[None][chore] remove executor config in kv cache creator #7526
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Signed-off-by: leslie-fang25 <[email protected]>
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📝 WalkthroughWalkthroughRefactors KvCacheCreator to accept explicit configuration parameters instead of a single ExecutorConfig, updates all internal references accordingly, and adjusts py_executor_creator to construct KvCacheCreator with granular args and to synchronize max_seq_len between the creator and executor_config during build and estimation flows. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
participant Caller as create_py_executor
participant Creator as KvCacheCreator
participant ExecCfg as executor_config
participant Sampler as Sampler
participant KVMgr as KVCacheManager
Note over Caller,Creator: Construction with explicit params (no ExecutorConfig)
Caller->>Creator: __init__(model_engine, draft_model_engine, mapping, net_max_seq_len, kv_connector_manager, max_num_tokens, max_beam_width, tokens_per_block, max_seq_len, max_batch_size, kv_cache_config, pytorch_backend_config, speculative_config)
activate Creator
Note over Creator,KVMgr: Build managers may adjust max_seq_len internally
Creator->>KVMgr: build_managers(tokens_per_block, max_seq_len, batch/beam limits, configs)
KVMgr-->>Creator: possibly_updated_max_seq_len
Creator->>Creator: self._max_seq_len = possibly_updated_max_seq_len
deactivate Creator
Caller->>ExecCfg: ExecCfg.max_seq_len = Creator._max_seq_len
Caller->>Sampler: update_sampler_max_seq_len(ExecCfg.max_seq_len)
break Estimation flow
Caller->>Creator: set Creator._max_seq_len = trial_max_seq_len
Caller->>KVMgr: rebuild_managers(...)
KVMgr-->>Caller: updated_max_seq_len
Caller->>Creator: Creator._max_seq_len = updated_max_seq_len
Caller->>ExecCfg: ExecCfg.max_seq_len = Creator._max_seq_len
Caller->>Sampler: update_sampler_max_seq_len(ExecCfg.max_seq_len)
end
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🧹 Nitpick comments (4)
tensorrt_llm/_torch/pyexecutor/py_executor_creator.py (2)
515-519: Ensure robust max_seq_len synchronizationThe synchronization between
kv_cache_creator._max_seq_lenandexecutor_config.max_seq_lenappears correct. However, there's a potential risk ifkv_cache_creator._max_seq_lenis uninitialized or modified unexpectedly. Consider adding a validation check.kv_cache_creator.build_managers(resources, estimating_kv_cache) -# Originally, executor_config.max_seq_len might be changed inside build_managers and used -# below in create_py_executor_instance. Since now, we are changing -# kv_cache_creator._max_seq_len instead, restore executor_config.max_seq_len. -executor_config.max_seq_len = kv_cache_creator._max_seq_len +# Originally, executor_config.max_seq_len might be changed inside build_managers and used +# below in create_py_executor_instance. Since now, we are changing +# kv_cache_creator._max_seq_len instead, restore executor_config.max_seq_len. +if hasattr(kv_cache_creator, '_max_seq_len'): + executor_config.max_seq_len = kv_cache_creator._max_seq_len +else: + logger.warning("kv_cache_creator._max_seq_len not set, keeping original executor_config.max_seq_len")
579-587: Add validation for max_seq_len restorationWhen restoring
executor_config.max_seq_lenafter estimation, the code assumeskv_cache_creator._max_seq_lenis properly set. Consider adding validation to ensure the value is reasonable.# Before estimating KV cache size, a minimal KV cache has been allocated using # create_kv_cache_manager above, which caps kv_cache_creator.max_seq_len. Restoring # the original value before creating the final KV cache. kv_cache_creator._max_seq_len = max_seq_len kv_cache_creator.build_managers(resources, False) # Originally, executor_config.max_seq_len might be changed again inside build_managers # Since now, we are changing kv_cache_creator.max_seq_len instead. # Restore executor_config.max_seq_len which has been used in create_py_executor_instance -executor_config.max_seq_len = kv_cache_creator._max_seq_len +if hasattr(kv_cache_creator, '_max_seq_len') and kv_cache_creator._max_seq_len > 0: + executor_config.max_seq_len = kv_cache_creator._max_seq_len +else: + logger.error(f"Invalid kv_cache_creator._max_seq_len: {getattr(kv_cache_creator, '_max_seq_len', 'not set')}") + executor_config.max_seq_len = max_seq_lentensorrt_llm/_torch/pyexecutor/_util.py (2)
361-361: Consider replacing assert with proper error handlingUsing
assertfor validation can be problematic in production as assertions can be disabled with Python's-Oflag. Consider using an explicit check with a proper exception.-assert model_engine.model.model_config.is_generation, "Only construct KV cache for generation models." +if not model_engine.model.model_config.is_generation: + raise ValueError("Only construct KV cache for generation models.")
155-158: Verify vocabulary bounds in dummy request generationThe random token generation could theoretically produce the same token repeatedly. While unlikely to cause issues, consider using
random.samplefor guaranteed uniqueness if needed.input_tokens = [ - random.randint(0, vocab_size - 1) for _ in range(input_seq_len) + random.randint(0, vocab_size - 1) for _ in range(input_seq_len) ] +# Note: Using random.randint is fine here as duplicate tokens are acceptable in input sequences
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📒 Files selected for processing (2)
tensorrt_llm/_torch/pyexecutor/_util.py(11 hunks)tensorrt_llm/_torch/pyexecutor/py_executor_creator.py(2 hunks)
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🧠 Learnings (2)
📚 Learning: 2025-08-14T21:04:50.248Z
Learnt from: thorjohnsen
PR: NVIDIA/TensorRT-LLM#6910
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0
Timestamp: 2025-08-14T21:04:50.248Z
Learning: In KV cache onboarding logic during prefill in cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, when calculating which blocks fall within the attention window, use getTokensPerBlock() to advance token indices rather than block->getUniqueTokens().size(), because the calculation needs to consider the post-prefill state where blocks will be filled to capacity, not their current token count.
Applied to files:
tensorrt_llm/_torch/pyexecutor/py_executor_creator.py
📚 Learning: 2025-08-14T15:38:01.771Z
Learnt from: MatthiasKohl
PR: NVIDIA/TensorRT-LLM#6904
File: cpp/tensorrt_llm/pybind/thop/bindings.cpp:55-57
Timestamp: 2025-08-14T15:38:01.771Z
Learning: In TensorRT-LLM Python bindings, tensor parameter collections like mla_tensor_params and spec_decoding_tensor_params are kept as required parameters without defaults to maintain API consistency, even when it might affect backward compatibility.
Applied to files:
tensorrt_llm/_torch/pyexecutor/_util.py
🧬 Code graph analysis (2)
tensorrt_llm/_torch/pyexecutor/py_executor_creator.py (2)
tensorrt_llm/_torch/attention_backend/trtllm.py (3)
tokens_per_block(581-585)max_seq_len(561-571)max_seq_len(574-578)tensorrt_llm/_torch/pyexecutor/_util.py (2)
try_prepare_estimation(208-219)build_managers(485-502)
tensorrt_llm/_torch/pyexecutor/_util.py (5)
tensorrt_llm/_torch/pyexecutor/model_engine.py (1)
PyTorchModelEngine(263-2464)tensorrt_llm/llmapi/llm_args.py (1)
KvCacheConfig(961-1095)tensorrt_llm/_torch/pyexecutor/config.py (1)
PyTorchConfig(16-114)tensorrt_llm/_torch/pyexecutor/resource_manager.py (1)
KVCacheManager(142-1011)tensorrt_llm/_torch/model_config.py (1)
get_bindings_model_config(431-528)
🪛 Ruff (0.12.2)
tensorrt_llm/_torch/pyexecutor/_util.py
361-361: Use of assert detected
(S101)
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🔇 Additional comments (4)
tensorrt_llm/_torch/pyexecutor/py_executor_creator.py (1)
495-509: Good refactor: explicit parameters improve clarityThe change from passing
executor_configto passing explicit configuration parameters toKvCacheCreatorimproves code clarity and reduces coupling between the executor configuration and KV cache creation logic.tensorrt_llm/_torch/pyexecutor/_util.py (3)
46-62: LGTM! Clean refactor to explicit parametersThe refactoring from
ExecutorConfigto explicit parameters in theKvCacheCreatorconstructor improves the API clarity and reduces coupling. The parameter names are descriptive and the keyword-only arguments enforce clear usage.
479-482: Good separation of concerns with max_seq_len updateThe logic for updating
self._max_seq_lenwhen the KV cache manager modifies it is well-placed. This ensures that any changes made by the KvCacheManager are captured and can be propagated back to the executor config.
217-219: Clean update to use new internal fieldThe change to update
self._kv_cache_config.max_tokensinstead of the executor config field is consistent with the refactoring approach.
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Signed-off-by: leslie-fang25 <[email protected]>
Signed-off-by: leslie-fang25 <[email protected]>
Summary by CodeRabbit
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Description
Remove executor config in kv cache creator
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