Log KV cache GiB usage and warn when max_num_seqs exceeds capacity#38408
Log KV cache GiB usage and warn when max_num_seqs exceeds capacity#38408ashishkamra wants to merge 1 commit intovllm-project:mainfrom
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This pull request refactors the KV cache concurrency calculation by introducing helper functions and adds detailed logging for GPU KV cache memory usage and workload capacity. A critical logic error was identified in the _blocks_per_request function, which incorrectly calculates the number of blocks for models with multiple KV cache groups, leading to underestimated concurrency and overestimated memory requirements. A suggestion was provided to simplify this calculation based on a single representative group's specification.
| def _blocks_per_request( | ||
| vllm_config: VllmConfig, kv_cache_config: KVCacheConfig | ||
| ) -> float: | ||
| """ | ||
| Get the maximum concurrency for the given KV cache configuration. | ||
| ) -> int: | ||
| """Return number of blocks needed per request at max_model_len. | ||
|
|
||
| Note: the num_layer_per_group factor appears in both numerator and | ||
| denominator and cancels out, so the result is correct regardless of | ||
| whether page_size_bytes already includes all layers (as in | ||
| UniformTypeKVCacheSpecs) or is per-layer. | ||
| """ | ||
| num_layer_per_group = max( | ||
| len(group.layer_names) for group in kv_cache_config.kv_cache_groups | ||
| ) | ||
| page_size = kv_cache_config.kv_cache_groups[0].kv_cache_spec.page_size_bytes | ||
| max_memory_usage_per_request = num_layer_per_group * max_memory_usage_bytes( | ||
| vllm_config, (group.kv_cache_spec for group in kv_cache_config.kv_cache_groups) | ||
| vllm_config, | ||
| (group.kv_cache_spec for group in kv_cache_config.kv_cache_groups), | ||
| ) | ||
| memory_per_block = ( | ||
| kv_cache_config.kv_cache_groups[0].kv_cache_spec.page_size_bytes | ||
| * num_layer_per_group | ||
| memory_per_block = page_size * num_layer_per_group | ||
| return cdiv(max_memory_usage_per_request, memory_per_block) |
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The calculation for _blocks_per_request seems incorrect for models with multiple KV cache groups (e.g., hybrid models). It appears to return num_groups * cdiv(max_model_len, block_size) instead of just cdiv(max_model_len, block_size).
This will cause get_max_concurrency_for_kv_cache_config to underestimate the maximum concurrency by a factor of num_groups, and the new logging for needed_kv_bytes will overestimate the required memory by the same factor.
The number of blocks required for a sequence should be independent of the number of KV cache groups, as blocks from the pool are allocated per sequence, and each block from the pool serves all groups (via memory sharing across layers).
A simpler and more correct implementation would calculate the blocks needed per layer for a single sequence, assuming all groups share the same block size.
| def _blocks_per_request( | |
| vllm_config: VllmConfig, kv_cache_config: KVCacheConfig | |
| ) -> float: | |
| """ | |
| Get the maximum concurrency for the given KV cache configuration. | |
| ) -> int: | |
| """Return number of blocks needed per request at max_model_len. | |
| Note: the num_layer_per_group factor appears in both numerator and | |
| denominator and cancels out, so the result is correct regardless of | |
| whether page_size_bytes already includes all layers (as in | |
| UniformTypeKVCacheSpecs) or is per-layer. | |
| """ | |
| num_layer_per_group = max( | |
| len(group.layer_names) for group in kv_cache_config.kv_cache_groups | |
| ) | |
| page_size = kv_cache_config.kv_cache_groups[0].kv_cache_spec.page_size_bytes | |
| max_memory_usage_per_request = num_layer_per_group * max_memory_usage_bytes( | |
| vllm_config, (group.kv_cache_spec for group in kv_cache_config.kv_cache_groups) | |
| vllm_config, | |
| (group.kv_cache_spec for group in kv_cache_config.kv_cache_groups), | |
| ) | |
| memory_per_block = ( | |
| kv_cache_config.kv_cache_groups[0].kv_cache_spec.page_size_bytes | |
| * num_layer_per_group | |
| memory_per_block = page_size * num_layer_per_group | |
| return cdiv(max_memory_usage_per_request, memory_per_block) | |
| def _blocks_per_request( | |
| vllm_config: VllmConfig, kv_cache_config: KVCacheConfig | |
| ) -> int: | |
| """Return number of blocks needed per request at max_model_len. | |
| Note: This assumes that all KV cache groups have the same block size. | |
| """ | |
| # All groups must have same block size. We take the spec from the first | |
| # group as representative for block size and max memory usage calculation | |
| # per layer. | |
| spec = kv_cache_config.kv_cache_groups[0].kv_cache_spec | |
| max_memory_per_layer = spec.max_memory_usage_bytes(vllm_config) | |
| bytes_per_block_per_layer = spec.page_size_bytes | |
| return cdiv(max_memory_per_layer, bytes_per_block_per_layer) |
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I double-checked this path, and the current _blocks_per_request math is intentional for hybrid KV configs.
In vLLM, blocks are consumed per KV cache group for a request, so for hybrid models the request block demand is the sum across groups (e.g., full-attn blocks + sliding-window blocks), not just cdiv(max_model_len, block_size) from one representative group.
That is why _blocks_per_request computes:
- numerator: num_layer_per_group * sum(group.max_memory_usage_bytes(...))
- denominator: num_layer_per_group * page_size
which simplifies to cdiv(sum_group_memory, page_size) (the num_layer_per_group factor cancels). This preserves existing concurrency semantics.
There is an existing test that reflects this behavior:
tests/v1/core/test_kv_cache_utils.py:1405 (kv_cache_config_hybrid_model) expects concurrency 3 for num_blocks=(1024 + 129) * 3, i.e., blocks/request is 1024 + 129, not 1024.
Also, the recent fix in this PR separates byte accounting for logging (_kv_cache_bytes_per_block) so UniformType no longer overcounts GiB, while keeping concurrency math unchanged.
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Here are the results for the PR for two models on a RTX 4060 with 8GB of VRAM:
Note: opt-125m had enough KV cache (92x concurrency vs 16 seqs requested) so no warning was emitted. Qwen3-0.6B triggered the warning because the GPU could only hold 25 full-length sequences but 64 were requested. |
…ds capacity - Extract _blocks_per_request() and _kv_cache_bytes_per_block() helpers from get_max_concurrency_for_kv_cache_config() to share block-demand math between concurrency calculation and the new log lines. - Fix _kv_cache_bytes_per_block() for UniformTypeKVCacheSpecs where page_size_bytes already sums across all layers (avoids overcounting). - Log GPU KV cache memory in GiB and block count after allocation. - Log KV cache demand for max_num_seqs × max_model_len vs allocated. - Warn when allocated KV cache cannot hold max_num_seqs full-length sequences so queueing behavior is explicit. Signed-off-by: Ashish Kamra <ashishkamra@gmail.com>
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Summary
max_num_seqsatmax_model_len.max_num_seqsto make queueing behavior explicit.Why this is not a duplicate
kv_cache_utils.Tests
python -m pytest tests/v1/core/test_kv_cache_utils.py -k \"max_concurrency\" -v-> not run successfully in this environment because.venv/bin/pythonis unavailable.AI Assistance