* bias softmax kernel
* bias softmax kernel
* remove debug comments
* remove debug comment
* windows build doesnt handle unary minus on unsigned type
* int64 => int treated as error
* only support cuda
* add bias softmax fusion tests
* PR comments
* more PR comments
* use MLTypeCallDispatcher
* break function into pieces
* add loop unroll and add to list for inference as well
* use std::min and move operator==
* revert std::min (doesnt work ci pipeline) and fix int to size_t error
* pr comments
* fixes for windows ci
* fix for windows ci
* pr comments on consistency
* p_model_
* fix formatting and add anonymous namespace
Co-authored-by: suffian khan <sukha@OrtTrainingDev1.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* remove shape inference and fix save large model problem
* remove unnecessary import
* refine code and add external format for quantize_qat
* remove initializers in tensors_to_calibrate
* small refine
Co-authored-by: t-yguo <t-yguo@microsoft.com>
This updates the NCHWc transformer to not interfere with quantized convolution models, based on observations from internal models. The tensor type for MaxPool must be float. The input to GlobalAveragePool/GlobalMaxPool must be in NCHWc format.
* Refactor TensorAt
locations* must be const and int64_t since our dims are int64_t
Remove unnecessary copy of locations.
Remove unnecesary casting and C-casting. Simplify implementation.
Add a check for string type.
Make CXX api return T& to fully expose C API in C++, const std::vector& by value as it
covers more ground and eliminate redundant copy.
Eliminate inner loop, compute strides first.
* add GetStartTime() for profiler
* add function in inference_session
* remove qualified name
* add the api in cxx_api.h
* rename starttime to StartTimeNs, expost profiling object
* rename GetProfilingStartTime
* move Ortapis to the right place
* move to the end
* add const for session
* const the right place
* use const auto instead of const auto* for session
* remove const for auto getstarttime
* remove const for auto getstarttime
add unit tests
* nit: update test name and add comments
* Initialize tensorrt perf script
* Add bert-squad dependencies
* Modified code to make ort inference with CUDA/Tensorrt
* Add get CUDA/TRT version
* uncomment bert-squad
* Add BERT-SQUAD inputs.json
* Add FastRCNN
* Make preprocess/validation in to common functions
* Add MaskRCNN and SSD and consolidate the code
* Add dependencies for MaskRCNN
* following modifications are made:
- create common fetch function to get inputs/outputs of model from ONNX model zoo.
- create common validation function to compare inference outputs with reference outputs from ONNX model zoo.
- move run/repeat time to argument list. (still working on other arguments, like fp16 or fp32, latency percentile).
- generate table in csv file to show the latency comparison (TRT vs CUDA) side by side.
* Add approache to analyze profling file and also update model related
settings
* Add models
* Add most of models from ONNX model zoo
* Add model input name and print all the model names at the end of run
* Add system info
* Add TRT fp16 support
* Refine the code
* Handle TRT fall back and modify the way to get input data
* Refine code
* Modify code
* Add more precise approach to measure inference
* Add io-binding
* Add YoLoV4
* Refine the code
* Refine the code
* Add models
* Add yolov4 notebook for jetson device
* Update notebook
* Update notebook
* Add CVS models
* Add missing model
* Add support of float16
* Add new way to get trt version
* Add "validate" and "benchmark" mode
* Add randomly generated input
* Refine perf script
* Refine the code.
* Add README
* Refine the code
* Update README.md
* Refine code
* Update README.md
* Remove all the model related python and instead using model_list.json as
models configuration.
Refine the benchmark.py
* Refine the code
Co-authored-by: Chi Lo <lochi@microsoft.com>
* Added config flags for VPU Fast Recompile
* clean-up ifdefs
* Add VPU Fast compile config option
Adds an option that enables Fast compilation of models to VPU
hardware specific format.
* Add config option to choose specific device id for inference
Inference of all subgraphs will be scheduled only on this device
even if other devices of the same type are available.
* Add Python API to list available device IDs
* code cleanup
* Add second C/C++ API with settings string parameter
Adds an additional C/C++ API that allows passing multiple
key-value pairs for settings as a single string. Multiple
settings are delimited by '\n' while the key and value
within a setting are delimited by '|'.
* Append 'Ex' to the extended C/C++ API
* Use set_providers Py API to set config options.
Uses Session.set_providers Python API to set EP runtime config
options as key/val pairs
Deprecated older module function definitions for config settings.
Updates documentation.
* avoid globals for py config options where possible
Co-authored-by: intel <you@example.com>
