{
  "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": {
    "embed_noseg_nopos_nosum": [
      {
        "name": "input_ids",
        "arg": "inputIdsT",
        "semantic": "input_ids",
        "buffer": { "type": "read-only-storage" },
        "elementType": "i32"
      },
      {
        "name": "word_embedding",
        "arg": "wordEmbeddingT",
        "semantic": "word_embedding",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "position_embedding",
        "arg": "positionEmbeddingT",
        "semantic": "position_embedding",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "output",
        "arg": "outputT",
        "semantic": "output",
        "buffer": { "type": "storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "params",
        "semantic": "kernel.params",
        "buffer": { "type": "uniform" },
        "struct": { "name": "Params", "fields": [{ "name": "tokens", "type": "u32", "value": "tokens" }] }
      }
    ],
    "embed_noseg_nopos_sum": [
      {
        "name": "input_ids",
        "arg": "inputIdsT",
        "semantic": "input_ids",
        "buffer": { "type": "read-only-storage" },
        "elementType": "i32"
      },
      {
        "name": "word_embedding",
        "arg": "wordEmbeddingT",
        "semantic": "word_embedding",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "position_embedding",
        "arg": "positionEmbeddingT",
        "semantic": "position_embedding",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "output",
        "arg": "outputT",
        "semantic": "output",
        "buffer": { "type": "storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "embedding_sum",
        "arg": "embeddingSumT",
        "semantic": "embedding_sum",
        "buffer": { "type": "storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "params",
        "semantic": "kernel.params",
        "buffer": { "type": "uniform" },
        "struct": { "name": "Params", "fields": [{ "name": "tokens", "type": "u32", "value": "tokens" }] }
      }
    ],
    "embed_noseg_posids_nosum": [
      {
        "name": "input_ids",
        "arg": "inputIdsT",
        "semantic": "input_ids",
        "buffer": { "type": "read-only-storage" },
        "elementType": "i32"
      },
      {
        "name": "position_ids",
        "arg": "positionIdsT",
        "semantic": "position_ids",
        "buffer": { "type": "read-only-storage" },
        "elementType": "i32"
      },
      {
        "name": "word_embedding",
        "arg": "wordEmbeddingT",
        "semantic": "word_embedding",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "position_embedding",
        "arg": "positionEmbeddingT",
        "semantic": "position_embedding",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "output",
        "arg": "outputT",
        "semantic": "output",
        "buffer": { "type": "storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "params",
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        "buffer": { "type": "uniform" },
        "struct": { "name": "Params", "fields": [{ "name": "tokens", "type": "u32", "value": "tokens" }] }
      }
    ],
    "embed_noseg_posids_sum": [
      {
        "name": "input_ids",
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        "semantic": "input_ids",
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        "elementType": "i32"
      },
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        "arg": "positionIdsT",
        "semantic": "position_ids",
        "buffer": { "type": "read-only-storage" },
        "elementType": "i32"
      },
      {
        "name": "word_embedding",
        "arg": "wordEmbeddingT",
        "semantic": "word_embedding",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "position_embedding",
        "arg": "positionEmbeddingT",
        "semantic": "position_embedding",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$aScalar"
      },
      {
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        "arg": "outputT",
        "semantic": "output",
        "buffer": { "type": "storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "embedding_sum",
        "arg": "embeddingSumT",
        "semantic": "embedding_sum",
        "buffer": { "type": "storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "params",
        "semantic": "kernel.params",
        "buffer": { "type": "uniform" },
        "struct": { "name": "Params", "fields": [{ "name": "tokens", "type": "u32", "value": "tokens" }] }
      }
    ],
    "embed_segdefault_nopos_nosum": [
      {
        "name": "input_ids",
        "arg": "inputIdsT",
        "semantic": "input_ids",
        "buffer": { "type": "read-only-storage" },
        "elementType": "i32"
      },
      {
        "name": "word_embedding",
        "arg": "wordEmbeddingT",
        "semantic": "word_embedding",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "position_embedding",
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        "semantic": "position_embedding",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "segment_embedding",
        "arg": "segmentEmbeddingT",
        "semantic": "segment_embedding",
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        "elementType": "$aScalar"
      },
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      },
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      }
    ],
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      {
        "name": "input_ids",
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        "semantic": "input_ids",
        "buffer": { "type": "read-only-storage" },
        "elementType": "i32"
      },
      {
        "name": "word_embedding",
        "arg": "wordEmbeddingT",
        "semantic": "word_embedding",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$aScalar"
      },
      {
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        "semantic": "position_embedding",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "segment_embedding",
        "arg": "segmentEmbeddingT",
        "semantic": "segment_embedding",
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        "elementType": "$aScalar"
      },
      {
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        "buffer": { "type": "storage" },
        "elementType": "$aScalar"
      },
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        "name": "embedding_sum",
        "arg": "embeddingSumT",
        "semantic": "embedding_sum",
        "buffer": { "type": "storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "params",
        "semantic": "kernel.params",
        "buffer": { "type": "uniform" },
        "struct": { "name": "Params", "fields": [{ "name": "tokens", "type": "u32", "value": "tokens" }] }
      }
    ],
    "embed_segdefault_posids_nosum": [
      {
        "name": "input_ids",
        "arg": "inputIdsT",
        "semantic": "input_ids",
        "buffer": { "type": "read-only-storage" },
        "elementType": "i32"
      },
      {
        "name": "position_ids",
        "arg": "positionIdsT",
        "semantic": "position_ids",
        "buffer": { "type": "read-only-storage" },
        "elementType": "i32"
      },
      {
        "name": "word_embedding",
        "arg": "wordEmbeddingT",
        "semantic": "word_embedding",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "position_embedding",
        "arg": "positionEmbeddingT",
        "semantic": "position_embedding",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$aScalar"
      },
      {
        "name": "segment_embedding",
        "arg": "segmentEmbeddingT",
        "semantic": "segment_embedding",
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        "elementType": "$aScalar"
      },
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        "elementType": "$aScalar"
      },
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        "name": "params",
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        "buffer": { "type": "uniform" },
        "struct": { "name": "Params", "fields": [{ "name": "tokens", "type": "u32", "value": "tokens" }] }
      }
    ],
    "embed_segdefault_posids_sum": [
      {
        "name": "input_ids",
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        "elementType": "i32"
      },
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        "semantic": "position_ids",
        "buffer": { "type": "read-only-storage" },
        "elementType": "i32"
      },
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      }
    ],
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    ],
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      },
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      },
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      },
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      }
    ],
    "embed_seg_posids_nosum": [
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        "semantic": "segment_ids",
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