### Description
<!-- What did you change and why was it needed? -->
Remove unused patching methods:
- `torch._C.Graph.constant`
- unpatch `torch._C.Node.__getitem__` and move the helper function to `symbolic_helper`
Add typing annotations
### Issue
<!-- Link to Issue ticket or RFP -->
#76254
### Testing
<!-- How did you test your change? -->
Unit tested
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83006
Approved by: https://github.com/BowenBao
Re-land #81953
Add `_type_utils` for handling data type conversion among JIT, torch and ONNX.
- Replace dictionary / list indexing with methods in ScalarType
- Breaking: **Remove ScalarType from `symbolic_helper`** and move it to `_type_utils`
- Deprecated: "cast_pytorch_to_onnx", "pytorch_name_to_type", "scalar_name_to_pytorch", "scalar_type_to_onnx", "scalar_type_to_pytorch_type" in `symbolic_helper`
- Deprecate the type mappings and lists. Remove all internal references
- Move _cast_func_template to opset 9 and remove its reference elsewhere (clean up). Added documentation for easy discovery
Why: List / dictionary indexing and lookup are error-prone and convoluted.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82995
Approved by: https://github.com/kit1980
Add `_type_utils` for handling data type conversion among JIT, torch and ONNX.
- Replace dictionary / list indexing with methods in ScalarType
- Breaking: **Remove ScalarType from `symbolic_helper`** and move it to `_type_utils`
- Breaking: **Remove "cast_pytorch_to_onnx", "pytorch_name_to_type", "scalar_name_to_pytorch", "scalar_type_to_onnx", "scalar_type_to_pytorch_type"** from `symbolic_helper`
- Deprecate the type mappings and lists. Remove all internal references
- Move _cast_func_template to opset 9 and remove its reference elsewhere (clean up). Added documentation for easy discovery
Why: List / dictionary indexing and lookup are error-prone and convoluted.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81953
Approved by: https://github.com/AllenTiTaiWang, https://github.com/BowenBao
### Description
RReLU behaves the same as LeakyReLU when it's on test mode ([paper](https://arxiv.org/pdf/1505.00853.pdf)), but onnx now only supports train mode behavior, which blocks the models using RReLU.
This PR adds test mode behavior into RReLU symbolic function, adds a runtime case to validate that the outcome now matches torch result, and updates related UT.
1. Extend RReLU symbolic function with test mode behavior
2. Add onnxruntime UT to validate the usage
3. update the existing RReLU UT
### Issue
Fix#82031
Also raise a document issue for torch #82677
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82678
Approved by: https://github.com/justinchuby, https://github.com/BowenBao
Pytorch has two similar layer normalization symbols `aten::layer_norm` and `aten::native_layer_norm`. This PR reuses `aten::layer_norm`'s exporter for exporting `aten::native_layer_norm` with a small refinement. A test is also included. This PR is required because JIT graphs generated from TorchDynamo and LazyTensor (with TS backend) may contain `native_layer_norm` instead of `layer_norm`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81754
Approved by: https://github.com/BowenBao, https://github.com/justinchuby
Part of #79263
Before: The output has `[1]` shape when the input is a scalar.
After: The output has `[]` shape, matching PyTorch's behavior.
The original comment along the code states `torch allows scalar self, and ONNX is ambiguous about whether this is allowed`. The fact seems to be that ONNX never clearly indicates whether scalar inputs are allowed for all the ONNX operators. At least in this case, a scalar input seems to be allowed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79846
Approved by: https://github.com/BowenBao
Part of #79263
This PR fixes the following matters:
1. Before this fix, the reduced output has `[1]` shape when `dim = None` and `keepdim = False`. Now the output is reduced to `[]` shape, which matches Pytorch's behavior.
2. Before this fix, the output is always casted to `Long`. Now the output is casted to the input's dtype.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79506
Approved by: https://github.com/justinchuby, https://github.com/BowenBao
When `TrainingMode.PRESERVE` is set for export, the exporter used to change the model's training mode based on some logic. Now we respect the option and not touch the model's training state.
- Previously `_set_training_mode`'s behavior doesn't match what the global variable expects. This PR removes the deprecated `_set_training_mode` and makes the type correct.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78583
Approved by: https://github.com/BowenBao
- Turn all string comparison on node types into `isinstance` checks
- Update error message in the device op to include the unexpected type's name: `RuntimeError: Unsupported: ONNX export of operator prim::device, output type should be 'DeviceObjType', not '<some unknown type>'. Please feel free to request support or submit a pull request on PyTorch GitHub.`
Tested:
Unit test in `test/onnx/test_pytorch_onnx_onnxruntime.py::TestONNXRuntime_opset13::test_to_device`
Follow up of #78085
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78114
Approved by: https://github.com/garymm
- Add quantization support for `interpolate`, `avgpool`, `sigmoid` and `add_relu`
- Return the inputs to ListUnpack if the previous node is ListConstruct so that `ListConstruct` and `ListUnpack` are canceled and removed in the jit passes. ONNX doesn't support them.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78103
Approved by: https://github.com/garymm
This is a simple fix addressing the exportation when the input to `torch.log2` is scalar. `log2(x)` will be exported as `log(x) / log(2)`, which creates a `log` node followed by a `div` node that divides it by a constant. The constant is constructed not as a scalar but as a tensor of shape `[1]`, so a scalar input here will get broadcasted creating the output tensor with shape `[1]`, while originally the torch model's output is a scalar.
```python
import torch
import onnx
import numpy as np
class Model(torch.nn.Module):
def forward(self, x):
return torch.log2(x)
x = torch.tensor(1.) # scalar
model = Model()
torch.onnx.export(model, (x, ), "output.onnx", opset_version=14,
output_names=['o0'], input_names=['i0'])
y_trh = model(x).numpy()
model = onnx.load("output.onnx")
print(model.graph.output[0])
import onnxruntime as ort
sess = ort.InferenceSession(
"output.onnx", providers=['CPUExecutionProvider'])
y_ort = sess.run(['o0'], {'i0': x.numpy()})[0]
assert y_ort.shape == y_trh.shape, 'shape mismatch, ORT is `{}` but PyTorch is `{}`'.format(
y_ort.shape, y_trh.shape)
```
The resulting ONNX model has an output of shape `[1]` and causes shape mismatch between ORT and PyTorch. The output:
```
name: "o0"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 1
}
}
}
}
Traceback (most recent call last):
File "test.py", line 501, in <module>
y_ort.shape, y_trh.shape)
AssertionError: shape mismatch, ORT is `(1,)` but PyTorch is `()`
```
After the fix, the output becomes:
```
name: "o0"
type {
tensor_type {
elem_type: 1
shape {
}
}
}
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78701
Approved by: https://github.com/justinchuby, https://github.com/BowenBao
The default for `torch.onnx.export` is `TrainingMode.EVAL`:
0d76299ff7/torch/onnx/__init__.py (L63)
That means that this warning is only printed when the caller overrides
that and explicitly specifies that they want training ops like Dropout.
We should assume the user knows what they're doing and not warn.
Also set `do_constant_folding=False` in the dropout related training tests. Without this, warnings are printed like:
```
UserWarning: It is recommended that constant folding be turned off ('do_constant_folding=False') when exporting the model in training-amenable mode
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78309
Approved by: https://github.com/justinchuby, https://github.com/BowenBao
Use pyupgrade(https://github.com/asottile/pyupgrade) and flynt to modernize python syntax
```sh
pyupgrade --py36-plus --keep-runtime-typing torch/onnx/**/*.py
pyupgrade --py36-plus --keep-runtime-typing test/onnx/**/*.py
flynt torch/onnx/ --line-length 120
```
- Use f-strings for string formatting
- Use the new `super()` syntax for class initialization
- Use dictionary / set comprehension
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77935
Approved by: https://github.com/BowenBao
Cleaning up onnx module imports to prepare for updating `__init__`.
- Simplify importing the `_C` and `_C._onnx` name spaces
- Remove alias of the symbolic_helper module in imports
- Remove any module level function imports. Import modules instead
- Alias `symbilic_opsetx` as `opsetx`
- Fix some docstrings
Requires:
- https://github.com/pytorch/pytorch/pull/77448
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77423
Approved by: https://github.com/BowenBao
Reduce circular dependencies
- Lift constants and flags from `symbolic_helper` to `_constants` and `_globals`
- Standardized constant naming to make it consistant
- Make `utils` strictly dependent on `symbolic_helper`, removing inline imports from symbolic_helper
- Move side effects from `utils` to `_patch_torch`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77142
Approved by: https://github.com/garymm, https://github.com/BowenBao
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)
Extending the support for quantization with per channel quantization.
An extra attribute `axis` can be found for per channel quantized tensors,
most commonly in quantized weight of Convolution or Linear module.
The PR adds support to correctly parse the `axis` attribute, and map to
ONNX representation in `QuantizeLinear` and `DequantizeLinear`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76002
Approved by: https://github.com/garymm
Currently, when an operator symbolic hits an unimplemented scenario, the symbolic may print a warning and return, allowing a non-ONNX operator be emitted into the graph
This PRs maintains this behavior for 1) Caffe2 builds or 2) non-caffe2 builds with `operator_export_type != ONNX`. If none of the conditions above are met, the converter raises a `RuntimeError` exception otherwise. This is needed so that exporter can detect detect unsupported ONNX operators when ATEN fallback is used (for non-caffe2 scenarios)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75468
Approved by: https://github.com/BowenBao
Currently ONNX exporter symbolics can emit ATen operators when `operator_export_type==ONNX_ATEN_FALLBACK`. However, this is a behavior specific to Caffe2 builds, as the intend use of `ONNX_ATEN_FALLBACK` is to emit ATen operators only when there is no ONNX equivalent.
The reason Caffe2 choses to emit ATen operators when ONNX counterpart exists is for performance on their particular engine implementation, which might not be true for other implementations. e.g. ONNX Runtime can optimize the generated ONNX graph into something more efficient
This PR must be merged only after https://github.com/pytorch/pytorch/pull/73954
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74680
Approved by: https://github.com/garymm, https://github.com/malfet
This PR introduces 3 BC changes:
First, this PR propagates `BUILD_CAFFE2` flag to `libtorch` and `libtorch_python`, which is necessary for non-caffe2 ONNX runtimes when using `ONNX_ATEN_FALLBACK` operator export type.
Second, as a complement of https://github.com/pytorch/pytorch/pull/68490, this PR refactors Caffe2's Aten ops symbolics to consider not only the `operator_export_type` (aka `ONNX_ATEN_FALLBACK`) to emit Caffe2 Aten ops, but also whether `BUILD_CAFFE2` (which is called `torch.onnx._CAFFE2_ATEN_FALLBACK` in python binding) is set.
Lastly, it renames `onnx::ATen` to `aten::ATen` for ONNX spec consistency in a BC fashion.
ONNX doesn't have `ATen` op on its spec, but PyTorch ONNX converter emits them. Non-Caffe2 backend engines would be mislead by such operator's name/domain. A non-ideal workaround would be to have Aten ops handled based on its name and ignore the (non-complaint) domain. Moreover, users could incorrectly file bugs to either ONNX or ONNX Runtime when they inspect the model and notice the presence of an unspecified ONNX operator.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73954
Approved by: https://github.com/BowenBao, https://github.com/malfet, https://github.com/garymm, https://github.com/jiafatom
There are a few ONNX operators do not support non-float (e.g., integer) inputs at early versions. For example, Clip supports non-float types until [opset 12](https://github.com/onnx/onnx/blob/main/docs/Changelog.md#type-constraints-280), that said older versions like [opset 6](https://github.com/onnx/onnx/blob/main/docs/Changelog.md#type-constraints-107) cannot deal with integer types.
I initially find such a bug in Clip (https://github.com/pytorch/pytorch/pull/70584), but later found more:
1. Clip < 12;
2. Min/Max < 12;
3. ReLU < 14;
4. Pad < 11;
In PyTorch, if we export Max-11 with integer inputs, actually the exportation will succeed; however, fail when imported by other frameworks like ONNXRuntime.
```python
import torch
class Net(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
def forward(self, x: torch.Tensor):
return torch.max(x, x + 1)
net = Net()
onnx_model = 'test.onnx'
torch.onnx.export(net, (torch.zeros((3, 3), dtype=torch.int32),),
onnx_model, verbose=True, opset_version=11)
```
This is an unexpected behavior as we want to ensure that every model exported by PyTorch is valid (https://github.com/pytorch/pytorch/pull/70584#issuecomment-1020636579). Theoretically, we can simply forbid such cases (e.g., `Clip<int>` < 12, `ReLU<int>` < 14). But actually we can enhance the compatibility and flexibility of PyTorch by simply casting inputs of those operators into float tensors, which allows the float operator functions, and then casting it back to original types.
This PR implements the second approach to achieve better compatibility in PyTorch.
@garymm @thiagocrepaldi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72401
Approved by: https://github.com/garymm, https://github.com/thiagocrepaldi
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73280
This PR adds a new attribute overload_name to the Aten node so that third party applications can implement calls to libtorch without using PyTorch source code.
This is necessary because torch's torch::jit::findOperatorFor(fullname) requires a full name, including operator and overload names.
ATen op was originally created for Caffe2, which leveraged the availability of the pytorch yaml files to create calls to the aten oeprators directly, not relying on torch::jit::findOperatorFor
The first part of the PR refactors all symbolics that create Aten ops, so that there is a single helper for this operator. Next all symbolics are updated to pass in the relevant overload name, if empty string is not applicable
Test Plan: Imported from OSS
Reviewed By: jbschlosser
Differential Revision: D34625645
Pulled By: malfet
fbshipit-source-id: 37d58cfb5231833768172c122efc42edf7d8609a
(cherry picked from commit e92f09117d3645b38bc3235b30aba4b4c7c71dfa)
Current we are unable to utilize ONNX's SpaceToDepth operator due to the lack of the mode_s attribute, hence we add an alternative symbolic in opset 9 to support pixel_unshuffle
- Adds support for pixel_unshuffle in opset9
- Adds support for dynamic input shapes for pixel_shuffle and pixel_unshuffle
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72449
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)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68490
The use of ATEN as a fallback operator during ONNX conversion is important for increasing operator coverage or even provide more efficient implementations over some ONNX ops.
Currently this feature is available through `OperatorExportTypes.ONNX_ATEN_FALLBACK`,
but it also performs changes to the graph that are runnable by Caffe2, only.
This PR introduces restricts caffe2-specific graph transformations for `ONNX_ATEN_FALLBACK`
operator export type for when pytorch is built with caffe2 support (aka BUILD_CAFFE2=1 during build)
The first version of this PR introduced a new operator export type `ONNX_ATEN__STRICT_FALLBACK`,
which essentially is the same as `ONNX_ATEN_FALLBACK` but without caffe2 transformations.
It was preferred to not introduce a new operator export type, but to refine the existing aten fallback one
## BC-breaking note
### The global constant `torch.onnx.PYTORCH_ONNX_CAFFE2_BUNDLE` is removed in favor of
a less visible `torch.onnx._CAFFE2_ATEN_FALLBACK`.
`PYTORCH_ONNX_CAFFE2_BUNDLE` is really a dead code flag always set to False.
One alternative would be fixing it, but #66658 disables Caffe2 build by default.
Making a Caffe2 feature a private one seems to make more sense for future deprecation.
### The method `torch.onnx.export` now defaults to ONNX when `operator_export_type` is not specified.
Previously `torch.onnx.export's operator_export_type` intended to default to `ONNX_ATEN_FALLBACK` when `PYTORCH_ONNX_CAFFE2_BUNDLE` was set, but it would never happen as `PYTORCH_ONNX_CAFFE2_BUNDLE` is always undefined
Co-authored-by: Nikita Shulga <nshulga@fb.com>
Test Plan: Imported from OSS
Reviewed By: jansel
Differential Revision: D32483781
Pulled By: malfet
fbshipit-source-id: e9b447db9466b369e77d747188685495aec3f124
(cherry picked from commit 5fb1eb1b19)
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/65993
This PR attempts to port `index_add` to structured kernels, but does more than that:
* Adds an `out=` variant to `index_add`
* Revises `native_functions.yaml` registrations, to not have multiple entries and instead pass default value to `alpha`.
* Changes in `derivatives.yaml` file for autograd functioning
* Revises error messages, please see: https://github.com/pytorch/pytorch/pull/65993#issuecomment-945441615
Follow-up PRs in near future will attempt to refactor the OpInfo test, and will give another look at tests in `test/test_torch.py` for this function. (hence the use of ghstack for this)
~This is WIP because there are tests failing for `Dimname` variant on mobile/android builds, and I'm working on fixing them.~
Issue tracker: https://github.com/pytorch/pytorch/issues/55070
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D32646426
fbshipit-source-id: b035ecf843a9a27d4d1e18b202b035adc2a49ab5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67808
torch.reciprocal implicitly casts the inputs to float, and ONNX
Reciprocal requires floating point inputs.
Also separate the reciprocal test from other tests, and test different
input types.
Test Plan: Imported from OSS
Reviewed By: msaroufim
Differential Revision: D32181307
Pulled By: malfet
fbshipit-source-id: 3e1109b3c85a49c51dc713656a900b4ee78c8340
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67806
Previously new_full would fail with errors like:
`TypeError: only integer tensors of a single element can be converted to an index`
And full_like would trigger warnings like:
`DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.`
Test Plan: Imported from OSS
Reviewed By: msaroufim
Differential Revision: D32181301
Pulled By: malfet
fbshipit-source-id: 2cf262cfef36c18e7b2423efe1e1d4fa3438f0ba
Co-authored-by: Bowen Bao <bowbao@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67271
* [ONNX] Use Reciprocal operator instead of Div(1, x).
This is a more readable and perhaps more performant way to export
torch.reciprocal.
* Use Reciprocal in caffe to operator to import onnx
Test Plan: Imported from OSS
Reviewed By: msaroufim
Differential Revision: D31962519
Pulled By: malfet
fbshipit-source-id: d926e75b1c8312b9a980c9a1207a1a93ba0c71e0
Co-authored-by: take-cheeze <takechi101010@gmail.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67270
* Add dim argument to all symbolic
* All symbolic depends on any symbolic
Test Plan: Imported from OSS
Reviewed By: msaroufim
Differential Revision: D31962518
Pulled By: malfet
fbshipit-source-id: f7ee05cf4eff5880fc508154267e060952b5b42d
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:
Replace `torch.tensor([numpy.arange(a, b, c)])` with `torch.arange(a, b, c).unsqueeze(0)`
Replace `tuple(numpy.add(a, b))` with `tuple( x + y for (x, y) in zip(a, b)`
As `numpy` is an optional dependency, it shouldn't be used in PyTorch core by default
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65188
Reviewed By: mruberry
Differential Revision: D31009490
Pulled By: malfet
fbshipit-source-id: 528e48f055bf9ac1de1fd7e94c0be41915df9a0b
Summary:
This is step 3/7 of https://github.com/pytorch/pytorch/issues/50276. It only adds support for the argument but doesn't implement new indexing modes yet.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62722
Test Plan:
Verified this is not FC breaking by adding logging to both meshgrid
overloads and then called meshgrid twice:
`meshgrid(*tensors)`
and
`meshgrid(*tensors, indexing='ij')`
This confirmed that the former signature triggered the original native
function and the latter signature triggered the new native function.
Reviewed By: H-Huang
Differential Revision: D30394313
Pulled By: dagitses
fbshipit-source-id: e265cb114d8caae414ee2305dc463b34fdb57fa6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62764Fixes#58733
- Support dynamic interleave for cases with dynamic repeat values
- Moved repeat_interleave symbolic from opset 11 to opset 13, as sequence as output types for loop outputs is needed for this change
Test Plan: Imported from OSS
Reviewed By: SplitInfinity
Differential Revision: D30375179
Pulled By: msaroufim
fbshipit-source-id: 787f96bf91d124fd0483761088c5f4ae930d96a9
Co-authored-by: Shubham Bhokare <shubhambhokare@gmail.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61560
1. Add a new symbolic function broadcast_tensors() to support exporting torch.broadcast_tensors() function. This is required by exporting torch.distribution.normal() function.
2. Add a new symbolic function normal() to support exporting torch.distribution.normal() function.
3. Add relative tests for normal and uniform ops as well.
Test Plan: Imported from OSS
Reviewed By: nikithamalgifb
Differential Revision: D29767995
Pulled By: SplitInfinity
fbshipit-source-id: acfe5e7801d00c0df8ca46966bbd6015fed0045e
Co-authored-by: Jay Zhang <jiz@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/59538
Four mealv2 models can export in torch 1.8.1, but fails when torch master introduces relu6 a few months back.
Test Plan: Imported from OSS
Reviewed By: nikithamalgifb, ansley
Differential Revision: D29046607
Pulled By: SplitInfinity
fbshipit-source-id: d9cf7050e4ac0dad892441305ffebc19ba84e2be
Co-authored-by: David <jiafa@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/58697
1. Add a symbolic function for aten::roll() op in symbolic_opset9.py.
2. Add a test with multiple scenarios as well.
Test Plan: Imported from OSS
Reviewed By: driazati, bhosmer
Differential Revision: D28714807
Pulled By: SplitInfinity
fbshipit-source-id: eae85f2dcf02737c9256a180f6905a935ca3f57e
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/58692
This is a fix for exporting fairseq models, see:
```python
model = torch.hub.load(github, 'conv.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')
model = torch.hub.load(github, 'conv.wmt17.en-de', tokenizer='moses', bpe='subword_nmt')
```
With this fix, and comment out model script one line `GradMultiply`, these two models can be exported successfully with perf met.
The original PR https://github.com/pytorch/pytorch/pull/57708 has merging issue, use this one instead.
Test Plan: Imported from OSS
Reviewed By: driazati
Differential Revision: D28714809
Pulled By: SplitInfinity
fbshipit-source-id: 71c2de6cec7ee05af68560996acf47d97af46fb2
Co-authored-by: David <jiafa@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58691
Note the first commit in this PR has its own pull request here since it seemed self-contained:
https://github.com/pytorch/pytorch/pull/57082
* [ONNX] simplify batch_first logic in RNN tests
* [ONNX] support GRU with packed input in scripting mode
This required two changes:
* Add as_tensor to symbolic_opset9.py
* Change torch::jit::pushPackingPastRnn to recognize and properly
replace another use of the batch_sizes output of prim::PackPadded.
Previously the code assumed that the first use was as input to the
RNN operator. However in some cases, it is also used to compute
max_batch_size. For example in this code:
https://github.com/pytorch/pytorch/blob/febff45/torch/nn/modules/rnn.py#L815-L815
With these changes the GRU tests now pass in scripting mode for opset
version >= 11.
Test Plan: Imported from OSS
Reviewed By: driazati
Differential Revision: D28714805
Pulled By: SplitInfinity
fbshipit-source-id: f19647a04533d9ec76399a8793b3f712ea0337d2
Co-authored-by: Gary Miguel <garymiguel@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58417
Same as title.
Test Plan:
Rely on CI signal.
Update unit test to exercise new code path as well.
Reviewed By: ngimel
Differential Revision: D28482927
fbshipit-source-id: 3ec8682810ed5c8547b1e8d3869924480ce63dcd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57830
This is PR is aiming to support tensor.index_add_() method in symbolic function. We leverage scatter_add() to implement this function while ONNX doesn't have a corresponding operator.
Notes:
1. 4 tests have been added for some scenarios.
2. If there are duplicated value in 'index' parameter, the export will still execute successfully but the results are wrong. Add a warning for every call to this symbolic function. And if we detect that the rank of 'index' is greater than the size of the 'dim' dimension, will raise an exception to stop exporting an incorrect ONNX file.
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision: D28393518
Pulled By: SplitInfinity
fbshipit-source-id: f487ca2c63fec47c6ab74f1a7783dae7f3b8d1ef
Co-authored-by: Jay Zhang <jiz@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57599
Currently, if we call tensor.to() method and pass a device as the parameter. It will fail, because in symbolic function of to() we didn't handle such case.
So add a check in the beginning of this symbolic function, if this is a device cast, we return self directly. A test has also been added.
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision: D28393523
Pulled By: SplitInfinity
fbshipit-source-id: c41e3c0293932fc90dedb544daadd9c5d4b48792
Co-authored-by: Jay Zhang <jiz@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57596
Add the corresponding symbolic function and test for fill_() function.
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision: D28393520
Pulled By: SplitInfinity
fbshipit-source-id: 3e177f88d3776d0d4a9d5e7ec7df4e6629738799
Co-authored-by: Jay Zhang <jiz@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49352
In this PR, we replace all definitions of slice to take None parameters for the start, end, and step. This will simplify the compiler logic
Test Plan:
test_jit test cases
Imported from OSS
Reviewed By: jamesr66a, nikithamalgifb
Differential Revision: D25929903
fbshipit-source-id: 5bfc6bad514a8aafbef2dacc706f95f867fe85f1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50903
First part of #50010. Also fixes#51127.
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D27911345
Pulled By: mruberry
fbshipit-source-id: 7138fddc935802918ab9ff19f4bc1b9f4d745d41
Summary:
Adjust how MutationRemover is used to avoid creating aliasDb multiple times for the same graph.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56675
Reviewed By: pbelevich
Differential Revision: D27945692
Pulled By: SplitInfinity
fbshipit-source-id: a6c548438e88ddee18ef03a6f0461ab9eaaaa829
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56173
* Create `InplaceConverter` and `ValueTracker` to keep track of aliases of values throughout the graph. For a given value, a new alias is created every time when there is an inplace operation, SetAttr, or through nested blocks owned by If/Loop nodes.
* Fix bug where controlflow node output types are not set, when the complete node is unable to run ONNX shape inference due to containing non-onnx node.
* Add symbolic for `__not__` ~~and `prim_min`~~(update: moved to a separate PR), and update `index_put` opset9 to support case of assignment without providing indices.
* Bump ORT version in CI test.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D27866138
Pulled By: SplitInfinity
fbshipit-source-id: ab5c9188740c50f783ceba4d54fda43c26e2fde7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56166
Support tensordot in symbolic function of opset 12, and add tests accordingly.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D27866140
Pulled By: SplitInfinity
fbshipit-source-id: 68e218cfbd630900fb92871fc7c0de3e7e8c8c3d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56165
Add implementation for cases when
- interleaving happens along dim which consist of dynamic axes
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D27866137
Pulled By: SplitInfinity
fbshipit-source-id: 7fef1b2c614f2e24a677b7ca0886bb37bd0ab479
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:
This PR adds a `padding_idx` parameter to `nn.EmbeddingBag` and `nn.functional.embedding_bag`. As with `nn.Embedding`'s `padding_idx` argument, if an embedding's index is equal to `padding_idx` it is ignored, so it is not included in the reduction.
This PR does not add support for `padding_idx` for quantized or ONNX `EmbeddingBag` for opset10/11 (opset9 is supported). In these cases, an error is thrown if `padding_idx` is provided.
Fixes https://github.com/pytorch/pytorch/issues/3194
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49237
Reviewed By: walterddr, VitalyFedyunin
Differential Revision: D26948258
Pulled By: jbschlosser
fbshipit-source-id: 3ca672f7e768941f3261ab405fc7597c97ce3dfc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54866
Replace decomposeLinear pre process pass with a symbolic
Test Plan: Imported from OSS
Reviewed By: nikithamalgifb
Differential Revision: D27408981
Pulled By: SplitInfinity
fbshipit-source-id: d2d76cab3383122a60df1f356742a33db56adc71
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53529
Supported for ONNX export after opset 10.
This is not exportable to opsets < 10 due to
1. onnx::IsInf is introduced in opset 10
2. onnx::Equal does not accept float tensor prior to opset 11
Test Plan: Imported from OSS
Reviewed By: pbelevich, malfet
Differential Revision: D26922418
Pulled By: SplitInfinity
fbshipit-source-id: 69bcba50520fa3d69db4bd4c2b9f88c00146fca7
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53305Fixes#52436
For opset 9 of onnx Pow, if X is int32, Y is float, we will cast back to int32 which is consistent with X type.
However, pytorch is still float. The aten graph sometimes does not bind with the type for operators,
we are fine with the float type and don't want to cast back.
Even if X, Y are int32, the resulting float32 and int32 makes no difference.
Test Plan: Imported from OSS
Reviewed By: pbelevich, malfet
Differential Revision: D26922425
Pulled By: SplitInfinity
fbshipit-source-id: f8c09524acee0de615df10a14310ca1dd583831e
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
