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"domain": "com.microsoft",
"name": "LinearAttentionGate",
"sinceVersion": 1,
"inputs": {
"aT": { "onnx": "a", "dtype": "T" },
"dtBiasT": { "onnx": "dt_bias", "dtype": "TF", "rank": 1 },
"decayScaleT": { "onnx": "decay_scale", "dtype": "TF", "rank": 1 },
"bT": { "onnx": "b", "dtype": "T", "optional": true }
},
"outputs": {
"decayT": { "onnx": "decay", "dtype": "T", "rank": "ranks.aT", "shape": "shapes.aT" },
"betaT": { "onnx": "beta", "dtype": "T", "rank": "ranks.aT", "optional": true, "shape": "shapes.aT" }
},
"typeConstraints": { "T": ["float32", "float16"], "TF": ["float32"] },
"tunables": { "WORKGROUP_SIZE": { "default": 64 } },
"derive": {
"deviceWorkgroupCap": "min(device.limits.maxComputeInvocationsPerWorkgroup, device.limits.maxComputeWorkgroupSizeX)",
"foldedDispatchCapacity": "min(device.limits.maxComputeWorkgroupsPerDimension, 65535) * min(device.limits.maxComputeWorkgroupsPerDimension, 65535)",
"numHeads": "dim(shapes.aT, ranks.aT - 1)",
"gateCount": "numel(shapes.aT)",
"headsVec4": "numHeads / 4",
"gateVec4Count": "gateCount / 4",
"gateDtype": "tensorDtypes.aT",
"gateDtypeOk": "(gateDtype == \"float32\" or gateDtype == \"float16\") and f16Ok(dtypes.T)",
"paramsOk": "ranks.aT >= 1 and numHeads > 0 and ranks.dtBiasT == 1 and ranks.decayScaleT == 1 and tensorDtypes.dtBiasT == \"float32\" and tensorDtypes.decayScaleT == \"float32\" and dim(shapes.dtBiasT, 0) == numHeads and dim(shapes.decayScaleT, 0) == numHeads",
"tensorContract": "gateDtypeOk and paramsOk and sameShape(shapes.decayT, shapes.aT) and tensorDtypes.decayT == gateDtype",
"betaContract": "tensorContract and present.bT and present.betaT and sameShape(shapes.bT, shapes.aT) and sameShape(shapes.betaT, shapes.aT) and tensorDtypes.bT == gateDtype and tensorDtypes.betaT == gateDtype",
"decayOnlyContract": "tensorContract and not present.betaT",
"workgroupFits": "tunables.WORKGROUP_SIZE > 0 and tunables.WORKGROUP_SIZE <= deviceWorkgroupCap",
"scalarDispatchFits": "ceilDiv(gateCount, tunables.WORKGROUP_SIZE) <= foldedDispatchCapacity",
"vec4DispatchFits": "numHeads % 4 == 0 and ceilDiv(gateVec4Count, tunables.WORKGROUP_SIZE) <= foldedDispatchCapacity",
"workgroupSize": "tunables.WORKGROUP_SIZE"
},
"when": ["workgroupFits"],
"bindings": {
"a": { "arg": "aT", "elementType": "$gateElement", "length": "$gateItems" },
"dt_bias": { "arg": "dtBiasT", "elementType": "$paramElement", "length": "$headItems" },
"decay_scale": { "arg": "decayScaleT", "elementType": "$paramElement", "length": "$headItems" },
"b": { "arg": "bT", "elementType": "$gateElement", "length": "$gateItems" },
"decay": { "arg": "decayT", "elementType": "$gateElement", "length": "$gateItems" },
"beta": { "arg": "betaT", "elementType": "$gateElement", "length": "$gateItems" }
},
"variants": [
{
"id": "vec4_gate_beta",
"priority": 30,
"when": ["betaContract", "vec4DispatchFits"],
"derive": {
"vectorized": true,
"hasBeta": "present.betaT",
"gateElement": "\"vec4<f16>\" if gateDtype == \"float16\" else \"vec4<f32>\"",
"paramElement": "\"vec4<f32>\"",
"headItems": "headsVec4",
"gateItems": "gateVec4Count"
},
"passes": [
{
"id": "main",
"name": "LinearAttentionGate.Vec4GateBeta",
"shader": "linear-attention-gate.wgsl.jinja",
"bindings": ["a", "dt_bias", "decay_scale", "b", "decay", "beta"],
"dispatch": {
"x": "min(ceilDiv((gateItems), (tunables.WORKGROUP_SIZE)), 65535)",
"y": "ceilDiv(ceilDiv((gateItems), (tunables.WORKGROUP_SIZE)), 65535)",
"z": 1
}
}
]
},
{
"id": "vec4_gate",
"priority": 20,
"when": ["decayOnlyContract", "vec4DispatchFits"],
"derive": {
"vectorized": true,
"hasBeta": "present.betaT",
"gateElement": "\"vec4<f16>\" if gateDtype == \"float16\" else \"vec4<f32>\"",
"paramElement": "\"vec4<f32>\"",
"headItems": "headsVec4",
"gateItems": "gateVec4Count"
},
"passes": [
{
"id": "main",
"name": "LinearAttentionGate.Vec4Gate",
"shader": "linear-attention-gate.wgsl.jinja",
"bindings": ["a", "dt_bias", "decay_scale", "decay"],
"dispatch": {
"x": "min(ceilDiv((gateItems), (tunables.WORKGROUP_SIZE)), 65535)",
"y": "ceilDiv(ceilDiv((gateItems), (tunables.WORKGROUP_SIZE)), 65535)",
"z": 1
}
}
]
},
{
"id": "scalar_gate_beta",
"priority": 10,
"when": ["betaContract", "scalarDispatchFits"],
"derive": {
"vectorized": false,
"hasBeta": "present.betaT",
"gateElement": "\"f16\" if gateDtype == \"float16\" else \"f32\"",
"paramElement": "\"f32\"",
"headItems": "numHeads",
"gateItems": "gateCount"
},
"passes": [
{
"id": "main",
"name": "LinearAttentionGate.ScalarGateBeta",
"shader": "linear-attention-gate.wgsl.jinja",
"bindings": ["a", "dt_bias", "decay_scale", "b", "decay", "beta"],
"dispatch": {
"x": "min(ceilDiv((gateItems), (tunables.WORKGROUP_SIZE)), 65535)",
"y": "ceilDiv(ceilDiv((gateItems), (tunables.WORKGROUP_SIZE)), 65535)",
"z": 1
}
}
]
},
{
"id": "scalar_gate",
"priority": 0,
"when": ["decayOnlyContract", "scalarDispatchFits"],
"derive": {
"vectorized": false,
"hasBeta": "present.betaT",
"gateElement": "\"f16\" if gateDtype == \"float16\" else \"f32\"",
"paramElement": "\"f32\"",
"headItems": "numHeads",
"gateItems": "gateCount"
},
"passes": [
{
"id": "main",
"name": "LinearAttentionGate.ScalarGate",
"shader": "linear-attention-gate.wgsl.jinja",
"bindings": ["a", "dt_bias", "decay_scale", "decay"],
"dispatch": {
"x": "min(ceilDiv((gateItems), (tunables.WORKGROUP_SIZE)), 65535)",
"y": "ceilDiv(ceilDiv((gateItems), (tunables.WORKGROUP_SIZE)), 65535)",
"z": 1
}
}
]
}
]
}
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