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Currently, MIGRAPHX_TRACE_EVAL=2 prints out the entire output buffer, but this can produce a lot of output. To make it easier to inspect and debug, using MIGRAPHX_TRACE_EVAL=2 now only prints 10 elements from the buffer(the first 5 and last 5) and shows any fp classifications found in the buffer(ie nans, infinity, etc). The previous behavior can still be enabled with MIGRAPHX_TRACE_EVAL=3.

This will also check the types using mypy on the CI.

* use the parse_resize to parse the upsample operator

Add cppcheck to examples

* 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

There have been hangs in the CI runs recently.  Github runner jobs
are failing due to exceeding file system size.  Upgrading to 0.0.11
resolves this issue.

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

nfnet update
3dunet requirements via pip
3dunet requirement and nb-clean

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

1. Previous implementation assumes inputs and outputs .pb files are ordered, but it is not the case. So, we should use the name of the tensors in the input/output .pb files to match the input and output in the onnx model. (This change applies to the BERT_Squad model)
2. When parsing a model with dynamic input shape, current implementation uses the default batch_size for the unknown dims, which can cause parsing error for some cases (e.g. mask_rcnn model). The solution is we first read an input to get the shape, then use these shapes to parse the onnx model.

Adds description for roctx knob of migraphx-driver in documentation.

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.

Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 2.5.1 to 2.5.2.
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](https://github.com/tensorflow/tensorflow/compare/v2.5.1...v2.5.2

)

---
updated-dependencies:
- dependency-name: tensorflow
  dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Chris Austen <causten@users.noreply.github.com>

* Check jit kernels files with clang-tidy

This provides a helper script to run rocTX markers with migraphx-driver and reduces the number of steps a user would go through running rocTX knob.
Run:
python roctx.py --run --onnx_file <ONNX_FILE> --migraphx_args "--onnx --gpu --fp16 --batch 16" --out outputfolder
Runs and parses the run output (JSON file). An example output is given below:

                                                     SUM  MIN  MAX
Marker start: gpu::convolution                      5272   10  563
Marker start: gpu::add_relu                          605   12   18
Marker start: gpu::gather                            299  145  154
Marker start: gpu::mul_add                           227   14   57
Marker start: gpu::sub                               177   13   42
Marker start: gpu::concat                            169   22   31
Marker start: gpu::triadd_relu                       163   15   18
Marker start: load                                   141    0    3
Marker start: hip::hip_copy_literal                  111    0    3
Marker start: gpu::add                                58   13   17
Marker start: broadcast                               52    0    3
Marker start: gpu::convert                            31   15   16
Marker start: slice                                   11    0    1
Marker start: gpu::pooling                             9    9    9
Marker start: step                                     2    2    2
Marker start: @param                                   2    0    1
Marker start: reshape                                  1    0    1
Marker start: hip::hip_allocate_memory                 1    1    1
Marker start: check_context::migraphx::version_...     0  ERR  ERR

TOTAL TIME: 7331 us

JSON FILE PATH: [...]/rpl_data_211019_195229_9369/input_results_211019_195229/trace.json
Parse:
python roctx.py --parse --json_path <JSON PATH FROM RUN>
Note: The parse knob is made available if the user wants to parse an already existing JSON output.

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.