Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598
ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18598 Turn on F401: Unused import warning.**
This was requested by someone at Facebook; this lint is turned
on for Facebook by default. "Sure, why not."
I had to noqa a number of imports in __init__. Hypothetically
we're supposed to use __all__ in this case, but I was too lazy
to fix it. Left for future work.
Be careful! flake8-2 and flake8-3 behave differently with
respect to import resolution for # type: comments. flake8-3 will
report an import unused; flake8-2 will not. For now, I just
noqa'd all these sites.
All the changes were done by hand.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14687478
fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
Summary:
So, we will keep the names of ONNX initializers the same as the names in PyTorch state dict.
Later, we will make this as the default behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17551
Reviewed By: dzhulgakov
Differential Revision: D14491920
Pulled By: houseroad
fbshipit-source-id: f355c02e1b90d7ebbebf4be7c0fb6ae208ec795f
Summary:
1) The changes in the new opset won't affect internal pipeline.
2) The CI won't be affected by the ONNX changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17736
Reviewed By: zrphercule
Differential Revision: D14358710
Pulled By: houseroad
fbshipit-source-id: 4ef15d2246b50f6875ee215ce37ecf92d555ca6a
Summary:
In discussion with houseroad, because Upsample op is being updated in ONNX https://github.com/onnx/onnx/pull/1773 and these tests are blocking it. These tests will be updated once the ONNX PR goes in.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17696
Differential Revision: D14338845
Pulled By: houseroad
fbshipit-source-id: cfaf8cf1ab578ae69dd3bf21b1c0681b572b9b6f
Summary:
Still wip, need more tests and correct handling for opset 8 in symbolics.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16068
Reviewed By: zrphercule
Differential Revision: D14185855
Pulled By: houseroad
fbshipit-source-id: 55200be810c88317c6e80a46bdbeb22e0b6e5f9e
Summary:
This PR does three things:
~~Allow `int64_t?` in function schema, which provide an elegant way of implementing null-able int arguments, as discussed in https://github.com/pytorch/pytorch/pull/15208#pullrequestreview-185230081~~
~~Originally implemented in https://github.com/pytorch/pytorch/pull/15235~~
~~Example:~~
```yaml
- func: myop(Tensor self, int64_t? dim=None) -> Tensor
variants: function
```
~~cc: zou3519~~
Edit: implemented in https://github.com/pytorch/pytorch/pull/15234
Previously tried in https://github.com/pytorch/pytorch/pull/12064. There was a problem that C++ does not have kwarg support, which makes it confusing to know whether `unique(t, 1)` actually means `unique(t, dim=1)` or `unique(t, sorted=1)`.
Now I think I have a better idea on how to implement this: there are two ATen operators: `unique` and `unique_dim`. `unique` has the same signature as in python, and exported to both python and C++. `unique_dim` has signature `unique_dim(tensor, dim, sorted=False, return_inverse=False)`, and only exported to C++, which could be used more naturally for a C++ user.
Differential Revision: D13540278
Pulled By: wanchaol
fbshipit-source-id: 3768c76a90b0881f565a1f890459ebccbdfe6ecd
Summary:
bypass-lint
- Change all Caffe2 builds to use setup.py instead of cmake
- Add a -cmake- Caffe2 build configuration that uses cmake and only builds cpp
- Move skipIfCI logic from onnx test scripts to the rest of CI logic
- Removal of old PYTHONPATH/LD_LIBRARY_PATH/etc. env management
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15917
Reviewed By: orionr
Differential Revision: D13637583
Pulled By: pjh5
fbshipit-source-id: c5c5639db0251ba12b6e4b51b2ac3b26a8953153
Summary:
* With the update of split output to dynamic list it breaks the export to onnx.
Now split ir becomes two ops: 1. Dynamic[] <= Split(), and 2. out1, out2, out3
<= Prim::ListUnpack. In this fix these two consecutive ops get fused when being
exported to onnx.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15092
Reviewed By: dzhulgakov
Differential Revision: D13583832
Pulled By: houseroad
fbshipit-source-id: 3eb18c871e750921ad6d5cc179254bee9bcf4c99
Summary:
We are going to have some breaking changes in ConstantLike and related operators in onnx, therefore it is better to disable all related tests for these operators for now.
These operators are not currently supported by caffe2, and are not included in our most recently released onnx, therefore we do not need to worry about internal/external production breaking.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15740
Differential Revision: D13582528
Pulled By: zrphercule
fbshipit-source-id: 92a890c1dc2a833969af69edfea85331bb4d562f
Summary:
max and reducemax are smashed together, we need to support one input case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15241
Reviewed By: yinghai
Differential Revision: D13473312
Pulled By: houseroad
fbshipit-source-id: 9b8c847286a2631b006ca900271bc0d26574101a
Summary:
`torch.expand` and `torch.ne` are used often in models and this PR adds ONNX export support for them. ArmenAg has created issue https://github.com/pytorch/pytorch/issues/10882 for this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15050
Differential Revision: D13453036
Pulled By: houseroad
fbshipit-source-id: 4724b4ffcebda6cd6b2acac51d6733cb27318daf
Summary:
This PR does the following:
1) Updates the ONNX export for `torch.zeros_like` and `torch.full_like` ops to use ONNX op `ConstantLike`. This reduces the export of experimental op `ConstantFill`, which may possibly be removed in future, see https://github.com/onnx/onnx/pull/1434).
2) It also adds export support for `torch.ones_like`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14903
Differential Revision: D13383700
Pulled By: houseroad
fbshipit-source-id: 566d00a943e9497172fcd5a034b638a650ab13a2
Summary:
This is probably slow but it should make the traces more understandable and make debugging easier. Any suggestions for how to make it faster (i.e. make it so we don't have to traverse all of locals() and globals()) would be appreciated
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13441
Differential Revision: D12879763
Pulled By: jamesr66a
fbshipit-source-id: b84133dc2ef9ca6cfbfaf2e3f9106784cc42951e
Summary:
We updated the description of upsample_op in onnx: https://github.com/onnx/onnx/pull/1467
Therefore, we need to support the new upsample_op in caffe2-onnx backend as well.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13272
Reviewed By: houseroad
Differential Revision: D12833656
Pulled By: zrphercule
fbshipit-source-id: 21af5282abaae12d2d044e4018a2b152aff79917
Summary:
The PR did two things:
1. fix the bug in erase_number_type on node inputs
2. handle negative indices for dim-reduce in caffe2
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12888
Reviewed By: houseroad
Differential Revision: D12833486
Pulled By: wanchaol
fbshipit-source-id: c3ceb400d91f0173b73ad95e392b010c3c14db7d
Summary:
This will enable the updated attribute and input format of operator upsample.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13152
Reviewed By: houseroad
Differential Revision: D12812491
Pulled By: zrphercule
fbshipit-source-id: d5db200365f1ab2bd1f052667795841d7ee6beb3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12794
common.py is used in base_module for almost all tests in test/. The
name of this file is so common that can easily conflict with other dependencies
if they happen to have another common.py in the base module. Rename the file to
avoid conflict.
Reviewed By: orionr
Differential Revision: D10438204
fbshipit-source-id: 6a996c14980722330be0a9fd3a54c20af4b3d380
Summary:
PyTorch exporting test and end to end cases.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10924
Reviewed By: Ac2zoom
Differential Revision: D9548210
Pulled By: houseroad
fbshipit-source-id: 2381d1ad92a4e07f97060eb65c9fd09f60ad3de6
Summary:
Trigonometry functions are newly added to ONNX in a recent PR https://github.com/onnx/onnx/pull/869
This PR makes pytorch support exporting graphs with trigonometry functions.
This PR might need to wait until it is ready to change
```python
_onnx_opset_version = 6
```
to
```python
_onnx_opset_version = 7
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/7540
Differential Revision: D9395041
Pulled By: bddppq
fbshipit-source-id: bdf3e9d212b911c8c4eacf5a0753bb092e4748d2
Summary:
Now, run `python test/onnx/test_operators.py --no-onnx`, we won't introduce any onnx python dependence. (No onnx/protobuf python packages needs to be installed)
The major changes:
- output pbtxt from C++ exporter directly, so the floating format may be slightly different. (This should be fine, since it's just to guard ONNX exporting.)
- ONNX python packages are only imported if we run the ONNX related checks. Those checks are disabled when using `--no-onnx` flag.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10151
Reviewed By: jamesr66a
Differential Revision: D9130706
Pulled By: houseroad
fbshipit-source-id: ea28cf5db8399929179698ee535137f209e9ce6f
Summary:
We only support special case. The original dim is not supported by ONNX.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9660
Reviewed By: bddppq
Differential Revision: D8965507
Pulled By: houseroad
fbshipit-source-id: 021dffdf0489c2d3a50bfd1e0c4cfd00d4a3d776
Summary:
```
This adds TensorIterator, a helper class for computing element-wise
operations that's intended to replace the CPU and CUDA apply utils
functions.
CPU kernels are implemented as functions that operate on strided 1-d
tensors compared to CPUApplyUtils which operated individual elements. This
allows the kernels to handle vectorization, while TensorIterator handles
parallelization and non-coalesced dimensions.
GPU kernels continue to operate on elements, but the number of
specializations is reduced. The contiguous case remains the same. The
non-contiguous case uses a single (reduced) shape for all operands and
the fast integer division from THCIntegerDivider. To avoid extra
specializations for indexing with 64-bits, large operations are split
into smaller operations that can be indexed with 32-bits.
Major semantic changes:
- No more s_add, s_mul, s_div, or s_sub. Broadcasting is handled by
TensorIterator. The autograd engine performs the reduction assuming
standard broadcasting if the gradient shape does not match the
expected shape. Functions that do not use standard broadcasting rules
should either continue to trace the expand calls or handle the
reduction in their derivative formula.
- Use ONNX v7, which supports broadcasting ops.
Performance impact:
- Small increased fixed overhead (~0.5 us)
- Larger overhead for wrapped numbers (~2.5 us)
- No significant change for ops on contiguous tensors
- Much faster worst-case performance for non-contiguous GPU tensors
- Faster CPU bias addition (~2x)
- Faster GPU bias addition (~30% faster)
Future work:
- Decrease overhead, especially for wrapping numbers in Tensors
- Handle general inter-type operations
- Extend to unary ops and reductions
- Use buffering for compute-bound operations on non-contiguous tensors
(pull in from CPUApplyUtils)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/8919
Differential Revision: D8677600
Pulled By: colesbury
fbshipit-source-id: 61bc9cc2a36931dfd00eb7153501003fe0584afd
Summary:
As in the title. Lets us simplify a lot of code.
Depends on #9363, so please review only the last commit.
zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9414
Reviewed By: zdevito
Differential Revision: D8836496
Pulled By: apaszke
fbshipit-source-id: 9b3c3d1f001a9dc522f8478abc005b6b86cfa3e3
