every finite input produces beta, while an infinite input produces NaN through 0*Infinity. Finite values, infinities, and NaN make the per-lane behavior observable."
},
"attrs": { "alpha": 0, "beta": 0.5 },
"inputs": {
"x": {
"dtype": "float32",
"shape": [8],
"data": { "kind": "values", "values": [0.0, 1.0, "Infinity", "-Infinity", "NaN", 3.5, -2.25, 0.75] }
}
},
"outputs": { "y": { "dtype": "float32", "shape": [8], "tolerance": 0, "allowNaN": true } }
}
]
}
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