[Bugfix][Attention][TurboQuant] Pad head_dim to power-of-2 for WHT

[TheTom]

Purpose

Fix a latent correctness bug in the TurboQuant rotation path for models with non-power-of-2 head_dim. Reproduced on microsoft/phi-2 (head_dim=80) with turboquant_4bit_nc:

File "vllm/v1/attention/backends/turboquant_attn.py", line 380, in do_kv_cache_update
    y = x_hat @ PiT
RuntimeError: mat1 and mat2 shapes cannot be multiplied (16384x80 and 128x128)

Root cause

_build_hadamard_cached(d) constructs the Sylvester Hadamard by doubling H until H.shape[0] >= d, then normalizes by sqrt(d):

H = torch.tensor([[1.0]])
while H.shape[0] < d:
    H = torch.cat([torch.cat([H, H], 1), torch.cat([H, -H], 1)], 0)
return (H / math.sqrt(d)).to(...)

For d=80 the loop overshoots to 128×128 and the result is normalized by 1/sqrt(80) — the matrix is the wrong size and not orthonormal at the constructed size. _ensure_on_device stores this 128×128 tensor as layer._tq_PiT; the MSE-K decode/store kernels then attempt q @ PiT with q at width 80 and PiT at 128×128.

The bug is path-specific:

• MSE-K presets (turboquant_4bit_nc, turboquant_3bit_nc, turboquant_k3v4_nc) call the rotation GEMM and crash at engine init.
• FP8-K (turboquant_k8v4) bypasses the WHT entirely (in-kernel FP8 cast, no rotation), so the broken matrix is built but never multiplied — the model loads but wastes VRAM on the unused buffer.

Summary of changes

• Add padded_head_dim = next_power_of_2(head_dim) and needs_padding to TurboQuantConfig. Pow-2 head_dim is identity.
• On the MSE-K path, run the WHT in padded_head_dim space throughout: zero-pad K and V at the kernel-launch boundary, run store/decode/continuation kernels with D=padded_head_dim, and slice the decode output back to head_dim before returning. Padded V columns hold zero quantization indices and contribute nothing to the reduction.
• FP8-K path is untouched (raw head_dim, no rotation, kernel masks non-pow-2 loads directly).
• For pow-2 head_dim (the common case: 64, 128, 256 — every current Qwen3, Llama, Mistral target), padded_head_dim == head_dim and every code path reduces to the prior behavior. Byte-counts in key_packed_size / value_packed_size are bitwise-identical.

Duplicate-work check

Searched open PRs and issues touching TurboQuant + head_dim / Hadamard / Phi-2 / non-power-of-2 before opening this PR. The closest references are:

• Tracking issue [Tracking issue]: TurboQuant/HIGGS Attention follow-ups #40069 (TurboQuant follow-ups) — does not list head_dim padding.
• PR [KV Cache][TurboQuant] Add official 3-bit and 4-bit grouped TurboQuant modes #39890 (erhan1209) adds new "official" 3-bit/4-bit grouped TQ presets but does not change _build_hadamard_cached or address non-pow-2 dim.
• PR [Attention][TurboQuant] Optimize k8v4 decode attention with GQA head grouping #40792 (hoseung2) optimizes k8v4 decode with GQA head grouping — orthogonal kernel optimization, unaffected by this fix.

No open PR addresses the rotation-shape mismatch on non-pow-2 head_dim. Issue #41413 was filed alongside this PR.

Test Plan / Results

Tested on AMD MI300X (gfx942), ROCm 7.2, vLLM ROCm 7.2.1 wheels.

Bug reproduction (Phi-2 d=80 + turboquant_4bit_nc):

Command (control + treatment):

python3 -c "
import os; os.environ['VLLM_ROCM_USE_AITER_FP4BMM']='0'
from vllm import LLM, SamplingParams
llm = LLM(model='microsoft/phi-2', dtype='bfloat16',
          kv_cache_dtype='turboquant_4bit_nc', max_model_len=2048,
          gpu_memory_utilization=0.40)
print(llm.generate(['The capital of France is'],
                   SamplingParams(max_tokens=32, temperature=0.0))[0].outputs[0].text)
"

Branch	Result
upstream main c2fb01331	RuntimeError: mat1 and mat2 shapes cannot be multiplied (16384x80 and 128x128)
this PR	Paris. ✓

No regression — Qwen3-8B (d=128) 3-chunk PPL on wikitext-2-raw/wiki.test.raw @ 8K:

Command:

python3 -c "
import os, math
os.environ['VLLM_ROCM_USE_AITER_FP4BMM']='0'
from vllm import LLM, SamplingParams
llm = LLM(model='Qwen/Qwen3-8B', dtype='bfloat16', max_model_len=8192,
          kv_cache_dtype='<preset>', gpu_memory_utilization=0.40,
          enable_prefix_caching=False, max_num_batched_tokens=512)
tok = llm.get_tokenizer()
ids = tok.encode(open('wiki.test.raw').read(), add_special_tokens=False)
chunks = [ids[i:i+8191] for i in range(0, len(ids)-8191, 8191)][:3]
total_lp, total_tok = 0.0, 0
sp = SamplingParams(max_tokens=1, temperature=0.0, prompt_logprobs=1)
for ch in chunks:
    o = llm.generate({'prompt_token_ids': ch}, sp, use_tqdm=False)[0]
    for i, lp_dict in enumerate(o.prompt_logprobs[1:]):
        if lp_dict and ch[i+1] in lp_dict:
            total_lp += lp_dict[ch[i+1]].logprob
            total_tok += 1
print(math.exp(-total_lp/total_tok))
"

Preset	upstream main	this PR	Δ
turboquant_k8v4	7.8630	7.8630	0
turboquant_4bit_nc	7.9041	7.9041	0

Both PPLs are byte-identical, same token count (24570).

Unit tests on this PR:

python3 -m pytest tests/quantization/test_turboquant.py -v

→ 130/130 passed in 28.34s.

Includes 14 new tests for the non-pow-2 head_dim path:

• padded_head_dim is identity for pow-2 head_dim (64, 128, 256)
• non-pow-2 head_dim rounds up correctly (80→128, 96→128, 192→256, 40→64)
• MSE preset at head_dim=80: key_packed_size=66, value_packed_size=68 (sized to padded 128)
• FP8 preset at head_dim=80: key_packed_size=80, value_packed_size=44 (head_dim-sized, FP8 path)
• Store + decode round-trip across {turboquant_k8v4, turboquant_4bit_nc} × {80, 96}: cosine similarity vs the stored V passes the same thresholds as the pow-2 case (>0.95 FP8, >0.85 MSE) and the returned tensor is sliced back to head_dim

AI assistance

This PR was prepared with AI assistance (Anthropic Claude). Each line of the diff was reviewed by the human submitter, the bug reproduction was run on the human's hardware (AMD MI300X dev cloud), and the no-regression PPL numbers are from runs the human supervised. Commits carry a Co-authored-by: Claude trailer per AGENTS.md.

Fixes #41413.

cc @vibhavagarwal5

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[gemini-code-assist]

Code Review

This pull request introduces support for non-power-of-2 head dimensions in the TurboQuant quantization path by padding to the next power of 2 for Walsh-Hadamard Transform (WHT) operations. Key changes include updating the configuration logic to handle padded dimensions, modifying Triton kernels to perform zero-padding and slicing, and adding comprehensive tests for these scenarios. A performance issue was noted regarding the allocation of workspace buffers in the decode path, which currently uses the unpadded head size and will trigger unnecessary re-allocations for models requiring padding.

[TheTom]

Thanks @gemini-code-assist — good catch. Fixed in 2c6fc7e: _decode_attention now sizes the mid_o_buf / output_buf / lse_buf workspace allocations to padded_head_dim on the MSE-K path (FP8-K stays at head_size since that path never enters WHT space). Pow-2 head_dim is unchanged because padded_head_dim == head_size.

Re-verified on Phi-2 (d=80) + turboquant_4bit_nc: still produces Paris. and no per-decode torch.empty (workspace buffer-fits checks now pass).

[gemini-code-assist]

Thanks for the update, @TheTom. It's great to hear that the fix is verified on Phi-2 and that the workspace buffer-fits checks are now passing. The padding approach for head_dim in the WHT path seems robust. I'll mark this as ready for CI.

[claude]

Claude Code Review

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