{
  "domain": "com.microsoft",
  "name": "SkipLayerNormalization",
  "sinceVersion": 1,
  "description": "Fuses skip addition with layer normalization. The rank-3 standard surface currently supports float32, required `beta`, no `bias` or residual output, hidden sizes divisible by four, and exact or documented broadcast skip shapes. The provider's rank-2 extension supports float32 output-only with optional `beta`, or `beta` with optional `bias` when emitting the residual; its float16 path requires `beta`, `bias`, a residual output, and four-wide hidden size. Other combinations, bfloat16, and training statistics are not implemented.",
  "inputs": [
    {
      "role": "input",
      "dtype": "T",
      "description": "Primary input normalized over the final hidden-size axis. Rank 3 is the public shape; rank 2 is an ONNX Runtime provider extension supported by this package."
    },
    {
      "role": "skip",
      "dtype": "T",
      "description": "Residual tensor. For rank-3 input it is exact shape, `(1, sequence_length, hidden_size)`, or `(sequence_length, hidden_size)`; rank-2 input requires exact shape."
    },
    { "role": "gamma", "dtype": "T", "rank": 1, "description": "Layer-norm scale weights of shape `(hidden_size)`." },
    {
      "role": "beta",
      "dtype": "T",
      "rank": 1,
      "optional": true,
      "description": "Layer-norm bias weights of shape `(hidden_size)`."
    },
    {
      "role": "bias",
      "dtype": "T",
      "rank": 1,
      "optional": true,
      "description": "Optional additive bias of shape `(hidden_size)` added to `input + skip` before normalization."
    }
  ],
  "outputs": [
    {
      "role": "output",
      "dtype": "T",
      "rank": "ranks.inputT",
      "shape": "shapes.inputT",
      "description": "Normalized output tensor with the same shape as `input`."
    },
    {
      "role": "input_skip_bias_sum",
      "dtype": "T",
      "rank": "ranks.inputT",
      "optional": true,
      "shape": "shapes.inputT",
      "description": "Sum of `input`, `skip`, and `bias` (when present) before normalization, with the same shape as `input`."
    }
  ],
  "attributes": { "epsilon": 9.999999960041972e-13 },
  "attributeDescriptions": { "epsilon": "Non-negative epsilon added to the variance before taking the square root." },
  "args": {
    "inputT": { "kind": "tensor", "semantic": "input", "role": "input" },
    "skipT": { "kind": "tensor", "semantic": "skip", "role": "input" },
    "gammaT": { "kind": "tensor", "semantic": "gamma", "role": "input" },
    "betaT": { "kind": "tensor", "semantic": "beta", "role": "input", "required": false },
    "biasT": { "kind": "tensor", "semantic": "bias", "role": "input", "required": false },
    "outputT": { "kind": "tensor", "semantic": "output", "role": "output" },
    "residualT": { "kind": "tensor", "semantic": "input_skip_bias_sum", "role": "output", "required": false }
  },
  "typeConstraints": { "T": ["float32", "float16"] },
  "derive": {
    "rowCount": "dim(shapes.inputT, 0)",
    "hiddenSize": "dim(shapes.inputT, 1)",
    "skipWg": "max(1, min(tunables.MAX_WORKGROUP_SIZE, device.limits.maxComputeInvocationsPerWorkgroup, device.limits.maxComputeWorkgroupSizeX, pow2ceil(hiddenSize)))",
    "skipWgVec4": "max(1, min(tunables.MAX_WORKGROUP_SIZE, device.limits.maxComputeInvocationsPerWorkgroup, device.limits.maxComputeWorkgroupSizeX, pow2ceil(ceilDiv(hiddenSize, 4))))",
    "portableWideExecution": "not has(device.adapterInfo, \"subgroupMinSize\") or device.adapterInfo.subgroupMinSize >= 32",
    "broadcastRows": "dim(shapes.inputT, 0) * dim(shapes.inputT, 1)",
    "broadcastHiddenSize": "dim(shapes.inputT, 2)",
    "broadcastSkipWgVec4": "max(1, min(tunables.MAX_WORKGROUP_SIZE, device.limits.maxComputeInvocationsPerWorkgroup, device.limits.maxComputeWorkgroupSizeX, pow2ceil(ceilDiv(broadcastHiddenSize, 4))))",
    "rowDispatchFits": "rowCount <= device.limits.maxComputeWorkgroupsPerDimension * device.limits.maxComputeWorkgroupsPerDimension",
    "broadcastDispatchFits": "broadcastRows <= device.limits.maxComputeWorkgroupsPerDimension * device.limits.maxComputeWorkgroupsPerDimension",
    "normResourcesFit": "skipWg * 8 <= device.limits.maxComputeWorkgroupStorageSize and skipWgVec4 * 8 <= device.limits.maxComputeWorkgroupStorageSize",
    "broadcastResourcesFit": "broadcastSkipWgVec4 * 8 <= device.limits.maxComputeWorkgroupStorageSize",
    "epsilonOk": "attrs.epsilon >= 0",
    "rank2CoreContract": "epsilonOk and ranks.inputT == 2 and ranks.skipT == 2 and ranks.gammaT == 1 and ranks.outputT == 2 and dim(shapes.inputT, 0) == dim(shapes.skipT, 0) and dim(shapes.inputT, 1) == dim(shapes.skipT, 1) and dim(shapes.inputT, 1) > 0 and dim(shapes.gammaT, 0) == dim(shapes.inputT, 1) and dim(shapes.outputT, 0) == dim(shapes.inputT, 0) and dim(shapes.outputT, 1) == dim(shapes.inputT, 1)",
    "residualOutputContract": "present.residualT and ranks.residualT == 2 and dim(shapes.residualT, 0) == dim(shapes.inputT, 0) and dim(shapes.residualT, 1) == dim(shapes.inputT, 1)",
    "outputOnlyContract": "not present.residualT",
    "betaContract": "false if not present.betaT else (ranks.betaT == 1 and dim(shapes.betaT, 0) == dim(shapes.inputT, 1))",
    "noBetaContract": "not present.betaT",
    "f32MainDtypes": "tensorDtypes.inputT == \"float32\" and tensorDtypes.skipT == \"float32\" and tensorDtypes.gammaT == \"float32\" and tensorDtypes.outputT == \"float32\"",
    "f16MainDtypes": "tensorDtypes.inputT == \"float16\" and tensorDtypes.skipT == \"float16\" and tensorDtypes.gammaT == \"float16\" and tensorDtypes.outputT == \"float16\"",
    "f32ResidualDtypes": "f32MainDtypes and tensorDtypes.residualT == \"float32\" if present.residualT else false",
    "f16ResidualDtypes": "f16MainDtypes and tensorDtypes.residualT == \"float16\" if present.residualT else false",
    "vec4Aligned": "dim(shapes.inputT, 1) % 4 == 0",
    "broadcastSkipShapeOk": "(ranks.skipT == 2 and dim(shapes.skipT, 0) == dim(shapes.inputT, 1) and dim(shapes.skipT, 1) == dim(shapes.inputT, 2)) or (ranks.skipT == 3 and ((dim(shapes.skipT, 0) == 1 and dim(shapes.skipT, 1) == dim(shapes.inputT, 1) and dim(shapes.skipT, 2) == dim(shapes.inputT, 2)) or sameShape(shapes.skipT, shapes.inputT)))",
    "broadcastOutputOnlyContract": "false if ranks.inputT != 3 or not present.betaT else (epsilonOk and not present.biasT and not present.residualT and dim(shapes.inputT, 2) % 4 == 0 and broadcastSkipShapeOk and ranks.gammaT == 1 and ranks.betaT == 1 and ranks.outputT == 3 and tensorDtypes.inputT == \"float32\" and tensorDtypes.skipT == \"float32\" and tensorDtypes.gammaT == \"float32\" and tensorDtypes.betaT == \"float32\" and tensorDtypes.outputT == \"float32\" and dim(shapes.inputT, 2) > 0 and dim(shapes.gammaT, 0) == dim(shapes.inputT, 2) and dim(shapes.betaT, 0) == dim(shapes.inputT, 2) and sameShape(shapes.outputT, shapes.inputT))",
    "hasSubgroups": "device.features.has(\"subgroups\") and device.wgslLanguageFeatures.has(\"subgroup_id\")",
    "hasF16": "device.features.has(\"shader-f16\")",
    "f32_beta_no_bias_residual_contract": "rank2CoreContract and residualOutputContract and betaContract and f32ResidualDtypes and not present.biasT and tensorDtypes.betaT == \"float32\"",
    "f32_beta_bias_residual_contract": "false if not present.biasT else (rank2CoreContract and residualOutputContract and betaContract and f32ResidualDtypes and ranks.biasT == 1 and tensorDtypes.betaT == \"float32\" and tensorDtypes.biasT == \"float32\" and dim(shapes.biasT, 0) == hiddenSize)",
    "f16_beta_bias_residual_contract": "false if not present.biasT else (hasF16 and rank2CoreContract and residualOutputContract and betaContract and f16ResidualDtypes and ranks.biasT == 1 and tensorDtypes.betaT == \"float16\" and tensorDtypes.biasT == \"float16\" and dim(shapes.biasT, 0) == hiddenSize)",
    "f32_no_beta_output_contract": "rank2CoreContract and outputOnlyContract and noBetaContract and f32MainDtypes and not present.biasT",
    "f32_beta_no_bias_output_only_contract": "rank2CoreContract and outputOnlyContract and betaContract and f32MainDtypes and not present.biasT and tensorDtypes.betaT == \"float32\"",
    "f32_beta_bias_output_only_contract": "false if not present.biasT else (rank2CoreContract and outputOnlyContract and betaContract and f32MainDtypes and ranks.biasT == 1 and tensorDtypes.betaT == \"float32\" and tensorDtypes.biasT == \"float32\" and dim(shapes.biasT, 0) == hiddenSize)"
  },
  "bindingSets": {
    "scalar_no_bias_residual": [
      {
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        "arg": "inputT",
        "semantic": "input",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$scalar"
      },
      {
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        "semantic": "skip",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$scalar"
      },
      {
        "name": "gamma",
        "arg": "gammaT",
        "semantic": "gamma",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$scalar",
        "length": "$HIDDEN_LEN"
      },
      {
        "name": "beta",
        "arg": "betaT",
        "semantic": "beta",
        "buffer": { "type": "read-only-storage" },
        "elementType": "$scalar",
        "length": "$HIDDEN_LEN"
      },
      {
        "name": "output",
        "arg": "outputT",
        "semantic": "output",
        "buffer": { "type": "storage" },
        "elementType": "$scalar"
      },
      {
        "name": "input_skip_bias_sum",
        "arg": "residualT",
        "semantic": "input_skip_bias_sum",
        "buffer": { "type": "storage" },
        "elementType": "$scalar"
      },
      {
        "name": "params",
        "semantic": "kernel.params",
        "buffer": { "type": "uniform" },
        "struct": {
          "name": "Params",
          "fields": [
            { "name": "rows", "type": "u32", "value": "rowCount" },
            { "name": "epsilon", "type": "f32", "value": "attrs.epsilon" }
          ]
        }
      }
    ],
    "scalar_bias_output_residual": [
      {
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        "arg": "inputT",
        "semantic": "input",
        "buffer": { "type": "read-only-storage" },
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      {
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        "semantic": "skip",
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        "elementType": "$scalar"
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      {
        "name": "bias",
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      },
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      },
      {
        "name": "input_skip_bias_sum",
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        "semantic": "input_skip_bias_sum",
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        "elementType": "$scalar"
      },
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        "name": "params",
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        "struct": {
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          "fields": [
            { "name": "rows", "type": "u32", "value": "rowCount" },
            { "name": "epsilon", "type": "f32", "value": "attrs.epsilon" }
          ]
        }
      }
    ],
    "vec4_bias_output_residual": [
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        "elementType": "$vectorScalar",
        "length": "$HIDDEN_LEN"
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      {
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            { "name": "rows", "type": "u32", "value": "rowCount" },
            {
              "name": "rowStride",
              "type": "u32",
              "value": "max(1, min(rowCount, device.limits.maxComputeWorkgroupsPerDimension))"
            },
            { "name": "epsilon", "type": "f32", "value": "attrs.epsilon" }
          ]
        }
      }
    ],
    "scalar_no_bias_output_only": [
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        "struct": {
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        "struct": {
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            { "name": "epsilon", "type": "f32", "value": "attrs.epsilon" }
          ]
        }
      }
    ],
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        "elementType": "$scalar"
      },
      {
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        "elementType": "$scalar"
      },
      {
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    ],
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            },
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              "value": "max(1, min(rowCount, device.limits.maxComputeWorkgroupsPerDimension))"
            },
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            },
            { "name": "epsilon", "type": "f32", "value": "attrs.epsilon" }
          ]
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    ],
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              "value": "max(1, min(rowCount, device.limits.maxComputeWorkgroupsPerDimension))"
            },
            { "name": "epsilon", "type": "f32", "value": "attrs.epsilon" }
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      },
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          "name": "Params",
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            { "name": "rows", "type": "u32", "value": "dim(shapes.inputT, 0) * dim(shapes.inputT, 1)" },
            {
              "name": "rowStride",
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              "value": "max(1, min(broadcastRows, device.limits.maxComputeWorkgroupsPerDimension))"
            },
            { "name": "epsilon", "type": "f32", "value": "attrs.epsilon" },
            { "name": "skipRows", "type": "u32", "value": "numel(shapes.skipT) / broadcastHiddenSize" }
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      "when": ["broadcastOutputOnlyContract", "broadcastResourcesFit", "broadcastDispatchFits"],
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        {
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            "shader": "norm-skip-row-vec4.wgsl.jinja",
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              "simplified": false,
              "hasBias": false,
              "hasBeta": true,
              "writeResidualSum": false,
              "usesF16": false,
              "broadcastSkip": true,
              "hidden": "broadcastHiddenSize",
              "hiddenVec": "broadcastHiddenSize / 4",
              "wg": "broadcastSkipWgVec4",
              "vecType": "\"vec4<f32>\"",
              "useSubgroups": "hasSubgroups"
            }
          },
          "subgroupCollectivesWidth": "portable",
          "bindings": "vec4_beta_broadcast_output_only",
          "dispatch": { "workgroups": "broadcastRows" }
        }
      ]
    },
    {
      "id": "beta_bias_vec4",
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      "when": ["f32_beta_bias_residual_contract", "vec4Aligned", "hasSubgroups or \"bias\" == \"bias\" or portableWideExecution", "normResourcesFit", "rowDispatchFits"],
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        "vectorScalar": "\"vec4<f32>\"",
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        "workgroupSize": "skipWg",
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      },
      "passes": [
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          "id": "normalize",
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            "shader": "norm-skip-row-vec4.wgsl.jinja",
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              "hidden": "hiddenSize",
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          },
          "subgroupCollectivesWidth": "portable",
          "bindings": "vec4_bias_output_residual",
          "dispatch": { "workgroups": "rowCount" }
        }
      ]
    },
    {
      "id": "beta_bias_row",
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        "hasBias": "\"bias\" == \"bias\"",
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          "subgroupCollectivesWidth": "portable",
          "bindings": "scalar_bias_output_residual",
          "dispatch": { "workgroups": "rowCount" },
          "constants": { "writeResidualSum": true }
        }
      ]
    },
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      "id": "beta_bias_vec4_f16",
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          "id": "main",
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              "vecType": "\"vec4<f16>\"",
              "useSubgroups": "hasSubgroups"
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          },
          "subgroupCollectivesWidth": "portable",
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          },
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          "bindings": "vec4_no_beta_output_only",
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          "subgroupCollectivesWidth": "portable",
          "bindings": "scalar_no_beta_output_only",
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        }
      ]
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      "id": "beta_no_bias_vec4",
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            }
          },
          "subgroupCollectivesWidth": "portable",
          "bindings": "vec4_no_bias_residual",
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      "id": "beta_no_bias_output_only_vec4",
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  "tunables": { "MAX_WORKGROUP_SIZE": 256 }
}