Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65967
Graph is an implementation detail. If user wants to get access to the
underlying graph, they should be able to explicitly dynamic cast instead.
ghstack-source-id: 141659819
Test Plan: no behavior change.
Reviewed By: gmagogsfm
Differential Revision: D31326153
fbshipit-source-id: a0e984f57c6013494b92a7095bf5bb660035eb84
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65966
ghstack-source-id: 141594521
Support exportation of "interface methods" from submodule to a mobile module. "Interface methods" are defined as methods which might be dynamically called in a module therefore need to be exported anyway, like virtual functions in C++.
Before this change the algorithm of exportation is a simple iteration through all toplevel methods. Now since we have indirect calls, we need to recursively walkthrough the call graph to find all potentially used methods, which means the order we export methods might break in old runtimes, to guarantee forward compatibility we need to export toplevel methods first, then extra methods, in this order toplevel methods will always be found first.
NOTE that interface methods exportations are disabled by default in this diff. We need to call torch._C._enable_mobile_interface_call_export to actaully enable it.
Test Plan: buck test mode/dev //caffe2/test:jit -- --exact 'caffe2/test:jit - test_export_opnames_interface (jit.test_misc.TestMisc)'
Reviewed By: qihqi, iseeyuan
Differential Revision: D31326155
fbshipit-source-id: 5be7234cca07691f62648a85133b6db65e427b53
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63129
1. Add an api to get `supported_types` from runtime, expose in c++ only.
2. Add an api to get `contained_types` from model, expose in both c++ and PyThon.
3. Add a field `contained_types_` in `type_parser.cpp` to track the contained types when parsing python string.
4. Expand `is_compatible` api to check type. When checking type, it will check the contained type list from the model with the support type list from runtime.
5. Expand the unittest for compatibility to cover type
6. Add unit test in python to check type list
ghstack-source-id: 139826944
Test Plan:
```
buck test mode/dev //caffe2/test/cpp/jit:jit -- --exact 'caffe2/test/cpp/jit:jit - LiteInterpreterTest.GetContainTypes'
buck test mode/dev //caffe2/test/cpp/jit:jit -- --exact 'caffe2/test/cpp/jit:jit - LiteInterpreterTest.isCompatibleSuccess'
buck test mode/dev //caffe2/test/cpp/jit:jit -- --exact 'caffe2/test/cpp/jit:jit - LiteInterpreterTest.isCompatibleFail'
buck test //caffe2/test:mobile
```
Reviewed By: iseeyuan
Differential Revision: D30231419
fbshipit-source-id: 8427f423ec28cc5de56411f15fd960d8595d6947
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61806
Currently, if you do `save_pickle` on a ScriptModule, then `save_pickle`
on a tensor, this would result in a `0.storage` tensor being written
*twice* to the zip archive. This would cause weird bugs on the
serializing side (this presented as a ASAN-detected heap buffer overflow
because we tried to read more memory from a tensor than we actually
had).
Turns out this was because when we did:
```
self.storage_context = self.script_module_serializer.storage_context()
```
it returned a new copy of the storage context, so we weren't actually
assigning unique names to tensors!!
This PR fixes the issue by making `(De)SerializationStorageContext`
non-copyable and fixing up the parts of the bindings that returned by
copy.
Differential Revision:
D29748969
D29748969
Test Plan: Imported from OSS
Reviewed By: Lilyjjo
Pulled By: suo
fbshipit-source-id: c2f89ab270e07e7a111fb35c545b5e07b804dc3c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52832
**Summary**
This commit adds `torch._C.ScriptList`, a list type that has reference
semantics across the Python/TorchScript boundary. That is, modifications
made in TorchScript to instances of `torch._C.ScriptList`
are visible in Python even when it is not returned from the function.
`torch._C.ScriptList` is implemented using a modified version of pybind's
`stl_bind.h`-style bindings attached to `ScriptList` and `ScriptListIterator`,
wrapper classes around `c10::impl::GenericList` and
`c10::impl::GenericList::iterator`. These bindings allow instances of
`torch._C.ScriptList` to be used as if it were a
regular `list` in Python. Reference semantics are achieved by simply
retrieving the `IValue` contained in `ScriptList` in `toIValue` (invoked
when converting Python arguments to `IValues` before calling TorchScript
code).
**Test Plan**
This commit adds `TestScriptList` to `test_list_dict.py`, a set of tests
that check that all of the common list operations are supported
and that instances have reference semantics across the
Python/TorchScript boundary.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D29478121
Pulled By: SplitInfinity
fbshipit-source-id: 652cc25cfa37debe28db9527504846f22abd8b54
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44324
**Summary**
This commit adds reference semantics to TorchScript class types;
modifications made to them within TorchScript will be visible in Python.
**Test Plan**
This commit adds a unit test to `TestClassType` that checks that
modifications made to a class type instance passed into TorchScript are
visible in Python after executing the scripted function or module.
**Fixes**
This commit closes#41421.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D24912807
Pulled By: SplitInfinity
fbshipit-source-id: d64ac6211012425b040b987e3358253016e84ca0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59735
1. Fixes ABA storage identity problem during serialization for `torch.package` by keeping reference of serialized storages through lifetime of `PackageExporter` to prevent reuse of memory address. Achieved by extending logic used in solution to mobile's same issue.
2. Adds determinism to naming scheme of serialized storages in export code paths which utilize `tensor_cdata_naming_scheme`(introduced 2nd mapping in `StorageContext`, now maps `storage cdata ptr` -> `unique id`, `unique id` -> `c10::Storage`)
3. Additionally uses presence of a storage in the `StorageContext` instance as marker for if a storage has been serialized or not, removing the need to scan the `PythonStreamWriter` for presence of the storage's serialization file
Test Plan: Imported from OSS
Reviewed By: suo
Differential Revision: D29075276
Pulled By: Lilyjjo
fbshipit-source-id: 15a5c30b1de99c5bd7079388f2db9b6ece2eca12
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52659
**Summary**
This commit adds `torch._C.ScriptDict`, a dictionary type that has reference
semantics across the Python/TorchScript boundary. That is, modifications
made to instances of `torch._C.ScriptDict` in TorchScript are visible in
Python even when it is not returned from the function. Instances can be
constructed by passing an instance of a Python dictionary to
`torch.jit.script`. In the case of an empty dictionary, its type is
assumed to be `Dict[str, Tensor]` to be consistent with the handling of
empty dictionaries in TorchScript source code.
`torch._C.ScriptDict` is implemented using a modified version of pybind's `stl_bind.h`-style bindings attached to `ScriptDict`, `ScriptDictIterator` and `ScriptDictKeyIterator`, wrapper classes around `c10::impl::GenericDict` and `c10::impl::GenericDict::iterator`. These bindings allow instances of `torch._C.ScriptDict` to be used as if it were a regular `dict` Python. Reference semantics are achieved by simply retrieving the `IValue` contained in `ScriptDict` in `toIValue` (invoked when converting Python arguments to `IValues` before calling TorchScript code).
**Test Plan**
This commit adds `TestScriptDict` to `test_list_dict.py`, a set of tests
that check that all of the common dictionary operations are supported
and that instances have reference semantics across the
Python/TorchScript boundary.
Differential Revision:
D27211605
D27211605
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Pulled By: SplitInfinity
fbshipit-source-id: 446d4e5328375791aa73eb9e8b04dfe3465af960
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57501
Add an api _get_model_ops_and_info to get root operators and versioning info of a model in both cxx and python, and the input can be from a file path or buffer.
ghstack-source-id: 129620112
Test Plan: unit test.
Reviewed By: xcheng16, raziel
Differential Revision: D28162765
fbshipit-source-id: 4413c1e906b8a872e4a717d849da37347adbbea4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57397
Introduces two main classes in C++ runtime:
ScriptProfile is the implementation for enalbing and disabling interpreter
profiling in C++. This should be only used from Python, and we will add
corresponding Python API in the next diff.
InstructionSpan is a utility class to instrument execution of each single
instruction. A start timestamp is recorded in the consturctor, and an end
timestamp is recorded in the destructor. During destruction, this will send
runtime data to all enabled ScriptProfile instances.
Test Plan:
build/bin/test_jit --gtest_filter='ScriptProfileTest.Basic'
Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D28133579
fbshipit-source-id: e7e30e96151367022793ab3ad323f01c51ad4a3b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57079
Testing onnx 1.9 release, we see that the old bug is triggered for the caffe2 test:
`pytest test/onnx/test_pytorch_onnx_caffe2_quantized.py::TestQuantizedOps::test_small_model`
This is because the graph inputs
```python
graph(%x.1 : Tensor,
%conv1._packed_params : __torch__.torch.classes.quantized.Conv2dPackedParamsBase,
%conv2._packed_params : __torch__.torch.classes.quantized.Conv2dPackedParamsBase,
%fc.bias : Float(10, strides=[1], requires_grad=0, device=cpu),
%fc.weight : Float(10, 72, strides=[72, 1], requires_grad=0, device=cpu)):
```
contains `Conv2dPackedParamsBase` which is a PackedParams.
When we do flatten, we will flatten to several tensors, then the shape inference for input misaligned.
This PR record how may tensors got flattened in PackeParams, and skip by these number rather than 1, then the UT passed.
Note that tuple case should still follow the original logic.
Test Plan: Imported from OSS
Reviewed By: SplitInfinity
Differential Revision: D28393949
Pulled By: malfet
fbshipit-source-id: 98d48aad27e5ca03fb10d260f8e625478d996ee2
Co-authored-by: David <jiafa@microsoft.com>
Summary:
Add an api to backport a model vn to model vi. It accept an input model (file or buffer) and output a model (file or buffer) with an expected bytecode version.
In this change, the input is a model and it can come from a file or buffer. The output is a model and can be either file path or buffer.
When backport fails, function return false with a warning message :
```
/Users/chenlai/pytorch/cmake-build-debug/bin/test_jit --gtest_filter=LiteInterpreterTest.BackPortByteCodeModelV4:LiteInterpreterTest/*.BackPortByteCodeModelV4:*/LiteInterpreterTest.BackPortByteCodeModelV4/*:*/LiteInterpreterTest/*.BackPortByteCodeModelV4 --gtest_color=no
Testing started at 2:32 PM ...
CUDA not available. Disabling CUDA and MultiCUDA tests
[W backport.cpp:419] Warning: Backport doesn't support backport to version3 (function _backport_for_mobile_impl)
Process finished with exit code 0
```
## Test
1. Run both `caffe2/test/cpp/jit/test_lite_interpreter.cpp` and `caffe2/test/mobile/test_bytecode.py`.
2. Run all prod models with backport api.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56802
ghstack-source-id: 128425510
Test Plan: CI
Reviewed By: raziel, iseeyuan
Differential Revision: D27844651
fbshipit-source-id: 8a803cf6c76433ee0a3049b1a5570585d569f8d6
Summary:
Add an api `_get_bytecode_version` to get version number given a bytecode model in both cxx and python, and the input can be both from file path and buffer.
## Test
CI (new added unit test will run as part of `pytorch_core-buck`)
1. run test_lite_interpreter.cpp
2. `python test/mobile/test_bytecode.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56801
ghstack-source-id: 128169647
Test Plan:
CI (new added unit test will run as part of `pytorch_core-buck`)
1. run test_lite_interpreter.cpp
2. `python test/mobile/test_bytecode.py`
Reviewed By: iseeyuan
Differential Revision: D27961417
fbshipit-source-id: f786cc9573d855feecff0b4fe8e5363e25f5728c
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54915
TorchScript and torch.package have different mangling schemes. To avoid
them interfering with each other, we should undo the torch.package
mangling before processing anything with TorchScript (since TS
independently makes sure that no names collide).
Test Plan: Imported from OSS
Reviewed By: SplitInfinity
Differential Revision: D27410472
Pulled By: suo
fbshipit-source-id: d1cc013c532d9abb7fb9615122bc465ded4785bb
Summary:
The code uses `torch::jit::jit_log_prefix` for handling recursive
indenting in most places in this function. There was one place that was
using "level", but it was buggy -- it would result in a compounding
superlinear indent. Note that changing it to "level+1" doesn't fix the
bug.
Before/after:
https://gist.github.com/silvasean/8ee3ef115a48de6c9c54fbc40838d8d7
The new code establishes a recursive invariant for
`Module::dump_to_str`: the function returns the module printed at the
base indent level (i.e. no indent). `torch::jit:log_prefix` is used
to prefix recursive calls. The code was already nearly there, except for
this spurious use of "level".
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52539
Reviewed By: navahgar
Differential Revision: D26773657
Pulled By: gmagogsfm
fbshipit-source-id: ab476f0738bf07de9f40d168dd038dbf62a9a79e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50670
This PR adds property support to Torchbind. There are two cases that it needs to work:
**Torchscript**
Inside Torchscript, we don't go through pybind so there is no issue with accessing properties through ClassType.
**Eager Mode**
In Eager Mode, Torchbind creates ScriptObject which we cannot dynamically add (aka access) properties after initializing it. (https://stackoverflow.com/questions/1325673/how-to-add-property-to-a-class-dynamically
) Therefore we created a Python wrapper (ScriptObjectWrapper) around ScriptObject where we can use property method to set properties. By doing so, we can look up wrapped object's property through __getattr__ method of the ScriptObjectWrapper. This logic is inspired from https://github.com/pytorch/pytorch/pull/44324
Test Plan:
test cases in test_torchbind.py
Imported from OSS
Reviewed By: pbelevich
Differential Revision: D26632781
fbshipit-source-id: dd690887cfda0c48ff0d104aa240ce0ab09055bc
Summary:
Previously `torch.jit.trace` relies on AutoGrad hooks to infer name of tensors in computation, including those of function/method arguments. This often doesn't work out because:
- These names often do not exist
- Tracer uses argument name of first tensor operation on each tensor as inferred argument names. These tensor operations have programmatically-generated names like `argument_1`
This PR extracts argument names directly from Python functions and pass them down to tracer, which then assigns them to correct graph inputs. This way, we always have the correct argument names captured in IR.
This is useful for both debugging and supporting using `InterfaceType` to represent traced modules.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51775
Reviewed By: izdeby
Differential Revision: D26273105
Pulled By: gmagogsfm
fbshipit-source-id: 934a385041137dc3731bb6fa8657b11532fed9e5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51312
Follow up to D24690094 (4a870f6518) exposing the api in python. Created matching unit test.
ghstack-source-id: 120611452
Test Plan: Ran unit test
Reviewed By: dhruvbird
Differential Revision: D26112765
fbshipit-source-id: ffe3bb97de0a4f08b31719b4b47dcebd7d2fd42a
Summary:
`ResolutionCallback` returns `py::object` (i.e. `Any`) rather than `py::function` (i.e. `Callable`)
Discovered while debugging test failures after updating pybind11
This also makes resolution code slightly faster, as it eliminates casts from object to function and back for every `py::object obj = rcb_(name);` statement.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51089
Reviewed By: jamesr66a
Differential Revision: D26069295
Pulled By: malfet
fbshipit-source-id: 6876caf9b4653c8dc8e568aefb6778895decea05
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/48732
add support for ScriptObject as attributes in symbolic trace.
Test Plan: OSS CI
Reviewed By: jamesr66a
Differential Revision: D25116185
fbshipit-source-id: c61993c84279fcb3c91f1d44fb952a8d80d0e552
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47586
relanding PR of https://github.com/pytorch/pytorch/pull/44492, and add
additional Capsule related wrapping to ensure we still have the correct
type in pybind11 to resolve Capsule as torch._C.CapsuleType
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D24822519
Pulled By: wanchaol
fbshipit-source-id: eaaea446fb54b56ed3b0d04c31481c64096e9459
Summary:
Original commit changeset: b9796e15074d
have weird issue happening with custom class + recursive scripting, unland this first to figure out more details
Test Plan: wait for sandcastle
Reviewed By: zhangguanheng66
Differential Revision: D24780498
fbshipit-source-id: 99a937a26908897556d3bd9f1b2b39f494836fe6
Summary:
With this PR, users can optionally provide a "doc_string" to describe a class or its method. doc_string for TorchBind classes and methods are stored as `doc_string` properties in `Function` and `ScriptClass`. These `dos_string` properties are then exposed in Python layer via PyBind for doc generation.
Fixes https://github.com/pytorch/pytorch/issues/46047
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46576
Reviewed By: wanchaol
Differential Revision: D24440636
Pulled By: gmagogsfm
fbshipit-source-id: bfa9b270a6c2d8bc769a88fad6be939cc6310412
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45789
Making sure that more tests invoke a run with a Fusion Group.
Test Plan: Imported from OSS
Reviewed By: Krovatkin
Differential Revision: D24169535
Pulled By: eellison
fbshipit-source-id: 54d7af434772ba52144b12d15d32ae30460c0c3c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45262
**Summary**
This commit adds an API for ignoring arbitrary module attributes during
scripting. A class attribute named `ignored_attributes` containing names
of attributes to ignore can be added to the class of the instance being
scripted. Attributes ignored in this fashion cannot be used in
`forward`, methods used by `forward` or by `exported` methods. They
are, however, copied to the `RecursiveScriptModule` wrapper and can be
used by `ignored` methods and regular Python code.
**Test Plan**
This commit adds unit tests to `TestScriptPy3` to test this new API.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D23971882
Pulled By: SplitInfinity
fbshipit-source-id: 8c81fb415fde7b78aa2f87e5d83a477e876a7cc3
Summary:
* Support propagating `dim_param` in ONNX by encoding as `ShapeSymbol` in `SymbolicShape` of outputs. If export is called with `dynamic_axes` provided, shape inference will start with these axes set as dynamic.
* Add new test file `test_pytorch_onnx_shape_inference.py`, reusing all test cases from `test_pytorch_onnx_onnxruntime.py`, but focus on validating shape for all nodes in graph. Currently this is not enabled in the CI, since there are still quite some existing issues and corner cases to fix. The test is default to run only at opset 12.
* Bug fixes, such as div, _len, and peephole.cpp passes for PackPadded, and LogSoftmaxCrossEntropy.
* This PR depends on existing PR such as 44332.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44920
Reviewed By: eellison
Differential Revision: D23958398
Pulled By: bzinodev
fbshipit-source-id: 00479d9bd19c867d526769a15ba97ec16d56e51d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45098
**Summary**
This commit adds support for default arguments in methods of class
types. Similar to how default arguments are supported for regular
script functions and methods on scripted modules, default values are
retrieved from the definition of a TorchScript class in Python as Python
objects, converted to IValues, and then attached to the schemas of
already compiled class methods.
**Test Plan**
This commit adds a set of new tests to TestClassType to test default
arguments.
**Fixes**
This commit fixes#42562.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D23844769
Pulled By: SplitInfinity
fbshipit-source-id: ceedff7703bf9ede8bd07b3abcb44a0f654936bd
Summary:
There's an annoying O(N^2) in module export logic that makes saving some of the models (if they have many classes) take eternity.
I'm not super familiar with this code to properly untangle the deps and make it a pure hash lookup. So I just added a side lookup table for raw pointers. It's still quadratic, but it's O(num_classes^2) instead of O(num_classes * num_references) which already gives huge savings.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44589
Test Plan:
Tested with one of the offending models - just loading a saving a Torchscript file:
```
Before:
load 1.9239683151245117
save 165.74712467193604
After:
load 1.9409027099609375
save 1.4711427688598633
```
Reviewed By: suo
Differential Revision: D23675278
Pulled By: dzhulgakov
fbshipit-source-id: 8f3fa7730941085ea20d9255b49a149ac1bf64fe
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42390
**Summary**
This commit extends support for properties to include
ScriptModules.
**Test Plan**
This commit adds a unit test that has a ScriptModule with
a user-defined property.
`python test/test_jit_py3.py TestScriptPy3.test_module_properties`
Test Plan: Imported from OSS
Reviewed By: eellison, mannatsingh
Differential Revision: D22880298
Pulled By: SplitInfinity
fbshipit-source-id: 74f6cb80f716084339e2151ca25092b6341a1560
