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"domain": "com.microsoft",
"name": "GemmaRotaryEmbedding",
"sinceVersion": 1,
"description": "Fuses the Gemma rotary-embedding tail: computes `sin` and `cos` from float32 `emb`, casts them to float16, then evaluates `q * cos + q_rot * sin` and the corresponding expression for `k`. `emb` has shape `(batch, seq, dim)` and is broadcast over the head axis of the `(batch, heads, seq, dim)` operands. Each product is rounded to float16 before the addition.",
"inputs": [
{
"role": "emb",
"dtype": "U",
"rank": 3,
"description": "Rotary angles with shape `(batch_size, seq_len, dim)`, shared by every head."
},
{
"role": "q",
"dtype": "T",
"rank": 4,
"description": "Query state with shape `(batch_size, num_heads, seq_len, dim)`."
},
{ "role": "q_rot", "dtype": "T", "rank": 4, "description": "Half-rotated query state, same shape as `q`." },
{ "role": "k", "dtype": "T", "rank": 4, "description": "Key state, same shape as `q`." },
{ "role": "k_rot", "dtype": "T", "rank": 4, "description": "Half-rotated key state, same shape as `q`." }
],
"outputs": [
{
"role": "output1",
"dtype": "T",
"rank": "ranks.qT",
"shape": "shapes.qT",
"description": "Rotary-embedded query, same shape as `q`."
},
{
"role": "output2",
"dtype": "T",
"rank": "ranks.qT",
"shape": "shapes.qT",
"description": "Rotary-embedded key, same shape as `q`."
}
],
"typeConstraints": { "T": ["float16"], "U": ["float32"] },
"args": {
"embT": { "kind": "tensor", "semantic": "emb", "role": "input", "dtype": "float32" },
"qT": { "kind": "tensor", "semantic": "q", "role": "input" },
"qRotT": { "kind": "tensor", "semantic": "q_rot", "role": "input" },
"kT": { "kind": "tensor", "semantic": "k", "role": "input" },
"kRotT": { "kind": "tensor", "semantic": "k_rot", "role": "input" },
"output1T": { "kind": "tensor", "semantic": "output1", "role": "output" },
"output2T": { "kind": "tensor", "semantic": "output2", "role": "output" }
},
"tunables": { "workgroupSize": 256 },
"derive": {
"batchSize": "dim(shapes.qT, 0)",
"numHeads": "dim(shapes.qT, 1)",
"seqLen": "dim(shapes.qT, 2)",
"headDim": "dim(shapes.qT, 3)",
"sameAsQ": "ranks.qRotT == 4 and ranks.kT == 4 and ranks.kRotT == 4 and sameShape(shapes.qRotT, shapes.qT) and sameShape(shapes.kT, shapes.qT) and sameShape(shapes.kRotT, shapes.qT)",
"dtypesMatch": "tensorDtypes.qRotT == tensorDtypes.qT and tensorDtypes.kT == tensorDtypes.qT and tensorDtypes.kRotT == tensorDtypes.qT and tensorDtypes.output1T == tensorDtypes.qT and tensorDtypes.output2T == tensorDtypes.qT",
"contract": "ranks.qT == 4 and ranks.embT == 3 and sameAsQ and dtypesMatch and tensorDtypes.qT == \"float16\" and f16Ok(dtypes.T) and tensorDtypes.embT == \"float32\" and dim(shapes.embT, 0) == batchSize and dim(shapes.embT, 1) == seqLen and dim(shapes.embT, 2) == headDim and sameShape(shapes.output1T, shapes.qT) and sameShape(shapes.output2T, shapes.qT)",
"vec4Ok": "headDim % 4 == 0"
},
"bindingSets": {
"scalar": [
{
"name": "emb",
"arg": "embT",
"semantic": "emb",
"buffer": { "type": "read-only-storage" },
"elementType": "f32"
},
{ "name": "q", "arg": "qT", "semantic": "q", "buffer": { "type": "read-only-storage" }, "elementType": "$scalar" },
{
"name": "q_rot",
"arg": "qRotT",
"semantic": "q_rot",
"buffer": { "type": "read-only-storage" },
"elementType": "$scalar"
},
{ "name": "k", "arg": "kT", "semantic": "k", "buffer": { "type": "read-only-storage" }, "elementType": "$scalar" },
{
"name": "k_rot",
"arg": "kRotT",
"semantic": "k_rot",
"buffer": { "type": "read-only-storage" },
"elementType": "$scalar"
},
{
"name": "output1",
"arg": "output1T",
"semantic": "output1",
"buffer": { "type": "storage" },
"elementType": "$scalar"
},
{
"name": "output2",
"arg": "output2T",
"semantic": "output2",
"buffer": { "type": "storage" },
"elementType": "$scalar"
},
{
"name": "params",
"semantic": "kernel.params",
"buffer": { "type": "uniform" },
"struct": {
"name": "Params",
"fields": [
{ "name": "count", "type": "u32", "value": "numel(shapes.qT)" },
{ "name": "seqDim", "type": "u32", "value": "(seqLen * headDim)" },
{ "name": "headSeqDim", "type": "u32", "value": "(numHeads * seqLen * headDim)" }
]
}
}
],
"vec4": [
{
"name": "emb",
"arg": "embT",
"semantic": "emb",
"buffer": { "type": "read-only-storage" },
"elementType": "vec4<f32>"
},
{ "name": "q", "arg": "qT", "semantic": "q", "buffer": { "type": "read-only-storage" }, "elementType": "$vector" },
{
"name": "q_rot",
"arg": "qRotT",
"semantic": "q_rot",
"buffer": { "type": "read-only-storage" },
"elementType": "$vector"
},
{ "name": "k", "arg": "kT", "semantic": "k", "buffer": { "type": "read-only-storage" }, "elementType": "$vector" },
{
"name": "k_rot",
"arg": "kRotT",
"semantic": "k_rot",
"buffer": { "type": "read-only-storage" },
"elementType": "$vector"
},
{
"name": "output1",
"arg": "output1T",
"semantic": "output1",
"buffer": { "type": "storage" },
"elementType": "$vector"
},
{
"name": "output2",
"arg": "output2T",
"semantic": "output2",
"buffer": { "type": "storage" },
"elementType": "$vector"
},
{
"name": "params",
"semantic": "kernel.params",
"buffer": { "type": "uniform" },
"struct": {
"name": "Params",
"fields": [
{ "name": "count", "type": "u32", "value": "numel(shapes.qT) / 4" },
{ "name": "seqDim", "type": "u32", "value": "(seqLen * headDim) / 4" },
{ "name": "headSeqDim", "type": "u32", "value": "(numHeads * seqLen * headDim) / 4" }
]
}
}
]
},
"variants": [
{
"id": "vec4",
"priority": 10,
"requires": { "features": ["shader-f16"] },
"when": ["contract", "vec4Ok", "tunables.workgroupSize >= 1", "floor(tunables.workgroupSize) == tunables.workgroupSize", "tunables.workgroupSize <= device.limits.maxComputeInvocationsPerWorkgroup", "tunables.workgroupSize <= device.limits.maxComputeWorkgroupSizeX"],
"constants": {
"vec4": true,
"scalar": "dtypes.T",
"vector": "\"vec4<f16>\"",
"workgroupSize": "tunables.workgroupSize"
},
"passes": [
{
"id": "main",
"name": "GemmaRotaryEmbedding.Vec4",
"shader": "gemma-rotary-embedding.wgsl.jinja",
"bindings": "vec4",
"dispatch": { "threads": "numel(shapes.qT) / 4", "workgroupSize": "constants.workgroupSize" }
}
]
},
{
"id": "scalar",
"priority": 0,
"requires": { "features": ["shader-f16"] },
"when": ["contract", "tunables.workgroupSize >= 1", "floor(tunables.workgroupSize) == tunables.workgroupSize", "tunables.workgroupSize <= device.limits.maxComputeInvocationsPerWorkgroup", "tunables.workgroupSize <= device.limits.maxComputeWorkgroupSizeX"],
"constants": {
"vec4": false,
"scalar": "dtypes.T",
"vector": "\"vec4<f16>\"",
"workgroupSize": "tunables.workgroupSize"
},
"passes": [
{
"id": "main",
"name": "GemmaRotaryEmbedding",
"shader": "gemma-rotary-embedding.wgsl.jinja",
"bindings": "scalar",
"dispatch": { "threads": "max(1, numel(shapes.qT))", "workgroupSize": "constants.workgroupSize" }
}
]
}
]
}
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