| { |
| "op": "ai.onnx.InstanceNormalization", |
| "fixtureArrays": { |
| "ort_rank3_batch2_repeated_epsilon_point3_input_input": [3.1513367, 9.283596, 1.4546119, 5.4617004, 8.519701, 1.2382338, 1.7930176, 5.1099434, 7.9195533, 7.638727, 8.065445, 3.8082376, 3.1513367, 9.283596, 1.4546119, 5.4617004, 8.519701, 1.2382338, 1.7930176, 5.1099434, 7.9195533, 7.638727, 8.065445, 3.8082376] |
| }, |
| "cases": [ |
| { |
| "name": "dispatch_cliff_ncl_16777216", |
| "attrs": { "epsilon": 0.00001 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 1, 16777217], |
| "data": { "kind": "cycle", "values": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6] } |
| }, |
| "scale": { "dtype": "float32", "shape": [1], "data": { "kind": "constant", "value": 1.0 } }, |
| "b": { "dtype": "float32", "shape": [1], "data": { "kind": "constant", "value": 0.0 } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [1, 1, 16777217], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "subgroup_vec4_2x4x32x32", |
| "attrs": { "epsilon": 0.00001 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [2, 4, 32, 32], |
| "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.21 } |
| }, |
| "scale": { |
| "dtype": "float32", |
| "shape": [4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.29, "cosStep": 0.11, "scale": 0.5 } |
| }, |
| "b": { |
| "dtype": "float32", |
| "shape": [4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.19, "scale": 0.25 } |
| } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 4, 32, 32], "tolerance": 0.000002 } } |
| }, |
| { |
| "name": "subgroup_scalar_1x2x13x5", |
| "attrs": { "epsilon": 0.00001 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 2, 13, 5], |
| "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.23 } |
| }, |
| "scale": { |
| "dtype": "float32", |
| "shape": [2], |
| "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.07, "scale": 0.4 } |
| }, |
| "b": { |
| "dtype": "float32", |
| "shape": [2], |
| "data": { "kind": "fillFloat32", "sinStep": 0.05, "cosStep": 0.13, "scale": 0.2 } |
| } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [1, 2, 13, 5], "tolerance": 0.000002 } } |
| }, |
| { |
| "name": "f32_yfold_many_channels_1x70000x4", |
| "provenance": { |
| "notes": "Compact sibling for the y-fold InstanceNormalization benchmark; preserves C>65535 with much smaller spatial work." |
| }, |
| "attrs": { "epsilon": 0.00001 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 70000, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.011, "cosStep": 0.019, "scale": 0.5 } |
| }, |
| "scale": { |
| "dtype": "float32", |
| "shape": [70000], |
| "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.023, "scale": 0.25, "offset": 1.0 } |
| }, |
| "b": { |
| "dtype": "float32", |
| "shape": [70000], |
| "data": { "kind": "fillFloat32", "sinStep": 0.013, "cosStep": 0.029, "scale": 0.1 } |
| } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [1, 70000, 4], "tolerance": 0.001, "relTolerance": 0.001 } } |
| }, |
| { |
| "name": "f32_batched_planes_1x257x64", |
| "provenance": { |
| "notes": "Compact correctness lock for the feature-independent lane-cohort plane batching used by the 70,000-row dispatch-cliff benchmark." |
| }, |
| "attrs": { "epsilon": 0.00001 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 257, 64], |
| "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.031, "scale": 0.5 } |
| }, |
| "scale": { |
| "dtype": "float32", |
| "shape": [257], |
| "data": { "kind": "fillFloat32", "sinStep": 0.013, "cosStep": 0.027, "scale": 0.25, "offset": 1.0 } |
| }, |
| "b": { |
| "dtype": "float32", |
| "shape": [257], |
| "data": { "kind": "fillFloat32", "sinStep": 0.019, "cosStep": 0.023, "scale": 0.1 } |
| } |
| }, |
| "outputs": { |
| "output": { "dtype": "float32", "shape": [1, 257, 64], "tolerance": 0.00001, "relTolerance": 0.00001 } |
| } |
| }, |
| { |
| "name": "nchw", |
| "attrs": { "epsilon": 0.00001 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 2, 2, 3], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, -1.0, -2.0, -3.0, -4.0, -5.0, -6.0] } |
| }, |
| "scale": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [1.0, 0.5] } }, |
| "b": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [0.0, 1.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [1, 2, 2, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "f32_tiny_variance_epsilon_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 per-instance variance collapses to zero so normalization is non-finite." |
| }, |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/instance_norm_op_test.cc", |
| "test": "InstanceNormalizationOpTest.InstanceNormNCHW", |
| "notes": "Valid epsilon=0 edge: normal inputs produce subnormal per-instance variance but finite order-one normalized outputs." |
| }, |
| "attrs": { "epsilon": 0 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 2, 2], |
| "data": { "kind": "values", "values": [1e-20, -1e-20, 2e-20, -2e-20] } |
| }, |
| "scale": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [1.0, 1.0] } }, |
| "b": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [0.0, 0.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [1, 2, 2], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "f32_subnormal_scale_rank3_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 affine scale collapses to zero, losing the tiny output it should preserve." |
| }, |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/instance_norm_op_test.cc", |
| "test": "InstanceNormalizationOpTest.InstanceNorm", |
| "notes": "Subnormal scale is a valid affine parameter; the normalized output should preserve tiny values." |
| }, |
| "attrs": { "epsilon": 0.00001 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 2, 2], |
| "data": { "kind": "values", "values": [-1.0, 1.0, -2.0, 2.0] } |
| }, |
| "scale": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [1e-40, -2e-40] } }, |
| "b": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [0.0, 0.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [1, 2, 2], "tolerance": 1e-44 } } |
| }, |
| { |
| "name": "f32_subnormal_scale_rank4_vec4_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 affine scale collapses to zero (vec4 path)." |
| }, |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/instance_norm_op_test.cc", |
| "test": "InstanceNormalizationOpTest.InstanceNormBatch1", |
| "notes": "Vec4 spatial companion for valid subnormal scale outputs." |
| }, |
| "attrs": { "epsilon": 0.00001 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 2, 2, 2], |
| "data": { "kind": "values", "values": [-3.0, -1.0, 1.0, 3.0, 4.0, 2.0, 0.0, -2.0] } |
| }, |
| "scale": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [1e-40, -2e-40] } }, |
| "b": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [0.0, 0.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [1, 2, 2, 2], "tolerance": 1e-44 } } |
| }, |
| { |
| "name": "ncdhw_rank5", |
| "attrs": { "epsilon": 0.00001 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 2, 2, 2, 2], |
| "data": { |
| "kind": "values", |
| "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, -1.0, -2.0, -3.0, -4.0, -5.0, -6.0, -7.0, -8.0] |
| } |
| }, |
| "scale": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [1.0, 0.5] } }, |
| "b": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [0.0, 1.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [1, 2, 2, 2, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "large_mean_small_variance_one_pass_cancellation", |
| "attrs": { "epsilon": 0.00001 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 2, 2, 3], |
| "data": { |
| "kind": "values", |
| "values": [8192.0, 8193.0, 8194.0, 8195.0, 8196.0, 8197.0, -8192.0, -8193.0, -8194.0, -8195.0, -8196.0, -8197.0] |
| } |
| }, |
| "scale": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [1.0, 0.5] } }, |
| "b": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [0.0, 1.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [1, 2, 2, 3], "tolerance": 0.001 } } |
| }, |
| { |
| "name": "zero_variance_bias_only", |
| "attrs": { "epsilon": 0.001 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 3, 2, 2], |
| "data": { "kind": "values", "values": [7.0, 7.0, 7.0, 7.0, -3.0, -3.0, -3.0, -3.0, 0.0, 0.0, 0.0, 0.0] } |
| }, |
| "scale": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [10.0, -2.0, 4.0] } }, |
| "b": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [0.5, -1.0, 2.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [1, 3, 2, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "large_epsilon_damps_output", |
| "attrs": { "epsilon": 0.5 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 2, 1, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 4.0, 8.0, -1.0, -2.0, -4.0, -8.0] } |
| }, |
| "scale": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [1.5, -0.5] } }, |
| "b": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [0.25, 2.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [1, 2, 1, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_webgpu_nchw_zero_variance_channel", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/instance_norm_op_test.cc", |
| "test": "InstanceNormalizationOpTest.InstanceNormNCHW_webgpu" |
| }, |
| "attrs": { "epsilon": 0.009999999776482582 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 2, 1, 3], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 2.0, 2.0, 2.0] } |
| }, |
| "scale": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [1.0, 1.0] } }, |
| "b": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [0.0, 2.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [1, 2, 1, 3], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "ort_webgpu_nchw_mixed_zero_variance", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/instance_norm_op_test.cc", |
| "test": "InstanceNormalizationOpTest.InstanceNormNCHW_webgpu_2" |
| }, |
| "attrs": { "epsilon": 0.009999999776482582 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 2, 2, 2], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 2.0, 2.0, 2.0, 2.0, 2.0] } |
| }, |
| "scale": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [1.0, 1.0] } }, |
| "b": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [0.0, 2.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [1, 2, 2, 2], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "ort_rank3_batch1_epsilon_point3", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/instance_norm_op_test.cc", |
| "test": "InstanceNormalizationOpTest.InstanceNormBatch1" |
| }, |
| "attrs": { "epsilon": 0.3 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [1, 3, 4], |
| "data": { |
| "kind": "values", |
| "values": [3.1513367, 9.283596, 1.4546119, 5.4617004, 8.519701, 1.2382338, 1.7930176, 5.1099434, 7.9195533, 7.638727, 8.065445, 3.8082376] |
| } |
| }, |
| "scale": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [1.0, 1.0, 1.0] } }, |
| "b": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [0.0, 0.0, 0.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [1, 3, 4], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "ort_rank3_batch2_distinct_epsilon_point3", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/instance_norm_op_test.cc", |
| "test": "InstanceNormalizationOpTest.InstanceNorm" |
| }, |
| "attrs": { "epsilon": 0.3 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [2, 3, 4], |
| "data": { |
| "kind": "values", |
| "values": [3.1513367, 9.283596, 1.4546119, 5.4617004, 8.519701, 1.2382338, 1.7930176, 5.1099434, 7.9195533, 7.638727, 8.065445, 3.8082376, 2.3667817, 2.8248506, 3.7754705, 5.861325, 5.058735, 3.2787242, 3.6843839, 9.755121, 2.7902672, 7.3974323, 8.283609, 8.488337] |
| } |
| }, |
| "scale": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [1.0, 1.0, 1.0] } }, |
| "b": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [0.0, 0.0, 0.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 3, 4], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "ort_rank3_batch2_repeated_epsilon_point3", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/instance_norm_op_test.cc", |
| "test": "InstanceNormalizationOpTest.InstanceNormBatch2" |
| }, |
| "attrs": { "epsilon": 0.3 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [2, 3, 4], |
| "data": { |
| "kind": "values", |
| "values": { "$ref": "#/fixtureArrays/ort_rank3_batch2_repeated_epsilon_point3_input_input" } |
| } |
| }, |
| "scale": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [1.0, 1.0, 1.0] } }, |
| "b": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [0.0, 0.0, 0.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float32", "shape": [2, 3, 4], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "ort_rank3_batch1_fp16", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/instance_norm_op_test.cc", |
| "test": "InstanceNormalizationOpTest.InstanceNormBatch1_fp16", |
| "notes": "Valid fp16 InstanceNormalization coverage for a rank-3 single-batch input." |
| }, |
| "attrs": { "epsilon": 0.3 }, |
| "inputs": { |
| "input": { |
| "dtype": "float16", |
| "shape": [1, 3, 4], |
| "data": { |
| "kind": "values", |
| "values": [3.1513367, 9.283596, 1.4546119, 5.4617004, 8.519701, 1.2382338, 1.7930176, 5.1099434, 7.9195533, 7.638727, 8.065445, 3.8082376] |
| } |
| }, |
| "scale": { "dtype": "float16", "shape": [3], "data": { "kind": "values", "values": [1.0, 1.0, 1.0] } }, |
| "b": { "dtype": "float16", "shape": [3], "data": { "kind": "values", "values": [0.0, 0.0, 0.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float16", "shape": [1, 3, 4], "tolerance": 0.005, "relTolerance": 0.005 } } |
| }, |
| { |
| "name": "ort_rank3_batch2_fp16", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/instance_norm_op_test.cc", |
| "test": "InstanceNormalizationOpTest.InstanceNormBatch2_fp16", |
| "notes": "Valid fp16 InstanceNormalization coverage for a rank-3 two-batch input." |
| }, |
| "attrs": { "epsilon": 0.3 }, |
| "inputs": { |
| "input": { |
| "dtype": "float16", |
| "shape": [2, 3, 4], |
| "data": { |
| "kind": "values", |
| "values": { "$ref": "#/fixtureArrays/ort_rank3_batch2_repeated_epsilon_point3_input_input" } |
| } |
| }, |
| "scale": { "dtype": "float16", "shape": [3], "data": { "kind": "values", "values": [1.0, 1.0, 1.0] } }, |
| "b": { "dtype": "float16", "shape": [3], "data": { "kind": "values", "values": [0.0, 0.0, 0.0] } } |
| }, |
| "outputs": { "output": { "dtype": "float16", "shape": [2, 3, 4], "tolerance": 0.005, "relTolerance": 0.005 } } |
| }, |
| { |
| "name": "ort_rank5_instance_norm_2", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/instance_norm_op_test.cc", |
| "test": "InstanceNormalizationOpTest.InstanceNorm_2" |
| }, |
| "attrs": { "epsilon": 0.3 }, |
| "inputs": { |
| "input": { |
| "dtype": "float32", |
| "shape": [2, 3, 2, 1, 3], |
| "data": { |
| "kind": "values", |
| "values": [2.676342, 4.1100464, 4.570907, 5.8493505, 4.772751, 7.1669755, 2.8400702, 8.903057, 1.2464883, 7.034208, 4.755743, 6.0282083, 2.2634823, 2.7829134, 8.206701, 9.7143545, 3.8208177, 7.2309036, 8.887503, 9.05146, 1.7653979, 1.351493, 2.5284739, 8.903282, 1.8851215, 4.7899685, 9.621006, 5.7984877, 7.226894, 3.8396406, 7.1785083, 8.511631, 1.1645945, 7.751299, 9.89975, 7.733491] |
| } |
| }, |
| "scale": { |
| "dtype": "float32", |
| "shape": [3], |
| "data": { "kind": "values", "values": [4.753198, 7.4829206, 1.0010294] } |
| }, |
| "b": { |
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