Currently there are a few type annotations that falsely state that mypy doesn't support recursive types.
Recursive type support is available in mypy for a few years already. It has been officially enabled in [version 0.991](https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html). Pyright even had support for recursive types earlier (https://github.com/microsoft/pyright/issues/569), so there is probably no reason not to model these types correctly.
This PR models these types properly now. Since this has turned a few implicit `Any` into fully typed variables that are not narrowed cleanly, a small number of type ignores were necessary.
Note that regarding the `Argument` it is desirable to model it in a covariant way (i.e. using `Sequence` and `Mapping`) instead of making it invariant unnecessarily (using `List` and `Dict`). If it were modeled invariant, it would for instance mean that a `List[Node]` would not type check as `Argument`, because invariance would mean that it really has to be a `List[Argument]` (i.e., including all the branches of the union type). Since even the name of the type "argument" strongly suggest that it is semantically used as "argument", having covariance natural anyway.
There are no chances in this PR that affect runtime behavior.
CC @Skylion007
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142300
Approved by: https://github.com/ezyang, https://github.com/Skylion007
Summary: Change fx graph module's _replace_hook from a single hook, to a list of hooks. This is to prepare to registering more hooks for inductor provenance tracking, where we might need to register multiple hooks for node replacement.
Test Plan:
```
buck run mode/dev-nosan caffe2/test:fx -- -r test_hooks_for_node_update
buck run mode/dev-nosan caffe2/test:test_export -- -r test_replace_hook
```
Differential Revision: D66726724
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142006
Approved by: https://github.com/zhxchen17
Part of #134054.
This corresponds to the pytorch mypy changes from D61493706. Updating takes so
long and touches so many files that it's impossible to land as a whole without conflicting with some other intermediate change.
So landing these 'type: ignore' for pytorch in advance of them actually being needed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134202
Approved by: https://github.com/Skylion007
Summary:
- make default DCE pass check schema,
- need to rebase onto https://github.com/pytorch/pytorch/pull/131651 after it's in phabricator (for now the change is manually added).
- mark Proxy dump as NotImplemented for better error msg
- Remove Proxy from tensors when dumping models, as Proxy cannot be dumped.
More details in https://docs.google.com/document/d/1G5vmTXjzxoyVGRI2kpA1gQukK_Glyg2NrE0Oh6Nlg9A/edit?usp=sharing.
Test Plan:
CI
```
- buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r qat_conv2d
- test_export.py
- buck2 run 'fbcode//mode/dev-nosan' fbcode//modai/test:test_modai -- -r test_qat_stinson_htp_export
- buck2 run 'fbcode//mode/dev-nosan' fbcode//vizard_projects/ml_depth/tests:test_model -- -r test_qat_model_et
- buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test:fx -- -r dce
- buck2 run 'fbcode//mode/dev-nosan' fbcode//bolt/nn/executorch/backends/tests:qnn_test -- -r test_qat_bias=False,use_3d_input=False
- buck2 run 'fbcode//mode/dev-nosan' fbcode//bolt/nn/executorch/backends/tests:qnn_test -- -r test_qat_bias=True,use_3d_input=False
- buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_fold_bn_erases_bn_node
```
Reviewed By: angelayi
Differential Revision: D60319175
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132764
Approved by: https://github.com/angelayi
The problem was we were shoving SymInts into the constant_args side
table. The root problem is that torch.fx.node.base_types, which we use
to determine what can be put in the graph, doesn't actually have SymInt
in it. This PR fixes base_types to include SymInt.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131363
Approved by: https://github.com/oulgen, https://github.com/justinchuby
The problem was we were shoving SymInts into the constant_args side
table. The root problem is that torch.fx.node.base_types, which we use
to determine what can be put in the graph, doesn't actually have SymInt
in it. This PR fixes base_types to include SymInt.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131363
Approved by: https://github.com/oulgen
Summary:
This diff reverts D59561509
D59561509: [FX][export] DCE pass, check schema for node impurity (#130395) by yushangdi causes the following test failure:
Tests affected:
- [cogwheel:cogwheel_mtia_cmf_m5_shrunk_test#test_flow_with_verification](https://www.internalfb.com/intern/test/844425041436985/)
Here's the Multisect link:
https://www.internalfb.com/multisect/6533402
Here are the tasks that are relevant to this breakage:
T191383430: 10+ tests unhealthy for ads_mtia_inference
The backout may land if someone accepts it.
If this diff has been generated in error, you can Commandeer and Abandon it.
Test Plan: NA
Differential Revision: D60029318
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131341
Approved by: https://github.com/angelayi
This is a copy of Brian's PR https://github.com/pytorch/pytorch/pull/128754, with some changes in the test_distributed_patterns.py unit tests to more closely reflect FSDP2 patterns. Also disabled two tests `test_input_mutation_storage_resize_up_down` and `test_input_mutation_storage_resize_not_supported` in test_aotdispatch.py until we figure out the right behavior for them.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129203
Approved by: https://github.com/bdhirsh
Currently if `x` is a CUDA tensor, calling `x.untyped_storage().resize_()` seems to always go into the `built without cuda` branch of `resize_storage_bytes_()` regardless of whether PyTorch is built with CUDA. I suspect this is because `inductor_ops.cpp` is only included in `libtorch_cpu.so` thus doesn't have the `USE_CUDA` information or ability to link to CUDA-related functions.
This PR moves `resize_storage_bytes_()` related custom op functions out of `inductor_ops.cpp` into its standalone file `resize_storage_bytes.cpp` to be included in `libtorch_python.so` instead. This mimics the setup for `StorageMethods.cpp`. This way, `resize_storage_bytes_()` can have access to the CUDA-related functions, which passes the CUDA unit test.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129215
Approved by: https://github.com/jansel
We have some (limited) support for `set_()` input mutations in `torch.compile`, but one restriction today is that we force them to run outside of the graph, in the opaque runtime epilogue.
This is a problem for ppFSDP. Why? The usage pattern of ppFSDP forward graphs look something like this:
```
def forward_fsdp(sacrificial_param, sharded_param, inp):
allgathered_param = allgather(sharded_param)
sacrificial_param.set_(allgathered_param) # hidden in an autograd.Function that we trace
out = matmul(sacrificial_param, inp)
sacrificial_param.untyped_storage().resize_(0)
return out
```
When we functionalize this graph, `sacrificial_param` sees two distinct types of input mutations, that we must preserve: a `set_`, and a `resize_`. Importantly, at runtime the `set_()` must run **before** the `resize_()`. Why? the `set_()` updates the storage of our sacrificial param to the allgather'd data, which allows the call to `sacrificial_param.resize_()` to free the allgathered data later. If we run the two mutations in reverse order, we will never free the allgathered data.
We want to put the `resize_()` mutation op inside of the graph (see next PR, also there's a much longer description in that PR for anyone interested). However, this will require us to put `set_()` in the graph as well, in order for them to run in the correct order.
In order to do this, I had to add some extra restrictions: You are now required to run `set_()` under `no_grad()` if you use it with `torch.compile`, and if you perform any other mutations to the input, those must be under no_grad as well (otherwise, the mutations may mutate the `grad_fn` of the input, making it no longer safe to keep in the graph). These restrictions are hopefully reasonable, since `set_()` doesn't see much usage today (and the original impetus for adding set_() support a few months ago was for fsdp anyway)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122981
Approved by: https://github.com/jansel
ghstack dependencies: #122433, #123646
Summary: This was a micro optimization that I thought would save time but it is not correct. For example, we cannot compare fake tensors.
Test Plan:
```
buck2 run 'fbcode//mode/opt' fbcode//langtech/edge/ns/tools/tests:test_ns_jit_traced_model_all_optimization_f328819347_portal_ns
```
now passes
Differential Revision: D55904083
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123616
Approved by: https://github.com/aakhundov
This is a lot of files changed! Don't panic! Here's how it works:
* Previously, we set `follow_imports = silent` for our mypy.ini configuration. Per https://mypy.readthedocs.io/en/stable/running_mypy.html#follow-imports, what this does is whenever we have an import to a module which is not listed as a file to be typechecked in mypy, we typecheck it as normal but suppress all errors that occurred in that file.
* When mypy is run inside lintrunner, the list of files is precisely the files covered by the glob in lintrunner.toml, but with files in excludes excluded.
* The top-level directive `# mypy: ignore-errors` instructs mypy to typecheck the file as normal, but ignore all errors.
* Therefore, it should be equivalent to set `follow_imports = normal`, if we put `# mypy: ignore-errors` on all files that were previously excluded from the file list.
* Having done this, we can remove the exclude list from .lintrunner.toml, since excluding a file from typechecking is baked into the files themselves.
* torch/_dynamo and torch/_inductor were previously in the exclude list, because they were covered by MYPYINDUCTOR. It is not OK to mark these as `# mypy: ignore-errors` as this will impede typechecking on the alternate configuration. So they are temporarily being checked twice, but I am suppressing the errors in these files as the configurations are not quite the same. I plan to unify the configurations so this is only a temporary state.
* There were some straggler type errors after these changes somehow, so I fixed them as needed. There weren't that many.
In the future, to start type checking a file, just remove the ignore-errors directive from the top of the file.
The codemod was done with this script authored by GPT-4:
```
import glob
exclude_patterns = [
...
]
for pattern in exclude_patterns:
for filepath in glob.glob(pattern, recursive=True):
if filepath.endswith('.py'):
with open(filepath, 'r+') as f:
content = f.read()
f.seek(0, 0)
f.write('# mypy: ignore-errors\n\n' + content)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118414
Approved by: https://github.com/thiagocrepaldi, https://github.com/albanD
Summary: Adding an experimental API to FX graph module to place "hooks" every time when we are changing or replacing nodes in a graph, so that we can properly update the new name in graph signature and potentially other places.
Test Plan:
buck test mode/opt -c fbcode.enable_gpu_sections=true caffe2/test/distributed/_tensor/experimental:tp_transform
buck test mode/opt caffe2/test:test_export -- -r test_replace_hook
Differential Revision: D52896531
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117825
Approved by: https://github.com/avikchaudhuri
Inductor codegen for `_assert_async` is currently disabled because we don't really understand how to codegen `scalar_to_tensor` on a Sympy expression. I initially tried to see if I could get this to work, but I got into some weird problem involving stride sorting, so I decided to fix it properly by not going through a tensor.
So we introduce an `_assert_scalar` which takes a scalar as an argument, avoiding needing to turn a SymBool into a tensor before asserting on it. I also add `_functional_assert_scalar` for good luck, although this doesn't do anything right now because https://github.com/pytorch/pytorch/pull/104203 still hasn't been landed.
I need to customize the codegen for this operator, so I decide to directly implement it in Inductor, rather than trying to treat it as a generic ExternKernel. This leads to the new AssertScalar IR node. This is written carefully so that it doesn't get DCE'd by Inductor.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114148
Approved by: https://github.com/jansel
Updates flake8 to v6.1.0 and fixes a few lints using sed and some ruff tooling.
- Replace `assert(0)` with `raise AssertionError()`
- Remove extraneous parenthesis i.e.
- `assert(a == b)` -> `assert a == b`
- `if(x > y or y < z):`->`if x > y or y < z:`
- And `return('...')` -> `return '...'`
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116591
Approved by: https://github.com/albanD, https://github.com/malfet
Applies PLW0108 which removes useless lambda calls in Python, the rule is in preview so it is not ready to be enabled by default just yet. These are the autofixes from the rule.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113602
Approved by: https://github.com/albanD
Summary:
Traditionally when user want to update the arguments for an FX node, the only way is to call the setter of .args property on nodes. This may be problematic when we insert a lot of arguments. Because of the semantics of the setter method, it has a worst case O(n) complexity.
Adding a new insert_arg provides us two benefits:
1. The operation is guaranteed to be O(1) cost.
2. User can express the intentation more directly, instead of writing code like `node.args = (arg,) + node.args`
Test Plan: caffe2/test:fx -- -r test_insert_arg
Reviewed By: suo
Differential Revision: D50574435
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111974
Approved by: https://github.com/angelayi
