Change Logs#

0.2.2#

#87: update the quantization tools to use a simplified dynamic linear quantization into float 8
#85: add load_model, save_model to help saving with/without external data
#82: fixes benchmark on multiple versions of onnxruntime

#79: update to onnxruntime v1.16.0
#77: helpers to benchmark a model
#74: add a function to enumerate all intermediate results with onnxruntime
#71, #72, #73: add function to analyse a profile produce by onnxruntime
#68, #69, #70: add CPU implementation for CustomGemmFloat8
#67: add a function to extract a subgraph of a model
#59, #60, #61, #62, #63, #65, #66, #68, #69, #70: add local functions to quantize into float 8, float 16
#57: add C implementation for DynamicQuantizeLinear (for experimentation)
#56: add C implementation to cast a float into float 8
#55, #58: add basic functionality to transform a graph, starts with basic quantization
#51: fix optimized TreeEnsembleRegressor and adds TreeEnsembleClassifier as custom ops
#50: add command line store to store intermediate outputs
#49: add option to save intermediate results in CReferenceEvaluator
#45: add option cuda-link to setup.py to specify how to link with CUDA library
#41: implements a custom kernel for RandomForestRegressor easier to optimize
#34: update to onnxruntime v1.15.1
#31: implement a custom CUDA kernel (gemm)
#32: update to onnxruntime v1.15.0
#27: add a custom kernel with parameters to onnxruntime
#26: add a custom kernel to onnxruntime
#24: use Eigen to implement Conv operator
#23: make pip wheel . work
#22: rename cmake into _cmake to avoid warnings related to cmake package
#19: minimal settings to use onnxruntime
#14: minimal setting to use CUDA
#8: support for C++ unit test