| { |
| "op": "ai.onnx.NonZero", |
| "tunableSpace": {}, |
| "cases": [ |
| { |
| "name": "f32_1024_capacity", |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1024], |
| "data": { "kind": "fillFloat32", "sinStep": 0.01, "cosStep": 0.02 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "uint32", "shape": [1, 1024] } } |
| }, |
| { |
| "name": "nonzero-rank2-u32-1m", |
| "preset": "smoke", |
| "inputs": { "x": { "dtype": "uint32", "shape": [1024, 1024], "dist": "linearMod", "mod": 4 } }, |
| "outputs": { "y": { "dtype": "uint32", "shape": [2, 786432] } }, |
| "bench": { |
| "primary": true, |
| "metrics": [ |
| { |
| "type": "bandwidth", |
| "value": "4 * numel(shapes.x) + 4 * numel(shapes.y) + 4 * (4 * ceilDiv(numel(shapes.x), 4) + 5 * ceilDiv(numel(shapes.x), 1024))" |
| } |
| ] |
| } |
| }, |
| { |
| "name": "nonzero-exact-capacity-1024-f32", |
| "preset": "smoke", |
| "provenance": { |
| "notes": "Legacy case name retained for longitudinal comparisons. At the measured crossover, 1024 elements now select the parallel scan. The traffic metric uses algorithm-independent logical I/O so an all-variant comparison weighs serial and parallel kernels fairly." |
| }, |
| "inputs": { "x": { "dtype": "float32", "shape": [1024], "dist": "constant", "value": 1 } }, |
| "outputs": { "y": { "dtype": "uint32", "shape": [1, 1024] } }, |
| "bench": { |
| "metrics": [ |
| { |
| "type": "bandwidth", |
| "value": "4 * numel(shapes.x) + 4 * numel(shapes.y) + 4 * (4 * ceilDiv(numel(shapes.x), 4) + 5 * ceilDiv(numel(shapes.x), 1024))" |
| } |
| ] |
| } |
| }, |
| { |
| "name": "nonzero-parallel-1280-f32-healthy", |
| "preset": "smoke", |
| "inputs": { "x": { "dtype": "float32", "shape": [1280], "dist": "constant", "value": 1 } }, |
| "outputs": { "y": { "dtype": "uint32", "shape": [1, 1280] } }, |
| "bench": { |
| "metrics": [ |
| { |
| "type": "bandwidth", |
| "value": "4 * numel(shapes.x) + 4 * numel(shapes.y) + 4 * (4 * ceilDiv(numel(shapes.x), 4) + 5 * ceilDiv(numel(shapes.x), 1024))" |
| } |
| ] |
| } |
| }, |
| { |
| "name": "nonzero-rank2-f32-1m-dense-100pct", |
| "preset": "smoke", |
| "inputs": { "x": { "dtype": "float32", "shape": [1024, 1024], "dist": "constant", "value": 1 } }, |
| "outputs": { "y": { "dtype": "uint32", "shape": [2, 1048576] } }, |
| "bench": { |
| "metrics": [ |
| { |
| "type": "bandwidth", |
| "value": "4 * numel(shapes.x) + 4 * numel(shapes.y) + 4 * (4 * ceilDiv(numel(shapes.x), 4) + 5 * ceilDiv(numel(shapes.x), 1024))" |
| } |
| ] |
| } |
| }, |
| { |
| "name": "nonzero-rank2-f16-1m-dense", |
| "preset": "smoke", |
| "inputs": { "x": { "dtype": "float16", "shape": [1024, 1024], "dist": "constant", "value": 1 } }, |
| "outputs": { "y": { "dtype": "uint32", "shape": [2, 1048576] } }, |
| "bench": { |
| "metrics": [ |
| { |
| "type": "bandwidth", |
| "value": "2 * numel(shapes.x) + 4 * numel(shapes.y) + 4 * (4 * ceilDiv(numel(shapes.x), 4) + 5 * ceilDiv(numel(shapes.x), 1024))" |
| } |
| ] |
| } |
| }, |
| { |
| "name": "nonzero-dispatch-cliff-16m-f32-sparse", |
| "preset": "smoke", |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [4096, 4096], |
| "data": { |
| "kind": "cycle", |
| "values": [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] |
| } |
| } |
| }, |
| "outputs": { "y": { "dtype": "uint32", "shape": [2, 1048576] } }, |
| "bench": { |
| "metrics": [ |
| { |
| "type": "bandwidth", |
| "value": "4 * numel(shapes.x) + 4 * numel(shapes.y) + 4 * (4 * ceilDiv(numel(shapes.x), 4) + 5 * ceilDiv(numel(shapes.x), 1024))" |
| } |
| ] |
| } |
| }, |
| { |
| "name": "nonzero-2dfold-boundary-16m-int32-exact", |
| "preset": "stress", |
| "inputs": { "x": { "dtype": "int32", "shape": [4096, 4097], "dist": "normal", "seed": 7, "scale": 2 } }, |
| "outputs": { "y": { "dtype": "uint32", "shape": [2, 16781312] } }, |
| "bench": { |
| "metrics": [ |
| { |
| "type": "bandwidth", |
| "value": "4 * numel(shapes.x) + 4 * numel(shapes.y) + 4 * (4 * ceilDiv(numel(shapes.x), 4) + 5 * ceilDiv(numel(shapes.x), 1024))" |
| } |
| ] |
| } |
| }, |
| { |
| "name": "nonzero-rank4-u32-1x32x512x512-exact-activation", |
| "preset": "stress", |
| "inputs": { "x": { "dtype": "uint32", "shape": [1, 32, 512, 512], "dist": "linearMod", "mod": 16 } }, |
| "outputs": { "y": { "dtype": "uint32", "shape": [4, 7864320] } }, |
| "bench": { |
| "metrics": [ |
| { |
| "type": "bandwidth", |
| "value": "4 * numel(shapes.x) + 4 * numel(shapes.y) + 4 * (4 * ceilDiv(numel(shapes.x), 4) + 5 * ceilDiv(numel(shapes.x), 1024))" |
| } |
| ] |
| } |
| }, |
| { |
| "name": "nonzero-rank3-u32-32x512x512-exact-activation", |
| "preset": "stress", |
| "provenance": { |
| "notes": "Rank-3 twin of the rank-4 activation case, same element count and density. The scatter writes one coordinate stream per rank, so it exists to place the items-per-thread threshold on measured rank-2/3/4 behaviour instead of extrapolating from two points." |
| }, |
| "inputs": { "x": { "dtype": "uint32", "shape": [32, 512, 512], "dist": "linearMod", "mod": 16 } }, |
| "outputs": { "y": { "dtype": "uint32", "shape": [3, 7864320] } }, |
| "bench": { |
| "metrics": [ |
| { |
| "type": "bandwidth", |
| "value": "4 * numel(shapes.x) + 4 * numel(shapes.y) + 4 * (4 * ceilDiv(numel(shapes.x), 4) + 5 * ceilDiv(numel(shapes.x), 1024))" |
| } |
| ] |
| } |
| } |
| ] |
| } |
|
|
