* Sort supported types order so we get a consistently generated order of types.
* Fix promote type to include all the input types and not just the first one.
Dev containers[1] provide a self-contained development environment that
can be tailored for a project. GitHub Codespaces[2] provide a cloud
hosted environment to run these containers in. This makes it easy to
provision a consistent development environment with developer tooling
already installed and configured that provide the following benefits:
1. Developer onboarding is simplified.
1. Easy to get environment setup and running
2. Reference environment is available, if developer is having issues
with local environment
2. Developer tooling is provided and automatically configured.
1. Python / C++ build tooling
2. Python / C++ code formatters / linters
3. Easy to provision cloud hosted environment via GitHub Codespace.
4. Easy to create ephemeral development environments to test new changes
1. Can be used to provision environments to test changes
and Pull Requests
This can ease several pain points that developers on-boarding to the
project can encounter. One of the problems I have seen with developers
new to the project (I am one of these) is having the baseline
development environment (Python / C++) and recommended tools (e.g. VS
Code Python / C++ extensions, linters, and autoformatters) installed and
configured to efficiently get started in the repository. For all
developers, this makes it easy to leverage ephemeral cloud hosted
development environments via GitHub Codespaces.
**Notes:**
- Compiling the project can run into trouble if the codespace has < 32
GB of RAM
1) https://docs.github.com/en/codespaces/setting-up-your-project-for-codespaces/introduction-to-dev-containers
2) https://docs.github.com/en/codespaces/overview
* consume onnx test data from github
* ensure tests
* update script and allow opset specification
* fix python format
* fix python format
* consume new filter format
* fix linting error
* use 3D grid to avoid the upper limit of grid dimension
* enrich tests
* Revert "use 3D grid to avoid the upper limit of grid dimension"
This reverts commit 2d5badf2fe8cd985f3f29ee2cb18fff13d07c2ab.
* change to a fix: switch the 1st and 2nd dim
This change updates the implementation or te argmax_out operator to 1)
set the output tensor correctly and 2) remove the unnecessary use of a
temporary tensor to store intermediate result of onnx ArgMax operation.
Previously, the argmax_out operator did not correctly update the out
tensor - it replaced the OrtValue instead of the memory backing the
OrtValue . To properly update the output tensor, we need to calculate
the expected shape of the out tensor.
We add the helper function calculate_reduction_shape to calculate the
shape of the reduced tensor from the input tensor, dimension to reduce,
and option to keep the reduced dimension or not. This is based on the
utility functions in aten/src/ATen/native/ReduceOpsUtils.h in the
PyTorch repository, but is tailored to be a bit more specific to our
current needs.
Notes:
We considered just directly leveraging PyTorch's utility functions (e.g.
get_reduction_shape) to calculate the shape of the reduced tensor from
aten/src/ATen/native/ReduceOpsUtils.h in the PyTorch repository, but
including this header file resulted in warnings around unused functions
that we need to handle. As we only need a limited functionality at the
moment, we instead implemented our own utility function to calculate the
reduction shape for our specific current needs. If we need a utility
function to more generally calculate the reduction shape, we could
consider switching to leveraging the utility methods in PyTorch.
* refactor test for model with undefined shapes
* add test for TVMso EP
* update build script for TVM EP tests
* fix pylint
* disable test for Windows
* fix black
* fix python format
* fix pylint
* fix python format
* replace Path.resolve with os.path.join
* fix python path issue
Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
* add scripts
* update docker scripts
* update build script
* create run script
* add test script
* add log 3 flags
* use the right build function
* build navi
* add clean script
* add pytorch like soln
* only build gfx 1030
* use HOST side var
* ignore logs
* update scripts
* GPU_WARP_SIZE_HOST
* update scripts
* remove scripts/amd
* match main
* add GPU_WARP_SIZE_HOST on cuda side
* match main
* correct gfx1030
* remove print
* move gfx add to rocm5.0
* remove inline
* make constexpr on cuda side
Fix bug where onnxruntime_USE_NCCL flag would default to ON, causing ORT to not build properly. New functionality: flag is ON when training is enabled and NCCL is not disabled. Flag is OFF otherwise
* Add file mapping for windows platform.
* Add unit test for file mapping for windows. Also add an error message for mis-aligned offset
* Add unit test for file mapping for windows. Also add an error message for mis-aligned offset
* Update data type to avoid warnings
* Compitable data type to avoid warnings. Update CreatFileMapping2 condition for winml compiling.
* Add type conversion to avoid warnings for X86 release build.
Co-authored-by: Ting Cao <ticao@microsoft.com>
