Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51340
**Summary**
`toIValue` assumes that any value passed for an argument of type
`torch.device` is a valid device object, even when it is not. This can
lead to device type arguments of functions being assigned incorrect
values (see #51098).
This commit adds an explicit check that the passed in object is indeed a
`torch.device` using `THPDevice_Check` and only then does is it
converted to an `IValue`. Since implicit conversion from strings to
devices is generally allowed, if `THPDevice_Check` fails, it is assumed
that the object is a string and an `IValue` containing a `c10::Device`
containing the passed in string is returned.
**Test Plan**
This commit adds a unit test to `test_jit.py` to test that invalid
strings passed as devices are not longer silently accepted.
**Fixes**
This commit fixes#51098.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D26187190
Pulled By: SplitInfinity
fbshipit-source-id: 48c990203431da30f9f09381cbec8218d763325b
Summary:
This simplifies our handling and allows passing CompilationUnits from Python to C++ defined functions via PyBind easily.
Discussed on Slack with SplitInfinity
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50614
Reviewed By: anjali411
Differential Revision: D25938005
Pulled By: SplitInfinity
fbshipit-source-id: 94aadf0c063ddfef7ca9ea17bfa998d8e7b367ad
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50593
There are no equivalent to torch.FloatTensor, torch.cuda.FloatTensor for complex
types. So `get_gpu_type` and `get_cpu_type` are broken for complex tensors.
Also found a few places that explicitly cast inputs to floating point types,
which would drop the imaginary component before running the test.
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D25954050
Pulled By: mruberry
fbshipit-source-id: 1fa8e5af233aa095c839d5e2f860564baaf92aef
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50074
Adds Conv-BN fusion for models that have been frozen. I haven't explicitly tested perf yet but it should be equivalent to the results from Chillee's PR [here](https://github.com/pytorch/pytorch/pull/476570) and [here](https://github.com/pytorch/pytorch/pull/47657#issuecomment-725752765). Click on the PR for details but it's a good speed up.
In a later PR in the stack I plan on making this optimization on by default as part of `torch.jit.freeze`. I will also in a later PR add a peephole so that there is not conv->batchnorm2d doesn't generate a conditional checking # dims.
Zino was working on freezing and left the team, so not really sure who should be reviewing this, but I dont care too much so long as I get a review �
Test Plan: Imported from OSS
Reviewed By: tugsbayasgalan
Differential Revision: D25856261
Pulled By: eellison
fbshipit-source-id: da58c4ad97506a09a5c3a15e41aa92bdd7e9a197
Summary:
This adds guarding for DifferentiableGraph nodes in order to not depend on
Also bailing out on required gradients for the CUDA fuser.
Fixes https://github.com/pytorch/pytorch/issues/49299
I still need to look into a handful of failing tests, but maybe it can be a discussion basis.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49433
Reviewed By: ngimel
Differential Revision: D25681374
Pulled By: Krovatkin
fbshipit-source-id: 8e7be53a335c845560436c0cceeb5e154c9cf296
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/49022
**BC-breaking note**:
Previously torch.stft took an optional `return_complex` parameter that indicated whether the output would be a floating point tensor or a complex tensor. By default `return_complex` was False to be consistent with the previous behavior of torch.stft. This PR changes this behavior so `return_complex` is a required argument.
**PR Summary**:
* **#49022 stft: Change require_complex warning to an error**
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D25658906
Pulled By: mruberry
fbshipit-source-id: 11932d1102e93f8c7bd3d2d0b2a607fd5036ec5e
Summary:
========
Fixes #{42915}
This commit adds support for Bitwise Shorthands in TorchScript, i.e : |=,&=,^=,<<=,>>=,**=
Testing:
======
This commit also adds test for the above fix in test_jit.py
The test can be invoked by
pytest -k augassign test/test_jit.py
Here is a snapshot of the testing:
<img width="1238" alt="image" src="https://user-images.githubusercontent.com/70345919/93105141-8f9f5300-f663-11ea-836b-3b52da6d2be5.png">
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44621
Reviewed By: mrshenli
Differential Revision: D23906344
Pulled By: nikithamalgifb
fbshipit-source-id: 4c93a7430a625f698b163609ccec15e51417d564
Summary:
Fixes https://github.com/pytorch/pytorch/issues/598
This is BC-breaking as we now explicitly don't call the hook when there are not Tensors at the top level of the output.
This feature was not working anyways as the returned grad_input/grad_output were wrong (not respecting the output structure and wrong inputs for multi-Node Module).
This is also BC-breaking as we now report the correct gradients for `nn.Module`s that contain multiple autograd `Node`s while we use to return bad results before.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46163
Reviewed By: ailzhang, mruberry
Differential Revision: D24894180
Pulled By: albanD
fbshipit-source-id: e1b5d193d2818eb2f51e2a2722c7405c8bd13c2b
Summary:
Fixes https://github.com/pytorch/pytorch/issues/49362
**Summary:**
This PR fixes the issue where invalid annotation types are used for a dictionary.
Unsupported assertion message is generated for all invalid annotations
**Test Case**:
python test/test_jit.py TestJit.test_dict_invalid_annotations
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49425
Reviewed By: navahgar
Differential Revision: D25601578
Pulled By: nikithamalgifb
fbshipit-source-id: 91633e3d0891bdcb5402f044a74d02fe352ecd6f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48660
We used to support tuple slicing without any step size before, but this PR extends this feature to support arbitrary step size. We do this by manually reconstructing a new tuple in the IR instead of relying on TupleSlice prim.
Test Plan:
python tests
Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D25359336
fbshipit-source-id: 28cde536f28dd8a00607814b2900765e177f0ed7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47695
The method_tests from common_methods_invoations.py are being migrated into a new OpInfo class-based testing framework. The work in this commit pulls out the functions embedded in the old method_tests logic and places them in a location that both the old method_tests and OpInfo tests can use
Specifically: created torch/testing/_internal/common_jit.py from functions and methods in torch/testing/_internal/jit_utils.py and test/test_jit.py. Also created new intermediate class JitCommonTestCase to house moved methods. Also slightly modified jit_metaprogramming_utils.py to work for OpInfo tests
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D25212437
Pulled By: Lilyjjo
fbshipit-source-id: 97bc52c95d776d567750e7478fac722da30f4985
Summary:
Fixes https://github.com/pytorch/pytorch/issues/46703
Previously, we would compile one side of an if-statement if it was a type-based expression we could statically resolve. I think it's reasonable to extend this metacompilation to booleans that are constant at compile time. There have been some instances where i've recommended unintuitive workarounds due to not having this behavior.
This is also possibly needed if we add boolean literals to schema declarations, which is a feature that might be needed to cleanup our `boolean_dispatch` mechanism.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46721
Reviewed By: ppwwyyxx
Differential Revision: D25008862
Pulled By: eellison
fbshipit-source-id: 5bc60a18f1021c010cb6abbeb5399c669fe04312
Summary:
Fix for https://github.com/pytorch/pytorch/issues/46122
For `Any`, we infer the type of the ivalue to set the ivalue's type tag. When we saw a Tensor, we would use a specialized Tensor type, so when `Dict[str, Tensor]` was passed in as any `Any` arg it would be inferred as `Dict[str, Float(2, 2, 2, 2)]` which breaks runtime `isinstance` checking.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46130
Reviewed By: glaringlee
Differential Revision: D24261447
Pulled By: eellison
fbshipit-source-id: 8a2bb26ce5b6c56c8dcd8db79e420f4b5ed83ed5
Summary:
inside IValue.h, we previously printed -0.0 as 0.0. Therefore, it was causing some inconsistency when using -0.0.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47081
Test Plan:
A new test case inside test_jit that divides a tensor by -0. and checks if it outputs -inf for all modes.
Fixes https://github.com/pytorch/pytorch/issues/46848
Reviewed By: mrshenli
Differential Revision: D24688572
Pulled By: gmagogsfm
fbshipit-source-id: 01a9d3f782e0711dd10bf24e6f3aa62eee72c895
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47211
The attribute is getting shadowed by the default one set on all modules,
and the __setattr__ on the TracedModule object prevents setting it correctly.
import torch
inp = torch.zeros(1, 3, 224, 224)
model = torch.hub.load('pytorch/vision:v0.6.0', 'mobilenet_v2', pretrained=True)
model.eval()
print(model.training)
with torch.no_grad():
traced = torch.jit.trace(model, inp)
print(traced.training)
traced.eval()
print(traced.training)
traced.training = False
print(traced.training)
torch.jit.freeze(traced)
Test Plan: Imported from OSS
Reviewed By: suo
Differential Revision: D24686690
Pulled By: zdevito
fbshipit-source-id: 9c1678dc68e9bf83176e9f5a20fa8f6bff5d69a0
Summary:
If there is no annotation given, we want to show users that the type is inferred
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46969
Test Plan:
Added a new test case that throws an error with the expected error message
Fixes https://github.com/pytorch/pytorch/issues/46326
Reviewed By: ZolotukhinM
Differential Revision: D24614450
Pulled By: gmagogsfm
fbshipit-source-id: dec555a53bfaa9cdefd3b21b5142f5e522847504
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:
It used to be that TorchScript only supported hashing of `int`, `float` and `str`. This PR adds hashing for many other types including `Tuple`, `bool`, `device` by implementing generic hashing on IValue.
* Tensor hashing follows eager behavior, which is identity-based (hash according to pointer address rather than tensor content).
Fixes https://github.com/pytorch/pytorch/issues/44038
This is based on suo's https://github.com/pytorch/pytorch/issues/44047, with some cleaning, more tests and fixing BC check issue.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46441
Reviewed By: robieta
Differential Revision: D24440713
Pulled By: gmagogsfm
fbshipit-source-id: 851f413f99b6f65084b551383ad21e558e7cabeb
Summary:
As per title. Limitations: only for batches of squared full-rank matrices.
CC albanD
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46284
Reviewed By: zou3519
Differential Revision: D24448266
Pulled By: albanD
fbshipit-source-id: d98215166268553a648af6bdec5a32ad601b7814
Summary:
Follow-up of https://github.com/pytorch/pytorch/issues/46461 with a similar goal
Makes them more readable and possibly faster. Care has to be taken because `map` applies the function immediately while `(x for x in xs)` is a generator expression which gets evaluated later. This is a benefit in some cases where it is not required to actually create the list of values in memory (e.g. when passing to `tuple` or `extend` or `join`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46462
Reviewed By: zou3519
Differential Revision: D24422343
Pulled By: ezyang
fbshipit-source-id: 252e33499c92ac0b15238f2df32681dbbda2b237
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46601
* except excluded tests and magic methods.
https://github.com/pytorch/pytorch/issues/38731
Previously, we'd only do run these tests for inplace operations. Since this is a lot more tests, fixed these issues that came up when running them -
- Updated schema of conj() to reflect existing behaviour.
- Updated deepEquals method in check_alias_annotation.cpp to re-use the overloaded == operator. Previous implementation did not cover all types of IValues.
- Corrected the order inputs are passed in during autograd testing of 'view' & 'reshape'.
- Subbed out atn::ger with the func its aliased to, atn::outer, for testing. The alias annotation checking code doesn't handle aliased operators properly.
ghstack-source-id: 114830903
Test Plan: Ran all tests in test:jit and verified they pass.
Reviewed By: eellison
Differential Revision: D24424955
fbshipit-source-id: 382d7e2585911b81b1573f21fff1d54a5e9a2054
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45929
We were checking `and` when we should have been checking `or`.
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D24148804
Pulled By: eellison
fbshipit-source-id: 9c394ea10ac91a588169d934b1e3208512c71b9d
Summary:
* Add a pass at end of runCleanupPasses to annotate `aten::warn` so that each has its unique id
* Enhanced interpreter so that it tracks which `aten::warn` has been executed before and skip them
* Improved insertInstruction so that it correctly checks for overflow
Fixes https://github.com/pytorch/pytorch/issues/45108
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45382
Reviewed By: mrshenli
Differential Revision: D24060677
Pulled By: gmagogsfm
fbshipit-source-id: 9221bc55b9ce36b374bdf614da3fe47496b481c1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43208
This PR adds gradcheck for complex. The logic used for complex gradcheck is described in Section 3.5.3 here: https://arxiv.org/pdf/1701.00392.pdf
More concretely, this PR introduces the following changes:
1. Updates get_numerical_jacobian to take as input a scalar value for vector (v). Adds gradcheck logic for C -> C, C-> R, R -> C. For R -> C functions, only the real value of gradient is propagated.
2. Adds backward definition for `torch.complex` and also adds a test to verify the definition added.
3. Updates backward for `mul`, `sin`, `cos`, `sinh`, `cosh`.
4. Adds tests for all `torch.real`, `torch.imag`, `torch.view_as_real`, `torch.view_as_complex`, `torch.conj`.
Follow up tasks:
1. Add more thorough tests for R -> C cases. Specifically, add R->C test variants for functions. for e.g., `torch.mul(complex_tensor, real_tensor)`
2. Add back commented test in `common_methods_invocation.py`.
3. Add more special case checking for complex gradcheck to make debugging easier.
4. Update complex autograd note.
5. disable complex autograd for operators not tested for complex.
Test Plan: Imported from OSS
Reviewed By: zou3519
Differential Revision: D23655088
Pulled By: anjali411
fbshipit-source-id: caa75e09864b5f6ead0f988f6368dce64cf15deb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44795
Today, we build our cpp tests twice, once as a standalone gtest binary,
and once linked in `libtorch_python` so we can call them from
`test_jit.py`.
This is convenient (it means that `test_jit.py` is a single entry point
for all our tests), but has a few drawbacks:
1. We can't actually use the gtest APIs, since we don't link gtest into
`libtorch_python`. We're stuck with the subset that we want to write
polyfills for, and an awkward registration scheme where you have to
write a test then include it in `tests.h`).
2. More seriously, we register custom operators and classes in these
tests. In a world where we may be linking many `libtorch_python`s, this
has a tendency to cause errors with `libtorch`.
So now, only tests that explicitly require cooperation with Python are
built into `libtorch_python`. The rest are built into
`build/bin/test_jit`.
There are tests which require that we define custom classes and
operators. In these cases, I've built thm into separate `.so`s that we
call `torch.ops.load_library()` on.
Test Plan: Imported from OSS
Reviewed By: SplitInfinity, ZolotukhinM
Differential Revision: D23735520
Pulled By: suo
fbshipit-source-id: d146bf4e7eb908afa6f96b394e4d395d63ad72ff
Summary:
* Implement tuple sort by traversing contained IValue types and generate a lambda function as comparator for sort.
* Tuple, class objects can now arbitrarily nest within each other and still be sortable
Fixes https://github.com/pytorch/pytorch/issues/43219
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43448
Reviewed By: eellison
Differential Revision: D23352273
Pulled By: gmagogsfm
fbshipit-source-id: b6efa8d00e112178de8256da3deebdba7d06c0e1
Summary:
We were hitting an assert error when you passed in an empty `List[List[int]]` - this fixes that error by not recursing into 0-element tensors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44652
Reviewed By: ZolotukhinM
Differential Revision: D23688247
Pulled By: eellison
fbshipit-source-id: d48ea24893044fae96bc39f76c0f1f9726eaf4c7
Summary:
This PR:
- updates div to perform true division
- makes torch.true_divide an alias of torch.div
This follows on work in previous PyTorch releases that first deprecated div performing "integer" or "floor" division, then prevented it by throwing a runtime error.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42907
Reviewed By: ngimel
Differential Revision: D23622114
Pulled By: mruberry
fbshipit-source-id: 414c7e3c1a662a6c3c731ad99cc942507d843927
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44493
This function allows to execute a graph exactly as it is, without going
through a graph executor which would run passes on the graph before
interpreting it. I found this feature extremely helpful when I worked on
a stress-testing script to shake out bugs from the TE fuser: I needed to
execute a very specific set of passes on a graph and nothing else, and
then execute exactly it.
Test Plan: Imported from OSS
Reviewed By: jamesr66a
Differential Revision: D23632505
Pulled By: ZolotukhinM
fbshipit-source-id: ea81fc838933743e2057312d3156b77284d832ef
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44398
These end up executing the same tests, so no reason to have them separate.
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D23600855
Pulled By: gchanan
fbshipit-source-id: 0952492771498bf813f1bf8e1d7c8dce574ec965
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43958
There is not any difference between these tests (I'm merging them), so let's merge them in the JIT as well.
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D23452337
Pulled By: gchanan
fbshipit-source-id: e6d13cdb164205eec3dbb7cdcd0052b02c961778
Summary:
Follow up to https://github.com/pytorch/pytorch/pull/41946/, to suggest enumerating a module as an alternative if a user tries indexing into a modulelist/sequential with a non-integer literal
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43361
Reviewed By: mrshenli
Differential Revision: D23602388
Pulled By: eellison
fbshipit-source-id: 51fa28d5bc45720529b3d45e92d367ee6c9e3316
Summary:
When the backward ops execute via the autograd engine evaluate_function(), the fn.release_variables() is called to release the SavedVariables. For the eager mode ops, this releases the saved inputs that was required for backward grad function. However, with TorchScript, we get a DifferentableGraph and the DifferentiableGraphBackward() doesn't implement a release_variables(). This leads to the SavedVariables to be alive longer. Implement release_variables() for DifferentiableGraphBackward to release these SavedVariables early.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42994
Reviewed By: izdeby
Differential Revision: D23503172
Pulled By: albanD
fbshipit-source-id: d87127498cfa72883ae6bb31d0e6c7056c4c36d4
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
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43284
The IR emitter looks for attributes on modules like:
1. Check the JIT type for the attribute
2. Check the originating Python class, in order to fulfill requests for, e.g. static methods or ignored methods.
In the case where you do:
```
inner_module = torch.jit.load("inner.pt")
wrapped = Wrapper(inner_module) # wrap the loaded ScriptModule in an nn.Module
torch.jit.script(wrapped)
```
The IR emitter may check for attributes on `inner_module`. There is no
originating Python class for `inner_module`, since it was directly
compiled from the serialized format.
Due to a bug in the code, we don't guard for this case an a segfault
results if the wrapper asks for an undefined attribute. The lookup in
this case looks like:
1. Check the JIT type for the attribute (not there!)
2. Check the originating Python class (this is a nullptr! segfault!)
This PR guards this case and properly just raises an attribute missing
compiler error instead of segfaulting.
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D23224337
Pulled By: suo
fbshipit-source-id: 0cf3060c427f2253286f76f646765ec37b9c4c49
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43847
It seems to slowdown two fastRNN benchmarks and does not speed up
others.
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D23416197
Pulled By: ZolotukhinM
fbshipit-source-id: 598144561979e84bcf6bccf9b0ca786f5af18383
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41371
**Summary**
This commit enables the use of `torch.no_grad()` in a with item of a
with statement within JIT. Note that the use of this context manager as
a decorator is not supported.
**Test Plan**
This commit adds a test case to the existing with statements tests for
`torch.no_grad()`.
**Fixes**
This commit fixes#40259.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D22649519
Pulled By: SplitInfinity
fbshipit-source-id: 7fa675d04835377666dfd0ca4e6bc393dc541ab9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43631
I added a new test for just profiler stuff - I don't think the test should go in test_jit.py. Maybe this should just go in test_tensorexpr_fuser, but I'm not really testing tensorexpr stuff either... LMK
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D23358810
Pulled By: eellison
fbshipit-source-id: 074238e1b60e4c4a919a052b7a5312b790ad5d82
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43093
without this it's hard to tell which module is going wrong
Test Plan:
```
> TypeError:
> 'numpy.int64' object in attribute 'Linear.in_features' is not a valid constant.
> Valid constants are:
> 1. a nn.ModuleList
> 2. a value of type {bool, float, int, str, NoneType, torch.device, torch.layout, torch.dtype}
> 3. a list or tuple of (2)
```
Reviewed By: eellison
Differential Revision: D23148516
fbshipit-source-id: b86296cdeb7b47c9fd69b5cfa479914c58ef02e6
Summary:
This PR:
- updates test_op_normalization.py, which verifies that aliases are correctly translated in the JIT
- adds torch.linalg.det as an alias for torch.det
- moves the torch.linalg.outer alias to torch.outer (to be consistent with NumPy)
The torch.linalg.outer alias was put the linalg namespace erroneously as a placeholder since it's a "linear algebra op" according to NumPy but is actually still in the main NumPy namespace.
The updates to test_op_normalization are necessary. Previously it was using method_tests to generate tests, and method_tests assumes test suites using it also use the device generic framework, which test_op_normalization did not. For example, some ops require decorators like `skipCPUIfNoLapack`, which only works in device generic test classes. Moving test_op_normalization to the device generic framework also lets these tests run on CPU and CUDA.
Continued reliance on method_tests() is excessive since the test suite is only interested in testing aliasing, and a simpler and more readable `AliasInfo` class is used for the required information. An example impedance mismatch between method_tests and the new tests, for example, was how to handle ops in namespaces like torch.linalg.det. In the future this information will likely be folded into a common 'OpInfo' registry in the test suite.
The actual tests performed are similar to what they were previously: a scripted and traced version of the op is run and the test verifies that both graphs do not contain the alias name and do contain the aliased name.
The guidance for adding an alias has been updated accordingly.
cc mattip
Note:
ngimel suggests:
- deprecating and then removing the `torch.ger` name
- reviewing the implementation of `torch.outer`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42802
Reviewed By: zou3519
Differential Revision: D23059883
Pulled By: mruberry
fbshipit-source-id: 11321c2a7fb283a6e7c0d8899849ad7476be42d1
Summary:
aten::sorted.str output type was incorrectly set to bool[] due to a copy-paste error. This PR fixes it.
Fixes https://fburl.com/0rv8amz7
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42853
Reviewed By: yf225
Differential Revision: D23054907
Pulled By: gmagogsfm
fbshipit-source-id: a62968c90f0301d4a5546e6262cb9315401a9729
Summary:
Raise and assert used to have a hard-coded error message "Exception". User provided error message was ignored. This PR adds support to represent user's error message in TorchScript.
This breaks backward compatibility because now we actually need to script the user's error message, which can potentially contain unscriptable expressions. Such programs can break when scripting, but saved models can still continue to work.
Increased an op count in test_mobile_optimizer.py because now we need aten::format to form the actual exception message.
This is built upon an WIP PR: https://github.com/pytorch/pytorch/pull/34112 by driazati
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41907
Reviewed By: ngimel
Differential Revision: D22778301
Pulled By: gmagogsfm
fbshipit-source-id: 2b94f0db4ae9fe70c4cd03f4048e519ea96323ad
Summary:
View ops as outputs of differentiable subgraphs can cause incorrect differentiation. For now, do not include them in the subgraph. This was observed with our autograd tests for MultiheadAttention and nn.Transformer, which currently fail with the legacy executor. This commit fixes those test failures.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42027
Reviewed By: pbelevich
Differential Revision: D22798133
Pulled By: eellison
fbshipit-source-id: 2f6c08953317bbe013933c6faaad20100376c039
Summary:
Currently constant pooling runs before const propagation, which can create more constants that need pooling. This can get in the way of serialization/deserialization stability because each time user serializes and deserializes a module, runCleanUpPasses is called upon it. Doing so multiple times would lead to different saved module.
This PR moves constant pooling after const propagation, which may slow down const propagation a little bit, but would otherwise side-step aforementioned problem.
test_constant_insertion in test_jit.py is also updated because after fixing the pass ordering, the number of constants is no longer a constant and it is extremely difficult to get the exact number with the current convoluted test structure. So for now, I changed the test to check only that CSE doesn't change number of "prim::constant" rather than comparing against a known number. Also left a TODO to improve this test.
ConstantPropagation pass is replaced by ConstantPropagationImmutableTypes because the latter is used in runCleanUpPasses. If not replaced, the former would create new CSE opportunities by folding more constants. This voids the purpose of the test case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41891
Reviewed By: colesbury
Differential Revision: D22701540
Pulled By: gmagogsfm
fbshipit-source-id: 8e60dbdcc54a93dac111d81b8d88fb39387224f5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41436
Constants are not executed as instructions, we should ignore them when counting subgraph size, as we ignore them in counting block size for loop unrolling.
Test Plan: Imported from OSS
Reviewed By: Krovatkin, ZolotukhinM
Differential Revision: D22600608
Pulled By: eellison
fbshipit-source-id: 9770b21c936144a3d6a1df89cf3be5911095187e
Summary:
* Add EnumType and AnyEnumType as first-class jit type
* Add Enum-typed IValue
* Enhanced aten::eq to support Enum
Supported:
Enum-typed function targuments
using Enum type and comparing them
TODO:
Add PyThon sugared value for Enum
Support getting name/value attrs of enums
Support Enum-typed return values
Support enum values of different types in same Enum class
Support serialization and deserialization
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41390
Reviewed By: eellison
Differential Revision: D22524388
Pulled By: gmagogsfm
fbshipit-source-id: 1627154a64e752d8457cd53270f3d14aea4b1150
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37587
Lifting RecordFunction up into the dispatcher code
Test Plan: Imported from OSS
Differential Revision: D21374246
fbshipit-source-id: 19f9c1719e6fd3990e451c5bbd771121e91128f7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41507
These fields have always been a part of tensor types, this change just
makes them serializable through IR dumps.
Test Plan: Imported from OSS
Reviewed By: Krovatkin, ngimel
Differential Revision: D22563661
Pulled By: ZolotukhinM
fbshipit-source-id: f01aaa130b7e0005bf1ff21f65827fc24755b360
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39343
Building on top of previous PR that adds fused add_relu op, this PR adds
a JIT pass to transform input graph to find all fusable instancs of add
+ relu and fuses them.
Test Plan:
python test/test_jit.py TestJit.test_add_relu_fusion
Imported from OSS
Differential Revision: D21822396
fbshipit-source-id: 12c7e8db54c6d70a2402b32cc06c7e305ffbb1be
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40718
Currently only constant except tensor must be inlined during serialization.
Tensor are stored in the contant table. This patch generalizes this capability
to any IValue. This is particularly useful for non ASCII string literal that
cannot be inlined.
Test Plan: Imported from OSS
Differential Revision: D22298169
Pulled By: bzinodev
fbshipit-source-id: 88cc59af9cc45e426ca8002175593b9e431f4bac
Summary:
Noticed while trying to script one of the models which happened to have numpy values as constants. Lacking the numpy prefix in the error message was quite confusing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41024
Differential Revision: D22426399
Pulled By: dzhulgakov
fbshipit-source-id: 06158b75355fac6871e4861f82fc637c2420e370
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40902
See the bottom of this stack for context.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D22360210
Pulled By: suo
fbshipit-source-id: 4275127173a36982ce9ad357aa344435b98e1faf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40939
Previously, when we would do shape analysis by running the op with representative inputs, we would always set the grad property to false. This led to a wrong static analysis when we would create differentiable subgraphs, and propagate shapes without also propagating requires_grad, and then uninline them.
Test Plan: Imported from OSS
Differential Revision: D22394676
Pulled By: eellison
fbshipit-source-id: 254e6e9f964b40d160befe0e125abe1b7aa2bd5e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40807
We pack a lot of logic into `jit/__init__.py`, making it unclear to
developers and users which parts of our API are public vs. internal. This
is one in a series of PRs intended to pull implementation out into
separate files, and leave `__init__.py` as a place to register the
public API.
This PR moves all the tracing-related stuff out, and fixes other spots up
as necessary. Followups will move other core APIs out.
The desired end-state is that we conform to the relevant rules in [PEP 8](https://www.python.org/dev/peps/pep-0008/#public-and-internal-interfaces). In particular:
- Internal implementation goes in modules prefixed by `_`.
- `__init__.py` exposes a public API from these private modules, and nothing more.
- We set `__all__` appropriately to declare our public API.
- All use of JIT-internal functionality outside the JIT are removed (in particular, ONNX is relying on a number internal APIs). Since they will need to be imported explicitly, it will be easier to catch new uses of internal APIs in review.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D22320645
Pulled By: suo
fbshipit-source-id: 0720ea9976240e09837d76695207e89afcc58270
Summary:
Define static script implementation of __len__ and __contains__ on any subclass derived from a type such as ModuleList, Sequential, or ModuleDict. Implement getitem for classes derived from ModuleDict.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40789
Reviewed By: eellison
Differential Revision: D22325159
Pulled By: wconstab
fbshipit-source-id: fc1562c29640fe800e13b5a1dd48e595c2c7239b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40753
The reference says that this op always returns a 1-D tensor, even if
the input and the mask are 0-D.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D22300354
Pulled By: ZolotukhinM
fbshipit-source-id: f6952989c8facf87d73d00505bf6d41573eff2d6
Summary:
Remove `skipIfRocm` from most jit tests and enable `RUN_CUDA_HALF` tests for ROCm.
These changes passed more than three rounds of CI testing against the ROCm CI.
CC ezyang xw285cornell sunway513
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40447
Differential Revision: D22190711
Pulled By: xw285cornell
fbshipit-source-id: bac44825a2675d247b3abe2ec2f80420a95348a3
Summary:
Partial support for slicing of Sequential containers.
- works around missing Sequential slice functionality
by converting to tuple
- only supports iteration of resulting tuple values,
not direct call() on the sliced sequential
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40445
Differential Revision: D22192469
Pulled By: wconstab
fbshipit-source-id: 61c85deda2d58f6e3bea2f1fa1d5d5dde568b9b5
Summary:
Currently, torchvision annotates `batched_nms` with `torch.jit.script` so the function gets compiled when it is traced and ONNX will work. Unfortunately, this means we are eagerly compiling batched_nms, which fails if torchvision isn't built with `torchvision.ops.nms`. As a result, torchvision doesn't work on torch hub right now.
`_script_if_tracing` could solve our problem here, but right now it does not correctly interact with recursive compilation. This PR fixes that bug.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40468
Reviewed By: jamesr66a
Differential Revision: D22195771
Pulled By: eellison
fbshipit-source-id: 83022ca0bab6d389a48a478aec03052c9282d2b7
Summary: NVIDIA's Apex is updating to no longer rely on this behavior, but we're reverting this Python2->Python3 update to unblock internal apex users.
Test Plan: Sandcaslte + OSS CI.
Reviewed By: ngimel
Differential Revision: D22146782
fbshipit-source-id: f9483d2cbf9dc3a469ad48a6c863edea3ae51070
Summary:
**Summary**
This commit adds support for with statements to PyTorch JIT. Each
of the with items in a with statement is represented in the JIT IR
as a pair of `prim::Enter` and `prim::Exit` nodes that call the
`__enter__` and `__exit__` methods defined on the context manager objects
returned by the expressions in the with item.
**Testing**
This commit adds unit tests for with statements with named with items,
nameless with items, and with statements that encounter exceptions.
```
$ python test/test_jit.py TestWith.test_with_as
Fail to import hypothesis in common_utils, tests are not derandomized
.
----------------------------------------------------------------------
Ran 1 test in 0.430s
OK
```
```
$ python test/test_jit.py TestWith.test_with_no_as
Fail to import hypothesis in common_utils, tests are not derandomized
.
----------------------------------------------------------------------
Ran 1 test in 0.264s
OK
```
```
$ python test/test_jit.py TestWith.test_with_exceptions
Fail to import hypothesis in common_utils, tests are not derandomized
Couldn't download test skip set, leaving all tests enabled...
.
----------------------------------------------------------------------
Ran 1 test in 1.053s
OK
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34705
Differential Revision: D22095945
Pulled By: SplitInfinity
fbshipit-source-id: f661565a834786725259b8ea014b4d7532f9419d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40144
as title, split remaining quantization tests out of test_jit to reduce
the size of test_jit
Test Plan: Imported from OSS
Differential Revision: D22085034
Pulled By: wanchaol
fbshipit-source-id: 0c8639da01ffc3e6a72e6f470837786c73a6b3f0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40142
test_jit is becoming huge again, which makes editor hard to load and
write new tests, this split out the tracer related tests.
Test Plan: Imported from OSS
Reviewed By: ailzhang
Differential Revision: D22085035
Pulled By: wanchaol
fbshipit-source-id: 696bee84985ecfbfeac8e2ee5c27f1bdda8de394
Summary:
BC-breaking note:
If a user is using one of these dunders directly they will not longer be available. Users should update to Python3 compatible dunders.
Original PR note:
`__div__` (and `__idiv__` and `__rdiv__`) are no longer special dunders in Python3. This PR replaces them with the `__truediv__` (`__itrudediv__`, `__rtruediv__`) dunders, since we no longer support Python2.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39151
Differential Revision: D22075713
Pulled By: mruberry
fbshipit-source-id: d318b47b51f7cc4c3728b1606a34d81e49ba0fa1
Summary:
Remove PY3 and PY34 checks from `torch/testing/_internal/common_utils.py`
Remove PY35 global var from `torch.jit.annotations`
Always call `try_get_real_signature` in `torch/jit/annotations.py`
Use `map` instead of `imap`, since Python-2 is no longer support, so map is always lazy.
Remove all pre Python-3.6 checks from `torch/_six.py` and `torch/_appdirs.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39879
Differential Revision: D22037811
Pulled By: malfet
fbshipit-source-id: af0c79f976569c2059d39ecb49c6b8285161734f
Summary:
Clearly expressing a type is inferred by PyTorch instead of explicitly annotated by user makes many error messages more user-friendly
Currently Type has two string conversion methods. str() for IR printing and python_str() for serialization and error message generation. If we want to include more information in type printing while maintaining serialization/deserialization correctness, we need to split python_str() into annotation_str() and repr_str().
annotation_str is solely responsible for serialization, it strictly matches format of python type annotation. repr_str() is responsible for generating a human-readable error message that includes information like "this type is inferred, not explicitly annotated"
Closes https://github.com/pytorch/pytorch/issues/39449
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39544
Differential Revision: D21978759
Pulled By: gmagogsfm
fbshipit-source-id: 733566f5a62e748b5ca4bb3c5943ebb6d5b664d0
Summary:
Currently torch.Tensor subclasses (like torch.nn.Parameter) isn't a supported type annotation to torch script inputs. This PR allows it to be treated like torch.Tensor for compilation.
Closes https://github.com/pytorch/pytorch/issues/38235
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39487
Differential Revision: D21885827
Pulled By: gmagogsfm
fbshipit-source-id: 1ec51829b132b7b0293a6c526d73497b23dae113
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39483
I fixed all of the new errors that occurred because of the upgrade.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D21884575
Pulled By: ezyang
fbshipit-source-id: 45c8e1f1ecb410c8d7c46dd3922ad70e982a0685
Summary:
Previously, on conversion from python -> c++ it was casted to double list through bad copy pasta. It's pretty unusual for someone to script a broadcasting list function directly since it's an internal api, so it was unlikely to affect anyone.
Fix for https://github.com/pytorch/pytorch/issues/39450
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39481
Reviewed By: jamesr66a
Differential Revision: D21870557
Pulled By: eellison
fbshipit-source-id: e704e5e87d2702a270b7d65c4df444246a134480
Summary:
**Summary**
This commit adds support for seralization and deserialization of
`ScriptModules` that have been lowered to a specific backend. Nothing
special was required to accomplish this, other than removing some code
in `unpickler.cpp` that guarded against the deserialization of `Any`
type objects. Now that lists and dicts are tagged with their types
during serialization, this check is no longer necessary.
**Test Plan**
This commit adds a unit test for testing that a lowered module still
produces the same results as Python and regular JIT after saving and
loading.
**Fixes**
This pull request fixes part of https://github.com/pytorch/pytorch/issues/37841.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38893
Differential Revision: D21825813
Pulled By: SplitInfinity
fbshipit-source-id: 77a7b84504e0dddf14c89b3ed5dd6b438c086f66
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38527
This PR solves issue (#37200).
Error is encountered during IR generation while trying to resolve the call to sum.
Should let user know it inferred the value for argument 'dim' to be of type 'Tensor'
because it was not annotated with an explicit type.
Test Plan:
Add code to reprodue the issue (#37200)
`python test/test_jit.py TestJit.test_inferred_as_tensor`
Differential Revision: D21743876
Pulled By: superwizard2019
fbshipit-source-id: 370ca32afea4d53b44d454f650f7d3006f86bcc6
Summary:
This updates assertEqual and assertEqual-like functions to either require both or neither of atol and rtol be specified. This should improve clarity around handling precision in the test suite, and it allows us to remove the legacy positional atol argument from assertEqual. In addition, the "message" kwarg is replace with a kwarg-only "msg" argument whose name is consistent with unittest's assertEqual argument.
In the future we could make "msg" an optional third positional argument to be more consistent with unittest's assertEqual, but requiring it be specified should be clear, and we can easily update the signature to make "msg" an optional positional argument in the future, too.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38872
Differential Revision: D21740237
Pulled By: mruberry
fbshipit-source-id: acbc027aa1d7877a49664d94db9a5fff91a07042
Summary:
This updates assertEqual and assertEqual-like functions to either require both or neither of atol and rtol be specified. This should improve clarity around handling precision in the test suite, and it allows us to remove the legacy positional atol argument from assertEqual. In addition, the "message" kwarg is replace with a kwarg-only "msg" argument whose name is consistent with unittest's assertEqual argument.
In the future we could make "msg" an optional third positional argument to be more consistent with unittest's assertEqual, but requiring it be specified should be clear, and we can easily update the signature to make "msg" an optional positional argument in the future, too.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38872
Differential Revision: D21717199
Pulled By: mruberry
fbshipit-source-id: 9feb856f94eee911b44f6c7140a1d07c1b026d3a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38746
Factors out testing of op alias normalization so that there is a registry used for tests.
Test Plan: Imported from OSS
Differential Revision: D21673107
Pulled By: eellison
fbshipit-source-id: e06653cdf24f14a4253dd054e4d402d171d16a11
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38735
Follow up to my comment https://github.com/pytorch/pytorch/pull/36597/#issuecomment-613674329
This adds a pass to convert op aliases into a normalized form. Having two ops generated in our IR that do the same thing makes the IR harder for downstream consumers of the IR, such as TorchScript passes but also ONNX, glow, etc.
Another solution would have been to fix our code generation to only emit `aten::abs` from the start. This seems trickier, and doesn't really buy us much if we still have to expose `aten::absolute` in C++, as glaringlee of the C++ API thinks we should.
Bike shedding: maybe this should be `CanonicalizeOps` instead
Test Plan: Imported from OSS
Differential Revision: D21673108
Pulled By: eellison
fbshipit-source-id: c328618907de1af22e07f57fd27fa619978c2817
Summary:
Updates our tests in preparation of integer division using torch.div and torch.addcdiv throwing a runtime error by avoiding integer division using torch.div. This creates a brief period where integer division using torch.div is untested, but that should be OK (since it will soon throw a runtime error).
These callsites were identified using https://github.com/pytorch/pytorch/issues/36897.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38621
Differential Revision: D21612823
Pulled By: mruberry
fbshipit-source-id: 749c03a69feae02590b4395335163d9bf047e162
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38453
Two fixes:
- RecordFunction in JIT interpreter should exist during the execution
of the frame, and not just when we enter the frame
- When creating a JIT continuation in wait instruction, we'd want to
preserve the original thread local context, right now when we resume
execution in continuation we preserve the thread local state of the
thread that set future value (i.e. executed a forked task)
Test Plan: unittest, CI
Reviewed By: ngimel
Differential Revision: D21565959
Pulled By: ilia-cher
fbshipit-source-id: 206b98e3bfb0052fc8e4031da778e372cc71afc1
Summary:
After an early return, we conditionalize all further execution. This means that currently the pattern of
`if return elif return elif return` generates better code than `if return if return if return`. It's obviously not good to have semantically equivalent code generate worse IR, so we should rewrite the graph to handle this case. This came up in https://github.com/pytorch/pytorch/pull/37171
```
torch.jit.script
def test_foo(x: bool, y: bool):
if x:
return 1
return 2
print(test_foo.code)
```
generates:
```
def test_foo(x: bool,
y: bool) -> int:
_0 = uninitialized(int)
if x:
_1, _2 = True, 1
else:
_1, _2 = False, _0
if _1:
_3 = _2
else:
_3 = 2
return _3
```
while
```
torch.jit.script
def test_foo(x: bool, y: bool):
if x:
return 1
else:
return 2
print(test_foo.code)
```
generates:
```
def test_foo(x: bool,
y: bool) -> int:
if x:
_0 = 1
else:
_0 = 2
return _0
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38282
Differential Revision: D21576733
Pulled By: eellison
fbshipit-source-id: 80cf1ad7fbda6d8d58557abbfb21c90eafae7488
Summary:
TorchScript currently doesn’t support `*args, **kwargs` in method signature, which is extensively used in DPER3 low-level modules’ forward method. In order to make DPER3 low-level modules scriptable, I was thinking about a solution of having a forward method *only* for TorchScript, and replace the forward method when we are not in scripting mode.
This solution works today, and I would like to add a test to make sure it will always work in the future.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38158
Differential Revision: D21485657
Pulled By: yf225
fbshipit-source-id: df7368e8a5265418be7c305e6666ffd76e595466
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35623
Python 2 has reached end-of-life and is no longer supported by PyTorch.
This test case is valid syntax in Python 3.
Test Plan: CI
Differential Revision: D20842874
Pulled By: dreiss
fbshipit-source-id: 9f12e046f827d4f9d5eca99b0b0b46f73e06ff51
Summary:
Previously, we weren't adding the location to implicit conversions, so the error message wouldn't show location when these ops failed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38442
Differential Revision: D21563500
Pulled By: eellison
fbshipit-source-id: 19dd786ab8580f11ed919aac669efeed0ef52dcb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37994
Before, reassigning a method in a module (like `forward = _forward`)
didn't work, because we look at the function object's name for our def
name when building AST. Mkae that overrideable to handle cases like
reassignment
Test Plan: Imported from OSS
Differential Revision: D21444535
Pulled By: suo
fbshipit-source-id: 4f045f18b5a146edc8005689af525d7d7ed8dd5f
Summary:
Fix for https://github.com/pytorch/pytorch/issues/37986
Follows the stack in https://github.com/pytorch/pytorch/pull/33783 stack to make functions in `torch/functional.py` resolve to their python implementations. Because the return type of `torch.unique` depends on `return_inverse` and `return_counts` I had to refactor the implementation to use our boolean_dispatch mechanism.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38156
Differential Revision: D21504449
Pulled By: eellison
fbshipit-source-id: 7efb1dff3b5c00655da10168403ac4817286ff59
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38253
This pass removes dropout and dropout_ nodes when training is false. It
requires to have run freeze_module pass which does both inlining and constant
propagation, without which training variable remains as attribute instead of
constant.
ghstack-source-id: 103939141
Test Plan: python test/test_jit.py TestScript.test_remove_dropout
Reviewed By: dreiss
Differential Revision: D21505863
fbshipit-source-id: 42ea45804e4653b625b6a254c8d8480757264aa8
Summary:
Followup of https://github.com/pytorch/pytorch/issues/37848 I realized that it's better to condition on `Value` type instead of token type. So now it also support indexing through list variables (used to be list literal only).
Also apparently our eager frontend accept indexing with float list as well, so matched this edge case behavior as well.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37966
Reviewed By: suo
Differential Revision: D21439642
Pulled By: ailzhang
fbshipit-source-id: cedb8431ef38747d4aa9909a6bbf8e954dbe0e25
Summary:
**Summary**
This commit adds `torch::jit::RegisterBackend`, an API that allows
external backends to be registered for the execution of JIT subgraphs
outside the JIT interpreter. In order to register an external backend,
one must extend the provided abstract class `PyTorchBackendInterface` and provide
two additional functions: one that creates an instance of the aforementioned subclass
of `PyTorchBackendInterface`, and another that preprocesses a `ScriptModule` so that
it can run on the backend. Then, a `ScriptModule` that can compile and execute a given
JIT subgraph using the functions provided at registration time is generated
for each registered backend.
**Testing**
This commit adds a unit test that uses a minimal test backend
to make sure that the registration endpoint and generated
`ScriptModule` work.
```
$ python test/test_jit.py TestBackends
Fail to import hypothesis in common_utils, tests are not derandomized
.
----------------------------------------------------------------------
Ran 1 test in 0.183s
OK
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35833
Differential Revision: D21231955
Pulled By: SplitInfinity
fbshipit-source-id: 452db1123d0e5d83f97fe5da8a00fdfdb50dbef9
Summary:
this is failing in the profiling_executor job
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37961
Differential Revision: D21434341
Pulled By: eellison
fbshipit-source-id: b34f94b1595ef6f6edee76cd200f951a2ef21f22
Summary:
The existing contextmanager only conditionally enabled_profiling_mode, which was counter intuitive. When we changed the default executor it broke internal benchmarking as a result.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37825
Differential Revision: D21404611
Pulled By: eellison
fbshipit-source-id: 306b3c333ef4eb44ab6a6e5ab4e0682e5ce312ce
Summary:
We used to only support indexing through
- numbers like `x[0, 1]`
- tuple like `x[(0, 1)]`
- tensor like `x[torch.tensor([0, 1])]`
This PR adds support for indexing through list which is equivalent to tensor.
- `x[[0, 1, 5]]`
- `x[[0, 1], [0, 1]]`
- `x[[[0, 1], [0, 1]], [[0, 1], [0, 1]]]`
Note for `x[[0, 1, 5]]` we had a bug in AST conversion code so we used to treat it like `x[0, 1, 5]` which means it might accidentally run and produce wrong result(fixes https://github.com/pytorch/pytorch/issues/37286 fixes https://github.com/pytorch/pytorch/issues/18616), now that it's fixed we probably want to mark it as BC breaking.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37848
Reviewed By: suo
Differential Revision: D21409840
Pulled By: ailzhang
fbshipit-source-id: 6f2d962885c6dc009cb384d98be1822f5ca7a189
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37842
Fixes https://github.com/pytorch/pytorch/issues/23993.
Previously our name lookup function for the tracer was looking in
f.globals for names. For example:
```
sample1 = torch.ones(1)
sample2 = torch.ones(1)
traced = torch.jit.trace(my_mod, ((sample1, sample2,),))
> produces a graph with something like:
> %sample1, %sample2 = prim::TupleUnpack(%input)
```
This is not great if you are, e.g. trace checking, because a non-local
bit of interpreter state is affected the graph produced:
```
traced = torch.jit.trace(my_mod, _clone_inputs((sample, sample,),))
> produces a graph with something like
> %0, %1 = prim::TupleUnpack(%input)
```
I have removed this functionality, as I don't think it provides huge
value. Things that look locally for names will still work, so e.g.
inputs, intermediate variables, and the like will be named correctly.
Test Plan: Imported from OSS
Differential Revision: D21406478
Pulled By: suo
fbshipit-source-id: 3c7066b95d4a6e9b528888309954b02dadbc1a07
Summary:
We were previously only looking at class attributes, so that didn't include methods etc, and would silently give wrong semantics. This makes hasAttr go through the same resolution as our other attribute lookups.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37424
Differential Revision: D21282633
Pulled By: eellison
fbshipit-source-id: 8e970f365c2740d137a02331739c2ed93747b918
Summary:
as a part of moving to the dynamic shapes we are now passing `frame_id` to each profiling callback. The implementation of that requires copying profiling callbacks into Interpreter, so `first`s are actually different for every run. The dynamic shapes merging algorithm won't be using `first`, but in the meantime, while we get there, this should be a good enough fix.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36806
Differential Revision: D21307173
Pulled By: Krovatkin
fbshipit-source-id: 7dade56ebcc72ebd40bb7f3d636c7b83c99b628f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37464
Fixes https://github.com/pytorch/pytorch/issues/23993.
There are two fixes here:
1. Previously our name lookup function for the tracer was looking in
f.globals for names. For example:
```
sample = torch.ones(1)
traced = torch.jit.trace(my_mod, ((sample, sample,),))
# produces a graph with something like
# %sample, %sample = prim::TupleUnpack(%input)
```
This is not great if you are, e.g. trace checking, because a non-local
bit of interpreter state is affected the graph produced:
```
traced = torch.jit.trace(my_mod, _clone_inputs((sample, sample,),))
# produces a graph with something like
# %0, %1 = prim::TupleUnpack(%input)
```
I have removed this functionality, as I don't think it provides huge
value. Things that look locally for names will still work, so e.g.
inputs, intermediate variables, and the like will be named correctly.
2. Previously, our input cloning for trace checking didn't do a memoized
deep copy. So:
```
_clone_inputs((sample, sample, sample))
```
produces a tuple with three non-aliased tensors. That's wrong! Use
copy.deepcopy with a memoization argument to fix this.
Test Plan: Imported from OSS
Differential Revision: D21297549
Pulled By: suo
fbshipit-source-id: 981d5879a4a244520dd68489767129ff357f1497
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37012
Removes an if statement in `torch.nn.functional.affine_grid`
Test Plan: Imported from OSS
Differential Revision: D21160755
Pulled By: eellison
fbshipit-source-id: 8b030936c9fbdb05b44abc9f254805d102f2acc2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36953
Add support for generic lists as a constant. generic dicts & tuples are already implemented. This is a pretty common pattern and cuts down on the number of non-tensor nodes executed in interpolate tests.
Test Plan: Imported from OSS
Differential Revision: D21160761
Pulled By: eellison
fbshipit-source-id: 1e6b7b25b7580f09067794772d44e615601c60c4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37088
For an inlined expression tree like `(e_0, (e_1, e_long))` the previous
algoritm only scanned the same statement as `e_long`, splitting the
inlined expressions across lines. Because it did not scan `e_0`, `e_0`
would still get emitted inline, causing it to reverse order with `e_1` and
`e_long`. The new algorithm scans starting at `e_long` and going all
the way back up the expression until it reaches the end of the inlined
statement. Caching of what has already been scanned has been added so that
if there was a second long long `e_long2` after `e_long`, it would not
rescan and re-inline the statements that were already split.
Test Plan: Imported from OSS
Differential Revision: D21180394
Pulled By: zdevito
fbshipit-source-id: 4d142c83a04c89a47d04282f67a513f82cf153c0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35615
Python 2 has reached end-of-life and is no longer supported by PyTorch.
Now we can clean up a lot of cruft that we put in place to support it.
These changes were all done manually, and I skipped anything that seemed
like it would take more than a few seconds, so I think it makes sense to
review it manually as well (though using side-by-side view and ignoring
whitespace change might be helpful).
Test Plan: CI
Differential Revision: D20842886
Pulled By: dreiss
fbshipit-source-id: 8cad4e87c45895e7ce3938a88e61157a79504aed
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36188
* Need to remove n^2 behavior for scanning whether to split or not
otherwise long inline chains will take a long time re-scanning.
Test Plan: Imported from OSS
Differential Revision: D20907254
Pulled By: zdevito
fbshipit-source-id: ebfc1a4eefc26d5806381e7afd75b7a9cd4cde97
Summary:
Our test suite used to set double as its default scalar type, and when it was switched to not do so (to be more consistent with how users experience PyTorch), a few tests had to still set the default scalar type to double to function properly. Now that the jit no longer creates double tensors so frequently, it appears that test_jit no longer needs to set double as its default scalar type, too.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36982
Differential Revision: D21152120
Pulled By: mruberry
fbshipit-source-id: ea6d3c1ad55552dc5affa1fe1bd0e5189849e6d7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34258
This PR allows both atol and rtol to be specified, uses defaults based on the prior analysis (spreadsheet attached to https://github.com/pytorch/pytorch/pull/32538), but retains the absolute tolerance behavior in cases where precision was previously specified explicitly.
Test Plan: Imported from OSS
Differential Revision: D21110255
Pulled By: nairbv
fbshipit-source-id: 57b3a004c7d5ac1be80ee765f03668b1b13f4a7e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36696
This PR add dictionary as a supported output of tracer under the strict
flag.
Test Plan: Imported from OSS
Reviewed By: houseroad
Differential Revision: D21056962
Pulled By: wanchaol
fbshipit-source-id: ace498182d636de853cf8a1efb3dc77f5d53db29
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36727
Looks like this was renamed by accident in 0cbd7fa46f
Test Plan:
Unit test.
Lint.
Differential Revision: D21076697
Pulled By: dreiss
fbshipit-source-id: dbd18cb41c7b26479984a7a7b12ad41a4c5b7658
Summary:
Previously we were always creating a double tensor from `torch.tensor(1.)`, whereas python eager uses the current default dtype. Fix for https://github.com/pytorch/pytorch/issues/36369
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36587
Differential Revision: D21043617
Pulled By: eellison
fbshipit-source-id: 38da303594f52e06941d86b6e57c4a06e7d36938
Summary:
With https://github.com/pytorch/pytorch/pull/35562, we are running peephole optimization on inlining to reduce the number of nodes that are copied.
The tracer encodes the sizes in the graph like:
```
graph(%0 : Double(7)):
%1 : Function = prim::Constant[name="tensor_size"]()
%2 : Tensor = prim::CallFunction(%1, %0)
return (%2)
```
however people would like to reuse the graph with different shapes so running size invalidations would invalidate that. long term it might be better for the tracer to not include shape information but there are downstream users of that.
Separates out FuseAddMM from peephole so that now there is a single `disable_size_optimizations` parameter, and onnx explicitly invokes fuseaddmm.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36404
Differential Revision: D20968974
Pulled By: eellison
fbshipit-source-id: 56f8f1699e3b0adeeccdfd5a67bb975fd41a2913
Summary:
Previously we were copying the bound method of the original class to the
new script module class, which causes `self` to be wrong. This PR
changes it so we fetch the unbound function, then bind it to the new
script module, then attach it to the module.
Fixes#28280
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36546
Pulled By: driazati
Differential Revision: D21023329
fbshipit-source-id: 6b3f8404700860151792f669a9c02fbd13365272
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36277
This PR introduce a flag to the tracer that guard the risky behaviors
like adding list/dict as output of the tracer. Currently to ensure not
BC breaking user, we throw warning if the tracer output is list, and
will throw error when the tracer output is dict to enforce using this
flag (next PR)
Test Plan: Imported from OSS
Differential Revision: D20998157
Pulled By: wanchaol
fbshipit-source-id: 0d2c55f1a263a48b1b92dd6ad54407815e0a6f72
Summary:
To make them compatible with python3.7 and python3.8
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36422
Test Plan: CI
Differential Revision: D21006399
Pulled By: malfet
fbshipit-source-id: 725df277ff3e4479fc2c39d16a30fbf301fde9e5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36411
This PR remove pytorch specific defined assertwarns and use the unit
test one, also format some tests
Test Plan: Imported from OSS
Differential Revision: D20998159
Pulled By: wanchaol
fbshipit-source-id: 1280ecff2dd293b95a639d13cc7417fc819c2201
Summary:
AnyType wasn't listed as a mutable type, so the assertion triggered (yay!). Also update the `isMutableTypeInternal(from) != isMutableTypeInternal` logic to be more encompassing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36178
Differential Revision: D20922356
Pulled By: eellison
fbshipit-source-id: 7060a62b18e98dc24b6004a66225c196aadb566e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35170
Looks like this was renamed by accident in 0cbd7fa46f
Test Plan:
Unit test.
Imported from OSS
Differential Revision: D20783298
fbshipit-source-id: 8fcc146284af022ec1afe8d651baf6721b190ad3
Summary:
In a comprehension like:
```
def f()->int:
i = 1
x = [i for i in range(7)]
return i
```
the variables inside the comprehension do not write to the function environment.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36105
Differential Revision: D20880699
Pulled By: eellison
fbshipit-source-id: 40af0f7470e0baeff7ef158cb461bf85c816d169
Summary:
Resubmit of https://github.com/pytorch/pytorch/pull/35424, only this time I run optimizations in the right order so the PR description is actually true.
This speeds up the inlining pass of FairSeq model from 180s -> 13s, and MaskRCNN model from 5s -> 1.5s.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35562
Differential Revision: D20738922
Pulled By: eellison
fbshipit-source-id: 1439cf9d1f0bc780e2d64a744694f8b3b7ba4b70
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35834
This handles the cases we did not handle before in AND and OR statements:
static_true || <unknown> -> static_true
static_false && <unknown> -> static_false
Test Plan: Imported from OSS
Differential Revision: D20801125
Pulled By: zdevito
fbshipit-source-id: 0ef94c3a14c7af91580fc5248a4ccfd9e8d6d481
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35720
When modules are saved, all relevant types are serialized according to
their qualified name with a compilation unit. Since qualified names are
guaranteed to be unique within a compilation unit, this normally works
fine.
On load, all types are registered in a compilation unit owned by the
script::Module. Type names are not unique across compilation units, so
if you load two modules with colliding type names, make them submodules
of yet another module, and save that module, there is the potential of a
name collision. See the added tests for examples if that description is
confusing.
The solution is to unique type names when serializing code by mangling
them if we detect a name collision.
Test Plan: Imported from OSS
Differential Revision: D20749423
Pulled By: suo
fbshipit-source-id: a8827ff1d4a89f3e7964dbbb49b4381863da3e6a
Summary:
This is a follow up from https://github.com/pytorch/pytorch/pull/34520, which removed specialized list ops. This removes templating from list ops.
it also has one minor other change, which is to move `aten::len(t[]) -> int` to `aten::len(Any[]) -> int` so that heterogenous tuples can be called with `len()`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35768
Differential Revision: D20772943
Pulled By: eellison
fbshipit-source-id: bc36a00920bc94ca8c5aa9eb7d5d7a640388ffbb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34674
Two changes to make sure the op_names dumped in export_opnames() are consistent to what are actually used in bytecode.
* Inline graph before dumping the operator names.
* Use code of the graph (which is used in bytecode) instead of the nodes of graph.
Test Plan: Imported from OSS
Differential Revision: D20610715
Pulled By: iseeyuan
fbshipit-source-id: 53fa9c3b36f4f242b7f2b99b421f4adf20d4b1f6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33020
This is a pass to create functional blocks. The other PRs in the stack help avoid some of the limitations that are are often found in graphs. It's possible that this would work well with a graph that is frozen. Follow up work items that will help this pass:
- We don't currently have any capacity in alias analysis to tell whether a Value that came from the wildcard set "re-escapes" back into the wildcard set.
- More comments on the semantics of the graph and correctness conditions
- We could consider using dynamic dag if the perf of this is a limitation.
- potential make Functional Graphs Functional Blocks instead, so that we do not repeatedly copy constants, also to make IR read easier.
Test Plan: Imported from OSS
Differential Revision: D20603188
Pulled By: eellison
fbshipit-source-id: 6822a6e65f4cc2676f8f6445fe8aa1cb858ebeeb
Summary:
Stacked PRs
* #34938 - [jit] Remove stray `script`
* **#34935 - [jit] Add lazy script decorator**
Some users maintain libraries of code that is largely trace-able but not
script-able. However, some functions may need to be `torch.jit.script`ed if
they contain control flow so the tracer will use the compiler version.
This however impacts library start up time as in #33418, so this PR adds
a workaround in the form of a `torch.jit._lazy_script_while_tracing`
that will only initialize the compiler if the function is called while
actually tracing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34935
Pulled By: driazati
Differential Revision: D20569778
fbshipit-source-id: d87c88c02b1abc86b283729ab8db94285d7d4853
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34804
We want to replicate the quantize node for return values in blocks of prim::If
in order to create the quantization patterns.
Test Plan:
python test/test_jit.py
Imported from OSS
Differential Revision: D20524453
fbshipit-source-id: 2268ac555f646158f4e1ffc98ccc8101d7504194
Summary:
add `id` function so to give uses a way of keeping a `seen` set of nn modules.
n practice, this is only used between values of `T` and `T` or `T` and `Optional[T]`, so in this implementation I made it so that None is the only value that can be zero. Python also only guarantees `id()` gives semantically meaningful results for pointer types.
EDIT: now only allowing id on class types
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34975
Reviewed By: driazati
Differential Revision: D20599564
Pulled By: eellison
fbshipit-source-id: 3c6666a9b9b0258198adc70969dd6332e3375e4f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35221
When weight is reused, we only need to insert one observer for the weight
Test Plan:
python test/test_jit.py
Imported from OSS
Differential Revision: D20602492
fbshipit-source-id: e003e6316f6615f3526f0d00fb7b722148b4749b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34411
To make sure dequantize and the node that uses the dequantized value reside in the
block, so that we can do quant fusion
Test Plan:
python test/test_jit.py
Imported from OSS
Differential Revision: D20519603
fbshipit-source-id: 3e4c68d0a73142716e19ea6a64ae3a5d6d51fa41
Summary:
This PR adds a preprocessing step in Conv2dBatchNorm folding.
It traverses the module to check if the bias of Conv2d module is set to
None. If it is, it assume that this a traced module and insert
Optional[Tensor] type bias.
Furthermore it insert getAttr for bias in the forward graph and fixes
_convolution op to take values from getAttr.
It also fixes parametere extraction from BN module which may not
have weight and bias attributes if affine was set to False. In scripted
mode such a BN module will get weight and bias attributes set to None.
For the case of eps it gets const propagated in tracing. This is also
fixed.
Few tests cases are added.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34932
Test Plan:
python test/test_jit.py TestJit.test_foldbn_trivial
python test/test_jit.py TestJit.test_foldbn_trivial_nobias
python test/test_jit.py TestJit.test_foldbn_in_submodule
python test/test_jit.py TestJit.test_foldbn_shared_classtype
python test/test_jit.py TestJit.test_foldbn_complex_cases
python test/test_jit.py TestJit.test_nofoldbn_complex_cases
Differential Revision: D20536478
Pulled By: kimishpatel
fbshipit-source-id: 4e842976a380d0575a71001bb4481390c08c259e
Summary:
add `id` function so to give uses a way of keeping a `seen` set of nn modules.
n practice, this is only used between values of `T` and `T` or `T` and `Optional[T]`, so in this implementation I made it so that None is the only value that can be zero. Python also only guarantees `id()` gives semantically meaningful results for pointer types.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34975
Differential Revision: D20549677
Pulled By: eellison
fbshipit-source-id: cca5ed4ef013f7540f93abf49f91f9830dfdca14
Summary:
https://github.com/pytorch/pytorch/issues/34563 accidentally introduced a lint error due to an unused import. This PR removes this import.
Jit tests run as expected after this change:
```
> python test/test_jit.py
.....
Ran 2435 tests in 100.077s
OK (skipped=140, expected failures=1)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34778
Differential Revision: D20459708
Pulled By: tugrulince
fbshipit-source-id: bb742085fafc849ff3d9507d1557556e01fbeb4b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34671
Like the python arg parser, this tries to convert to the schema in order.
It introduces schema_match_exception which gets thrown when the schema doesn't match,
allowing the overload handler to try the next option.
Behavior will not 100% match the schema argument parser but should work for
simple cases using custom binding.
Test Plan: Imported from OSS
Differential Revision: D20432206
Pulled By: zdevito
fbshipit-source-id: 280839a2205ea3497db3a9b5741fccc1e2bff9a8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34190
inplace modification of ClassType might affect other tests, so we want to do non-inplace modifications.
Actually the inplace argument will be removed soon.
Test Plan:
ci
Imported from OSS
Differential Revision: D20451765
fbshipit-source-id: e87ad528c4e7f84f5774b94a8e3e85568269682d
Summary:
With this PR, we can now support left and right shift operators in the JIT engine for <int, int> and <Tensor, int>.
Updated tests pass as expected:
```
> python test/test_jit.py
...
Ran 2427 tests in 84.861s
OK (skipped=139, expected failures=1)
```
Running the following code with Python results in the output below:
```
> cat ~/expressions.py
import torch
torch.jit.script
def fn(a, b):
# type: (int, int)
return (
a << b, # supported
b >> a, # supported
a & b,
a | b,
a ^ b
)
print(fn.graph)
```
```
> python ~/expressions.py
graph(%a.1 : int,
%b.1 : int):
%4 : int = aten::leftshift(%a.1, %b.1) # /home/ince/expressions.py:7:8
%7 : int = aten::rightshift(%b.1, %a.1) # /home/ince/expressions.py:8:8
%10 : int = aten::__and__(%a.1, %b.1) # /home/ince/expressions.py:9:8
%13 : int = aten::__or__(%a.1, %b.1) # /home/ince/expressions.py:10:8
%16 : int = aten::__xor__(%a.1, %b.1) # /home/ince/expressions.py:11:8
%17 : (int, int, int, int, int) = prim::TupleConstruct(%4, %7, %10, %13, %16)
return (%17)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34563
Differential Revision: D20434209
Pulled By: tugrulince
fbshipit-source-id: 886386c59755106e17b84778b8e495b80a6269cd
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/33927
Test Plan:
test will be added in later PRs
Imported from OSS
Differential Revision: D20354879
fbshipit-source-id: 03976f4b86c46dbdc4e45764a1e72f1a3855a404
Summary:
`torch.nn.functional.interpolate` was written as a builtin op when we scripted the standard library, because it has four possible overloads. As a result, whenever we make a change to `interpolate`, we need to make changes in two places, and it also makes it impossible to optimize the interpolate op. The builtin is tech debt.
I talked with ailzhang, and the symbolic script changes are good to remove (i guess that makes a third place we needed to re-implement interpolate).
I'm trying to get rid of unneccessary builtin operators because we're standardizing mobile bytecode soon, so we should try to get this landed as soon as possible.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34514
Differential Revision: D20391089
Pulled By: eellison
fbshipit-source-id: abc84cdecfac67332bcba6b308fca4db44303121
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33853
Quant fusion relies on inline, but inline will break the CallFunction("linaer", ...) into a if block
it will be hard to recognize this block and swap it with quantized::linear, in order to
preserve the op, we will swap all quantized functional linear into aten::linear.
They might produce different backward graph, but this is called in the step before we get quantized
model, so it shouldn't affect anything.
We'll integrate this with convert_script later in the new "finalize_quant" API
Test Plan:
python test/test_jit.py
Imported from OSS
Differential Revision: D20343873
fbshipit-source-id: 423e03bf893b79267d2dc97bc997ee1bfe54ec0f
Summary:
Previously when emitting subscripts we only emitted actual values, but
now they may sometimes emit a `ModuleValue`, so it should stay as a
`SugaredValue`. This allows for the result of the subscript to be
treated as a real module (i.e. you can just do `self.modlist[1](inputs)`
instead of `self.modlist[1].forward(inputs)`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34320
Pulled By: driazati
Differential Revision: D20345642
fbshipit-source-id: 2bedf9a454af747b704422f6bbb8370cbdf4bf61
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33481
We have to propagate observed property of values through ops like max_pool2d, flatten and
avoid inserting duplicated observers.
For example:
```
x1 = self.conv(x)
x2 = maxpool(x1)
x3 = self.conv(x2)
```
If x1 is observed, we should propagate this information through maxpool and
we should consider x2 as observed as well.
Test Plan:
python test/test_jit.py
Imported from OSS
Differential Revision: D20261897
fbshipit-source-id: 7de354a3ccb2b6e1708f5c743d4d9f7272691a93
Summary:
`ConcreteModuleTypeBuilder` used to keep parameters together with all others attributes in an `unordered_map` often leading to reordering them while building up the type. Parameter order is semantically meaningful, so we need to preserve it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34131
Differential Revision: D20331542
Pulled By: suo
fbshipit-source-id: 5b860025f7902654d6099751d3fb14b12f6f5a67
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32814
We skip quantization for the intermediate values for patterns like `Conv - ReLU`,
but currently we didn't skip quantizing the input/output of the graphs of matched modules,
since we now changed the way we add observers, this also needs to be updated.
Test Plan:
python test/test_jit.py -- 'TestJit.test_insert_observers_skip_values'
Imported from OSS
Differential Revision: D20208785
fbshipit-source-id: ce30f2c4c8ce737500d0b41357c80ec8b33aecf9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33834
This changes how we report Tracebacks to make them more clear when
there are both serialized and non-serialized ranges. It now looks like:
```
Traceback (most recent call last):
File "foo.py", line 25, in <module>
s2(a, b)
File "/scratch/zdevito/pytorch/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
RuntimeError: The following operation failed in the TorchScript interpreter.
Traceback of TorchScript, serialized code (most recent call last):
File "code/__torch__.py", line 7, in forward
x: Tensor,
y: Tensor) -> Tensor:
return (self).bar(x, y, )
~~~~~~~~~ <--- HERE
def bar(self: __torch__.Moo,
x: Tensor,
File "code/__torch__.py", line 11, in bar
x: Tensor,
y: Tensor) -> Tensor:
_0 = (self).baz(x, y, )
~~~~~~~~~ <--- HERE
_1 = torch.ones([3], dtype=None, layout=None, device=None, pin_memory=None)
return torch.add(_0, _1, alpha=1)
File "code/__torch__.py", line 17, in baz
x: Tensor,
y: Tensor) -> Tensor:
return torch.add(x, y, alpha=1)
~~~~~~~~~ <--- HERE
Traceback of TorchScript, original code (most recent call last):
File "foo.py", line 11, in forward
def forward(self, x, y):
return self.bar(x, y)
~~~~~~~~ <--- HERE
File "foo.py", line 9, in bar
def bar(self, x, y):
return self.baz(x, y) + torch.ones(3)
~~~~~~~~ <--- HERE
File "foo.py", line 7, in baz
def baz(self, x, y):
return x + y
~~~~~ <--- HERE
RuntimeError: The size of tensor a (4) must match the size of tensor b (5) at non-singleton dimension 1
```
It follows Python convension of having the most important information last
and reading from the bottom up.
Changes:
* Moved the error message to the end, to copy Python
* Report original traceback separate from serialized traceback
* Make sure root functions have names in the interpreter trace.
Test Plan: Imported from OSS
Differential Revision: D20126136
Pulled By: zdevito
fbshipit-source-id: fd01f9985e5d74e04c4d064c02e8bc320f4fac13
