Summary:
If JIT constant propagation doesn't work, we have to handle the ListConstructor in symbolic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19102
Reviewed By: zrphercule
Differential Revision: D14875588
Pulled By: houseroad
fbshipit-source-id: d25c847d224d2d32db50aae1751100080e115022
Summary:
Almost there, feel free to review.
these c10 operators are exported to _caffe2 domain.
TODO:
- [x] let the onnx checker pass
- [x] test tensor list as argument
- [x] test caffe2 backend and converter
- [x] check the c10 schema can be exported to onnx
- [x] refactor the test case to share some code
- [x] fix the problem in ONNX_ATEN_FALLBACK
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18210
Reviewed By: zrphercule
Differential Revision: D14600916
Pulled By: houseroad
fbshipit-source-id: 2592a75f21098fb6ceb38c5d00ee40e9e01cd144
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:
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:
Add support of count_include_pad end to end test for AveragePool
We can export AveragePool from PyTorch with count_include_pad attribute. However, we don't directly support it in Caffe2's ONNX backend.
We also want to check whether we can pass the end to end test for average pool operator with count_include_pad attribute (pytorch => onnx => caffe2)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17034
Reviewed By: houseroad
Differential Revision: D14060186
Pulled By: dwarakrajagopal
fbshipit-source-id: 10dae532611c71f8c8cfc3fa701cc7c1c1c02695
Summary:
Before this pr, rsub did not convert two elements into the same dtype, therefore "1 - x" may export to an onnx model that two elements of rsub having different dtype.
By adding this symbolic patch this bug should be fixed.
Related test cases also created.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15707
Differential Revision: D13583042
Pulled By: zrphercule
fbshipit-source-id: 3a2de47a1a8d1ded1a0adfb911adbe6ac729cdef
Summary:
Short term solution, export group norm as an ATen op to unblock users.
Long term will add GroupNorm to onnx.
Add an end to end test for this one.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15569
Differential Revision: D13554293
Pulled By: houseroad
fbshipit-source-id: b4974c9ea2a1b81338ca1e5c6747efe2715d7932
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:
There should really be a single place to erase or do special treatment to the prim::ListConstruct during ONNX export, this will make it consistent across different calls. e.g it will give a correct output graph in the following case:
```python
class Test(torch.nn.Module):
def forward(self, input):
return torch.cat([input, torch.zeros(input.size(0), 1).type_as(input)], dim=1)
```
Before this PR, we have the onnx graph as:
```
graph(%0 : Byte(2, 3)) {
%1 : Long() = onnx::Constant[value={0}](), scope: Test
%2 : Dynamic = onnx::Shape(%0), scope: Test
%3 : Long() = onnx::Gather[axis=0](%2, %1), scope: Test
%4 : Long() = onnx::Constant[value={1}](), scope: Test
%5 : Dynamic = onnx::Unsqueeze[axes=[0]](%3)
%6 : Dynamic = onnx::Unsqueeze[axes=[0]](%4)
%7 : int[] = onnx::Concat[axis=0](%5, %6)
%8 : Float(2, 1) = onnx::ConstantFill[dtype=1, input_as_shape=1, value=0](%7), scope: Test
%9 : Byte(2, 1) = onnx::Cast[to=2](%8), scope: Test
%10 : Byte(2, 4) = onnx::Concat[axis=1](%0, %9), scope: Test
return (%10);
}
```
Which is wrong since onnx does not have a concept of `int[]`, here is the onnx graph after this PR:
```
graph(%0 : Byte(2, 3)) {
%1 : Long() = onnx::Constant[value={0}](), scope: Test
%2 : Dynamic = onnx::Shape(%0), scope: Test
%3 : Long() = onnx::Gather[axis=0](%2, %1), scope: Test
%4 : Long() = onnx::Constant[value={1}](), scope: Test
%5 : Dynamic = onnx::Unsqueeze[axes=[0]](%3)
%6 : Dynamic = onnx::Unsqueeze[axes=[0]](%4)
%7 : Dynamic = onnx::Concat[axis=0](%5, %6)
%8 : Float(2, 1) = onnx::ConstantFill[dtype=1, input_as_shape=1, value=0](%7), scope: Test
%9 : Byte(2, 1) = onnx::Cast[to=2](%8), scope: Test
%10 : Byte(2, 4) = onnx::Concat[axis=1](%0, %9), scope: Test
return (%10);
}
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13195
Differential Revision: D12812541
Pulled By: wanchaol
fbshipit-source-id: db6be8bf0cdc85c426d5cbe09a28c5e5d860eb3e
Summary:
Using Transpose + Reshape, not using DepthToSpace, since they are not available in C2 yet.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12192
Reviewed By: BIT-silence
Differential Revision: D10129913
Pulled By: houseroad
fbshipit-source-id: b60ee6d53b8ee95fd22f12e628709b951a83fab6
Summary:
ATenOp was handling `torch.where` incorrectly. Whereas the `torch.where` overload (and `aten::` function) had arguments in the order `Tensor condition, Tensor self, Tensor other`, ATenOp was emitting code that assumed that `self` was the 0th argument, and thus was trying to interpret the wrong value as the condition.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12353
Differential Revision: D10218435
Pulled By: jamesr66a
fbshipit-source-id: afe31c5d4f941e5fa500e6b0ef941346659c8d95
Summary:
Requires https://github.com/onnx/onnx/pull/1377
This PR makes it so that slices with dynamic boundary values can be exported from pytorch and run in caffe2 via ONNX.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11255
Differential Revision: D9790216
Pulled By: jamesr66a
fbshipit-source-id: 6adfcddc5788df4d34d7ca98341077140402a3e2
Summary:
Many constructors like `torch.zeros` or `torch.randn` didn't support
size tracing correctly which is fixed by this pass. Same issue has been
fixed in legacy tensor constructors.
Additionally, new tensor constructors, which do not participate in
tracing (most notably `torch.tensor`, `torch.as_tensor` and
`torch.from_numpy`) raise a warning when they are used.
Finally, entering a traceable operation disables the tracing in its body.
This is needed because
zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11288
Reviewed By: ezyang
Differential Revision: D9751183
Pulled By: apaszke
fbshipit-source-id: 51444a39d76a3e164adc396c432fd5ee3c8d5f7f
Summary:
Operators like aten::chunk used to return a number of tensors, but
now return a list. To make it easier to do shape prop through
aten::chunk and fuse it, I've also introduced prim::ConstantChunk,
which behaves like the previous implementation (has a variable length
output list).
The downside of this PR is that the introduction of more lists to the IR causes the LSTM and MiLSTM graphs to be considered as non-differentiable by the graph executor. I verified that they are still optimize correctly, and my next patch (that changes how the specializations/differentiation works) will restore those.
zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10949
Reviewed By: zdevito
Differential Revision: D9556823
Pulled By: apaszke
fbshipit-source-id: 33e63b17fc7247cac6cfc05eb7eb9bf069b499ee
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:
This fixes multiple bugs in the handling of negative indices in both slicing and gather operations. These were uncovered by @[1466077526:Elias Ellison]'s diff D9493614, which made it so that we actually emit negative indices when we see them in PyTorch code.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10973
Reviewed By: jhcross
Differential Revision: D9546183
Pulled By: jamesr66a
fbshipit-source-id: 6cb0e84e8ad399e47e24a96c44025f644c17b375
Summary:
zdevito et al came to the conclusion that the ONNX spec does not mandate the widening conversion of integral types when serializing tensor data into raw_data, as opposed to serializing the data into int32_data. PyTorch recently made this change in the export code, which caused import in caffe2 to break because it did not match semantics. This fixes that
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10718
Differential Revision: D9423712
Pulled By: jamesr66a
fbshipit-source-id: 479fbae67b028bf4f9c1ca1812c2c7b0c6cccd12
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:
We can't rely on the ATen fallback pathway here because we need to parse out the constant attributes explicitly
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10513
Reviewed By: dzhulgakov
Differential Revision: D9322133
Pulled By: jamesr66a
fbshipit-source-id: 52af947e6c44532ef220cb4b94838ca838b5df06
