[Quant] Add TurboQuant 4-bit (tq4) KV cache quantization#39008
[Quant] Add TurboQuant 4-bit (tq4) KV cache quantization#39008wizzense wants to merge 5 commits intovllm-project:mainfrom
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Add `--kv-cache-dtype tq4` for TurboQuant 4-bit KV cache compression with rotation pre-processing. TQ4 achieves near-optimal MSE within 2.7x of the information-theoretic lower bound by applying a random orthogonal rotation before per-token-head scalar quantization. This first PR stores quantized values as int8 with a 4-bit range (-8 to +7), reusing the existing per-token-head scale infrastructure. A follow-up PR will add int4 bit-packing for 2x additional memory savings. Changes: - Add "tq4" to CacheDType and STR_DTYPE_TO_TORCH_DTYPE - Add KVQuantMode.TQ4 (value=4) to kv_cache_interface - Add triton_reshape_and_cache_flash_tq4() wrapper with rotation pre-processing and QUANT_MAX=7, QUANT_MIN=-8 - Add tq4_rotation.py: deterministic orthogonal matrix via QR decomposition with fixed seed, cached per (head_dim, device) - Wire TQ4 into TritonAttentionImpl (init, forward, kv_cache_update) - Update is_quantized_kv_cache() and kv_cache_uses_per_token_head_scales() - Add tests for config routing, rotation orthogonality, quantization quality, and compression ratio Reference: Zandieh et al., "TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate", arXiv:2504.19874, 2025. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Code Review
This pull request introduces TurboQuant 4-bit (tq4) KV cache quantization, which utilizes rotation pre-processing to achieve near-optimal compression. The implementation includes rotation matrix generation, integration into the V1 attention backend, and a new Triton-based reshape and cache operation. Review feedback identifies a potential cross-device error when using lru_cache for GPU tensors, a numerical stability issue in the rotation matrix generation where torch.sign() could return zero, and a potential crash if key and value head dimensions differ. Additionally, it is noted that performing rotations in eager mode may introduce performance overhead compared to a fused kernel approach.
| @lru_cache(maxsize=4) | ||
| def get_tq4_rotation( | ||
| head_dim: int, | ||
| device: str = "cuda", | ||
| seed: int = 42, | ||
| ) -> torch.Tensor: |
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Using lru_cache with a generic device="cuda" string can lead to correctness issues in multi-GPU environments if the same Python process manages multiple devices (e.g., during unit testing). The cache will store the tensor on the CUDA device that was active during the first call. Subsequent calls with the same arguments but a different active CUDA device will return the tensor from the original device, causing cross-device operation errors.
Consider caching the rotation matrix on the CPU and moving it to the target device within the function without caching the device-resident tensor.
| q, r = torch.linalg.qr(gaussian) | ||
| # Ensure deterministic sign (Haar measure requires this correction) | ||
| d = torch.diag(r) | ||
| ph = d.sign() |
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torch.sign() returns 0 for zero inputs. If any diagonal element of r is exactly zero (which is theoretically possible with torch.randn), ph will contain a zero, and the subsequent multiplication will zero out a column of q, making it non-orthogonal. It is safer to use a method that ensures signs are strictly ±1.
| ph = d.sign() | |
| ph = torch.where(d >= 0, 1.0, -1.0) |
| if rotation_matrix is not None: | ||
| # key: [num_tokens, num_kv_heads, head_size] | ||
| # rotation_matrix: [head_size, head_size] | ||
| key = torch.matmul(key.float(), rotation_matrix.T).to(key.dtype) | ||
| value = torch.matmul(value.float(), rotation_matrix.T).to(value.dtype) |
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This implementation assumes that head_size (for keys) and head_size_v (for values) are identical when applying the rotation matrix. If a model uses different dimensions for keys and values (which is supported by FullAttentionSpec), torch.matmul(value.float(), rotation_matrix.T) will crash because rotation_matrix is sized for head_size.
Additionally, performing these matmuls in eager mode for every KV cache update introduces significant overhead, especially during prefill. In a performance-critical system like vLLM, the rotation should ideally be fused into the quantization kernel or handled more efficiently to avoid extra kernel launches and memory allocations.
- tq4_rotation: cache rotation on CPU, move to target device per-call to avoid cross-device errors in multi-GPU setups (lru_cache pinning) - tq4_rotation: use torch.where(d >= 0, 1.0, -1.0) instead of d.sign() to guarantee strictly ±1 (sign() returns 0 for zero inputs) - triton_reshape_and_cache_flash_tq4: guard value rotation when head_size_v != head_size (models with separate value head dims) - Update tests for new _compute_rotation_cpu cache structure Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
TQ4 previously stored 4-bit quantized values in full int8 bytes, providing no memory savings over INT8 per-token-head quantization. This commit adds actual nibble packing: two signed 4-bit values (-8..7) are encoded into a single uint8 byte (high nibble = even dim, low nibble = odd dim), halving the cache data footprint. Changes: - torch_utils: TQ4 dtype changed from int8 to uint8 (packed bytes) - kv_cache_interface: AttentionSpec/FullAttentionSpec page size calculations halve head_size for TQ4 mode - triton_reshape_and_cache_flash: new _tq4_pack_and_cache Triton kernel loads K/V with stride-2 even/odd pattern, quantizes to 4-bit, and packs pairs into uint8 bytes - triton_attn: forward() unpacks uint8 nibbles to int8 before calling unified_attention; get_kv_cache_shape halves data dim; do_kv_cache_update strips inline scale padding before packing - tests: pack/unpack roundtrip, page size halving, compression ratio validation (~3.76x vs FP16), extreme value coverage The persistent KV cache allocation is now half-sized, enabling 2x more blocks (and thus longer context) for the same GPU memory. The attention kernel itself remains unchanged -- temporary unpacked int8 tensors are created per forward pass and freed immediately. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Preliminary Benchmark ResultsRan GSM8K and MMLU against a live TQ4 KV cache instance on RTX 5090 (32GB). Setup
Accuracy
Note: This model already has 4-bit AWQ weight quantization, so TQ4 KV cache is stacking on top of weight quant. Accuracy delta on an FP16-weight model (e.g., Llama-3.1-8B-Instruct) will be smaller — planning that run next. Memory
Throughput
Summary~5% accuracy drop for 1.94x more KV cache blocks (3.76x vs FP16). The trade-off is favorable for memory-constrained deployments — doubling context window at the cost of a few percentage points. Will follow up with Llama-3.1-8B-Instruct (FP16 weights) benchmarks for a cleaner apples-to-apples comparison against FP8 KV cache. |
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Closing in favor of consolidating into #38479, which has a more complete implementation (fused decode kernels, custom attention backend, comprehensive benchmarks on 2xRTX PRO 6000). Our contributions that may be useful for #38479:
Happy to contribute any of the above directly to #38479. @vibhavagarwal5 let me know what would be most useful. |
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Summary
Adds
--kv-cache-dtype tq4— TurboQuant 4-bit KV cache quantization with nibble packing and rotation pre-processing. Delivers 3.76x compression vs FP16 (1.88x vs FP8) by packing two 4-bit values per uint8 byte.head_size // 2How it works
Cache layout:
[num_blocks, block_size, num_kv_heads, head_size // 2]uint8 + per-token-head float32 scales.Memory savings
For Llama 3.1 8B at 32K context: KV cache drops from 2.0 GB (FP8) to 1.1 GB (TQ4).
Usage
What changed
"tq4"inCacheDType,KVQuantMode.TQ4 = 4, dtype =uint8AttentionSpec.real_page_size_byteshalved for TQ4 (packed dims)_tq4_pack_and_cache— stride-2 even/odd loads, absmax scale, quantize, nibble-pack into uint8tq4_rotation.py— deterministic QR-based random orthogonal matrix (cached on CPU, moved to device per-call for multi-GPU safety)unified_attention— zero changes to the attention kernel itselfDesign decisions
triton_unified_attention.py) is untouched. Packed data is unpacked to int8 inforward()before calling attention. This adds a temporary tensor but keeps the hot path clean. Fused packed-attention is a follow-up.real_page_size_bytesreturns half the data size for TQ4, so vLLM allocates ~2x more blocks for the same VRAM budget.Quality (head_dim=128, 10K random vectors)
Follow-up PRs
Related
Test plan
vllm serve --kv-cache-dtype tq4with Llama-3.1-8B (requires GPU CI)🤖 Generated with Claude Code