[CI/Build] VLM Test Consolidation

[alex-jw-brooks]

*This PR is large because it's touching pretty much all of the VLM tests - the place where they are consolidated into, i.e., where new model tests would be added is here. The place that each of these eventually land to run the vLLM/HF runners (for all of the tests types) is here!

Overview

Most of the multi-modal image/video tests in vLLM do the same thing, but have small tweaks to things like

• prompt format / model-specific multimodal tokens
• data types
• number of logprobs being compared
• decorators (e.g., @large_gpu_test, which may control spawning the tests in a new process) and so on.

However, the structure of the tests themselves is very similar, and pretty much always consists of some boilerplate to configure instances of HfRunner / VllmRunner and compare greedy logprobs in some common _run_test function, which will then be wrapped in a test_ function with different options, e.g., size_factors, being parametrized.

This PR aims to consolidate a lot of the redundancy in vLLM's multimodal tests, starting with the visual tests, but in a way that can also (hopefully) be easily extended to other types, e.g., audio.

The way this is accomplished is by defining five types of test, which can cover most of our visual model tests:

• single image
• multiple images
• visual embeddings (i.e., vLLM gets image embeddings, HF model gets images)
• video
• custom inputs, i.e., some other prepepared input for some edge-case for specific models

and defining test wrappers for each of them, each of which leverage common utilities and invoke a common run_test, which is written to be model-agnostic. As such, to add a test for a new type of model, you should be able to (mostly) add a new object configuring a model for the VLM_TEST_SETTINGS & only need to worry about things that are model-specific, e.g., post-processing on the VLLM runner output.

Example 1: GLM-4

Here is an example of an object describing the GLM-4 tests.

    "glm4": VLMTestInfo(
        models="THUDM/glm-4v-9b",
        test_type=VLMTestType.IMAGE,
        prompt_formatter=identity,
        fork_new_process_for_each_test=True,
        img_idx_to_prompt=lambda idx: "",
        max_model_len=2048,
        max_num_seqs=2,
        dtype="bfloat16",
        get_stop_token_ids=lambda tok: [151329, 151336, 151338],
        skip=(get_memory_gb() < 48), # Large GPU test
        patch_hf_runner=vlm_utils.glm_patch_hf_runner,
    ),

Which indicates that we should run a test single image test, using the default single image prompt / image token, without additional prompt formatting. This will actually result in four tests, because it runs the default size factors as separate pytest cases. Additionally, each test case will run in a separate process, similar to if we wrote a test with @large_gpu_test(min_gb=48) to ensure resources are still cleaned up correctly.

Example 2: Phi3v

Phi3v is a model that supports multiple images, so it has single & mutli image tests. This is a pretty good example of what our multi-image tests look like most of the time, but there are a few quirks specific to the model, e.g., needing to use eager attention when running the HF model. The option below configures both the single & multi-image tests for the default size factors.

    "phi3v": VLMTestInfo(
        models="microsoft/Phi-3.5-vision-instruct",
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<|user|>\n{img_prompt}<|end|>\n<|assistant|>\n", # noqa: E501
        img_idx_to_prompt=lambda idx: f"<|image_{idx}|>\n",
        max_model_len=4096,
        max_num_seqs=2,
        # use eager mode for hf runner, since phi3v didn't work with flash_attn
        model_kwargs={"_attn_implementation": "eager"},
        use_tokenizer_eos=True,
        vllm_output_post_proc=vlm_utils.phi3v_vllm_to_hf_output,
        num_logprobs=10,
    ),

So as a result of the default size factors, when this gets picked up, it will run 4 single image tests (one per size factor) and 4 multi image tests (one per size factor). Settings like max_model_len and max_num_seqs etc which are common are used in all the test types.

Other Implementation details

• Each VLMTestInfo has the flexibility to set the things we would normally parametrize, e.g., size_factors. This is accomplished by consuming all of the VLM_TEST_SETTINGS, dropping things that are marked as skipped / tests that don't have the corresponding test_type, and then taking an itertools product over specific fields (e.g.,models, num_tokens, size_factors/fixed_sizes, dtype etc) for each test that matched so that each combination will run as its own test.

• Each test type has two implementations; the normal one, and a heavy version which is identical, but runs each test in a new process. If a test sets fork_new_process_for_each_test=True, it will run in the heavy version, which is wrapped in the @fork_new_process_for_each_test decorator.

  • This can be paired with the skip field to achieve the same effect of some of our existing decorators for cleaning up resources. For example, setting VLMTestInfo(..., fork_new_process_for_each_test=True, skip=(get_memory_gb() < 48)) will behave like @large_gpu_test(min_gb=48).

• A lot of the current tests use very slight variations of the same prompts. This PR is aligning most of them on a common prompts, so the scores will change slightly. The settings for most models should (hopefully) be the same though; for each model, I ensured that if the prompts matched, the vLLM runner/hf runner produced the same output as I was porting them.

• In some cases, e.g., paligemma, the prompt is quite different, and using the default one fails the logprobs check. This is why in some cases the prompt is set directly for some models.

• There are a couple of other tests here that didn't really fit (e.g., the quantized InternVL test and error case for Llava). For now, I left these where they were, since I thought it might be a good idea to figure out if we prefer this to somewhere In between (e.g., keeping in model's tests in its own file, but using common test runner here) first

• To handle stuff like audio, I think it should be pretty similar to handling images vs. video; if we choose to make this sort of change, hopefully we can just add a new test_type to pass audio through the common wrapper for the vllm/hf runners

• For stuff like quantized tests - some thoughts for what would feel nice might be a good idea, but I imagine we probably could just pass the model / quantized model, and if its set, create two vLLM runners instead of a HF/vLLM runner

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