File size: 10,052 Bytes
1098d2e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 | {
"op": "com.microsoft.BiasGelu",
"cases": [
{
"name": "dispatch_cliff_vec4_f32",
"requires": { "limits": { "maxBufferSize": 268435488, "maxStorageBufferBindingSize": 268435488 } },
"provenance": {
"notes": "Its largest tensor is 268435488 bytes, so the case needs an adapter whose maxBufferSize and maxStorageBufferBindingSize both reach it — declared, because the WebGPU guaranteed minimums (256 MiB / 128 MiB) do not, and a device at them must report the case inapplicable rather than fail allocating it."
},
"inputs": {
"A": { "dtype": "float32", "shape": [8388609, 8], "data": { "kind": "linspace", "start": -2.0, "end": 2.0 } },
"B": {
"dtype": "float32",
"shape": [8],
"data": { "kind": "values", "values": [0.25, -0.25, 0.5, -0.5, 0.1, -0.1, 0.0, 1.0] }
}
},
"outputs": { "C": { "dtype": "float32", "shape": [8388609, 8], "tolerance": 0.0001 } }
},
{
"name": "ort_float32_rank2_bias_vector",
"provenance": {
"source": "onnxruntime/test/contrib_ops/element_wise_ops_test.cc",
"test": "BiasGeluTest.Float",
"notes": "Small deterministic rank-2 instance of ORT's bias-vector GELU coverage."
},
"inputs": {
"A": {
"dtype": "float32",
"shape": [2, 4],
"data": { "kind": "values", "values": [-1.5, -0.5, 0.5, 1.5, 2.0, -2.0, 0.25, -0.25] }
},
"B": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [0.25, -0.25, 0.5, -0.5] } }
},
"outputs": {
"C": {
"dtype": "float32",
"shape": [2, 4],
"tolerance": 0.000001,
"data": {
"kind": "values",
"values": [-0.13206221, -0.16997051, 0.8413447, 0.8413447, 2.222495, -0.02750505, 0.5800295, -0.16997051]
}
}
}
},
{
"name": "ort_float16_rank2_bias_vector",
"provenance": {
"source": "onnxruntime/test/contrib_ops/element_wise_ops_test.cc",
"test": "BiasGeluTest.MLFloat16",
"notes": "Compact deterministic f16 projection of ORT's bias-vector GELU coverage."
},
"inputs": {
"A": {
"dtype": "float16",
"shape": [2, 4],
"data": { "kind": "values", "values": [-1.5, -0.5, 0.5, 1.5, 2.0, -2.0, 0.25, -0.25] }
},
"B": { "dtype": "float16", "shape": [4], "data": { "kind": "values", "values": [0.25, -0.25, 0.5, -0.5] } }
},
"outputs": {
"C": {
"dtype": "float16",
"shape": [2, 4],
"tolerance": 0.001,
"data": {
"kind": "values",
"values": [-0.1320623, -0.16997046, 0.8413447, 0.8413447, 2.222495, -0.02750498, 0.58002954, -0.16997046]
}
}
}
},
{
"name": "ort_float32_rank3_bias_vector",
"provenance": {
"source": "onnxruntime/test/contrib_ops/element_wise_ops_test.cc",
"test": "BiasGeluTest.Float",
"notes": "Small deterministic rank-3 instance of ORT's bias-vector GELU coverage."
},
"inputs": {
"A": {
"dtype": "float32",
"shape": [2, 2, 3],
"data": { "kind": "values", "values": [-1.0, 0.0, 1.0, 2.0, -2.0, 0.5, -0.5, 0.25, -0.25, 3.0, -3.0, 0.0] }
},
"B": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [0.5, -0.5, 0.25] } }
},
"outputs": {
"C": {
"dtype": "float32",
"shape": [2, 2, 3],
"tolerance": 0.000001,
"data": {
"kind": "values",
"values": [-0.15426877, -0.15426877, 1.1179378, 2.4844761, -0.015524104, 0.58002949, 0.0, -0.10032342, 0.0, 3.4991858, -0.00081422925, 0.14967658]
}
}
}
},
{
"name": "ort_float32_rank4_bias_vector",
"provenance": {
"source": "onnxruntime/test/contrib_ops/element_wise_ops_test.cc",
"test": "BiasGeluTest.Float",
"notes": "Small deterministic rank-4 instance of ORT's bias-vector GELU coverage."
},
"inputs": {
"A": {
"dtype": "float32",
"shape": [1, 2, 2, 3],
"data": { "kind": "values", "values": [-1.0, 0.0, 1.0, 2.0, -2.0, 0.5, -0.5, 0.25, -0.25, 3.0, -3.0, 0.0] }
},
"B": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [0.25, -0.25, 0.5] } }
},
"outputs": {
"C": {
"dtype": "float32",
"shape": [1, 2, 2, 3],
"tolerance": 0.000001,
"data": {
"kind": "values",
"values": [-0.16997051, -0.10032342, 1.3997892, 2.2224948, -0.027505063, 0.8413447, -0.10032342, 0.0, 0.14967658, 3.2481246, -0.0018753314, 0.34573123]
}
}
}
},
{
"name": "ort_float32_empty_middle_dim",
"provenance": {
"source": "onnxruntime/test/contrib_ops/element_wise_ops_test.cc",
"test": "BiasGeluTest.Float",
"notes": "Additional edge: empty tensors should preserve shape and produce no values."
},
"inputs": {
"A": { "dtype": "float32", "shape": [2, 0, 4], "data": { "kind": "values", "values": [] } },
"B": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [0.25, -0.25, 0.5, -0.5] } }
},
"outputs": { "C": { "dtype": "float32", "shape": [2, 0, 4], "data": { "kind": "values", "values": [] } } }
},
{
"name": "empty_zero_hidden",
"provenance": {
"notes": "Zero-length last (bias-broadcast) axis: the bias vector itself is empty, so no kernel may size a binding from the hidden extent."
},
"inputs": {
"A": { "dtype": "float32", "shape": [2, 0], "data": { "kind": "values", "values": [] } },
"B": { "dtype": "float32", "shape": [0], "data": { "kind": "values", "values": [] } }
},
"outputs": { "C": { "dtype": "float32", "shape": [2, 0], "data": { "kind": "values", "values": [] } } }
},
{
"name": "f32_subnormal_linear_region_zero_bias_vec4_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/contrib_ops/element_wise_ops_test.cc",
"test": "BiasGeluTest.Float",
"notes": "BiasGelu with zero bias reduces to exact erf-form GELU; finite subnormal inputs should produce the x/2 linear-region tail through the vec4 bias path."
},
"inputs": {
"A": {
"dtype": "float32",
"shape": [1, 4],
"data": { "kind": "values", "values": [1e-40, -1e-40, 1e-38, -1e-38] }
},
"B": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [0.0, 0.0, 0.0, 0.0] } }
},
"outputs": {
"C": {
"dtype": "float32",
"shape": [1, 4],
"tolerance": 2e-45,
"data": {
"kind": "values",
"values": [4.99997305055738e-41, -4.99997305055738e-41, 4.999999675228202e-39, -4.999999675228202e-39]
}
}
}
},
{
"name": "f32_subnormal_linear_region_zero_bias_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/contrib_ops/element_wise_ops_test.cc",
"test": "BiasGeluTest.Float",
"notes": "Scalar-path companion for BiasGelu subnormal linear-region behavior with zero bias."
},
"inputs": {
"A": { "dtype": "float32", "shape": [1, 3], "data": { "kind": "values", "values": [1e-40, -1e-40, 1e-38] } },
"B": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [0.0, 0.0, 0.0] } }
},
"outputs": {
"C": {
"dtype": "float32",
"shape": [1, 3],
"tolerance": 2e-45,
"data": { "kind": "values", "values": [4.99997305055738e-41, -4.99997305055738e-41, 4.999999675228202e-39] }
}
}
},
{
"name": "scalar_dispatch_cliff_2d_fold",
"inputs": {
"A": { "dtype": "float32", "shape": [8388609, 3], "data": { "kind": "linspace", "start": -2.0, "end": 2.0 } },
"B": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [0.1, -0.1, 0.3] } }
},
"outputs": { "C": { "dtype": "float32", "shape": [8388609, 3], "tolerance": 0.0001 } }
},
{
"name": "scalar_rank1_odd_hidden_bias_broadcast",
"inputs": {
"A": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [1.5, -1.5, 0.7] } },
"B": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [0.2, -0.2, 0.3] } }
},
"outputs": {
"C": {
"dtype": "float32",
"shape": [3],
"tolerance": 0.000001,
"data": { "kind": "values", "values": [1.62423876, -0.07576124, 0.84134474] }
}
}
},
{
"name": "scalar_numel_div4_but_last_dim_not",
"inputs": {
"A": {
"dtype": "float32",
"shape": [4, 3],
"data": { "kind": "values", "values": [-1.0, 0.5, 2.0, 1.5, -0.5, -1.5, 0.0, 1.0, -2.0, 0.25, -0.25, 0.75] }
},
"B": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [0.2, -0.3, 0.1] } }
},
"outputs": { "C": { "dtype": "float32", "shape": [4, 3], "tolerance": 0.000001 } }
}
]
}
|