onnxruntime

838b17a3 - Generalize noop_with_empty_axes handling across all Reduce operators (#26436)

Generalize noop_with_empty_axes handling across all Reduce operators (#26436) ### Description <!-- Describe your changes. --> This PR fixes the behavior of the reduction operators so it's aligned with the ONNX specification. [See ONNX ReduceSumSquare Specification](https://onnx.ai/onnx/operators/onnx__ReduceSumSquare.html) for the definition of noop_with_empty_axes and expected behavior when axes=[] or when the axes input is not provided. Main changes: - Added a new helper function ApplyNoopEmptyAxesElementwise<AGG>() to handle the case axes=[] and noop_with_empty_axes=1 for all reduction operators (ReduceSumSquare, ReduceL1, ReduceL2, ReduceLogSum, ReduceLogSumExp, etc.). This function performs elementwise operations according to the aggregator type: If the aggregator defines Pre/Post operations (e.g., abs, square, sqrt, log), they are applied elementwise on each element of the input without reduction. Otherwise, a direct memory copy (memcpy) is performed to efficiently produce an identical output. - Introduced a compile-time trait system ReduceAggTraits to detect whether each aggregator defines a PreOp and/or PostOp. This allows compile-time specialization and avoids redundant runtime checks. - Updated the generic reduction paths (CommonReduce1Loop and CommonReduce2Loops) to invoke ApplyNoopEmptyAxesElementwise<AGG>() when axes=[] and noop_with_empty_axes=1. These paths are used by all reduction operators, ensuring consistent handling across the entire operators. - Fixed the conditional inside CommonFastReduceCopy: replaced the previous unconditional memcpy and return true with a safe return false, since empty-axes cases are now fully handled upstream by ApplyNoopEmptyAxesElementwise<AGG>(). This keeps the control flow explicit and prevents unintended fallback copies. - Added unit tests for all affected reduction operators (ReduceSumSquare, ReduceL1, ReduceL2, ReduceLogSum, ReduceLogSumExp),and corrected one test that previously expected identity output but now correctly applies Pre/Post operations per the ONNX specification. These updates ensure spec-compliant behavior for all Reduce ops and slightly improve performance for identity cases. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? -->Before this fix, some Reduce operators (e.g., ReduceSumSquare, ReduceL1, ReduceL2, ReduceLogSum, ReduceLogSumExp) did not follow the ONNX spec when axes=[] and noop_with_empty_axes=1. Per the ONNX specification: No reduction should occur if axes is empty and noop_with_empty_axes=1. Operators with Pre/Post (e.g., abs, square, sqrt, log) must apply them elementwise. Others should return the input unchanged. Fixes #26288 Fixes #25095 **Note:** This fix currently applies to the CPU Execution Provider only. Other EPs will need a similar update to align with this behavior. <!-- -If it fixes an open issue, please link to the issue here. -->