Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68491
* Allows implementing symbolic functions for domains other than `aten`, for example `prim`, in symbolic_opset#.py.
* Allows symbolic function to access extra context if needed, through `SymbolicFunctionState`.
* Particularly, the `prim::PythonOp` special case can access node without the need of passing node through inputs. Updates will be made downstreams, and in a follow-up PR we will remove the previous workaround in exporter.
* `prim::Loop`, `prim::If`, etc are now moved outside of `_run_symbolic_function` from utils.py, and to symbolic_opset9.py.
Motivation for this change:
- Better maintainability and reducing complexity. Easier to add symbolic for operators, both simple and complex ones (that need additional context), without the former needing to know the existence of the latter.
- The design idea was long outdated. prim ops are no longer rare special cases, and they shouldn't all be handled inside `_run_symbolic_function`. As a result this function becomes too clumsy. There were also prim ops symbolic added in symbolic_opset#.py with signature `prim_[opname]`, creating separation and confusion.
Test Plan: Imported from OSS
Reviewed By: jansel
Differential Revision: D32483782
Pulled By: malfet
fbshipit-source-id: f9affc31b1570af30ffa6668da9375da111fd54a
Co-authored-by: BowenBao <bowbao@microsoft.com>
(cherry picked from commit 1e04ffd2fd)
Cover more cases of scope inferencing where consecutive nodes don't have valid scope information. Usually these nodes are created in some pass where authors forgot to assign meaningful scope to them.
* One rule of `InferScope` is to check if the current node's outputs' users share the same scope. Recursively run `InferScope` on the user nodes if they are missing scope as well. Since the graph is SSA, the depth is finite.
* Fix one pass that missed scope information for a new node.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71897
Summary:
- PyTorch and ONNX has supported BFloat16, add this to unblock some mixed-precision training model.
- Support PyTorch TNLG model to use BFloat16 tensors for the inputs/outputs of the layers that run on the NPU.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66788
Reviewed By: jansel
Differential Revision: D32283510
Pulled By: malfet
fbshipit-source-id: 150d69b1465b2b917dd6554505eca58042c1262a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67805
Also fix Reduce ops on binary_cross_entropy_with_logits
The graph says the output is a scalar but with `keepdims=1`
(the default), the output should be a tensor of rank 1. We set keep
`keepdims=0` to make it clear that we want a scalar output.
This previously went unnoticed because ONNX Runtime does not strictly
enforce shape inference mismatches if the model is not using the latest
opset version.
Test Plan: Imported from OSS
Reviewed By: msaroufim
Differential Revision: D32181304
Pulled By: malfet
fbshipit-source-id: 1462d8a313daae782013097ebf6341a4d1632e2c
Co-authored-by: Bowen Bao <bowbao@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64578
* Fix remainder export for edge case when input is negative. New export relies on true_divide export.
* Simplified true_divide export. Cleaned up redundant code which is handled by scalar type analysis pass. Removed dependency on `onnx::Where`, thus supports opset 7 & 8.
Fixes#60179
Test Plan: Imported from OSS
Reviewed By: jansel
Differential Revision: D30919601
Pulled By: malfet
fbshipit-source-id: 0f78621c0ac3bdb6bf4225e049ba5f470dc8ab12
Co-authored-by: BowenBao <bowbao@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61558
When we construct an empty list by python list comprehension, we need to avoid converting the node without inputs to onnx::Concat in shape_type_inference.cpp and peephole.cpp because it will create an invalid Concat node which doesn't have inputs.
In addition, update the code to avoid passing a Sequence input to an onnx::Cast node which doesn't accept Sequence data type as an input.
Add tests for the validation as well.
Test Plan: Imported from OSS
Reviewed By: nikithamalgifb
Differential Revision: D29767989
Pulled By: SplitInfinity
fbshipit-source-id: f97f172ff20eebda4c3744c7a934df36716f12a2
Co-authored-by: fatcat-z <jiz@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60246
* Adds support for linspace op
* Modifies arange symbolic in opset 9 to replicate the same behavior in which dtype is determined (similar to opset 11) as in https://pytorch.org/docs/stable/generated/torch.arange.html
* Enabled some arange unit tests which were disabled for opset 9
Test Plan: Imported from OSS
Reviewed By: zou3519, ZolotukhinM
Differential Revision: D29494911
Pulled By: SplitInfinity
fbshipit-source-id: bddff18a90f8a78121c8ecdd1dafc15c69962d66
Co-authored-by: Shubham Bhokare <shubhambhokare@gmail.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60245
Fix after b9bdb07a0261ab5a0b1038f290fa03af6ce0415f. Improving previous fix on two aspects
* Not only checks 0 on first dimension for empty tensor.
* Do not assume empty tensor when shape is not accessible.
Test Plan: Imported from OSS
Reviewed By: zou3519, ZolotukhinM
Differential Revision: D29494917
Pulled By: SplitInfinity
fbshipit-source-id: 02587c3c3be0510312c1a1959f28cab12d81812d
Co-authored-by: BowenBao <bowbao@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59537
PyTorch sum over empty tensor gives 0, while ONNX produces an error.
torch.sum will be translated into onnx::ReduceSum op. Per the definition of ReduceSum, update the keepdims attribute for this scenario.
Test Plan: Imported from OSS
Reviewed By: nikithamalgifb, ansley
Differential Revision: D29046604
Pulled By: SplitInfinity
fbshipit-source-id: 6f5f3a66cb8eda8b5114b8474dda6fcdbae73469
Co-authored-by: fatcat-z <jiz@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58695
As PEP8 says: "Pick a rule and stick to it." [1]
[1] https://www.python.org/dev/peps/pep-0008/#string-quotes
Test Plan: Imported from OSS
Reviewed By: driazati
Differential Revision: D28714811
Pulled By: SplitInfinity
fbshipit-source-id: c95103aceb1725c17c034dc6fc8216627f189548
Co-authored-by: Gary Miguel <garymiguel@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57598
Add a doc string to explain what it does and how to use it.
Remove hack around a bug in Python 2's functools.wrap().
Python 2 is no longer supported.
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision: D28393519
Pulled By: SplitInfinity
fbshipit-source-id: aae8c5e7b49e2ad2d24a0e86f8ba47f1cd080e46
Co-authored-by: Gary Miguel <garymiguel@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56163
* [ONNX] Improve index_put symbolic to handle singular Bool updates (#53690)
Adds support for cases where the updates to the index_put node is a single Bool value, such as the case shown below
```
mask[indices] = True
```
Fixes#53507
* [ONNX] Support primitive type input/outputs and attributes (#53550)
Support primitive type attributes. Needed for Silero model.
* [ONNX] Fix if output shape mismatch error & Fix graph input directly used as output (#53219)
Fix if output shape mismatch error & Fix graph input directly used as output
* Add support for hann_window operator.
* [ONNX] Replace decomposeLinear pre process pass with a symbolic (#53077)
Replace decomposeLinear pre process pass with a symbolic
* Add a test case for dtype is None.
* Resolve flake8 issue.
* Remove one unused test case.
* Add support for hann_window operator.
* Add a test case for dtype is None.
* Remove one unused test case.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D27866145
Pulled By: SplitInfinity
fbshipit-source-id: e0b43df9ecd1a95cd7ac297213aba453bbaf2913
Co-authored-by: Shubham Bhokare <32080845+shubhambhokare1@users.noreply.github.com>
Co-authored-by: Negin Raoof <neginmr@utexas.edu>
Co-authored-by: Bowen Bao <bowbao@microsoft.com>
Co-authored-by: Ksenija Stanojevic <KsenijaS@users.noreply.github.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53312
- Add support for aten::repeat_interleave
- NOTE: Also adds fix for cases with split op where input tensor sizes are not known but _outputs is provided
Test Plan: Imported from OSS
Reviewed By: pbelevich, malfet
Differential Revision: D26922422
Pulled By: SplitInfinity
fbshipit-source-id: 5362d0d8ccfdc14c15e1ae73fd70c4c113f823e6
Summary:
Context: https://github.com/pytorch/pytorch/pull/53299#discussion_r587882857
These are the only hand-written parts of this diff:
- the addition to `.github/workflows/lint.yml`
- the file endings changed in these four files (to appease FB-internal land-blocking lints):
- `GLOSSARY.md`
- `aten/src/ATen/core/op_registration/README.md`
- `scripts/README.md`
- `torch/csrc/jit/codegen/fuser/README.md`
The rest was generated by running this command (on macOS):
```
git grep -I -l ' $' -- . ':(exclude)**/contrib/**' ':(exclude)third_party' | xargs gsed -i 's/ *$//'
```
I looked over the auto-generated changes and didn't see anything that looked problematic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53406
Test Plan:
This run (after adding the lint but before removing existing trailing spaces) failed:
- https://github.com/pytorch/pytorch/runs/2043032377
This run (on the tip of this PR) succeeded:
- https://github.com/pytorch/pytorch/runs/2043296348
Reviewed By: walterddr, seemethere
Differential Revision: D26856620
Pulled By: samestep
fbshipit-source-id: 3f0de7f7c2e4b0f1c089eac9b5085a58dd7e0d97
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52350
When onnx export creates a 0-dim tensor of constant type, this action overrides the type promotion logic as quoted in #9515. In order to prevent this from happening this PR adds the following functionality.
If the data type is a floating point type, it is converted to a 0-dim double tensor, else it is converted to a 0-dim tensor of its original type
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision: D26490325
Pulled By: SplitInfinity
fbshipit-source-id: 4c47c69c9b6523d2e45b74c2541d6d8ca7e28fc9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51518
* enable remaining test in opset13
* add comments for error version test info
* fix comments:opset12 unbind problem
* add ignore[no-redef]
* fix format
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D26203122
Pulled By: SplitInfinity
fbshipit-source-id: e7d95bd2ce13f79f11965be82f640379cd55ff0f
Co-authored-by: hwangdeyu <deyhuang@qq.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51517
Fix get/set attributes when getting/setting a model parameter.
This PR also fixes inplace ops in If blocks.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D26203116
Pulled By: SplitInfinity
fbshipit-source-id: bed6ee6dd92b5b43febc8c584a6872290f8fe33f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50954
* Replace optional parameters of Resize with placeholder for ops13.
* Use common methods to handle different versions.
* Correct flake8 issue.
* Update per comments.
* Add something to trigger CI again.
* Trigger another round of CI.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D26050882
Pulled By: SplitInfinity
fbshipit-source-id: aea6205a1ba4a0621fe1ac9e0c7d94b92b6d8f21
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50906
In opset 13, squeeze/unsqueeze is updated to take axes as input, instead of attribute.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D26050883
Pulled By: SplitInfinity
fbshipit-source-id: 7b5faf0e016d476bc75cbf2bfee6918d77e8aecd
Summary:
`isCompleteTensor()` only returns true when both scalar type and shape is present. All dimensions in the shape must be static. This high requirement is unnecessary for many use cases such as when only rank or scalar type needs to be known.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48162
Reviewed By: malfet
Differential Revision: D25340823
Pulled By: bzinodev
fbshipit-source-id: 1fef61f44918f4339dd6654fb725b18cd58d99cf
Summary:
Onnx op Gather index need be int32 or int64. However, we don't have this Cast in our converter.
Therefore, it fails the following UT (for opset 11+)
`seq_length.type().scalarType()` is None, so `_arange_cast_helper()` cannot treat it as all integral, then it will cast all to float. Then this float value will be used as Gather index, hence it throws error in ORT about float type index.
The fix is that we need cast Gather index type to Long if it is not int/long.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47653
Reviewed By: heitorschueroff
Differential Revision: D25298056
Pulled By: mruberry
fbshipit-source-id: 05e3a70ccfd74612233c63ec5bb78e060b211909
Summary:
Fixes https://github.com/pytorch/pytorch/issues/45215
Still need to resolve a few mypy issues before a review. In special, there is an error which I don't know how to solve, see:
```python
torch/onnx/utils.py:437: error: Name 'is_originally_training' is not defined [name-defined]
if training is None or training == TrainingMode.EVAL or (training == TrainingMode.PRESERVE and not is_originally_training):
```
`is_originally_training` is used but never defined/imported on [`torch/onnx/utils.py`](ab5cc97fb0/torch/onnx/utils.py (L437)),
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45258
Reviewed By: zhangguanheng66
Differential Revision: D25254920
Pulled By: ezyang
fbshipit-source-id: dc9dc036da43dd56b23bd6141e3ab92e1a16e3b8
Summary:
- rand/randn: the type signature of int[] is different in scripting, thus failing the check.
- where: scripting produces dynamic cases which are supported by `unbind` export of higher opsets.
- test_list_pass: this test fails when using new scripting api, should be fixed by https://github.com/pytorch/pytorch/issues/45369
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45793
Reviewed By: mrshenli
Differential Revision: D24566096
Pulled By: bzinodev
fbshipit-source-id: 6fe0925c66dee342106d71c9cbc3c95cabe639f7
Summary:
* Support propagating `dim_param` in ONNX by encoding as `ShapeSymbol` in `SymbolicShape` of outputs. If export is called with `dynamic_axes` provided, shape inference will start with these axes set as dynamic.
* Add new test file `test_pytorch_onnx_shape_inference.py`, reusing all test cases from `test_pytorch_onnx_onnxruntime.py`, but focus on validating shape for all nodes in graph. Currently this is not enabled in the CI, since there are still quite some existing issues and corner cases to fix. The test is default to run only at opset 12.
* Bug fixes, such as div, _len, and peephole.cpp passes for PackPadded, and LogSoftmaxCrossEntropy.
* This PR depends on existing PR such as 44332.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44920
Reviewed By: eellison
Differential Revision: D23958398
Pulled By: bzinodev
fbshipit-source-id: 00479d9bd19c867d526769a15ba97ec16d56e51d
Summary:
* Support sequence type (de)serialization, enables onnx shape inference on sequence nodes.
* Fix shape inference with block input/output: e.g. Loop and If nodes.
* Fix bugs in symbolic discovered by coverage of onnx shape inference.
* Improve debuggability: added more jit logs. For simplicity, the default log level, when jit log is enabled, will not dump ir graphs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43929
Reviewed By: albanD
Differential Revision: D23674604
Pulled By: bzinodev
fbshipit-source-id: ab6aacb16d0e3b9a4708845bce27c6d65e567ba7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43025
- Use new overloads that better reflect the arguments to interpolate.
- More uniform interface for upsample ops allows simplifying the Python code.
- Also reorder overloads in native_functions.yaml to give them priority.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37177
ghstack-source-id: 106938111
Test Plan:
test_nn has pretty good coverage.
Relying on CI for ONNX, etc.
Didn't test FC because this change is *not* forward compatible.
To ensure backwards compatibility, I ran this code before this change
```python
def test_func(arg):
interp = torch.nn.functional.interpolate
with_size = interp(arg, size=(16,16))
with_scale = interp(arg, scale_factor=[2.1, 2.2], recompute_scale_factor=False)
with_compute = interp(arg, scale_factor=[2.1, 2.2])
return (with_size, with_scale, with_compute)
traced_func = torch.jit.trace(test_func, torch.randn(1,1,1,1))
sample = torch.randn(1, 3, 7, 7)
output = traced_func(sample)
assert not torch.allclose(output[1], output[2])
torch.jit.save(traced_func, "model.pt")
torch.save((sample, output), "data.pt")
```
then this code after this change
```python
model = torch.jit.load("model.pt")
sample, golden = torch.load("data.pt")
result = model(sample)
for r, g in zip(result, golden):
assert torch.allclose(r, g)
```
Reviewed By: AshkanAliabadi
Differential Revision: D21209991
fbshipit-source-id: 5b2ebb7c3ed76947361fe532d1dbdd6faa3544c8
Summary:
It is often that the conversion from torch operator to onnx operator requires input rank/dtype/shape to be known. Previously, the conversion depends on tracer to provide these info, leaving a gap in conversion of scripted modules.
We are extending the export with support from onnx shape inference. If enabled, onnx shape inference will be called whenever an onnx node is created. This is the first PR introducing the initial look of the feature. More and more cases will be supported following this PR.
* Added pass to run onnx shape inference on a given node. The node has to have namespace `onnx`.
* Moved helper functions from `export.cpp` to a common place for re-use.
* This feature is currently experimental, and can be turned on through flag `onnx_shape_inference` in internal api `torch.onnx._export`.
* Currently skipping ONNX Sequence ops, If/Loop and ConstantOfShape due to limitations. Support will be added in the future.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40628
Reviewed By: mrshenli
Differential Revision: D22709746
Pulled By: bzinodev
fbshipit-source-id: b52aeeae00667e66e0b0c1144022f7af9a8b2948
Summary:
in `_jit_pass_onnx`, symbolic functions are called for each node for conversion. However, there are nodes that cannot be converted without additional context. For example, the number of outputs from split (and whether it is static or dynamic) is unknown until the point where it is unpacked by listUnpack node. This pass does a preprocess, and prepares the nodes such that enough context can be received by the symbolic function.
* After preprocessing, `_jit_pass_onnx` should have enough context to produce valid ONNX nodes, instead of half baked nodes that replies on fixes from later postpasses.
* `_jit_pass_onnx_peephole` should be a pass that does ONNX specific optimizations instead of ONNX specific fixes.
* Producing more valid ONNX nodes in `_jit_pass_onnx` enables better utilization of the ONNX shape inference https://github.com/pytorch/pytorch/issues/40628.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41832
Reviewed By: ZolotukhinM
Differential Revision: D22968334
Pulled By: bzinodev
fbshipit-source-id: 8226f03c5b29968e8197d242ca8e620c6e1d42a5
Summary:
Shape is passed to _reshape_to_tensor as a Constant and cannot infer shape of the input when model is exported with dynamic axes set. Instead of a Constant pass output of a subgraph Shape-Slice-Concat to compute the shape for the Reshape node in _reshape_to_tensor function.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40418
Reviewed By: hl475
Differential Revision: D22480127
Pulled By: houseroad
fbshipit-source-id: 11853adb6e6914936871db1476916699141de435
Summary:
In issue https://github.com/pytorch/pytorch/issues/36997 the user encountered a non-meaningful error message when trying to export the model to ONNX. The Pad operator in opset 9 requires the list of paddings to be constant. This PR tries to improve the error message given to the user when this is not the case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39651
Reviewed By: hl475
Differential Revision: D21992262
Pulled By: houseroad
fbshipit-source-id: b817111c2a40deba85e4c6cdb874c1713312dba1
Summary:
When an op involves creating a tensor of a certain type (such as torch.ones(...)), the tracer creates a `prim::Constant` node with an integer value representing the type. The mapping from the torch type to integers maps:
```
torch.complex32 -> 8
torch.complex64 -> 9
torch.complex128 -> 10
torch.bool -> 11
```
However, when the ONNX exporter maps back the integer to torch type, 10 is mapped to bool, 9 is mapped to complex128 and 8 is mapped to complex64.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40006
Reviewed By: hl475
Differential Revision: D22158019
Pulled By: houseroad
fbshipit-source-id: 42fbd6b56566017ff03382c4faf10d30ffde3802
Summary:
This PR adds a new operator export type to exporter: ONNX_FALLTHROUGH
This new type allows ops that are not supported to pass through.
This PR also removes all aten ops in ONNX operator export type mode.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37239
Reviewed By: hl475
Differential Revision: D21440509
Pulled By: houseroad
fbshipit-source-id: 38b826677cf3431ea44868efebefe1ff51c9aa75
Summary:
the rand N like function had required args which were not being used.
As such modified the method signature to give default values so when
scripting does not provide these arguments which are not even being
used, no error is thrown.
Additionally modified the const checker for handling prim::Constant as
well
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32830
Reviewed By: hl475
Differential Revision: D19731715
Pulled By: houseroad
fbshipit-source-id: a3cacb3977eecb88b122e0ceb654fdbf1c8286c1
Summary:
The PR https://github.com/pytorch/pytorch/pull/31791 adds support for float[] constant, which affects some cases of ONNX interpolate support.
This PR adds float[] constants support in ONNX, updates interpolate in ONNX, and re-enable the disabled tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32554
Reviewed By: hl475
Differential Revision: D19566596
Pulled By: houseroad
fbshipit-source-id: 843f62c86126fdf4f9c0117b65965682a776e7e9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29694
This PR adds preliminary support required to be able to run quantized pytorch models on a C2 backend.
For quantized ops we use a custom domain name 'caffe2' to register the ops if they are in the "quantized" namespace.
The change also adds JIT pass to unpack the quantized weights and insert the unpacked values into the graph.
The actual tensor values are looked up from the params dict.
Test Plan:
python test/onnx/test_pytorch_onnx_caffe2.py TestQuantizedOps
Imported from OSS
Reviewed By: houseroad
Differential Revision: D18467130
fbshipit-source-id: 53ebd8c43935f7d7e74305dad6c231a2247df176
Summary:
- Add support for missing case where interpolate is exported with missing shape information in scripting
- Add warnings
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29489
Reviewed By: hl475
Differential Revision: D18438872
Pulled By: houseroad
fbshipit-source-id: d01f833bec0cc4e881ddc18e7054d22f54e9886b
Summary:
Fixes https://github.com/pytorch/pytorch/issues/17662
I'm not sure if `arange` needs to be in python_arg_parser at all, given the schemas in native_functions.yaml. In any case this at least fixes the dytpe mismatch.
In follow up PRs I will try to handle some of the other ops that do type inference at the python level, like randint.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27629
Differential Revision: D17885939
Pulled By: eellison
fbshipit-source-id: f97a8bc722b7ab77de1c42a992e49a4a3175ad60
Summary:
This PR makes Caffe2 compatible with TensorRT 6. To make sure it works well, new unit test is added. This test checks PyTorch->ONNX->TRT6 inference flow for all classification models from TorhchVision Zoo.
Note on CMake changes: it has to be done in order to import onnx-tensorrt project. See https://github.com/pytorch/pytorch/issues/18524 for details.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26426
Reviewed By: hl475
Differential Revision: D17495965
Pulled By: houseroad
fbshipit-source-id: 3e8dbe8943f5a28a51368fd5686c8d6e86e7f693
Summary:
We currently support exporting traced interpolate ops to ONNX.
Scripting interpolate op invokes aten::__interpolate in the Torch IR (instead of aten::upsample_[mode][dim]d), which we do not support yet.
This PR implements the ONNX symbolic for __interpolate() to support exporting interpolate in scripting scenarios.
Related open issue: https://github.com/pytorch/pytorch/issues/25807
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27566
Reviewed By: hl475
Differential Revision: D17817731
Pulled By: houseroad
fbshipit-source-id: e091793df503e2497f24821cf2954ff157492c75
Summary:
Exporting a scripted module to ONNX, with ops like torch.zeros(), fails when the dtype is not specified.
This PR adds support to exporting scripted torch.zeros() ops (and similar ops) without specifying the dtype (dtype will default to float).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27577
Reviewed By: hl475
Differential Revision: D17822318
Pulled By: houseroad
fbshipit-source-id: b2d4300b869e782a9b72534fea1263eb83744953
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26778
- Add support for linear and cubic interpolate in opset 11.
- Add support for 1d and 3d interpolate in nearest mode for opset 7 and 8.
- Add tests for all cases of interpolate in ORT tests (nearest/linear/cubic, 1d/2d/3d, upsample/downsample).
Original PR resolved: https://github.com/pytorch/pytorch/pull/24805
Reviewed By: hl475
Differential Revision: D17564911
Pulled By: houseroad
fbshipit-source-id: 591e1f5b361854ace322eca1590f8f84d29c1a5d
Summary:
- Add support for linear and cubic interpolate in opset 11.
- Add support for 1d and 3d interpolate in nearest mode for opset 7 and 8.
- Add tests for all cases of interpolate in ORT tests (nearest/linear/cubic, 1d/2d/3d, upsample/downsample).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24805
Reviewed By: hl475
Differential Revision: D17330801
Pulled By: houseroad
fbshipit-source-id: 1bdefff9e72f5e70c51f4721e1d7347478b7505b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24284
This PR finishes the unification of all Tensor types into a single object.
ProfiledTensorType is renamed to TensorType and the old TensorType is
deleted.
Notes:
* Fixes bug in merge for VaryingShape by changing its representation to an
optional list of optional ints.
* Removes ProfiledTensorType::create(type) invocations that can now
simply be expect calls on tensor type.
Test Plan: Imported from OSS
Differential Revision: D16794034
Pulled By: zdevito
fbshipit-source-id: 10362398d0bb166d0d385d74801e95d9b87d9dfc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24077
This replaces all uses of DimensionedTensorType with ProfiledTensorType.
For places where we propagate shape information, we still follow the
dimension-only propagation rules, meaning that even if full size information
is known on inputs the outputs will only have dimension information.
This fixes several bugs in existing implentations that this change uncovered:
* requires_grad was not propgated correctly across loops
* requires_grad on ProfiledTensorType returned false when requires_grad information
is unknown but the conservative result is true
* some equality code on ProfiledTensorType contained bugs.
Test Plan: Imported from OSS
Reviewed By: suo
Differential Revision: D16729581
Pulled By: zdevito
fbshipit-source-id: bd9f823c1c6b1d06a236a1b5b2b2fcdf0245edce
Summary:
Support exporting
* Standard tensor indexing like
```
x = torch.ones(4, 5)
ind = torch.tensor([0, 1])
return x[ind]
```
* [Advanced indexing](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing) like
```
x = torch.ones(4,5,6,7,8)
ind1 = torch.tensor([0, 1])
ind2 = torch.tensor([[3], [2]])
ind3 = torch.tensor([[2, 2], [4, 5]])
return x[2:4, ind1, None, ind2, ind3, :]
```
It would be ideal if ONNX can natively support indexing in future opsets, but for opset <= 10 it will always need this kind of workarounds.
There are still various limitations, such as not supporting advanced indexing with negative indices, not supporting mask indices of rank > 1, etc. My feeling is that these are less common cases that requires great effort to support using current opset, and it's better to not make the index export more cumbersome than it already is.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21716
Reviewed By: zrphercule
Differential Revision: D15902199
Pulled By: houseroad
fbshipit-source-id: 5f1cc687fc9f97da18732f6a2c9dfe8f6fdb34a6
Summary:
This is an extension to the original PR https://github.com/pytorch/pytorch/pull/21765
1. Increase the coverage of different opsets support, comments, and blacklisting.
2. Adding backend tests for both caffe2 and onnxruntime on opset 7 and opset 8.
3. Reusing onnx model tests in caffe2 for onnxruntime.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22421
Reviewed By: zrphercule
Differential Revision: D16225518
Pulled By: houseroad
fbshipit-source-id: 01ae3eed85111a83a0124e9e95512b80109d6aee
Summary:
re-apply changes reverted in:
https://github.com/pytorch/pytorch/pull/22412
Also change log_softmax to take positional arguments. Long-term we do want the kwarg-only interface, but seems to currently be incompatible with jit serialization.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22456
Differential Revision: D16097159
Pulled By: nairbv
fbshipit-source-id: 8cb73e9ca18fc66b35b873cf4a574b167a578b3d
Summary:
This change is backwards incompatible in *C++ only* on mean(), sum(), and prod() interfaces that accepted either of:
```
Tensor sum(IntArrayRef dim, bool keepdim=false) const;
Tensor sum(IntArrayRef dim, ScalarType dtype) const;
```
but now to specify both the dim and dtype will require the keepdim parameter:
```
Tensor sum(IntArrayRef dim, bool keepdim=false, c10::optional<ScalarType> dtype=c10::nullopt) const;
```
[xla ci]
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21088
Reviewed By: ailzhang
Differential Revision: D15944971
Pulled By: nairbv
fbshipit-source-id: 53473c370813d9470b190aa82764d0aea767ed74
Summary:
In onnx spec, the supported input/output type for `And` and `Or` is `Bool` only.
Thus in exporting, cast to/from `Bool` is inserted for input/output.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17894
Reviewed By: zrphercule
Differential Revision: D15103148
Pulled By: houseroad
fbshipit-source-id: 3e1068ea236c743260d42882fb11f0e3a21707e6
