Reference: https://docs.astral.sh/ruff/formatter/black/#assert-statements
> Unlike Black, Ruff prefers breaking the message over breaking the assertion, similar to how both Ruff and Black prefer breaking the assignment value over breaking the assignment target:
>
> ```python
> # Input
> assert (
> len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
>
> # Black
> assert (
> len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
> # Ruff
> assert len(policy_types) >= priority + num_duplicates, (
> f"This tests needs at least {priority + num_duplicates} many types."
> )
> ```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144546
Approved by: https://github.com/malfet
This is one of a series of PRs to update us to PEP585 (changing Dict -> dict, List -> list, etc). Most of the PRs were completely automated with RUFF as follows:
Since RUFF UP006 is considered an "unsafe" fix first we need to enable unsafe fixes:
```
--- a/tools/linter/adapters/ruff_linter.py
+++ b/tools/linter/adapters/ruff_linter.py
@@ -313,6 +313,7 @@
"ruff",
"check",
"--fix-only",
+ "--unsafe-fixes",
"--exit-zero",
*([f"--config={config}"] if config else []),
"--stdin-filename",
```
Then we need to tell RUFF to allow UP006 (as a final PR once all of these have landed this will be made permanent):
```
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -40,7 +40,7 @@
[tool.ruff]
-target-version = "py38"
+target-version = "py39"
line-length = 88
src = ["caffe2", "torch", "torchgen", "functorch", "test"]
@@ -87,7 +87,6 @@
"SIM116", # Disable Use a dictionary instead of consecutive `if` statements
"SIM117",
"SIM118",
- "UP006", # keep-runtime-typing
"UP007", # keep-runtime-typing
]
select = [
```
Finally running `lintrunner -a --take RUFF` will fix up the deprecated uses.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145101
Approved by: https://github.com/bobrenjc93
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
ghstack dependencies: #127122, #127123, #127124, #127125
Simplifies and optimizes dict construction using the `fromkeys` classmethod ctor. This also makes it really obvious when all the keys will have the same static value, which could be a bug if unintentional. It is also significantly faster than using a dict comprehension. The rule is in preview, but I am adding a forward fix for when it becomes stable.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118637
Approved by: https://github.com/albanD
# Summary
This PR introduces a new Tensor subclass that is designed to be used with torch.nn.functional.scaled_dot_product_attention. Currently we have a boolean `is_causal` flag that allows users to do do causal masking without the need to actually create the "realized" attention bias and pass into sdpa. We originally added this flag since there is native support in both fused kernels we support. This provides a big performance gain ( the kernels only need to iterate over ~0.5x the sequence, and for very large sequence lengths this can provide vary large memory improvements.
The flag was introduced when the early on in the kernel development and at the time it was implicitly meant to "upper_left" causal attention. This distinction only matters when the attention_bias is not square. For a more detailed break down see: https://github.com/pytorch/pytorch/issues/108108. The kernels default behavior has since changed, largely due to the rise of autogressive text generation. And unfortunately this would lead to a BC break. In the long term it may actually be beneficial to change the default meaning of `is_causal` to represent lower_right causal masking.
The larger theme though is laid here: https://github.com/pytorch/pytorch/issues/110681. The thesis being that there is alot of innovation in SDPA revolving around the attention_bias being used. This is the first in hopefully a few more attention_biases that we would like to add. The next interesting one would be `sliding_window` which is used by the popular mistral model family.
Results from benchmarking, I improved the meff_attention perf hence the slightly decreased max perf.
```Shell
+---------+--------------------+------------+-----------+-----------+-----------+-----------+----------------+----------+
| Type | Speedup | batch_size | num_heads | q_seq_len | k_seq_len | embed_dim | dtype | head_dim |
+---------+--------------------+------------+-----------+-----------+-----------+-----------+----------------+----------+
| Average | 1.2388050062214226 | | | | | | | |
| Max | 1.831672915579016 | 128 | 32 | 1024 | 2048 | 2048 | torch.bfloat16 | 64 |
| Min | 0.9430534166730135 | 1 | 16 | 256 | 416 | 2048 | torch.bfloat16 | 128 |
+---------+--------------------+------------+-----------+-----------+-----------+-----------+----------------+----------+
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114823
Approved by: https://github.com/cpuhrsch
