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"op": "ai.onnx.CausalConvWithState",
"cases": [
{
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},
{
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},
{
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},
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},
{
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},
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{
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}
},
{
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},
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}
},
{
"name": "zero_length_reemits_past_state",
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"weightT": { "shape": [1, 1, 3], "dtype": "float32", "data": { "kind": "values", "values": [1.0, 1.0, 1.0] } },
"pastStateT": { "shape": [1, 1, 2], "dtype": "float32", "data": { "kind": "values", "values": [4.0, 5.0] } }
},
"outputs": {
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}
},
{
"name": "vec4_zero_state_f16_silu",
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"data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.23, "scale": 0.25 }
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"data": { "kind": "fillFloat32", "sinStep": 0.27, "cosStep": 0.11, "scale": 0.2 }
}
},
"outputs": {
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"presentStateT": { "shape": [2, 3, 3], "dtype": "float16" }
}
},
{
"name": "vec4_bias_no_state_swish",
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"data": { "kind": "fillFloat32", "sinStep": 0.21, "cosStep": 0.13, "scale": 0.25 }
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"data": { "kind": "fillFloat32", "sinStep": 0.09, "cosStep": 0.31, "scale": 0.2 }
},
"biasT": {
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"data": { "kind": "fillFloat32", "sinStep": 0.33, "cosStep": 0.07, "scale": 0.1 }
}
},
"outputs": {
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"presentStateT": { "shape": [1, 2, 2], "dtype": "float32" }
}
},
{
"name": "vec4_state_no_bias",
"inputs": {
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"dtype": "float32",
"data": { "kind": "fillFloat32", "sinStep": 0.15, "cosStep": 0.25, "scale": 0.25 }
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"data": { "kind": "fillFloat32", "sinStep": 0.35, "cosStep": 0.05, "scale": 0.2 }
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"pastStateT": {
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}
},
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"presentStateT": { "shape": [1, 2, 1], "dtype": "float32" }
}
},
{
"name": "vec4_state_bias_silu",
"attrs": { "activation": "silu" },
"inputs": {
"inputT": {
"shape": [1, 2, 8],
"dtype": "float32",
"data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.27, "scale": 0.25 }
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"weightT": {
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"data": { "kind": "fillFloat32", "sinStep": 0.25, "cosStep": 0.13, "scale": 0.2 }
},
"biasT": {
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"data": { "kind": "fillFloat32", "sinStep": 0.39, "cosStep": 0.05, "scale": 0.1 }
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},
"outputs": {
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"presentStateT": { "shape": [1, 2, 3], "dtype": "float32" }
}
},
{
"name": "tiled_zero_state",
"inputs": {
"inputT": {
"shape": [1, 1, 256],
"dtype": "float32",
"data": { "kind": "fillFloat32", "sinStep": 0.03, "cosStep": 0.05, "scale": 0.25 }
},
"weightT": {
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"data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.09, "scale": 0.1 }
}
},
"outputs": {
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"presentStateT": { "shape": [1, 1, 31], "dtype": "float32" }
}
},
{
"name": "tiled_bias_no_state_silu",
"attrs": { "activation": "silu" },
"inputs": {
"inputT": {
"shape": [1, 1, 256],
"dtype": "float32",
"data": { "kind": "fillFloat32", "sinStep": 0.04, "cosStep": 0.06, "scale": 0.25 }
},
"weightT": {
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"data": { "kind": "fillFloat32", "sinStep": 0.08, "cosStep": 0.1, "scale": 0.1 }
},
"biasT": { "shape": [1], "dtype": "float32", "data": { "kind": "constant", "value": 0.05 } }
},
"outputs": {
"outputT": { "shape": [1, 1, 256], "dtype": "float32" },
"presentStateT": { "shape": [1, 1, 31], "dtype": "float32" }
}
},
{
"name": "tiled_state_no_bias",
"inputs": {
"inputT": {
"shape": [1, 1, 256],
"dtype": "float32",
"data": { "kind": "fillFloat32", "sinStep": 0.05, "cosStep": 0.07, "scale": 0.25 }
},
"weightT": {
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"dtype": "float32",
"data": { "kind": "fillFloat32", "sinStep": 0.09, "cosStep": 0.11, "scale": 0.1 }
},
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"data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.17, "scale": 0.25 }
}
},
"outputs": {
"outputT": { "shape": [1, 1, 256], "dtype": "float32" },
"presentStateT": { "shape": [1, 1, 31], "dtype": "float32" }
}
},
{
"name": "tiled_state_bias_f16_swish",
"attrs": { "activation": "swish" },
"inputs": {
"inputT": {
"shape": [1, 1, 256],
"dtype": "float16",
"data": { "kind": "fillFloat32", "sinStep": 0.06, "cosStep": 0.08, "scale": 0.25 }
},
"weightT": {
"shape": [1, 1, 32],
"dtype": "float16",
"data": { "kind": "fillFloat32", "sinStep": 0.1, "cosStep": 0.12, "scale": 0.1 }
},
"biasT": { "shape": [1], "dtype": "float16", "data": { "kind": "constant", "value": -0.05 } },
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"shape": [1, 1, 31],
"dtype": "float16",
"data": { "kind": "fillFloat32", "sinStep": 0.14, "cosStep": 0.18, "scale": 0.25 }
}
},
"outputs": {
"outputT": { "shape": [1, 1, 256], "dtype": "float16" },
"presentStateT": { "shape": [1, 1, 31], "dtype": "float16" }
}
},
{
"name": "tiled_zero_state_unaligned_k35_weight_tile_pad",
"inputs": {
"inputT": {
"shape": [1, 1, 256],
"dtype": "float32",
"data": { "kind": "fillFloat32", "sinStep": 0.03, "cosStep": 0.05, "scale": 0.25 }
},
"weightT": {
"shape": [1, 1, 35],
"dtype": "float32",
"data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.09, "scale": 0.1 }
}
},
"outputs": {
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"presentStateT": { "shape": [1, 1, 34], "dtype": "float32" }
},
"provenance": {
"notes": "Kernel length 3 mod 4. The shared tiled kernel consumes four taps per iteration, so this shape reaches it only through the zero-padded weight tile; ai.onnx carries its own copy of that gate, so it needs its own lock."
}
}
]
}
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