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:
Update freezing api for 1.8, and add a corresponding C++ API. The `optimize` flag hasn't been publicly released yet, so we are able to change it without breaking BC. I will submit a PR to branch release as well, there are a few more days to do that
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52337
Reviewed By: ejguan
Differential Revision: D26491833
Pulled By: eellison
fbshipit-source-id: 6dcd74eb8f76db64ac53183d03dabdd0f101f4b5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49588
**Summary**
`BufferPolicy::valid` uses `!typ->is_parameter(i)` to check if an
attribute is a buffer or not; it should use `type->is_buffer(i)` instead.
It also removes a forward compatibility gate in `python_print.cpp` that
has prevented the preservation of buffer metadata during serialization
in fbcode. Without this, the first change (to `BufferPolicy`) does not
work correctly in fbcode.
**Test Plan**
It is difficult to write an additional test that would have failed before this
commit because the two booleans `is_parameter` and `is_buffer` are never set
to `true` at the same time.
**Fixes**
This commit fixes#48746.
Test Plan: Imported from OSS
Reviewed By: xw285cornell
Differential Revision: D25633250
Pulled By: SplitInfinity
fbshipit-source-id: e727f8506f16d2e2b28f3d76a655f6528e7ac6cb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49053
**Summary**
`BufferPolicy::valid` uses `!typ->is_parameter(i)` to check if an
attribute is a buffer or not; it should use `type->is_buffer(i)` instead.
**Test Plan**
It is difficult to write an additional test that would have failed before this
commit because the two booleans `is_parameter` and `is_buffer` are never set
to `true` at the same time.
**Fixes**
This commit fixes#48746.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D25434956
Pulled By: SplitInfinity
fbshipit-source-id: ff2229058abbafed0b67d7b26254d406e5f7b074
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/42133
Test Plan:
We save a module with module debugging information as follows.
```
import torch
m = torch.jit.load('./detect.pt')
# Save module without debug info
m._save_for_lite_interpreter('./detect.bc')
# Save module with debug info
m._save_for_lite_interpreter('./detect.bc', _save_debug_info_in_bytecode=True)
```
Size of the file without module debugging information: 4.508 MB
Size of the file with module debugging information: 4.512 MB
Reviewed By: kimishpatel
Differential Revision: D22803740
Pulled By: taivu1998
fbshipit-source-id: c82ea62498fde36a1cfc5b073e2cea510d3b7edb
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:
**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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32684
Previously we have `clone` and `clone_instance`, where `clone` will clone both type
and value, and `clone_instance` only clone the value, both of them are shallow copies.
We need to re-evaluate whether we should expose them as a user facing API.
I think we should hide `clone`, but `clone_instance` might be useful as well, especially
when we are copying a model with very large weights, people might just want to do shallow copy.
This PR adds a `deepcopy` that might be useful as a user API, which deep copies the values, including
Tensor, but we didn't deepcopy `Blob`, `Capsule`, `Future` or `PyObject`.
For more discussions please see the following issue.
fixes: https://github.com/pytorch/pytorch/issues/32519
Test Plan: Imported from OSS
Differential Revision: D21220756
fbshipit-source-id: 476bf11fe82c08fac36e7457879a09f545ffdc5e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35115
This commit runs the newly added tools/clang_format.py on the JIT
codebase and includes all of the formatting changes thus produced.
Testing:
Ran the script, CI.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D20568523
Pulled By: SplitInfinity
fbshipit-source-id: e09bdb982ccf090eecfb7c7b461b8d0681eef82b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34677
1. Remove remaining uses of `script::` namespace from the codebase,
2. Add one more typedef for `script::ExtraFilesMap` which is part of the
public interface.
Pull Request resolved: #34580
Test Plan: Imported from OSS
Reviewed By: zdevito
Differential Revision: D20431739
Pulled By: suo
fbshipit-source-id: a29d369c755b6506c53447ca1f286b6339222c9a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34623
The bandaid of "AT_WARN" keeps introducing new warnings. Let's get rid
of it entirely.
Close#34502
Test Plan: Imported from OSS
Differential Revision: D20420112
Pulled By: albanD
fbshipit-source-id: 7160c113cb4deb2d2f50a375356f423fe5e86f50
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
Summary:
This patch enables folding GetAttr nodes with their corresponding
values. _jit_pass_freeze_module API returns a new TorchScipt module
where all function calls and get attributes are inlined.
Usage:
frozen_model = torch._C._freeze_module(scrited_model._c)
frozen_model.forward(...)
This API currently optimizes the forward method. We will follow up to
to preserve and optimize methods and attributes that are annotated as
torch.jit.interface.
Several future improvements to JIT optimizations are required to maximize
clean up/de-sugar the graph and eliminate redundancies.
Ideally, we want to produce a graph that can easily be lowered to
GLOW and other low-level backends.
__
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32178
Differential Revision: D19419640
Pulled By: bzinodev
fbshipit-source-id: 52baffaba9bca2cd60a8e747baa68d57711ad42b
