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68 lines
2.4 KiB
Python
68 lines
2.4 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 mllama's multimodal preprocessing and profiling."""
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import pytest
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from torch import prod
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from transformers import Llama4Config
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.profiling import MultiModalProfiler
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from ...utils import build_model_context
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@pytest.mark.parametrize("model_id", ["meta-llama/Llama-Guard-4-12B"])
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@pytest.mark.parametrize("max_model_len", [4096, 8192, 25600, 131072])
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def test_profiling(model_id: str, max_model_len: int):
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model_config_kwargs = {
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"max_model_len": max_model_len,
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}
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ctx = build_model_context(
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model_id,
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model_config_kwargs=model_config_kwargs,
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limit_mm_per_prompt={"image": 1},
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)
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mm_config = ctx.get_mm_config()
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processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
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profiler = MultiModalProfiler(processor)
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decoder_dummy_data = profiler.get_decoder_dummy_data(
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max_model_len,
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mm_counts=mm_config.limit_per_prompt,
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)
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dummy_mm_data = processor.dummy_inputs.get_dummy_processor_inputs(
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max_model_len,
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mm_counts=mm_config.limit_per_prompt,
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)
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hf_config = ctx.get_hf_config(Llama4Config)
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mm_kwargs = processor.apply(
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prompt=dummy_mm_data.prompt,
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mm_data=dummy_mm_data.mm_data,
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hf_processor_mm_kwargs=dict(),
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)["mm_kwargs"]
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image_size = hf_config.vision_config.image_size
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patch_size = hf_config.vision_config.patch_size
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downsample_ratio = int(
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round(1.0 / (hf_config.vision_config.pixel_shuffle_ratio**2)))
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tokens_per_patch = ((image_size // patch_size)**2) // downsample_ratio
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chunks_per_image = prod(mm_kwargs["patches_per_image"])
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total_num_patches = chunks_per_image * tokens_per_patch
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num_tiles = mm_kwargs["aspect_ratios"][0][0] * mm_kwargs["aspect_ratios"][
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0][1] # x-y seperator tokens
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total_tokens = total_num_patches.item() + num_tiles.item(
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) + 3 # image start, image, image end
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profiled_tokens = profiler.get_mm_max_contiguous_tokens(
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max_model_len,
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mm_counts=mm_config.limit_per_prompt,
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)
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assert total_tokens == profiled_tokens["image"]
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assert total_tokens == sum(
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placeholder.length for placeholder in
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decoder_dummy_data.multi_modal_placeholders["image"])
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