(l-onnx-doc-ReduceLogSum)= # ReduceLogSum (l-onnx-op-reducelogsum-18)= ## ReduceLogSum - 18 ### Version - **name**: [ReduceLogSum (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#ReduceLogSum) - **domain**: `main` - **since_version**: `18` - **function**: `True` - **support_level**: `SupportType.COMMON` - **shape inference**: `True` This version of the operator has been available **since version 18**. ### Summary Computes the log sum of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or undefined otherwise. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Function Body The function definition for this operator. ``` < domain: "", opset_import: ["" : 18] > ReduceLogSum (data, axes) => (reduced) { reduced_sum = ReduceSum (data, axes) reduced = Log (reduced_sum) } ``` ### Attributes * **keepdims - INT** (default is `'1'`): Keep the reduced dimension or not, default 1 means keep reduced dimension. * **noop_with_empty_axes - INT** (default is `'0'`): Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor. ### Inputs Between 1 and 2 inputs. - **data** (heterogeneous) - **T**: An input tensor. - **axes** (optional, heterogeneous) - **tensor(int64)**: Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data). ### Outputs - **reduced** (heterogeneous) - **T**: Reduced output tensor. ### Type Constraints * **T** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int32)`, `tensor(int64)`, `tensor(uint32)`, `tensor(uint64)` ): Constrain input and output types to numeric tensors. ```{toctree} text_diff_ReduceLogSum_13_18 ``` (l-onnx-op-reducelogsum-13)= ## ReduceLogSum - 13 ### Version - **name**: [ReduceLogSum (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#ReduceLogSum) - **domain**: `main` - **since_version**: `13` - **function**: `False` - **support_level**: `SupportType.COMMON` - **shape inference**: `True` This version of the operator has been available **since version 13**. ### Summary Computes the log sum of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or undefined otherwise. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. ### Attributes * **axes - INTS** : A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data). * **keepdims - INT** (default is `'1'`): Keep the reduced dimension or not, default 1 means keep reduced dimension. ### Inputs - **data** (heterogeneous) - **T**: An input tensor. ### Outputs - **reduced** (heterogeneous) - **T**: Reduced output tensor. ### Type Constraints * **T** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int32)`, `tensor(int64)`, `tensor(uint32)`, `tensor(uint64)` ): Constrain input and output types to numeric tensors. ```{toctree} text_diff_ReduceLogSum_11_18 text_diff_ReduceLogSum_11_13 ``` (l-onnx-op-reducelogsum-11)= ## ReduceLogSum - 11 ### Version - **name**: [ReduceLogSum (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#ReduceLogSum) - **domain**: `main` - **since_version**: `11` - **function**: `False` - **support_level**: `SupportType.COMMON` - **shape inference**: `True` This version of the operator has been available **since version 11**. ### Summary Computes the log sum of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. ### Attributes * **axes - INTS** : A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data). * **keepdims - INT** (default is `'1'`): Keep the reduced dimension or not, default 1 means keep reduced dimension. ### Inputs - **data** (heterogeneous) - **T**: An input tensor. ### Outputs - **reduced** (heterogeneous) - **T**: Reduced output tensor. ### Type Constraints * **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int32)`, `tensor(int64)`, `tensor(uint32)`, `tensor(uint64)` ): Constrain input and output types to high-precision numeric tensors. ```{toctree} text_diff_ReduceLogSum_1_18 text_diff_ReduceLogSum_1_13 text_diff_ReduceLogSum_1_11 ``` (l-onnx-op-reducelogsum-1)= ## ReduceLogSum - 1 ### Version - **name**: [ReduceLogSum (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#ReduceLogSum) - **domain**: `main` - **since_version**: `1` - **function**: `False` - **support_level**: `SupportType.COMMON` - **shape inference**: `True` This version of the operator has been available **since version 1**. ### Summary Computes the log sum of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or undefined otherwise. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. ### Attributes * **axes - INTS** : A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. * **keepdims - INT** (default is `'1'`): Keep the reduced dimension or not, default 1 means keep reduced dimension. ### Inputs - **data** (heterogeneous) - **T**: An input tensor. ### Outputs - **reduced** (heterogeneous) - **T**: Reduced output tensor. ### Type Constraints * **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int32)`, `tensor(int64)`, `tensor(uint32)`, `tensor(uint64)` ): Constrain input and output types to high-precision numeric tensors.