| { |
| "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" } |
| } |
| ] |
| } |
| ] |
| } |
|
|
