[ROCm] Eliminate redundant MoE buffer copies in AITER fused experts

[frida-andersson]

Purpose

Eliminate redundant __amd_rocclr_copyBuffer DMA kernel launches from the ROCm AITER fused MoE decode path by passing the caller's output tensor directly as AITER's destination buffer.

On DeepSeek-V3.2 TP4 (4x MI355X), this removes 116 copy kernels per decode step and yields a 2.3% per-step improvement (500 us savings), with a secondary benefit from reduced memory bandwidth contention on the custom allreduce.

Changes

• _aiter_ops.py: Add moe_buf parameter to rocm_aiter_fused_moe custom op. Change return type to void and declare mutates_args=["moe_buf"] so torch.compile tracks the in-place write correctly. Pre-allocate moe_buf at the Python call site when not provided. Use AITER's output_buffer_override context manager when available, fall back to allocate+copy otherwise.
• rocm_aiter_fused_moe.py: Thread moe_buf=output from AiterExperts.apply() through rocm_aiter_fused_experts. Add identity check to skip self-copy when AITER wrote directly into the output tensor.
• modular_kernel.py: Add final_output parameter to _fused_experts. When expert workspaces are both empty (AITER manages its own buffers), write directly into the caller's output tensor, bypassing the intermediate allocation and finalize copy.
• topk_weight_and_reduce.py: Add identity check in TopKWeightAndReduceNoOP.apply() to skip copy when output already aliases fused_expert_output.

4 files, ~60 lines net.

Test Result

Performance (DeepSeek-V3.2, 1K input / 100 output, TP4, 4x MI355X)

Metric	Baseline	This PR	Delta
__amd_rocclr_copyBuffer calls/step	177	61	-116 (-66%)
copyBuffer total time	743 us	251 us	-492 us
Total decode step	21,952 us	21,456 us	-496 us (-2.3%)

Before — two copyBuffer calls between MoE GEMM and allreduce:

After — copies eliminated, MoE GEMM feeds directly into allreduce:

Accuracy (GSM8K 5-shot, exact_match, TP4)

Config	Score	±
This PR	0.9431	0.0064

Test Plan

• Profile confirms copyBuffer reduction (177 → 61 calls)
• Kernel dispatch unchanged (same MoE GEMM variants, same dense GEMM backend)
• DeepSeek-V3.2 TP4 decode produces identical outputs
• No regressions with torch.compile / HIP graphs
• Quark MoE paths unchanged (don't pass moe_buf, use default alloc)

Dependencies

Requires AITER commit da318d0 which adds two things to aiter/fused_moe.py:

1. output_buffer_override(buf) — a thread-local context manager. While active, fused_moe writes its result into buf instead of allocating a fresh moe_buf.
2. _maybe_take_override_buf(M, model_dim, dtype, device) — called inside fused_moe at the point where moe_buf is normally allocated. If a matching override buffer exists (same shape, dtype, device, contiguous), it is used; otherwise allocation proceeds as before.

The AITER diff is ~40 lines. Prior art: ROCm/aiter#2663 by @tpopp (closed).

When output_buffer_override is not importable (older AITER), this vLLM PR falls back to the existing allocate+copy path — no crash, no regression, just no speedup.

Related PRs

• #39393 (@tpopp) — related output-copy elimination attempt (CLOSED, prior art)
• #40341 — bias dtype fix (no overlap, already addressed upstream)

Co-authored-by: Jouni Hartikainen <jhartika@amd.com>

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[gemini-code-assist]

Code Review

This pull request introduces an optimization for ROCm AITER fused MoE by enabling the use of pre-allocated output buffers, which reduces redundant memory copies. The implementation updates the aiter operator to support buffer overrides and modifies the modular MoE kernel to pass a final output tensor when workspaces are empty. Feedback focuses on performance overhead from inline imports and feature checks in the execution hot path, and suggests refining the workspace bypass logic in the modular kernel to ensure safety and avoid redundant memory allocations.

[gemini-code-assist]

Importing and checking for output_buffer_override inside the custom op implementation will incur significant Python overhead on every call if the user has an older version of aiter where this function is missing. Since this is in the hot path of MoE execution, this check should be cached at the module level or within rocm_aiter_ops to avoid repeated ImportError exceptions.

[gemini-code-assist]

Importing nullcontext inside the function adds unnecessary overhead to the hot path. Please move this import to the top of the file.

[gemini-code-assist]

This optimization assumes that any expert implementation with empty workspaces is safe to write directly into final_output. While this is true for AiterExperts (which uses TopKWeightAndReduceNoOP), it might be unsafe for other modular experts if they don't handle the case where output aliases fused_expert_output. Additionally, _allocate_buffers was already called at line 1233, which might have reserved workspace memory for fused_out that is now being bypassed. Consider making this optimization more explicit or ensuring that _allocate_buffers is aware of the final_output override to avoid redundant workspace reservations.