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

* Add mean op onnx parser and unit tests
* Refactor parse_mean to use add_broadcastable_binary_op

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

Add HardSwish to HardSigmoid parser

HardSwish formula is y = x * HardSigmoid<alpha=1/6, beta=0.5>(x)
HardSigmoid parser sets alpha to 1/6 and adds the mul instruction if op name is HardSwish

Resolves #1062

Add onnx parser for operator GreaterOrEqual

Add onnx parser and unit tests for Softsign

* Add onnx parser and unit test

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

Make clip a pointwise op

Add HardSigmoid onnx parser and unit tests
Produces mathematical equivalent to ONNX operator through combination of existing pointwise ops.
Resolves #1028

* Add matcher for conv_bias pointwise
* Add fusion op

Fix bug caused by casting time seed to float

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.

Fuse last instruction in fuse_pointwise
This is also fixes a bug with using an invalid iterator.

simple variable rename

Fix pointwise compile error with half sqrt 

Fix whitespace bug in fusable_conv matcher and add unit test

Resolves a problem in parsing the ssd-10 model.

The problem is, after inserting contiguous in the auto_contiguous pass, standard output shape of some operators becomes non-standard. Then, if the next operator requires standard input shape, an exception is throw.

For example, if we pass the following model:
Input (standard shape) -> transpose (transposed) -> softmax (transposed) -> transpose (standard) -> gather.
It works fine, and no contiguous is required.

In the auto_contiguous pass, a contiguous is inserted after the first transpose. Then we need to replace the first transpose with the contiguous and recompute all shapes. When it comes to the gather operator, its input is a transposed shape, and an exception is thrown.

The solution is in the recompute_shape() function. If it is called by the auto_contiguous pass and shape of an instruction is changed, and the shape is non_standard, we do not recompute shape of its output. The reason is: since its output shape is non_standard, a contiguous op will be added after the instruction, which will recompute shape for later operators.

* Check jit kernels files with clang-tidy

Allows --fp16 to be used in the driver to compare the target fp16 result and the ref fp32 result.

Do compilation in parallel

Currently, eliminate_contiguous will never remove contiguous for operators that use module inputs due to the fact that it doesn't pass the module inputs to compute_shape.

- Update to pass the module inputs correctly to compute_shape
- Fix the overloads of compute_shape so that when passed an empty vector of module inputs it will call the overload without module inputs
- Add tests with contiguous and pointwise module function.
- Move add_pointwise function to a seperate header to reuse across different tests

Currently we have the option of passing in --batch to the driver to change the batch size when the model has a dynamic dim value. We can use this flag to adjust the perf report's rate.

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 

In migraphx, DepthToSpace (d2s) is implemented as reshape --> transpose --> contiguous --> reshape.

If there is trailing binary pointwise operator after depthToSpace then, migraphx can move binary operator before contiguous and reshape of the depthtospce.

So, it becomes reshape-->transpose-->binary_op-->contiguous-->reshape.

Explicit contiguous wouldn't be required since binary_op outputs standard shape. So, it becomes reshape-->transpose-->binary-->reshape.

simplify_reshapes already has matcher that can do this transformation. This PR adds test for cases like depthtospace +binary op.

solves #905

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.

In migraphx, DepthToSpace (d2s) is implemented as reshape --> transpose --> contiguous --> reshape.

This PR adds matcher to find d2s + unary pointwise ops.

Application of the matcher moves the pointwise unary operation before the contiguous and reshape of the d2s.
So it becomes
reshape --> transpose --> unary --> contiguous --> reshape.

Motivation is that, later pointwise module would be created out of unary --> contiguous --> reshape. Codegen for this pointwise module can write out buffer such that explicit contiguous and reshape wouldn't be required.

This transformation is not always guaranteed to improve performance, since unary op will operate on non-standard shape. So, we would need some tuning mechanism to make decision.

#905 pending PR for binary operations.

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. 

Adds a pass to fuse pointwise operators into one "pointwsie" op that has a submodule which does the calculation.

Designed to allow a user to format the values needed for the json_string: migraphx::operation("reduce_mean", "{axes : [%i, %i, %i, %i]}", axes[0], axes[1], axes[2], axes[3]) instead of needing to use string concat or stringstream

Enable a cppcheck rule to catch these redundant casts in the future