feat: enable Triton kernels on MUSA

[Arlo-mt]

Summary

Thank you for merging the previous PR that added the MUSA backend support. Building on that foundation, this PR adds MUSA support for Triton-backed RL-Kernel operators.

MUSA tensors are not reported as CUDA tensors by PyTorch, so the existing
.is_cuda and torch.cuda.is_available() checks could incorrectly reject
MUSA execution or select the CPU fallback. This change adds explicit MUSA
device handling while preserving the existing CUDA and ROCm paths.

Changes

• Add MUSA device detection through torch_musa and torch.musa.
• Add the MUSA device and backend constants.
• Update Triton operator validation to accept MUSA tensors.
• Enable MUSA Triton dispatch for:
  • logp
  • grpo_loss
  • ratio_kl
  • linear_logp
  • det_gemm
  • batch_invariant_logp
  • rms_norm
  • embedding
  • silu
  • swiglu
  • rope
• Keep PyTorch fallback paths available when a Triton backend is unavailable.
• Add MUSA-specific dispatch and linear_logp forward/backward coverage.
• Update accuracy and dispatch tests to distinguish MUSA from CPU and CUDA.

This PR does not add MUSA native C++/MUSA kernels. Native MUSA kernel support
is handled separately in the MUSA-support-native-kernels branch.

Validation

Validated locally on:

• GPU: Moore Threads S5000
• PyTorch: 2.9.0.post1+musa5.1.2mp22
• Triton: 3.2.0
• MUSA architecture: mp_22

Results:

• MUSA Triton operator smoke tests: 5/5 passed
• MUSA dispatch and accuracy tests: 7/7 passed
• CPU-safe and dispatch unit tests: 100 passed
• Python compilation and whitespace checks passed

Compatibility

• CUDA and ROCm-specific dispatch paths are unchanged.
• CUDA-only tests such as SM90, NCCL, and CUDA extension tests remain
  CUDA-specific.

Summary by CodeRabbit

• New Features

  • Expanded MUSA accelerator support across Triton activation, embedding, loss, normalization, rotary-embedding, and matrix multiplication operations.
  • MUSA dispatch now prioritizes validated Triton implementations while retaining appropriate fallbacks.
  • Enabled additional Triton backends for log-probability, embedding, and RMS normalization operations.
  • Added MUSA support for contract-aware attention dispatch.
  • Improved deterministic matrix multiplication validation, including matching input-device requirements.

• Tests

  • Expanded MUSA coverage for dispatch behavior, correctness, and numerical accuracy across supported operations.
  • Added validation for MUSA-specific backend selection and computation results.

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---

📝 Walkthrough

Walkthrough

This change enables MUSA support across Triton operator validation and registry dispatch. It adds Triton backend identifiers, prioritizes Triton implementations for MUSA operations, and updates dispatch, correctness, and dtype-alignment tests.

Changes

MUSA Triton support

Layer / File(s)	Summary
Triton operator device validation rl_engine/kernels/ops/triton/...	Activation, embedding, loss, GEMM, RMSNorm, and RoPE operators now accept MUSA tensors. Error messages and documentation reflect the supported accelerator set.
MUSA backend registry wiring rl_engine/kernels/registry.py	OpBackend adds Triton RMSNorm, LogP, and embedding identifiers. MUSA priority maps add Triton backends with PyTorch fallbacks where configured.
MUSA dispatch and accuracy tests rl_engine/tests/test_dispatch.py, tests/test_*	Tests verify MUSA backend selection, deterministic GEMM dispatch, MUSA linear-logp correctness, active-device allocation, and matching reference output dtypes.
Formatting cleanup rl_engine/integrations/vllm_runtime.py, rl_engine/kernels/ops/cuda/attention/flash_attn.py	Existing conditional expressions and statements are reformatted without behavior changes.

Estimated code review effort: 3 (Moderate) | ~20 minutes

Merge Risk: ⚪ Minimal · up to f430e

This PR adds MUSA support for Triton-backed operators while preserving CUDA, ROCm, and fallback behavior; no actionable merge-blocking risk remains beyond normal checks and review.

Sequence Diagram(s)

sequenceDiagram
  participant OpRegistry
  participant MUSAOperation
  participant TritonOperator
  OpRegistry->>MUSAOperation: resolve MUSA operation
  MUSAOperation->>TritonOperator: select prioritized Triton backend
  TritonOperator-->>MUSAOperation: execute with MUSA tensors

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🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 12.50% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 40 functions across 20 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly and concisely summarizes the main change: enabling Triton kernels on MUSA. It matches the pull request objectives and modified files.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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Actionable comments posted: 3

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Inline comments:
In `@rl_engine/kernels/ops/triton/matmul/det_gemm.py`:
- Around line 149-150: Validate exact device equality for the GEMM operands in
both entry points before calling _TritonDetGemmFn.apply, rejecting cases where
a.device differs from b.device; retain the existing supported-device checks and
ensure _triton_gemm cannot launch with mixed-device pointers.

In `@rl_engine/kernels/registry.py`:
- Line 578: Add a MUSA ws2_attention entry in the MUSA backend map used by
KernelRegistry.get_attention_op, with OpBackend.PYTORCH_CP_ATTENTION as the
first candidate, matching the ROCm configuration. Extend the dispatch test to
call get_attention_op for MUSA and verify the candidate is returned.

In `@tests/test_rms_norm.py`:
- Around line 243-245: Update the MUSA branch in the RMS norm test to condition
its type assertion on Triton availability: expect RMSNormTritonOp when Triton is
available and NativeRMSNormOp otherwise, while preserving the forward attribute
check for the selected operation.

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ℹ️ Review info

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Configuration used: defaults

Review profile: CHILL

Plan: Team

Run ID: b39c53a5-154a-4785-852d-97d2957d84cb

📥 Commits

Reviewing files that changed from the base of the PR and between 01b4ae4 and e896df3.

📒 Files selected for processing (18)

• rl_engine/kernels/ops/triton/activation/swiglu.py
• rl_engine/kernels/ops/triton/linear/embedding.py
• rl_engine/kernels/ops/triton/loss/batch_invariant_logp.py
• rl_engine/kernels/ops/triton/loss/grpo_loss.py
• rl_engine/kernels/ops/triton/loss/linear_logp.py
• rl_engine/kernels/ops/triton/loss/ratio_kl.py
• rl_engine/kernels/ops/triton/matmul/det_gemm.py
• rl_engine/kernels/ops/triton/rmsnorm_triton.py
• rl_engine/kernels/ops/triton/rotary_embedding/rope.py
• rl_engine/kernels/registry.py
• rl_engine/tests/test_dispatch.py
• tests/test_batch_invariant_logp.py
• tests/test_grpo_loss.py
• tests/test_linear_logp.py
• tests/test_logp.py
• tests/test_op_accuracy.py
• tests/test_ratio_kl.py
• tests/test_rms_norm.py

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[zhangj1an]

Thanks! The change is well-scoped (musa priority map only, fallbacks preserved) and I've verified it doesn't affect existing CUDA/NPU/CPU dispatch.