Move Repeat_interleave

[ayerofieiev-tt]

tt_eager --> ttnn per op plan

We propose next order to breakdown this work into smaller pieces:

1. Replace usage of a given op in C++ with ttnn analog (example)
2. Replace usage in Python [Models, Demos, Examples, tests] (example)
3. Remove tt_lib bindings and entries from docs (bindings, docs)
4. Restructure op consolidating code in ttnn (see new structure)

Replacing usage in C++

Each of such usage should be replaced with a ttnn analog. For example repeat --> ttnn::repeat. This should be done for each operation. Missing operations should be added to ttnn.

• usually there are lots of usage in composite_ops.cpp, complex_ops.cpp, backward_ops.cpp
• lots of usage in tests, example 1, example 2

Replacing usage in Python

For every unary op, look for next entries in Tests/Sweeps, Demos, Models, Examples:

• ttl.tensor.repeat
• tt_lib.tensor.repeat
• ttnn.primary.tensor.repeat and replace them with ttnn.repeat. Example

⚠️ tt_lib operations might sometimes have a slightly different interface

• arguments may be in different order
• arguments may be positional when ttnn arguments may be kw only
• output_mem_config --> memory_config
• optional_output_tensor --> optional_tensor
• output_dtype --> dtype

Testing

For the best coverage, I recommend to run these workflows. If some of them fails, check if it is the same fail as on main:

• Post commit CI
• [post-commit] models tests
• Device perf regressions and output report
• Model perf regressions and output report
• Nightly fast dispatch model regression CI
• T3000 frequent tests
• T3000 demo tests

[ayerofieiev-tt]

Discussed, we want it as the Op in the data movement folder

[KalaivaniMCW]

Merged to main #10719