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| module | lgtm status | approve status |
|---|---|---|
| torch_npu/utils | ✅ liangsongwei, htchu (2/2) | ✅ liangsongwei, htchu (2/1) |
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变更摘要
本 PR 主要修复 torch_npu/utils/combine_tensors.py 中 is_combined_tensor_valid 函数的连续性判断逻辑缺失问题。原函数仅检查 tensor 是否为空以及其数据指针是否落在合并 tensor 的地址范围内，但未验证 tensor 自身的连续性。该修复补充了对 tensor.is_contiguous() 的校验，使 is_combined_tensor_valid 与 npu_combine_tensors 中的连续性检查保持一致，确保合并 tensor 有效性判定的完整性。
主要改动
is_combined_tensor_valid函数新增连续性检查: 在遍历 tensor 列表的有效性判定条件中增加了not tensor.is_contiguous()条件，当 tensor 不连续时返回False，修复了此前仅检查地址范围而忽略连续性导致的判定漏洞。- 与
npu_combine_tensors的校验逻辑对齐:npu_combine_tensors在组合 tensor 前已要求输入 tensor 必须连续，本次修改使is_combined_tensor_valid的校验标准与之统一，避免出现组合时通过但验证时未检出的不一致情况。
/approve
1. Origin pull request:
https://gitcode.com/Ascend/pytorch/merge_requests/39043
2. Original pull request related issue(s):
https://gitcode.com/Ascend/pytorch/issues/2456
3. Original pull request related commit(s):
Signed-off-by: l1919_snow lixinyu68@huawei.com
Signed-off-by: l1919_snow lixinyu68@huawei.com
格式
Signed-off-by: l1919_snow lixinyu68@huawei.com
格式修改
Signed-off-by: l1919_snow lixinyu68@huawei.com