This PR adds support for `SymInt`s in python. Namely,
* `THPVariable_size` now returns `sym_sizes()`
* python arg parser is modified to parse PyObjects into ints and `SymbolicIntNode`s
* pybind11 bindings for `SymbolicIntNode` are added, so size expressions can be traced
* a large number of tests added to demonstrate how to implement python symints.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78135
Approved by: https://github.com/ezyang
Original PR: #77295
Original commit message:
On GPU, conv errors if not all its inputs have the same dtype.
In the case of autocasting during freezing, what we see is:
1) inputs to conv are casted to half
2) inputs to batchnorm are not casted, so many are still floats
3) we try to fold conv + batchnorm, by finding different weight and bias such that conv(input, new_weight, new_bias) is equivalent to the original conv -> batchnorm.
If conv previously had an optional bias, then during freezing we will temporarily create a zero-valued bias as a placeholder for conv_bias. We want to construct it to have the same dtype as the weight input to conv, to avoid errors on GPU.
Reland changes:
There's a memory leak from cuda caching allocator that is a side effect of this fix. The memory leak causes the test to fail, though for some reason it didn't fail on CI in the last PR. This skips the tests for now.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77617
Approved by: https://github.com/eellison
On GPU, conv errors if not all its inputs have the same dtype.
In the case of autocasting during freezing, what we see is:
1) inputs to conv are casted to half
2) inputs to batchnorm are not casted, so many are still floats
3) we try to fold conv + batchnorm, by finding different weight and bias such that conv(input, new_weight, new_bias) is equivalent to the original conv -> batchnorm.
If conv previously had an optional bias, then during freezing we will temporarily create a zero-valued bias as a placeholder for conv_bias. We want to construct it to have the same dtype as the weight input to conv, to avoid errors on GPU.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77295
Approved by: https://github.com/eellison
Summary:
In order to categorize exceptions/errors, the observability /migration team faced a problem that currently the exception is shown as RuntimeError, and hard to categorize.
The solution to this problem is to be able to get the original python exception's class name and msg, and hopefully to recreate a python exception from that.
TO support this approach, we did the following in this diff:
(1) TorchScript to translate JITException so that it does not show as RuntimeError
(2) record python exception class name, original message during translation.
Then, later, the python exception can be reconstructed.
(3) Added a new decorator to reconstruct the python exception and then rethrow it.
Test Plan:
buck test //caffe2/torch/fb/translate_exception/tests:test_rethrow mode/dev-tsan
```
More details at https://www.internalfb.com/intern/buck/build/1180a788-3767-48e5-a64d-06d284b91a17
BUILD SUCCEEDED
Tpx test run coordinator for Facebook. See https://fburl.com/tpx for details.
Running with tpx session id: 24ae6c7c-a647-404e-8f12-d12c762bf728
Trace available for this run at /tmp/tpx-20220507-195320.698499-24ae6c7c-a647-404e-8f12-d12c762bf728/trace.log
RemoteExecution session id: reSessionID-24ae6c7c-a647-404e-8f12-d12c762bf728-tpx
Started reporting to test run: https://www.internalfb.com/intern/testinfra/testrun/8162774413147962
✓ ListingSuccess: caffe2/torch/fb/translate_exception/tests:test_rethrow : 3 tests discovered (27.233)
✓ Pass: caffe2/torch/fb/translate_exception/tests:test_rethrow - test_one_parameter (test_rethrow.TestTranslateRethrowPythonException) (28.467)
✓ Pass: caffe2/torch/fb/translate_exception/tests:test_rethrow - test_no_parameter (test_rethrow.TestTranslateRethrowPythonException) (28.495)
✓ Pass: caffe2/torch/fb/translate_exception/tests:test_rethrow - test_2_parameter_with_torch_script_only (test_rethrow.TestTranslateRethrowPythonException) (28.708)
Summary
Pass: 3
ListingSuccess: 1
If you need help understanding your runs, please follow the wiki: https://fburl.com/posting_in_tpx_users
Finished test run: https://www.internalfb.com/intern/testinfra/testrun/8162774413147962
```
Differential Revision: D36166520
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77093
Approved by: https://github.com/qihqi
Adds support for scripting ParameterDicts and getattr() on them. It does
not support iterating on ParameterDicts because torch/nn/container.py
implementation of ParameterDict.items() uses a generator, which is not
supported by torchscript. torch/nn/container.py would need to be updated
so that iter gets correctly registered in python_sugared_value.cpp
Added a test in test_module_containers.py
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77143
Approved by: https://github.com/eellison
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 makes prims look as if they were defined in native_functions.yaml
but they're still all written in Python. You now need to give a full
schema string for your prims. The returned prim object is now
torch.ops.prim overload (prims are not allowed to be overloaded,
so we return the overload, not the overload packet, for speed.)
Signed-off-by: Edward Z. Yang <ezyangfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77117
Approved by: https://github.com/mruberry, https://github.com/albanD
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
Re-landing #68111/#74596
## Description
v0.5 PR of this [RFC](https://github.com/pytorch/pytorch/issues/49444).
On the basis of #50256, the below improvements are included:
* The [v0.5 release branch](https://github.com/oneapi-src/oneDNN/releases/tag/graph-v0.5) of the oneDNN Graph API is used
* The fuser now works with the profiling graph executor. We have inserted type check nodes to guard the profiled tensor properties.
### User API:
The optimization pass is disabled by default. Users could enable it by:
```
torch.jit.enable_onednn_fusion(True)
```
`torch.jit.freeze` should be used after tracing (recommended) or scripting a model.
### Performance:
[pytorch/benchmark](https://github.com/pytorch/benchmark) tool is used to compare the performance:
* SkyLake 8180 (1 socket of 28 cores):
* SkyLake 8180 (single thread):
* By mapping hardswish to oneDNN Graph, it’s 8% faster than PyTorch JIT (NNC + OFI)
** We expect performance gain after mapping transpose, contiguous & view to oneDNN graph ops
### Directory structure of the integration code
Fuser-related code is placed under:
```
torch/csrc/jit/codegen/onednn/
```
Optimization pass registration is done in:
```
torch/csrc/jit/passes/onednn_graph_fuser.h
```
CMake for the integration code is in:
```
caffe2/CMakeLists.txt
cmake/public/mkldnn.cmake
cmake/Modules/FindMKLDNN.cmake
```
## Limitations
* In this PR, we only support Pytorch-oneDNN-Graph integration on Linux platform. Support on Windows and MacOS will be enabled as a next step.
* We have only optimized the inference use-case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76622
Approved by: https://github.com/eellison
This allows us to provide OpOverloadPacket.overloads method that
lists all of the overloads.
This isn't tested; will be exercised in the next PR.
Signed-off-by: Edward Z. Yang <ezyangfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76814
Approved by: https://github.com/mruberry
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73284
Some important ops won't support optional type until opset 16,
so we can't fully test things end-to-end, but I believe this should
be all that's needed. Once ONNX Runtime supports opset 16,
we can do more testing and fix any remaining bugs.
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D34625646
Pulled By: malfet
fbshipit-source-id: 537fcbc1e9d87686cc61f5bd66a997e99cec287b
Co-authored-by: BowenBao <bowbao@microsoft.com>
Co-authored-by: neginraoof <neginmr@utexas.edu>
Co-authored-by: Nikita Shulga <nshulga@fb.com>
(cherry picked from commit 822e79f31ae54d73407f34f166b654f4ba115ea5)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76485
Adds an environment variable `PYTORCH_JIT_ENABLE_NVFUSER` for
controlling whether or not nvfuser is enabled. This required changing
the PassManager behavior to support the case where nvfuser gets enabled
by default when PYTORCH_JIT_ENABLE_NVFUSER=1.
Previously the solution for turning nvfuser on or off was to use the
PassManager to register or un-register the pass. That works fine if the
pass starts of _disabled_, but causes issues once we try to enable the
pass by default.
The main issue with enabling by default is with the validation check to
see whether NVFuser can be turned on. The check relies on
at::globalContext().hasCUDA(), which requires CUDAHooks to be registered
before hasCUDA() wil work correctly. At static initialization time it's
difficult to ensure that CUDAHooks will be registered _before_ we
attempt to register the nvfuser pass. In OSS it worked fine, but in
internal builds it would fail on ROCm builds.
To fix this, we switch the control of NVFuser enablement to a check in
the pass. i.e. previously, we enabled/disabled nvfuser by registering or
de-registering the pass in pass manager; now, the pass is always
registered in pass manager, and enablement is done by a check within the
nvfuser pass.
Remaining TODO: Connect this with NNC so that in cases where NNC is
available but not NVFuser (i.e. on AMD gpus), NNC can be turned on
automatically.
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D35982618
Pulled By: davidberard98
fbshipit-source-id: fd5b76bc0b8c8716c96fdc04bebfb15026a7ef60
(cherry picked from commit ff14603ff5ac8d9b6c749c4f111f4a8be8023b7f)
- Allow registering custom decompositions
- Add easier API for invoking decompositions
- Shorten API names (no users yet)
I am doing these as one pr because they are fairly short/simple and because github first does not support ghstack yet.
cc @Chillee @zou3519
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76252
Approved by: https://github.com/davidberard98
Summary:
## Original commit message:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73368
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:
## Original 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 ~]$
```
## Additional test:
`buck test mode/dev-tsan //caffe2/benchmarks/static_runtime:static_runtime_cpptest -- --exact 'caffe2/benchmarks/static_runtime:static_runtime_cpptest - StaticRuntime.to'` passes
test jest.fbios.startup_cold_start.local.simulator f333356873 -
Differential Revision: D35196883
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74869
Approved by: https://github.com/gmagogsfm
