| { |
| "op": "ai.onnx.Mean", |
| "cases": [ |
| { |
| "name": "max_arity_float16_positions", |
| "provenance": { |
| "notes": "Synthetic five-input float16 Mean contract fixture; element-varying operands make every bounded input position affect a non-constant exact mean." |
| }, |
| "inputs": { |
| "a": { "dtype": "float16", "shape": [5], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0] } }, |
| "b": { "dtype": "float16", "shape": [5], "data": { "kind": "values", "values": [2.0, 3.0, 4.0, 5.0, 6.0] } }, |
| "c": { "dtype": "float16", "shape": [5], "data": { "kind": "values", "values": [3.0, 4.0, 5.0, 6.0, 7.0] } }, |
| "d": { "dtype": "float16", "shape": [5], "data": { "kind": "values", "values": [4.0, 5.0, 6.0, 7.0, 8.0] } }, |
| "e": { "dtype": "float16", "shape": [5], "data": { "kind": "values", "values": [5.0, 6.0, 7.0, 8.0, 9.0] } } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float16", |
| "shape": [5], |
| "tolerance": 0, |
| "data": { "kind": "values", "values": [3.0, 4.0, 5.0, 6.0, 7.0] } |
| } |
| } |
| }, |
| { |
| "name": "same_shape", |
| "inputs": { |
| "a": { |
| "dtype": "float32", |
| "shape": [2, 3], |
| "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29 } |
| }, |
| "b": { |
| "dtype": "float32", |
| "shape": [2, 3], |
| "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.07 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "f32_subnormal_two_input_mean_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: Metal flushes float32 subnormals to zero; the subnormal addends/mean cannot be reproduced on GPU." |
| }, |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.Mean_6", |
| "notes": "The mean of equal finite subnormal values is the same subnormal value; flushing the accumulation or division erases the signal." |
| }, |
| "inputs": { |
| "a": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [1e-40, -1e-40] } }, |
| "b": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [1e-40, -1e-40] } } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [2], |
| "tolerance": 2e-45, |
| "data": { "kind": "values", "values": [1e-40, -1e-40] } |
| } |
| } |
| }, |
| { |
| "name": "f32_subnormal_two_input_mean_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: Metal flushes float32 subnormals to zero; the subnormal addends/mean cannot be reproduced on GPU." |
| }, |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.Mean_6", |
| "notes": "Vec4 companion: the mean of equal finite subnormal values should remain subnormal in the vectorized path." |
| }, |
| "inputs": { |
| "a": { |
| "dtype": "float32", |
| "shape": [4], |
| "data": { "kind": "values", "values": [1e-40, -1e-40, 1e-39, -1e-39] } |
| }, |
| "b": { |
| "dtype": "float32", |
| "shape": [4], |
| "data": { "kind": "values", "values": [1e-40, -1e-40, 1e-39, -1e-39] } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [4], |
| "tolerance": 2e-45, |
| "data": { "kind": "values", "values": [1e-40, -1e-40, 1e-39, -1e-39] } |
| } |
| } |
| }, |
| { |
| "name": "float16_vec4_same_shape", |
| "inputs": { |
| "a": { |
| "dtype": "float16", |
| "shape": [8], |
| "data": { "kind": "values", "values": [1.0, -2.0, 3.5, -4.0, 0.25, 10.0, -100.0, 0.001] } |
| }, |
| "b": { |
| "dtype": "float16", |
| "shape": [8], |
| "data": { "kind": "values", "values": [0.5, 2.0, -1.5, 4.0, 0.75, -5.0, 100.0, -0.001] } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float16", |
| "shape": [8], |
| "tolerance": 0.001, |
| "data": { "kind": "values", "values": [0.75, 0.0, 1.0, 0.0, 0.5, 2.5, 0.0, 0.0] } |
| } |
| } |
| }, |
| { |
| "name": "same_shape_vec4_three_input", |
| "inputs": { |
| "a": { |
| "dtype": "float32", |
| "shape": [8], |
| "data": { "kind": "values", "values": [1.0, 5.0, -2.0, 4.0, 0.0, 6.0, 10.0, -10.0] } |
| }, |
| "b": { |
| "dtype": "float32", |
| "shape": [8], |
| "data": { "kind": "values", "values": [3.0, 2.0, -4.0, 8.0, 1.0, 1.0, 9.0, -9.0] } |
| }, |
| "c": { |
| "dtype": "float32", |
| "shape": [8], |
| "data": { "kind": "values", "values": [0.0, 7.0, -3.0, 2.0, -1.0, 8.0, 11.0, -11.0] } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [8], |
| "tolerance": 0.000001, |
| "data": { |
| "kind": "values", |
| "values": [1.3333333333333333, 4.666666666666667, -3.0, 4.666666666666667, 0.0, 5.0, 10.0, -10.0] |
| } |
| } |
| } |
| }, |
| { |
| "name": "float16_vec4_three_input", |
| "provenance": { |
| "notes": "Route lock for the f16 three-input vec4 path. the summean-vec4 kernel has a separate f16 branch that widens each operand to f32, accumulates, then narrows on store; until now only two-input f16 renders existed, so on that branch neither the extra-operand loop that folds C in nor the non-arity-2 divisor (total / 3.0, as opposed to the 0.5 * total shortcut the two-input case takes) was ever emitted. Both numerator and denominator are therefore checked here: with C dropped, or with the 0.5 shortcut applied to three operands, every lane moves. Every operand, every partial sum and every quotient is exactly representable in float16 (each total is 3x a dyadic value), so the expected output is the exact (a + b + c) / 3 and needs no tolerance." |
| }, |
| "inputs": { |
| "a": { |
| "dtype": "float16", |
| "shape": [8], |
| "data": { "kind": "values", "values": [1.0, -2.0, 0.5, 10.0, 0.125, 4.0, -16.0, 0.25] } |
| }, |
| "b": { |
| "dtype": "float16", |
| "shape": [8], |
| "data": { "kind": "values", "values": [2.0, -3.25, -1.0, 6.0, -0.25, 3.0, -20.0, 0.25] } |
| }, |
| "c": { |
| "dtype": "float16", |
| "shape": [8], |
| "data": { "kind": "values", "values": [1.5, -1.5, 2.0, 8.0, -0.25, 2.0, -12.0, 0.25] } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float16", |
| "shape": [8], |
| "tolerance": 0, |
| "data": { "kind": "values", "values": [1.5, -2.25, 0.5, 8.0, -0.125, 3.0, -16.0, 0.25] } |
| } |
| } |
| }, |
| { |
| "name": "broadcast_rank4", |
| "inputs": { |
| "a": { |
| "dtype": "float32", |
| "shape": [2, 3, 4, 5], |
| "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29 } |
| }, |
| "b": { |
| "dtype": "float32", |
| "shape": [1, 3, 1, 5], |
| "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.07 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 3, 4, 5], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank0_lhs_scalar_broadcast", |
| "inputs": { |
| "a": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [10.0] } }, |
| "b": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [2.0, 4.0, 6.0, 8.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_three_inputs_same_shape", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.Mean_6" |
| }, |
| "inputs": { |
| "a": { |
| "dtype": "float32", |
| "shape": [3, 3], |
| "data": { "kind": "values", "values": [1.0, 0.0, 1.0, -1.0, 1.1, -100.0, -5.0, 0.01, -10.0] } |
| }, |
| "b": { |
| "dtype": "float32", |
| "shape": [3, 3], |
| "data": { "kind": "values", "values": [1.0, 0.0, 2.0, -2.0, 2.2, 65.0, -1.0, 0.02, -1.0] } |
| }, |
| "c": { |
| "dtype": "float32", |
| "shape": [3, 3], |
| "data": { "kind": "values", "values": [1.0, 0.0, 3.0, -3.0, 3.3, 65.0, -3.0, 0.03, -1.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_validated_four_inputs_same_shape_variadic", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.Mean_6", |
| "notes": "Extends ORT's same-shape Mean coverage to a valid four-input ONNX variadic node." |
| }, |
| "inputs": { |
| "a": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "constant", "value": 1.0 } }, |
| "b": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "constant", "value": 3.0 } }, |
| "c": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "constant", "value": 5.0 } }, |
| "d": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "constant", "value": 7.0 } } |
| }, |
| "outputs": { |
| "y": { "dtype": "float32", "shape": [2, 2], "tolerance": 0, "data": { "kind": "constant", "value": 4.0 } } |
| } |
| }, |
| { |
| "name": "four_input_same_shape_vec4_reference_generated", |
| "provenance": { |
| "notes": "Reference-generated four-operand coverage. Mean was doubly exposed: reducing only A..C also divided by three, so both the numerator and the denominator were wrong. Every element moves if either is, which makes this a total detector rather than a partial one." |
| }, |
| "inputs": { |
| "a": { |
| "dtype": "float32", |
| "shape": [2, 3, 2, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.25 } |
| }, |
| "b": { |
| "dtype": "float32", |
| "shape": [2, 3, 2, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.07, "scale": 0.25 } |
| }, |
| "c": { |
| "dtype": "float32", |
| "shape": [2, 3, 2, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.13, "scale": 0.25 } |
| }, |
| "d": { |
| "dtype": "float32", |
| "shape": [2, 3, 2, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.05, "cosStep": 0.37, "scale": 0.25 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 3, 2, 8], "tolerance": 0.000001, "relTolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_four_inputs_nan_infinity_variadic", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.Mean_6", |
| "notes": "Extends ORT's variadic Mean coverage with a fourth input that exposes NaN propagation and inf-minus-inf cancellation." |
| }, |
| "inputs": { |
| "a": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [1.0, "Infinity", 1.0, 8.0] } }, |
| "b": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [3.0, "-Infinity", 5.0, 4.0] } }, |
| "c": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [5.0, 2.0, "NaN", -4.0] } }, |
| "d": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [7.0, 6.0, 9.0, "NaN"] } } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [4], |
| "tolerance": 0, |
| "allowNaN": true, |
| "data": { "kind": "values", "values": [4.0, "NaN", "NaN", "NaN"] } |
| } |
| } |
| }, |
| { |
| "name": "ort_three_inputs_multidirectional_broadcast", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.Mean_8" |
| }, |
| "inputs": { |
| "a": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } }, |
| "b": { "dtype": "float32", "shape": [3, 1], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } }, |
| "c": { |
| "dtype": "float32", |
| "shape": [3, 3], |
| "data": { "kind": "values", "values": [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3, 3], "tolerance": 0.000003 } } |
| }, |
| { |
| "name": "three_input_broadcast_vec4_mean", |
| "provenance": { |
| "notes": "Reaches broadcast_three_input_vec4. The output's innermost dimension is four-aligned, `a` keeps it (one aligned vec4 load) and `b` and `c` broadcast it (scalar splats), so the variant's three per-input element types are not all the same -- which is what the vec4 broadcast binding set exists for. Its four-input sibling was already reachable from four_input_scalar_d_broadcast_mean; this one had nothing." |
| }, |
| "inputs": { |
| "a": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, -1.0, -2.0, -3.0, -4.0] } |
| }, |
| "b": { "dtype": "float32", "shape": [2, 1], "data": { "kind": "values", "values": [0.5, -0.5] } }, |
| "c": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [2.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_four_inputs_multidirectional_broadcast", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.Mean_8", |
| "notes": "Extends ORT's multidirectional broadcast case to a valid four-input ONNX variadic Mean node." |
| }, |
| "inputs": { |
| "a": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } }, |
| "b": { "dtype": "float32", "shape": [3, 1], "data": { "kind": "values", "values": [10.0, 20.0, 30.0] } }, |
| "c": { "dtype": "float32", "shape": [3, 1, 1], "data": { "kind": "values", "values": [100.0, 200.0, 300.0] } }, |
| "d": { |
| "dtype": "float32", |
| "shape": [1, 1, 3], |
| "data": { "kind": "values", "values": [1000.0, 2000.0, 3000.0] } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [3, 3, 3], |
| "tolerance": 0, |
| "data": { |
| "kind": "values", |
| "values": [277.75, 528.0, 778.25, 280.25, 530.5, 780.75, 282.75, 533.0, 783.25, 302.75, 553.0, 803.25, 305.25, 555.5, 805.75, 307.75, 558.0, 808.25, 327.75, 578.0, 828.25, 330.25, 580.5, 830.75, 332.75, 583.0, 833.25] |
| } |
| } |
| } |
| }, |
| { |
| "name": "onnx_backend_example_three_inputs", |
| "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_mean_example" }, |
| "inputs": { |
| "a": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [3.0, 0.0, 2.0] } }, |
| "b": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [1.0, 3.0, 4.0] } }, |
| "c": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [2.0, 6.0, 6.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "onnx_backend_one_input_identity", |
| "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_mean_one_input" }, |
| "inputs": { "a": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [3.0, 0.0, 2.0] } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "onnx_backend_mean_two_inputs", |
| "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_mean_two_inputs" }, |
| "inputs": { |
| "a": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [3.0, 0.0, 2.0] } }, |
| "b": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [1.0, 3.0, 4.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_dim_zero_equal_rank", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.DimWithZeroHandling", |
| "notes": "Projected from ORT's binary elementwise zero-dimension Add coverage to generic ONNX multidirectional broadcasting." |
| }, |
| "inputs": { |
| "a": { "dtype": "float32", "shape": [3, 1], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } }, |
| "b": { "dtype": "float32", "shape": [3, 0], "data": { "kind": "values", "values": [] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3, 0], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_dim_zero_scalar_broadcast", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", |
| "test": "MathOpTest.DimWithZeroHandling", |
| "notes": "Projected from ORT's binary elementwise zero-dimension Add coverage to generic ONNX multidirectional broadcasting." |
| }, |
| "inputs": { |
| "a": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [1.0] } }, |
| "b": { "dtype": "float32", "shape": [0], "data": { "kind": "values", "values": [] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [0], "tolerance": 0 } } |
| }, |
| { |
| "name": "single_input_empty_shape", |
| "inputs": { "a": { "dtype": "float32", "shape": [0], "data": { "kind": "values", "values": [] } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [0], "tolerance": 0 } } |
| }, |
| { |
| "name": "f16_broadcast_differing_shapes", |
| "inputs": { |
| "a": { "dtype": "float16", "shape": [1, 4], "data": { "kind": "values", "values": [1.0, 2.0, 4.0, 8.0] } }, |
| "b": { |
| "dtype": "float16", |
| "shape": [4, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.41 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [4, 4], "tolerance": 0.004 } } |
| }, |
| { |
| "name": "rank7_broadcast_two_input", |
| "inputs": { |
| "a": { |
| "dtype": "float32", |
| "shape": [1, 2, 1, 2, 1, 2, 3], |
| "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23 } |
| }, |
| "b": { |
| "dtype": "float32", |
| "shape": [2, 1, 2, 1, 2, 1, 3], |
| "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.19 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 2, 2, 2, 2, 2, 3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "four_input_scalar_d_broadcast_mean", |
| "inputs": { |
| "a": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| }, |
| "b": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0] } |
| }, |
| "c": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0] } |
| }, |
| "d": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [100.0] } } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "tolerance": 0.000001, |
| "data": { "kind": "values", "values": [31.75, 32.5, 33.25, 34.0, 34.75, 35.5, 36.25, 37.0] } |
| } |
| } |
| }, |
| { |
| "name": "five_input_same_shape_variadic", |
| "inputs": { |
| "a": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [1.0, 9.0, -3.0, 4.0] } }, |
| "b": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [2.0, 8.0, -4.0, 3.0] } }, |
| "c": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [3.0, 7.0, -5.0, 2.0] } }, |
| "d": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [4.0, 6.0, -6.0, 1.0] } }, |
| "e": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [5.0, 5.0, -7.0, 0.0] } } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [4], |
| "tolerance": 0, |
| "data": { "kind": "values", "values": [3.0, 7.0, -5.0, 2.0] } |
| } |
| } |
| }, |
| { |
| "name": "rank8_broadcast_two_input", |
| "inputs": { |
| "a": { |
| "dtype": "float32", |
| "shape": [1, 2, 1, 2, 1, 2, 2, 3], |
| "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23 } |
| }, |
| "b": { |
| "dtype": "float32", |
| "shape": [2, 1, 2, 1, 2, 1, 2, 3], |
| "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.19 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 2, 2, 2, 2, 2, 2, 3], "tolerance": 0.000001 } } |
| } |
| ] |
| } |
|
|
