ARC uses dind-rootless which causes bind mounts to always be mounted as the "root" user inside the container rather than the "jenkins" user as expected. We run chown to ensure that the workspace gets mapped to the jenkins user as well as a trap to ensure this change gets reverted when the script ends for any reason. This is the same workaround as in #122922 but adapted for onnx tests.
Issue: pytorch/ci-infra#112
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123641
Approved by: https://github.com/jeanschmidt, https://github.com/seemethere
Introduce `Modularize` pass that analyzes the flat `fx.GraphModule` and creates nested
layers of sub `fx.GraphModule`s along with the `call_module` fx nodes that invokes them.
The analysis is done on the meta data "nn_module_stack", which captures the `nn.Module`
each flat `fx.Node` belongs to.
`FxOnnxInterpreter` is updated to support `call_module`. The related sub module linked
by `node.target` is exported as an ONNX model local function. The `call_module` node itself
is exported as an ONNX node, associated with the ONNX model local function by op_type.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105618
Approved by: https://github.com/justinchuby
Fixes https://github.com/pytorch/pytorch/pull/95676#issuecomment-1460588229
PS: It doesn't seem the exported ONNX_proto having type now. I wonder if there was a ONNX pass doing this for us (converting torch dtype to onnx dtype during exporting.)
Type promotion issue would be raised with an error if we want to set type
```python
onnxscript_value.dtype = expected_value.dtype
```
onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Shape inference error(s): (op_type:aten_add, node name: aten_add_1): [ShapeInferenceError] (op_type:Add, node name: n3): B has inconsistent type tensor(int64)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96349
Approved by: https://github.com/justinchuby, https://github.com/wschin
* CI Test environment to install onnx and onnx-script.
* Add symbolic function for `bitwise_or`, `convert_element_type` and `masked_fill_`.
* Update symbolic function for `slice` and `arange`.
* Update .pyi signature for `_jit_pass_onnx_graph_shape_type_inference`.
Co-authored-by: Wei-Sheng Chin <wschin@outlook.com>
Co-authored-by: Ti-Tai Wang <titaiwang@microsoft.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94564
Approved by: https://github.com/abock
