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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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| "bench": { |
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| "metrics": [ |
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| "bench": { |
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| "metrics": [ |
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| "value": "2 * args.batch * args.seq * (args.qHeads + 2 * args.kvHeads) * args.dk * args.dv" |
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| "keyT": { "shape": [1, 32, 32], "dtype": "float32", "dist": "normal", "seed": 209, "scale": 0.2 }, |
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| "presentStateT": { "shape": [1, 2, 16, 16], "dtype": "float32" } |
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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." |
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| "vars": { "batch": 4, "seq": 1536, "qHeads": 4, "kvHeads": 2, "dk": 16, "dv": 16 }, |
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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 } |
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| "outputs": { |
| "outputT": { "shape": [4, 1536, 64], "dtype": "float16" }, |
| "presentStateT": { "shape": [4, 2, 16, 16], "dtype": "float16" } |
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| "bench": { |
| "primary": true, |
| "metrics": [ |
| { "type": "gflops", "value": "2 * args.batch * args.seq * (args.qHeads + args.kvHeads) * args.dk * args.dv" } |
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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 }, |
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| "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 }, |
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| "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": { |
| "outputT": { "shape": [8, 1536, 48], "dtype": "float32" }, |
| "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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| "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": { |
| "queryT": { "shape": [4, 1536, 64], "dtype": "float16", "dist": "normal", "seed": 311, "scale": 0.2 }, |
| "keyT": { "shape": [4, 1536, 32], "dtype": "float16", "dist": "normal", "seed": 312, "scale": 0.2 }, |
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| "bench": { |
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| "metrics": [ |
| { "type": "gflops", "value": "2 * args.batch * args.seq * (args.qHeads + args.kvHeads) * args.dk * args.dv" } |
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| }, |
| "provenance": { |
| "source": "synthetic benchmark", |
| "notes": "A long-sequence scalar recurrence uses the zero-state branch with no entry state." |
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| { |
| "name": "linear-attention-gated-delta-f32-bonsai-m16-h48-kv16-dk128-dv128", |
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| "vars": { "batch": 1, "seq": 16, "qHeads": 48, "kvHeads": 16, "dk": 128, "dv": 128 }, |
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| "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 }, |
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| "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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| "outputs": { |
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| "presentStateT": { "shape": [1, 16, 128, 128], "dtype": "float32", "dist": "empty" } |
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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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| { |
| "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 }, |
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| "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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| "outputT": { "shape": [1, 16, 6144], "dtype": "float16", "dist": "empty" }, |
| "presentStateT": { "shape": [1, 16, 128, 128], "dtype": "float16", "dist": "empty" } |
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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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| ] |
| } |
| }, |
| { |
| "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 }, |
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| "shape": [1, 1, 2048], |
| "dtype": "float32", |
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| "signed": false, |
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| "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" } |
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| "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 }, |
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| "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 |
| } |
| }, |
| "outputs": { |
| "outputT": { "shape": [1, 512, 4096], "dtype": "float32" }, |
| "presentStateT": { "shape": [1, 16, 128, 128], "dtype": "float32" } |
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| "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." |
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| "vars": { "batch": 1, "seq": 2048, "qHeads": 32, "kvHeads": 16, "dk": 128, "dv": 128 }, |
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| "q_num_heads": 32, |
| "kv_num_heads": 16, |
| "update_rule": "gated_delta", |
| "scale": 0.08838834764831843, |
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| "inputs": { |
| "queryT": { "shape": [1, 2048, 4096], "dtype": "float32", "dist": "normal", "seed": 8300, "scale": 0.3 }, |
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| "presentStateT": { "shape": [1, 16, 128, 128], "dtype": "float32" } |
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| "bench": { |
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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." |
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| "attrs": { "q_num_heads": 4, "kv_num_heads": 2, "update_rule": "linear", "state_window": 3, "scale": 0.375 }, |
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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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| }, |
| { |
| "name": "bench-linear_geometry_float32_float32_g0_state", |
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| "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." |
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| "dtype": "float32", |
| "shape": [2, 33, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.31, "scale": 0.05 } |
| }, |
| "valueT": { |
| "dtype": "float32", |
| "shape": [2, 33, 14], |
| "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.31, "scale": 0.05 } |
| }, |
| "pastStateT": { |
| "dtype": "float16", |
| "shape": [3, 2, 2, 8, 7], |
| "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.31, "scale": 0.05 } |
| } |
| }, |
| "outputs": { |
| "outputT": { "dtype": "float32", "shape": [2, 33, 28] }, |
| "presentStateT": { "dtype": "float16", "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))" |
| } |
| ] |
| } |
| }, |
| { |
| "name": "bench-linear_geometry_float32_float16_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 }, |
| "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 } |
| } |
| }, |
| "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))" |
| } |
| ] |
| } |
| }, |
| { |
| "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))" |
| } |
| ] |
| } |
| } |
| ] |
| } |
|
|
