[Attention][TurboQuant] Optimize k8v4 decode attention with GQA head grouping#40792
[Attention][TurboQuant] Optimize k8v4 decode attention with GQA head grouping#40792hoseung2 wants to merge 3 commits intovllm-project:mainfrom
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Signed-off-by: hoseung-kim <hoseung.kim@navercorp.com>
Signed-off-by: hoseung-kim <hoseung.kim@navercorp.com>
hoseung2
requested review from
LucasWilkinson,
MatthewBonanni,
mgoin,
pavanimajety,
robertgshaw2-redhat and
yewentao256
as code owners
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Code Review
This pull request introduces a new GQA-grouped Triton kernel for TurboQuant decoding to improve performance for FP8 keys, along with corresponding unit tests. The review feedback highlights a potential bug in the head grouping logic that could cause incorrect results with specific head configurations and provides a more robust mapping implementation. Furthermore, the reviewer identified several blocks of unreachable code within the new kernel—specifically the MSE-quantized key path and 3-bit value quantization logic—that should be removed to simplify the implementation and reduce compilation overhead.
| VALID_BLOCK_H: tl.constexpr = BLOCK_H if KV_GROUP_SIZE > BLOCK_H else KV_GROUP_SIZE | ||
| kv_head = head_group_id // tl.cdiv(KV_GROUP_SIZE, BLOCK_H) | ||
| cur_head = head_group_id * VALID_BLOCK_H + tl.arange(0, BLOCK_H) | ||
| mask_h = cur_head < (head_group_id + 1) * VALID_BLOCK_H | ||
| mask_h = mask_h & (cur_head < Q_HEAD_NUM) |
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The current GQA grouping logic is fragile and will produce incorrect results if KV_GROUP_SIZE is greater than BLOCK_H (16) and not a multiple of it (e.g., KV_GROUP_SIZE=24). In such cases, CTAs will "bleed" across KV head boundaries because the mapping of head_group_id to kv_head and cur_head assumes perfect alignment. For example, if KV_GROUP_SIZE=24, the second CTA (head_group_id=1) would process heads 16-31 using kv_head=0 data, even though heads 24-31 belong to kv_head=1.
A more robust approach is to explicitly calculate the number of CTAs needed per KV head and map them accordingly. This ensures that each CTA only processes query heads belonging to its assigned KV head.
| VALID_BLOCK_H: tl.constexpr = BLOCK_H if KV_GROUP_SIZE > BLOCK_H else KV_GROUP_SIZE | |
| kv_head = head_group_id // tl.cdiv(KV_GROUP_SIZE, BLOCK_H) | |
| cur_head = head_group_id * VALID_BLOCK_H + tl.arange(0, BLOCK_H) | |
| mask_h = cur_head < (head_group_id + 1) * VALID_BLOCK_H | |
| mask_h = mask_h & (cur_head < Q_HEAD_NUM) | |
| # Map head_group_id → KV head + Q head range | |
| heads_per_kv_head: tl.constexpr = tl.cdiv(KV_GROUP_SIZE, BLOCK_H) | |
| kv_head = head_group_id // heads_per_kv_head | |
| group_idx = head_group_id % heads_per_kv_head | |
| cur_head = kv_head * KV_GROUP_SIZE + group_idx * BLOCK_H + tl.arange(0, BLOCK_H) | |
| mask_h = (cur_head < (kv_head + 1) * KV_GROUP_SIZE) & (cur_head < Q_HEAD_NUM) |
| else: | ||
| # MSE unpack → centroid gather → k_dequant [BLOCK_KV, BLOCK_D] | ||
| mse_addrs0 = slot_bases[:, None] + mse_byte_idx[None, :] | ||
| mse_raw0 = tl.load( | ||
| KV_cache_ptr + mse_addrs0, | ||
| mask=kv_mask[:, None] & d_mask[None, :], | ||
| other=0, | ||
| ).to(tl.int32) | ||
| mse_raw1 = tl.load( | ||
| KV_cache_ptr + mse_addrs0 + 1, | ||
| mask=kv_mask[:, None] & d_mask[None, :], | ||
| other=0, | ||
| ).to(tl.int32) | ||
| raw16 = mse_raw0 | (mse_raw1 << 8) | ||
| mse_idx = (raw16 >> mse_bit_shift[None, :]) & mse_mask | ||
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| c_vals = tl.load( | ||
| Centroids_ptr + mse_idx, | ||
| mask=kv_mask[:, None] & d_mask[None, :], | ||
| other=0.0, | ||
| ) | ||
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| if NORM_CORRECTION: | ||
| c_norm_sq = tl.sum( | ||
| tl.where(d_mask[None, :], c_vals * c_vals, 0.0), axis=1 | ||
| ) | ||
| c_inv_norm = 1.0 / tl.sqrt(c_norm_sq + 1e-16) | ||
| c_vals = c_vals * c_inv_norm[:, None] | ||
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| # term1 = q_rot @ c_vals^T : [BLOCK_H, BLOCK_KV] | ||
| term1 = tl.dot(q_rot.to(tl.float16), tl.trans(c_vals.to(tl.float16))) | ||
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| norm_bases = slot_bases + MSE_BYTES | ||
| n_lo = tl.load(KV_cache_ptr + norm_bases, mask=kv_mask, other=0).to( | ||
| tl.uint16 | ||
| ) | ||
| n_hi = tl.load(KV_cache_ptr + norm_bases + 1, mask=kv_mask, other=0).to( | ||
| tl.uint16 | ||
| ) | ||
| vec_norms = (n_lo | (n_hi << 8)).to(tl.float16, bitcast=True).to(tl.float32) | ||
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| scores = vec_norms[None, :] * term1.to(tl.float32) * ATTN_SCALE | ||
| scores = tl.where(mask_h[:, None] & kv_mask[None, :], scores, -float("inf")) | ||
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This else block contains logic for MSE-quantized keys using tl.dot. However, the launcher (line 842) only routes to this kernel when key_fp8 is True. This makes the entire MSE path dead code in this kernel. Since the PR description explicitly states that MSE-key presets should continue using the original scalar kernel due to performance regressions, this unreachable code should be removed to maintain clarity and reduce compilation overhead.
| if VQB == 3: | ||
| val_addrs0 = val_bases[:, None] + val_byte_idx[None, :] | ||
| val_raw0 = tl.load( | ||
| KV_cache_ptr + val_addrs0, | ||
| mask=kv_mask[:, None] & d_mask[None, :], | ||
| other=0, | ||
| ).to(tl.int32) | ||
| val_raw1 = tl.load( | ||
| KV_cache_ptr + val_addrs0 + 1, | ||
| mask=kv_mask[:, None] & d_mask[None, :], | ||
| other=0, | ||
| ).to(tl.int32) | ||
| raw16_val = val_raw0 | (val_raw1 << 8) | ||
| v_idx = ((raw16_val >> val_bit_shift[None, :]) & 0x7).to(tl.float32) | ||
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| sc_bases = val_bases + VAL_DATA_BYTES | ||
| sc_lo = tl.load(KV_cache_ptr + sc_bases, mask=kv_mask, other=0).to( | ||
| tl.uint16 | ||
| ) | ||
| sc_hi = tl.load(KV_cache_ptr + sc_bases + 1, mask=kv_mask, other=0).to( | ||
| tl.uint16 | ||
| ) | ||
| v_scales = ( | ||
| (sc_lo | (sc_hi << 8)).to(tl.float16, bitcast=True).to(tl.float32) | ||
| ) | ||
| zr_lo = tl.load(KV_cache_ptr + sc_bases + 2, mask=kv_mask, other=0).to( | ||
| tl.uint16 | ||
| ) | ||
| zr_hi = tl.load(KV_cache_ptr + sc_bases + 3, mask=kv_mask, other=0).to( | ||
| tl.uint16 | ||
| ) | ||
| v_zeros = (zr_lo | (zr_hi << 8)).to(tl.float16, bitcast=True).to(tl.float32) | ||
| values = v_idx * v_scales[:, None] + v_zeros[:, None] |
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The if VQB == 3 block is dead code because the only preset currently routed to this kernel is turboquant_k8v4, which uses 4-bit values (VQB=4). The 3-bit value presets (turboquant_3bit_nc) are MSE-based and thus use the original scalar kernel. This unreachable logic should be removed.
| VALID_BLOCK_H = min(BLOCK_H, kv_group_size) | ||
| head_groups = triton.cdiv(Hq, VALID_BLOCK_H) |
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To support the robust GQA grouping logic suggested in the kernel, the launcher's grid calculation should be updated to ensure CTAs are correctly partitioned per KV head.
| VALID_BLOCK_H = min(BLOCK_H, kv_group_size) | |
| head_groups = triton.cdiv(Hq, VALID_BLOCK_H) | |
| heads_per_kv_head = triton.cdiv(kv_group_size, BLOCK_H) | |
| head_groups = Hk * heads_per_kv_head |
Signed-off-by: hoseung-kim <hoseung.kim@navercorp.com>
1 participant
Summary
This PR optimizes the
turboquant_k8v4decode attention path by addingGQA head grouping and
tl.dot-based QK/PV computation.The new kernel follows the same grouping pattern used by vLLM's standard
Triton decode attention kernel
triton_decode_attention.py::_fwd_grouped_kernel_stage1:multiple query heads that share the same KV head are processed in one CTA,
allowing K/V loads to be reused across the GQA group.
Across the tested A100 and H100 configurations, this improves
turboquant_k8v4throughput by +16.5% to +27.2%, with the largest gainson Qwen3-32B where the GQA ratio is higher.
Current kernel behavior
The existing TurboQuant decode kernel processes one query head per CTA:
For GQA models, multiple query heads share the same KV head. Since each query
head is handled by a separate CTA, CTAs in the same GQA group independently load
the same K/V cache data. For example:
This means the same KV head can be loaded 4x or 8x for a single decode step.
The current kernel also computes QK and PV with element-wise reductions:
This keeps the implementation simple, but it does not use the tensor-core
friendly
tl.dotpath used by the standard Triton decode attention kernel.Change
This PR adds
_tq_grouped_decode_stage1for the FP8-key TurboQuant path.Instead of launching one CTA per query head, the grouped kernel processes
BLOCK_Hquery heads per CTA:With
BLOCK_H = 16andBLOCK_KV = 16, this allows:tl.dottl.dotThe original
_tq_decode_stage1kernel is kept unchanged and remains thefallback for MHA and MSE-key TurboQuant presets.
Scope: FP8-key path only
This optimization is currently enabled only when both conditions are true:
In that case, the launcher routes to the grouped kernel:
The MSE-key presets (
turboquant_{4bit,k3v4,3bit}_nc) still use the originalscalar kernel. Those paths include additional per-token dequantization work
(bit unpacking, centroid lookup, and norm correction), and simply increasing
the KV tile size to
BLOCK_KV = 16regressed performance in microbenchmarks.Results
Benchmarked with
vllm bench throughputusing 200 prompts,--gpu-memory-utilization 0.90, and--attention-config '{"flash_attn_version": 2}'.The BF16 sanity runs with
--kv-cache-dtype autostayed within measurementnoise, confirming that the change does not affect the non-TurboQuant path.
turboquant_k8v4throughputValues are total throughput in tok/s.
The gain is larger on Qwen3-32B than Qwen3-4B because the grouped kernel
amortizes K/V loads across the GQA group. Qwen3-4B uses GQA=4, while
Qwen3-32B uses GQA=8.
Kernel changes
vllm/v1/attention/ops/triton_turboquant_decode.py_tq_grouped_decode_stage1for the FP8-key TurboQuant pathkv_group_size > 1 and key_fp8_tq_decode_stage1as the fallback for MHA and MSE-key presetstl.dotfor both QK scoring and PV accumulation in the grouped pathTest plan
tests/quantization/test_turboquant.pyturboquant_k8v4withkv_group_size=4and8those presets to the new kernel
All TurboQuant quantization tests pass locally:
Implementation notes
BLOCK_H = 16: follows the standard grouped decode kernel and enablestensor-core-friendly QK/PV shapes
BLOCK_KV = 16: smallest KV tile size used for thetl.dotpathnum_warps = 4,num_stages = 2: same configuration as the referencegrouped decode kernel
I also tried larger
BLOCK_KVvalues such as 32 and 64. They were slightlyfaster in some microbenchmarks, but increased register pressure and occasional
spills, so this PR keeps
BLOCK_KV = 16.Reproduction
Related
_fwd_grouped_kernel_stage1