* try to run inside 4.3.1 container
* no \ in container run command
* remove networking options
* try with adding video render groups
* add job to build docker image
* try without 1st stage
* change alpha, beta to float
* try adding service connection
* retain huggingface directory
* static video and render gid
* use runtime expression for variables
* install torch-ort
* pin sacrebleu==1.5.1
* update curves for rocm 4.3.1
* try again
* disable determinism and only check tail of loss curve and with a much larger threshold of 0.05
* disable RoBERTa due to high run variablity on ROCm 4.3.1
* put reduction unit tests back in
* initial change for eager/ortmodule integration
* pdate to latest pytorch api
* add test model;fix torch version issue
* fix comments in pr
* fix python test break
* fix api change
* fix comments in PR
* pass device into the fw function
* updates for picking pnnx commit
* add tests filter to c# tests
* plus test fixes
* fix versioning for contrib ops
* fix tests
* test filter for optional ops
* more versioning related updates
* fix test
* fix layernorm spec
* more updates
* update docs
* add more test filters
* more filters
* update binary size threshold
* update docs
* draft - enable model local function
* enable model local functions in ORT
* update to latest rel onnx commit
* plus tests
* plus more updates
* plus updates
* test updates
* Fix for nested functions + shape inference
* plus bug fix and updates per review
* plus fixes per review
* plus test updates
* plus updates per review
* plus fixes
* fix a test
* Include pytorch_export_contrib_ops in inference builds
Rename / move it from tools/python/register_custom_ops_pytorch_exporter
to onnxruntime/python/tools/pytorch_export_contrib_ops.
Rationale for inclusion in inference builds:
This code is potentially useful for anyone using ORT, not just training.
Rationale for new name:
"Contrib op" is the nomenclature used within ORT to refer to the set of
ops that are not in the standard op set but are included by default with
ORT. This is more specific than "custom op", which is what the PyTorch
exporter uses to refer to any non-standard op.
Step 1 of addressing #8818. After this is merged I will update the docs.
* Enable test_pytorch_export_contrib_ops.py in CI
Fixes AB#1342330
* Use PROTOBUF_LIB instead of protobuf::libprotbuf
* Moved setdlopenflags to _pybind_state.py
* Copy the generated _pybind_state.py to required location for Windows.
* special case concat and split when sizes are equal
* add tests for 16 and 32 inputs with same dim
* add tests for 16/64 inputs on concat or 16/64 outputs on split
* try eliminate windows warning
* outter => outer
* test running hf bert-large
* try again
* try again
* include other models
* correct names
* disable deberta-v2-xxlarge
* avoid torch.distributed
* add compare json loss and perf for bert-large to test
* fix sed expression
* remove pytest
* add more models
* move unit tests u
* display samples/sec
* support register external ep lib inforation; make eager mode share the same ep pools with training workloads
* fix inference code
* fix build break
* fix the message
The following ops have been added to the DNNL execution provider
Abs, Elu, Exp, Log, *Relu, Round, Sigmoid, Softplus, Sqrt, and Tanh
*Relu op was moved from its individual file to the elementwise operators
The error tolerance for the LogGrad unit test had to be decreased slightly
when using OneDNN. Still investigating why a differet tolerance value is
needed.
DnnlSubgraph::AddKernels() member function was moved to the top of the file
since this is eddited every time a new operator is added to the the execution
provider this places the code at the top which mean less scrooling when
adding new kernels.
Signed-off-by: George Nash <george.nash@intel.com>
