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/127124
Approved by: https://github.com/Skylion007
ghstack dependencies: #127122, #127123
Adds a ruff lint rule to ban raising raw exceptions. Most of these should at the very least be runtime exception, value errors, type errors or some other errors. There are hundreds of instance of these bad exception types already in the codebase, so I have noqa'd most of them. Hopefully this error code will get commiters to rethink what exception type they should raise when they submit a PR.
I also encourage people to gradually go and fix all the existing noqas that have been added so they can be removed overtime and our exception typing can be improved.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124570
Approved by: https://github.com/ezyang
* Enable PERF402. Makes code more efficient and succinct by removing useless list copies that could be accomplished either via a list constructor or extend call. All test cases have noqa added since performance is not as sensitive in that folder.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115505
Approved by: https://github.com/malfet
Testing out some new rules that are in beta, I think I will apply this one codebase wide once it's out of preview. Replaces the hack of using `[:]` to do copies of list with the proper copy method. More efficient and more readable.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112990
Approved by: https://github.com/ezyang
Summary:
In SelectiveBuildOperator, we can specify argument `include_all_overloads`. If True, all overloaded operators (for example, `aten::to.dtype_layout`, `aten::to.prim_Device"` are considered as overloaded operators of `aten::to`), will be built and linked to the final binary. This can significantly increases the final binary size, which could be a deal breaker for on-device deployment.
In this diff, we make back-compatible changes to add new arguments `--not-include-all-overloads-static-root-ops` and `--not-include-all-overloads-closure-ops`. When they are set, we set `include_all_overloads` flag to False for static root ops and closure ops, and rely on code analyzer to decide the actual used overloaded operator.
Test Plan:
- unit test
```
buck test //xplat/caffe2/tools:gen_operators_yaml_test
```
- See test plan in D48771544 where we reduce the shared lib file `libmrengine.lib` from 16653072 bytes to 13686032 bytes.
- See detailed document: https://fburl.com/gdoc/mc93h6kb
Reviewed By: larryliu0820
Differential Revision: D48772302
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108396
Approved by: https://github.com/larryliu0820
Summary: Removes the dependency on the unified YAML file
Test Plan:
Smoke test via some caffe2 tests.
```
buck2 run xplat/caffe2:supported_mobile_models_test
```
Build a major FoA app that uses model tracing and confirm it still works.
```
buck2 build fb4a
```
CI/CD for the rest. If operator tracing / bundling was broken, I'd hope in the 1000+ tests spawned by this change should catch it.
Differential Revision: D44946368
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99122
Approved by: https://github.com/dhruvbird
Preferring dash over underscore in command-line options. Add `--command-arg-name` to the argument parser. The old arguments with underscores `--command_arg_name` are kept for backward compatibility.
Both dashes and underscores are used in the PyTorch codebase. Some argument parsers only have dashes or only have underscores in arguments. For example, the `torchrun` utility for distributed training only accepts underscore arguments (e.g., `--master_port`). The dashes are more common in other command-line tools. And it looks to be the default choice in the Python standard library:
`argparse.BooleanOptionalAction`: 4a9dff0e5a/Lib/argparse.py (L893-L895)
```python
class BooleanOptionalAction(Action):
def __init__(...):
if option_string.startswith('--'):
option_string = '--no-' + option_string[2:]
_option_strings.append(option_string)
```
It adds `--no-argname`, not `--no_argname`. Also typing `_` need to press the shift or the caps-lock key than `-`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94505
Approved by: https://github.com/ezyang, https://github.com/seemethere
With ufmt in place https://github.com/pytorch/pytorch/pull/81157, we can now use it to gradually format all files. I'm breaking this down into multiple smaller batches to avoid too many merge conflicts later on.
This batch (as copied from the current BLACK linter config):
* `tools/**/*.py`
Upcoming batchs:
* `torchgen/**/*.py`
* `torch/package/**/*.py`
* `torch/onnx/**/*.py`
* `torch/_refs/**/*.py`
* `torch/_prims/**/*.py`
* `torch/_meta_registrations.py`
* `torch/_decomp/**/*.py`
* `test/onnx/**/*.py`
Once they are all formatted, BLACK linter will be removed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81285
Approved by: https://github.com/suo
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68180
Since we've open sourced the tracing-based selective build, we can deprecate the
op-dependency-graph-based selective build and the static analyzer tool that
produces the dependency graph.
ghstack-source-id: 143108377
Test Plan: CIs
Reviewed By: seemethere
Differential Revision: D32358467
fbshipit-source-id: c61523706b85a49361416da2230ec1b035b8b99c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62185
This file can take 5 minutes on its own to compile, and is the single limiting
factor for compile time of `libtorch_cpu` on a 32-core threadripper. Instead,
sharding into 5 files that take around 1 minute each cuts a full minute off the
overall build time.
This also factors out the `.findSchemaOrThrow(...).typed` step so the code can
be shared between `call` and `redispatch`.
Test Plan: Imported from OSS
Reviewed By: bdhirsh
Differential Revision: D29962049
Pulled By: albanD
fbshipit-source-id: be5df05fbea09ada0d825855f1618c25a11abbd8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59573
To do mobile selective build, we have several options:
1. static dispatch;
2. dynamic dispatch + static analysis (to create the dependency graph);
3. dynamic dispatch + tracing;
We are developing 3. For open source, we used to only support 1, and
currently we support both 1 and 2.
This file is only used for 2. It was introduced when we deprecated
the static dispatch (1). The motivation was to make sure we have a
low-friction selective build workflow for dynamic dispatch (2).
As the name indicates, it is the *default* dependency graph that users
can try if they don't bother to run the static analyzer themselves.
We have a CI to run the full workflow of 2 on every PR, which creates
the dependency graph on-the-fly instead of using the committed file.
Since the workflow to automatically update the file has been broken
for a while, it started to confuse other pytorch developers as people
are already manually editing it, and it might be broken for some models
already.
We reintroduced the static dispatch recently, so we decide to deprecate
this file now and automatically turn on static dispatch if users run
selective build without providing the static analysis graph.
The tracing-based selective build will be the ultimate solution we'd
like to provide for OSS, but it will take some more effort to polish
and release.
Differential Revision:
D28941020
D28941020
Test Plan: Imported from OSS
Reviewed By: dhruvbird
Pulled By: ljk53
fbshipit-source-id: 9977ab8568e2cc1bdcdecd3d22e29547ef63889e
Summary:
This PR greatly simplifies `mypy-strict.ini` by strictly typing everything in `.github` and `tools`, rather than picking and choosing only specific files in those two dirs. It also removes `warn_unused_ignores` from `mypy-strict.ini`, for reasons described in https://github.com/pytorch/pytorch/pull/56402#issuecomment-822743795: basically, that setting makes life more difficult depending on what libraries you have installed locally vs in CI (e.g. `ruamel`).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59117
Test Plan:
```
flake8
mypy --config mypy-strict.ini
```
Reviewed By: malfet
Differential Revision: D28765386
Pulled By: samestep
fbshipit-source-id: 3e744e301c7a464f8a2a2428fcdbad534e231f2e
Summary:
I'd like the following pattern (a natural composition of Amp with full fwd+bwd capture) to work:
```python
# Create "static_input" with dummy data, run warmup iterations,
# call optimizer.zero_grad(set_to_none=True), then
g = torch.cuda._Graph()
s.wait_stream(torch.cuda.current_stream())
with torch.cuda.stream(s):
optimizer.zero_grad(set_to_none=True)
g.capture_begin()
with autocast():
out = model(static_input)
loss = loss_fn(out)
scaler.scale(loss).backward()
g.capture_end()
torch.cuda.current_stream().wait_stream(s)
# Training loop:
for b in data:
# optimizer.zero_grad() deliberately omitted, replay()'s baked-in backward will refill statically held .grads
static_input.copy_(b)
g.replay()
scaler.step(optimizer)
scaler.update()
```
Right now `GradScaler` can't work with this pattern because `update()` creates the scale tensor for the next iteration out of place. This PR changes `update()` to act in place on a long-lived scale tensor that stays static across iterations.
I'm not sure how this change affects XLA (see https://github.com/pytorch/pytorch/pull/48570), so we shouldn't merge without approval from ailzhang yaochengji.
Tagged bc-breaking because it's a change to the amp update utility function in native_functions.yaml. The function was never meant to be user-facing though.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55562
Reviewed By: zou3519
Differential Revision: D28046159
Pulled By: ngimel
fbshipit-source-id: 02018c221609974546c562f691e20ab6ac611910
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50611
Removed the unused old-style code to prevent it from being used.
Added all autograd/gen_pyi sources to mypy-strict.ini config.
Confirmed byte-for-byte compatible with the old codegen:
```
Run it before and after this PR:
.jenkins/pytorch/codegen-test.sh <baseline_output_dir>
.jenkins/pytorch/codegen-test.sh <test_output_dir>
Then run diff to compare the generated files:
diff -Naur <baseline_output_dir> <test_output_dir>
```
Confirmed clean mypy-strict run:
```
mypy --config mypy-strict.ini
```
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D25929730
Pulled By: ljk53
fbshipit-source-id: 1fc94436fd4a6b9b368ee0736e99bfb3c01d38ef
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49220
Since all ops are c10-full, we can remove .impl_UNBOXED now.
This also removes the ability of KernelFunction or CppFunction to store unboxedOnly kernels.
ghstack-source-id: 119450489
Test Plan: waitforsandcastle
Reviewed By: ezyang
Differential Revision: D25490225
fbshipit-source-id: 32de9d591e6a842fe18abc82541580647e9cfdad
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48308
The original regex that I added didn't correctly match namespaces that started with an underscore (e.g. `_test`), which caused a master-only test to fail.
The only change from the previous commit is that I updated the regex like so:
before: `^.*TORCH_LIBRARY_IMPL_init_([^_]+)_([^_]+)_[0-9]+(\(.*)?$`
after: `^.*TORCH_LIBRARY_IMPL_init_([_]*[^_]+)_([^_]+)_[0-9]+(\(.*)?$`
I added in a `[_]*` to the beginning of the namespace capture. I did the same for the `_FRAGMENT` regex.
Verified that running `ANALYZE_TEST=1 tools/code_analyzer/build.sh` (as the master-only test does) produces no diff in the output.
Fixing regex pattern to allow for underscores at the beginning of the
namespace
This reverts commit 3c936ecd3c68f395dad01f42935f20ed8068da02.
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D25123295
Pulled By: bdhirsh
fbshipit-source-id: 54bd1e3f0c8e28145e736142ad62a18806bb9672
Summary:
`__ROOT__` ops are only used in full-jit. To make size compact, disable using it in inference. Since FL is still in fill-jit, keep it for training only.
It saves -17 KB for fbios.
TODO: when FL is migrated to lite_trainer, remove `__ROOT__` to save size in training too.
Test Plan: CI
Reviewed By: dhruvbird
Differential Revision: D24686838
fbshipit-source-id: 15214cebb9d8defa3fdac3aa0d73884b352aa753
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46057
The code analyser (that uses LLVM and runs in the OSS PyTorch git repo) already produces a YAML file which contains base operator names and the operators that they depend on. Currently, this operator dependency graph is converted into a python dictionary to be imported in BUCK and used there. However, it is mostly fed into other executables by serializing the JSON and the consumer pieces this JSON together by concatenating each argument together. This seems unnecessary. Instead, this diff retains the original YAML file and makes all consumers consume that same YAML file.
ghstack-source-id: 114641582
Test Plan: Build Lite Predictor + sandcastle.
Reviewed By: iseeyuan
Differential Revision: D24186303
fbshipit-source-id: eecf41bf673d90b960c3efe7a1271249f0a4867f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45722
This diff does a bunch of things:
1. Introduces some abstractions as detailed in https://fb.quip.com/2oEzAR5MKqbD to help with selective build related codegen in multiple files.
2. Adds helper methods to combine operators, debug info, operator lists, etc...
3. Currently, the selective build machinery querying `op_registration_whitelist` directly at various places in the code. `op_registration_whitelist` is a list of allowed operator names (without overload name). We want to move to a world where the overload names are also included so that we can be more selective about which operators we include. To that effect, it makes sense to hide the checking logic in a separate abstraction and have the build use that abstraction instead of putting all this selective build specific logic in the code-generator itself. This change is attempting to do just that.
4. Updates generate_code, unboxing-wrapper codegen, and autograd codegen to accept the operator selector paradigm as opposed to a selected operator list.
5. Update `tools/code_analyzer/gen_op_registration_allowlist.py` to expose providing an actual structured operator dependency graph in addition to a serialized string.
There are a bunch of structural changes as well:
1. `root_op_list.yaml` and `combined_op_list.yaml` are now actual YAML files (not a space separated list of operator names)
2. `generate_code.py` accepts only paths to operator list YAML files (both old style as well as new style) and not list of operator names on the command line as arguments
3. `gen.py` optionally also accepts a custom build related operators YAML path (this file has information about which operators to register in the generated library).
ghstack-source-id: 114578753
(Note: this ignores all push blocking failures!)
Test Plan:
`buck test caffe2/test:selective_build`
Generated YAML files after the change:
{P143981979}
{P143982025}
{P143982056}
Ensure that the generated files are same before and after the change:
```
[dhruvbird@devvm2490 /tmp/TypeDefault.cpp] find -name "*.cpp" | xargs md5sum
d72c3d125baa7b77e4c5581bbc7110d2 ./after_change/gen_aten/TypeDefault.cpp
42353036c83ebc7620a7159235b9647f ./after_change/lite_predictor_lib_aten/TypeDefault.cpp
d72c3d125baa7b77e4c5581bbc7110d2 ./before_change/gen_aten/TypeDefault.cpp
42353036c83ebc7620a7159235b9647f ./before_change/lite_predictor_lib_aten/TypeDefault.cpp
```
`VariableTypes_N.cpp` are generated the same both before and after the change:
```
[dhruvbird@devvm2490 /tmp/VariableType] find -name "*.cpp" | xargs -n 1 md5sum | sort
3be89f63fd098291f01935077a60b677 ./after/VariableType_2.cpp
3be89f63fd098291f01935077a60b677 ./before/VariableType_2.cpp
40a3e59d64e9dbe86024cf314f127fd6 ./after/VariableType_4.cpp
40a3e59d64e9dbe86024cf314f127fd6 ./before/VariableType_4.cpp
a4911699ceda3c3a430f08c64e8243fd ./after/VariableType_1.cpp
a4911699ceda3c3a430f08c64e8243fd ./before/VariableType_1.cpp
ca9aa611fcb2a573a8cba4e269468c99 ./after/VariableType_0.cpp
ca9aa611fcb2a573a8cba4e269468c99 ./before/VariableType_0.cpp
e18f639ed23d802dc4a31cdba40df570 ./after/VariableType_3.cpp
e18f639ed23d802dc4a31cdba40df570 ./before/VariableType_3.cpp
```
Reviewed By: ljk53
Differential Revision: D23837010
fbshipit-source-id: ad06b1756af5be25baa39fd801dfdf09bc565442
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44148
Automatically remove the build_code_analyzer folder each time build.sh is run
ghstack-source-id: 111458413
Test Plan:
Run build.sh with different options and compare the outputs (should be different).
Ex:
`ANALYZE_TORCH=1 DEPLOY=1 BASE_OPS_FILE=/path/to/baseops MOBILE_BUILD_FLAGS='-DBUILD_MOBILE_AUTOGRAD=OFF' tools/code_analyzer/build.sh `
should produce a shorter file than
`ANALYZE_TORCH=1 DEPLOY=1 BASE_OPS_FILE=/path/to/baseops MOBILE_BUILD_FLAGS='-DBUILD_MOBILE_AUTOGRAD=ON' tools/code_analyzer/build.sh`
Reviewed By: iseeyuan
Differential Revision: D23503886
fbshipit-source-id: 9b95d4365540da0bd2d27760e1315caed5f44eec
