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A high-throughput and memory-efficient inference and serving engine for LLMs

Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git

Synced Nov 2, 09:45 UTC

[Bugfix] Use storage_block_size in KV cache reshape for compressed specs (DeepSeek V4) (#43988)

Signed-off-by: zixi-qi <zixi@inferact.ai> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

qizixi committed May 29, 2026 at 11:14 UTC d07ad0693bc7f5c993bfb473f02a71df8f8c7109
1 file changed +6 -1
vllm/v1/worker/gpu/attn_utils.py
+6 -1
@@ -199,7 +199,12 @@ def _reshape_kv_cache(
199
200 if isinstance(kv_cache_spec, AttentionSpec):
201 has_attn = True
202 - num_blocks_per_kv_block = kv_cache_spec.block_size // kernel_block_size
202 + # Use storage_block_size: it equals block_size for uncompressed
203 + # specs but is smaller for compressed ones (DeepSeek V4), which
204 + # store block_size tokens in block_size // compress_ratio slots.
205 + num_blocks_per_kv_block = (
206 + kv_cache_spec.storage_block_size // kernel_block_size
207 + )
208 kernel_num_blocks = num_blocks * num_blocks_per_kv_block
209 kv_cache_shape = group.backend.get_kv_cache_shape(
210 kernel_num_blocks,