Moved path_lib.h/cc from onnxruntime/core/framework to onnxruntime/core/platform and from the onnxruntime_framework to the onnxruntime_common libraries.
* Add notebooks for GPU and CPU inference of PyTorch BERT SQuAD model
* update bert_optimization.py: Do not add duplicated logger handler
* Add machineinfo.py to show machine configuration for notebook.
* Update bert performance test tool:
(1) Set OpenMP environment variable before importing onnxruntime.
(2) Use sub-process for each test
(3) Allow test multiple batch_size
(4) Add latency percentile
(5) Add warmup
1. Fix onnxruntime server docker file build failure. Tested with the notebook in ONNX tutorial, it works well.
2. Delete the docker files for the other EPs, because currently they don't work and I don't have enough time to update them.
Speed up TfIdf.
Build Trie like structure to quickly exclude dead-ends.
Use ParallelFor() for each of the rows processing.
Make it non-template, batch it.
Check for short tail within the inner loop.
Fixes a bug in TopK cuda implementation when input size is between GridDim::maxThreadsPerBlock and GridDim::maxThreadsPerBlock * 2. In this case, the BitonicTopK will generate all-zero outputs.
* Simplify Normalizer as the spec only requires support for 2D input.
Tried using eigen (LpNorm<1>(), and norm()) on each row but that was much slower.
* Remove unused variable
* Use MlasComputeLogistic instead of manually computing values.
* Update test script to allow the tolerance to be specified when checking float output from logreg_iris.onnx.
1. Do not reuse the main thread.
2. Do not plus one when mlas calculate the number of tasks to schedule. (It was me put the plus one there)
This is the second try of #1839
It's known that this change has negative performance impact on some of the models.
* Avoid use of vectors for tracking reader/writer offsets as it adds too much overhead if there are a lot of readers or writers.
Tracy found improvements in resnet34-ssd1200 and BERT Squad with this approach.
* api goverannce draft
* Update CONTRIBUTING.md
updated for ABI proposals
* Update CONTRIBUTING.md
* Update CONTRIBUTING.md
* Incomplete, a draft iteartion of 2 more changes - api docs and high levle design
* pushing to see how the picture size works on screen.
* added 2 charts on api choice and distribution choice
* details on contract checking
* lint cleanup and links
* PR feedback.
* fixed markdown and lists
* more markdown and lists
* fixed broken links
* PR feedback
* commas
* PR comments from nick
* PR feedback
* fixed build section
Co-authored-by: Nick Geisler <36938193+ngeisler11@users.noreply.github.com>
Discussed with Faith, because the data size is very small and changes are gradual, there is no need to delete the old data. We want to keep all the history.
* update GeluFusion to support pattern from PyTorch 1.4;
* Fix a bug that missing the check of an edge between mul2 and root.
* update script to fuse gelu from PyTorch 1.4
* Add test for python optimizer
This change fixes#3129. When running onnxruntime as dll on Windows, CUDA does some internal cleanups when process exits. After this, any call to CUDA would cause crash. Delayload makes thread_local destructor to happen after CUDA cleanup, thus the crash.
Override native package name. Preserve managed package name the same.
Specify pckage name for validation purposes.
Fix up validation package name parameter.
(1) Add performance test tool for bert model.
(2) Add accuracy test tool to compare inference results of original and optimized bert models.
(3) Add test data generator tool to create test data for onnxruntime_perf_test.exe
(4) Improve bert optimization script: Verify model producer for model_type; Add warning if model is not fully optimized.
(5) Add shape optimizer tool to assist developing optimization script.
(6) Update readme.
Previously, we put the "bin" folder of all the CUDA verions in the system PATH. And 10.2 is in the front. It's a mess.
So I've removed all of them from the system PATH env. But I need to add one of them back through build scripts.
(The problem only affect the C# test, not the C/C++ tests that forked from build.py).
* add dml gpu pipelines
* add x86 to the gpu dml dev build pipeline
* Enable DML x86 builds
* Fix uint64_t -> size_t warning
* fix warnings
* enable dml on x86 ci builds
* operatorHelper 773 error uint32_t vs uint64_t
* operatorHelper 773 error uint32_t vs uint64_t
* make x86 pipeline use the gpu pool
* more warnings
* fix x86 directml path
* make dml nuget package
* disable tf_pnasnet_large
* disable zfnet512
* make validation use wildcards
* disable x86 dml gpu tests
* add args.
* update gpu.yml
* change nupkg wildcard
* add debug statements
* package x86 dml nupkg
* dont drop managed nuget again from dml pipeline build
* Add DML EULA
* directml license should be renamed to not clobber the existing license
* casing on dml package....
* {} to ()
* fix license name
* disable dml from x86 ci
* typo and cr feedback
* remove featurizers
* ship the dml pdb as well
