{
  "domain": "com.microsoft",
  "name": "SkipLayerNormalization",
  "sinceVersion": 1,
  "inputs": {
    "inputT": { "onnx": "input", "dtype": "T" },
    "skipT": { "onnx": "skip", "dtype": "T" },
    "gammaT": { "onnx": "gamma", "dtype": "T", "rank": 1 },
    "betaT": { "onnx": "beta", "dtype": "T", "rank": 1, "optional": true },
    "biasT": { "onnx": "bias", "dtype": "T", "rank": 1, "optional": true }
  },
  "outputs": {
    "outputT": { "onnx": "output", "dtype": "T", "rank": "ranks.inputT", "shape": "shapes.inputT" },
    "meanT": {
      "onnx": "mean",
      "dtype": "U",
      "optional": true,
      "rank": "ranks.inputT",
      "shape": "prefix(shapes.inputT, ranks.inputT - 1) + [1]"
    },
    "invStdT": {
      "onnx": "inv_std_var",
      "dtype": "U",
      "optional": true,
      "rank": "ranks.inputT",
      "shape": "prefix(shapes.inputT, ranks.inputT - 1) + [1]"
    },
    "residualT": {
      "onnx": "input_skip_bias_sum",
      "dtype": "T",
      "rank": "ranks.inputT",
      "optional": true,
      "shape": "shapes.inputT"
    }
  },
  "attributes": { "epsilon": { "default": 9.999999960041972e-13 } },
  "typeConstraints": { "T": ["float32", "float16"], "U": ["float32"] },
  "tunables": { "MAX_WORKGROUP_SIZE": { "default": 256 } },
  "derive": {
    "rowCount": "numel(shapes.inputT) / max(1, dim(shapes.inputT, -1))",
    "hiddenSize": "dim(shapes.inputT, -1)",
    "skipWg": "max(1, min(tunables.MAX_WORKGROUP_SIZE, device.limits.maxComputeInvocationsPerWorkgroup, device.limits.maxComputeWorkgroupSizeX)) if hiddenSize == 1 else (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))))",
    "rowDispatchFits": "rowCount <= min(device.limits.maxComputeWorkgroupsPerDimension, 65535) * min(device.limits.maxComputeWorkgroupsPerDimension, 65535)",
    "normResourcesFit": "skipWg * 8 <= device.limits.maxComputeWorkgroupStorageSize and skipWgVec4 * 8 <= device.limits.maxComputeWorkgroupStorageSize",
    "epsilonOk": "attrs.epsilon >= 0",
    "coreContract": "epsilonOk and (ranks.inputT == 2 or ranks.inputT == 3) and ranks.skipT == ranks.inputT and ranks.gammaT == 1 and ranks.outputT == ranks.inputT and sameShape(shapes.inputT, shapes.skipT) and sameShape(shapes.outputT, shapes.inputT) and dim(shapes.inputT, -1) > 0 and dim(shapes.gammaT, 0) == dim(shapes.inputT, -1)",
    "residualOutputContract": "present.residualT and sameShape(shapes.residualT, shapes.inputT)",
    "outputOnlyContract": "not present.residualT",
    "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",
    "hasSubgroups": "device.features.has(\"subgroups\") and device.wgslLanguageFeatures.has(\"subgroup_id\")",
    "hasF16": "device.features.has(\"shader-f16\")",
    "statsRequested": "present.meanT or present.invStdT",
    "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))))",
    "broadcastDispatchFits": "broadcastRows <= min(device.limits.maxComputeWorkgroupsPerDimension, 65535) * min(device.limits.maxComputeWorkgroupsPerDimension, 65535)",
    "broadcastResourcesFit": "broadcastSkipWgVec4 * 8 <= device.limits.maxComputeWorkgroupStorageSize",
    "betaContract": "false if not present.betaT else (ranks.betaT == 1 and dim(shapes.betaT, 0) == dim(shapes.inputT, -1))",
    "noBetaContract": "not present.betaT",
    "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))",
    "f32_beta_no_bias_residual_contract": "coreContract 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 (coreContract 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 coreContract 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": "coreContract and outputOnlyContract and noBetaContract and f32MainDtypes and not present.biasT",
    "f32_beta_no_bias_output_only_contract": "coreContract 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 (coreContract and outputOnlyContract and betaContract and f32MainDtypes and ranks.biasT == 1 and tensorDtypes.betaT == \"float32\" and tensorDtypes.biasT == \"float32\" and dim(shapes.biasT, 0) == hiddenSize)",
    "f16_no_beta_output_contract": "hasF16 and coreContract and outputOnlyContract and noBetaContract and f16MainDtypes and not present.biasT",
    "f16_beta_no_bias_output_only_contract": "hasF16 and coreContract and outputOnlyContract and betaContract and f16MainDtypes and not present.biasT and tensorDtypes.betaT == \"float16\"",
    "f16_beta_bias_output_only_contract": "false if not present.biasT else (hasF16 and coreContract and outputOnlyContract and betaContract and f16MainDtypes and ranks.biasT == 1 and tensorDtypes.betaT == \"float16\" and tensorDtypes.biasT == \"float16\" and dim(shapes.biasT, 0) == hiddenSize)",
    "statsContract": "statsRequested and coreContract and f16Ok(dtypes.T) and (not present.biasT or (ranks.biasT == 1 and dim(shapes.biasT, 0) == hiddenSize)) and (not present.residualT or sameShape(shapes.residualT, shapes.inputT)) and (not present.betaT or (ranks.betaT == 1 and dim(shapes.betaT, 0) == hiddenSize))"
  },
  "bindings": {
    "input": { "arg": "inputT", "elementType": "$vectorScalar" },
    "skip": { "arg": "skipT", "elementType": "$vectorScalar" },
    "gamma": { "arg": "gammaT", "elementType": "$vectorScalar", "length": "$HIDDEN_LEN" },
    "beta": { "arg": "betaT", "elementType": "$vectorScalar", "length": "$HIDDEN_LEN" },
    "output": { "arg": "outputT", "elementType": "$vectorScalar" },
    "bias": { "arg": "biasT", "elementType": "$vectorScalar", "length": "$HIDDEN_LEN" },
    "input_skip_bias_sum": { "arg": "residualT", "elementType": "$vectorScalar" },
    "params_main": {
      "name": "params",
      "struct": [
        { "name": "rows", "type": "u32", "value": "rowCount" },
        {
          "name": "rowStride",
          "type": "u32",
          "value": "max(1, min(rowCount, min(device.limits.maxComputeWorkgroupsPerDimension, 65535)))"
        },
        { "name": "epsilon", "type": "f32", "value": "attrs.epsilon" }
      ]
    },
    "input_main": { "arg": "inputT", "name": "input", "elementType": "$scalar" },
    "skip_main": { "arg": "skipT", "name": "skip", "elementType": "$scalar" },
    "bias_main": { "arg": "biasT", "name": "bias", "elementType": "$scalar", "length": "$HIDDEN_LEN" },
    "gamma_main": { "arg": "gammaT", "name": "gamma", "elementType": "$scalar", "length": "$HIDDEN_LEN" },
    "beta_main": { "arg": "betaT", "name": "beta", "elementType": "$scalar", "length": "$HIDDEN_LEN" },
    "output_main": { "arg": "outputT", "name": "output", "elementType": "$scalar" },
    "input_skip_bias_sum_main": { "arg": "residualT", "name": "input_skip_bias_sum", "elementType": "$scalar" },
    "params__uniform": {
      "name": "params",
      "struct": [
        { "name": "rows", "type": "u32", "value": "rowCount" },
        { "name": "epsilon", "type": "f32", "value": "attrs.epsilon" }
      ]
    },
    "mean": { "arg": "meanT", "elementType": "f32" },
    "inv_std_var": { "arg": "invStdT", "elementType": "f32" },
    "row_stats": { "scratch": "rowStats", "elementType": "vec2<f32>" },
    "row_stats_read": {
      "scratch": "rowStats",
      "name": "row_stats",
      "buffer": "read-only-storage",
      "elementType": "vec2<f32>"
    },
    "params_stats": { "name": "params", "struct": [{ "name": "rows", "type": "u32", "value": "rowCount" }] }
  },
  "variants": [
    {
      "id": "hidden1_f32_no_beta",
      "priority": 20,
      "when": ["f32_no_beta_output_contract", "hiddenSize == 1", "normResourcesFit", "rowDispatchFits", "not present.meanT and not present.invStdT"],
      "derive": {
        "simplified": false,
        "hasBias": false,
        "hasBeta": "present.betaT",
        "writeResidualSum": false,
        "useSubgroups": false,
        "scalar": "dtypes.T",
        "workgroupSize": "skipWg",
        "HIDDEN_LEN": "hiddenSize"
      },
      "passes": [
        {
          "id": "main",
          "name": "SkipLayerNormalization.Hidden1",
          "shader": "norm-skip-row.wgsl.jinja",
          "bindings": ["gamma_main", "output_main", "params__uniform"],
          "dispatch": {
            "x": "min(ceilDiv((rowCount), (skipWg)), 65535)",
            "y": "ceilDiv(ceilDiv((rowCount), (skipWg)), 65535)",
            "z": 1
          }
        }
      ]
    },
    {
      "id": "hidden1_f32_beta",
      "priority": 20,
      "when": ["(f32_beta_no_bias_output_only_contract or f32_beta_bias_output_only_contract)", "hiddenSize == 1", "normResourcesFit", "rowDispatchFits", "not present.meanT and not present.invStdT"],
      "derive": {
        "simplified": false,
        "hasBias": false,
        "hasBeta": "present.betaT",
        "writeResidualSum": false,
        "useSubgroups": false,
        "scalar": "dtypes.T",
        "workgroupSize": "skipWg",
        "HIDDEN_LEN": "hiddenSize"
      },
      "passes": [
        {
          "id": "main",
          "name": "SkipLayerNormalization.Hidden1",
          "shader": "norm-skip-row.wgsl.jinja",
          "bindings": ["gamma_main", "beta_main", "output_main", "params__uniform"],
          "dispatch": {
            "x": "min(ceilDiv((rowCount), (skipWg)), 65535)",
            "y": "ceilDiv(ceilDiv((rowCount), (skipWg)), 65535)",
            "z": 1
          }
        }
      ]
    },
    {
      "id": "hidden1_f16_no_beta",
      "priority": 20,
      "when": ["f16_no_beta_output_contract", "hiddenSize == 1", "normResourcesFit", "rowDispatchFits", "not present.meanT and not present.invStdT"],
      "derive": {
        "simplified": false,
        "hasBias": false,
        "hasBeta": "present.betaT",
        "writeResidualSum": false,
        "useSubgroups": false,
        "scalar": "dtypes.T",
        "workgroupSize": "skipWg",
        "HIDDEN_LEN": "hiddenSize"
      },
      "passes": [
        {
          "id": "main",
          "name": "SkipLayerNormalization.Hidden1",
          "shader": "norm-skip-row.wgsl.jinja",
          "bindings": ["gamma_main", "output_main", "params__uniform"],
          "dispatch": {
            "x": "min(ceilDiv((rowCount), (skipWg)), 65535)",
            "y": "ceilDiv(ceilDiv((rowCount), (skipWg)), 65535)",
            "z": 1
          }
        }
      ]
    },
    {
      "id": "hidden1_f16_beta",
      "priority": 20,
      "when": ["(f16_beta_no_bias_output_only_contract or f16_beta_bias_output_only_contract)", "hiddenSize == 1", "normResourcesFit", "rowDispatchFits", "not present.meanT and not present.invStdT"],
      "derive": {
        "simplified": false,
        "hasBias": false,
        "hasBeta": "present.betaT",
        "writeResidualSum": false,
        "useSubgroups": false,
        "scalar": "dtypes.T",
        "workgroupSize": "skipWg",
        "HIDDEN_LEN": "hiddenSize"
      },
      "passes": [
        {
          "id": "main",
          "name": "SkipLayerNormalization.Hidden1",
          "shader": "norm-skip-row.wgsl.jinja",
          "bindings": ["gamma_main", "beta_main", "output_main", "params__uniform"],
          "dispatch": {
            "x": "min(ceilDiv((rowCount), (skipWg)), 65535)",
            "y": "ceilDiv(ceilDiv((rowCount), (skipWg)), 65535)",
            "z": 1
          }
        }
      ]
    },
    {
      "id": "beta_output_only_vec4_broadcast",
      "priority": 19,
      "when": ["broadcastOutputOnlyContract", "broadcastResourcesFit", "broadcastDispatchFits", "not present.meanT and not present.invStdT"],
      "derive": { "vectorScalar": "\"vec4<f32>\"", "HIDDEN_LEN": "broadcastHiddenSize / 4" },
      "passes": [
        {
          "id": "main",
          "name": "SkipLayerNormalization.BroadcastSkip",
          "shader": "norm-skip-row-vec4.wgsl.jinja",
          "derive": {
            "simplified": false,
            "hasBias": false,
            "hasBeta": true,
            "writeResidualSum": false,
            "broadcastSkip": true,
            "hidden": "broadcastHiddenSize",
            "hiddenVec": "broadcastHiddenSize / 4",
            "wg": "broadcastSkipWgVec4",
            "vecType": "\"vec4<f32>\"",
            "useSubgroups": "hasSubgroups"
          },
          "bindings": [
            "input",
            "skip",
            "gamma",
            "beta",
            "output",
            {
              "name": "params",
              "struct": [
                { "name": "rows", "type": "u32", "value": "dim(shapes.inputT, 0) * dim(shapes.inputT, 1)" },
                {
                  "name": "rowStride",
                  "type": "u32",
                  "value": "max(1, min(broadcastRows, min(device.limits.maxComputeWorkgroupsPerDimension, 65535)))"
                },
                { "name": "epsilon", "type": "f32", "value": "attrs.epsilon" },
                { "name": "skipRows", "type": "u32", "value": "numel(shapes.skipT) / broadcastHiddenSize" }
              ]
            }
          ],
          "dispatch": { "x": "min(broadcastRows, 65535)", "y": "ceilDiv(broadcastRows, 65535)", "z": 1 }
        }
      ]
    },
    {
      "id": "beta_bias_vec4",
      "priority": 15,
      "when": ["f32_beta_bias_residual_contract", "vec4Aligned", "hasSubgroups or \"bias\" == \"bias\" or portableWideExecution", "normResourcesFit", "rowDispatchFits", "not present.meanT and not present.invStdT"],
      "derive": { "vectorScalar": "\"vec4<f32>\"", "hasBias": "\"bias\" == \"bias\"", "HIDDEN_LEN": "hiddenSize / 4" },
      "passes": [
        {
          "id": "normalize",
          "name": "SkipLayerNormalization.Vec4.Normalize",
          "shader": "norm-skip-row-vec4.wgsl.jinja",
          "derive": {
            "simplified": false,
            "hasBeta": true,
            "writeResidualSum": true,
            "hidden": "hiddenSize",
            "hiddenVec": "hiddenSize / 4",
            "wg": "skipWgVec4",
            "vecType": "\"vec4<f32>\"",
            "useSubgroups": "hasSubgroups"
          },
          "bindings": ["input", "skip", "bias", "gamma", "beta", "output", "input_skip_bias_sum", "params_main"],
          "dispatch": { "x": "min(rowCount, 65535)", "y": "ceilDiv(rowCount, 65535)", "z": 1 }
        }
      ]
    },
    {
      "id": "beta_bias_row",
      "priority": 5,
      "when": ["f32_beta_bias_residual_contract", "normResourcesFit", "rowDispatchFits", "not present.meanT and not present.invStdT"],
      "derive": {
        "simplified": false,
        "useSubgroups": "hasSubgroups",
        "hasBeta": true,
        "writeResidualSum": true,
        "hasBias": "\"bias\" == \"bias\"",
        "scalar": "\"f32\"",
        "workgroupSize": "skipWg",
        "HIDDEN_LEN": "hiddenSize"
      },
      "passes": [
        {
          "id": "normalize",
          "name": "SkipLayerNormalization.Row.Normalize",
          "shader": "norm-skip-row.wgsl.jinja",
          "derive": { "writeResidualSum": true },
          "bindings": ["input_main", "skip_main", "bias_main", "gamma_main", "beta_main", "output_main", "input_skip_bias_sum_main", "params__uniform"],
          "dispatch": {
            "x": "min(ceilDiv((rowCount * (1 if hiddenSize == 1 else skipWg)), (skipWg)), 65535)",
            "y": "ceilDiv(ceilDiv((rowCount * (1 if hiddenSize == 1 else skipWg)), (skipWg)), 65535)",
            "z": 1
          }
        }
      ]
    },
    {
      "id": "beta_no_bias_vec4",
      "priority": 20,
      "when": ["f32_beta_no_bias_residual_contract", "vec4Aligned", "hasSubgroups or portableWideExecution", "normResourcesFit", "rowDispatchFits", "not present.meanT and not present.invStdT"],
      "derive": {
        "vectorScalar": "\"vec4<f32>\"",
        "hasBias": "\"no_bias\" == \"bias\"",
        "HIDDEN_LEN": "hiddenSize / 4"
      },
      "passes": [
        {
          "id": "main",
          "name": "SkipLayerNormalization.Vec4",
          "shader": "norm-skip-row-vec4.wgsl.jinja",
          "derive": {
            "simplified": false,
            "hasBeta": true,
            "writeResidualSum": true,
            "hidden": "hiddenSize",
            "hiddenVec": "hiddenSize / 4",
            "wg": "skipWgVec4",
            "vecType": "\"vec4<f32>\"",
            "useSubgroups": "hasSubgroups"
          },
          "bindings": ["input", "skip", "gamma", "beta", "output", "input_skip_bias_sum", "params_main"],
          "dispatch": { "x": "min(rowCount, 65535)", "y": "ceilDiv(rowCount, 65535)", "z": 1 }
        }
      ]
    },
    {
      "id": "beta_no_bias_row",
      "priority": 10,
      "when": ["f32_beta_no_bias_residual_contract", "normResourcesFit", "rowDispatchFits", "not present.meanT and not present.invStdT"],
      "derive": {
        "simplified": false,
        "useSubgroups": "hasSubgroups",
        "hasBeta": true,
        "writeResidualSum": true,
        "hasBias": "\"no_bias\" == \"bias\"",
        "scalar": "\"f32\"",
        "workgroupSize": "skipWg",
        "HIDDEN_LEN": "hiddenSize"
      },
      "passes": [
        {
          "id": "main",
          "name": "SkipLayerNormalization.Row",
          "shader": "norm-skip-row.wgsl.jinja",
          "bindings": ["input_main", "skip_main", "gamma_main", "beta_main", "output_main", "input_skip_bias_sum_main", "params__uniform"],
          "dispatch": {
            "x": "min(ceilDiv((rowCount * (1 if hiddenSize == 1 else skipWg)), (skipWg)), 65535)",
            "y": "ceilDiv(ceilDiv((rowCount * (1 if hiddenSize == 1 else skipWg)), (skipWg)), 65535)",
            "z": 1
          }
        }
      ]
    },
    {
      "id": "beta_bias_vec4_f16",
      "priority": 21,
      "when": ["f16_beta_bias_residual_contract", "vec4Aligned", "normResourcesFit", "rowDispatchFits", "not present.meanT and not present.invStdT"],
      "derive": { "vectorScalar": "\"vec4<f16>\"", "HIDDEN_LEN": "hiddenSize / 4" },
      "passes": [
        {
          "id": "main",
          "name": "SkipLayerNormalization.Vec4",
          "shader": "norm-skip-row-vec4.wgsl.jinja",
          "derive": {
            "simplified": false,
            "hasBias": true,
            "hasBeta": true,
            "writeResidualSum": true,
            "hidden": "hiddenSize",
            "hiddenVec": "hiddenSize / 4",
            "wg": "skipWgVec4",
            "vecType": "\"vec4<f16>\"",
            "useSubgroups": "hasSubgroups"
          },
          "bindings": ["input", "skip", "bias", "gamma", "beta", "output", "input_skip_bias_sum", "params_main"],
          "dispatch": { "x": "min(rowCount, 65535)", "y": "ceilDiv(rowCount, 65535)", "z": 1 }
        }
      ]
    },
    {
      "id": "no_beta_output_only_vec4",
      "priority": 20,
      "when": ["f32_no_beta_output_contract", "vec4Aligned", "normResourcesFit", "rowDispatchFits", "not present.meanT and not present.invStdT"],
      "derive": { "vectorScalar": "\"vec4<f32>\"", "HIDDEN_LEN": "hiddenSize / 4" },
      "passes": [
        {
          "id": "main",
          "name": "SkipLayerNormalization.NoBetaOutputOnly.Vec4",
          "shader": "norm-skip-row-vec4.wgsl.jinja",
          "derive": {
            "simplified": false,
            "hasBias": "present.biasT",
            "hasBeta": "present.betaT",
            "writeResidualSum": false,
            "hidden": "hiddenSize",
            "hiddenVec": "hiddenSize / 4",
            "wg": "skipWgVec4",
            "vecType": "\"vec4<f32>\"",
            "useSubgroups": "hasSubgroups"
          },
          "bindings": ["input", "skip", "gamma", "output", "params_main"],
          "dispatch": { "x": "min(rowCount, 65535)", "y": "ceilDiv(rowCount, 65535)", "z": 1 }
        }
      ]
    },
    {
      "id": "no_beta_output_only_row",
      "priority": 10,
      "when": ["f32_no_beta_output_contract", "normResourcesFit", "rowDispatchFits", "hiddenSize != 1", "not present.meanT and not present.invStdT"],
      "derive": {
        "simplified": false,
        "hasBias": "present.biasT",
        "hasBeta": "present.betaT",
        "writeResidualSum": false,
        "useSubgroups": "hasSubgroups",
        "scalar": "\"f32\"",
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