GitLab

* LpNormalization ONNX parser

Add exclusive and reverse modes to gpu implementation of prefix_scan_sum, which completes support for ONNX op CumSum

The get_context may change in the future(when we support multi-targets) so make this experimental for now.

API includes following
create_module,
get_main_module
add_instruction without module args
add_instruction with module args
add_parameter
add_return

This adds iterators to tensor_view, which can allow kernels to work with non-standard shapes like for roialign.

To improve the performance of indexing when using the iterators, the shape class was updated to use integral_constants since the compiler doesn't always fold the const values. An integral_constant will at least enforce that in the AST.

Finally, since index calculations with single integers are improved, I also updated pointwise to use single index rather than multi index. There is about 4% improvement in some cases.

Have git ignore build directory and compiled python
Add ignores from other ROCm projects that look applicable
Ignore downloaded models in test/
Remove including visual studio settings

change max number of groups in a kernel to 1B for greater performance

* Show the operator fields in the driver

The module::debug_print(ins) is very slow, which makes the trave_eval==1/2 very slow. The reason is printing an ins involves search the whole module to get the instruction, the print it.  This change is to fix that by calling module::print() to get names of all instructions of a program, then print the instruction by getting its name from a hash map.

Add Celu ONNX operator

Add python API to construct shape class

Add a callable C++ API to migraphx

* Implement size ONNX operator and tests

add_common_op for parse_clip
Should fix #1119

Adds EyeLike ONNX parser and unit tests.

Changed the pooling values for two structures from strings to specialized enum classes. Many test and operator parsing changes to support this. Introduces one new source file, op_enums.cpp.

Boost the max number of workgroups for pointwise ops by matching what we are doing in launch.hpp

better performance doing it this way

Add onnx parser and ref and gpu implementations of ONNX op ScatterND