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fix: Fixing kv_cache_events unit tests [nvbug5362412] #6265

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merged 1 commit into from
Jul 25, 2025
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fix: Fixing kv_cache_events unit tests [nvbug5362412] #6265

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11 changes: 2 additions & 9 deletions tests/unittest/llmapi/test_llm_kv_cache_events.py
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Original file line number Diff line number Diff line change
@@ -1,10 +1,8 @@
import asyncio
import time

import pytest

import tensorrt_llm
from tensorrt_llm._tensorrt_engine import LLM
from tensorrt_llm import LLM
from tensorrt_llm._torch.pyexecutor.llm_request import LlmRequest
from tensorrt_llm._torch.pyexecutor.resource_manager import KVCacheManager
from tensorrt_llm._utils import KVCacheEventSerializer
Expand All @@ -16,7 +14,6 @@

default_model_name = "llama-models-v2/TinyLlama-1.1B-Chat-v1.0"
llama_model_path = get_model_path(default_model_name)

global_kvcache_config = KvCacheConfig(free_gpu_memory_fraction=0.4,
event_buffer_max_size=1024,
enable_block_reuse=True,
Expand Down Expand Up @@ -50,8 +47,7 @@ def create_llm(tensor_parallel_size=1):
return LLM(model=llama_model_path,
tensor_parallel_size=tensor_parallel_size,
kv_cache_config=global_kvcache_config,
enable_autotuner=False,
backend="pytorch")
enable_autotuner=False)


def create_llm_request(id, input_tokens, new_tokens=1):
Expand Down Expand Up @@ -103,7 +99,6 @@ def test_kv_cache_event_data_serialization():
serialized_event = KVCacheEventSerializer.serialize(events)


@pytest.mark.skip(reason="https://nvbugs/5362412")
def test_expected_kv_cache_events():
llm = create_llm()
sampling_params = SamplingParams(max_tokens=6, temperature=0.01)
Expand All @@ -122,7 +117,6 @@ def test_expected_kv_cache_events():
assert event["data"]["type"] == "stored"


@pytest.mark.skip(reason="https://nvbugs/5362412")
def test_kv_cache_event_async_api():
llm = create_llm()
sampling_params = SamplingParams(max_tokens=6, temperature=0.01)
Expand Down Expand Up @@ -150,7 +144,6 @@ async def main():
asyncio.run(main())


@pytest.mark.skip(reason="https://nvbugs/5362412")
def test_llm_kv_events_api():
llm = create_llm()
sampling_params = SamplingParams(max_tokens=6, temperature=0.01)
Expand Down