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"domain": "ai.onnx",
"name": "GroupNormalization",
"sinceVersion": 21,
"description": "Applies group normalization to the input: `y = scale * (x - mean) / sqrt(variance + epsilon) + bias`, where mean and variance are computed per instance per group of channels. The number of groups `num_groups` must divide the channel count `C` evenly; when `num_groups == C` this is equivalent to InstanceNormalization, and when `num_groups == 1` it is equivalent to LayerNormalization. The normalization stage supports TensorProto `stash_type` values `1` (float32) and `10` (float16).",
"inputs": [
{
"role": "X",
"dtype": "T",
"description": "Input data tensor of shape `(N x C x D1 x ... x Dn)` where `N` is batch size and `C` is the number of channels."
},
{ "role": "scale", "dtype": "T", "rank": 1, "description": "Scale tensor of shape `(C)`, one value per channel." },
{ "role": "bias", "dtype": "T", "rank": 1, "description": "Bias tensor of shape `(C)`, one value per channel." }
],
"outputs": [
{
"role": "Y",
"dtype": "T",
"rank": "ranks.X",
"description": "Normalized output tensor of the same shape as `X`.",
"shape": "shapes.X"
}
],
"attributes": { "epsilon": 0.00001, "stash_type": 1 },
"attributeDescriptions": {
"epsilon": "Small value added to the variance denominator to avoid division by zero.",
"num_groups": "Required number of groups to divide the channels into; must be a divisor of `C`.",
"stash_type": "TensorProto element type used for the normalization stage: `1` computes in float32, while `10` computes in float16. Normalized values are cast back to the input type before scale and bias are applied."
},
"attributeConstraints": { "num_groups": { "required": true }, "stash_type": { "values": [1, 10] } },
"typeConstraints": { "T": ["float32", "float16"] },
"args": {
"x": { "kind": "tensor", "semantic": "X", "role": "input" },
"scale": { "kind": "tensor", "semantic": "scale", "role": "input" },
"bias": { "kind": "tensor", "semantic": "bias", "role": "input" },
"y": { "kind": "tensor", "semantic": "Y", "role": "output" }
},
"tunables": {
"WORKGROUP_SIZE": 256,
"MAX_STATS_SPLITS": 64,
"STATS_VALUES_PER_SPLIT": 4096,
"SPLIT_STATS_MIN_HIDDEN": 65536,
"SPLIT_STATS_MAX_ROWS": 256
},
"derive": {
"deviceWorkgroupCap": "min(device.limits.maxComputeInvocationsPerWorkgroup, device.limits.maxComputeWorkgroupSizeX)",
"groupAttributesOk": "attrs.num_groups >= 1",
"groupShapeOk": "groupAttributesOk and f16Ok(dtypes.T) and ranks.X >= 3 and ranks.scale == 1 and ranks.bias == 1 and ranks.Y == ranks.X and sameShape(shapes.Y, shapes.X) and dim(shapes.scale, 0) == dim(shapes.X, 1) and dim(shapes.bias, 0) == dim(shapes.X, 1) and dim(shapes.X, 1) % attrs.num_groups == 0",
"groupContractOk": "groupShapeOk and attrs.stash_type == onnxDtypeCode(\"float32\")",
"groupStashF16Ok": "groupShapeOk and attrs.stash_type == onnxDtypeCode(\"float16\")",
"groupRows": "dim(shapes.X, 0) * attrs.num_groups if groupAttributesOk else 0",
"groupSpatial": "inner(shapes.X, 1)",
"groupChannelsPerGroup": "dim(shapes.X, 1) / attrs.num_groups if groupAttributesOk else 0",
"groupHidden": "groupChannelsPerGroup * groupSpatial",
"normDeviceWorkgroupCap": "min(tunables.WORKGROUP_SIZE, deviceWorkgroupCap)",
"normWorkgroupCap": "max(1, pow2ceil(normDeviceWorkgroupCap + 1) / 2)",
"groupScalarWorkgroup": "min(normWorkgroupCap, pow2ceil(groupHidden))",
"groupVec4Workgroup": "min(normWorkgroupCap, pow2ceil(groupHidden / 4))",
"hasSubgroupId": "device.features.has(\"subgroups\") and device.wgslLanguageFeatures.has(\"subgroup_id\")",
"groupRowWorkgroupBytes": "normWorkgroupCap * 2 * 4",
"groupRowCovered": "groupContractOk and groupRowWorkgroupBytes <= device.limits.maxComputeWorkgroupStorageSize",
"groupSplitCount": "min(tunables.MAX_STATS_SPLITS, device.limits.maxComputeWorkgroupsPerDimension, pow2ceil(ceilDiv(groupHidden, tunables.STATS_VALUES_PER_SPLIT)))",
"groupPartialBytes": "groupRows * groupSplitCount * 2 * 4",
"groupSplitCovered": "groupRowCovered and groupRows <= tunables.SPLIT_STATS_MAX_ROWS and groupRows <= device.limits.maxComputeWorkgroupsPerDimension and groupHidden >= tunables.SPLIT_STATS_MIN_HIDDEN and groupPartialBytes <= device.limits.maxStorageBufferBindingSize and groupPartialBytes <= device.limits.maxBufferSize"
},
"bindingSets": {
"norm": [
{
"name": "x",
"arg": "x",
"semantic": "X",
"buffer": { "type": "read-only-storage" },
"elementType": "$ioElement"
},
{
"name": "scale",
"arg": "scale",
"semantic": "scale",
"buffer": { "type": "read-only-storage" },
"elementType": "$scalar"
},
{
"name": "bias",
"arg": "bias",
"semantic": "bias",
"buffer": { "type": "read-only-storage" },
"elementType": "$scalar"
},
{ "name": "y", "arg": "y", "semantic": "Y", "buffer": { "type": "storage" }, "elementType": "$ioElement" },
{
"name": "params",
"semantic": "kernel.params",
"buffer": { "type": "uniform" },
"struct": {
"name": "Params",
"fields": [
{ "name": "rows", "type": "u32", "value": "groupRows" },
{
"name": "rowStride",
"type": "u32",
"value": "max(1, min(groupRows, device.limits.maxComputeWorkgroupsPerDimension))"
}
]
}
}
],
"splitPartials": [
{ "name": "x", "arg": "x", "semantic": "X", "buffer": { "type": "read-only-storage" }, "elementType": "$scalar" },
{ "name": "partials", "semantic": "partials", "buffer": { "type": "storage" }, "elementType": "vec2<f32>" },
{
"name": "params",
"semantic": "kernel.params",
"buffer": { "type": "uniform" },
"struct": { "name": "Params", "fields": [{ "name": "rows", "type": "u32", "value": "groupRows" }] }
}
],
"splitApply": [
{ "name": "x", "arg": "x", "semantic": "X", "buffer": { "type": "read-only-storage" }, "elementType": "$scalar" },
{
"name": "scale",
"arg": "scale",
"semantic": "scale",
"buffer": { "type": "read-only-storage" },
"elementType": "$scalar"
},
{
"name": "bias",
"arg": "bias",
"semantic": "bias",
"buffer": { "type": "read-only-storage" },
"elementType": "$scalar"
},
{
"name": "partials",
"semantic": "partials",
"buffer": { "type": "read-only-storage" },
"elementType": "vec2<f32>"
},
{ "name": "y", "arg": "y", "semantic": "Y", "buffer": { "type": "storage" }, "elementType": "$scalar" },
{
"name": "params",
"semantic": "kernel.params",
"buffer": { "type": "uniform" },
"struct": { "name": "Params", "fields": [{ "name": "rows", "type": "u32", "value": "groupRows" }] }
}
]
},
"variants": [
{
"id": "group_stash_f16_serial",
"priority": 1000,
"when": "groupStashF16Ok",
"constants": {
"scalar": "dtypes.T",
"ioElement": "dtypes.T",
"usesF16": "dtypes.T == \"f16\"",
"hiddenSize": "groupHidden",
"spatial": "groupSpatial",
"channelsPerGroup": "groupChannelsPerGroup",
"numGroups": "attrs.num_groups",
"epsilon": "attrs.epsilon"
},
"passes": [
{
"id": "main",
"name": "GroupNormalization.StashF16Serial",
"shader": "group-normalization-stash-f16-serial.wgsl.jinja",
"bindings": "norm",
"dispatch": { "workgroups": "groupRows" }
}
]
},
{
"id": "group_splitk",
"priority": 120,
"when": ["groupSplitCovered"],
"constants": {
"scalar": "dtypes.T",
"usesF16": "dtypes.T == \"f16\"",
"hiddenSize": "groupHidden",
"spatial": "groupSpatial",
"channelsPerGroup": "groupChannelsPerGroup",
"numGroups": "attrs.num_groups",
"workgroupSize": "normWorkgroupCap",
"split": "groupSplitCount",
"epsilon": "attrs.epsilon"
},
"intermediates": [{ "id": "partials", "dtype": "float32", "shape": "[groupRows * groupSplitCount, 2]" }],
"passes": [
{
"id": "partials",
"name": "GroupNormalization.SplitKPartials",
"shader": "group-normalization-splitk-partials.wgsl.jinja",
"bindings": "splitPartials",
"dispatch": { "workgroups": "groupRows", "z": "groupSplitCount" }
},
{
"id": "apply",
"name": "GroupNormalization.SplitKApply",
"shader": "group-normalization-splitk-apply.wgsl.jinja",
"bindings": "splitApply",
"dispatch": { "workgroups": "groupRows", "z": "groupSplitCount" }
}
]
},
{
"id": "group_subgroup_vec4",
"priority": 110,
"when": ["groupRowCovered", "groupSpatial % 4 == 0"],
"constants": {
"scalar": "dtypes.T",
"ioElement": "\"vec4<\" ~ dtypes.T ~ \">\"",
"vectorScalar": "\"vec4<\" ~ dtypes.T ~ \">\""
},
"passes": [
{
"id": "main",
"name": "GroupNormalization.group_subgroup_vec4",
"source": {
"shader": "norm-row-stats.wgsl.jinja",
"inputs": {
"mode": "\"group\"",
"vec4": true,
"scalar": "dtypes.T",
"usesF16": "dtypes.T == \"f16\"",
"hidden": "groupHidden",
"wg": "groupVec4Workgroup",
"epsilon": "attrs.epsilon",
"numGroups": "attrs.num_groups",
"cpg": "groupChannelsPerGroup",
"hiddenVec": "groupHidden / 4",
"vecType": "\"vec4<\" ~ dtypes.T ~ \">\"",
"spatialVec": "groupSpatial / 4",
"combineSubgroups": "hasSubgroupId"
}
},
"subgroupCollectivesWidth": "portable",
"bindings": "norm",
"dispatch": { "workgroups": "groupRows" }
}
]
},
{
"id": "group_subgroup",
"priority": 100,
"when": ["groupRowCovered"],
"constants": { "scalar": "dtypes.T", "ioElement": "dtypes.T" },
"passes": [
{
"id": "main",
"name": "GroupNormalization.group_subgroup",
"source": {
"shader": "norm-row-stats.wgsl.jinja",
"inputs": {
"mode": "\"group\"",
"vec4": false,
"scalar": "dtypes.T",
"usesF16": "dtypes.T == \"f16\"",
"hidden": "groupHidden",
"wg": "groupScalarWorkgroup",
"epsilon": "attrs.epsilon",
"numGroups": "attrs.num_groups",
"cpg": "groupChannelsPerGroup",
"spatial": "groupSpatial",
"combineSubgroups": "hasSubgroupId"
}
},
"subgroupCollectivesWidth": "portable",
"bindings": "norm",
"dispatch": { "workgroups": "groupRows" }
}
]
}
]
}
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