from typing import List, Optional, Tuple, Type, overload import pytest from transformers import AutoConfig, AutoModelForVision2Seq, AutoTokenizer from vllm.multimodal.utils import rescale_image_size from vllm.sequence import SampleLogprobs from ..conftest import (IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner, _ImageAssets) from .utils import check_logprobs_close pytestmark = pytest.mark.vlm _LIMIT_IMAGE_PER_PROMPT = 4 HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({ "stop_sign": "[INST] \nWhat's the content of the image? [/INST]", "cherry_blossom": "[INST] \nWhat is the season? [/INST]", }) models = ["llava-hf/llava-v1.6-mistral-7b-hf"] def vllm_to_hf_output(vllm_output: Tuple[List[int], str, Optional[SampleLogprobs]], model: str): """Sanitize vllm output to be comparable with hf output.""" output_ids, output_str, out_logprobs = vllm_output config = AutoConfig.from_pretrained(model) image_token_id = config.image_token_index tokenizer = AutoTokenizer.from_pretrained(model) eos_token_id = tokenizer.eos_token_id hf_output_ids = [ token_id for idx, token_id in enumerate(output_ids) if token_id != image_token_id or output_ids[idx - 1] != image_token_id ] assert output_str[0] == " " hf_output_str = output_str[1:] if hf_output_ids[-1] == eos_token_id: hf_output_str = hf_output_str + tokenizer.decode(eos_token_id) return hf_output_ids, hf_output_str, out_logprobs @overload def run_test( hf_runner: Type[HfRunner], vllm_runner: Type[VllmRunner], image_assets: _ImageAssets, model: str, *, size_factors: List[float], dtype: str, max_tokens: int, num_logprobs: int, tensor_parallel_size: int, distributed_executor_backend: Optional[str] = None, ): ... @overload def run_test( hf_runner: Type[HfRunner], vllm_runner: Type[VllmRunner], image_assets: _ImageAssets, model: str, *, sizes: List[Tuple[int, int]], dtype: str, max_tokens: int, num_logprobs: int, tensor_parallel_size: int, distributed_executor_backend: Optional[str] = None, ): ... def run_test( hf_runner: Type[HfRunner], vllm_runner: Type[VllmRunner], image_assets: _ImageAssets, model: str, *, size_factors: Optional[List[float]] = None, sizes: Optional[List[Tuple[int, int]]] = None, dtype: str, max_tokens: int, num_logprobs: int, tensor_parallel_size: int, distributed_executor_backend: Optional[str] = None, ): images = [asset.pil_image for asset in image_assets] if size_factors is not None: inputs_per_image = [( [prompt for _ in size_factors], [rescale_image_size(image, factor) for factor in size_factors], ) for image, prompt in zip(images, HF_IMAGE_PROMPTS)] elif sizes is not None: inputs_per_image = [( [prompt for _ in sizes], [image.resize(size) for size in sizes], ) for image, prompt in zip(images, HF_IMAGE_PROMPTS)] else: raise ValueError("You must provide either `size_factors` or `sizes`") _run_test(hf_runner, vllm_runner, inputs_per_image, model, dtype=dtype, max_tokens=max_tokens, num_logprobs=num_logprobs, tensor_parallel_size=tensor_parallel_size, distributed_executor_backend=distributed_executor_backend) def _run_test( hf_runner: Type[HfRunner], vllm_runner: Type[VllmRunner], inputs: List[Tuple[List[str], PromptImageInput]], model: str, dtype: str, max_tokens: int, num_logprobs: int, tensor_parallel_size: int, distributed_executor_backend: Optional[str] = None, ): # max_model_len should be greater than image_feature_size with vllm_runner(model, dtype=dtype, max_model_len=10240, tensor_parallel_size=tensor_parallel_size, distributed_executor_backend=distributed_executor_backend, enforce_eager=True, limit_mm_per_prompt={"image": _LIMIT_IMAGE_PER_PROMPT }) as vllm_model: vllm_outputs_per_image = [ vllm_model.generate_greedy_logprobs(prompts, max_tokens, num_logprobs=num_logprobs, images=images) for prompts, images in inputs ] with hf_runner(model, dtype=dtype, auto_cls=AutoModelForVision2Seq) as hf_model: hf_outputs_per_image = [ hf_model.generate_greedy_logprobs_limit(prompts, max_tokens, num_logprobs=num_logprobs, images=images) for prompts, images in inputs ] for hf_outputs, vllm_outputs in zip(hf_outputs_per_image, vllm_outputs_per_image): # TODO: Check whether using original CLIPVisionModel can improve # consistency against HF check_logprobs_close( outputs_0_lst=hf_outputs, outputs_1_lst=[ vllm_to_hf_output(vllm_output, model) for vllm_output in vllm_outputs ], name_0="hf", name_1="vllm", ) @pytest.mark.parametrize("model", models) @pytest.mark.parametrize( "size_factors", [ # No image [], # Single-scale [1.0], # Single-scale, batched [1.0, 1.0, 1.0], # Multi-scale [0.25, 0.5, 1.0], ], ) @pytest.mark.parametrize("dtype", ["half"]) @pytest.mark.parametrize("max_tokens", [128]) @pytest.mark.parametrize("num_logprobs", [5]) def test_models(hf_runner, vllm_runner, image_assets, model, size_factors, dtype, max_tokens, num_logprobs) -> None: """Inference result should be the same between hf and vllm. All the image fixtures for the test is under tests/images. For huggingface runner, we provide the PIL images as input. For vllm runner, we provide MultiModalDataDict objects and corresponding MultiModalConfig as input. Note, the text input is also adjusted to abide by vllm contract. The text output is sanitized to be able to compare with hf. """ run_test( hf_runner, vllm_runner, image_assets, model, size_factors=size_factors, dtype=dtype, max_tokens=max_tokens, num_logprobs=num_logprobs, tensor_parallel_size=1, ) @pytest.mark.parametrize("model", models) @pytest.mark.parametrize( "sizes", [[(1669, 2560), (2560, 1669), (183, 488), (488, 183)]], ) @pytest.mark.parametrize("dtype", ["half"]) @pytest.mark.parametrize("max_tokens", [128]) @pytest.mark.parametrize("num_logprobs", [5]) def test_models_fixed_sizes(hf_runner, vllm_runner, image_assets, model, sizes, dtype, max_tokens, num_logprobs) -> None: run_test( hf_runner, vllm_runner, image_assets, model, sizes=sizes, dtype=dtype, max_tokens=max_tokens, num_logprobs=num_logprobs, tensor_parallel_size=1, ) @pytest.mark.parametrize("model", models) @pytest.mark.parametrize("dtype", ["half"]) @pytest.mark.parametrize("max_tokens", [128]) @pytest.mark.parametrize("num_logprobs", [5]) def test_models_multiple_image_inputs(hf_runner, vllm_runner, image_assets, model, dtype, max_tokens, num_logprobs) -> None: stop_sign = image_assets[0].pil_image cherry_blossom = image_assets[1].pil_image inputs = [( [ "[INST] \nDescribe 2 images. [/INST]", "[INST] \nDescribe 2 images. [/INST]", "[INST] \nDescribe 4 images. [/INST]", "[INST] \nWhat is the season? [/INST]" ], [ [stop_sign, cherry_blossom], # Images with different sizes and aspect-ratios [ rescale_image_size(stop_sign, 0.1), stop_sign, ], [ stop_sign, rescale_image_size(stop_sign, 0.25), cherry_blossom.resize((183, 488)), cherry_blossom.resize((488, 183)) ], cherry_blossom, ])] _run_test( hf_runner, vllm_runner, inputs, model, dtype=dtype, max_tokens=max_tokens, num_logprobs=num_logprobs, tensor_parallel_size=1, )