| { |
| "op": "ai.onnx.LRN", |
| "fixtureArrays": { |
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| }, |
| "cases": [ |
| { |
| "name": "size_greater_than_channels", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/lrn_op_test.cc", |
| "test": "LRNTest.SizeGreaterThanChannels" |
| }, |
| "attrs": { "alpha": 0.001, "beta": 0.75, "bias": 1, "size": 5 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 2, 2], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 3, 2, 2], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "size_equals_channels", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/lrn_op_test.cc", |
| "test": "LRNTest.SizeEqualsChannels" |
| }, |
| "attrs": { "alpha": 0.0001, "beta": 0.75, "bias": 1, "size": 3 }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 3, 1, 1], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 3, 1, 1], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "multiple_batches", |
| "provenance": { "source": "onnxruntime/test/providers/cpu/nn/lrn_op_test.cc", "test": "LRNTest.MultipleBatches" }, |
| "attrs": { "alpha": 0.01, "beta": 0.5, "bias": 1, "size": 3 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 3, 2, 2], |
| "data": { |
| "kind": "values", |
| "values": [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0, 1.05, 1.1, 1.15, 1.2] |
| } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 3, 2, 2], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "zero_input", |
| "provenance": { "source": "onnxruntime/test/providers/cpu/nn/lrn_op_test.cc", "test": "LRNTest.ZeroInput" }, |
| "attrs": { "alpha": 0.001, "beta": 0.75, "bias": 1, "size": 3 }, |
| "inputs": { "x": { "dtype": "float32", "shape": [1, 3, 2, 2], "data": { "kind": "constant", "value": 0.0 } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 3, 2, 2] } } |
| }, |
| { |
| "name": "f32_tiny_square_bias_zero_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; the subnormal squared input collapses to zero so the bias=0 LRN denominator is zero instead of subnormal." |
| }, |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/lrn_op_test.cc", |
| "test": "LRNTest.SizeEqualsChannels", |
| "notes": "Valid bias=0 edge: tiny normal inputs have positive subnormal squares, so size=1 LRN should produce finite +/-1-style outputs." |
| }, |
| "attrs": { "alpha": 1, "beta": 0.5, "bias": 0, "size": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 4, 1, 1], |
| "data": { "kind": "values", "values": [1e-20, -1e-20, 2e-20, -2e-20] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 4, 1, 1], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "f32_tiny_square_bias_zero_vec4w_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; the subnormal squared input collapses to zero (vec4 path)." |
| }, |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/lrn_op_test.cc", |
| "test": "LRNTest.LargerSpatialDims", |
| "notes": "Same finite bias=0 tiny-square edge as the scalar case, shaped to exercise the vectorized W-lane kernel." |
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| "attrs": { "alpha": 1, "beta": 0.5, "bias": 0, "size": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 4, 1, 4], |
| "data": { |
| "kind": "values", |
| "values": [1e-20, -1e-20, 2e-20, -2e-20, -1e-20, 1e-20, -2e-20, 2e-20, 3e-20, -3e-20, 4e-20, -4e-20, -3e-20, 3e-20, -4e-20, 4e-20] |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 4, 1, 4], "tolerance": 0.00001 } } |
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| { |
| "name": "ort_many_channels_sliding_window", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/lrn_op_test.cc", |
| "test": "LRNTest.ManyChannels", |
| "notes": "C > size exercises the channel sliding-window path instead of only clamped edge windows." |
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| "attrs": { "alpha": 0.0001, "beta": 0.75, "bias": 1, "size": 3 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 8, 2, 2], |
| "data": { |
| "kind": "values", |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 8, 2, 2], "tolerance": 0.00001 } } |
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| { |
| "name": "size_one_channelwise_window", |
| "attrs": { "alpha": 0.5, "beta": 1, "bias": 1, "size": 1 }, |
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| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 1, 2], |
| "data": { "kind": "values", "values": [-2.0, -1.0, 0.0, 1.0, 2.0, 3.0] } |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 3, 1, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "tiny_alpha_bias_beta_dominates", |
| "attrs": { "alpha": 1e-8, "beta": 0.5, "bias": 4, "size": 5 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 2, 1, 3], |
| "data": { "kind": "values", "values": [-6.0, -2.0, 0.0, 2.0, 4.0, 8.0] } |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 2, 1, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "f32_unaligned_width_w6_scalar_compact", |
| "provenance": { |
| "notes": "Compact sibling for the LRN unaligned-width scalar benchmark; width=6 bypasses the vec4-W path while preserving a larger spatial loop." |
| }, |
| "attrs": { "alpha": 0.0001, "beta": 0.75, "bias": 1, "size": 5 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 16, 32, 6], |
| "data": { "kind": "fillFloat32", "sinStep": 0.011, "cosStep": 0.017, "scale": 1.0 } |
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| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 16, 32, 6], "tolerance": 0.00001, "relTolerance": 0.00001 } } |
| }, |
| { |
| "name": "float16_small", |
| "attrs": { "alpha": 0.001, "beta": 0.75, "bias": 2, "size": 3 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 2, 2, 2], |
| "data": { "kind": "values", "values": [0.9, 0.2, 0.7, 0.4, 0.3, 0.6, 0.8, 0.1] } |
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| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 2, 2, 2], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "ort_lrn_1_bias2_size5", |
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| "attrs": { "alpha": 0.001, "beta": 0.75, "bias": 2, "size": 5 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 2, 5, 5], |
| "data": { |
| "kind": "values", |
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| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2, 5, 5], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "ort_lrn_2_default_bias", |
| "provenance": { "source": "onnxruntime/test/providers/cpu/nn/lrn_op_test.cc", "test": "LRNTest.LRN_2" }, |
| "attrs": { "alpha": 0.0001, "beta": 0.75, "bias": 1, "size": 5 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 3, 4, 4], |
| "data": { |
| "kind": "values", |
| "values": [0.97540224, 0.76555133, 0.44334042, 0.81262767, 0.80408305, 0.45893553, 0.39771056, 0.34420514, 0.94965851, 0.93253171, 0.42878076, 0.85962552, 0.14810622, 0.89759219, 0.34574565, 0.70201623, 0.15821661, 0.48984697, 0.94124645, 0.32628751, 0.15926595, 0.59950596, 0.88879001, 0.98674315, 0.80416244, 0.71297693, 0.94821811, 0.48053992, 0.16935933, 0.11691149, 0.22425655, 0.89018708, 0.75404555, 0.68191183, 0.31341696, 0.86113745, 0.11833113, 0.67812026, 0.92196965, 0.2754015, 0.80969357, 0.06198973, 0.01612644, 0.12104732, 0.26516402, 0.55688673, 0.88051248, 0.02725011, 0.05843787, 0.75477105, 0.14542344, 0.97296566, 0.90956807, 0.43174571, 0.76335925, 0.45990494, 0.40780696, 0.29402575, 0.54538655, 0.28858703, 0.56942707, 0.75392908, 0.66404897, 0.0093868, 0.6678884, 0.60093129, 0.54297262, 0.16187565, 0.81088668, 0.93738687, 0.17667364, 0.61121237, 0.46496955, 0.11731055, 0.09468836, 0.80042875, 0.63450789, 0.6306234, 0.01279899, 0.06116414, 0.61256766, 0.5070824, 0.60149539, 0.84250051, 0.99870878, 0.08305351, 0.25943553, 0.58913916, 0.19776763, 0.29520017, 0.21649282, 0.46786523, 0.93280208, 0.87324554, 0.63763618, 0.58810955] |
| } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 3, 4, 4], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "ort_larger_spatial_dims_pattern", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/lrn_op_test.cc", |
| "test": "LRNTest.LargerSpatialDims" |
| }, |
| "attrs": { "alpha": 0.001, "beta": 0.75, "bias": 1, "size": 3 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 8, 8], |
| "data": { "kind": "cycle", "values": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 3, 8, 8], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "onnx_backend_lrn", |
| "attrs": { "alpha": 0.00019999999494757503, "beta": 0.5, "bias": 2, "size": 3 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [5, 5, 5, 5], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_lrn_input_x" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [5, 5, 5, 5] } }, |
| "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_lrn" } |
| }, |
| { |
| "name": "onnx_backend_lrn_default", |
| "attrs": { "size": 3 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [5, 5, 5, 5], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_lrn_input_x" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [5, 5, 5, 5] } }, |
| "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_lrn_default" } |
| }, |
| { |
| "name": "vec4w_float16_width8", |
| "attrs": { "alpha": 0.001, "beta": 0.75, "bias": 1.5, "size": 3 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 2, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.23 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 3, 2, 8], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "vec4w_size5_width12_clamped_window", |
| "attrs": { "alpha": 0.0001, "beta": 0.75, "bias": 1, "size": 5 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 4, 3, 12], |
| "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.29 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 4, 3, 12], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "ort_larger_spatial_dims_mod7", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/lrn_op_test.cc", |
| "test": "LRNTest.LargerSpatialDims", |
| "notes": "Uses ORT's modulo-7 fill pattern over a 1x3x128x128 tensor to stress spatial indexing without inline bulk data." |
| }, |
| "attrs": { "alpha": 0.001, "beta": 0.75, "bias": 1, "size": 3 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 128, 128], |
| "data": { "kind": "cycle", "values": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7] } |
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| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 3, 128, 128], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "empty_zero_dim", |
| "attrs": { "alpha": 0.0001, "beta": 0.75, "bias": 1, "size": 3 }, |
| "inputs": { "x": { "dtype": "float32", "shape": [0, 3, 2, 2], "data": { "kind": "values", "values": [] } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [0, 3, 2, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "empty_zero_dim_f16", |
| "attrs": { "alpha": 0.001, "beta": 0.75, "bias": 2, "size": 3 }, |
| "inputs": { "x": { "dtype": "float16", "shape": [0, 2, 2, 2], "data": { "kind": "values", "values": [] } } }, |
| "outputs": { "y": { "dtype": "float16", "shape": [0, 2, 2, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "f16_vec4w_path_w4_min_aligned", |
| "attrs": { "alpha": 0.001, "beta": 0.75, "bias": 1.5, "size": 3 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [2, 4, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.31 } |
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| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 4, 3, 4], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "f32_size_gt_channels_vec4w_fully_clamped_window", |
| "attrs": { "alpha": 0.001, "beta": 0.75, "bias": 1, "size": 7 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 4, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.27 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 4, 3, 4], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "f16_scalar_path_odd_w5_channel_window", |
| "attrs": { "alpha": 0.001, "beta": 0.75, "bias": 1.5, "size": 3 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 4, 2, 5], |
| "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.23 } |
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| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 4, 2, 5], "tolerance": 0.002 } } |
| } |
| ] |
| } |
|
|
