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Change the "scatter" struct and op to a base/child set of three: scatter_none, scatter_add, scatter_mul to mirror Onnx' ScatterElements op. and its three reduction options. (Onnx Scatter op is deprecated and is equivalent to scatter_none.)

Provides both a reference op. and update to Onnx parsing. Tests updated and new test case added.

When create a tensor_view with vector date type, the last dimension of the shape should be divided by the vec_size.

This adds the infrastructure so we can compile everything in parallel, whereas before only pointwise kernels were compiled in parallel. This will also directly integrate with lowering and the gpu-driver. The kernels for pointwise and roialign are using this infrastructure. Scatternd is not since it does require standard shape.

This also makes it easier to add new runtime compiled kernels in the future.

* Use ccache for runtime compilation

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

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.

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

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

Implements the IsNaN operator, ref, gpu, and onnx parser.

Update the base version of clang-format from 5.0 to 10.0

wrapped in a any_ptr class so the type can be checked at runtime for a mismatch.

Make doc/CMakeLists.txt standalone
Switch to use rocm-cmake modules for document generation
Add CONFIGURE_DEPENDS to file(GLOB) so it will update without an explicit cmake run
Add STRINGS property for build type to make it easier to switch build types with ccmake
Various fixes and improvements

There is now a MIGRAPHX_DISABLE_POINTWISE_FUSION to disable it

This causes incorrect memory coloring, which was causing the accuracy failures in the vision model when enabling the pointwise fusions. Resnet50, inceptionv3 and inceptionv4 do verify now in the driver.

Enforce types to avoid compilation error in pointwise fusions
This fixes compile failure: gpt-2, fp16 on Navi

* Enable auto vectorization
* Handle vector types with convert function
* Dont vectorize when it will cause problems with preload

allow nonstd shape for the arg ops, non-standard shapes include broadcast, slice and transpose

* Improve handling of generator expressions when getting the flags for hip

* Add matcher for conv_bias pointwise
* Add fusion op

Changed the number of threads in a block from 256 to 128
Increased the max number of blocks in the kernel from 256 to 1M.
For the case that the axis is the last dimension, we removed the computation of index since it is not required.

With these change, we can get about 2x speedup compared to the develop branch for the softmax op used in the BertSquad model.

simple variable rename

Fix pointwise compile error with half sqrt 

Fix whitespace bug in fusable_conv matcher and add unit test

* Check jit kernels files with clang-tidy

Do compilation in parallel

This enables the pointwise fusions using the MIGRAPHX_ENABLE_POINTWISE_FUSION env variable. Its disabled by default since MIOpen fusions need to be refactored.

This also adds a compile_ops pass to compile the pointwise modules. All tests except test_gpu_fast_math passes with MIGRAPHX_ENABLE_POINTWISE_FUSION=1 set.

* Add workaround for devices that do not support miopen conv fusions

Moving our Docker file from ROCm 4.3 to 4.5 
Add Navi base GPUs in to the CI infrastructure 

This PR is the ref implementation of the nonmaxsuppression operator. It always returns the max possible output shape, which is the problem tracked in issue #948.

GPU implementation of the roialign operator, using the jit approach to reduce the lib size.

Implementation of the roialign operator. For now, we have only the ref implementation. When we run a model on the GPU, we fall back the execution to use the ref implementation.

pthread linking errors on SLES. 

This PR is for the nonzero operator with static output shape.  
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>
Co-authored-by: mvermeulen <5479696+mvermeulen@users.noreply.github.com>

Previously dot operator was defined as C = alpha * A . B + beta * C where * is scalar multiplication and . is dot product or matrix multiplication depending on dimension of the inputs.

Aim is to have the definition of dot operator as C = A . B without having alpha or beta.

In order to achieve the same effect as alpha and beta (1) it multiplies the one of the inputs to the dot operator with alpha value. (2) if beta is present then, multiplies the C with beta and then adds into the output from step 1.

Add multinomial op to onnx parser with ref and GPU implementations.

The onnx parser inserts a literal of shape {batch_size, sample_size} with random values in the range [0, 1) and inserts existing ops to compute the cumulative density function. The multinomial operator multiplies the random values by the sum of the CDF and returns the index of the first element of the CDF that is greater than the result, representing samples randomly drawn from [0, class_size) that follow the log-probability distribution.

Resolves #821
Co-authored-by: Shucai Xiao <shucai@gmail.com>