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| # 64 segments stay faster until the 16-segment grid has about two | ||
| # programs per CU (measured on MI325X, 304 CUs). | ||
| if on_gfx942(): |
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Can you use get_cdna_version() == 3 here instead of on_gfx942()? Also, have you measured gfx950? If 64 segments are faster there too, can we make this in (3, 4)?
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Updated to get_cdna_version() == 3 and adjusted the tests for CDNA2/3/4 selection in 0e9224d.
I only have MI325X (gfx942), so I haven't benchmarked gfx950. I'll keep CDNA4 disabled until it's measured.
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Verified on 0e9224d: get_cdna_version() == 3 gates the 64-segment path, and the occupancy formula is unchanged from the previous revision. MI325X (gfx942, get_cdna_version() == 3), ROCm 10.0 image vllm/vllm-openai-rocm:v0.33.0 (HIP 7.15, torch 2.13.0, triton 3.8.0), PR head files overlaid. pytest tests/v1/attention/test_triton_attn_segments.py tests/v1/attention/test_triton_attn_metadata_builder.py: 60 passed, 0 failed/skipped. The CDNA3 cases (64 vs 16 segments at the occupancy boundary) pass, and the CDNA2 and CDNA4 cases stay at 16 segments. CDNA4 stays disabled until it is measured on gfx950.
AI assistance was used. Signed-off-by: Oliver Yu <[email protected]>
Keep the 64-segment path scoped to tested CDNA3 hardware. Update metadata-builder coverage for CDNA2, CDNA3 and CDNA4. AI assistance was used. Signed-off-by: Oliver Yu <[email protected]>
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Overview
TritonAttentionMetadataBuilderalready uses 64 instead of 16 split-KV segments when a decode batch under-fills the device, but only on SM120 (#58482). This enables the same rule on gfx942.Claims
TRITON_ATTNon MI325X: TPOT -14% at 8K context, -36% at 32K, -54% at 100K (Qwen3-8B, bf16 KV).(2 * num_compute_units - 1) // (16 * num_kv_heads)requests, 4 for 8 KV heads).Validation
Kernel, GB/s, 16 vs 64 segments (graph captured, MI325X):
End to end: Qwen3-8B (dummy weights, YaRN 131K override),
--attention-backend TRITON_ATTN, batch 1, median TPOT over 2-4 requests, two runs with the GPUs swapped:Correctness: passkey retrieval at 30K tokens, batch 1 (the 64-segment path), Qwen3-1.7B: 30/30 before and after, 29/30 outputs byte-identical. GSM8K (1319, Qwen3-1.7B) was run with this and the unified-attention PR together: 888 -> 895.
Tests (MI325X, tests tree = latest main + this change):
tests/v1/attention/test_triton_attn_segments.pyandtest_triton_attn_metadata_builder.py: 58 passed. The two existing tests that mockis_cuda=Truenow also mockis_rocm=False; without that, 16 of their cases take the new ROCm branch on a ROCm runner.Not covered: fp8 KV cache, speculative decoding with a real draft model (an ngram run, k=3, batch 1, 32K: 9.55 -> 6.90 ms; random prompts, so this only measures the verify path), MI300X / MI308X.
Details
The cutoff counts requests, like the SM120 rule. gfx942 uses two programs per CU instead of one because 64 segments stayed faster until the 16-segment grid reached about that point (measured on MI325X, 304 CUs). The new branch runs only when
on_gfx942().Related: #58482 and #56148 (merged, SM120 and multi-query split-K). No open PR changes the segment count on ROCm.
AI assistance was used.
Pull Request Checklist
I used vLLM's
/pr-checklistskill. (Mandatory for agents, optional for humans).AI assistance was used during the creation of this PR.
Design Fit: Minimizes impact on core components, reuses existing functionality, and justifies added complexity.
Testing and Validation: Validates the change and ensures any added tests are meaningful and reliable, with CI coverage or documented CI resource constraints and validation performed outside CI.
Code Quality and Style: Keeps code and comments clear and concise, and updates relevant documentation and examples.
Pull Request Contents: Includes a brief summary and relevant links, supports claims with evidence, explains root causes and implementation trade-offs, and follows the contributing guide.