[None][fix] Moving transfer timeout test to test_llm_pytorch, fixing broken kv transfer timeout #8892
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📝 WalkthroughWalkthroughThe changes refactor KV cache timeout handling by updating the internal data structure in the executor to store triplets instead of pairs, moving a context-only timeout test from one test file to another, and adding corresponding imports to support the relocated test. Changes
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tensorrt_llm/_torch/pyexecutor/py_executor.py (1)
1661-1674: KV timeout check now matches triplet storage — consider tiny robustness and naming nitUnpacking triplets from ctx_in_transmission_requests is correct given values are stored as (req, block_id, counter). Two minor nits:
- Avoid shadowing builtin type; rename parameter to phase.
- If mixed tuple shapes ever appear, index the first element defensively.
Apply:
-def flag_if_kv_transfer_timed_out(req: LlmRequest, type: str) -> None: +def flag_if_kv_transfer_timed_out(req: LlmRequest, phase: str) -> None: @@ - logger.warning( - f"Terminating {type} request {req.py_request_id} due to KV cache transfer timeout" - ) + logger.warning( + f"Terminating {phase} request {req.py_request_id} due to KV cache transfer timeout" + ) @@ - for req, _, _ in self.ctx_in_transmission_requests.values(): - flag_if_kv_transfer_timed_out(req, "context") + for entry in self.ctx_in_transmission_requests.values(): + req = entry[0] # supports (req, ...) tuple shapes + flag_if_kv_transfer_timed_out(req, "context")tests/unittest/llmapi/test_llm_pytorch.py (1)
968-1023: Make the timeout test deterministic and faster (avoid sleep(5), brittle len==1)Current assertions can be flaky and slow:
- sleep(5) overshoots kv_transfer_timeout_ms; prefer polling bounded by the configured timeout.
- len(results) == 1 is brittle; use the latest sample (results[-1]) after ensuring non-empty.
Apply:
@@ - results = llm.get_stats(2) - assert len(results) == 1 - context_only_used_num_blocks = results[0]["kvCacheStats"]["usedNumBlocks"] - print(f"Context only used num blocks: {context_only_used_num_blocks}") + results = llm.get_stats(2) + assert results, "no stats returned after context-only request" + context_only_used_num_blocks = results[-1]["kvCacheStats"]["usedNumBlocks"] + print(f"Context only used num blocks: {context_only_used_num_blocks}") @@ - # Sleep 5 seconds to allow context only request to time out - time.sleep(5) + # Wait for timeout to elapse with a bounded poll (<= ~2x the configured timeout) + timeout_s = 2.0 * (llm._executor.kv_cache_transceiver.kv_transfer_timeout_ms / 1000.0) + deadline = time.time() + max(1.0, timeout_s) + while time.time() < deadline: + # Drain any interim stats (best-effort) to reduce noise for the next check + _ = llm.get_stats(0.25) + time.sleep(0.1) @@ - results = llm.get_stats(2) - assert len(results) == 1 - final_used_num_blocks = results[0]["kvCacheStats"]["usedNumBlocks"] + results = llm.get_stats(2) + assert results, "no stats returned after regular request" + final_used_num_blocks = results[-1]["kvCacheStats"]["usedNumBlocks"] @@ - assert final_used_num_blocks == 0 + assert final_used_num_blocks == 0This keeps the intent while reducing flakiness and test time.
Run this test locally to confirm stability across multiple iterations (e.g., 10x loop) and across CI backends.
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Learning: In cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, updateSequenceCacheBlockOffsets is specifically for updating bookkeeping when blocks are added during the context phase, not for refreshing offsets after detach operations. During detach operations, GenerationRequest::removeFrontBlock handles the necessary cache block bookkeeping internally.
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Learning: In tensorrt_llm/_torch/pyexecutor/_util.py, when calling model_engine.set_lora_model_config(), pass model_binding_config.mlp_hidden_size directly without multiplying by mapping.tp_size, as the mlp_hidden_size from get_bindings_model_config() is already the per-TP rank value needed for LoRA weight packaging.
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tests/unittest/llmapi/test_llm.py (4)
cpp/include/tensorrt_llm/batch_manager/llmRequest.h (1)
tensorrt_llm(39-275)cpp/include/tensorrt_llm/batch_manager/cacheTransceiver.h (1)
tensorrt_llm(40-279)tensorrt_llm/llmapi/llm_args.py (2)
EagleDecodingConfig(575-692)KvCacheConfig(1265-1409)cpp/tensorrt_llm/executor/kvCacheRetentionConfig.cpp (1)
KvCacheRetentionConfig(45-97)
tests/unittest/llmapi/test_llm_pytorch.py (3)
tensorrt_llm/llmapi/llm.py (3)
LLM(1103-1119)generate(259-341)get_stats(519-530)tensorrt_llm/disaggregated_params.py (1)
DisaggregatedParams(15-92)tensorrt_llm/llmapi/llm_args.py (1)
CacheTransceiverConfig(1444-1469)
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🔇 Additional comments (2)
tests/unittest/llmapi/test_llm.py (1)
34-36: Import cleanup aligns with test moveDropping timeout-related imports here is consistent with relocating the test to the PyTorch suite. LGTM.
tests/unittest/llmapi/test_llm_pytorch.py (1)
2-2: Additions for timeout test are appropriateThe new imports (time, DisaggregatedParams, CacheTransceiverConfig, KvCacheConfig) are correct for the new test. LGTM.
Also applies to: 9-13
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