On this line, we see that the bw_compiler that dynamo uses for AotAutograd automatically disables the backward runnable:
05dd638ee9/torch/_dynamo/backends/common.py (L76)
This disables dynamo in the bw_compiler but also disables the runnable the compiler returns.
On a AOTAutogradCache hit, however, we never call the bw_compiler! So we don't disable dynamo properly. This only has an effect on certain cases of cpu tensors' backwards, where the backward is being done in python land, and dynamo unnecessarily tries to trace through the inductor generated code. It also only matters if the backward is being accessed outside of dynamo itself (say, in a graph break in eager mode), since dynamo properly disables the forward function already.
```
I0605 09:58:40.135000 3981970 torch/_dynamo/eval_frame.py:517] TorchDynamo attempted to trace the following frames: [
I0605 09:58:40.135000 3981970 torch/_dynamo/eval_frame.py:517] * fn /home/jjwu/test.py:9
I0605 09:58:40.135000 3981970 torch/_dynamo/eval_frame.py:517] * cast /data/users/jjwu/a/pytorch-env/lib/python3.10/typing.py:1737
I0605 09:58:40.135000 3981970 torch/_dynamo/eval_frame.py:517] * call /tmp/torchinductor_jjwu/rq/crq327nhoyjzog5n3qlchauucdrunrtutwmmoh7ipoe2ngnson5s.py:35
I0605 09:58:40.135000 3981970 torch/_dynamo/eval_frame.py:517] * fn /home/jjwu/test.py:9
I0605 09:58:40.135000 3981970 torch/_dynamo/eval_frame.py:517] * cast /data/users/jjwu/a/pytorch-env/lib/python3.10/typing.py:1737
I0605 09:58:40.135000 3981970 torch/_dynamo/eval_frame.py:517] * call /tmp/torchinductor_jjwu/rq/crq327nhoyjzog5n3qlchauucdrunrtutwmmoh7ipoe2ngnson5s.py:35
I0605 09:58:40.135000 3981970 torch/_dynamo/eval_frame.py:517] ]
```
This PR fixes the issue and adds a unit test showing that with or without cache hit, the frames dynamo is tracing is identical.
Fixes https://github.com/pytorch/pytorch/issues/154536
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155251
Approved by: https://github.com/bdhirsh, https://github.com/anijain2305
This implements a new wrapper class AOTDispatchCompiler wrapper, which is just a wrapper around a callable that returns an OutputCode. We can then use it in AOTDispatch to decide whether or not to use the cache: if fw_compiler, bw_compiler and inference_compiler are all AOTDispatchCompilers, then we enable caching.
This type is pretty close to _CompiledFxGraphCallable, except it's not allowed to take any kwargs. Not sure how to consolidate the two ideas together just yet: unfortunately, there's no way to properly annotate the types to make them related. But a lot of the time, the input to this function will be a partially applied _CompiledFxGraphCallable.
This allows the PR above this one to enable AOTAutogradCache everywhere, but not increase instruction count or enable cache on unit tests that use aot_eager or other non inductor compilers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142205
Approved by: https://github.com/oulgen, https://github.com/bdhirsh
Fixes a bunch of benchmarks that failed with cudagraph errors including `tlp python benchmarks/dynamo/timm_models.py --device cuda --inductor --accuracy --amp --training --only resmlp_12_224` when `specialize_float=False`
Also brings down number of overall failures (with keep-going) from 108 => 62. I'd estimate >80% of those 62 are wobbly expect tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140346
Approved by: https://github.com/ezyang
ghstack dependencies: #140983, #141003
Type annotations for compile_fx.
- Some of the stuff here is pretty complicated (functions which return functions that take functions) so I bailed on those and used `Any` just to get the rest landed.
- There are also changes to type signatures in other files which I did just to let mypy know more about the types in compile_fx.py.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138033
Approved by: https://github.com/Skylion007
## save&load support for OptimizedModule
[Issue Description](https://github.com/pytorch/pytorch/pull/101651)
English is not my native language; please excuse typing errors.
This pr is based on commit b9588101c4d3411b107fdc860acfa8a72c642f91\
I'll do something with the merge conflicts later
### test result for test/dynamo
Conclusion:\
It performs the same as before as far as I can see.
ENV(CPU only):\
platform linux -- Python 3.10.14, pytest-7.3.2, pluggy-1.5.0\
configfile: pytest.ini\
plugins: anyio-3.7.1, cpp-2.3.0, flakefinder-1.1.0, xdist-3.3.1, xdoctest-1.1.0, metadata-3.1.1, html-4.1.1, hypothesis-5.35.1, rerunfailures-14.0
#### before this pr:
[before](https://github.com/pytorch/pytorch/files/15329370/before.md)
#### after this pr:
[after](https://github.com/pytorch/pytorch/files/15329376/after.md)
### some changes
1. add test_save_and_load to test/dynamo/test_modules.py with & without "backend='inductor'"
2. add \_\_reduce\_\_ function to OptimizedModule and derived classes of _TorchDynamoContext for pickling & unpickling
3. change the wrappers into wrapper classes ( including convert_frame_assert, convert_frame, catch_errors_wrapper in torch/_dynamo/convert_frame.py & wrap_backend_debug in torch/_dynamo/repro/after_dynamo.py )
4. change self.output.compiler_fn into innermost_fn(self.output.compiler_fn) in torch/_dynamo/symbolic_convert.py to get the origin compiler_fn and to avoid the "compiler_fn is not eager" condition
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126374
Approved by: https://github.com/msaroufim, https://github.com/jansel
# Note: Returning Fake Tensors on First AOT Autograd Call
#
# Inductor will optimize strides of outputs when it deems it profitable.
# For instance, converting to channels last. When we split the graph here
# into multiple inductor compilations, we need to make sure that the
# output strides of one compilation is appropriately passed to the subsequent
# compilations. However, the mapping from inductor output to dynamo output
# is non-trivial due to aot_autograd's deduping, de-aliasing, mutation, re-writing,
# subclass handling, etc. In order to replay all this logic we set a flag such that
# the first invocation of inductor in aot_autograd will return Fake Tensors with
# appropriate strides. Then, all of aot autograd's runtime logic is replayed.
# This gives us the appropriately strided outputs here which will reflect runtime strides.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120523
Approved by: https://github.com/yf225, https://github.com/bdhirsh
The original motivation for MYPYINDUCTOR was a faster type checking configuration that only checked a subset of files. With the removal of `follow_imports = ignore`, we are now able to use dmypy to do fast incremental typechecking, eliminating the need for this.
Perhaps erroneously, when I tee'ed up this PR I elected to delete the `follow_imports = skip` designations in the mypy-inductor.ini. This lead to a number of extra type error suppressions that I manually edited. You will need to review.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118432
Approved by: https://github.com/Skylion007
ghstack dependencies: #118414, #118418
For a while now, we've been re-running our functionalization analysis pass twice - once for get metadata when dedup'ing, and an entire second time during aot_dispatch_base/autograd.
This should also probably speed up compile times pretty noticeably, since we're going from:
(a) inference-only trace case: 3 fw traces -> 2 fw traces
(b) autograd trace case: 2 fw traces + 1 joint trace -> 1 fw trace + 1 joint trace
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95992
Approved by: https://github.com/ezyang
