![]() * Roll back expand, fix, add tests for reduce grad. * Fixes for Expand, Where, ConcatGrad ReduceSumGrad. * Fixes for Where, ConcatGrad and ReduceSumGrad ( #3415) * Get onnxruntime/core/providers/cuda/math/matmul_ from ort_training. * Get onnxruntime/core/providers/cuda/cu from ort_training. * Get onnxruntime/contrib_ops/cuda/bert/fast_ from ort_training. * Get onnxruntime/core/providers/cuda/tensor/slice.h from ort_training. ![]() * Rename ONNX OPTIONAL to OPTIONAL_VALUE. * create pipeline for ci frontend tests ( #3422)Ĭreate pipeline for nightly python front-end e2e tests * Fix onnxruntime_unittests.cmake after merge. * Address comments around bfc arena ( #3460) Raise rtol to avoid expected CI test failure in onnxruntime_test_ort_trainer.py * safeint for region bytes in bfc arena and code clean up ( #3447) * frontend test to use random seed ( #3209) * View Op - new unit tests and add support for tensor memcpy by offset/size ( #3439) * Disable gradient clipping for E2E test. * Reapply commit 131c65d Fix memory regression issue. Graph::SetInputs() was causing some issues - it seems to not be working correctly.Īlso added some mixed precision Python frontend tests. The generated loss scale input name is passed back to the frontend.Īlso fixed how inputs were added during the training graph configuration. In particular, now addition of loss scaling is done unconditionally if mixed precision is enabled. Made some fixes to enable loss scale to be wired up to ORT from the Python frontend. * Enable loss scale input from Python frontend ( #3327) * Disable GradientCheckerTest tests for GPU/Debug build ( #3407) * Revert Session and InferenceSession implementation * Revert _SliceKernel cuda implementation * Update MixedPrecisionScale Doman and Opset * Update LayerNormalizationGrad Domain and OpSet ![]() * Update Op's Domain and Version ( #3356) * add pipeline graph split script ( #3275) * Fix code-base after breaking API changes * Address master merge PR comments ( #3348) * Don't cast to fp16 in LayernormGrad ( #3328) * add weight decay mode to support both pytorch and huggingface's adamw * Expose frozen_weights in PyTorch Frontend ( #3317) * Add bias correction in Adam & Lamb for C++ frontend & python frontend ( #3301) * handle cases where onnx model is provided at initialization * Update ort_trainer.py with lazy onnx export ( #3244) * Forgot to swap order in the implementation after spec changed Each stage of pipeline contains FW and BW of a subset of the model and are scheduled in one worker thread for each microbatch. Implement pipeline event generator with OneFWOneBW schedule in timeline. * Implement pipeline event generator ( #3206) ( #3243)Īdd control flag to c++ and python frontend * move training_session.py into a subfolder per reviewer's comment * missed a forward declaration in ort_pybind_ * update according to reviewer's comments ![]() * remove training python files from inferencing build * Remove orttraining/tools/scripts/profile directory. * Rewrite WindowsEnv::DeleteFolder(), some other clean up. * Use SafeInt for allocation calculation, fix typo. * Handle ImportError for training imports. * Fixed issues with Python and inference-only build. * Revert change from RelWithDebInfo to Release in. Disable ONNX tests that don't have op implementation for the latest opset. Regenerate csharp/test/Microsoft.ML.OnnxRuntime.Tests/OnnxMl.cs Reverse integrate changes from *.in.proto files in github ONNX repo. We want to implement SoftmaxCrossentropy and NegativeLossLikelihoodLoss forward training ops for opset-12 but that requires ONNX submodule to point to the latest commit to have the latest and greatest ONNX spec! * Merged PR 5688: Upgrade ONNX submodule to the latest from github ONNX master. TBD - broken UTs related to MatmulIntegerOpTest (works on v100/windows, though) fp16/128 need to reduce batch size from 66 to 64 to avoid OOM issue cc code, leverage HasCudaEnvironment() insteadĤ. enable/add UTs for GatherNDGrad and reduction_ops using half You may find sub models and profiling json under onnxruntime/test if you run "onnxruntime_test_all -gtest_filter=GradientGraphBuilderTest.TrainingSession_WithPipeline"ģ. This is a draft of graph cut and wait/record to demonstrate cut and Wait/Record design. * Initial implementation of graph cut and pipeline * Add back orttraining-linux-gpu-inference-only-ci-pipeline.yml. * Change Tensor::ByteOffset() to use ptrdiff_t. ![]()
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