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* use the parse_resize to parse the upsample operator

* 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

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

Fix bug caused by casting time seed to float

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

Fix pointwise compile error with half sqrt 

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.

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.

This PR is to turn on a few gemm unit test with int8 input datatype. Before rocm4.4, int8 input data type requires matrix size to be no less than 4 in rocblas implementation. Because of this limitation, we turned off a few gemm unit tests with int8 input data type.

This limitation is removed in rocm4.4, so after we upgrade to rocm4.5, we can turn on these unit tests. Also we change to unit test conv_bn_add to adding instructions to module instead of program.
Co-authored-by: kahmed10 <15948690+kahmed10@users.noreply.github.com>

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.

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

Added features:
This enables wrapping each migraphx operator with rocTX markers.
It adds new knob trace to migraphx-driver binary.

Limitation:

rocTX standalone does not output a file, it needs to be used with rocprof. Example command line:

/opt/rocm/bin/rocprof -i ./in.txt --hip-trace --roctx-trace --flush-rate 10ms --timestamp on -d cagri_out --obj-tracking on /opt/rocm/bin/migraphx-driver trace ./resnet50-v2-7.onnx --onnx --gpu
Co-authored-by: Shucai Xiao <shucai@gmail.com>

Inverse of DepthToSpace op
Co-authored-by: Shucai Xiao <shucai@gmail.com>

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>

Add RandomNormal, RandomNormalLike, RandomUniform, and RandomUniformLike to onnx parser and onnx tests

Each pair of Random*/Random*Like is implemented using a single op_parser because the ops share the same essential attributes and algorithm with the difference that Random*Like get the output type and/or shape from an input argument and Random* take both from attributes.

Resolves #907
Resolves #959

Supports 1,11,13 ONNX Operator Set

Checks wavefront size, then changes implementation and number of threads for DPP reduce

Add forward compatibility support for compile options 

Needed to bypass passes when fusing pointwise operators into a module.

This reverts commit 9e43cb8b.

This PR aims to remove alpha and beta attributes from dot operator completely.

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.

make file_options struct an opaque object for ABI compatibility, uses make generate to auto-generate and includes  modified tests.
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>