**BC-breaking note**:
This PR deprecates `torch.lu` in favor of `torch.linalg.lu_factor`.
A upgrade guide is added to the documentation for `torch.lu`.
Note this PR DOES NOT remove `torch.lu`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77636
Approved by: https://github.com/malfet
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74353
Repatched `d00de0d43598522b8f6ab2de553b6aaf6768faa5` by Nora Belrose (norabelrose). With following changes:
* Register fake source of generated methods in linecache so that inspect.get_source will succeed.
* this patching is only triggered if the given dataclass passed to torch.jit.script previously. Effectively we make this feature opt-in.
## Original Summary:
Fixes https://github.com/pytorch/pytorch/issues/72901.
Since we can't get access to the source code for synthesized magic methods on dataclasses, we have to synthesize our own versions. torch/jit/_dataclass_impls.py has the code that does this.
What's supported
Synthesized __init__, __eq__, and the comparison magic methods when order=True is set on the dataclass decorator
Default values for fields
__post_init__, including using InitVar fields inside of __post_init__, on Python 3.8+
Overriding __eq__ or any of the comparison magic methods to provide your own implementation
What's not supported
Default factory initializers for fields
Frozen dataclasses
InitVar on Python 3.7
__repr__ and __hash__ (these are actually implemented, but the TorchScript interpreter won't call them)
Using the != operator on dataclasses inside TorchScript; this is because TorchScript requires that you implement __ne__ to use this operator, whereas in regular Python the != operator will resolve to the negation of whatever is returned by __eq__ if there's no __ne__. Dataclasses don't actually synthesize an __ne__ method for this reason. I've been toying with different ways to fix this but != is not working in this PR at the moment.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74889
Test Plan:
unittest
Also run previously failed test:
```
buck test mode/dev-nosan //fblearner/flow/projects/fluent2/definition/transformers/contrib/faim/test:tests -- --exact 'fblearner/flow/projects/fluent2/definition/transformers/contrib/faim/test:tests - test_mixmatch_multiclass (fblearner.flow.projects.fluent2.definition.transformers.contrib.faim.test.faim_mixmatch_test.TestFaimTransformerMixMatch)'
```
passes
Reviewed By: zhxchen17
Differential Revision: D35206262
Pulled By: qihqi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76771
Approved by: https://github.com/seemethere
PR https://github.com/pytorch/pytorch/pull/77042 has fixed the new folding conv-bn data type issue but missing the case when original conv has no bias input.
In this PR:
- Fix the new folding conv-bn's bias data type issue, when conv has no bias but weight as lower precision datatype, the new generated bias data type should be same as conv's weight.
- Move the Autocast JIT Trace UT from `test_jit.py` to `test_jit_autocast.py`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78241
Approved by: https://github.com/davidberard98
Consider the following JIT graph, where the type of `%a` and `%b` are out of sync with tuple `%c`.
Before:
```
graph(%a : Float(123), %b : Float(4, 5, 6)):
c : (Tensor, Tensor) = prim::TupleConstruct(%a, %b)
return (%c)
```
After:
```
graph(%a : Float(123), %b : Float(4, 5, 6)):
c : (Float(123), Float(4, 5, 6)) = prim::TupleConstruct(%a, %b)
return (%c)
```
This PR adds a pass `RefineTypes(...)` to update all such instances with the correct type. This is also available via Python by using `torch._C._jit_pass_refine_types(...)`.
A unit test has been added for unnamed tuples, but no test exists for `NamedTuple` (though it was tested manually) since it isn't supported by the parser:
```
RuntimeError:
unknown type specifier:
graph(%a : Float(123), %b : Float(4, 5, 6)):
%c : NamedTuple(Tensor : Tuple, Tensor : Tuple) = prim::TupleConstruct(%a, %b)
~~~~~~~~~~ <--- HERE
return (%c)
```
cc: @ke1337 @antoniojkim @wconstab @eellison
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76919
Approved by: https://github.com/eellison
This PR ...
Makes the following testing changes:
- Updates stride testing in test_python_reference_consistency to only check strides of dimensions with length > 1
- Creates reference inputs for reshape
- Creates reference inputs for chunk
- Extends the sample inputs for unsqueeze
- Extends the sample inputs for stack -- test_conj_view and test_neg_view are now xfailed
- https://github.com/pytorch/pytorch/issues/77046
Makes the following architecture changes:
- Adds the refs.special (sub)module
- Adds the refs.nn.functional (sub)module
Adds the following prims:
- expand_dims
- view_of
- rev
- clone
Adds the following references:
- flatten
- squeeze
- unsqueeze
- special.i0e
- special.i1e
- logical_or
- logical_and
- isclose
- flip
- stack
- nn.functional.elu
- chunk
- clone
- narrow
Identifies the following bugs in PyTorch today:
- https://github.com/pytorch/pytorch/issues/77054
- https://github.com/pytorch/pytorch/issues/77055
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77043
Approved by: https://github.com/ngimel
For the most part, PrimTorch refs have the same signature as their
ATen equivalents. I modify most PrimTorch refs to register themselves
as decompositions, using the prim name they wrap to find the aten name
(except for a few cases where the prim/aten names mismatch). There are
some exclusions, falling into one of two categories:
- The torch equivalent was already implemented as a CompositeImplicitAutograd
decomposition in C++
- The ref doesn't support enough features (e.g., the real deal has more
kwargs / overloads than are currently implemented)
PrimTorch refs are written as a single function that supports all
overloads, and this style is convenient for cases where we have a bundle
of overloads for what morally is a single overload with a Union type
on an argument (which we ought to have supported in
native_functions.yaml but blah); to support registering a single decomp
for all the overloads, we modify register_decomposition to register
to ALL overloads if you pass it an overload packet. This is technically
BC breaking but no tests started failing because of it.
Signed-off-by: Edward Z. Yang <ezyangfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76835
Approved by: https://github.com/Chillee, https://github.com/mruberry
Summary:
This enables previous change made at D35196883 (b34b192d6b)
Previous change is landed for 2 weeks to make sure that the format change introduced here will be handed in code.
Test Plan: existing tests
Differential Revision: D36074453
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76688
Approved by: https://github.com/gmagogsfm
crossref is a new strategy for performing tests when you want
to run a normal PyTorch API call, separately run some variation of
the API call (e.g., same thing but all the arguments are meta tensors)
and then cross-reference the results to see that they are consistent.
Any logic you add to CrossRefMode will get run on *every* PyTorch API
call that is called in the course of PyTorch's test suite. This can
be a good choice for correctness testing if OpInfo testing is not
exhaustive enough.
For now, the crossref test doesn't do anything except verify that
we can validly push a mode onto the torch function mode stack for all
functions.
Signed-off-by: Edward Z. Yang <ezyangfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75988
Approved by: https://github.com/seemethere
This PR makes the following improvements:
- moves the custom skip list for test_normalize_operator_exhaustive in test_fx_experimental to use the typical OpInfo skip architecture. The skips were updated to xfails, and that identified some operators which were no longer failing the test
- redundant tests with OpInfo-based testing in test_jit.py were removed
- test_dtypes was improved so its error messages are clear and it makes test_nondifferentiable redundant; the latter test has been removed
- OpInfo.supports_complex_autograd() is removed in favor of a more accurate and general test for whether the particular dtype is in the backward dtypes of the operator
- gradchecks have been improved to verify that an operator doesn't support grad if it claims not to
- gradchecks have been improved to test the gradient of all input tensors that require gradient
- the concept of "default test dtypes" has been removed
- excessive and mostly redundant out testing for elementwise unary operators has been removed
- metadata for whether an op supports nuanced "safe casting" to out behavior has been removed from OpInfos
- numerous skips have been converted to xfails
- numerous OpInfos have had their metadata fixed based on the new checks
- jit-specific utilities in common_methods_invocations.py have been moved to jit_programming_utils.py
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75951
Approved by: https://github.com/ngimel
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75119
Add support for parsing Tensor constants like Double(4, 4) ... by initializing random tensors. This makes saving IR and then parsing it lossy, so I have it toggled as default not on, but is useful in cases like repro-ing Fusions with tensor constants post-freezing.
cc Krovatkin
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D35373999
Pulled By: eellison
fbshipit-source-id: a5c8d9f93f23a7442258fc745ed6b6def330dca8
(cherry picked from commit 32dd6567522973563bd452bf486ed27b02e4e35c)
Summary:
This PR introduces `SymInt` type to Pytorch which will be used by LTC and AOTAutograd for tracing size arithmetic and tests.
`SymInt` is a C++ union structure [int64_t, SymbolicIntNode*] that wraps around an int64_t field where the value of the field could be an index into a list of `shared_ptr<SymbolicIntNode>` or a real int.
This PR doesn't add any support for actually tracing symbolic ints. i.e. data_ for now can only contain real ints.
```
Goal 1: just to show we can add a type to PyTorch core. (wraps int) LANDEABLE
Finalize the naming - symint
Want the name to be short
Does invoke “size” - NO
SInt/SymInt/SymbolicInt
SInt could mean signed int
sym_int or symint or SymInt (originally it was “int”; capitalized implies object semantics, whereas lowercase implies value semantics)
JIT schema - symint
C++ - symint
```
See more details here: https://docs.google.com/document/d/1iiLNwR5ohAsw_ymfnOpDsyF6L9RTUaHMpD8 (d843f63f2a)YLw-jxEw
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74861
Reviewed By: qihqi, ngimel
Differential Revision: D35226230
Pulled By: Krovatkin
fbshipit-source-id: 34acf342bd50fcaa4d8d5dd49c2fd6a98823a5b3
(cherry picked from commit 218643f63ef181cabb92d13a6e837eb64f2dda3c)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74353
Repatched `d00de0d43598522b8f6ab2de553b6aaf6768faa5` by Nora Belrose (norabelrose). With following changes:
* Register fake source of generated methods in linecache so that inspect.get_source will succeed.
* this patching is only triggered if the given dataclass passed to torch.jit.script previously. Effectively we make this feature opt-in.
## Original Summary:
Fixes#72901.
Since we can't get access to the source code for synthesized magic methods on dataclasses, we have to synthesize our own versions. torch/jit/_dataclass_impls.py has the code that does this.
What's supported
Synthesized __init__, __eq__, and the comparison magic methods when order=True is set on the dataclass decorator
Default values for fields
__post_init__, including using InitVar fields inside of __post_init__, on Python 3.8+
Overriding __eq__ or any of the comparison magic methods to provide your own implementation
What's not supported
Default factory initializers for fields
Frozen dataclasses
InitVar on Python 3.7
__repr__ and __hash__ (these are actually implemented, but the TorchScript interpreter won't call them)
Using the != operator on dataclasses inside TorchScript; this is because TorchScript requires that you implement __ne__ to use this operator, whereas in regular Python the != operator will resolve to the negation of whatever is returned by __eq__ if there's no __ne__. Dataclasses don't actually synthesize an __ne__ method for this reason. I've been toying with different ways to fix this but != is not working in this PR at the moment.
Test Plan:
unittest
Also run previously failed test:
```
buck test mode/dev-nosan //fblearner/flow/projects/fluent2/definition/transformers/contrib/faim/test:tests -- --exact 'fblearner/flow/projects/fluent2/definition/transformers/contrib/faim/test:tests - test_mixmatch_multiclass (fblearner.flow.projects.fluent2.definition.transformers.contrib.faim.test.faim_mixmatch_test.TestFaimTransformerMixMatch)'
```
passes
Differential Revision: D35206262
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74889
Approved by: https://github.com/zhxchen17
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73938
This is a first step in porting and making usable all of the decompositions defined in [functorch](https://github.com/pytorch/functorch/blob/main/functorch/_src/decompositions.py#L349) in core and in JIT as well as C++.
The decompositions are defined in python, scripted and inlined, and then serialized as C++ code which TorchScript can parse. The workflow is edit python decomposition file then run [tools/codegen/decompositions/gen_jit_decompositions.py](https://github.com/pytorch/pytorch/pull/73938/files#diff-6adef2116be233c3524e3b583e373ab0ffc9169beb6c1f6d96b5d0385e75afa1).
Decompositions are mapped to their corresponding aten schemas via the schema in their python def. This allows multiple decompositions for an overloaded op like `aten.var` (shown here in the example).
This is just a first PR, i'm sure there will be many follows ups such as:
- making these runnable in C++ with simple executor
- porting over more decompositions from AOT Autograd
- Using opinfos / more robust testing
- Categorizing decompositions
- Hooking in decompositions at various points of JIT execution
Test Plan: Imported from OSS
Reviewed By: gchanan
Differential Revision: D34938126
Pulled By: eellison
fbshipit-source-id: 9559a7cb731982e3a726f2f95af498b84fb09c13
(cherry picked from commit a4e0e748791e378e7e12a9dd0b63fb3c62dc1890)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73875
Previously we had a few settings:
- getExecutor - which toggled between Profiling Executor and Legacy
- getGraphOptimize - if true, overrides PE/Legacy to run with simple executor (no optimizations)
and then...
- getProfilingMode - which would set PE to 0 specializtions.
The last mode is redundant with getGraphOptimize, we should just remove it and use getGraphOptimize in these cases. It would lead to potentially invalid combinations of logic - what does mean if getProfilingMode is true but getExecutor is set to false ? This would lead to a bug in specialize_autograd_zero in this case, see: https://github.com/pytorch/pytorch/blob/master/torch%2Fcsrc%2Fjit%2Fpasses%2Fspecialize_autogradzero.cpp#L93.
The tests here are failing but get fixed with the PR above it, so i'll squash for landing.
Test Plan: Imported from OSS
Reviewed By: cpuhrsch
Differential Revision: D34938130
Pulled By: eellison
fbshipit-source-id: 1a9c0ae7f6d1cfddc2ed3499a5af611053ae5e1b
(cherry picked from commit cf69ce3d155ba7d334022c42fb2cee54bb088c23)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74353
Repatched `d00de0d43598522b8f6ab2de553b6aaf6768faa5` by Nora Belrose (norabelrose). With following changes:
* Register fake source of generated methods in linecache so that inspect.get_source will succeed.
* this patching is only triggered if the given dataclass passed to torch.jit.script previously. Effectively we make this feature opt-in.
## Original Summary:
Fixes#72901.
Since we can't get access to the source code for synthesized magic methods on dataclasses, we have to synthesize our own versions. torch/jit/_dataclass_impls.py has the code that does this.
What's supported
Synthesized __init__, __eq__, and the comparison magic methods when order=True is set on the dataclass decorator
Default values for fields
__post_init__, including using InitVar fields inside of __post_init__, on Python 3.8+
Overriding __eq__ or any of the comparison magic methods to provide your own implementation
What's not supported
Default factory initializers for fields
Frozen dataclasses
InitVar on Python 3.7
__repr__ and __hash__ (these are actually implemented, but the TorchScript interpreter won't call them)
Using the != operator on dataclasses inside TorchScript; this is because TorchScript requires that you implement __ne__ to use this operator, whereas in regular Python the != operator will resolve to the negation of whatever is returned by __eq__ if there's no __ne__. Dataclasses don't actually synthesize an __ne__ method for this reason. I've been toying with different ways to fix this but != is not working in this PR at the moment.
Test Plan:
unittest
Also run previously failed test:
```
buck test mode/dev-nosan //fblearner/flow/projects/fluent2/definition/transformers/contrib/faim/test:tests -- --exact 'fblearner/flow/projects/fluent2/definition/transformers/contrib/faim/test:tests - test_mixmatch_multiclass (fblearner.flow.projects.fluent2.definition.transformers.contrib.faim.test.faim_mixmatch_test.TestFaimTransformerMixMatch)'
```
passes
Reviewed By: zhxchen17
Differential Revision: D34808842
fbshipit-source-id: 02f807cff1ea99e606333960225c71a239743a4b
(cherry picked from commit ec885a2bc04f9e5f65838fa5704d9a05815ebd37)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74387
Make temporary python bindings for flatbuffer to test ScriptModule save / load.
(Note: this ignores all push blocking failures!)
Test Plan: unittest
Reviewed By: iseeyuan
Differential Revision: D34968080
fbshipit-source-id: d23b16abda6e4b7ecf6b1198ed6e00908a3db903
(cherry picked from commit 5cbbc390c5f54146a1c469106ab4a6286c754325)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73748
This adds CPU-only slow test jobs, which previously would never run.
Includes fixes/skips for slow tests which fail (they need to be skipped now because they used to never run)
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision: D34628803
Pulled By: davidberard98
fbshipit-source-id: c090ab7bf7bda9e24ec5cdefa6fd35c6310dbac0
(cherry picked from commit 06f7a94a57cc7023e9c5442be8298d20cd011144)
Summary:
Original commit changeset: f5a792555c88
Original Phabricator Diff: D34398107 (d00de0d435)
Backing out as this broke fluent2 tests
Test Plan: sandcastle
Reviewed By: qihqi
Differential Revision: D34597363
fbshipit-source-id: 26bbe64b981aeb53b901cda61557614d9f28700e
(cherry picked from commit f17adfed8125ef84efaf2c8923c11a751eb7fb98)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/72901.
Since we can't get access to the source code for synthesized magic methods on dataclasses, we have to synthesize our own versions. `torch/jit/_dataclass_impls.py` has the code that does this.
What's supported
- Synthesized `__init__`, `__eq__`, and the comparison magic methods when `order=True` is set on the dataclass decorator
- Default values for fields
- `__post_init__`, including using `InitVar` fields inside of `__post_init__`, on Python 3.8+
- Overriding `__eq__` or any of the comparison magic methods to provide your own implementation
What's not supported
- Default factory initializers for fields
- Frozen dataclasses
- `InitVar` on Python 3.7
- `__repr__` and `__hash__` (these are actually implemented, but the TorchScript interpreter won't call them)
- Using the `!=` operator on dataclasses inside TorchScript; this is because TorchScript requires that you implement `__ne__` to use this operator, whereas in regular Python the `!=` operator will resolve to the negation of whatever is returned by `__eq__` if there's no `__ne__`. Dataclasses don't actually synthesize an `__ne__` method for this reason. I've been toying with different ways to fix this but `!=` is not working in this PR at the moment.
qihqi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73066
Reviewed By: mrshenli
Differential Revision: D34398107
Pulled By: qihqi
fbshipit-source-id: f5a792555c88f3631f97837a96687e4890660a32
(cherry picked from commit ea7f077dc49a4ee75ca0d1409aedd85228952881)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73253
This PR allows TS schema_matching to match input arg with self for aten operators. This is because, operators in their functional form have input as paremeter instead of self.
fixes: https://github.com/pytorch/pytorch/issues/71994
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D34427556
Pulled By: tugsbayasgalan
fbshipit-source-id: 96c2340d605c59634bf6e37db1db6025d93a933a
(cherry picked from commit 45a593d73bc5e6308dd80a4a29afed8e318a0a1c)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72596
debug_pkl file inside of pytorch's .pt file consists of a list of SourceRanges. Each SourceRange points to a Source which is a stack track, filename, and start, end numbers. Those are emitted in debug_pkl file as strings.
Since many SourceRange shares the same source, the string for trace can be deduped.
The newer format saves a set of unique traces in a tuple, then each SourceRange will save the offset of it's trace w.r.t. position in that tuple. (i.e. manually applying dictionary compression).
The above helps with smaller file size. On loading, if we copy each trace to Source as string the runtime memory would still blowup.
To mitigate this, we use SourceView directly instead of source which will take the reference of string inside of Deserializer and make that into string_view. This is safe because Deserializer is hold by Unpickler by shared_ptr, and Unpickler is also hold by shared_ptr by another Source object. That Source object will be alive during the model construction.
Test Plan:
unit test
Took original file (312271638_930.predictor.disagg.local); loaded with `torch.jit.load` save again with `torch.jit.save`. Unzip both, look at contents:
```
[qihan@devvm5585.vll0 ~]$ du archive -h
4.0K archive/xl_model_weights
3.7M archive/extra
8.0K archive/code/__torch__/caffe2/torch/fb/model_transform/splitting
8.0K archive/code/__torch__/caffe2/torch/fb/model_transform
8.0K archive/code/__torch__/caffe2/torch/fb
8.0K archive/code/__torch__/caffe2/torch
8.0K archive/code/__torch__/caffe2
20M archive/code/__torch__/torch/fx/graph_module
20M archive/code/__torch__/torch/fx
8.0K archive/code/__torch__/torch/classes
20M archive/code/__torch__/torch
20M archive/code/__torch__
20M archive/code
2.7M archive/constants
35M archive
[qihan@devvm5585.vll0 ~]$ du resaved -h
4.0K resaved/extra
8.0K resaved/code/__torch__/caffe2/torch/fb/model_transform/splitting
8.0K resaved/code/__torch__/caffe2/torch/fb/model_transform
8.0K resaved/code/__torch__/caffe2/torch/fb
8.0K resaved/code/__torch__/caffe2/torch
8.0K resaved/code/__torch__/caffe2
1.3M resaved/code/__torch__/torch/fx/graph_module
1.3M resaved/code/__torch__/torch/fx
8.0K resaved/code/__torch__/torch/classes
1.4M resaved/code/__torch__/torch
1.4M resaved/code/__torch__
1.4M resaved/code
2.7M resaved/constants
13M resaved
[qihan@devvm5585.vll0 ~]$
```
Reviewed By: JasonHanwen
Differential Revision: D33994011
fbshipit-source-id: 8e6224c6e942e91c3403f686c8f0937d1002ed41
(cherry picked from commit a7014dd4029308c95007f362a57c31796d686647)
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 1d276baca3)
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 b47cc935ee)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69645
As noted in code comment:
existing device operator is registered with input name `a`, which prevents torch.device(type="cuda") from working. add shim-layer here
Test Plan: Imported from OSS
Reviewed By: jbschlosser
Differential Revision: D33515231
Pulled By: eellison
fbshipit-source-id: c04af8158a9568a20cd5fbbbd573f6efab98fd60
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65645
This is a retry of PR: https://github.com/pytorch/pytorch/pull/59492
Latest Changes: Added more tests, added the getOrCreateDB pattern, updated logic to remove unnecessary checks
addressed all comments.
Adding code to find common expressions from the two subblocks of an if
operation and hoist them before the if block.
This also allows Dead Code Elimination to
then eliminate some if blocks.
Test Plan: python test_jit.py TestIfHoisting
Reviewed By: eellison
Differential Revision: D33302065
Pulled By: Gamrix
fbshipit-source-id: a5a184a480cf07354359aaca344c6e27b687a3c2
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:
1. Split the test `test_save_load.py` to two files. Basically move the operator versioning related changes to `test_save_load_for_op_versions.py`.
2. Add mobile module related test to `test_save_load_for_op_versions.py`
How to run:
```
buck test mode/opt //caffe2/test:jit
or
python test/test_jit.py TestSaveLoadForOpVersion
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70090
ghstack-source-id: 146103547
Test Plan:
```
buck test mode/opt //caffe2/test:jit
python test/test_jit.py TestSaveLoadForOpVersion
```
Reviewed By: tugsbayasgalan
Differential Revision: D33180767
fbshipit-source-id: dd31e313c81e90b598ea9dd5ad04a853c017f994
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69614
Previously sparse COO tensors were ignored during freezing, because
`tryInsertConstant` would fail during `freeze_module.cpp`, and because
hashes weren't implemented for COO tensor IValues.
Test Plan: Imported from OSS
Reviewed By: mrshenli
Differential Revision: D32954620
Pulled By: davidberard98
fbshipit-source-id: a91f97fdfc2152b417f43a6948100c94970c0831
Summary:
As per title. This in particular allows to more easily override backward function for which the underlying backend returns `None`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67793
Reviewed By: zou3519
Differential Revision: D32242962
Pulled By: albanD
fbshipit-source-id: 6e114def90ee9499161e1303d301ba7fd003ff89
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68672
This PR adds `python_module: sparse` to `native_function.yaml`.
These functions would appear in `torch._C._sparse` namespace instead of
just `torch`.
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D32517813
fbshipit-source-id: 7c3d6df57a24d7c7354d0fefe1b628dc89be9431
Summary:
Adds native_dropout to have a reasonable target for torchscript in auto diff. native_dropout has scale and train as arguments in its signature, this makes native_dropout more consistent with other operators and removes conditionals in the autodiff definition.
cc gmagogsfm
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63937
Reviewed By: mruberry
Differential Revision: D32477657
Pulled By: ngimel
fbshipit-source-id: d37b137a37acafa50990f60c77f5cea2818454e4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68367
- bmm_test.py was using syntax not allowed in 3.6
- Some suppressions were not placed on the correct line.
With this file,
```
lintrunner --paths-cmd='git grep -Il .'
```
passes successfully.
Test Plan: Imported from OSS
Reviewed By: janeyx99, mrshenli
Differential Revision: D32436644
Pulled By: suo
fbshipit-source-id: ae9300c6593d8564fb326822de157d00f4aaa3c2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68311
prim::SetAttr is listed as an op with side effects, but in AliasDb, `analyzeSetAttr` already accounts for its behavior. By removing it from the list of ops with side effects, dead code elimination will work in a few other scenarios.
Test Plan: Imported from OSS
Reviewed By: mrshenli
Differential Revision: D32409510
fbshipit-source-id: 52ed9e19f92afb95c669ad3c2440f72f9515ba4c
Summary:
CAFFE2 has been deprecated for a while, but still included in every PyTorch build.
We should stop building it by default, although CI should still validate that caffe2 code is buildable.
Build even fewer dependencies when compiling mobile builds without Caffe2
Introduce `TEST_CAFFE2` in torch.common.utils
Skip `TestQuantizedEmbeddingOps` and `TestJit.test_old_models_bc` is code is compiled without Caffe2
Should be landed after https://github.com/pytorch/builder/pull/864
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66658
Reviewed By: driazati, seemethere, janeyx99
Differential Revision: D31669156
Pulled By: malfet
fbshipit-source-id: 1cc45e2d402daf913a4685eb9f841cc3863e458d
Summary:
All of the pooling modules except MaxUnpool and LPPool return either a
Tensor or [Tensor, Tensor]. The current type annotations are inaccurate,
and prevent scripting the module if return_indices is set as True in the
module.
There's not a great way to make this agree with mypy because the
overload is dependent on the value of return_indices, an attribute.
I tried changing the annotations from `Tensor` to
`Union[Tensor, Tuple[Tensor, Tensor]]`, but that breaks a bunch of uses
that have return_indices=False.
For example, this breaks:
4e94e84f65/torch/nn/modules/container.py (L139)
Also clean up how test names were being constructed in test_jit, since
otherwise we were getting name collisions when there were two tests on
the same nn.Module.
Fixes https://github.com/pytorch/pytorch/issues/45904
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65847
Reviewed By: ZolotukhinM
Differential Revision: D31462517
Pulled By: eellison
fbshipit-source-id: 6f9e8df1be6c75e5e1e9bae07cf3ad3603ba59bd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65097
Previously, BatchMM would skip any block containing any mutable
operators. Now it will avoid batching any operation whose inputs or
outputs are ever mutated. Specifically: consider a tree of ADD, T,
and MM nodes rooted at an ADD node. If any input or output to any
node in the tree is ever mutated, then the entire tree will be ignored
by BatchMM.
Test Plan: python test/test_jit.py TestBatchMM
Reviewed By: eellison
Differential Revision: D30973515
Pulled By: davidberard98
fbshipit-source-id: 9d836faa1ef0c9e3fefe0ffc0bd265f275471f48
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63552
In this PR, we want to exclude these 2 cases in the `Autocast` weight cache usages:
- Using `torch.jit.trace` under the `Autocast`
As report in https://github.com/pytorch/pytorch/issues/50231 and several other discussions, using `torch.jit.trace` under the `Autocast`, the trace process would hit Autocast's weight cache and fails. So we should disable weight cache under the trace process.
- Using `Autocast` with `Grad mode`
- Usually we are using `Grad mode` for training. Since in the training phase, the weight will change in every step. So we doesn't need to cache the weight.
- For the recommended `Autocast` training case in the [doc](https://pytorch.org/docs/stable/amp.html), `Autocast` will clear the cache every step leaving the context. We should disable it to save the clear operations.
```
model = Net().cuda()
optimizer = optim.SGD(model.parameters(), ...)
for input, target in data:
optimizer.zero_grad()
with autocast():
output = model(input)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()
```
Test Plan: Imported from OSS
Reviewed By: mrshenli
Differential Revision: D30644913
Pulled By: ezyang
fbshipit-source-id: ad7bc87372e554e7aa1aa0795e9676871b3974e7
Summary:
This PR is created to replace https://github.com/pytorch/pytorch/pull/53180 PR stack, which has all the review discussions. Reason for needing a replacement is due to a messy Sandcastle issue.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64234
Reviewed By: gmagogsfm
Differential Revision: D30656444
Pulled By: ansley
fbshipit-source-id: 77536c8bcc88162e2c72636026ca3c16891d669a
Summary:
RecursiveScriptModule has its customized `__copy__` and `__deepcopy__` defined. The warning/error that says it is not copiable is outdated
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64085
Reviewed By: rohan-varma
Differential Revision: D30598623
Pulled By: gmagogsfm
fbshipit-source-id: 0701d8617f42d818bc7b88244caee4cd47fbe976
Summary:
Turns on BN in autodiff:
1. outputs an empty tensor for running stats to by pass autodiff issue on None;
2. fixing BN inference backward in cudnn & miopen, where backward falls back to native batchnorm kernel instead;
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57321
Reviewed By: albanD, ngimel
Differential Revision: D30250419
Pulled By: jansel
fbshipit-source-id: a62553789c20fb50a820003a056f40d9d642dfaa
