# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """ Test that ``InputProcessor.inject_into_mm_cache()`` correctly injects pre-processed mm_kwargs into the processor cache and reports MM cache hit rate metrics accurately. This is used by frameworks like Dynamo that run the HF processor on a frontend and transfer pre-processed mm_kwargs to the backend, avoiding redundant processing. """ import logging import pytest import regex as re from tests.entrypoints.multimodal.conftest import TEST_IMAGE_ASSETS from vllm import LLM, SamplingParams from vllm.renderers.params import ChatParams from vllm.v1.metrics import loggers as stat_loggers from vllm.v1.metrics.reader import Counter, Metric def _make_messages(image_url: str): return [ { "role": "user", "content": [ { "type": "image_url", "image_url": {"url": image_url}, }, ], } ] def _get_counter_value(metrics: list[Metric], name: str): metric = next(m for m in metrics if m.name == name) assert isinstance(metric, Counter) return metric.value def _get_mm_cache_stats(metrics: list[Metric]): mm_cache_queries = _get_counter_value(metrics, "vllm:mm_cache_queries") mm_cache_hits = _get_counter_value(metrics, "vllm:mm_cache_hits") return mm_cache_queries, mm_cache_hits def _get_mm_cache_log(llm: LLM, caplog_vllm: pytest.LogCaptureFixture) -> float: caplog_vllm.clear() with caplog_vllm.at_level(logging.INFO, logger=stat_loggers.__name__): llm.llm_engine.do_log_stats() assert len(caplog_vllm.records) == 1 msg = caplog_vllm.records[0].getMessage() assert "MM cache hit rate" in msg match = re.search(r"MM cache hit rate: ([0-9.]+)%", msg) assert match is not None return float(match.group(1)) @pytest.mark.parametrize("image_urls", [TEST_IMAGE_ASSETS[:2]], indirect=True) @pytest.mark.parametrize("mm_processor_cache_type", ["lru", "shm"]) def test_inject_into_mm_cache( num_gpus_available, image_urls, mm_processor_cache_type, caplog_vllm, multimodal_llm_factory, ): """Test that inject_into_mm_cache() injects pre-processed mm_kwargs into the processor cache and MM cache hit metrics are updated correctly. Steps: 1. Two normal requests (same image) -> cache miss then hit (baseline) 2. Extract cached kwargs, call inject_into_mm_cache with a new hash, then generate with a pre-rendered input -> verifies injection works """ llm = multimodal_llm_factory( model="llava-hf/llava-1.5-7b-hf", max_model_len=4096, max_num_seqs=5, enforce_eager=True, disable_log_stats=False, limit_mm_per_prompt={"image": 2}, mm_processor_cache_type=mm_processor_cache_type, ) # Step 1: Normal requests to populate the cache llm.chat(_make_messages(image_urls[0])) assert _get_mm_cache_stats(llm.get_metrics()) == (1, 0) llm.chat(_make_messages(image_urls[0])) assert _get_mm_cache_stats(llm.get_metrics()) == (2, 1) assert _get_mm_cache_log(llm, caplog_vllm) == pytest.approx(50.0) # Step 2: Use a second image to get valid expanded tokens and # placeholder positions via the renderer. llm.chat(_make_messages(image_urls[1])) queries_before = _get_mm_cache_stats(llm.get_metrics())[0] # 3 renderer = llm.llm_engine.renderer cache = renderer.mm_processor_cache assert cache is not None, "Processor cache should be enabled" _, eng_prompts = renderer.render_chat( [_make_messages(image_urls[1])], ChatParams(), ) eng_input = eng_prompts[0] # Inject pre-processed mm_kwargs with a NEW hash via public API new_mm_hash = "deadbeef" * 8 mm_hashes = {"image": [new_mm_hash]} mm_kwargs = eng_input["mm_kwargs"] llm.llm_engine.input_processor.inject_into_mm_cache(mm_hashes, mm_kwargs) # Build pre-rendered input (no externally_processed flag needed) pre_rendered_input = { "type": "multimodal", "prompt_token_ids": eng_input["prompt_token_ids"], "mm_kwargs": mm_kwargs, "mm_hashes": mm_hashes, "mm_placeholders": eng_input["mm_placeholders"], } llm.generate( pre_rendered_input, sampling_params=SamplingParams(max_tokens=1), ) # Verify cache was queried and injection happened queries_after = _get_mm_cache_stats(llm.get_metrics())[0] assert queries_after > queries_before, ( "Cache should have been queried for the injected item" ) mm_rate = _get_mm_cache_log(llm, caplog_vllm) assert mm_rate >= 0.0, "MM cache hit rate should be reported" @pytest.mark.parametrize("image_urls", [TEST_IMAGE_ASSETS[:1]], indirect=True) def test_inject_into_mm_cache_without_cache( num_gpus_available, image_urls, multimodal_llm_factory, ): """Test that inject_into_mm_cache works gracefully when processor cache is disabled (mm_processor_cache_gb=0). Should not crash. """ llm = multimodal_llm_factory( model="llava-hf/llava-1.5-7b-hf", max_model_len=4096, max_num_seqs=5, enforce_eager=True, disable_log_stats=False, limit_mm_per_prompt={"image": 2}, mm_processor_cache_gb=0, ) # Run a normal chat request first to warm up the model. llm.chat(_make_messages(image_urls[0])) # Use the renderer to get a proper EngineInput with expanded tokens renderer = llm.llm_engine.renderer _, eng_prompts = renderer.render_chat( [_make_messages(image_urls[0])], ChatParams(), ) eng_input = eng_prompts[0] mm_hashes = {"image": ["abcd1234" * 8]} mm_kwargs = eng_input["mm_kwargs"] # inject_into_mm_cache should not crash even without cache llm.llm_engine.input_processor.inject_into_mm_cache(mm_hashes, mm_kwargs) # Build and generate with pre-rendered input pre_rendered_input = { "type": "multimodal", "prompt_token_ids": eng_input["prompt_token_ids"], "mm_kwargs": mm_kwargs, "mm_hashes": mm_hashes, "mm_placeholders": eng_input["mm_placeholders"], } result = llm.generate( pre_rendered_input, sampling_params=SamplingParams(max_tokens=1), ) assert len(result) == 1, "Should produce one output" assert len(result[0].outputs) >= 1, "Should have at least one output sequence"