| { |
| "fixtureArrays": { |
| "ort_seed_123_input_x": [1.0856307, 0.99734545, 0.2829785, 1.5062947, 0.5786002, 1.6514366, 2.4266791, 0.42891264, 1.2659363, 0.8667404, 0.6788862, 0.09470897, 1.4913896, 0.638902, 0.44398195, 0.43435127, 2.20593, 2.1867862, 1.004054, 0.3861864, 0.7373686, 1.4907321, 0.9358339, 1.175829, 1.2538806, 0.6377515, 0.9071052, 1.4286807, 0.14006872, 0.8617549, 0.25561938, 2.798589, 1.7715331, 0.69987726, 0.92746246, 0.17363568, 0.002845916, 0.6882227, 0.87953633, 0.28362733, 0.8053665, 1.7276695, 0.3908998, 0.57380587, 0.33858904, 0.011830495, 2.3923652, 0.41291216, 0.978736, 2.2381434, 1.2940853, 1.0387882, 1.7437122, 0.79806274, 0.02968323, 1.0693159, 0.8907064, 1.7548862, 1.4956441, 1.0693927], |
| "onnx_backend_softmax_input_x": [1.764052391052246, 0.40015721321105957, 0.978738009929657, 2.2408931255340576, 1.8675580024719238, 0.9772778749465942, 0.9500884413719177, 0.15135720372200012, 0.10321885347366333, 0.4105985164642334, 0.14404356479644775, 1.4542734622955322, 0.7610377073287964, 0.12167501449584961, 0.44386324286460876, 0.3336743414402008, 1.4940791130065918, 0.2051582634449005, 0.3130677044391632, 0.8540957570075989, 2.5529897212982178, 0.653618574142456, 0.8644362092018127, 0.7421650290489197, 2.269754648208618, 1.4543657302856445, 0.04575851559638977, 0.18718385696411133, 1.5327792167663574, 1.4693588018417358, 0.154947429895401, 0.37816253304481506, 0.8877857327461243, 1.980796456336975, 0.34791216254234314, 0.15634897351264954, 1.2302906513214111, 1.202379822731018, 0.38732680678367615, 0.302302747964859, 1.0485529899597168, 1.420017957687378, 1.7062702178955078, 1.950775384902954, 0.5096521973609924, 0.4380742907524109, 1.2527953386306763, 0.7774903774261475, 1.6138978004455566, 0.21274028718471527, 0.8954665660858154, 0.38690251111984253, 0.5108051300048828, 1.18063223361969, 0.02818222902715206, 0.4283318817615509, 0.06651721894741058, 0.30247190594673157, 0.6343221068382263, 0.3627411723136902] |
| }, |
| "cases": [ |
| { |
| "name": "dispatch_cliff_online_2dfold_65540x40", |
| "attrs": { "axis": -1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [65540, 40], |
| "data": { "kind": "fillFloat32", "sinStep": 0.011, "cosStep": 0.007, "scale": 2.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [65540, 40], "tolerance": 0.0001, "relTolerance": 0.0001 } } |
| }, |
| { |
| "name": "wide_rows_257x65535", |
| "attrs": { "axis": -1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [257, 65535], |
| "data": { "kind": "fillFloat32", "sinStep": 0.011, "cosStep": 0.007, "scale": 2.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [257, 65535], "tolerance": 1e-8, "relTolerance": 0.0001 } }, |
| "provenance": { |
| "notes": "A 257-row softmax over 65,535 columns; the tolerance, scaled to the roughly 1/65,535 outputs, exposes a dropped column or an incorrectly rescaled denominator." |
| } |
| }, |
| { |
| "name": "axis1_3x5", |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [3, 5], |
| "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.17 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3, 5], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_simple_axis1", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/softmax_test.cc", |
| "test": "SoftmaxOperator.Simple" |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 3], "data": { "kind": "values", "values": [-1.0, 0.0, 1.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_large_number_axis1", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/softmax_test.cc", |
| "test": "SoftmaxOperator.LargeNumber" |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [0.0, 1.0, 2.0, 3.0, 10000.0, 10001.0, 10002.0, 10003.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "f32_large_gap_subnormal_tail_gpu_gap", |
| "skipGpu": { |
| "category": "permanent", |
| "reason": "Portable WGSL floating-point semantics do not guarantee preservation of the subnormal values required by this fixture. Backend evidence: WebGPU/Metal flushes subnormals to zero in f32; bit-exact subnormal preservation is unattainable on GPU." |
| }, |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/softmax_test.cc", |
| "test": "SoftmaxOperator.LargeNumber", |
| "notes": "An 87.5-point logit gap leaves a valid positive subnormal probability tail in ONNX Runtime's CPU provider; stable softmax should not flush it to zero." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { "x": { "dtype": "float32", "shape": [1, 2], "data": { "kind": "values", "values": [0.0, -87.5] } } }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 2], |
| "tolerance": 2e-45, |
| "data": { "kind": "values", "values": [1.0, 9.982351397596697e-39] } |
| } |
| } |
| }, |
| { |
| "name": "max_negative_padding_regression", |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 5], |
| "data": { |
| "kind": "values", |
| "values": [-3.4028234663852886e+38, -3.4028234663852886e+38, -3.4028234663852886e+38, -3.4028234663852886e+38, -3.4028234663852886e+38, -1000.0, -1001.0, -1002.0, -1003.0, -1004.0] |
| } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 5], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "large_positive_stability", |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [1000.0, 1001.0, 999.0, -1000.0, 80.0, 80.0, 79.0, 78.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "all_negative_infinity_returns_nan", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/softmax_test.cc", |
| "test": "SoftmaxOperator.webgpu_nan" |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { "x": { "dtype": "float32", "shape": [2, 4], "data": { "kind": "negativeInfinity" } } }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "tolerance": 0, |
| "allowNaN": true, |
| "data": { "kind": "values", "values": ["NaN", "NaN", "NaN", "NaN", "NaN", "NaN", "NaN", "NaN"] } |
| } |
| } |
| }, |
| { |
| "name": "f16_all_negative_infinity_returns_nan", |
| "attrs": { "axis": 1 }, |
| "inputs": { "x": { "dtype": "float16", "shape": [1, 4], "data": { "kind": "negativeInfinity" } } }, |
| "outputs": { |
| "y": { |
| "dtype": "float16", |
| "shape": [1, 4], |
| "tolerance": 0, |
| "allowNaN": true, |
| "data": { "kind": "values", "values": ["NaN", "NaN", "NaN", "NaN"] } |
| } |
| } |
| }, |
| { |
| "name": "positive_infinity_rows_return_nan", |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": ["Infinity", 1.0, 2.0, -3.0, 4.0, 1.0, 5.0, 2.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 4], "tolerance": 0.000001, "allowNaN": true } } |
| }, |
| { |
| "name": "singleton_axis_mixed_finite_nonfinite", |
| "attrs": { "axis": 0 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 4], |
| "data": { "kind": "values", "values": [0.25, "Infinity", "-Infinity", "NaN"] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 4], "tolerance": 0, "allowNaN": true } } |
| }, |
| { |
| "name": "axis1_2x64_cross_subgroup", |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 64], |
| "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.07, "scale": 2.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 64], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "longrow_split_axis1_1x65536", |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 65536], |
| "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.031, "scale": 3.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 65536], "tolerance": 0.00001, "relTolerance": 0.00001 } } |
| }, |
| { |
| "name": "longrow_split_all_negative_infinity", |
| "attrs": { "axis": 1 }, |
| "inputs": { "x": { "dtype": "float32", "shape": [1, 65536], "data": { "kind": "negativeInfinity" } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 65536], "tolerance": 0, "allowNaN": true } } |
| }, |
| { |
| "name": "ort_dim_with_zero_axis0", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/softmax_test.cc", |
| "test": "SoftmaxOperator.DimWithZero", |
| "notes": "Shape [1,0] has an empty reduced axis, so Softmax produces an empty output without reading or writing elements." |
| }, |
| "attrs": { "axis": 0 }, |
| "inputs": { "x": { "dtype": "float32", "shape": [1, 0], "data": { "kind": "values", "values": [] } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 0], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "empty_last_axis_dim_zero", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/softmax_test.cc", |
| "test": "SoftmaxOperator.DimWithZero", |
| "notes": "A zero-length last axis produces an empty output, and dispatch setup must not divide by the reduced extent." |
| }, |
| "attrs": { "axis": -1 }, |
| "inputs": { "x": { "dtype": "float32", "shape": [1, 0], "data": { "kind": "values", "values": [] } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 0], "tolerance": 0 } } |
| }, |
| { |
| "name": "empty_axis0_dim_zero", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/softmax_test.cc", |
| "test": "SoftmaxOperator.DimWithZero", |
| "notes": "A zero-length leading reduction axis produces an empty output without dividing by the reduced extent." |
| }, |
| "attrs": { "axis": 0 }, |
| "inputs": { "x": { "dtype": "float32", "shape": [0, 1], "data": { "kind": "values", "values": [] } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [0, 1], "tolerance": 0 } } |
| }, |
| { |
| "name": "axis_minus_one_rank3", |
| "attrs": { "axis": -1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.21 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 3, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "stable_3pass_float32_min_uniform_regression", |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 64], "data": { "kind": "constant", "value": -3.4028234663852886e+38 } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 64], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "axis0_non_last_rank2", |
| "attrs": { "axis": 0 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [3, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 2.0, 1.0, 4.0, 3.0, 3.0, 4.0, 1.0, 2.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank6_last_axis", |
| "attrs": { "axis": -1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 2, 1, 2, 1, 3], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, -1.0, 0.0, 1.0, 4.0, 4.0, 5.0, -2.0, -3.0, -4.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2, 1, 2, 1, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank3_axis1_middle_strided", |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 3, 4], |
| "data": { |
| "kind": "values", |
| "values": [1.0, 2.0, 3.0, 4.0, 2.0, 3.0, 4.0, 5.0, 3.0, 4.0, 5.0, 6.0, -1.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 4.0] |
| } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 3, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_rank3_axis0_seed123", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/softmax_test.cc", |
| "test": "SoftmaxOperator.ThreeAndFourDimsAxis0", |
| "notes": "Uses a seeded rank-3 input to validate opset-13 axis-0 semantics." |
| }, |
| "attrs": { "axis": 0 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [3, 4, 5], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/ort_seed_123_input_x" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3, 4, 5], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_rank4_axis0_seed123", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/softmax_test.cc", |
| "test": "SoftmaxOperator.ThreeAndFourDimsAxis0", |
| "notes": "Uses a seeded rank-4 input to validate opset-13 axis-0 semantics." |
| }, |
| "attrs": { "axis": 0 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 4, 5], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/ort_seed_123_input_x" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 3, 4, 5], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_opset13_rank3_axis1_seed123", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/softmax_test.cc", |
| "test": "SoftmaxOperator.ThreeAndFourDimsSecondLastAxis_opset13" |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [3, 4, 5], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/ort_seed_123_input_x" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3, 4, 5], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_opset13_rank4_axis2_seed123", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/softmax_test.cc", |
| "test": "SoftmaxOperator.ThreeAndFourDimsSecondLastAxis_opset13" |
| }, |
| "attrs": { "axis": 2 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 4, 5], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/ort_seed_123_input_x" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 3, 4, 5], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank3_axis_minus2_middle_strided_large_values", |
| "attrs": { "axis": -2 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 3, 2], |
| "data": { |
| "kind": "values", |
| "values": [1000.0, -1000.0, 1001.0, -1001.0, 999.0, -999.0, -50.0, 50.0, -51.0, 49.0, -52.0, 48.0] |
| } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 3, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "f16_last_axis_large_ties", |
| "attrs": { "axis": -1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [10.0, 10.0, 9.0, -10.0, -12.0, -12.0, -13.0, -14.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 4], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "ort_simple_fp16_axis1", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/softmax_test.cc", |
| "test": "SoftmaxOperator.Simple_fp16" |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 3], "data": { "kind": "values", "values": [-1.0, 0.0, 1.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 3], "tolerance": 0.001 } } |
| }, |
| { |
| "name": "ort_opset13_rank3_axis2_seed123", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/softmax_test.cc", |
| "test": "SoftmaxOperator.ThreeAndFourDimsLastAxis_opset13" |
| }, |
| "attrs": { "axis": 2 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [3, 4, 5], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/ort_seed_123_input_x" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3, 4, 5], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_opset13_rank4_axis3_seed123", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/softmax_test.cc", |
| "test": "SoftmaxOperator.ThreeAndFourDimsLastAxis_opset13" |
| }, |
| "attrs": { "axis": 3 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 4, 5], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/ort_seed_123_input_x" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 3, 4, 5], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_opset13_rank3_default_axis_seed123", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/softmax_test.cc", |
| "test": "SoftmaxOperator.ThreeAndFourDimsDefaultAxis_opset13" |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [3, 4, 5], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/ort_seed_123_input_x" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3, 4, 5], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_opset13_rank4_default_axis_seed123", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/softmax_test.cc", |
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| "x": { |
| "dtype": "float32", |
| "shape": [65537, 4], |
| "data": { "kind": "cycle", "values": [0.1, 0.5, 0.9, 0.3, 0.7] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [65537, 4], "tolerance": 0.0001 } } |
| }, |
| { |
| "name": "dispatch_cliff_strided_rows_140000", |
| "provenance": { |
| "notes": "A strided Softmax over 140,000 outer rows uses a three-row workgroup dispatch. Distinct row values verify folded row indexing and the final over-dispatch bound." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [70000, 4, 2], |
| "data": { "kind": "cycle", "values": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [70000, 4, 2], "tolerance": 0.0001 } } |
| }, |
| { |
| "name": "rank7_last_axis", |
| "provenance": { "notes": "A rank-7 tensor exercises last-axis Softmax with high-rank row decomposition." }, |
| "attrs": { "axis": -1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 1, 1, 1, 1, 1, 3], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, -1.0, 0.0, 1.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 1, 1, 1, 1, 1, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank7_axis1_strided", |
| "provenance": { |
| "notes": "Exercises strided Softmax over axis 1 of a rank-7 tensor, with expectations from ONNX Runtime's CPU provider." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 3, 1, 1, 1, 1, 1], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, -1.0, 0.0, 1.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 3, 1, 1, 1, 1, 1], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "online_wg_vec4_f16_2x8192", |
| "provenance": { |
| "notes": "A float16 tensor with two 8,192-element rows exercises vec4<f16> loads and stores, float16 code generation, and cross-subgroup combination on the online workgroup route. A relative tolerance scaled to the approximately 1/8,192 outputs makes denominator rescaling, missing partials, or duplicate lanes observable." |
| }, |
| "attrs": { "axis": -1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [2, 8192], |
| "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.029, "scale": 2.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 8192], "tolerance": 0.000002, "relTolerance": 0.002 } } |
| }, |
| { |
| "name": "strided_online_lane_tail_f32_8x1024x130_axis1", |
| "provenance": { |
| "notes": "A large contiguous inner dimension exercises one-lane strided Softmax; 1,040 rows leave a partial final workgroup." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [8, 1024, 130], |
| "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.029, "scale": 2.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [8, 1024, 130], "tolerance": 0.00002, "relTolerance": 0.00002 } } |
| }, |
| { |
| "name": "strided_online_lane_tail_f16_8x1024x130_axis1", |
| "provenance": { |
| "notes": "Float16 input exercises one-lane strided accumulation and output conversion with a partial final workgroup. The tolerance is scaled to the roughly 1/130 outputs so denominator rescaling or a dropped lane is observable." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [8, 1024, 130], |
| "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.029, "scale": 2.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [8, 1024, 130], "tolerance": 0.00001, "relTolerance": 0.003 } } |
| }, |
| { |
| "name": "strided_3pass_f16_rank3_axis1", |
| "provenance": { |
| "notes": "A rank-3 float16 tensor of shape [4,8,16] exercises the max, exponential-sum, and normalization passes along strided axis 1." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [4, 8, 16], |
| "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.07, "scale": 2.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [4, 8, 16], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "strided_positive_infinity_rows_return_nan_axis1", |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 3, 4], |
| "data": { |
| "kind": "values", |
| "values": ["Infinity", 1.0, 2.0, -3.0, 3.0, 0.5, -1.0, 2.0, 7.0, 4.0, 5.0, 6.0, 0.0, 1.0, 2.0, 3.0, -2.0, -1.0, 0.0, 1.0, 4.0, 5.0, 6.0, 7.0] |
| } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 3, 4], "tolerance": 0.000001, "allowNaN": true } } |
| }, |
| { |
| "name": "strided_all_negative_infinity_nan_row_axis1", |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 2, 3], |
| "data": { |
| "kind": "values", |
| "values": ["-Infinity", "-Infinity", "-Infinity", "-Infinity", "-Infinity", "-Infinity", 1.0, 2.0, 3.0, 4.0, 5.0, 6.0] |
| } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [2, 2, 3], |
| "tolerance": 0.000001, |
| "allowNaN": true, |
| "data": { |
| "kind": "values", |
| "values": ["NaN", "NaN", "NaN", "NaN", "NaN", "NaN", 0.04742587317756678, 0.04742587317756678, 0.04742587317756678, 0.9525741268224334, 0.9525741268224334, 0.9525741268224334] |
| } |
| } |
| } |
| }, |
| { |
| "name": "strided_vec4_empty_reduce_axis_dim_zero_axis1", |
| "attrs": { "axis": 1 }, |
| "inputs": { "x": { "dtype": "float32", "shape": [2, 0, 4], "data": { "kind": "values", "values": [] } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 0, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "strided_inner_dim_zero_axis1_scalar", |
| "attrs": { "axis": 1 }, |
| "inputs": { "x": { "dtype": "float32", "shape": [3, 4, 0], "data": { "kind": "values", "values": [] } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3, 4, 0], "tolerance": 0 } } |
| }, |
| { |
| "name": "strided_mixed_neg_inf_and_finite_axis1", |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 4, 2], |
| "data": { |
| "kind": "values", |
| "values": ["-Infinity", "-Infinity", 0.5, -2.5, "-Infinity", 0.0, 1.25, "-Infinity"] |
| } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 4, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "packed_rows_tail_axis1_33x32", |
| "provenance": { |
| "notes": "33 rows of 32 columns leave one row past a multiple of 32; that final row must normalize independently of the others." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [33, 32], |
| "data": { "kind": "fillFloat32", "sinStep": 0.037, "cosStep": 0.019, "scale": 2.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [33, 32], "tolerance": 0.000001, "relTolerance": 0.000001 } } |
| }, |
| { |
| "name": "many_short_rows_axis1_64x32", |
| "provenance": { |
| "notes": "Sixty-four 32-column rows exercise packed short-row Softmax at a correctness-test scale." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [64, 32], |
| "data": { "kind": "fillFloat32", "sinStep": 0.037, "cosStep": 0.019, "scale": 2.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [64, 32], "tolerance": 0.000001, "relTolerance": 0.000001 } } |
| }, |
| { |
| "name": "many_rows_axis1_2048x32", |
| "provenance": { |
| "notes": "Two thousand forty-eight rows of width 32 exercise packed short-row Softmax at sustained density." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2048, 32], |
| "data": { "kind": "fillFloat32", "sinStep": 0.037, "cosStep": 0.019, "scale": 2.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2048, 32], "tolerance": 0.000001, "relTolerance": 0.000001 } } |
| }, |
| { |
| "name": "strided_scalar4_tail_f32_rank3_axis1_inner6", |
| "provenance": { |
| "notes": "Four adjacent strided rows with a two-element inner tail check normalized output independently per row." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 17, 6], |
| "data": { "kind": "fillFloat32", "sinStep": 0.071, "cosStep": 0.043, "scale": 2.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 17, 6], "tolerance": 0.00001, "relTolerance": 0.00001 } } |
| }, |
| { |
| "name": "strided_scalar4_tail_f16_rank3_axis1_inner6", |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [2, 17, 6], |
| "data": { "kind": "fillFloat32", "sinStep": 0.071, "cosStep": 0.043, "scale": 2.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 17, 6], "tolerance": 0.003, "relTolerance": 0.003 } } |
| }, |
| { |
| "name": "strided_empty_inner_f16_plain_3pass", |
| "provenance": { |
| "notes": "A float16 tensor with a zero-length inner dimension exercises the plain three-pass strided implementation, since scalar-four processing requires a nonempty inner dimension and vectorized processing requires float32." |
| }, |
| "attrs": { "axis": 0 }, |
| "inputs": { "x": { "dtype": "float16", "shape": [2, 0], "data": { "kind": "values", "values": [] } } }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 0], "tolerance": 0 } } |
| }, |
| { |
| "name": "strided_rowmax_scratch_over_128mib_f16_capacity_fallback", |
| "provenance": { |
| "notes": "With 33,554,433 strided rows, an f32 row-maximum scratch buffer would exceed 128 MiB while the float16 input remains about 64 MiB. The case therefore exercises single-pass online normalization under the minimum storage-binding limit." |
| }, |
| "attrs": { "axis": 0 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 33554433], |
| "data": { "kind": "cycle", "values": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 33554433], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "rank8_last_axis", |
| "attrs": { "axis": -1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 1, 1, 1, 1, 1, 2, 3], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, -1.0, 0.0, 1.0, 0.5, -0.5, 2.5, 4.0, 1.0, -2.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 1, 1, 1, 1, 1, 2, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "packed_rows_64x8_f16", |
| "provenance": { |
| "notes": "A float16 tensor with 64 rows of width 8 exercises the packed-rows online route with aligned vec4 storage." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [64, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.037, "cosStep": 0.019, "scale": 2.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [64, 8], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "subgroup_rows_f16_96x512", |
| "attrs": { "axis": -1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [96, 512], |
| "data": { "kind": "fillFloat32", "sinStep": 0.21, "cosStep": 0.09, "scale": 2.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [96, 512], "tolerance": 0.0001, "relTolerance": 0.02 } } |
| }, |
| { |
| "name": "subgroup_rows_f32_64x1024", |
| "attrs": { "axis": -1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [64, 1024], |
| "data": { "kind": "fillFloat32", "sinStep": 0.013, "cosStep": 0.029, "scale": 3.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [64, 1024], "tolerance": 0.0001, "relTolerance": 0.0001 } } |
| }, |
| { |
| "name": "subgroup_rows_f32_rank3_4x20x256_neg_inf_padding", |
| "attrs": { "axis": -1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [4, 20, 256], |
| "data": { |
| "kind": "cycle", |
| "values": [1.0, "-Infinity", 2.0, 0.5, "-Infinity", -1.0, 3.0, 0.25, "-Infinity", 1.5, -2.0] |
| } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [4, 20, 256], "tolerance": 0.0001, "relTolerance": 0.0001 } } |
| }, |
| { |
| "name": "subgroup_rows_f32_65x64_tail_row", |
| "attrs": { "axis": -1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [65, 64], |
| "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.11, "scale": 4.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [65, 64], "tolerance": 0.0001, "relTolerance": 0.0001 } } |
| }, |
| { |
| "name": "packed_rows_scalar_lane_tail_2081x33", |
| "provenance": { |
| "notes": "Thirty-three columns require a ragged scalar lane tail in packed-row Softmax. With 2,081 rows, the final row group is also partial, exercising inactive lanes across barriers." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2081, 33], |
| "data": { "kind": "fillFloat32", "sinStep": 0.041, "cosStep": 0.023, "scale": 2.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2081, 33], "tolerance": 0.000001, "relTolerance": 0.000001 } } |
| }, |
| { |
| "name": "online_stream_axis31_f16", |
| "provenance": { |
| "notes": "Exercises the per-lane stream-count boundary, reduction tail, or column tail. f16 comparison includes its storage rounding." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [2, 31, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.029, "scale": 4.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 31, 8], "tolerance": 1e-7, "relTolerance": 0.001 } } |
| }, |
| { |
| "name": "online_stream_axis32_f16", |
| "provenance": { |
| "notes": "Exercises the per-lane stream-count boundary, reduction tail, or column tail. f16 comparison includes its storage rounding." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [2, 32, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.029, "scale": 4.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 32, 8], "tolerance": 1e-7, "relTolerance": 0.001 } } |
| }, |
| { |
| "name": "online_stream_axis33_f16", |
| "provenance": { |
| "notes": "Exercises the per-lane stream-count boundary, reduction tail, or column tail. f16 comparison includes its storage rounding." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [2, 33, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.029, "scale": 4.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 33, 8], "tolerance": 1e-7, "relTolerance": 0.001 } } |
| }, |
| { |
| "name": "online_stream_axis64_f16", |
| "provenance": { |
| "notes": "Exercises the per-lane stream-count boundary, reduction tail, or column tail. f16 comparison includes its storage rounding." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [2, 64, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.029, "scale": 4.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 64, 8], "tolerance": 1e-7, "relTolerance": 0.001 } } |
| }, |
| { |
| "name": "online_stream_axis65_f16", |
| "provenance": { |
| "notes": "Exercises the per-lane stream-count boundary, reduction tail, or column tail. f16 comparison includes its storage rounding." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [2, 65, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.029, "scale": 4.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 65, 8], "tolerance": 1e-7, "relTolerance": 0.001 } } |
| }, |
| { |
| "name": "online_stream_columns_tail_f16", |
| "provenance": { |
| "notes": "Exercises the per-lane stream-count boundary, reduction tail, or column tail. f16 comparison includes its storage rounding." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [129, 67, 130], |
| "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.029, "scale": 4.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [129, 67, 130], "tolerance": 1e-7, "relTolerance": 0.001 } } |
| }, |
| { |
| "name": "online_stream_nonfinite_f16", |
| "provenance": { |
| "notes": "Mixes NaNs with large finite maxima across independent streams, alongside positive infinity, all-negative-infinity, and finite groups. The tail crosses both the stream period and the input recipe period." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [2, 67, 8], |
| "data": { |
| "kind": "cycle", |
| "values": ["NaN", 1.0, "-Infinity", "Infinity", 0.0, "NaN", 5.0, -1000.0, 0.0, 3.0, "-Infinity", 2.0, 5.0, -1000.0, "-Infinity", 0.0, 1000.0, 5.0, "-Infinity", 4.0, "NaN", 1000.0, 7.0, "-Infinity"] |
| } |
| } |
| }, |
| "outputs": { |
| "y": { "dtype": "float16", "shape": [2, 67, 8], "tolerance": 1e-7, "relTolerance": 0.001, "allowNaN": true } |
| } |
| }, |
| { |
| "name": "online_stream_four_way_tail_f16", |
| "provenance": { |
| "notes": "Exercises the per-lane stream-count boundary, reduction tail, or column tail. f16 comparison includes its storage rounding." |
| }, |
| "attrs": { "axis": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [2, 129, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.029, "scale": 4.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 129, 8], "tolerance": 1e-7, "relTolerance": 0.001 } }, |
| "tunables": { "STRIDED_ONLINE_STREAMS": 4 } |
| } |
| ] |
| } |
|
|
