File size: 21,742 Bytes
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"cases": [
{
"name": "inner_broadcast_consecutive_row_groups",
"provenance": {
"source": "onnxruntime/contrib_ops/cuda/math/bias_softmax_impl.cu",
"notes": "Distinguishes flattened inner-broadcast row grouping from outer mode and right-aligned broadcasting."
},
"attrs": { "axis": -1, "is_inner_broadcast": 1 },
"inputs": {
"data": { "dtype": "float32", "shape": [2, 3, 2], "data": { "kind": "constant", "value": 0.0 } },
"bias": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [0.0, 2.0, 2.0, 0.0] } }
},
"outputs": {
"output": {
"dtype": "float32",
"shape": [2, 3, 2],
"tolerance": 0.000001,
"relTolerance": 0.000001,
"data": {
"kind": "values",
"values": [0.119202922022, 0.880797077978, 0.119202922022, 0.880797077978, 0.119202922022, 0.880797077978, 0.880797077978, 0.119202922022, 0.880797077978, 0.119202922022, 0.880797077978, 0.119202922022]
}
}
}
},
{
"name": "outer_broadcast_cycles_bias_rows",
"provenance": {
"source": "onnxruntime/contrib_ops/cuda/math/bias_softmax_impl.cu",
"notes": "Non-broadcastable data and bias shapes exercise cyclic outer-broadcast row selection."
},
"attrs": { "axis": -1, "is_inner_broadcast": 0 },
"inputs": {
"data": { "dtype": "float32", "shape": [2, 3, 2], "data": { "kind": "constant", "value": 0.0 } },
"bias": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [0.0, 2.0, 2.0, 0.0] } }
},
"outputs": {
"output": {
"dtype": "float32",
"shape": [2, 3, 2],
"tolerance": 0.000001,
"relTolerance": 0.000001,
"data": {
"kind": "values",
"values": [0.119202922022, 0.880797077978, 0.880797077978, 0.119202922022, 0.119202922022, 0.880797077978, 0.880797077978, 0.119202922022, 0.119202922022, 0.880797077978, 0.880797077978, 0.119202922022]
}
}
}
},
{
"name": "ort_inner_broadcast_full_suffix_softmax",
"provenance": {
"source": "onnxruntime/test/contrib_ops/bias_softmax_op_test.cc",
"test": "BiasSoftmaxTest.InnerBroadcastFullBiasBatch",
"notes": "Small deterministic suffix-softmax case."
},
"attrs": { "axis": 1, "is_inner_broadcast": 1 },
"inputs": {
"data": { "dtype": "float32", "shape": [1, 2, 2], "data": { "kind": "values", "values": [0.0, 0.0, 0.0, 0.0] } },
"bias": { "dtype": "float32", "shape": [1, 2, 2], "data": { "kind": "values", "values": [0.0, 1.0, 2.0, 3.0] } }
},
"outputs": {
"output": {
"dtype": "float32",
"shape": [1, 2, 2],
"tolerance": 0.000001,
"relTolerance": 0.000001,
"data": { "kind": "values", "values": [0.03205860328, 0.087144318742, 0.23688281809, 0.643914259888] }
}
}
},
{
"name": "ort_inner_broadcast_empty_bias_batch",
"provenance": {
"source": "onnxruntime/test/contrib_ops/bias_softmax_op_test.cc",
"test": "BiasSoftmaxTest.InnerBroadcastEmptyBiasBatch",
"notes": "Compact deterministic projection where one bias batch broadcasts across two input batches."
},
"attrs": { "axis": 1, "is_inner_broadcast": 1 },
"inputs": {
"data": {
"dtype": "float32",
"shape": [2, 2, 2],
"data": { "kind": "values", "values": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] }
},
"bias": { "dtype": "float32", "shape": [1, 2, 2], "data": { "kind": "values", "values": [0.0, 1.0, 2.0, 3.0] } }
},
"outputs": {
"output": {
"dtype": "float32",
"shape": [2, 2, 2],
"tolerance": 0.000001,
"relTolerance": 0.000001,
"data": {
"kind": "values",
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}
}
}
},
{
"name": "ort_inner_broadcast_negative_axis",
"provenance": {
"source": "onnxruntime/test/contrib_ops/bias_softmax_op_test.cc",
"test": "BiasSoftmaxTest.InnerBroadcastNegativeAxis",
"notes": "Negative-axis inner-broadcast case."
},
"attrs": { "axis": -2, "is_inner_broadcast": 1 },
"inputs": {
"data": {
"dtype": "float32",
"shape": [2, 2, 2],
"data": { "kind": "values", "values": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] }
},
"bias": { "dtype": "float32", "shape": [1, 2, 2], "data": { "kind": "values", "values": [0.0, 1.0, 2.0, 3.0] } }
},
"outputs": {
"output": {
"dtype": "float32",
"shape": [2, 2, 2],
"tolerance": 0.000001,
"relTolerance": 0.000001,
"data": {
"kind": "values",
"values": [0.03205860328, 0.087144318742, 0.23688281809, 0.643914259888, 0.03205860328, 0.087144318742, 0.23688281809, 0.643914259888]
}
}
}
},
{
"name": "ort_outer_broadcast_negative_axis",
"provenance": {
"source": "onnxruntime/test/contrib_ops/bias_softmax_op_test.cc",
"test": "BiasSoftmaxTest.OuterBroadcastNegativeAxis",
"notes": "Negative-axis outer-broadcast case."
},
"attrs": { "axis": -1, "is_inner_broadcast": 0 },
"inputs": {
"data": {
"dtype": "float32",
"shape": [2, 2, 3],
"data": { "kind": "values", "values": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] }
},
"bias": {
"dtype": "float32",
"shape": [2, 3],
"data": { "kind": "values", "values": [0.0, 1.0, 2.0, 2.0, 1.0, 0.0] }
}
},
"outputs": {
"output": {
"dtype": "float32",
"shape": [2, 2, 3],
"tolerance": 0.000001,
"relTolerance": 0.000001,
"data": {
"kind": "values",
"values": [0.09003057317, 0.244728471055, 0.665240955775, 0.665240955775, 0.244728471055, 0.09003057317, 0.09003057317, 0.244728471055, 0.665240955775, 0.665240955775, 0.244728471055, 0.09003057317]
}
}
}
},
{
"name": "ort_outer_broadcast_full_bias_batch",
"provenance": {
"source": "onnxruntime/test/contrib_ops/bias_softmax_op_test.cc",
"test": "BiasSoftmaxTest.OuterBroadcastFullBiasBatch",
"notes": "Compact deterministic projection where every input batch has its own full bias row."
},
"attrs": { "axis": -1, "is_inner_broadcast": 0 },
"inputs": {
"data": {
"dtype": "float32",
"shape": [2, 2, 3],
"data": { "kind": "values", "values": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] }
},
"bias": {
"dtype": "float32",
"shape": [2, 2, 3],
"data": { "kind": "values", "values": [0.0, 1.0, 2.0, 2.0, 1.0, 0.0, -1.0, 0.0, 1.0, 1.0, 0.0, -1.0] }
}
},
"outputs": {
"output": {
"dtype": "float32",
"shape": [2, 2, 3],
"tolerance": 0.000001,
"relTolerance": 0.000001,
"data": {
"kind": "values",
"values": [0.09003057317, 0.244728471055, 0.665240955775, 0.665240955775, 0.244728471055, 0.09003057317, 0.09003057317, 0.244728471055, 0.665240955775, 0.665240955775, 0.244728471055, 0.09003057317]
}
}
}
},
{
"name": "f32_large_gap_subnormal_tail_gpu_gap",
"skipGpu": {
"category": "permanent",
"reason": "Portable WGSL floating-point semantics do not guarantee preservation of subnormal values. The 87.5-point logit gap requires a positive subnormal probability tail, which may be flushed to zero."
},
"provenance": {
"source": "onnxruntime/test/contrib_ops/bias_softmax_op_test.cc",
"test": "BiasSoftmaxTest.InnerBroadcastFullBiasBatch",
"notes": "An 87.5-point logit gap leaves a positive subnormal probability tail after suffix softmax over data+bias."
},
"attrs": { "axis": 1, "is_inner_broadcast": 1 },
"inputs": {
"data": { "dtype": "float32", "shape": [1, 2], "data": { "kind": "values", "values": [0.0, -87.5] } },
"bias": { "dtype": "float32", "shape": [1, 2], "data": { "kind": "values", "values": [0.0, 0.0] } }
},
"outputs": {
"output": {
"dtype": "float32",
"shape": [1, 2],
"tolerance": 2e-45,
"relTolerance": 0,
"data": { "kind": "values", "values": [1.0, 9.982351397596697e-39] }
}
}
},
{
"name": "ort_outer_broadcast_empty_bias_batch",
"provenance": {
"source": "onnxruntime/test/contrib_ops/bias_softmax_op_test.cc",
"test": "BiasSoftmaxTest.OuterBroadcastEmptyBiasBatch",
"notes": "Compact deterministic projection where one outer-broadcast bias row is reused for every input batch."
},
"attrs": { "axis": -1, "is_inner_broadcast": 0 },
"inputs": {
"data": {
"dtype": "float32",
"shape": [2, 2, 3],
"data": { "kind": "values", "values": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] }
},
"bias": { "dtype": "float32", "shape": [1, 3], "data": { "kind": "values", "values": [0.0, 1.0, 2.0] } }
},
"outputs": {
"output": {
"dtype": "float32",
"shape": [2, 2, 3],
"tolerance": 0.000001,
"relTolerance": 0.000001,
"data": {
"kind": "values",
"values": [0.09003057317, 0.244728471055, 0.665240955775, 0.09003057317, 0.244728471055, 0.665240955775, 0.09003057317, 0.244728471055, 0.665240955775, 0.09003057317, 0.244728471055, 0.665240955775]
}
}
}
},
{
"name": "empty_zero_dim",
"attrs": { "axis": 1, "is_inner_broadcast": 1 },
"inputs": {
"data": { "dtype": "float32", "shape": [0, 2, 2], "data": { "kind": "values", "values": [] } },
"bias": { "dtype": "float32", "shape": [0, 2, 2], "data": { "kind": "values", "values": [] } }
},
"outputs": { "output": { "dtype": "float32", "shape": [0, 2, 2], "tolerance": 0 } }
},
{
"name": "f16_default_axis_two_element_row",
"attrs": { "is_inner_broadcast": 1 },
"inputs": {
"data": { "dtype": "float16", "shape": [1, 2], "data": { "kind": "values", "values": [0.0, 0.0] } },
"bias": { "dtype": "float16", "shape": [1, 2], "data": { "kind": "values", "values": [0.0, 0.0] } }
},
"outputs": {
"output": {
"dtype": "float16",
"shape": [1, 2],
"tolerance": 0,
"data": { "kind": "values", "values": [0.5, 0.5] }
}
}
},
{
"name": "dispatch_cliff_rows_65537",
"provenance": {
"notes": "65,537 short rows cross the per-dimension workgroup limit. The packed route grid-strides over rows, while the generic route reconstructs row indices from a two-dimensional dispatch."
},
"attrs": { "axis": 1, "is_inner_broadcast": 0 },
"inputs": {
"data": {
"dtype": "float32",
"shape": [65537, 4],
"data": { "kind": "cycle", "values": [0.1, 0.2, 0.3, 0.4, 0.5] }
},
"bias": { "dtype": "float32", "shape": [65537, 4], "data": { "kind": "constant", "value": 0.0 } }
},
"outputs": { "output": { "dtype": "float32", "shape": [65537, 4], "tolerance": 0.0001 } }
},
{
"name": "fully_masked_row_neg_inf_bias",
"provenance": {
"notes": "The additive bias is -Infinity across the entire suffix block. The expected result uses the package's all-zero convention for a fully masked row, preventing NaNs from propagating into attention."
},
"attrs": { "axis": 1, "is_inner_broadcast": 0 },
"inputs": {
"data": { "dtype": "float32", "shape": [1, 4], "data": { "kind": "values", "values": [0.0, 0.0, 0.0, 0.0] } },
"bias": {
"dtype": "float32",
"shape": [1, 4],
"data": { "kind": "values", "values": ["-Infinity", "-Infinity", "-Infinity", "-Infinity"] }
}
},
"outputs": {
"output": {
"dtype": "float32",
"shape": [1, 4],
"tolerance": 0.000001,
"data": { "kind": "values", "values": [0.0, 0.0, 0.0, 0.0] }
}
}
},
{
"name": "attn_rows_axis3_row1_all_neg_inf_bias_2x8x4x64",
"provenance": {
"notes": "Data has shape [2, 8, 4, 64], and bias [1, 1, 4, 64] uses outer broadcasting. Bias query row 1 is -Infinity at every key, so 16 of the 64 softmax rows are fully masked and must be all zero; the other rows remain finite. This operator's zero-denominator rule differs from applying ONNX Softmax directly to data plus bias, which would produce NaN for the fully masked rows."
},
"attrs": { "axis": 3, "is_inner_broadcast": 0 },
"inputs": {
"data": {
"dtype": "float32",
"shape": [2, 8, 4, 64],
"data": { "kind": "fillFloat32", "scale": 0.5, "sinStep": 0.017, "cosStep": 0.031 }
},
"bias": {
"dtype": "float32",
"shape": [1, 1, 4, 64],
"data": {
"kind": "values",
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}
}
},
"outputs": {
"output": { "dtype": "float32", "shape": [2, 8, 4, 64], "tolerance": 0.000001, "relTolerance": 0.000001 }
}
},
{
"name": "axis0_full_reduce_single_block",
"provenance": {
"notes": "With axis=0, one softmax block covers the whole tensor. The bias supplies one complete flattened row with shape [4,6]."
},
"attrs": { "axis": 0, "is_inner_broadcast": 0 },
"inputs": {
"data": { "dtype": "float32", "shape": [4, 6], "data": { "kind": "linspace", "start": -3.0, "end": 3.0 } },
"bias": {
"dtype": "float32",
"shape": [4, 6],
"data": {
"kind": "values",
"values": [0.5, -0.5, 1.0, -1.0, 0.25, -0.25, 0.5, -0.5, 1.0, -1.0, 0.25, -0.25, 0.5, -0.5, 1.0, -1.0, 0.25, -0.25, 0.5, -0.5, 1.0, -1.0, 0.25, -0.25]
}
}
},
"outputs": { "output": { "dtype": "float32", "shape": [4, 6], "tolerance": 0.000001, "relTolerance": 0.000001 } }
},
{
"name": "bias_fold_skip_middle_dim_axis1",
"provenance": {
"notes": "Bias [1,3,4] broadcasts across the batch axis of data [2,3,4]. axis=1 reduces each 12-element suffix while the size-one bias axis is skipped during broadcast indexing."
},
"attrs": { "axis": 1, "is_inner_broadcast": 0 },
"inputs": {
"data": { "dtype": "float32", "shape": [2, 3, 4], "data": { "kind": "linspace", "start": -2.0, "end": 2.0 } },
"bias": { "dtype": "float32", "shape": [1, 3, 4], "data": { "kind": "linspace", "start": -1.0, "end": 1.0 } }
},
"outputs": {
"output": { "dtype": "float32", "shape": [2, 3, 4], "tolerance": 0.000001, "relTolerance": 0.000001 }
}
},
{
"name": "many_two_element_rows_axis1",
"provenance": {
"notes": "Many two-element softmax rows exercise the scalar-row fallback and folded dispatch accounting used by `biassoftmax-f32-launchbound-262144x2-axis1`."
},
"attrs": { "axis": 1, "is_inner_broadcast": 0 },
"inputs": {
"data": {
"dtype": "float32",
"shape": [4096, 2],
"data": { "kind": "fillFloat32", "scale": 0.25, "sinStep": 0.17, "cosStep": 0.31 }
},
"bias": { "dtype": "float32", "shape": [1, 2], "data": { "kind": "values", "values": [0.125, -0.25] } }
},
"outputs": {
"output": { "dtype": "float32", "shape": [4096, 2], "tolerance": 0.000001, "relTolerance": 0.000001 }
}
},
{
"name": "longrow_split_inner_broadcast_axis1_4x65536_groups2",
"provenance": {
"notes": "A long split row with consecutive inner-broadcast groups checks that each group reads the correct bias row across the full reduction."
},
"attrs": { "axis": 1, "is_inner_broadcast": 1 },
"inputs": {
"data": {
"dtype": "float32",
"shape": [4, 65536],
"data": { "kind": "fillFloat32", "scale": 0.5, "sinStep": 0.0017, "cosStep": 0.0031 }
},
"bias": {
"dtype": "float32",
"shape": [2, 65536],
"data": { "kind": "fillFloat32", "scale": 0.25, "sinStep": 0.0011, "cosStep": 0.0023 }
}
},
"outputs": {
"output": { "dtype": "float32", "shape": [4, 65536], "tolerance": 0.000001, "relTolerance": 0.00001 }
}
},
{
"name": "longrow_split_f16_axis1_4x65536_groups2",
"provenance": {
"notes": "Exercises float16 storage on the split long-row path over 65536 elements. A -30-to-0 ramp concentrates probability mass so normal float16 outputs and underflowing tail zeros coexist, while the row statistics remain in float32 scratch."
},
"attrs": { "axis": 1, "is_inner_broadcast": 1 },
"inputs": {
"data": { "dtype": "float16", "shape": [4, 65536], "data": { "kind": "linspace", "start": -30.0, "end": 0.0 } },
"bias": { "dtype": "float16", "shape": [2, 65536], "data": { "kind": "constant", "value": 0.0 } }
},
"outputs": { "output": { "dtype": "float16", "shape": [4, 65536], "tolerance": 1e-7, "relTolerance": 0.01 } }
},
{
"name": "attn_rows_axis2_4x64x256",
"provenance": {
"notes": "An attention-score layout with broadcast bias exercises many axis-2 softmax rows at a compact scale representative of `biassoftmax-f32-attn-32x512x512`."
},
"attrs": { "axis": 2, "is_inner_broadcast": 0 },
"inputs": {
"data": {
"dtype": "float32",
"shape": [4, 64, 256],
"data": { "kind": "fillFloat32", "scale": 0.5, "sinStep": 0.017, "cosStep": 0.031 }
},
"bias": {
"dtype": "float32",
"shape": [1, 64, 256],
"data": { "kind": "fillFloat32", "scale": 0.25, "sinStep": 0.011, "cosStep": 0.023 }
}
},
"outputs": {
"output": { "dtype": "float32", "shape": [4, 64, 256], "tolerance": 0.000001, "relTolerance": 0.000001 }
}
},
{
"name": "rank8_inner_broadcast_full_suffix_softmax",
"attrs": { "axis": 1, "is_inner_broadcast": 1 },
"inputs": {
"data": {
"dtype": "float32",
"shape": [1, 2, 1, 2, 1, 2, 2, 2],
"data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.27 }
},
"bias": {
"dtype": "float32",
"shape": [1, 2, 1, 2, 1, 2, 2, 2],
"data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.11 }
}
},
"outputs": { "output": { "dtype": "float32", "shape": [1, 2, 1, 2, 1, 2, 2, 2], "tolerance": 0.000002 } }
}
]
}
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