Re: [PR] [TOPI] Fix strided_slice_with_axes to handle negative axis values [tvm]

2026-03-19 Thread via GitHub 


tlopex merged PR #18917:
URL: https://github.com/apache/tvm/pull/18917


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Re: [PR] [TOPI] Fix strided_slice_with_axes to handle negative axis values [tvm]

2026-03-19 Thread via GitHub 


mshr-h commented on PR #18917:
URL: https://github.com/apache/tvm/pull/18917#issuecomment-4095726556

   cc @tlopex @guan404ming 


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Re: [PR] [TOPI] Fix strided_slice_with_axes to handle negative axis values [tvm]

2026-03-19 Thread via GitHub 


gemini-code-assist[bot] commented on code in PR #18917:
URL: https://github.com/apache/tvm/pull/18917#discussion_r2963809939


##
include/tvm/topi/transform.h:
##
@@ -904,28 +904,41 @@ inline Tensor strided_slice_with_axes(const Tensor& x, 
const ffi::Array
   std::string slice_mode = "end",
   std::string name = 
"T_strided_slice_with_axes",
   std::string tag = kInjective) {
-  const size_t src_tensor_dim = x->shape.size();
-  TVM_FFI_ICHECK(axes.size() <= src_tensor_dim);
+  const int64_t src_tensor_dim = static_cast(x->shape.size());
+  TVM_FFI_ICHECK(static_cast(axes.size()) <= src_tensor_dim);
   TVM_FFI_ICHECK(axes.size() == begin.size() && axes.size() == end.size() &&
  axes.size() == strides.size());
 
+  // Normalize negative axes
+  ffi::Array normalized_axes;
+  for (size_t i = 0; i < axes.size(); ++i) {
+int64_t axis = axes[i].IntValue();
+if (axis < 0) {
+  axis += src_tensor_dim;
+}
+TVM_FFI_ICHECK(axis >= 0 && axis < src_tensor_dim)
+<< "Axis " << axes[i].IntValue() << " is out of bounds for tensor with 
" << src_tensor_dim
+<< " dimensions";
+normalized_axes.push_back(Integer(axis));
+  }

Review Comment:
   ![medium](https://www.gstatic.com/codereviewagent/medium-priority.svg)
   
   While normalizing axes, it's good practice to also check for duplicate axes. 
Currently, if an axis is repeated, the slice parameters for that axis will be 
silently overwritten, which can lead to unexpected behavior. Other operators 
like `transpose` already perform this check. You can use a `std::unordered_set` 
to efficiently track seen axes and ensure each axis is specified only once.
   
   ```suggestion
 ffi::Array normalized_axes;
 std::unordered_set seen_axes;
 for (size_t i = 0; i < axes.size(); ++i) {
   int64_t axis = axes[i].IntValue();
   if (axis < 0) {
 axis += src_tensor_dim;
   }
   TVM_FFI_ICHECK(axis >= 0 && axis < src_tensor_dim)
   << "Axis " << axes[i].IntValue() << " is out of bounds for tensor 
with " << src_tensor_dim
   << " dimensions";
   auto [it, inserted] = seen_axes.insert(axis);
   TVM_FFI_ICHECK(inserted) << "Axis " << axis << " is specified more than 
once.";
   normalized_axes.push_back(Integer(axis));
 }
   ```



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Re: [PR] [TOPI] Fix strided_slice_with_axes to handle negative axis values [tvm]

2026-03-19 Thread via GitHub 


gemini-code-assist[bot] commented on PR #18917:
URL: https://github.com/apache/tvm/pull/18917#issuecomment-4095270468

   ## Summary of Changes
   
   Hello, I'm Gemini Code Assist[^1]! I'm currently reviewing this pull request 
and will post my feedback shortly. In the meantime, here's a summary to help 
you and other reviewers quickly get up to speed!
   
   This pull request addresses a critical bug in the `strided_slice_with_axes` 
function where negative axis values were not properly handled, leading to 
runtime errors. The changes introduce a robust mechanism to normalize these 
negative indices to their positive equivalents, ensuring the slicing operation 
functions as expected and improving the stability of tensor manipulations. A 
dedicated test case has been added to validate this fix.
   
   ### Highlights
   
   * **Negative Axis Handling**: Implemented normalization for negative axis 
values in `strided_slice_with_axes` to convert them into positive, zero-based 
indices. This prevents `IndexError` during `LegalizeOps` when negative axes are 
used directly.
   * **API Integration**: The normalized axes are now consistently used when 
calling `StridedSliceCanonicalizeBegin`, `StridedSliceOutputShape`, and within 
the compute lambda for index calculation, ensuring correct behavior across the 
slicing logic.
   * **Test Coverage**: Added a new test case 
`test_strided_slice_negative_axes` to verify that `strided_slice_with_axes` 
correctly processes negative axis inputs after the `LegalizeOps` pass.
   
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