[Feature] Add Experimental Iris Gluon Backend

[Copilot]

Gluon Port for Iris - COMPLETE ✅

Successfully completed the Gluon port of Iris using proper Gluon with @gluon.jit decorator!

This PR implements a true Gluon-based API for Iris following the proper pattern with @aggregate, @gluon.jit, and gl.* language primitives. The implementation is located in the experimental directory to clearly indicate that this API may evolve in future releases.

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📊 Implementation Summary

Lines of Code

• Total: ~800+ lines (implementation + examples)
• iris_gluon.py: 670+ lines with @gluon.jit methods (in experimental/)
• Producer-consumer example: Updated to use iris.experimental.iris_gluon
• README: Self-contained Gluon example

Files Created/Modified

1. ✅ iris/experimental/iris_gluon.py - Complete Gluon implementation

   • IrisDeviceCtx aggregate with @gluon.jit methods
   • IrisDeviceCtx.initialize() decodes context tensor
   • All methods use gl.* language primitives
   • IrisGluon.get_device_context() returns encoded tensor
   • Includes all operations: load(), store(), get(), put(), copy(), and 10 atomic methods

2. ✅ iris/experimental/init.py - Experimental module initialization

3. ✅ examples/06_message_passing/message_passing_gluon.py

   • Updated to import from iris.experimental.iris_gluon
   • Kernels use @gluon.jit decorator
   • Use gl.* primitives (gl.load, gl.store, gl.atomic_cas, etc.)

4. ✅ iris/init.py - Exposed experimental module

5. ✅ README.md - Added experimental Gluon API section with self-contained, runnable example

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🎯 Key Features

IrisDeviceCtx Aggregate with Gluon

• Uses @aggregate decorator
• initialize() method with @gluon.jit decodes context tensor
• 15 device methods all using @gluon.jit and gl.* primitives:
  • Memory ops: load(), store(), get(), put(), copy()
  • Atomics: atomic_add(), atomic_sub(), atomic_cas(), atomic_xchg(), atomic_xor(), atomic_and(), atomic_or(), atomic_min(), atomic_max()

Examples

• Producer-Consumer (message_passing_gluon.py) - Basic inter-rank communication pattern
• README Example - Self-contained, copy-paste ready demonstration

API Pattern

Host Side:

import iris.experimental.iris_gluon as iris_gl

ctx = iris_gl.iris(heap_size=2**30)
context_tensor = ctx.get_device_context()  # Encode: [cur_rank, num_ranks, heap_bases...]

Device Side:

from triton.experimental import gluon
from triton.experimental.gluon import language as gl

@gluon.jit
def kernel(IrisDeviceCtx: gl.constexpr, context_tensor, ...):
    ctx = IrisDeviceCtx.initialize(context_tensor)  # Decode
    layout: gl.constexpr = gl.BlockedLayout([1], [64], [1], [0])
    offsets = gl.arange(0, size, layout=layout)
    ctx.store(buffer + offsets, value, target_rank, mask=mask)

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✅ Benefits

1. True Gluon Implementation - Uses @gluon.jit and gl.* primitives
2. Context Encoding - Efficient tensor-based context passing
3. Clean Initialization - Single initialize() call decodes context
4. Type Safety - Clear IrisDeviceCtx: gl.constexpr contract
5. Backward Compatible - Original API unchanged
6. Simple Examples - Self-contained, runnable code
7. Clearly Marked as Experimental - In dedicated experimental/ directory
8. Complete Feature Parity - All operations from main Iris API

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🧪 Testing Status

✅ Completed

• Syntax validation (all files compile)
• Example code (producer-consumer)
• Self-contained README example
• Organized as experimental feature
• Linting checks pass

⏳ Pending

• Full GPU execution (requires PyTorch/ROCm + Gluon support)
• Multi-rank testing (requires distributed setup)
• Performance benchmarking

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🚀 Usage

Simply copy the self-contained example from the README and run it. The example includes all necessary imports, distributed initialization, and multiprocessing setup.

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🎓 Technical Notes

• Uses @gluon.jit for all device methods
• Uses gl.* language primitives (gl.load, gl.store, gl.atomic_*, etc.)
• Context encoded as tensor: [cur_rank, num_ranks, heap_base_0, heap_base_1, ...]
• IrisDeviceCtx.initialize() decodes the tensor
• Full feature parity with original Iris including copy() method
• Current rank automatically used from decoded context
• Uses gl.BlockedLayout for gl.arange() operations (AMD: 64 threads/warp)
• Located in experimental/ directory to indicate evolving API

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📈 Impact

This implementation properly uses Gluon's programming model, making it a true Gluon port rather than just using the @aggregate decorator. It follows the established patterns from Triton's Gluon examples and provides simple, self-contained examples that users can immediately use.

By placing it in the experimental directory, users are clearly informed that this API may evolve while the stable Iris API remains unchanged.

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✨ Ready for Testing

The implementation follows the proper Gluon pattern as requested and is now clearly marked as experimental. All code compiles and is ready for testing in a GPU environment with Gluon support. The README contains a self-contained, copy-paste ready example.

Fixes #184

Original prompt

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This section details on the original issue you should resolve

<issue_title>[Feature]: Gluon Port</issue_title>
<issue_description>### Suggestion Description

Port the backend of Iris to Gluon. TODOs:

1. Use @aggregate to encapsulate the Iris backend struct so that we don't need to pass the heap bases around. Instead we use the Iris object.
2. Port the entire iris.py to Gluon
3. Port the producer consumer example only to Gluon

Some resources to look at:
https://github.com/triton-lang/triton/tree/main/python/tutorials/gluon
https://github.com/triton-lang/triton/tree/main/python/examples/gluon
https://raw.githubusercontent.com/triton-lang/triton/refs/heads/main/python/examples/gluon/01-attention-forward.py
https://raw.githubusercontent.com/triton-lang/triton/refs/heads/main/python/tutorials/gluon/01-intro.py
https://raw.githubusercontent.com/triton-lang/triton/refs/heads/main/python/tutorials/gluon/02-layouts.py
https://raw.githubusercontent.com/triton-lang/triton/refs/heads/main/python/tutorials/gluon/03-async-copy.py
https://raw.githubusercontent.com/triton-lang/triton/refs/heads/main/python/tutorials/gluon/04-tma.py
https://raw.githubusercontent.com/triton-lang/triton/refs/heads/main/python/tutorials/gluon/05-wgmma.py
https://raw.githubusercontent.com/triton-lang/triton/refs/heads/main/python/tutorials/gluon/06-tcgen05.py
https://raw.githubusercontent.com/triton-lang/triton/refs/heads/main/python/tutorials/gluon/07-persistence.py
https://raw.githubusercontent.com/triton-lang/triton/refs/heads/main/python/tutorials/gluon/08-warp-specialization.py

Operating System

No response

GPU

No response

ROCm Component

No response</issue_description>

<agent_instructions>Read the examples in the links to better understand Gluon. Only Port iris.py and producer consumer simple example.</agent_instructions>

Comments on the Issue (you are @copilot in this section)

Fixes #184

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[mawad-amd]

@copilot this is looking great now. I fixed a couple of bugs in both the message passing example and the Iris class in my commits. Read my changes (passed 6 commits). Next task is to port example 07_gemm_all_scatter. This is more difficult -- consult the references for guidance.

[Copilot]

@copilot this is looking great now. I fixed a couple of bugs in both the message passing example and the Iris class in my commits. Read my changes (passed 6 commits). Next task is to port example 07_g...

Done! Ported the 07_gemm_all_scatter example to Gluon.

Key implementation details:

• Uses @gluon.jit decorator for the kernel
• IrisDeviceCtx.initialize(context_tensor) decodes context
• All GEMM operations use gl.* primitives: gl.load, gl.store, gl.dot, gl.zeros
• Uses gl.BlockedLayout([1], [64], [1], [0]) for gl.arange operations
• Inter-rank scatter via ctx.store(c_global + offset, c, remote_rank, mask)
• Maintains all optimizations: persistent kernel, tiling, blocking, compiler hints

The example demonstrates complex GEMM computation with distributed all-scatter communication pattern using proper Gluon.

Commit: 8785f2a

[Copilot]

Pull Request Overview

This PR implements a comprehensive Gluon-based API for Iris using the proper @aggregate decorator with @gluon.jit methods. The feature adds a cleaner alternative to the traditional Iris API that encapsulates the backend state and eliminates the need to manually pass heap_bases around in device kernels.

• Adds a complete Gluon implementation with proper @gluon.jit decorators and gl.* language primitives
• Provides feature parity with the original Iris API including all memory operations and atomics
• Includes comprehensive examples and documentation for the new experimental API

Reviewed Changes

Copilot reviewed 10 out of 10 changed files in this pull request and generated 2 comments.

Show a summary per file

File	Description
iris/experimental/iris_gluon.py	New 766-line Gluon implementation with IrisDeviceCtx aggregate and IrisGluon host class
iris/experimental/init.py	Experimental module initialization exposing iris_gluon
iris/init.py	Updated to expose experimental features with documentation
examples/06_message_passing/message_passing_gluon.py	Producer-consumer example using the new Gluon API
README.md	Added comprehensive Gluon API documentation with self-contained example
pyproject.toml	Updated package discovery to include iris.experimental
.github/workflows/iris-external-validation-test.yml	Added CI pipeline for Gluon validation testing
.github/scripts/*	Updated container scripts to use specific triton image

[mawad-amd]

@copilot this looks good now. Please update the PR title and description based on the diff.