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  "tunableSpace": {
    "dvGroups": [2, 4, 8],
    "tileV": [4, 8, 16],
    "gatedTileV": [2, 4, 8],
    "chunkSize": [16, 32],
    "chunkTileV": [16, 32]
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        "keyT": { "shape": [1, 32, 32], "dtype": "float32", "dist": "normal", "seed": 202, "scale": 0.2 },
        "valueT": { "shape": [1, 32, 32], "dtype": "float32", "dist": "normal", "seed": 203, "scale": 0.2 }
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        "presentStateT": { "shape": [1, 2, 16, 16], "dtype": "float32" }
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        "metrics": [
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        "keyT": { "shape": [1, 32, 32], "dtype": "float32", "dist": "normal", "seed": 203, "scale": 0.2 },
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        "betaT": { "shape": [1, 32, 2], "dtype": "float32", "dist": "normal", "seed": 207, "scale": 0.1 }
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      "bench": {
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        "metrics": [
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      "vars": { "batch": 1, "seq": 32, "qHeads": 4, "kvHeads": 2, "dk": 16, "dv": 16 },
      "attrs": { "q_num_heads": 4, "kv_num_heads": 2, "update_rule": "linear", "scale": 0.25 },
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        "presentStateT": { "shape": [1, 2, 16, 16], "dtype": "float32" }
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      "bench": {
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        ]
      }
    },
    {
      "name": "linear-attention-linear-state-scalar-f16-seq1536-pathology",
      "preset": "stress",
      "provenance": {
        "source": "synthetic benchmark",
        "notes": "Long-sequence supplied-state case at head_dim_k 16. It exercises the recurrent small-dk route's serial token recurrence and distinguishes it from the chunked prefill decomposition."
      },
      "vars": { "batch": 4, "seq": 1536, "qHeads": 4, "kvHeads": 2, "dk": 16, "dv": 16 },
      "attrs": { "q_num_heads": 4, "kv_num_heads": 2, "update_rule": "linear", "scale": 0.25 },
      "inputs": {
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        "keyT": { "shape": [4, 1536, 32], "dtype": "float16", "dist": "normal", "seed": 315, "scale": 0.2 },
        "valueT": { "shape": [4, 1536, 32], "dtype": "float16", "dist": "normal", "seed": 316, "scale": 0.2 },
        "pastStateT": { "shape": [4, 2, 16, 16], "dtype": "float16", "dist": "normal", "seed": 317, "scale": 0.1 }
      },
      "outputs": {
        "outputT": { "shape": [4, 1536, 64], "dtype": "float16" },
        "presentStateT": { "shape": [4, 2, 16, 16], "dtype": "float16" }
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      "bench": {
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        "metrics": [
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    {
      "name": "linear-attention-gated_delta-scalar-headdimk6-seq1536-stress",
      "preset": "stress",
      "vars": { "batch": 8, "seq": 1536, "qHeads": 4, "kvHeads": 4, "dk": 6, "dv": 12 },
      "attrs": { "q_num_heads": 4, "kv_num_heads": 4, "update_rule": "gated_delta", "scale": 0.25 },
      "inputs": {
        "queryT": { "shape": [8, 1536, 24], "dtype": "float32", "dist": "normal", "seed": 301, "scale": 0.2 },
        "keyT": { "shape": [8, 1536, 24], "dtype": "float32", "dist": "normal", "seed": 302, "scale": 0.2 },
        "valueT": { "shape": [8, 1536, 48], "dtype": "float32", "dist": "normal", "seed": 303, "scale": 0.2 },
        "pastStateT": { "shape": [8, 4, 6, 12], "dtype": "float32", "dist": "normal", "seed": 304, "scale": 0.1 },
        "decayT": { "shape": [8, 1536, 4], "dtype": "float32", "dist": "normal", "seed": 305, "scale": 0.1 },
        "betaT": { "shape": [8, 1536, 4], "dtype": "float32", "dist": "normal", "seed": 306, "scale": 0.1 }
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      "outputs": {
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        "presentStateT": { "shape": [8, 4, 6, 12], "dtype": "float32" }
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      "bench": {
        "primary": true,
        "metrics": [
          {
            "type": "gflops",
            "value": "2 * args.batch * args.seq * (args.qHeads + 2 * args.kvHeads) * args.dk * args.dv"
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        ]
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    },
    {
      "name": "linear-attention-linear-scalar-f16-seq1536-stress",
      "preset": "stress",
      "vars": { "batch": 4, "seq": 1536, "qHeads": 4, "kvHeads": 2, "dk": 16, "dv": 16 },
      "attrs": { "q_num_heads": 4, "kv_num_heads": 2, "update_rule": "linear", "scale": 0.25 },
      "inputs": {
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        "keyT": { "shape": [4, 1536, 32], "dtype": "float16", "dist": "normal", "seed": 312, "scale": 0.2 },
        "valueT": { "shape": [4, 1536, 32], "dtype": "float16", "dist": "normal", "seed": 313, "scale": 0.2 }
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      "outputs": {
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        "presentStateT": { "shape": [4, 2, 16, 16], "dtype": "float16" }
      },
      "bench": {
        "primary": true,
        "metrics": [
          { "type": "gflops", "value": "2 * args.batch * args.seq * (args.qHeads + args.kvHeads) * args.dk * args.dv" }
        ]
      },
      "provenance": {
        "source": "synthetic benchmark",
        "notes": "A long-sequence scalar recurrence uses the zero-state branch with no entry state."
      }
    },
    {
      "name": "linear-attention-gated-delta-f32-bonsai-m16-h48-kv16-dk128-dv128",
      "preset": "stress",
      "vars": { "batch": 1, "seq": 16, "qHeads": 48, "kvHeads": 16, "dk": 128, "dv": 128 },
      "attrs": { "q_num_heads": 48, "kv_num_heads": 16, "update_rule": "gated_delta", "scale": 0.08838834764831845 },
      "inputs": {
        "queryT": { "shape": [1, 16, 6144], "dtype": "float32", "dist": "normal", "seed": 320, "scale": 0.05 },
        "keyT": { "shape": [1, 16, 2048], "dtype": "float32", "dist": "normal", "seed": 321, "scale": 0.05 },
        "valueT": { "shape": [1, 16, 2048], "dtype": "float32", "dist": "normal", "seed": 322, "scale": 0.05 },
        "pastStateT": { "shape": [1, 16, 128, 128], "dtype": "float32", "dist": "normal", "seed": 323, "scale": 0.02 },
        "decayT": { "shape": [1, 16, 16], "dtype": "float32", "dist": "normal", "seed": 324, "scale": 0.08 },
        "betaT": { "shape": [1, 16, 16], "dtype": "float32", "dist": "normal", "seed": 325, "scale": 0.08 }
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        "presentStateT": { "shape": [1, 16, 128, 128], "dtype": "float32", "dist": "empty" }
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        "metrics": [
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    {
      "name": "linear-attention-gated-delta-f16-bonsai-m16-h48-kv16-dk128-dv128",
      "preset": "stress",
      "vars": { "batch": 1, "seq": 16, "qHeads": 48, "kvHeads": 16, "dk": 128, "dv": 128 },
      "attrs": { "q_num_heads": 48, "kv_num_heads": 16, "update_rule": "gated_delta", "scale": 0.08838834764831845 },
      "inputs": {
        "queryT": { "shape": [1, 16, 6144], "dtype": "float16", "dist": "normal", "seed": 326, "scale": 0.05 },
        "keyT": { "shape": [1, 16, 2048], "dtype": "float16", "dist": "normal", "seed": 327, "scale": 0.05 },
        "valueT": { "shape": [1, 16, 2048], "dtype": "float16", "dist": "normal", "seed": 328, "scale": 0.05 },
        "pastStateT": { "shape": [1, 16, 128, 128], "dtype": "float16", "dist": "normal", "seed": 329, "scale": 0.02 },
        "decayT": { "shape": [1, 16, 16], "dtype": "float16", "dist": "normal", "seed": 330, "scale": 0.08 },
        "betaT": { "shape": [1, 16, 16], "dtype": "float16", "dist": "normal", "seed": 331, "scale": 0.08 }
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      "outputs": {
        "outputT": { "shape": [1, 16, 6144], "dtype": "float16", "dist": "empty" },
        "presentStateT": { "shape": [1, 16, 128, 128], "dtype": "float16", "dist": "empty" }
      },
      "bench": {
        "primary": true,
        "metrics": [
          {
            "type": "gflops",
            "value": "2 * args.batch * args.seq * (args.qHeads + 2 * args.kvHeads) * args.dk * args.dv"
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        ]
      }
    },
    {
      "name": "linear-attention-qwen3next-decode-s1",
      "preset": "model",
      "provenance": {
        "notes": "Qwen3-Next class defaults (linear_num_value_heads 32, linear_num_key_heads 16, linear_key_head_dim 128, linear_value_head_dim 128) at a decode step, where the recurrence carries the whole cost. Float uniforms use scale/offset: log-decay spans log(0.9) to 0, beta spans 0.1 to 0.9, and keys are bounded by 1/sqrt(dk) so squared key norms do not exceed one."
      },
      "vars": { "batch": 1, "seq": 1, "qHeads": 32, "kvHeads": 16, "dk": 128, "dv": 128 },
      "attrs": {
        "q_num_heads": 32,
        "kv_num_heads": 16,
        "update_rule": "gated_delta",
        "scale": 0.08838834764831843,
        "chunk_size": 64
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      "inputs": {
        "queryT": { "shape": [1, 1, 4096], "dtype": "float32", "dist": "normal", "seed": 8100, "scale": 0.3 },
        "keyT": {
          "shape": [1, 1, 2048],
          "dtype": "float32",
          "dist": "uniform",
          "seed": 8101,
          "scale": 0.08838834764831843
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        "valueT": { "shape": [1, 1, 2048], "dtype": "float32", "dist": "normal", "seed": 8102, "scale": 0.3 },
        "pastStateT": { "shape": [1, 16, 128, 128], "dtype": "float32", "dist": "normal", "seed": 8103, "scale": 0.1 },
        "decayT": {
          "shape": [1, 1, 2048],
          "dtype": "float32",
          "dist": "uniform",
          "seed": 8104,
          "signed": false,
          "offset": -0.10536051565782628,
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        "betaT": {
          "shape": [1, 1, 16],
          "dtype": "float32",
          "dist": "uniform",
          "seed": 8105,
          "signed": false,
          "offset": 0.1,
          "scale": 0.8
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      },
      "outputs": {
        "outputT": { "shape": [1, 1, 4096], "dtype": "float32" },
        "presentStateT": { "shape": [1, 16, 128, 128], "dtype": "float32" }
      },
      "bench": {
        "metrics": [
          {
            "type": "gflops",
            "value": "2 * args.batch * args.seq * (args.qHeads + 2 * args.kvHeads) * args.dk * args.dv"
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      }
    },
    {
      "name": "linear-attention-qwen3next-prefill-s512",
      "preset": "model",
      "provenance": {
        "notes": "Qwen3-Next class defaults over a 512-token prefill chunk. Float uniforms use scale/offset: log-decay spans log(0.9) to 0, beta spans 0.1 to 0.9, and keys are bounded by 1/sqrt(dk) so squared key norms do not exceed one."
      },
      "vars": { "batch": 1, "seq": 512, "qHeads": 32, "kvHeads": 16, "dk": 128, "dv": 128 },
      "attrs": {
        "q_num_heads": 32,
        "kv_num_heads": 16,
        "update_rule": "gated_delta",
        "scale": 0.08838834764831843,
        "chunk_size": 64
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      "inputs": {
        "queryT": { "shape": [1, 512, 4096], "dtype": "float32", "dist": "normal", "seed": 8200, "scale": 0.3 },
        "keyT": {
          "shape": [1, 512, 2048],
          "dtype": "float32",
          "dist": "uniform",
          "seed": 8201,
          "scale": 0.08838834764831843
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        "valueT": { "shape": [1, 512, 2048], "dtype": "float32", "dist": "normal", "seed": 8202, "scale": 0.3 },
        "pastStateT": { "shape": [1, 16, 128, 128], "dtype": "float32", "dist": "normal", "seed": 8203, "scale": 0.1 },
        "decayT": {
          "shape": [1, 512, 2048],
          "dtype": "float32",
          "dist": "uniform",
          "seed": 8204,
          "signed": false,
          "offset": -0.10536051565782628,
          "scale": 0.10536051565782628
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        "betaT": {
          "shape": [1, 512, 16],
          "dtype": "float32",
          "dist": "uniform",
          "seed": 8205,
          "signed": false,
          "offset": 0.1,
          "scale": 0.8
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      },
      "outputs": {
        "outputT": { "shape": [1, 512, 4096], "dtype": "float32" },
        "presentStateT": { "shape": [1, 16, 128, 128], "dtype": "float32" }
      },
      "bench": {
        "metrics": [
          {
            "type": "gflops",
            "value": "2 * args.batch * args.seq * (args.qHeads + 2 * args.kvHeads) * args.dk * args.dv"
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        ]
      }
    },
    {
      "name": "linear-attention-qwen3next-prefill-s2048",
      "preset": "model",
      "provenance": {
        "notes": "Qwen3-Next class defaults over a 2048-token prefill chunk, eight chunk_size 64 blocks per workgroup pass. Float uniforms use scale/offset: log-decay spans log(0.9) to 0, beta spans 0.1 to 0.9, and keys are bounded by 1/sqrt(dk) so squared key norms do not exceed one."
      },
      "vars": { "batch": 1, "seq": 2048, "qHeads": 32, "kvHeads": 16, "dk": 128, "dv": 128 },
      "attrs": {
        "q_num_heads": 32,
        "kv_num_heads": 16,
        "update_rule": "gated_delta",
        "scale": 0.08838834764831843,
        "chunk_size": 64
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      "inputs": {
        "queryT": { "shape": [1, 2048, 4096], "dtype": "float32", "dist": "normal", "seed": 8300, "scale": 0.3 },
        "keyT": {
          "shape": [1, 2048, 2048],
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          "dist": "uniform",
          "seed": 8301,
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        "valueT": { "shape": [1, 2048, 2048], "dtype": "float32", "dist": "normal", "seed": 8302, "scale": 0.3 },
        "pastStateT": { "shape": [1, 16, 128, 128], "dtype": "float32", "dist": "normal", "seed": 8303, "scale": 0.1 },
        "decayT": {
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          "dtype": "float32",
          "dist": "uniform",
          "seed": 8304,
          "signed": false,
          "offset": -0.10536051565782628,
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        "betaT": {
          "shape": [1, 2048, 16],
          "dtype": "float32",
          "dist": "uniform",
          "seed": 8305,
          "signed": false,
          "offset": 0.1,
          "scale": 0.8
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      "outputs": {
        "outputT": { "shape": [1, 2048, 4096], "dtype": "float32" },
        "presentStateT": { "shape": [1, 16, 128, 128], "dtype": "float32" }
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      "bench": {
        "metrics": [
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            "value": "2 * args.batch * args.seq * (args.qHeads + 2 * args.kvHeads) * args.dk * args.dv"
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    },
    {
      "name": "bench-linear_geometry_float32_float32_g0_zero",
      "provenance": {
        "notes": "Small-head linear recurrence with shared keys, standard or inverse query grouping, odd value dimensions, independent state dtype, and retained or empty-sequence state windows."
      },
      "attrs": { "q_num_heads": 4, "kv_num_heads": 2, "update_rule": "linear", "state_window": 3, "scale": 0.375 },
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        "keyT": {
          "dtype": "float32",
          "shape": [2, 33, 8],
          "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.31, "scale": 0.05 }
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        "valueT": {
          "dtype": "float32",
          "shape": [2, 33, 14],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.31, "scale": 0.05 }
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      },
      "outputs": {
        "outputT": { "dtype": "float32", "shape": [2, 33, 28] },
        "presentStateT": { "dtype": "float32", "shape": [3, 2, 2, 8, 7] }
      },
      "preset": "stress",
      "bench": {
        "primary": true,
        "metrics": [
          {
            "type": "gflops",
            "value": "dim(shapes.queryT, 0) * dim(shapes.queryT, 1) * (dim(shapes.valueT, 2) / attrs.kv_num_heads) * (2 * (dim(shapes.queryT, 2) / attrs.q_num_heads) * (attrs.kv_num_heads + max(attrs.q_num_heads, attrs.kv_num_heads)) + max(attrs.q_num_heads, attrs.kv_num_heads))"
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        ]
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    },
    {
      "name": "bench-linear_geometry_float32_float32_g0_state",
      "provenance": {
        "notes": "Small-head linear recurrence with shared keys, standard or inverse query grouping, odd value dimensions, independent state dtype, and retained or empty-sequence state windows."
      },
      "attrs": { "q_num_heads": 4, "kv_num_heads": 2, "update_rule": "linear", "state_window": 3, "scale": 0.375 },
      "inputs": {
        "queryT": {
          "dtype": "float32",
          "shape": [2, 33, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.31, "scale": 0.05 }
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          "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.31, "scale": 0.05 }
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        "valueT": {
          "dtype": "float32",
          "shape": [2, 33, 14],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.31, "scale": 0.05 }
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        "pastStateT": {
          "dtype": "float32",
          "shape": [3, 2, 2, 8, 7],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.31, "scale": 0.05 }
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        "presentStateT": { "dtype": "float32", "shape": [3, 2, 2, 8, 7] }
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      "preset": "stress",
      "bench": {
        "primary": true,
        "metrics": [
          {
            "type": "gflops",
            "value": "dim(shapes.queryT, 0) * dim(shapes.queryT, 1) * (dim(shapes.valueT, 2) / attrs.kv_num_heads) * (2 * (dim(shapes.queryT, 2) / attrs.q_num_heads) * (attrs.kv_num_heads + max(attrs.q_num_heads, attrs.kv_num_heads)) + max(attrs.q_num_heads, attrs.kv_num_heads))"
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        ]
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    },
    {
      "name": "bench-linear_geometry_float32_float32_g1_zero",
      "provenance": {
        "notes": "Small-head linear recurrence with shared keys, standard or inverse query grouping, odd value dimensions, independent state dtype, and retained or empty-sequence state windows."
      },
      "attrs": { "q_num_heads": 2, "kv_num_heads": 4, "update_rule": "linear", "state_window": 0, "scale": 0 },
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      "bench": {
        "primary": true,
        "metrics": [
          {
            "type": "gflops",
            "value": "dim(shapes.queryT, 0) * dim(shapes.queryT, 1) * (dim(shapes.valueT, 2) / attrs.kv_num_heads) * (2 * (dim(shapes.queryT, 2) / attrs.q_num_heads) * (attrs.kv_num_heads + max(attrs.q_num_heads, attrs.kv_num_heads)) + max(attrs.q_num_heads, attrs.kv_num_heads))"
          }
        ]
      }
    },
    {
      "name": "bench-linear_geometry_float32_float16_g1_state",
      "provenance": {
        "notes": "Small-head linear recurrence with shared keys, standard or inverse query grouping, odd value dimensions, independent state dtype, and retained or empty-sequence state windows."
      },
      "attrs": { "q_num_heads": 2, "kv_num_heads": 4, "update_rule": "linear", "state_window": 0, "scale": 0 },
      "inputs": {
        "queryT": {
          "dtype": "float32",
          "shape": [1, 17, 12],
          "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.31, "scale": 0.05 }
        },
        "keyT": {
          "dtype": "float32",
          "shape": [1, 17, 12],
          "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.31, "scale": 0.05 }
        },
        "valueT": {
          "dtype": "float32",
          "shape": [1, 17, 20],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.31, "scale": 0.05 }
        },
        "pastStateT": {
          "dtype": "float16",
          "shape": [1, 4, 6, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.31, "scale": 0.05 }
        }
      },
      "outputs": {
        "outputT": { "dtype": "float32", "shape": [1, 17, 20] },
        "presentStateT": { "dtype": "float16", "shape": [1, 4, 6, 5] }
      },
      "preset": "stress",
      "bench": {
        "primary": true,
        "metrics": [
          {
            "type": "gflops",
            "value": "dim(shapes.queryT, 0) * dim(shapes.queryT, 1) * (dim(shapes.valueT, 2) / attrs.kv_num_heads) * (2 * (dim(shapes.queryT, 2) / attrs.q_num_heads) * (attrs.kv_num_heads + max(attrs.q_num_heads, attrs.kv_num_heads)) + max(attrs.q_num_heads, attrs.kv_num_heads))"
          }
        ]
      }
    }
  ]
}