BaseKVCacheMethod.apply_kv_cache

[captainpete]

What this is

Following up on @kylesayrs's suggestion in #sig-quantization for scoping out what a generalized apply method on BaseKVCacheMethod looks like; one that's responsible for transforms + quantization + writing to the paged KV cache, and replacing the hardcoded flag approach in #1.

This is a design sketch for the KV cache transform path: so that transforms on standard-format KV caches (FP16/BF16 for now) have a home without touching attention kernels or backends. A side effect of centralising this is that correctness invariants (e.g. scale computation ordering relative to transforms) can be enforced once at model load, so each transform implementation doesn't need to get it right independently.

I've based this on the previous PR so we can hook something up end-to-end as a worked example. Keen to get feedback on the interface before proposing anything upstream. There were a few approaches to this but this one fell out as the cleanest so far. If we're happy with the interface I can drop the Hadamard commits for an upstream PR.

The interface

class BaseKVCacheMethod:
    def apply_query(
        self,
        layer: torch.nn.Module,
        query: torch.Tensor,        # [num_tokens, num_heads, head_size]
    ) -> torch.Tensor:
        return query  # identity by default

    def apply_kv_cache(
        self,
        layer: torch.nn.Module,
        key: torch.Tensor,          # [num_tokens, num_kv_heads, head_size]
        value: torch.Tensor,        # [num_tokens, num_kv_heads, head_size_v]
        kv_cache: torch.Tensor,
        slot_mapping: torch.Tensor,
    ) -> None:
        layer.impl.do_kv_cache_update(layer, key, value, kv_cache, slot_mapping)

apply_query is called unconditionally on every forward pass, after RoPE and reshape, before the attention kernel. apply_kv_cache is called only when a cache write should occur. The two concerns are explicitly separate: Q transform runs unconditionally regardless of whether a cache write occurs.

Both methods are called from apply_kv_cache_update, a new custom op inside Attention.forward() that replaces the inline unified_kv_cache_update call for MHA. The op handles all the existing guards internally (forward_includes_kv_cache_update, kv_sharing_target_layer_name, slot_mapping) and returns a dummy dependency tensor for torch.compile ordering.

The worked example: Hadamard rotation from PR1

class CompressedTensorsKVCacheMethod(BaseKVCacheMethod):
    def apply_query(self, layer, query):
        scheme = getattr(layer, "_ct_kv_transform", None)
        if scheme is not None and scheme.type == "hadamard":
            query = ops.hadacore_transform(query)
        return query

    def apply_kv_cache(self, layer, key, value, kv_cache, slot_mapping):
        scheme = getattr(layer, "_ct_kv_transform", None)
        if scheme is not None and scheme.type == "hadamard":
            key = ops.hadacore_transform(key)
        super().apply_kv_cache(layer, key, value, kv_cache, slot_mapping)

_ct_kv_transform is a TransformScheme | None set at create_weights time by reading the checkpoint's transform_config. All validation (unsupported scheme types, head_dim mismatches, power-of-two checks, ROCm guard) raises at model load so failures surface before serving.

Edge cases found while hooking this up

apply_query split. An earlier version had a single apply_kv_cache that transformed both Q and K. This worked in practice for standard decoder-only inference: every forward pass that produces new tokens also writes new K/V to the cache, so the write-side gate (should_write) fires on every step and Q is never left untransformed. But it coupled two independent concerns: whether Q needs rotating before attention, and whether there is a cache write to perform. The coupling was accidental. Separating them into apply_query (unconditional) and apply_kv_cache (write-side only) makes the intent explicit and avoids relying on that coincidence holding in all serving configurations.

calculate_kv_scales ordering. Because all transforms go through apply_kv_cache, the incompatibility with calculate_kv_scales=True can be caught once: create_weights raises a ValueError if both are active. Any future transform added via this interface gets the check automatically. Related: vllm-project#34863, vllm-project#39418.

ROCm/Triton fused kernel. RopeKVCacheFusionPass matched unified_kv_cache_update in the traced graph; since MHA now calls apply_kv_cache_update instead, the pattern never fired. Fixed by updating the pattern to match apply_kv_cache_update. Layers whose quant method overrides either apply_query or apply_kv_cache are excluded from fusion -- the fused triton kernel has no injection point between RoPE and the cache write. The exclusion check is:

qm = getattr(layer, "quant_method", None)
if qm is not None and (
    type(qm).apply_kv_cache is not BaseKVCacheMethod.apply_kv_cache
    or type(qm).apply_query is not BaseKVCacheMethod.apply_query
):
    continue  # use the unfused apply_kv_cache_update path

So any quant method that overrides either method is automatically excluded and the fusion pass doesn't need knowledge of CT internals.

splitting_ops. apply_kv_cache_update has the same per-layer string parameter as unified_kv_cache_update and needs to be in splitting_ops for the same reason.

Pre-existing mypy false positive in compilation.py. PassConfig.enabled_fusions triggers an arg-type error under --follow-imports skip. Fixed with # type: ignore[arg-type]. Confirmed pre-existing against upstream HEAD.

In-flight PRs that interact with this

vllm-project#39074 -- dynamic INT2/INT4 KV transforms; a parallel path for checkpoint-free dynamic quantisation.

vllm-project#34863, vllm-project#39418 -- scale propagation bugs in CompressedTensorsKVCacheMethod; see calculate_kv_scales above.

vllm-project#36858 -- RoPE+FP8+cache fusion for FlashInfer; its new RopeQuantReshapeKVCachePattern also matches unified_kv_cache_update and will need the same one-line update to apply_kv_cache_update if a redesign like this happens first. It restructures RopeKVCacheFusionPass.__init__ into if cuda / elif rocm branches with for is_neox inside -- the layer-level guard would sit between fused_rope_kvcache_supported() and for is_neox in that new structure. The guard is also correct for the CUDA path: FlashInfer FP8 quantization is baked into the kernel via quant_key, not apply_kv_cache, so FP8 layers are not incorrectly excluded.

vllm-project#38646 -- RoPE+cache fusion for MLA; uses unified_mla_kv_cache_update which I didn't touch, it looks compatible as-is.

vllm-project#36007, vllm-project#36244 -- sentinel string proposals for unified_kv_cache_update; same would apply to apply_kv_cache_update if those land.

Out of scope

• MLA
• Other model architectures. The worked example uses is_hadamard_transform_weight which is currently llama-only from PR1. Other architectures using CompressedTensorsKVCacheMethod with a transform_config would need equivalent lines for introducing Hadamard.
• apply_query and apply_kv_cache have the same call-site scope: the use_output=True MHA path with fresh K/V being written. The use_output=False path (MLA, forward_includes_kv_cache_update backends) goes straight to the attention kernel; Q transforms there would need to be handled inside the kernel. Cross-attention with pre-cached K (encoder-decoder) also skips both hooks since key is None. These are the same architectural boundaries the PR already targets.

Tests

.venv/bin/python -m pytest tests/quantization/test_hadamard_kv_dispatch.py -v
PATH="$(pwd)/.venv/bin:$PATH" .venv/bin/python -m pytest tests/quantization/test_compressed_tensors.py -v
VLLM_WORKER_MULTIPROC_METHOD=spawn PATH="$(pwd)/.venv/bin:$PATH" .venv/bin/python tests/quantization/test_hadamard_kq_r3_e2e.py
PATH="$(pwd)/.venv/bin:$PATH" VLLM_WORKER_MULTIPROC_METHOD=spawn .venv/bin/python -m pytest tests/compile/passes/test_rope_kvcache_fusion.py -v

Using my dusty RTX 3090 struggling on 2/3 fans in the Australian heat:

17 passed in 5.37s
17 passed, 2 skipped (kv_cache_fp8_per_attn_head requires FA3; w4a8_fp8 requires SM90)
E2E: 240.2 tok/s, coherent outputs, rotation enabled
32 skipped (ROCm/aiter hardware not available)

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[kylesayrs]

From a semantic perspective, it's definitely a little confusing to apply query in a KV cache method, but I think from an implementation perspective I think this is natural and makes sense, as we don't have (and probably shouldn't have) an interface for quantized attention.