.. _op_ai_onnx_Sum: Sum === - **Domain**: ``ai.onnx`` - **Since version**: 13 Element-wise sum of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check `the doc `_. **Inputs** - **data_0** (*T*): List of tensors for sum. **Outputs** - **sum** (*T*): Output tensor. **Type Constraints** - **T**: Constrain input and output types to float tensors. Allowed types: tensor(bfloat16), tensor(double), tensor(float), tensor(float16). Examples -------- **test_cc_sum_bcast** .. code-block:: text Node: Sum(data_0, data_1) -> (sum) .. code-block:: text Inputs: data_0: shape=(2, 2), dtype=float32 [[1., 2.], [3., 4.]] data_1: shape=(), dtype=float32 10. Outputs: sum: shape=(2, 2), dtype=float32 [[11., 12.], [13., 14.]] **test_cc_sum_example** .. code-block:: text Node: Sum(data_0, data_1, data_2) -> (sum) .. code-block:: text Inputs: data_0: shape=(3,), dtype=float32 [1., 0., 1.] data_1: shape=(3,), dtype=float32 [3., 4., 5.] data_2: shape=(3,), dtype=float32 [6., 0., 5.] Outputs: sum: shape=(3,), dtype=float32 [10., 4., 11.] **test_cc_sum_one_input** .. code-block:: text Node: Sum(data_0) -> (sum) .. code-block:: text Inputs: data_0: shape=(3,), dtype=float32 [1., 2., 3.] Outputs: sum: shape=(3,), dtype=float32 [1., 2., 3.] **test_cc_sum_two_inputs** .. code-block:: text Node: Sum(data_0, data_1) -> (sum) .. code-block:: text Inputs: data_0: shape=(2, 3), dtype=float32 [[1., 2., 3.], [4., 5., 6.]] data_1: shape=(2, 3), dtype=float32 [[10., 20., 30.], [40., 50., 60.]] Outputs: sum: shape=(2, 3), dtype=float32 [[11., 22., 33.], [44., 55., 66.]] **test_sum_example** .. code-block:: text Node: Sum(data_0, data_1, data_2) -> (sum) .. code-block:: text Inputs: data_0: shape=(3, 4, 5), dtype=float32 [[[-1.6207618 , -0.26159498, -1.6894207 , -0.5941726 , 0.23757638], [-0.77598375, 1.7195183 , 0.87334126, -0.47817174, 0.16062957], [-1.1535512 , -1.0489278 , -0.8616083 , -0.25931633, 0.1662263 ], [-1.19579 , -0.09554294, -0.7415066 , 1.8345209 , 0.66055524]], [[-0.37864083, -1.2763816 , -0.20821808, 0.79001987, -0.305968 ], [-0.13338329, -0.26947752, -0.68312395, -0.02752227, 1.2929565 ], [-2.3823667 , -1.3751754 , 0.08531325, -1.0451319 , 0.5773258 ], [ 0.5335717 , 1.7940854 , -0.25193027, -0.42498377, 0.62358594]], [[-0.21925525, 0.9713564 , -0.12102764, 0.72468317, -0.30977774], [ 1.0066974 , 0.20055684, 0.35697085, -0.9946885 , 1.5686967 ], [-2.015447 , -0.80032647, -1.3453012 , -1.3160369 , 0.09626406], [ 0.00318402, 1.1699535 , -1.1138605 , -0.72566646, -0.04194619]]] data_1: shape=(3, 4, 5), dtype=float32 [[[-3.2327610e-01, 5.3205568e-01, 1.5432492e+00, -6.9176567e-01, 1.9503884e+00], [-1.7234032e+00, -1.0676117e+00, -1.6856503e+00, -3.3474345e-02, -9.3504351e-01], [ 8.5077822e-01, 4.7314724e-01, -3.6031015e-02, 1.1229317e+00, 1.0024427e-01], [-1.3745983e-01, -4.5334585e-02, 4.9413046e-01, 5.0943595e-01, 2.7962857e-01]], [[ 1.6754329e+00, -1.1071100e+00, 3.6536074e-01, 9.5587057e-01, 9.5825948e-02], [-3.4379008e+00, -2.1401776e-01, 1.0207568e+00, 1.5230697e-01, 1.7962818e-01], [ 1.9142120e-01, 1.2306712e-04, -1.4188983e+00, -1.1545553e+00, -3.6328000e-01], [-1.1123656e+00, -2.7028066e-01, 1.0528580e+00, -1.3669981e+00, -1.9538646e+00]], [[-8.5295737e-01, 1.2235709e+00, -9.7294033e-02, 8.7098098e-01, 3.7720674e-01], [ 1.0523090e+00, -5.4001397e-01, 5.1259655e-01, 3.2776645e-01, -2.8144464e-01], [ 2.0450303e-01, -5.3457934e-01, 7.0617035e-02, -4.4991007e-01, 7.2952157e-01], [-1.1638440e+00, 6.6377270e-01, -1.4313440e+00, -1.1646674e+00, -1.5334731e+00]]] data_2: shape=(3, 4, 5), dtype=float32 [[[-1.9504728 , -0.384003 , 0.6175111 , 0.4860437 , 0.14837483], [ 0.4243168 , 0.7555941 , 1.2209445 , -0.8658253 , -0.2577181 ], [-1.1415162 , -0.40477493, -0.87081665, 0.88169414, 0.38555267], [-1.5191493 , -1.1147851 , -1.6198577 , 0.18040422, -0.61582077]], [[ 1.0728145 , -0.32273647, -1.4748397 , 1.3682464 , 0.69247085], [ 1.4945487 , -1.8275837 , 1.1269883 , -1.1244485 , 0.19961263], [ 0.8090156 , 0.8177859 , -0.01459381, -1.4260765 , 1.5820233 ], [-1.1409763 , 0.3658983 , 0.5160113 , -1.0337888 , 0.6509004 ]], [[-0.2102342 , -0.90819484, -0.16215591, -0.6403306 , -0.68816286], [ 2.1505942 , 0.07699525, -0.6226731 , -0.6401388 , 0.8192693 ], [-1.3715283 , -0.58853245, 0.3524632 , 0.0612135 , 0.20318082], [-0.23185334, 0.7497727 , 0.7727014 , 0.7761635 , -0.32464558]]] Outputs: sum: shape=(3, 4, 5), dtype=float32 [[[-3.8945107 , -0.11354232, 0.47133964, -0.7998946 , 2.3363397 ], [-2.0750701 , 1.4075007 , 0.40863544, -1.3774714 , -1.032132 ], [-1.4442892 , -0.9805554 , -1.768456 , 1.7453096 , 0.6520232 ], [-2.8523993 , -1.2556626 , -1.8672338 , 2.5243611 , 0.32436305]], [[ 2.3696065 , -2.706228 , -1.317697 , 3.1141367 , 0.4823288 ], [-2.0767355 , -2.311079 , 1.4646212 , -0.99966383, 1.6721972 ], [-1.3819299 , -0.5572664 , -1.348179 , -3.625764 , 1.7960691 ], [-1.7197702 , 1.889703 , 1.3169391 , -2.8257706 , -0.6793782 ]], [[-1.2824467 , 1.2867324 , -0.3804776 , 0.95533353, -0.62073386], [ 4.2096004 , -0.26246187, 0.2468943 , -1.307061 , 2.1065214 ], [-3.1824722 , -1.9234383 , -0.9222209 , -1.7047335 , 1.0289664 ], [-1.3925134 , 2.583499 , -1.7725033 , -1.1141703 , -1.900065 ]]] Differences with previous version (8) ------------------------------------- **SchemaDiff**: ``Sum`` (domain ``'ai.onnx'``) * old version: 8 * new version: 13 * breaking: no **Type constraints:** * changed 'T': added types: ['tensor(bfloat16)'] Version History --------------- - :doc:`Version 8 ` - :doc:`Version 6 ` - :doc:`Version 1 `