Fixes the string_view errors and reland the work. The previous changes in torch/csrc/utils/invalid_arguments.cpp were too aggressive and not tested thoroughly. They are discarded.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110518
Approved by: https://github.com/ezyang
In almost all cases this is only included for writing the output formatter, which
only uses `std::ostream` so including `<ostream>` is sufficient.
The istream header is ~1000 lines so the difference is non-trivial.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106914
Approved by: https://github.com/lezcano
In almost all cases this is only included for writing the output formatter, which
only uses `std::ostream` so including `<ostream>` is sufficient.
The istream header is ~1000 lines so the difference is non-trivial.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106914
Approved by: https://github.com/lezcano
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74119
implemented function to generate source as ExtraFilesMap and constants
wrote function to construct jit module given (ivalue, source,
constant) tripple.
Test Plan: unittest
Reviewed By: pavithranrao
Differential Revision: D34803945
fbshipit-source-id: 2edc798407fe68294cb4c3c7516f5bd143df88c3
(cherry picked from commit 35e54e166b8f0f5cfe8f08c07866b59ae61ee79d)
Summary:
Follow up to https://github.com/pytorch/pytorch/issues/68095
This also changes the files from the ATen folder to include c10's `Export.h` instead since they can't ever be exporting `TORCH_PYTHON_API`.
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse SciPioneer H-Huang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69585
Reviewed By: mrshenli
Differential Revision: D32958594
Pulled By: albanD
fbshipit-source-id: 1ec7ef63764573fa2b486928955e3a1172150061
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/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:
BC NOTE:
This change makes it so modules saved with torch.jit.save in PyTorch 1.6 can be loaded by previous versions of PyTorch unless they use torch.div or (soon) torch.full. It also lets tensors saved using torch.save be loaded by previous versions. So this is the opposite of BC-breaking, but I'm using that label to highlight this issue since we don't have a "BC-improving" label.
PR NOTE:
When an operator's semantics change in PyTorch we want to do two things:
1) Preserve the semantics of older serialized Torchscript programs that use the operator
2) Ensure the new semantics are respected
Historically, this meant writing a Versioned Symbol that would remap older versions of the operator into current PyTorch code (1), and bumping the produced file format version (2). Unfortunately, bumping the produced file format version is a nuclear option for ensuring semantics are respected, since it also prevents older versions of PyTorch from loading anything (even tensors!) from newer versions.
Dynamic versioning addresses the nuclear consequences of bumping the produced file format version by only bumping it when necessary. That is, when an operator with changed semantics is detected in the serialized Torchscript. This will prevent Torchscript programs that use the changed operator from loading on earlier versions of PyTorch, as desired, but will have no impact on programs that don't use the changed operator.
Note that this change is only applicable when using torch.jit.save and torch.jit.load. torch.save pickles the given object using pickle (by default), which saves a function's Python directly.
No new tests for this behavior are added since the existing tests for versioned division in test_save_load already validate that models with div are loaded correctly at version 4.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40279
Reviewed By: dzhulgakov
Differential Revision: D22168291
Pulled By: mruberry
fbshipit-source-id: e71d6380e727e25123c7eedf6d80e5d7f1fe9f95
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34515
Once upon a time we thought this was necessary. In reality it is not, so
removing it.
For backcompat, our public interface (defined in `api/`) still has
typedefs to the old `script::` names.
There was only one collision: `Pass` as a `Stmt` and `Pass` as a graph
transform. I renamed one of them.
Test Plan: Imported from OSS
Differential Revision: D20353503
Pulled By: suo
fbshipit-source-id: 48bb911ce75120a8c9e0c6fb65262ef775dfba93
