* squashed commit for standalone tvm execution provider
* critical fix for correct python build with stvm ep
* get tuning log file from ep options. It has priority over AUTOTVM_TUNING_LOG
* updates and fixes
* update parsing of stvm provider options
* add support of external data for onnx model
* add conditional dump of subgraphs
* remove unused code
* get input tensor shapes through provider options. get output shapes for fixed input ones by TVM API
* support AUTO_TVM tuning log file inside ORT. Selector for Ansor and Auto_TVM is provider option (tuning_type)
* add fp16
* add functionality of conversion of model layout to NHWC if need. Necessary parameter was added to STVM provider options
* fix license text in header. fix log format
* small fixes
* fix issues from flake8
* remove model proto construction from GetCapability
* reserve memory for vector of DLTensors
* add simple tutorial for STVM EP
* STVM docs
* jroesch/tvm -> apache/tvm
* remove dead code, unneccessary logs and comments
* fix in readme
* improve tutorial notebook
* tvm update
* update STVM_EP.md
* fix default value
* update STVM_EP.md
* some TODOs for the future development
* shorten long lines
* add hyperlink to STVM_EP.md
* fix Linux CI error
* fix error in csharp test
Co-authored-by: Jared Roesch <jroesch@octoml.ai>
Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
Co-authored-by: KJlaccHoeUM9l <wotpricol@mail.ru>
Update Python API to allow more flexibility for setting providers and provider options.
The providers argument (InferenceSession/TrainingSession constructors, InferenceSession.set_providers()) now also accepts a tuple of (name, options dict).
Fix get_available_providers() API (and the corresponding function in the C API) to return the providers in default priority order. Now it can be used as a starting point for the providers argument and maintain the default priority order.
Convert some usages of the deprecated global configuration functions to use EP-specific options instead.
Update some EP-specific option parsing to fail on unknown options.
Other clean up.
* Update version to 1.6.0
* Add v 1.5.3 info
* Updating WindowsAI and ONNX version
Co-authored-by: Du Li <duli@OrtTrainingDev0.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
1. Reflect int8 GEMV improvements for multi-threading from #2696
2. Add notes on multi-threading control using OpenMP
3. Add samples of running multi-isa AOT, and show int8 GEMM differences between AVX and AVX2
4. Add rnn_benchmark example to resolve#1993
* [NupharEP] Add parallel schedule to JIT function name
Update Nuphar docker to use Python 3.6 and ubuntu 18.04
* Update notebook
* Avoid JIT cache file name conflict
* Fixed a bug of missing tvm in python wheel
* Put Nuphar Python scripts into wheel
* Add note book tutorial
* Some improvements in symbolic shape inference for quantized models
