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87 lines
3.2 KiB
Python
87 lines
3.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Tests for Llama4's multimodal preprocessing kwargs."""
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import pytest
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.transformers_utils.tokenizer import encode_tokens
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from ....conftest import ImageTestAssets
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from ...utils import build_model_context
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@pytest.mark.parametrize("model_id", ["meta-llama/Llama-4-Scout-17B-16E-Instruct"])
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@pytest.mark.parametrize("mm_processor_kwargs", [{}])
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@pytest.mark.parametrize("num_imgs", [1, 5])
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@pytest.mark.parametrize("mm_processor_cache_gb", [0, 4])
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@pytest.mark.parametrize("tokenized_prompt", [True, False])
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def test_processor_override(
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image_assets: ImageTestAssets,
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model_id: str,
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mm_processor_kwargs: dict,
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num_imgs: int,
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mm_processor_cache_gb: int,
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tokenized_prompt: bool,
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):
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"""Ensure llama4 processor works properly."""
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ctx = build_model_context(
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model_id,
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mm_processor_kwargs=mm_processor_kwargs,
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limit_mm_per_prompt={"image": num_imgs},
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mm_processor_cache_gb=mm_processor_cache_gb,
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)
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processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
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config = processor.info.get_hf_config()
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tokenizer = processor.info.get_tokenizer()
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hf_processor = processor.info.get_hf_processor()
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vocab = tokenizer.get_vocab()
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prompt = (
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"<|begin_of_text|><|header_start|>user<|header_end|>"
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+ "<|image|>" * num_imgs
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+ "<|eot|><|header_start|>assistant<|header_end|>"
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)
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mm_data = {
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"image": [
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image_assets[(i % len(image_assets))].pil_image for i in range(num_imgs)
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]
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}
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if tokenized_prompt:
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prompt = encode_tokens(tokenizer, prompt)
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processed_inputs = processor.apply(prompt, mm_data, mm_processor_kwargs)
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mm_data = processed_inputs["mm_kwargs"].get_data()
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# place holder replacements
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prompt_token_ids = processed_inputs["prompt_token_ids"]
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assert prompt_token_ids.count(config.boi_token_index) == num_imgs
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assert prompt_token_ids.count(config.eoi_token_index) == num_imgs
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assert prompt_token_ids.count(vocab[hf_processor.image_token]) == num_imgs
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aspect_ratios = mm_data["aspect_ratios"]
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num_x_separators = num_y_separators = 0
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for tiles_y, tiles_x in aspect_ratios:
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if tiles_x * tiles_y > 1:
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num_x_separators += (tiles_x - 1) * tiles_y
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num_y_separators += tiles_y
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assert prompt_token_ids.count(vocab[hf_processor.tile_token]) == num_x_separators
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assert (
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prompt_token_ids.count(vocab[hf_processor.tile_global_token])
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== num_y_separators
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)
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# image token offsets
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img_locs = processed_inputs["mm_placeholders"].get("image", [])
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assert len(img_locs) == num_imgs
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assert [img_loc.offset for img_loc in img_locs] == [
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i for i, v in enumerate(prompt_token_ids) if v == config.boi_token_index
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]
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# patch sizes and masks
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num_patches_per_chunk = processor.info.get_patch_per_chunk(config.vision_config)
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assert (
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prompt_token_ids.count(config.image_token_index)
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== sum(mm_data["patches_per_image"]) * num_patches_per_chunk
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)
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assert len(mm_data["pixel_values"]) == sum(mm_data["patches_per_image"])
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