Vibe-coded with Codex, after collecting a backtrace, see https://chatgpt.com/s/cd_68438be8a1248191adbfa0a5f000e60b
Even though, check for empty tensor list exists in `at::cat` crash might happens while resolving named dimension to position, by calling `dimname_to_position(tensors[0], dim)`, see backtrace below
```
(lldb) up
frame #1: 0x00000001101146dc libtorch_cpu.dylib`at::TensorBase::has_names(this=0x0000000000000000) const at TensorBase.h:559:10
556 bool has_names() const {
557 // If a user is using unnamed tensors, then we can short-circuit right here.
558 // Otherwise, impl::has_names attempts to retrieve names.
-> 559 if (!impl_->has_named_tensor_meta()) {
560 return false;
561 }
562 return impl::has_names(unsafeGetTensorImpl());
(lldb) up
frame #2: 0x00000001101144c4 libtorch_cpu.dylib`at::dimname_to_position(tensor=0x0000000000000000, dim=Dimname @ 0x000000016fdfe348) at NamedTensorUtils.cpp:23:3
20 int64_t dimname_to_position(const Tensor& tensor, Dimname dim) {
21 TORCH_CHECK(dim.type() != NameType::WILDCARD,
22 "Please look up dimensions by name, got: name = None.");
-> 23 TORCH_CHECK(tensor.has_names(),
24 "Name ", dim, " not found in ", toDimnameRepr(tensor), ".");
25 const auto names = tensor.names();
26
```
TODOs:
- May be move test from `test_tensor_creation.py` to OpInfo (not sure which one is more readable)
- Replace `TORCH_CHECK` with `TORCH_CHECK_VALUE` and adjust unit tests
Fixes https://github.com/pytorch/pytorch/issues/155306
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155383
Approved by: https://github.com/cyyever, https://github.com/ezyang
ghstack dependencies: #155382
We have broken tests on Aarch64 which are not enabled upstream, this PR will fix and enable those tests.
```
AssertionError: Tensor-likes are not equal!
Mismatched elements: 2 / 3 (66.7%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: 1.0842021724855044e-19 at index (1,)
To execute this test, run the following from the base repo dir:
python test/test_tensor_creation_ops.py TestTensorCreationCPU.test_float_to_int_conversion_nonfinite_cpu_int64
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145367
Approved by: https://github.com/malfet
Periodically run testsuite for s390x
**Dependencies update**
Package z3-solver is updated from version 4.12.2.0 to version 4.12.6.0. This is a minor version update, so no functional change is expected.
The reason for update is build on s390x. pypi doesn't provide binary build for z3-solver for versions 4.12.2.0 or 4.12.6.0 for s390x. Unfortunately, version 4.12.2.0 fails to build with newer gcc used on s390x builders, but those errors are fixed in version 4.12.6.0. Due to this minor version bump fixes build on s390x.
```
# pip3 install z3-solver==4.12.2.0
...
In file included from /tmp/pip-install-756iytc6/z3-solver_ce6f750b780b4146a9a7c01e52672071/core/src/util/region.cpp:53:
/tmp/pip-install-756iytc6/z3-solver_ce6f750b780b4146a9a7c01e52672071/core/src/util/region.cpp: In member function ‘void* region::allocate(size_t)’:
/tmp/pip-install-756iytc6/z3-solver_ce6f750b780b4146a9a7c01e52672071/core/src/util/tptr.h:29:62: error: ‘uintptr_t’ does not name a type
29 | #define ALIGN(T, PTR) reinterpret_cast<T>(((reinterpret_cast<uintptr_t>(PTR) >> PTR_ALIGNMENT) + \
| ^~~~~~~~~
/tmp/pip-install-756iytc6/z3-solver_ce6f750b780b4146a9a7c01e52672071/core/src/util/region.cpp:82:22: note: in expansion of macro ‘ALIGN’
82 | m_curr_ptr = ALIGN(char *, new_curr_ptr);
| ^~~~~
/tmp/pip-install-756iytc6/z3-solver_ce6f750b780b4146a9a7c01e52672071/core/src/util/region.cpp:57:1: note: ‘uintptr_t’ is defined in header ‘<cstdint>’; did you forget to ‘#include <cstdint>’?
56 | #include "util/page.h"
+++ |+#include <cstdint>
57 |
```
**Python paths update**
On AlmaLinux 8 s390x, old paths:
```
python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())'
/usr/lib/python3.12/site-packages
```
Total result is `/usr/lib/python3.12/site-packages/torch;/usr/lib/python3.12/site-packages`
New paths:
```
python -c 'import site; print(";".join([x for x in site.getsitepackages()] + [x + "/torch" for x in site.getsitepackages()]))'
/usr/local/lib64/python3.12/site-packages;/usr/local/lib/python3.12/site-packages;/usr/lib64/python3.12/site-packages;/usr/lib/python3.12/site-packages;/usr/local/lib64/python3.12/site-packages/torch;/usr/local/lib/python3.12/site-packages/torch;/usr/lib64/python3.12/site-packages/torch;/usr/lib/python3.12/site-packages/torch
```
```
# python -c 'import torch ; print(torch)'
<module 'torch' from '/usr/local/lib64/python3.12/site-packages/torch/__init__.py'>
```
`pip3 install dist/*.whl` installs torch into `/usr/local/lib64/python3.12/site-packages`, and later it's not found by cmake with old paths:
```
CMake Error at CMakeLists.txt:9 (find_package):
By not providing "FindTorch.cmake" in CMAKE_MODULE_PATH this project has
asked CMake to find a package configuration file provided by "Torch", but
CMake did not find one.
```
https://github.com/pytorch/pytorch/actions/runs/10994060107/job/30521868178?pr=125401
**Builders availability**
Build took 60 minutes
Tests took: 150, 110, 65, 55, 115, 85, 50, 70, 105, 110 minutes (split into 10 shards)
60 + 150 + 110 + 65 + 55 + 115 + 85 + 50 + 70 + 105 + 110 = 975 minutes used. Let's double it. It would be 1950 minutes.
We have 20 machines * 24 hours = 20 * 24 * 60 = 20 * 1440 = 28800 minutes
We currently run 5 nightly binaries builds, each on average 90 minutes build, 15 minutes test, 5 minutes upload, 110 minutes total for each, 550 minutes total. Doubling would be 1100 minutes.
That leaves 28800 - 1100 = 27700 minutes total. Periodic tests would use will leave 25750 minutes.
Nightly binaries build + nightly tests = 3050 minutes.
25750 / 3050 = 8.44. So we could do both 8 more times for additional CI runs for any reason. And that is with pretty good safety margin.
**Skip test_tensorexpr**
On s390x, pytorch is built without llvm.
Even if it would be built with llvm, llvm currently doesn't support used features on s390x and test fails with errors like:
```
JIT session error: Unsupported target machine architecture in ELF object pytorch-jitted-objectbuffer
unknown file: Failure
C++ exception with description "valOrErr INTERNAL ASSERT FAILED at "/var/lib/jenkins/workspace/torch/csrc/jit/tensorexpr/llvm_jit.h":34, please report a bug to PyTorch. Unexpected failure in LLVM JIT: Failed to materialize symbols: { (main, { func }) }
```
**Disable cpp/static_runtime_test on s390x**
Quantization is not fully supported on s390x in pytorch yet.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125401
Approved by: https://github.com/malfet
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Fixes #ISSUE_NUMBER
Similar to #143682, for large maximum values we were sampling integers via % and it doesn't provide uniform distribution. Here we limit the max skew to approx 1% (random32 is used for max values `<= 2**32 / 128`)
This comes with significant perf penalty, especially for cuda, but it's a pretty bad bug, so we'll have to figure out what can be done to improve it.
`torch.compile` has always been producing correct results for this, and it's performance is also significantly better than current eager (eager is ~660 GB/s on H100, torch.compile 1200 GB/s), so we have to figure out why torch.compile is better.
`__launch_bounds__` slightly regress perf, so perhaps we can figure out how to specify them better, but it's only 20-30 GB/s, so the big difference is still unexplained.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143787
Approved by: https://github.com/eqy
Fixes #ISSUE_NUMBER
Similar to #143682, for large maximum values we were sampling integers via % and it doesn't provide uniform distribution. Here we limit the max skew to approx 1% (random32 is used for max values `<= 2**32 / 128`)
This comes with significant perf penalty, especially for cuda, but it's a pretty bad bug, so we'll have to figure out what can be done to improve it.
`torch.compile` has always been producing correct results for this, and it's performance is also significantly better than current eager (eager is ~660 GB/s on H100, torch.compile 1200 GB/s), so we have to figure out why torch.compile is better.
`__launch_bounds__` slightly regress perf, so perhaps we can figure out how to specify them better, but it's only 20-30 GB/s, so the big difference is still unexplained.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143787
Approved by: https://github.com/eqy
Related to #107302
We saw `test_float_to_int_conversion_nonfinite` failed as we upgrade to NumPy 2.
It is caused by the undefined behavior of `numpy` casting `inf`, `-inf` and `nan` from `np.float32` to other dtypes.
The test is using NumPy as reference for the ground truth. (see line 1013-1015)
However, these behaviors are undefined in NumPy.
If you do `np.array([float("inf")]).astype(np.uint8, casting="safe")`, it results in an error `TypeError: Cannot cast array data from dtype('float64') to dtype('uint8') according to the rule 'safe'`.
The undefined behaviors are always subject to change.
This PR address this issue by passing concrete values as the ground truth references.
In the future, even NumPy changes its behavior the test would still remain stable.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138131
Approved by: https://github.com/drisspg
Summary: Float8 is becoming and increasingly popular datatype now that it is well supported on GPUs. This diff enables FP8 to work with `torch.cat`. This is pretty straight forward since memory operations dont vary based on the input dtype, but can be quite helpful for FP8 based models.
Test Plan:
```
buck2 run mode/opt -c fbcode.enable_gpu_sections=true -c fbcode.platform=platform010 -c fbcode.nvcc_arch=h100a -c fbcode.platform010_cuda_version=12 //caffe2/test:tensor_creation -- -r test_cat_all_dtypes_and_devices
```
Differential Revision: D64443965
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138046
Approved by: https://github.com/eqy, https://github.com/qchip, https://github.com/jianyuh
Update ruff to 0.4.1 .
This version fixes a lot false negatives/false positives, is 20-40% faster, and has various other bug fixes.
Below is a before and after table showing the execution time of ruff lint and ruff format in milliseconds courtesy of https://astral.sh/blog/ruff-v0.4.0
| Repository | Linter (v0.3) | Linter (v0.4) | Formatter (v0.3) | Formatter (v0.4) |
|----------------------------------------------------|---------------|---------------|------------------|------------------|
| [pytorch/pytorch](https://github.com/pytorch/pytorch) | 328.7 | 251.8 | 351.1 | 274.9 |
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124549
Approved by: https://github.com/ezyang
