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Remove patches that conflict with upstream review comments; submit optimized patches later. - #43

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XYenChi wants to merge 20 commits into
RuyiAI-Stack:riscvfrom
XYenChi:regression
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Remove patches that conflict with upstream review comments; submit optimized patches later.#43
XYenChi wants to merge 20 commits into
RuyiAI-Stack:riscvfrom
XYenChi:regression

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@XYenChi

@XYenChi XYenChi commented Aug 24, 2026

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变更说明 / Summary

Remove patch and will push better version according to upstream's suggestion.

关联 Issue / Related issue

验证方式 / Validation

检查清单 / Checklist

  • The change is focused and contains no unrelated modifications.
  • I have followed the target repository's contribution guidelines.
  • I have added or updated tests where applicable.
  • I have updated related documentation where applicable.
  • I have run the relevant formatting, lint, build, and test checks.
  • I have described known limitations or compatibility impact.

XYenChi and others added 18 commits August 24, 2026 03:00
* Add RISC-V 64 BLOCK_LIST

* Skip long time testcase

Co-authored-by: Cursor <cursoragent@cursor.com>
* Add riscv64 ci with PR
⭐ Run Main Diff base and head
Push to riscv
From https://github.com/RuyiAI-Stack/pytorch
 * branch              riscv      -> FETCH_HEAD
fatal: Not a valid object name origin/main
Error:   ❌  Failure - Main Diff base and head
Error: exit status 128
* mklnn is unavailable on RISC-V

* Remove test_cpu_select_algorithm from block_list

* Fix block list format
These cases are too slow on riscv64, adding them to here simply

Drop test_torch from the list because it is one core case
for action, these yaml must be merged first then take effect, so merge
it skipping ci
bytes_to_scalar previously round-tripped raw bytes through Python
float/complex values (via ctypes) before constructing the tensor. This
loses NaN bit patterns on architectures (such as RISC-V) that
canonicalize NaNs in floating-point loads/conversions, causing
test_bytes_to_scalar_cpu_{float32,float64,complex64,complex128} to
fail with mismatched storage bytes.

Construct the scalar tensor by reinterpreting the raw bytes as the
target dtype via tensor.view(dtype), so all input bit patterns
(including NaN payloads) are preserved exactly.

Test Plan:
python test/test_torch.py TestTorchDeviceTypeCPU.test_bytes_to_scalar_cpu_complex64
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3 participants