[ROCm] Add AITER RoPE + KV cache fusion for MLA prefill and decode

[khairulkabir1661]

Summary

This PR adds AITER fused kernel support for DeepSeek MLA attention on AMD ROCm, implementing RoPE + KV cache fusion for both prefill and decode paths. This builds on top of vllm-project#38299 (RMSNorm + FP8 quantization fusion).

Changes

Core Functionality

• AITER fused decode kernel: Fuses RoPE application and KV cache writes for decode tokens using AMD's AITER library
• Prefill RoPE + KV cache fusion: Separate fusion path for prefill tokens
• Mixed batch handling: Correctly handles batches containing both prefill and decode tokens

Implementation Details

• vllm/model_executor/layers/attention/mla_attention.py:
  • Add _run_aiter_fused_decode() for fused decode path
  • Add prefill fusion in forward_impl()
  • Update unified_mla_kv_cache_update() to skip KV writes when using fused paths
  • Add custom ops for torch.compile/CUDA graph compatibility
• vllm/model_executor/layers/mla.py:
  • Pass RoPE caches and modules to MLAAttention
  • Skip RoPE in mla.py when using fused path (applied in custom op instead)
• vllm/envs.py:
  • Add VLLM_ROCM_USE_AITER flag to enable AITER kernels (default: enabled on ROCm)

Code Quality

• Clean up verbose comments throughout mla_attention.py
• Remove unused parameters and debug logging
• Simplify docstrings and inline comments

Testing

Tested on AMD MI300X with DeepSeek-V3:

• ✅ Mixed batches (prefill + decode)
• ✅ Pure prefill batches
• ✅ Pure decode batches
• ✅ CUDA graph mode
• ✅ Eager mode

Performance

Expected improvements on AMD MI300X:

• Reduced memory bandwidth usage (fused RoPE + KV cache write)
• Better kernel launch overhead (fewer separate operations)

Dependencies

• Requires: [ROCm] Add AITER RMSNorm+FP8 quantization fusion for MLA vllm-project/vllm#38299 (RMSNorm + FP8 quantization fusion)
• AITER library must be installed on ROCm systems

🤖 Generated with Claude Code

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