268 lines
8.8 KiB
Python
268 lines
8.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import copy
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from functools import partial
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from typing import Optional
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import numpy as np
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import pytest
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from PIL import Image
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from vllm.config import ModelConfig
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from vllm.inputs import InputProcessingContext
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.processing import ProcessingCache
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from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
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from ....multimodal.utils import random_audio, random_image, random_video
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from ...registry import HF_EXAMPLE_MODELS
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def _test_processing_correctness(
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model_id: str,
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hit_rate: float,
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num_batches: int,
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simplify_rate: float,
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ignore_mm_keys: Optional[list[str]] = None,
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):
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model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id)
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model_info.check_available_online(on_fail="skip")
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model_info.check_transformers_version(on_fail="skip")
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model_config = ModelConfig(
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model_id,
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task="auto",
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tokenizer=model_info.tokenizer or model_id,
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tokenizer_mode=model_info.tokenizer_mode,
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trust_remote_code=model_info.trust_remote_code,
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seed=0,
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dtype="float16",
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revision=None,
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hf_overrides=model_info.hf_overrides,
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)
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model_cls = MULTIMODAL_REGISTRY._get_model_cls(model_config)
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factories = MULTIMODAL_REGISTRY._processor_factories[model_cls]
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ctx = InputProcessingContext(
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model_config,
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tokenizer=cached_tokenizer_from_config(model_config),
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)
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# Ensure that it can fit all of the data
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cache = ProcessingCache(capacity=1 << 30)
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processing_info = factories.info(ctx)
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supported_mm_limits = processing_info.get_supported_mm_limits()
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limit_mm_per_prompt = {
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modality: 3 if limit is None else limit
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for modality, limit in supported_mm_limits.items()
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}
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model_config.get_multimodal_config().limit_per_prompt = limit_mm_per_prompt
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baseline_processor = factories.build_processor(ctx, cache=None)
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cached_processor = factories.build_processor(ctx, cache=cache)
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dummy_inputs = baseline_processor.dummy_inputs
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tokenizer = baseline_processor.info.get_tokenizer()
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rng = np.random.RandomState(0)
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input_to_hit = {
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"image": Image.new("RGB", size=(128, 128)),
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"video": np.zeros((4, 128, 128, 3), dtype=np.uint8),
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"audio": (np.zeros((512, )), 16000),
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}
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input_factory = {
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"image":
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partial(random_image, rng, min_wh=128, max_wh=256),
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"video":
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partial(random_video,
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rng,
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min_frames=2,
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max_frames=8,
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min_wh=128,
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max_wh=256),
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"audio":
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partial(random_audio, rng, min_len=512, max_len=1024, sr=16000),
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}
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tokenizer_encode_kwargs = {}
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if model_config.hf_config.model_type in ("mllama", "whisper", "ultravox"):
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# For some multimodal models, tokenizer will always add bos_token
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# at the beginning of prompt by default, causing hf_processor outputs
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# incorrect token ids. So we need use `add_special_tokens=False` here
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# to leave bos_token to be added by the processor.
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tokenizer_encode_kwargs = {"add_special_tokens": False}
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for batch_idx in range(num_batches):
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mm_data = {
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k:
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[(input_to_hit[k] if rng.rand() < hit_rate else input_factory[k]())
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for _ in range(rng.randint(limit + 1))]
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for k, limit in limit_mm_per_prompt.items()
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}
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mm_counts = {k: len(vs) for k, vs in mm_data.items()}
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prompt = dummy_inputs.get_dummy_processor_inputs(
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model_config.max_model_len,
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mm_counts,
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).prompt_text
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# Drop unnecessary keys and test single -> multi conversion
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if rng.rand() < simplify_rate:
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for k in list(mm_data.keys()):
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if not mm_data[k]:
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del mm_data[k]
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elif len(mm_data[k]) == 1:
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mm_data[k] = mm_data[k][0]
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baseline_result = baseline_processor.apply(
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prompt,
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mm_data=mm_data,
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hf_processor_mm_kwargs={},
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)
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cached_result = cached_processor.apply(
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prompt,
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mm_data=mm_data,
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hf_processor_mm_kwargs={},
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)
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assert _drop_mm_kwargs_keys(
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baseline_result, ignore_mm_keys) == _drop_mm_kwargs_keys(
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cached_result, ignore_mm_keys), (
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f"Failed ({batch_idx=}, {prompt=}, {mm_data=})")
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baseline_tokenized_result = baseline_processor.apply(
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tokenizer.encode(prompt, **tokenizer_encode_kwargs),
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mm_data=mm_data,
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hf_processor_mm_kwargs={},
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)
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assert _drop_mm_kwargs_keys(
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baseline_result, ignore_mm_keys) == _drop_mm_kwargs_keys(
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baseline_tokenized_result, ignore_mm_keys), (
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f"Failed ({batch_idx=}, {prompt=}, {mm_data=})")
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cached_tokenized_result = cached_processor.apply(
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tokenizer.encode(prompt, **tokenizer_encode_kwargs),
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mm_data=mm_data,
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hf_processor_mm_kwargs={},
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)
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assert _drop_mm_kwargs_keys(
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cached_result, ignore_mm_keys) == _drop_mm_kwargs_keys(
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cached_tokenized_result, ignore_mm_keys), (
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f"Failed ({batch_idx=}, {prompt=}, {mm_data=})")
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# yapf: disable
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@pytest.mark.parametrize("model_id", [
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"rhymes-ai/Aria",
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"Salesforce/blip2-opt-2.7b",
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"facebook/chameleon-7b",
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"deepseek-ai/deepseek-vl2-tiny",
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"microsoft/Florence-2-base",
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"adept/fuyu-8b",
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"google/gemma-3-4b-it",
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"THUDM/glm-4v-9b",
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"h2oai/h2ovl-mississippi-800m",
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"OpenGVLab/InternVL2-1B",
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"HuggingFaceM4/Idefics3-8B-Llama3",
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"llava-hf/llava-1.5-7b-hf",
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"llava-hf/llava-v1.6-mistral-7b-hf",
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"llava-hf/LLaVA-NeXT-Video-7B-hf",
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"llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
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"meta-llama/Llama-3.2-11B-Vision-Instruct",
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"TIGER-Lab/Mantis-8B-siglip-llama3",
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"mistral-community/pixtral-12b",
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"openbmb/MiniCPM-o-2_6",
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"openbmb/MiniCPM-V-2_6",
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"allenai/Molmo-7B-D-0924",
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"allenai/Molmo-7B-O-0924",
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"nvidia/NVLM-D-72B",
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"Qwen/Qwen-VL-Chat",
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"Qwen/Qwen2-VL-2B-Instruct",
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"Qwen/Qwen2.5-VL-3B-Instruct",
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"Qwen/Qwen2-Audio-7B-Instruct",
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"fixie-ai/ultravox-v0_5-llama-3_2-1b",
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"openai/whisper-large-v3",
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"google/paligemma-3b-mix-224",
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"google/paligemma2-3b-ft-docci-448",
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])
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@pytest.mark.parametrize("hit_rate", [0.3, 0.5, 1.0])
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@pytest.mark.parametrize("num_batches", [32])
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@pytest.mark.parametrize("simplify_rate", [1.0])
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# yapf: enable
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def test_processing_correctness(
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model_id: str,
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hit_rate: float,
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num_batches: int,
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simplify_rate: float,
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):
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ignore_mm_keys = None
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if 'ultravox' in model_id:
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# In Ultravox, the audio_features can be different depending on padding
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# The slight difference should not be a problem though, since
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# attention_mask lets us ignore the difference.
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ignore_mm_keys = ['audio_features']
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_test_processing_correctness(
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model_id,
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hit_rate=hit_rate,
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num_batches=num_batches,
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simplify_rate=simplify_rate,
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ignore_mm_keys=ignore_mm_keys,
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)
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# yapf: disable
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@pytest.mark.parametrize("model_id", ["microsoft/Phi-3.5-vision-instruct"])
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@pytest.mark.parametrize("hit_rate", [0.3, 0.5, 1.0])
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@pytest.mark.parametrize("num_batches", [32])
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@pytest.mark.parametrize("simplify_rate", [1.0])
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# yapf: enable
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def test_processing_correctness_phi3v(
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model_id: str,
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hit_rate: float,
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num_batches: int,
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simplify_rate: float,
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):
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# HACK - this is an attempted workaround for the following bug
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# https://github.com/huggingface/transformers/issues/34307
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from transformers import AutoImageProcessor # noqa: F401
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from transformers import AutoProcessor # noqa: F401
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AutoImageProcessor.from_pretrained(model_id, trust_remote_code=True)
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_test_processing_correctness(
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model_id,
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hit_rate=hit_rate,
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num_batches=num_batches,
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simplify_rate=simplify_rate,
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)
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def _drop_mm_kwargs_keys(result: dict,
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ignore_mm_keys: Optional[list[str]] = None) -> dict:
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"""Drop specified keys from result['mm_kwargs'].
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This is mainly to avoid doing exact match of audio_features in ultravox.
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Args:
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result: Result to drop keys from
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ignore_mm_keys: List of keys to ignore, e.g. ['audio_features']
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"""
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if not ignore_mm_keys:
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return result
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if 'mm_kwargs' in result:
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result = copy.deepcopy(result)
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mm_kwargs = result['mm_kwargs']
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for key in ignore_mm_keys:
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mm_kwargs.pop(key, None)
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for items in mm_kwargs._items_by_modality.values():
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for item in items:
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for key in ignore_mm_keys:
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item.pop(key, None)
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return result
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