* Changed internal loss scale to 1-D
* added test
Co-authored-by: root <root@525204a066204ea794f942530b05ae7f000000.axlncovkyjne5caro2tmz3zryb.xx.internal.cloudapp.net>
* Fixes for Expand, Where, ConcatGrad ReduceSumGrad.
* Roll back expand, fix, add tests for reduce grad.
* Roll back CPU Expand change.
* Fix after merge.
Co-authored-by: Vincent Wang <weicwang@microsoft.com>
Made some fixes to enable loss scale to be wired up to ORT from the Python frontend. In particular, now addition of loss scaling is done unconditionally if mixed precision is enabled. The generated loss scale input name is passed back to the frontend.
Also fixed how inputs were added during the training graph configuration. Graph::SetInputs() was causing some issues - it seems to not be working correctly.
Also added some mixed precision Python frontend tests.
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
1. Support the new fields for Constant in opset 12
2. Support SparseTensor in the Constant node by converting to dense tensor when lifting the Constant to an initializer. Will make a model with a sparse tensor in a Constant work but isn't an overly efficient approach.
* added support for ios crosscompilation under linux
* reverted cmake generator change
* if --ios is added protoc can be compiled for host system
* accidently reverted change to compile protoc for host system for ios if protoc exe is not set
* wdata is now used
* accidentally pasted CMAKE_OSX_ARCHITECTURES into CmakeLists.txt, also made bad merge on build.py previously
* removed print
* fixed typeo, deleted commented statements for earlier debugging
* reverted accidental delete
* added asmmacro.h for aarch64 asm
now MlasSgemmKernel**** gets underscore added if needed
no need anymote to differentiate between iOS arm64 and normal amr64 build
onnxruntime.cmake: added check if iOSCross is set to properly set RPATH
* removed 2 spaces
* fix: logcial error fixed, now protoc gets compiled if not supplied with --path_to_protoc_exe
* removed unecessarily added spaces
* removed some more spaces
Fix 3 bugs:
node names duplicate in calibration augment_graph if the name of node to quantize is empty.
If output nodes are quantized, output value are quantized and not dequantized back
Gather with data type int64 should not be quantized
* Avoid "infinite" loop in optimizer
When symbolic dimensions are present and can be overridden,
FreeDimensionOverrideTransformer always sets modified flag to true. As a
consequence, the optimizer loops until the iteration limit is reached.
1. Copy tensorflow's thread pool class to ORT, so that we can get a better implementation of thread pool based parallelfor
2. Copy Eigen's thread pool class to ORT
3. Support thread affinity
4. Remove RNN kernel’s private thread pool
5. Modify pool kernels to use the thread pool when openmp is disabled.
* skip optional inputs for scan subgraphs
We may have cases where the subgraph has optionial inputs that appear
in both subgraph's input and initializer, but not in the node's input.
In such cases, the input model might be invalid, but let's not choke
on it. Instead, let's issue a warning, skip the optional inputs,
and keep going forward.
* address CR feedback
* Fixed two issues in symbolic_shape_infer script
This change addressed #3293
There were two issues in the script:
* We need to handle a special case for infer_Reshape, where input_shape
is empty and target shape_value is [-1]. In such case, we need to
get sympy data for the output dim (or create one if it doesn't exist).
* We need to update computed dims for newly-created shape for Range op
* also call _update_computed_dims for _infer_Expand
addressed CR feedback
* added ai.onnx into opset list
* instead of manipulating _infer_Reshape, call _update_computed_dims
from _infer_Expand to update newly-computed dims
