* Add multithread unit test and put lock on library call
* update code
* remove debug code
* add comment
* add one session multi-threads inference
* Put lock for build engine all the time
* Update naming and comment
* remove unnecessary lock
* Revert "remove unnecessary lock"
This reverts commit 9c2317b1d2273dec0ebdeb52160bc757839e5edc.
* rename info to options for TVM EP
* transfer options processing from TVMExecutionProvider to TVMEPOptions
* transfer TVMRunner to separated files
* implement TVMCompiler class
* replace CompileFunc by TVMCompiler object. update TVMRunner. now it does not depend on TvmExecutionProvider
* correct logging of TVM EP options
* RunnerImpl, GERunnerImpl and VMRunnerImpl were implemented
* add prepareComputeInfo method
* remove update_output_shapes flag
* embed all TVM EP dependences to tvm namespace. transfer model compilation from TVMRunner. connect TVMRunnerImpl to TVMRunner
* refactor compileModel method
* small cleaning
* separate TVM EP options data store and processing
* replace TvmTensorShape by InlinedVector with max_size 5
* correct indentation
* update TVM hash
Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
* add c-api test for package
* fix bug for running c-api test for package
* refine run application script
* remove redundant code
* include CUDA test
* Remove testing CUDA EP temporarily
* fix bug
* Code refactor
* try to fix YAML bug
* try to fix YAML bug
* try to fix YAML bug
* fix bug for multiple directories in Pipelines
* fix bug
* add comments and fix bug
* Update c-api-noopenmp-packaging-pipelines.yml
* Remove failOnStandardError flag in Pipelines
* Tweaks to the model utils
* Add handling for a dim_value of -1 when replacing the entire input shape. This occurs in models exported from PaddlePaddle
* make pytorch helpers accessible in package
* make QDQ helpers accessible in package
* add more qdq softmax test case
* add rest of additional nnapi qdq test case
* comment out
* update
* update concat test case
* extract zp/scale point retrieval out
* fix
* fix helper function for scale/zp and address some pr comments
* use enum class expectedepnodeassignments
* fix default value for ExpectedEPNodeAssignment
* update
* refine some comments
* refine some minor comments
* refine pr comments
* update
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Add runtime optimization support to ONNX -> ORT format conversion script.
Replace `--optimization_level`, `--use_nnapi`, and `--use_coreml` with a new `--optimization_style` option.
* creating a test for printing ort tensor
* modifying comment for error case
* Using Output Grabber to assert the print output
* modifying the print ort test
* removing comments
* removing sys import
* Use numpy.percentile to get the lookup value.
* Use 1.0 as float value rather than integer.
* Add missing cdf parameter for `np.percentile`.
* Use 100. instead of 1.0
* Remove print.
* Update from @yufenglee
* Add support for opset 16 to transpose optimizer.
Only change required is for GridSample to be added to the layout sensitive ops. The existing handling for layout transpose works with that as the first input and first output are layout sensitive.
Update the optimize to be able to return an error message if it fails.
This PR is just for making onnxruntime passing Binskim rules.
Below is how I made it:
git clone absl repo, checkout the version we are using
Then apply our patch file
Make modifications
Regenerate the patch file by "git diff > C:\src\onnxruntime\cmake\patch\xxx.patch"
Then submit the change to our repo
You will need to repeat the steps when you need to advance the absl commit or add more changes to it.
When returning by value in a cross DLL call, the hash table
even though containing all the entries that are originally there
can not find at least some of them. Reverting to std::unordered_set
pending further investigation.
* Add micro-benchmarks for Attention and SkipLayerNormalization ops.
* Add choices for argument provider and precision.
* Automatically select CUDA or ROCM execution provider.
Current training optimizer kernels include CPU headers
that affects changes that we can make in the CPU code with C++14 compiler and
other refactoring efforts. Rearrange the kernel according to the established patterns
and do not include headers that are not needed.
