This PR applies clang-tidy readability checks to jit sources and all headers in the code base.
`readability-redundant-inline-specifier` is suppressed because it incurs too many changes. `readability-redundant-inline-specifier` is used to detect redundant inline specifiers on function and variable declarations. There are many in-class method definitions that are marked inline.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164652
Approved by: https://github.com/Skylion007
This PR applies clang-tidy readability checks to jit sources and all headers in the code base.
`readability-redundant-inline-specifier` is suppressed because it incurs too many changes. `readability-redundant-inline-specifier` is used to detect redundant inline specifiers on function and variable declarations. There are many in-class method definitions that are marked inline.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164652
Approved by: https://github.com/Skylion007
Summary:
Seeing errors like when testing sigmoid for inline_cvr and perevent_cvr models.
```
terminate called after throwing an instance of 'c10::Error'
what(): forward() Expected a value of type 'Dict[int, Tuple[Tensor, Tensor, Tensor]]' for argument 'event_based_features' but instead found type 'Dict[Any, Any]'.
```
Let empty dict pass type check.
please, do NOT use any of the following flags, those are result of manual interventions in other parts of the system, misuse of them can be very painful for both detect and recover:
Test Plan:
```
MODEL_ENTITY_ID=691508446
SNAPSHOT_ID=0
OTHER_MODEL_ENTITY_ID=649645886
OTHER_SNAPSHOT_ID=0
MODULE=local
buck2 run mode/opt caffe2/torch/fb/model_transform/fx2trt/packaging:load_net_predictor -- \
--loadMode=BenchmarkAB \
--inputNetFile=/data/users/${USER}/models/${MODEL_ENTITY_ID}/${SNAPSHOT_ID}/${MODEL_ENTITY_ID}_${SNAPSHOT_ID}${suffix} \
--otherNetFile=/data/users/${USER}/models/${OTHER_MODEL_ENTITY_ID}/${OTHER_SNAPSHOT_ID}/${OTHER_MODEL_ENTITY_ID}_${OTHER_SNAPSHOT_ID}${suffix} \
--moduleName=${module} \
--submodToDevice "" \
--benchmarkDontRebatchSamples=true \
--sampleInputFilePath=/data/users/${USER}/models/${MODEL_ENTITY_ID}/${SNAPSHOT_ID}/archive_.predictor.disagg.gpu.local/data/sample_inputs/local.pt
```
Reviewed By: yjhao
Differential Revision: D69871393
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147480
Approved by: https://github.com/henryoier, https://github.com/jeanschmidt
Summary:
When Static Runtime graph node has sub-blocks, the memory planner does not consider sub-blocks' inputs as a node's input in memory planner. As the result, such nodes' inputs' lifetime is incorrect and corresponding tensor memory is released earlier than required and causes errors.
Differential Revision: D69195886
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146855
Approved by: https://github.com/swolchok
In almost all cases this is only included for writing the output formatter, which
only uses `std::ostream` so including `<ostream>` is sufficient.
The istream header is ~1000 lines so the difference is non-trivial.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106914
Approved by: https://github.com/lezcano
In almost all cases this is only included for writing the output formatter, which
only uses `std::ostream` so including `<ostream>` is sufficient.
The istream header is ~1000 lines so the difference is non-trivial.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106914
Approved by: https://github.com/lezcano
Avoid referring to std::vector<T> members and constructor/desctructors when T is incomplete.
Referring to incomplete members is [not legal](https://timsong-cpp.github.io/cppwp/n4868/vector#overview-4) according to the C++ standard.
Non-noexcept constructors need access to members' destructors. As of C++20, std::vector's destructor is constexpr and so forcefully requires a complete type for the vector's elements.
These issues cause build errors in newer toolchains under c++20 mode.
Fix them by moving code that needs complete types to a different place where the type is already defined.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93978
Approved by: https://github.com/Skylion007
Summary:
* add human readable type and ivalue printout
* fix internal linter warnings
Test Plan:
error message now looks like e.g.
```
E0315 16:27:32.409082 422313 ExceptionTracer.cpp:222] exception stack complete
terminate called after throwing an instance of 'c10::Error'
what(): List[int] is not a subtype of List[int]; schema arg name: 'split_sizes', ivalue: [1, 1]
```
Differential Revision: D44112297
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96903
Approved by: https://github.com/davidberard98
Not only is this change usually shorter and more readable, it also can yield better performance. size() is not always a constant time operation (such as on LinkedLists), but empty() always is.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93236
Approved by: https://github.com/malfet
These PR fixes a number of bugs found by Svace static analyzer:
1. DEREF_AFTER_FREE at qnnpack_utils.h:
Pointer '&convolution->zero_buffer' is dereferenced at qnnpack_utils.h:258 after the referenced memory was deallocated at operator-delete.c:25 by passing as 1st parameter to function 'pytorch_qnnp_delete_operator' at qnnpack_utils.h:251.
2. DEREF_AFTER_NULL at impl.cpp:
After having been compared to NULL value at impl.cpp:1892, pointer 'schema' is passed as 2nd parameter in call to function 'c10::operator<<' at impl.cpp:1921, where it is dereferenced at function_schema_inl.h:13.
3. DEREF_OF_NULL at stmt.h:
After having been compared to NULL value at stmt.h:744, pointer 'body->_M_ptr' is passed in call to function 'torch::jit::tensorexpr::malformed_input::malformed_input' at stmt.h:745, where it is dereferenced at exceptions.h:67.
4. DEREF_OF_NULL at loopnest.h:
Pointer 'f->ptr' that can have only NULL value (checked at loopnest.cpp:1482), is passed in call to function 'torch::jit::tensorexpr::malformed_input::malformed_input' at loopnest.cpp:1483, where it is dereferenced at exceptions.h:67.
This is the same error as 3: forwarding a nullptr to malformed_input().
4. TAINTED_INT.LOOP in python_arg_parser:
Integer value 'this->size' obtained from untrusted source at python_arg_parser.cpp:118 without checking its bounds is used as a loop bound at python_arg_parser.cpp:698 by calling function 'torch::FunctionParameter::set_default_str' at python_arg_parser.cpp:133.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85705
Approved by: https://github.com/kit1980
Summary:
The pass introduces an `fb::` operator and thus cannot be used in OSS.
The test failure was not exposed because the Static Runtime tests have been disabled in OSS for a while. The Dev Infra folks encountered this failure when re-enabling the tests.
Test Plan: Existing tests
Differential Revision: D40724547
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87799
Approved by: https://github.com/huydhn
Summary:
Someone was running into problems where
1) Static Runtime enablement would fail
2) We would try to fall back to the JIT interpreter *after trying to create `StaticModule`*
3) The fallback fails because Static Runtime mangled the graph.
We don't want to prevent Static Runtime from mutating its input due to memory concerns. The intent of `canEnableStaticRuntime` is to catch issues in the module before Static Runtime messes with it.
With this diff, `StaticModule` instantiation can be avoided by querying `canEnableStaticRuntime` and the issue is fixed.
Test Plan: New unit test
Differential Revision: D40564452
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87396
Approved by: https://github.com/tenpercent
Summary: Split `quantized_linear_unpacked_weight_v2` into `linear_prepack` and `quantized_linear` so that the prepacking operation may be eliminated by constant folding.
Test Plan:
Fixes a huge regression in an internal model:
```
Before
89.6141 ms. 99.0923%. fb::quantized_linear_unpacked_weight_v2 (12 nodes)
After
0.806852 ms. 53.5365%. quantized::linear (12 nodes, out variant)
(prepacking eliminated)
```
Differential Revision: D39622530
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85289
Approved by: https://github.com/davidberard98
Summary: Apparently static runtime's list construct return value is always a `GenericList`, so we cannot use the `toOptionalTensorList` method in the general case -- we must convert each item individually.
Test Plan: New unit test
Differential Revision: D39628979
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85298
Approved by: https://github.com/tenpercent
