https://github.com/pytorch/pytorch/issues/144893
```
python benchmarks/dynamo/timm_models.py --only poolformer_m36 --accuracy --no-translation-validatio --training --amp --device cuda --backend inductor
```
`--float32`, `--bfloat16` - passes the accuracy
`--disable-cudagraph` does not change the result
accuracy_fail only for `--amp` and gives `0.048` res_error, on 1-element result Tensor.
This fails with `0.01` tolerance.
If to increase tolerance to 0.04 it passes. I have not reproduced "eager_two_runs_differ" on H100.
I think this is a true distribution of results with `--amp`, so increasing tolerance to 0.04 for ano case only makes it passing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145375
Approved by: https://github.com/desertfire
Mitigates the deterministic benchmark regression: https://github.com/pytorch/pytorch/issues/144775#issuecomment-2593411844. and maybe the dashboard issue.
fx.Node.is_impure is unexpectedly a hot spot. It gets called for every node in the graph whenever we invoke DCE, which should be okay, EXCEPT we invoke DCE on the full graph ~10 times at various stages of torch.compile, and an insane number of times (>O(parameters)) for the subgraphs traced by the pattern matcher.
I considered addressing this problem by reducing the amount of times DCE is called, but I think we can only trim the ones from the pattern matcher, which will require some refactor/caching solution that I leave out of this PR.
torch.Tag.nondeterministic_seeded is provided by native_functions.yml and is implemented as a list. Most of the time, it has <=2 elements, so it's not really worth it to turn it into a set for fast lookup.
Using the deterministic instruction count benchmarks
```python
# before
aotdispatcher_partitioner_cpu,compile_time_instruction_count,8914894946
aotdispatcher_partitioner_cpu,compile_time_instruction_count,8866669058
# after
aotdispatcher_partitioner_cpu,compile_time_instruction_count,8770562314
aotdispatcher_partitioner_cpu,compile_time_instruction_count,8779547794
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145118
Approved by: https://github.com/ezyang, https://github.com/zou3519
The benchmark is failing with the following error
```
File "/var/lib/jenkins/workspace/benchmarks/gpt_fast/benchmark.py", line 333, in <module>
main(output_file=args.output, only_model=args.only)
File "/var/lib/jenkins/workspace/benchmarks/gpt_fast/benchmark.py", line 308, in main
lst = func(device)
File "/var/lib/jenkins/workspace/benchmarks/gpt_fast/benchmark.py", line 66, in run_mlp_layer_norm_gelu
us_per_iter = benchmarker.benchmark(compiled_mod, (x,)) * 1000
File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/_inductor/runtime/benchmarking.py", line 39, in wrapper
return fn(self, *args, **kwargs)
TypeError: benchmark() missing 1 required positional argument: 'fn_kwargs'
```
An example error is https://github.com/pytorch/pytorch/actions/runs/12862761823/job/35858912555
I also assign `oncall: pt2` as the owner of this job going forward.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145235
Approved by: https://github.com/nmacchioni
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
This PR adds the most basic custom benchmarker (i.e. a benchmarker that is not provided by Triton), which we call `InductorBenchmarker`. This new benchmarker is very basic in principal, and very closely follows Triton's `do_bench` implementation with slight changes such as flushing the exact L2 cache size (Triton defaults to 256mb), using a buffer zero for warmup (Triton uses the benchmarked kernel itself, I found that buffer zeroes are more consistent), and returning the min runtime (Triton can return min, among other things, currently Inductor picks median).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133058
Approved by: https://github.com/eellison
ghstack dependencies: #144315
The regex-based parser would erroneously split on commas in nested brackets, for example, it would do the following parse which is wrong:
'M: [(32, 16), (64, 32)], ZPB: 2' -> ['M: [(32, 16)', ' (64, 32)]', 'ZPB: 2']
The new manual parser handles this situation the right way:
'M: [(32, 16), (64, 32)], ZPB: 2' -> ['M: [(32, 16), (64, 32)]', 'ZPB: 2']
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144297
Approved by: https://github.com/XuehaiPan, https://github.com/jeffdaily
Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
Changes:
1. Bump `ruff` from 0.7.4 to 0.8.4
2. Change `%`-formatted strings to f-string
3. Change arguments with the `__`-prefix to positional-only arguments with the `/` separator in function signature.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143753
Approved by: https://github.com/Skylion007
Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
Fixes#130559
* Intro
This PR adds support for `@contextmanager` in Dynamo. We chose to limit the
scope of this work to only `@contextmanager` and plan to handle generators fully
in #141055 (still in draft).
* Motivation
Dynamo lacks support for generator functions. When it encounters one, it traces
it as if it were a regular function. This is problematic because it can lead to
incorrect behavior. To illustrate, consider the test case below:
```python
import torch
import contextlib
@contextlib.contextmanager
def set_default_dtype(dtype):
old_dtype = torch.get_default_dtype()
try:
torch.set_default_dtype(dtype)
yield
finally:
torch.set_default_dtype(old_dtype)
@torch.compile(backend="eager", fullgraph=True)
def fn():
with set_default_dtype(torch.float64):
x = torch.tensor([3.0, 3.0 + 5.0j])
return x
```
Before this work, Dynamo would not stop at the `yield`, and the graph produced
would contain both calls to `set_default_dtype` executed one after the other.
This is incorrect because the context manager should execute code before and
after the `yield`.
* List of changes
`YIELD_VALUE` now raises an exception (`YieldValueOp`) to signal that control
flow must be suspended and returned to the caller. Additionally, `RETURN_VALUE`
behaves differently in a generator function. Unlike regular functions, where
`RETURN_VALUE` indicates the final result, in generators it signifies that the
generator is exhausted and implicitly raises `StopIteration`.
A new `VariableTracker` named `FunctionDecoratedByContextlibContextManagerVariable`
was introduced to handle `@contextmanager`. This variable tracker acts not just
as a wrapper for the original function but also maintains an internal `tx`
(InstructionTranslator) object to suspend and return control flow to the parent
tracer when a `yield` is encountered.
* Corner cases
Returning a context manager from a compiled function is not supported. This
would require PyTorch to synchronize the generator state between Dynamo and the
interpreter. Any attempt to return it will result in an `IncorrectUsage`
exception.
Graph breaks require special handling as well. In the event of a graph break,
the frame associated with the context manager is skipped, and the context
manager runs in eager mode.
* This PR is breaking my code
There is a configuration flag (`enable_trace_contextlib`) that can be set to
`False` to disable tracing context managers. If this still causes crashes,
please revert this PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136033
Approved by: https://github.com/zou3519
