| { |
| "domain": "ai.onnx", |
| "name": "MeanVarianceNormalization", |
| "sinceVersion": 13, |
| "inputs": { "x": { "onnx": "X", "dtype": "T" } }, |
| "outputs": { "y": { "onnx": "Y", "dtype": "T", "rank": "ranks.x", "shape": "shapes.x" } }, |
| "attributes": { "axes": { "default": [0, 2, 3] } }, |
| "typeConstraints": { "T": ["float32", "float16"] }, |
| "tunables": { |
| "WORKGROUP_SIZE": { "default": 256 }, |
| "SERIAL_WORKGROUP_SIZE": { "default": 256 }, |
| "SERIAL_TINY_WORKGROUP_SIZE": { "default": 64 }, |
| "SERIAL_MAX_REDUCTION": { "default": 128 }, |
| "SERIAL_MIN_ROWS": { "default": 256 }, |
| "TREE_MEDIUM_WORKGROUP_SIZE": { "default": 64 }, |
| "VEC4_MIN_REDUCTION": { "default": 8 }, |
| "FLAT_SPLIT_MIN_ELEMENTS": { "default": 65536 }, |
| "FLAT_SPLIT_TARGET_ELEMENTS": { "default": 4096 }, |
| "MAX_FLAT_SPLITS": { "default": 256 }, |
| "PACKED_WORKGROUP_SIZE": { "default": 512 } |
| }, |
| "derive": { |
| "deviceWorkgroupCap": "min(device.limits.maxComputeInvocationsPerWorkgroup, device.limits.maxComputeWorkgroupSizeX)", |
| "foldedDispatchCapacity": "min(device.limits.maxComputeWorkgroupsPerDimension, 65535) * min(device.limits.maxComputeWorkgroupsPerDimension, 65535)", |
| "shapeContract": "ranks.x >= 3 and ranks.x <= 8 and ranks.y == ranks.x and sameShape(shapes.y, shapes.x) and f16Ok(dtypes.T)", |
| "reduceCount": "(dim(shapes.x, 0) if hasAxis(attrs.axes, 0, ranks.x) else 1) * (dim(shapes.x, 1) if hasAxis(attrs.axes, 1, ranks.x) else 1) * (dim(shapes.x, 2) if hasAxis(attrs.axes, 2, ranks.x) else 1) * (dim(shapes.x, 3) if ranks.x >= 4 and hasAxis(attrs.axes, 3, ranks.x) else 1) * (dim(shapes.x, 4) if ranks.x >= 5 and hasAxis(attrs.axes, 4, ranks.x) else 1) * (dim(shapes.x, 5) if ranks.x >= 6 and hasAxis(attrs.axes, 5, ranks.x) else 1) * (dim(shapes.x, 6) if ranks.x >= 7 and hasAxis(attrs.axes, 6, ranks.x) else 1) * (dim(shapes.x, 7) if ranks.x >= 8 and hasAxis(attrs.axes, 7, ranks.x) else 1)", |
| "shortGroup": "reduceCount >= 2 and reduceCount <= 4", |
| "rowCount": "numel(shapes.x) / max(1, reduceCount)", |
| "allAxesReduced": "hasAxis(attrs.axes, 0, ranks.x) and hasAxis(attrs.axes, 1, ranks.x) and hasAxis(attrs.axes, 2, ranks.x) and (ranks.x < 4 or hasAxis(attrs.axes, 3, ranks.x)) and (ranks.x < 5 or hasAxis(attrs.axes, 4, ranks.x)) and (ranks.x < 6 or hasAxis(attrs.axes, 5, ranks.x)) and (ranks.x < 7 or hasAxis(attrs.axes, 6, ranks.x)) and (ranks.x < 8 or hasAxis(attrs.axes, 7, ranks.x))", |
| "vec4Eligible": "((ranks.x == 3 and hasAxis(attrs.axes, 2, 3) and (dim(shapes.x, 2) % 4 == 0 or (hasAxis(attrs.axes, 1, 3) and dim(shapes.x, 1) * dim(shapes.x, 2) % 4 == 0) or (hasAxis(attrs.axes, 0, 3) and hasAxis(attrs.axes, 1, 3) and numel(shapes.x) % 4 == 0))) or (ranks.x == 4 and hasAxis(attrs.axes, 3, 4) and (dim(shapes.x, 3) % 4 == 0 or (hasAxis(attrs.axes, 2, 4) and dim(shapes.x, 2) * dim(shapes.x, 3) % 4 == 0) or (hasAxis(attrs.axes, 1, 4) and hasAxis(attrs.axes, 2, 4) and dim(shapes.x, 1) * dim(shapes.x, 2) * dim(shapes.x, 3) % 4 == 0) or (hasAxis(attrs.axes, 0, 4) and hasAxis(attrs.axes, 1, 4) and hasAxis(attrs.axes, 2, 4) and numel(shapes.x) % 4 == 0))) or (ranks.x == 5 and hasAxis(attrs.axes, 4, 5) and (dim(shapes.x, 4) % 4 == 0 or (hasAxis(attrs.axes, 3, 5) and dim(shapes.x, 3) * dim(shapes.x, 4) % 4 == 0) or (hasAxis(attrs.axes, 2, 5) and hasAxis(attrs.axes, 3, 5) and dim(shapes.x, 2) * dim(shapes.x, 3) * dim(shapes.x, 4) % 4 == 0) or (hasAxis(attrs.axes, 1, 5) and hasAxis(attrs.axes, 2, 5) and hasAxis(attrs.axes, 3, 5) and dim(shapes.x, 1) * dim(shapes.x, 2) * dim(shapes.x, 3) * dim(shapes.x, 4) % 4 == 0) or (allAxesReduced and numel(shapes.x) % 4 == 0))) or (ranks.x == 6 and hasAxis(attrs.axes, 5, 6) and (dim(shapes.x, 5) % 4 == 0 or (hasAxis(attrs.axes, 4, 6) and dim(shapes.x, 4) * dim(shapes.x, 5) % 4 == 0) or (hasAxis(attrs.axes, 3, 6) and hasAxis(attrs.axes, 4, 6) and dim(shapes.x, 3) * dim(shapes.x, 4) * dim(shapes.x, 5) % 4 == 0) or (hasAxis(attrs.axes, 2, 6) and hasAxis(attrs.axes, 3, 6) and hasAxis(attrs.axes, 4, 6) and dim(shapes.x, 2) * dim(shapes.x, 3) * dim(shapes.x, 4) * dim(shapes.x, 5) % 4 == 0) or (hasAxis(attrs.axes, 1, 6) and hasAxis(attrs.axes, 2, 6) and hasAxis(attrs.axes, 3, 6) and hasAxis(attrs.axes, 4, 6) and dim(shapes.x, 1) * dim(shapes.x, 2) * dim(shapes.x, 3) * dim(shapes.x, 4) * dim(shapes.x, 5) % 4 == 0) or (allAxesReduced and numel(shapes.x) % 4 == 0))))", |
| "maxWorkgroupSize": "min(tunables.WORKGROUP_SIZE, deviceWorkgroupCap)", |
| "minSubgroupSize": "device.adapterInfo.subgroupMinSize if has(device.adapterInfo, \"subgroupMinSize\") else 1", |
| "useSubgroups": "device.features.has(\"subgroups\") and has(device.adapterInfo, \"subgroupMinSize\") and minSubgroupSize > 0", |
| "scalarWorkgroupSize": "min(maxWorkgroupSize, tunables.TREE_MEDIUM_WORKGROUP_SIZE) if not useSubgroups and reduceCount > tunables.TREE_MEDIUM_WORKGROUP_SIZE and reduceCount <= 2 * tunables.TREE_MEDIUM_WORKGROUP_SIZE else min(maxWorkgroupSize, max(1, pow2ceil(reduceCount)))", |
| "vectorWorkgroupSize": "min(maxWorkgroupSize, max(1, pow2ceil(ceilDiv(reduceCount, 4))))", |
| "serialWorkgroupSize": "min(device.limits.maxComputeInvocationsPerWorkgroup, device.limits.maxComputeWorkgroupSizeX, (tunables.SERIAL_TINY_WORKGROUP_SIZE if reduceCount <= 4 else tunables.SERIAL_WORKGROUP_SIZE))", |
| "rowDispatchFits": "rowCount <= foldedDispatchCapacity", |
| "serialDispatchFits": "ceilDiv(rowCount, serialWorkgroupSize) <= foldedDispatchCapacity", |
| "applyDispatchFits": "ceilDiv(numel(shapes.y), maxWorkgroupSize) <= foldedDispatchCapacity", |
| "scalarStorageFits": "scalarWorkgroupSize * 8 <= device.limits.maxComputeWorkgroupStorageSize", |
| "vectorStorageFits": "vectorWorkgroupSize * 8 <= device.limits.maxComputeWorkgroupStorageSize", |
| "flatSplit": "min(tunables.MAX_FLAT_SPLITS, pow2ceil(ceilDiv(numel(shapes.x), tunables.FLAT_SPLIT_TARGET_ELEMENTS)))", |
| "flatScratchBytes": "flatSplit * 8", |
| "flatPathFits": "flatSplit <= min(device.limits.maxComputeWorkgroupsPerDimension, 65535) and flatScratchBytes <= device.limits.maxStorageBufferBindingSize and flatScratchBytes <= device.limits.maxBufferSize and maxWorkgroupSize * 8 <= device.limits.maxComputeWorkgroupStorageSize and applyDispatchFits", |
| "reducedSpan": "1 + ((dim(shapes.x, 0) - 1) * inner(shapes.x, 0) if ranks.x > 0 and hasAxis(attrs.axes, 0, ranks.x) else 0) + ((dim(shapes.x, 1) - 1) * inner(shapes.x, 1) if ranks.x > 1 and hasAxis(attrs.axes, 1, ranks.x) else 0) + ((dim(shapes.x, 2) - 1) * inner(shapes.x, 2) if ranks.x > 2 and hasAxis(attrs.axes, 2, ranks.x) else 0) + ((dim(shapes.x, 3) - 1) * inner(shapes.x, 3) if ranks.x > 3 and hasAxis(attrs.axes, 3, ranks.x) else 0) + ((dim(shapes.x, 4) - 1) * inner(shapes.x, 4) if ranks.x > 4 and hasAxis(attrs.axes, 4, ranks.x) else 0) + ((dim(shapes.x, 5) - 1) * inner(shapes.x, 5) if ranks.x > 5 and hasAxis(attrs.axes, 5, ranks.x) else 0) + ((dim(shapes.x, 6) - 1) * inner(shapes.x, 6) if ranks.x > 6 and hasAxis(attrs.axes, 6, ranks.x) else 0) + ((dim(shapes.x, 7) - 1) * inner(shapes.x, 7) if ranks.x > 7 and hasAxis(attrs.axes, 7, ranks.x) else 0)", |
| "packedRows": "4 if reduceCount % 2 != 0 else (2 if reduceCount % 4 != 0 else 1)", |
| "packedVectors": "reduceCount * packedRows / 4", |
| "packedWorkgroupSize": "tunables.PACKED_WORKGROUP_SIZE if tunables.PACKED_WORKGROUP_SIZE <= deviceWorkgroupCap else min(tunables.SERIAL_TINY_WORKGROUP_SIZE, deviceWorkgroupCap)" |
| }, |
| "when": ["shapeContract"], |
| "bindings": { |
| "x": { "elementType": "$scalar" }, |
| "params": { "struct": [{ "name": "count", "type": "u32", "value": "numel(shapes.x)" }] }, |
| "x_main": { "name": "x", "elementType": "$ioElement" }, |
| "y_main": { "name": "y", "elementType": "$ioElement" }, |
| "params_rows": { "name": "params", "struct": [{ "name": "rows", "type": "u32", "value": "rowCount" }] } |
| }, |
| "variants": [ |
| { |
| "id": "empty_noop", |
| "priority": 200, |
| "when": ["numel(shapes.x) == 0"], |
| "passes": [ |
| { |
| "id": "noop", |
| "name": "MeanVarianceNormalization.Empty", |
| "shader": "noop.wgsl.jinja", |
| "bindings": [{ "name": "params", "struct": [{ "name": "dummy", "type": "u32", "value": 0 }] }], |
| "dispatch": { "x": 0 } |
| } |
| ] |
| }, |
| { |
| "id": "all_axes_flat_split", |
| "priority": 120, |
| "when": ["numel(shapes.x) > 0", "allAxesReduced", "numel(shapes.x) >= tunables.FLAT_SPLIT_MIN_ELEMENTS", "flatPathFits"], |
| "derive": { "scalar": "dtypes.T" }, |
| "intermediates": [ |
| { "id": "partials", "dtype": "float32", "shape": "[flatSplit, 2]" }, |
| { "id": "stats", "dtype": "float32", "shape": "[2]" } |
| ], |
| "passes": [ |
| { |
| "id": "partials", |
| "name": "MeanVarianceNormalization.FlatPartials", |
| "shader": "norm-flat-splitk-partials.wgsl.jinja", |
| "derive": { |
| "workgroupSizeSpec": "maxWorkgroupSize", |
| "splitSpec": "flatSplit", |
| "usesF16Spec": "dtypes.T == \"f16\"" |
| }, |
| "bindings": ["x", { "name": "partials", "elementType": "vec2<f32>" }, "params"], |
| "dispatch": { "x": "flatSplit" } |
| }, |
| { |
| "id": "combine", |
| "name": "MeanVarianceNormalization.FlatCombine", |
| "shader": "norm-flat-splitk-combine.wgsl.jinja", |
| "derive": { "splitSpec": "flatSplit" }, |
| "bindings": [ |
| { "name": "partials", "buffer": "read-only-storage", "elementType": "vec2<f32>" }, |
| { "name": "stats", "elementType": "f32", "length": 2 }, |
| "params" |
| ], |
| "dispatch": { "x": "min(1, 65535)", "y": "ceilDiv(1, 65535)", "z": 1 } |
| }, |
| { |
| "id": "apply", |
| "name": "MeanVarianceNormalization.FlatApply", |
| "shader": "norm-flat-apply.wgsl.jinja", |
| "derive": { "workgroupSizeSpec": "maxWorkgroupSize", "usesF16Spec": "dtypes.T == \"f16\"" }, |
| "bindings": [ |
| "x", |
| { "name": "stats", "buffer": "read-only-storage", "elementType": "f32", "length": 2 }, |
| { "arg": "y", "elementType": "$scalar" }, |
| { "name": "params", "struct": [{ "name": "count", "type": "u32", "value": "numel(shapes.y)" }] } |
| ], |
| "dispatch": { |
| "x": "min(ceilDiv((numel(shapes.y)), (maxWorkgroupSize)), 65535)", |
| "y": "ceilDiv(ceilDiv((numel(shapes.y)), (maxWorkgroupSize)), 65535)", |
| "z": 1 |
| } |
| } |
| ] |
| }, |
| { |
| "id": "packed_short_groups", |
| "priority": 116, |
| "when": ["numel(shapes.x) > 0", "shortGroup", "rowCount >= tunables.SERIAL_MIN_ROWS", "reducedSpan == reduceCount", "ceilDiv(rowCount / packedRows, packedWorkgroupSize) <= foldedDispatchCapacity"], |
| "derive": { |
| "packedTail": "rowCount % packedRows != 0", |
| "ioElement": "dtypes.T if packedTail else \"vec4<\" ~ dtypes.T ~ \">\"", |
| "usesF16Spec": "dtypes.T == \"f16\"" |
| }, |
| "passes": [ |
| { |
| "id": "main", |
| "name": "MeanVarianceNormalization.PackedShortGroups", |
| "shader": "mean-variance-normalization-packed-rows.wgsl.jinja", |
| "bindings": ["x_main", "y_main", "params_rows"], |
| "dispatch": { |
| "x": "min(ceilDiv((ceilDiv(rowCount, packedRows)), (packedWorkgroupSize)), 65535)", |
| "y": "ceilDiv(ceilDiv((ceilDiv(rowCount, packedRows)), (packedWorkgroupSize)), 65535)", |
| "z": 1 |
| } |
| } |
| ] |
| }, |
| { |
| "id": "serial_rows", |
| "priority": 115, |
| "when": ["numel(shapes.x) > 0", "reduceCount <= tunables.SERIAL_MAX_REDUCTION", "rowCount >= tunables.SERIAL_MIN_ROWS", "serialDispatchFits"], |
| "derive": { "scalar": "dtypes.T", "ioElement": "dtypes.T" }, |
| "passes": [ |
| { |
| "id": "main", |
| "name": "MeanVarianceNormalization.SerialRows", |
| "shader": "mean-variance-normalization-serial-rows.wgsl.jinja", |
| "derive": { |
| "xShape": "shapes.x", |
| "reduce": ["hasAxis(attrs.axes, 0, ranks.x)", "hasAxis(attrs.axes, 1, ranks.x)", "hasAxis(attrs.axes, 2, ranks.x)", "hasAxis(attrs.axes, 3, ranks.x)", "hasAxis(attrs.axes, 4, ranks.x)", "hasAxis(attrs.axes, 5, ranks.x)", "hasAxis(attrs.axes, 6, ranks.x)", "hasAxis(attrs.axes, 7, ranks.x)"], |
| "workgroupSizeSpec": "serialWorkgroupSize", |
| "usesF16Spec": "dtypes.T == \"f16\"" |
| }, |
| "bindings": ["x_main", "y_main", "params_rows"], |
| "dispatch": { |
| "x": "min(ceilDiv((rowCount), (serialWorkgroupSize)), 65535)", |
| "y": "ceilDiv(ceilDiv((rowCount), (serialWorkgroupSize)), 65535)", |
| "z": 1 |
| } |
| } |
| ] |
| }, |
| { |
| "id": "cooperative_vec4", |
| "priority": 110, |
| "when": ["numel(shapes.x) > 0", "reduceCount >= tunables.VEC4_MIN_REDUCTION", "vec4Eligible", "rowDispatchFits", "vectorStorageFits"], |
| "derive": { "scalar": "dtypes.T", "ioElement": "\"vec4<\" ~ dtypes.T ~ \">\"" }, |
| "passes": [ |
| { |
| "id": "main", |
| "name": "MeanVarianceNormalization.CooperativeVec4", |
| "shader": "mean-variance-normalization-subgroup.wgsl.jinja", |
| "derive": { |
| "xShape": "shapes.x", |
| "reduce": ["hasAxis(attrs.axes, 0, ranks.x)", "hasAxis(attrs.axes, 1, ranks.x)", "hasAxis(attrs.axes, 2, ranks.x)", "hasAxis(attrs.axes, 3, ranks.x)", "hasAxis(attrs.axes, 4, ranks.x)", "hasAxis(attrs.axes, 5, ranks.x)", "hasAxis(attrs.axes, 6, ranks.x)", "hasAxis(attrs.axes, 7, ranks.x)"], |
| "wg": "vectorWorkgroupSize", |
| "scalar": "dtypes.T", |
| "vecType": "\"vec4<\" ~ dtypes.T ~ \">\"", |
| "usesF16Spec": "dtypes.T == \"f16\"", |
| "vectorizedSpec": true |
| }, |
| "bindings": ["x_main", "y_main", "params_rows"], |
| "dispatch": { "x": "min(rowCount, 65535)", "y": "ceilDiv(rowCount, 65535)", "z": 1 } |
| } |
| ] |
| }, |
| { |
| "id": "cooperative_scalar", |
| "priority": 100, |
| "when": ["numel(shapes.x) > 0", "rowDispatchFits", "scalarStorageFits"], |
| "derive": { "scalar": "dtypes.T", "ioElement": "dtypes.T" }, |
| "passes": [ |
| { |
| "id": "main", |
| "name": "MeanVarianceNormalization.CooperativeScalar", |
| "shader": "mean-variance-normalization-subgroup.wgsl.jinja", |
| "derive": { |
| "xShape": "shapes.x", |
| "reduce": ["hasAxis(attrs.axes, 0, ranks.x)", "hasAxis(attrs.axes, 1, ranks.x)", "hasAxis(attrs.axes, 2, ranks.x)", "hasAxis(attrs.axes, 3, ranks.x)", "hasAxis(attrs.axes, 4, ranks.x)", "hasAxis(attrs.axes, 5, ranks.x)", "hasAxis(attrs.axes, 6, ranks.x)", "hasAxis(attrs.axes, 7, ranks.x)"], |
| "wg": "scalarWorkgroupSize", |
| "scalar": "dtypes.T", |
| "usesF16Spec": "dtypes.T == \"f16\"", |
| "vectorizedSpec": false |
| }, |
| "bindings": ["x_main", "y_main", "params_rows"], |
| "dispatch": { "x": "min(rowCount, 65535)", "y": "ceilDiv(rowCount, 65535)", "z": 1 } |
| } |
| ] |
| } |
| ] |
| } |
|
|
