DSV4 on MI300X: throughput tuning (optimise, +6 commits on port)

[fergusfinn]

Summary

Performance work on top of #11. Six commits that drop overhead around the matmuls in the sparse MLA decode hot loop and the MXFP4 MoE.

Commits (6, bottom-up, on top of port)

• 29aaf1bbc DSV4 no-LoRA: direct W2 reduce + shape dump (split out of an earlier mixed commit; rides on top of the routing fix in Port DeepSeek-V4-Flash serving to MI300X #11)
• 266c6c84d skip sparse top-k when full window already covers seqlen
• 27888ec80 sparse MLA: pass output buffer through, avoid copy
• 501595f95 cache static bf16 projection weights
• a767c4d13 sparse MLA decode: launch-shape + occupancy tuning
• 330979094 MXFP4 OGS tile / ramp / epilogue tuning

Stacked PR

Base: port/dsv4-mi300x-bringup (#11). Cannot land until that PR is merged.

Test plan

• Throughput benchmark: aggregate tok/s and per-user tok/s on N×MI300X with DSV4 at saturation
• Output correctness matches port-only baseline within tolerance
• No regressions on small-M ramp or large-M steady state shapes

AI assistance

Prepared with Claude. All commits reviewed by the submitter.

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[doubleword-code]

Summary

This PR introduces throughput tuning optimizations for DeepSeek-V4 on AMD MI300X (CDNA3) GPUs across three files. The changes include:

1. OGS tile/ramp/epilogue tuning for MoE operations - applies smaller M tiles (32/64 vs default 128) for high-throughput serving shapes
2. Sparse MLA decode launch configuration tuning - dynamic block_h/block_k/num_warps selection based on query count and sparsity
3. Full-window topk optimization - skips expensive topk computation when seq_len <= topk_tokens
4. BF16 projection weight caching - avoids redundant dtype conversions in_rocm_inv_rope_einsum
5. Profiling infrastructure - shape dump and memory metrics utilities gated behind environment variables

The changes are well-structured and follow existing patterns in the codebase. The optimization logic appears sound for the target hardware (MI300X/CDNA3). Approved with minor non-blocking suggestions.

Research notes

• Triton kernel launch parameters: num_warps is correctly passed as a keyword argument to kernel launches
• AMD CDNA3 architecture: 4 SIMDs per CU, 128 threads/CU, 64-thread wavefronts - the warps values (4, 8) used are appropriate
• OGS (Optimized GEMM Specification): The constraint override pattern via update_opt_flags_constraints is the intended mechanism for tile tuning
• HIP graph capture safety: Shape dump functions correctly check torch.cuda.is_current_stream_capturing() to avoid illegal host copies during capture

Suggested next steps

1. Non-blocking: Consider adding thread-safety documentation or using threading.Lock for the global shape dump counters if multi-threaded profiling is anticipated
2. Nit: Standardize environment variable access pattern (prefer os.environ.get() consistently over mixing with os.getenv())
3. Documentation: Add a section to ROCm documentation or a comment block listing all new environment variables introduced by this PR for discoverability

General findings

Pattern consistency: The env var helper functions differ slightly between files:

• gpt_oss_triton_kernels_moe.py: Uses os.environ.get() with explicit empty string check
• rocm_aiter_mla_sparse.py: Uses os.getenv() with try/except for int conversion

Both approaches work correctly, but consolidating to a single pattern would improve maintainability.

Cache invalidation edge case: The bf16 weight cache at lines 1287-1304 uses data_ptr() as part of the cache key. This correctly handles tensor reallocation but would not detect in-place value modifications. This is an extremely rare pattern in PyTorch (tensors are typically immutable after creation in inference paths), so this is acceptable but worth noting.

Global counter behavior: The module-level counters (_MOE_SHAPE_DUMP_COUNT, _DSV4_SPARSE_DECODE_SHAPE_CALLS) accumulate across the process lifetime. The env vars *_LIMIT and *_STRIDE provide adequate control for production use. For long-running training jobs with periodic evaluation, users may need to manually reset these via environment or accept that counting continues across phases.

General findings (auto-demoted from inline due to pre-validation)

• Nit vllm/v1/attention/ops/rocm_aiter_mla_sparse.py:1287 — Cache invalidation relies on data_ptr().
  • (demoted: code self-check failed at vllm/v1/attention/ops/rocm_aiter_mla_sparse.py:1287: diff has n_local_groups, -1, wo_a.weight_scale_inv.shape[-1], model claimed cache_key = ()

[doubleword-code]

Non-blocking: Module-level global counter without explicit synchronization.

Why it matters: While Python's GIL protects simple integer increment operations, this counter could exhibit unexpected behavior in multi-process scenarios (each process gets its own copy) or if the module is reloaded. For the intended profiling use case, this is acceptable.

Suggested fix: If cross-process coordination is ever needed, consider using a file-based counter or multiprocessing.Value. For now, a comment documenting the expected single-process-per-GPU pattern would suffice.

[doubleword-code]

Nit: Boolean flag without atomic protection.

Why it matters: In a multi-threaded scenario, multiple threads could theoretically pass the if not _MOE_SHAPE_DUMP_WARNED check simultaneously before either sets it to True, resulting in duplicate warnings. The GIL makes this unlikely in practice.

Suggested fix: Consider using a module-level lock or accepting that duplicate warnings are harmless for this debug feature.

[doubleword-code]

Non-blocking: Launch configuration selection logic has overlapping conditions.

Why it matters: Looking at lines 114-134, both the extra_per_query <= 8 and else branches contain identical logic for num_queries >= 256 and num_queries >= 80 cases (both set block_h, block_k = 64, 16 and 32, 16 respectively). The differentiation is only in the num_warps assignment (conditional vs unconditional) and the num_queries < 32 case. This structure is correct but could be clearer.

Suggested fix: Consider refactoring to extract the common cases:

if num_queries >= 256:
    block_h, block_k = 64, 16
    num_warps = 8 if extra_per_query > 0 else 4  # or just always 8?
elif num_queries >= 80:
    block_h, block_k = 32, 16
# ... etc

This would make the actual differentiating factors more obvious.

[doubleword-code]

Non-blocking: Same pattern as _MOE_SHAPE_DUMP_COUNT - module-level global counter.

Why it matters: See comment at gpt_oss_triton_kernels_moe.py:41. For profiling/debugging features that are disabled in production (no dump_dir set), this is acceptable.

Suggested fix: Consider adding a comment noting that this counter persists for the process lifetime and is reset only on module reload.

[doubleword-code]

Nit: Inconsistent with _env_int in gpt_oss_triton_kernels_moe.py.

Why it matters: This version (lines 29-35) uses os.getenv() with try/except, while the version at gpt_oss_triton_kernels_moe.py:46 uses os.environ.get() with explicit empty string check. Both handle the same cases correctly, but having two implementations increases maintenance burden.

Suggested fix: Consolidate to a single utility function in a shared module (e.g., vllm.utils.env_utils) or pick one pattern and use it consistently across ROCm-specific code.

[doubleword-code]

Non-blocking: Assertion depends on upstream metadata builder.

Why it matters: This assertion (and the similar one at line 513) assumes seq_lens_cpu_upper_bound is always populated by the CommonAttentionMetadata builder. If a future change modifies the metadata construction path, this could fail. The assertion is appropriate here as it documents the precondition.

Suggested fix: No change required. The assertion is good defensive programming. Consider adding a comment referencing where this field is populated (e.g., # Set by CommonAttentionMetadata builder in worker.py).

[fergusfinn]

Superseded by #16, which keeps the throughput work but rewrites the branch into a six-commit linear attribution history with the fixed e2e benchmark numbers in the PR body.

[fergusfinn]

Closing in favor of #16.