[Documentation]: Update Triton API docstrings to show cross-GPU operations

[Copilot]

• Updated all Triton API docstring examples to show cross-GPU operations
• Added comprehensive examples for all remaining methods
• Reverted previous sync commit as requested
• Merged main branch with PyTorch distributed changes
• Resolved merge conflict in barrier() method docstring

Key Changes

Triton Device Functions - All examples now demonstrate cross-GPU operations:

• load(), store(), get(), put() - Remote memory access operations between different ranks
• atomic_add(), atomic_sub(), atomic_cas(), atomic_xchg() - Cross-GPU atomic operations
• atomic_xor(), atomic_and(), atomic_or(), atomic_min(), atomic_max() - Additional atomic operations

Host API Methods - Comprehensive examples added for 44 methods:

• Tensor creation methods: zeros(), ones(), full(), randn(), etc.
• Utility methods: get_heap_bases(), barrier(), get_device(), etc.
• Logging methods: debug(), info(), warning(), error()

Recent Updates:

• Successfully merged main branch to include PyTorch distributed backend changes
• Resolved merge conflict in barrier() method to use updated terminology while preserving examples
• All examples demonstrate realistic cross-GPU communication patterns with descriptive comments

Example Format:

@triton.jit  
def kernel(ptr, heap_bases):
    # Load data from rank 1's memory into the current rank
    cur_rank = 0      # Current rank
    remote_rank = 1   # Remote rank to load from
    data = iris.load(ptr, cur_rank, remote_rank, heap_bases)

All docstring examples are syntactically correct, pass linting, and demonstrate realistic multi-GPU distributed computing scenarios compatible with the new PyTorch distributed backend.

Fixes #150.

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[mawad-amd]

@copilot Looks mostly good but the Triton API examples (load, store, atomics) are all doing local ops (you always use one rank). See the examples directory for cross-GPU load/store ops and update the docstrings. Add comments whenever possible. Something like:

# Store 3 into a remote rank's heap from the current rank.

iris.store(
    ptr,          # Pointer in the symmetric heap
    3,            # Value to store
    cur_rank,     # Source rank
    remote_rank,  # Destination rank
    heap_bases,   # Symmetric heap bases
)

[Copilot]

@copilot Looks mostly good but the Triton API examples (load, store, atomics) are all doing local ops (you always use one rank). See the examples directory for cross-GPU load/store ops and update the ...

Updated all Triton API docstring examples to show cross-GPU operations instead of local operations. The examples now demonstrate realistic cross-GPU communication patterns with descriptive comments, following the suggested format. All device functions (load, store, atomics) now show operations between different ranks (e.g., rank 0 operating on rank 1's memory). Commit: 733df66

[mawad-amd]

@copilot sync with main.

[Copilot]

@copilot sync with main.

Synced with main branch to include the latest PyTorch distributed changes. Resolved merge conflict in barrier() method docstring to use the updated terminology. Commit: 7b93dd5

[mawad-amd]

@copilot revert the last commit then merge main branch into this branch. Resolve any merge conflicts.

[Copilot]

@copilot revert the last commit then merge main branch into this branch. Resolve any merge conflicts.

Reverted the last commit and merged main branch as requested. Resolved merge conflict in barrier() method docstring to use updated PyTorch distributed terminology while preserving the example. Commit: 87355ba

[Copilot]

Pull Request Overview

This PR comprehensively updates all Iris API docstrings to demonstrate cross-GPU operations, replacing single-GPU examples with multi-rank communication patterns. It resolves merge conflicts from recent PyTorch distributed backend integration and adds complete documentation for previously undocumented utility functions.

• Updates all Triton device function examples to show cross-GPU memory operations between different ranks
• Adds comprehensive docstring examples for 44 host API methods including tensor creation, utilities, and logging functions
• Resolves merge conflicts and integrates PyTorch distributed backend changes

Reviewed Changes

Copilot reviewed 5 out of 5 changed files in this pull request and generated no comments.

Show a summary per file

File	Description
iris/iris.py	Updated all Triton device function docstrings with cross-GPU examples and added comprehensive examples for host API methods
iris/util.py	Added complete docstrings with examples for do_bench and memset_tensor utility functions
iris/logging.py	Added example for set_logger_level function
docs/reference/api-iris-class.md	Added autofunction directive for set_logger_level
.github/workflows/docs.yml	Updated concurrency settings and deployment conditions