Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74785
Fix for https://github.com/facebookresearch/torchdynamo/issues/93
Because the constructor follow a non-standard input schema (variadic integers), they are handled specially in ir_emitter.
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D35362762
Pulled By: eellison
fbshipit-source-id: 960badf08ba2ab0818af5fd331aff3542051250f
(cherry picked from commit bd579dead5a5206fc6e5b535ecf4f99ae67ee135)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72899
Reland D33282878 (911d527b87). This is the frontend change.
ghstack-source-id: 149204031
Test Plan: Refer to D33282878 (911d527b87). Also check CI
Reviewed By: gmagogsfm
Differential Revision: D34252127
fbshipit-source-id: 27b17ddd4d05d904eb91fd9ee094d9121f00e388
(cherry picked from commit 1d276baca308110ac40111ccd622400b3bbdc864)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70471
Reland D33282878 (911d527b87). This is the frontend change.
ghstack-source-id: 149114933
Test Plan: Refer to D33282878 (911d527b87). Also check CI
Reviewed By: gmagogsfm
Differential Revision: D33342569
fbshipit-source-id: 57984ac67ae2c56c38f72d3b1fb69105901fb472
(cherry picked from commit b47cc935ee1fd7aa63aa453a323a637bc2c22f3c)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71443
cogwheel test inline_cvr_infer_canary_pyper_model_publish is timing out.
The convert_fx call takes > 20 mins for local and local_ro sub modules, which used to take ~ 2 mins.
Test Plan:
Fblearn flow run
* the following cmd took 1113 seconds before the diff and 5002 seconds after.
flow-cli clone-locally 320014219 --run-as-secure-group pytorch_at_scale --operators pyper_model_publish_workflow.pyper_model_publish_workflow.process_torch_package_model_files.process_non_sparse_parameters[0]
Cogwheel test
* Cogwheel test with packages in B3588 (the last good run) took 4694.48s
* Cogwheel test with packages in B3590 (the first timeout) took 13975.83s
* Cogwheel test with the following packages took 4535.04s
* all packages in B3588 except the model publish
* the model publish built with D33469839 (043e84b3d2) reversed (created D33633570)
Reviewed By: albanD, jerryzh168
Differential Revision: D33633570
fbshipit-source-id: dc5e777c48a90c551641a3f79126461f6a60449e
(cherry picked from commit 03ab65023a9f4175584ddac1cca7eab51397c84a)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67254
Fixes https://github.com/pytorch/pytorch/issues/65997
BC breaking:
`output = torch.ops._test.leaky_relu(self=torch.tensor(-1.0))` now fails with the error `TypeError: __call__() got multiple values for argument 'self'` since we call into `OpOverloadBundle`'s `__call__` method that has `self` bound to it as its first argument.
Follow up work:
1. disallow `default` as an overload name for aten operators.
2. Add a method to obtain a list of all overloads (exclude the ones registered by JIT)
3. Add methods/properties to `OpOverload` to access more schema information (types of input and output args etc)
cc ezyang gchanan
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D33469839
Pulled By: anjali411
fbshipit-source-id: c3fc43460f1c7c9651c64b4d46337be21c400621
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67254
Fixes https://github.com/pytorch/pytorch/issues/65997
TODO: disallow `default` as an overload name for aten operators.
BC breaking:
`output = torch.ops._test.leaky_relu(self=torch.tensor(-1.0))` now fails with the error `TypeError: __call__() got multiple values for argument 'self'` since we call into `OpOverloadBundle`'s `__call__` method that has `self` bound to it as its first argument.
cc ezyang gchanan
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D33262228
Pulled By: anjali411
fbshipit-source-id: 600dbf511514ea9b41aea3e6b1bc1102dab08909
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70339
When a python program is translated to TorchScript, the python exception type is dropped. This makes users's life hard when they need to categorize errors based more than only exception message.
Here we make the change so when we raise a python exception, we record the fully qualified class name for the exception. Later on when the TorchScript is interpreted, a special exception CustomJITException is thrown. User can get the python class name from CustomJITException::getPythonClassName .
Note that, this diff does not customize the mapping from C++ exception to Python exception. It's left to the users to do whatever mapping they want.
Code under scripts/shunting are just my own experimental code. I can split them out if requested.
ghstack-source-id: 146221879
Test Plan: buck test mode/opt //caffe2/test:jit
Reviewed By: gmagogsfm
Differential Revision: D33282878
fbshipit-source-id: 910f67a764519f1053a48589d1a34df69001525d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65967
Graph is an implementation detail. If user wants to get access to the
underlying graph, they should be able to explicitly dynamic cast instead.
ghstack-source-id: 141659819
Test Plan: no behavior change.
Reviewed By: gmagogsfm
Differential Revision: D31326153
fbshipit-source-id: a0e984f57c6013494b92a7095bf5bb660035eb84
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65345
FooType::get() can return a const reference. Inconveniently, converting shared_ptr<FooType> to shared_ptr<Type> requires a copy & refcount bump, so to properly take advantage of this in unshapedType() we need to take a const Type& in isSubtypeOf(), which is good practice anyway -- don't require a shared_ptr if you don't need to take ownership.
ghstack-source-id: 140044165
Test Plan:
CI
perf says c10::unshapedType time decreased from 2.8% to 2.2% during static runtime startup, though I expect this to be generally beneficial.
Reviewed By: hlu1
Differential Revision: D31027361
fbshipit-source-id: 676feb81db9f74ad7b8651d8774f4ecb4cfa6ab8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65610
- Replace HIP_PLATFORM_HCC with USE_ROCM
- Dont rely on CUDA_VERSION or HIP_VERSION and use USE_ROCM and ROCM_VERSION.
- In the next PR
- Will be removing the mapping from CUDA_VERSION to HIP_VERSION and CUDA to HIP in hipify.
- HIP_PLATFORM_HCC is deprecated, so will add HIP_PLATFORM_AMD to support HIP host code compilation on gcc.
cc jeffdaily sunway513 jithunnair-amd ROCmSupport amathews-amd
Reviewed By: jbschlosser
Differential Revision: D30909053
Pulled By: ezyang
fbshipit-source-id: 224a966ebf1aaec79beccbbd686fdf3d49267e06
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58887
There are some callsites of `torch.distributed.rpc.XXX` APIs that are compiled
or not based on `USE_RPC`. However, `torch::deploy`, at least for now,
is compiled with `USE_RPC=1`, but the `torch.distributed.rpc.XXX` APIs used by
the aforementioned pieces of code are not available (i.e.
`torch.distributed.rpc.is_available()` returns `False`). This can cause
Torchscript compilation to fail, even if the code being compiled doesn't use
RPC.
This commit fixes this problem (at least temporarily) by predicating the use
all thse `torch.distributed.rpc` APIs on the value of
`torch.distributed.rpc.is_available()`.
Test Plan: Ran packaged XLM-R model with C++ benchmark.
Reviewed By: suo
Differential Revision: D28660925
fbshipit-source-id: fbff7c7ef9596549105e79f702987a53b04ba6f9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53410
**Summary**
This commit enables indexing into `ModuleList` using a non-literal
index if the LHS of the assignment statement of which the indexing is
the RHS is annotated with an interface type.
This feature already exists for `ModuleDict`, and this commit builds on
top of that implementation. A `prim::ModuleContainerIndex` operator is
emitted for any statement of the form `lhs: InterfaceType =
module_container[idx]`. The same operator has to be used for both
`ModuleDict` and `ModuleList` because serialization does not preserve
the metadata that indicates whether a `Module` is a `ModuleDict` or
`ModuleList`.
**Testing**
This commit extends the existing unit tests for non-literal `ModuleDict`
indexing to test non-literal `ModuleList` indexing.
**Fixes**
This commit fixes#47496.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D26857597
Pulled By: SplitInfinity
fbshipit-source-id: d56678700a264d79aae3de37ad6b08b080175f7c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51177
**Summary**
This commit adds support for static methods to TorchBind. Just like
pybind, the API for declaring a static method is `def_static(...)`. A
static method must be called on the class directly, and can be called
both in Python as well as TorchScript.
Support for static methods is implemented in a manner similar to that of
instance methods. Registered static functions are wrapped in a layer of
unboxing logic, their schemas are inferred using templates and
metaprogramming, and they are added to the `ClassType` object
corresponding to the TorchBind class on which they are registered.
ScriptClass has been extended to support a `__getattr__` function so
that static methods of TorchBind classes can be invoked in Python. The
implementation of `__getattr__` returns `ScriptClassFunctionPtr`, a
version of `StrongFunctionPtr` without a compilation unit (since the
functions of a TorchBind class live inside the TorchBind registry).
Within TorchScript, TorchBind static functions are desugared in
`PythonClassValue::attr` by looking them up on the class type of the
`PythonClassValue` instance.
**Test Plan**
This commit adds a unit test that tests a simple static method on a
TorchBind class.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D26356942
Pulled By: SplitInfinity
fbshipit-source-id: 1b6a9bc2e5f3e22071ad78e331a0201fbbf7ab30
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50228
`fastmod -m 'expect(<((at|c10)::)?\w+Type>\(\)\s*)->'
'expectRef${1}.'`
Presuming it builds, this is a safe change: the result of `expect()`
wasn't being saved anywhere, so we didn't need it, so we can take a
reference instead of a new `shared_ptr`.
ghstack-source-id: 119782961
Test Plan: CI
Reviewed By: SplitInfinity
Differential Revision: D25837374
fbshipit-source-id: 86757b70b1520e3dbaa141001e7976400cdd3b08
Summary:
=======
This PR addresses the following:
* Adds JIT support for CUDA Streams
* Adds JIT support for CUDA Events
* Adds JIT support for CUDA Stream context manager
Testing:
======
python test/test_jit.py -v TestCUDA
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48020
Reviewed By: navahgar
Differential Revision: D25725749
Pulled By: nikithamalgifb
fbshipit-source-id: b0addeb49630f8f0c430ed7badeca43bb9d2535c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47300
**Summary**
This commit augments the module interface subtyping check that is done
before the emission of the `prim::ModuleDictIndex` operator so that the
error message that is printed if the subtyping check fails provides more
information on which methods do not match.
**Test Plan**
Existing unit tests for `prim::ModuleDictIndex`. Compilation of `ModWithWrongAnnotation` now produces this error:
```
Attribute module is not of annotated type __torch__.jit.test_module_containers.ModuleInterface: Method on class '__torch__.jit.test_module_containers.DoesNotImplementInterface' (1) is not compatible with interface '__torch__.jit.test_module_containers.ModuleInterface' (2)
(1) forward(__torch__.jit.test_module_containers.DoesNotImplementInterface self, Tensor inp) -> ((Tensor, Tensor))
(2) forward(InterfaceType<ModuleInterface> self, Any inp) -> (Any)
:
```
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D24709538
Pulled By: SplitInfinity
fbshipit-source-id: 6b6cb75e4b2b12b08576a5530b4b90cbcad9b6e5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45716
**Summary**
This commit enables indexing into `ModuleDict` using a non-literal
index if the `ModuleDict` is annotated with `Dict[str, X]`, where `X` is
a module interface type. These annotations must be expressed using a
class attribute named `__annotations__`, which is a `Dict[str, Type]`
where the keys are the names of module attributes and the values are
their types.
The approach taken by this commit is that these annotations are stored
as "hints" along with the corresponding module attributes in the
`ConcreteSubmoduleTypeBuilder` instance for each module (which might be
a `ModuleDict`). These hints are passed into the `ModuleValue` that is
created for desugaring operations on submodules so that indexing into a
`ModuleDict` can be emitted as a getitem op into a dict emitted into the
graph that represents the `ModuleDict`.
**Test Plan**
This commit adds unit tests to `TestModuleContainers` to test this
feature (`test_typed_module_dict`).
Differential Revision: D24070606
Test Plan: Imported from OSS
Reviewed By: ansley
Pulled By: SplitInfinity
fbshipit-source-id: 6019a7242d53d68fbfc1aa5a49df6cfc0507b992
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46686
I was trying to page this code back in after a while and some things
stuck out as unnecessarily confusing.
1. Improve documentation of closures and fork stuff to be more accurate
to how we use them today.
2. Change `prim::LocalVariableScope` to `prim::ListComprehension`. It is
only ever used for a list comprehensions, and in general the nodes
emitted by `ir_emitter` should correspond to concrete operations or
language features rather than semantic constraints.
3. Change the somewhat mysterious "inputs" and "attributes" argument
names throughout the codebase to be the more obvious "args" and "kwargs"
that they generally represent (I think "inputs" and "attributes" come
from the AST naming).
Test Plan: Imported from OSS
Reviewed By: navahgar, jamesr66a
Differential Revision: D24464197
Pulled By: suo
fbshipit-source-id: 1f4b1475b58b5690a0b204e705caceff969533b4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45262
**Summary**
This commit adds an API for ignoring arbitrary module attributes during
scripting. A class attribute named `ignored_attributes` containing names
of attributes to ignore can be added to the class of the instance being
scripted. Attributes ignored in this fashion cannot be used in
`forward`, methods used by `forward` or by `exported` methods. They
are, however, copied to the `RecursiveScriptModule` wrapper and can be
used by `ignored` methods and regular Python code.
**Test Plan**
This commit adds unit tests to `TestScriptPy3` to test this new API.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D23971882
Pulled By: SplitInfinity
fbshipit-source-id: 8c81fb415fde7b78aa2f87e5d83a477e876a7cc3
Summary:
PyObject_IsSubclass may set python live exception bit if given object is not a class. `IsNamedTuple` is currently using it incorrectly, which may trip all following python operations in debug-build python. Normal release-build python is not affected because `assert` is no-op in release-build.
Fixes https://github.com/pytorch/pytorch/issues/43577
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44769
Reviewed By: jamesr66a
Differential Revision: D23725584
Pulled By: gmagogsfm
fbshipit-source-id: 2dabd4f8667a045d5bf75813500876c6fd81542b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42390
**Summary**
This commit extends support for properties to include
ScriptModules.
**Test Plan**
This commit adds a unit test that has a ScriptModule with
a user-defined property.
`python test/test_jit_py3.py TestScriptPy3.test_module_properties`
Test Plan: Imported from OSS
Reviewed By: eellison, mannatsingh
Differential Revision: D22880298
Pulled By: SplitInfinity
fbshipit-source-id: 74f6cb80f716084339e2151ca25092b6341a1560
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43043
This add the support for rpc_sync in TorchScript in a way similar to
rpc_async
Test Plan: Imported from OSS
Reviewed By: mrshenli
Differential Revision: D23252039
Pulled By: wanchaol
fbshipit-source-id: 8a05329cb8a24079b2863178b73087d47273914c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43298
IR emitter uses `ModuleValue` to represent ScriptModules and emit IR for
attribute access, submodule access, etc.
`ModuleValue` relies on two pieces of information, the JIT type of the
module, and the `ConcreteModuleType`, which encapsulates Python-only
information about the module.
ScriptModules loaded from a package used to create a dummy
ConcreteModuleType without any info in it. This led to divergences in
behavior during compilation.
This PR makes the two ways of constructing a ConcreteModuleType equivalent,
modulo any py-only information (which, by definition, is never present in
packaged files anyway).
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D23228738
Pulled By: suo
fbshipit-source-id: f6a660f42272640ca1a1bb8c4ee7edfa2d1b07cc
