GitLab

Add in the reshape parameter to better mirror what we're seeing in the retinanet
network block that's causing the issue

This doesn't seem to match yet for the gathernd nonzero op.

Used to generate a subset of the broken op in retinanet to speed up
debugging/matching

We can't change the behaviour of the nonzero op and we currently pad the output
with zeros. This unfortunately obfuscates the following cases:

1. When the only nonzero element is the first index - the whole tensor is padded
with zeros its not obvious if the first value is valid index or padded

2. When the nonzero elements vector is used for indicies. The resulting vector
   with the padded value of 0 is still a valid index thus gather/gatherND and other ops
   will assume the 0 index is valid and operate accordingly.

In this case, by adding a sentinel value of the number of static elements used
by the desired shape, the resulting nonzero output can now track how many elements
are valid by determining the value in the correct range.

Originally I intended to use -1 but not all datatypes use this if say, we're dealing with
unsigned values in our vectors or booleans.
Signed-off-by: Ted Themistokleous <ted.themistokleous@amd.com>

Use std::numeric_limits::min/max() functions plus the appropriate value to encode -inf/inf 

Enable retrieving the code object to do tuning in the future.

Refactor supported gfx archs

back out changes for rocm-5.5

* use half hip functions to compute max and min
* add verify test for min and max

Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>

Co-authored-by: Sam Wu <sam.wu2@amd.com>
Co-authored-by: Paul <pfultz2@yahoo.com>

Move CI to support the rocm5.5 release

Adding support for broadcasted scalars to unsqueeze op.

Specifying steps other than 1 is disallowed in this implementation since we want the output the always be a tensor. We can support varying step sizes if we allow a broadcasted scalar output from this op.

Co-authored-by: causten <causten@users.noreply.github.com>

Example of using the C++ API to run an ONNX model with dynamic batch

Remove various file not required for what we use Github runners for

Python API with documentation updates

Adds support for slice,transpose,contigous and reshape fusions into input tensors for a fused mlir kernel.

add option to print tf supported ops
sort both onnx and tf ops alphabetically

When multiplying either the input or output across the K dimensions then the multiple can be applied to the constant which can then be folded with propagate_const.

Exposed the mlir_enabled() call the decide for lowering pipeline's enablement
Disabled the rewrite quantization pipeline in mlir compilation
Added quant convolution as anchor ops
Fixed the return type expectations
Added the fall back hip implementation for quantizelinear and dequantizelinear
Will need advises to improve the implementation for quantizelinear

Relies on Removed split_single_dyn_dim compile flag #1711
Exposes dynamic_dimension as a opaque object with dynamic_dimensions and optimals
Exposes ONNX dyn_input_dims and default_dyn_dim to run with dynamic batch
Updates api.py to be able to create objects from aggregate initialization (used for dynamic_dimension)
Uses offload copy for now

Improves the constant propagation for bert models. Larger batch size no longer use as large of constants.  Also improves the speed of model compilation 

update rocBLAS version check to support 3.0 and above with simplified logic

Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 2.9.3 to 2.11.1.
- [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/v2.9.3...v2.11.1)

---
updated-dependencies:
- dependency-name: tensorflow
  dependency-type: direct:production
...

Updates the hip::copy_to_gpu and hip::copy_from_gpu operators to work with dynamic shapes

Allows for offload_copy to be used with dynamic batch

Changed assert in select_module because the argument might now be smaller with how offload_copy will work with dynamic batch. (maximum buffer size will be used)

This fixes #1700