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{
"domain": "com.microsoft",
"name": "EmbedLayerNormalization",
"sinceVersion": 1,
"description": "BERT embedding fusion: looks up word and position tables, optionally adds a segment table, then applies layer normalization. A segment table without IDs uses row 0. `embedding_sum` is the pre-normalization sum. `mask_index` is the first zero or the sequence length; without `mask`, it is zero. Batch and sequence dimensions must be non-empty.",
"inputs": [
{
"role": "input_ids",
"dtype": "T1",
"rank": 2,
"description": "Word ids of shape `(batch_size, sequence_length)`."
},
{
"role": "segment_ids",
"dtype": "T1",
"rank": 2,
"optional": true,
"description": "Segment ids `(batch_size, sequence_length)`. Requires `segment_embedding`; when omitted with that table present, every token uses row 0. Values must be valid non-negative table-row indices."
},
{
"role": "word_embedding",
"dtype": "T",
"rank": 2,
"description": "Non-empty word embedding table `(vocab, hidden_size)`. Every `input_ids` value must be a valid non-negative row index."
},
{
"role": "position_embedding",
"dtype": "T",
"rank": 2,
"description": "Non-empty position embedding table `(max_positions, hidden_size)`. Without `position_ids`, it must contain at least `sequence_length` rows."
},
{
"role": "segment_embedding",
"dtype": "T",
"rank": 2,
"optional": true,
"description": "Non-empty segment embedding table `(segments, hidden_size)`. If `segment_ids` is absent, row 0 is used for every token."
},
{ "role": "gamma", "dtype": "T", "rank": 1, "description": "Layer-normalization scale of shape `(hidden_size)`." },
{ "role": "beta", "dtype": "T", "rank": 1, "description": "Layer-normalization bias of shape `(hidden_size)`." },
{
"role": "mask",
"dtype": "T1",
"rank": 2,
"optional": true,
"description": "Attention mask of shape `(batch_size, sequence_length)`. Only used to produce `mask_index`."
},
{
"role": "position_ids",
"dtype": "T1",
"rank": 2,
"optional": true,
"description": "Position ids `(batch_size, sequence_length)`, or `(1, sequence_length)` to share one row across the batch. Values must be valid non-negative table-row indices; absent uses the position within the sequence."
}
],
"outputs": [
{
"role": "output",
"dtype": "T",
"rank": 3,
"shape": "[dim(shapes.inputIdsT, 0), dim(shapes.inputIdsT, 1), hidden]",
"description": "Normalized embeddings of shape `(batch_size, sequence_length, hidden_size)`."
},
{
"role": "mask_index",
"dtype": "T1",
"rank": 1,
"optional": true,
"shape": "[dim(shapes.inputIdsT, 0)]",
"description": "Position of the first zero in each mask row, or `sequence_length` when no zero exists; shape `(batch_size)`. It is zero when the optional mask input is absent."
},
{
"role": "embedding_sum",
"dtype": "T",
"rank": 3,
"optional": true,
"shape": "[dim(shapes.inputIdsT, 0), dim(shapes.inputIdsT, 1), hidden]",
"description": "The summed embeddings before normalization, including the segment term when present. Float16 uses staged `(word + segment) + position`; float32 uses `(word + position) + segment`."
}
],
"attributes": { "epsilon": 9.999999960041972e-13 },
"attributeDescriptions": {
"epsilon": "Non-negative epsilon added to the layer-normalization variance before taking the square root.",
"mask_index_type": "Optional shape-inference hint for the `mask_index` output type. The schema's `T1` constraint fixes the runtime tensor type to int32."
},
"attributeConstraints": { "mask_index_type": { "values": [0, 1] } },
"typeConstraints": { "T": ["float32", "float16"], "T1": ["int32"] },
"args": {
"inputIdsT": { "kind": "tensor", "semantic": "input_ids", "role": "input", "dtype": "int32" },
"segmentIdsT": { "kind": "tensor", "semantic": "segment_ids", "role": "input", "dtype": "int32", "required": false },
"wordEmbeddingT": { "kind": "tensor", "semantic": "word_embedding", "role": "weights" },
"positionEmbeddingT": { "kind": "tensor", "semantic": "position_embedding", "role": "weights" },
"segmentEmbeddingT": { "kind": "tensor", "semantic": "segment_embedding", "role": "weights", "required": false },
"gammaT": { "kind": "tensor", "semantic": "gamma", "role": "weights" },
"betaT": { "kind": "tensor", "semantic": "beta", "role": "weights" },
"maskT": { "kind": "tensor", "semantic": "mask", "role": "input", "dtype": "int32", "required": false },
"positionIdsT": {
"kind": "tensor",
"semantic": "position_ids",
"role": "input",
"dtype": "int32",
"required": false
},
"outputT": { "kind": "tensor", "semantic": "output", "role": "output" },
"maskIndexT": { "kind": "tensor", "semantic": "mask_index", "role": "output", "dtype": "int32", "required": false },
"embeddingSumT": { "kind": "tensor", "semantic": "embedding_sum", "role": "output", "required": false }
},
"tunables": { "WORKGROUP_SIZE": 128, "MASK_WORKGROUP_SIZE": 64 },
"derive": {
"batchSize": "dim(shapes.inputIdsT, 0)",
"sequenceLength": "dim(shapes.inputIdsT, 1)",
"tokens": "batchSize * sequenceLength",
"hidden": "dim(shapes.wordEmbeddingT, 1)",
"epsilonValue": "attrs.epsilon",
"epsilonOk": "epsilonValue >= 0",
"tableShapeOk": "ranks.wordEmbeddingT == 2 and dim(shapes.wordEmbeddingT, 0) > 0 and ranks.positionEmbeddingT == 2 and dim(shapes.positionEmbeddingT, 0) > 0 and dim(shapes.positionEmbeddingT, 1) == hidden and ranks.gammaT == 1 and ranks.betaT == 1 and dim(shapes.gammaT, 0) == hidden and dim(shapes.betaT, 0) == hidden and hidden > 0",
"segmentContract": "(not present.segmentIdsT or present.segmentEmbeddingT) and (ranks.segmentIdsT == 2 and sameShape(shapes.segmentIdsT, shapes.inputIdsT) if present.segmentIdsT else true) and (ranks.segmentEmbeddingT == 2 and dim(shapes.segmentEmbeddingT, 0) > 0 and dim(shapes.segmentEmbeddingT, 1) == hidden if present.segmentEmbeddingT else true)",
"positionIdsContract": "(ranks.positionIdsT == 2 and dim(shapes.positionIdsT, 1) == sequenceLength and (dim(shapes.positionIdsT, 0) == batchSize or dim(shapes.positionIdsT, 0) == 1) if present.positionIdsT else dim(shapes.positionEmbeddingT, 0) >= sequenceLength)",
"broadcastPositionIds": "dim(shapes.positionIdsT, 0) == 1 if present.positionIdsT else false",
"maskContract": "ranks.maskT == 2 and sameShape(shapes.maskT, shapes.inputIdsT) if present.maskT else true",
"maskIndexTypeOk": "not has(attrs, \"mask_index_type\") or attrs.mask_index_type == 0 or attrs.mask_index_type == 1",
"ioShapeOk": "ranks.inputIdsT == 2 and ranks.outputT == 3 and dim(shapes.outputT, 0) == batchSize and dim(shapes.outputT, 1) == sequenceLength and dim(shapes.outputT, 2) == hidden and tensorDtypes.outputT == tensorDtypes.wordEmbeddingT and tensorDtypes.positionEmbeddingT == tensorDtypes.wordEmbeddingT and tensorDtypes.gammaT == tensorDtypes.wordEmbeddingT and tensorDtypes.betaT == tensorDtypes.wordEmbeddingT and f16Ok(tensorDtypes.wordEmbeddingT)",
"embeddingSumContract": "ranks.embeddingSumT == 3 and sameShape(shapes.embeddingSumT, shapes.outputT) and tensorDtypes.embeddingSumT == tensorDtypes.wordEmbeddingT if present.embeddingSumT else true",
"maskIndexShapeOk": "ranks.maskIndexT == 1 and dim(shapes.maskIndexT, 0) == batchSize if present.maskIndexT else true",
"embedContractOk": "epsilonOk and tableShapeOk and segmentContract and positionIdsContract and maskContract and maskIndexTypeOk and ioShapeOk and embeddingSumContract and maskIndexShapeOk and batchSize > 0 and sequenceLength > 0",
"dispatchFits": "tunables.WORKGROUP_SIZE <= device.limits.maxComputeInvocationsPerWorkgroup and tunables.MASK_WORKGROUP_SIZE <= device.limits.maxComputeInvocationsPerWorkgroup"
},
"constants": {
"aScalar": "dtypes.T",
"scalar": "dtypes.T",
"usesF16": "dtypes.T == \"f16\"",
"hidden": "hidden",
"sequenceLength": "sequenceLength",
"epsilon": "epsilonValue",
"workgroupSize": "tunables.WORKGROUP_SIZE",
"maskWorkgroupSize": "tunables.MASK_WORKGROUP_SIZE",
"wordRows": "dim(shapes.wordEmbeddingT, 0)",
"positionRows": "dim(shapes.positionEmbeddingT, 0)",
"segmentRows": "dim(shapes.segmentEmbeddingT, 0) if present.segmentEmbeddingT else 1",
"hasSegment": "present.segmentEmbeddingT",
"hasSegmentIds": "present.segmentIdsT",
"hasPositionIds": "present.positionIdsT",
"broadcastPositionIds": "broadcastPositionIds",
"writeEmbeddingSum": "present.embeddingSumT",
"hasMask": "present.maskT",
"HIDDEN_LEN": "hidden"
},
"bindingSets": {
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"elementType": "$aScalar"
},
{
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},
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},
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"buffer": { "type": "uniform" },
"struct": { "name": "Params", "fields": [{ "name": "tokens", "type": "u32", "value": "tokens" }] }
}
],
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