We have 4 different version of inductor benchmark Docker images used in CI at the moment:
1. `pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9-inductor-benchmarks` is used by almost all inductor jobs including nightly benchmark
2. `pytorch-linux-jammy-cuda12.8-cudnn9-py3.12-gcc9-inductor-benchmarks` runs inductor unit tests with python 3.12
3. `pytorch-linux-jammy-cuda12.8-cudnn9-py3.13-gcc9-inductor-benchmarks` runs inductor unit tests with python 3.13
4. `pytorch-linux-jammy-py3-gcc11-inductor-benchmarks` runs inductor unit tests on CPU
My proposal here is to clean up (2) and (3) and to keep (1) under the same setup from https://ghcr.io/pytorch/torchbench. Simplicity is the key here as inductor workflows are getting more and more complex:
1. Unit tests for Python variant like 3.12 and 3.13 were useful when they were first added to CI. They are much less useful now. [Flambeau](https://hud.pytorch.org/flambeau/s/3876ec7b-43f0-42c6-bfbf-899035e5bb77) shows a 0.97 correlation between them. And we are also moving to 3.14 nowadays. I want to choose 3.12 for (1), but will do this separately. This is also what TorchBench and vLLM are using on CI.
1. We are gradually cleaning up 3.9 on CI https://github.com/pytorch/pytorch/issues/161167
Another BE change here is to rename the jobs various inductor workflows because I think names like `linux-jammy-cuda12_8-py3_10-gcc9-inductor-build` is too long and confusing to look at, better just use human-friendly names like `inductor-build`. Other information is already spelled out in the build environment.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161536
Approved by: https://github.com/zou3519
Reland of https://github.com/pytorch/pytorch/pull/159923
Couple of fixes:
1. When we run into an operation we didn't proxy, we end up emitting fake constants. We detect this and warn using the FQN of the lifted constant. We warn because some internal users complained it was regressing their exportability.
2. Previous attribute mutation detection logic in non-strict didn't account for nested module structure. This fixes silent incorrectness issue of exporting esm and qwen in non-strict
3. We modify yolov3 to fix the previous silent incorrect behaviour
4. We use strict export for levit_128 because it errors in non-strict due to more strict side effect checking
When upgrading torchbench pin, opacus_cifar10 seems to not run on eager anymore. I verified this by pushing a temporary PR on master with new pin. So i added it to expect_fail list.
Differential Revision: [D81133908](https://our.internmc.facebook.com/intern/diff/D81133908)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161589
Approved by: https://github.com/avikchaudhuri
Including below changes,
- Add XPU support package 2025.2 build and test in CI for both Linux and Windows
- Keep XPU support package 2025.1 build in CI to ensure no break issue until PyTorch 2.9 release
- Upgrade XPU support package from 2025.1 to 2025.2 in CD for both Linux and Windows
- Rename Linux CI job name & image name to n & n-1
- Update XPU runtime pypi packages dependencies of CD wheels
- Remove deprecated support package version docker image build
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158733
Approved by: https://github.com/EikanWang, https://github.com/atalman
setup vllm test logics.
1. install wheels generated from previous build stage
2. generate and install vllm test pkg list on run time based on the torch wheels in the instance
3. run test based on the pre-defined test plan
notice the test-plan format is temporary for some basic vllm testing
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160361
Approved by: https://github.com/atalman, https://github.com/huydhn
Differential Revision: D79694055
Couple of fixes:
1. When we run into an operation we didn't proxy, we end up emitting fake constants. We detect this and error using the FQN of the lifted constant
2. Previous attribute mutation detection logic in non-strict didn't account for nested module structure. This fixes silent incorrectness issue of exporting esm and qwen in non-strict
3. We modify yolov3 to fix the previous silent incorrect behaviour
When upgrading torchbench pin, opacus_cifar10 seems to not run on eager anymore. I verified this by pushing a temporary PR on master with new pin. So i added it to expect_fail list.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159923
Approved by: https://github.com/avikchaudhuri
Removing tb-nightly because we found issues when importing tensorboard as having both tb-nightly and tensorboard causes issues when pip would report 2.18.0 (pinned tensorboard) but importing in a python shell would report 2.13.XXX. This mismatch causes issues when running tests in a numpy2.X environment. e.g.
```
/var/lib/jenkins/pytorch# PYTORCH_TEST_WITH_ROCM=1 python test/test_monitor.py TestMonitorTensorboard.test_event_handler
/opt/venv/lib/python3.12/site-packages/redis/connection.py:77: UserWarning: redis-py works best with hiredis. Please consider installing
warnings.warn(msg)
/opt/venv/lib/python3.12/site-packages/google/protobuf/internal/well_known_types.py:91: DeprecationWarning: datetime.datetime.utcfromtimestamp() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.fromtimestamp(timestamp, datetime.UTC).
_EPOCH_DATETIME_NAIVE = datetime.datetime.utcfromtimestamp(0)
E
======================================================================
ERROR: test_event_handler (__main__.TestMonitorTensorboard.test_event_handler)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/var/lib/jenkins/pytorch/test/test_monitor.py", line 116, in setUp
from tensorboard.backend.event_processing import (
File "/opt/venv/lib/python3.12/site-packages/tensorboard/backend/event_processing/plugin_event_multiplexer.py", line 25, in <module>
from tensorboard.backend.event_processing import (
File "/opt/venv/lib/python3.12/site-packages/tensorboard/backend/event_processing/plugin_event_accumulator.py", line 25, in <module>
from tensorboard.backend.event_processing import event_file_loader
File "/opt/venv/lib/python3.12/site-packages/tensorboard/backend/event_processing/event_file_loader.py", line 21, in <module>
from tensorboard import dataclass_compat
File "/opt/venv/lib/python3.12/site-packages/tensorboard/dataclass_compat.py", line 33, in <module>
from tensorboard.plugins.hparams import metadata as hparams_metadata
File "/opt/venv/lib/python3.12/site-packages/tensorboard/plugins/hparams/metadata.py", line 32, in <module>
NULL_TENSOR = tensor_util.make_tensor_proto(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/lib/python3.12/site-packages/tensorboard/util/tensor_util.py", line 405, in make_tensor_proto
numpy_dtype = dtypes.as_dtype(nparray.dtype)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/lib/python3.12/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py", line 677, in as_dtype
if type_value.type == np.string_ or type_value.type == np.unicode_:
^^^^^^^^^^
File "/opt/venv/lib/python3.12/site-packages/numpy/__init__.py", line 400, in __getattr__
raise AttributeError(
AttributeError: `np.string_` was removed in the NumPy 2.0 release. Use `np.bytes_` instead.
----------------------------------------------------------------------
Ran 1 test in 0.355s
FAILED (errors=1)
```
After removing tb-nightly and ensuring that tensorboard 2.18.0 is the only tensoboard in the env:
```
root@rocm-framework-47:/var/lib/jenkins/pytorch# PYTORCH_TEST_WITH_ROCM=1 python test/test_monitor.py TestMonitorTensorboard.test_event_handler
.
----------------------------------------------------------------------
Ran 1 test in 0.409s
OK
```
```
>>> import tensorboard
>>> print(tensorboard.__version__)
2.13.0a20230426
```
```:/# pip show tensorboard
Name: tensorboard
Version: 2.18.0
Summary: TensorBoard lets you watch Tensors Flow
Home-page: https://github.com/tensorflow/tensorboard
Author: Google Inc.
Author-email: packages@tensorflow.org
License: Apache 2.0
Location: /opt/venv/lib/python3.12/site-packages
Requires: absl-py, grpcio, markdown, numpy, packaging, protobuf, setuptools, six, tensorboard-data-server, werkzeug
Required-by:
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160996
Approved by: https://github.com/huydhn
