wehub-resource-sync
288 lines
9.4 KiB
Python
288 lines
9.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Mapping
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import pytest
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import torch
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from PIL import Image as PILImage
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from vllm.model_executor.models.gemma4_mm import (
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Gemma4ForConditionalGeneration,
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Gemma4ImagePixelInputs,
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)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import MultiModalFieldConfig
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from vllm.utils.mem_constants import GiB_bytes
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from ....conftest import ImageTestAssets
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from ...utils import build_model_context
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# TODO: to be updated to "google/gemma-4-e2b-it" once the models are available
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GEMMA4_MODEL_ID = "google/gemma-4-E2B-it"
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def test_gemma4_image_schema_accepts_variable_patch_counts():
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Gemma4ImagePixelInputs(
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pixel_values=[
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torch.randn(10080, 768),
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torch.randn(2520, 768),
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],
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pixel_position_ids=[
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torch.zeros(10080, 2, dtype=torch.long),
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torch.zeros(2520, 2, dtype=torch.long),
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],
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)
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def test_gemma4_image_batching_keeps_variable_patch_counts_unstacked():
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field = MultiModalFieldConfig.batched("image").field
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elems = field.build_elems(
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"image",
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"pixel_values",
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[torch.randn(10080, 768), torch.randn(2520, 768)],
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)
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reduced = field.reduce_data(list(elems))
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assert isinstance(reduced, list)
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assert [tensor.shape for tensor in reduced] == [
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torch.Size([10080, 768]),
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torch.Size([2520, 768]),
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]
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@pytest.mark.parametrize(
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"image_width,image_height,max_soft_tokens",
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[
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# Production repro: a 3x900 image (extreme aspect ratio) made the
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# prompt-side estimator return 289 while the HF Gemma 4 image
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# processor's vision tower output capped at 280, producing the
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# "Attempted to assign 280 multimodal tokens to 289 placeholders"
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# mismatch that crashed EngineCore.
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(900, 3, 280),
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(3, 900, 280),
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# Same pathology should hold for the video-frame budget (70 tokens).
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(900, 3, 70),
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# And for any other supported budget.
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(4000, 2, 1120),
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],
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)
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@pytest.mark.parametrize("model_id", [GEMMA4_MODEL_ID])
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def test_compute_num_soft_tokens_does_not_exceed_max_soft_tokens(
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model_id: str,
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image_width: int,
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image_height: int,
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max_soft_tokens: int,
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):
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"""Regression for the Gemma 3/4 multimodal crash.
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`_compute_num_soft_tokens` must never return a value larger than
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`max_soft_tokens`. The HF Gemma 4 image processor clamps its vision
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tower output to that value; if the prompt-side estimator returns more,
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the prompt has more `image` placeholder tokens than the encoder will
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fill, and `_merge_multimodal_embeddings` raises `ValueError` deep in
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the model forward.
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"""
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ctx = build_model_context(
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model_id,
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mm_processor_kwargs={"do_pan_and_scan": True},
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limit_mm_per_prompt={"image": 1},
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)
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processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
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num_soft_tokens = processor.info._compute_num_soft_tokens(
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image_width=image_width,
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image_height=image_height,
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max_soft_tokens=max_soft_tokens,
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)
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assert num_soft_tokens <= max_soft_tokens, (
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f"_compute_num_soft_tokens returned {num_soft_tokens} for "
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f"image_width={image_width}, image_height={image_height}, "
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f"max_soft_tokens={max_soft_tokens} — exceeds the cap that the HF "
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f"image processor enforces on its vision tower output. This is "
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f"the placeholder/encoder count mismatch that crashes EngineCore."
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)
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@pytest.mark.parametrize(
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("mm_processor_kwargs", "expected_image_tokens"),
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[
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({}, 280),
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({"max_soft_tokens": 70}, 70),
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({"max_soft_tokens": 280}, 280),
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({"max_soft_tokens": 1120}, 1120),
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({"images_kwargs": {"max_soft_tokens": 560}}, 560),
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({"images_kwargs": None}, 280),
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({"images_kwargs": "not-a-dict"}, 280),
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],
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)
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@pytest.mark.parametrize("model_id", [GEMMA4_MODEL_ID])
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def test_get_mm_max_tokens_per_item_respects_configured_max_soft_tokens(
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model_id: str,
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mm_processor_kwargs: dict[str, object],
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expected_image_tokens: int,
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):
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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": 1, "video": 1},
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)
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processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
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tokens = processor.info.get_mm_max_tokens_per_item(
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seq_len=ctx.model_config.max_model_len,
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mm_counts={"image": 1, "video": 1},
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)
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assert tokens is not None
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assert tokens["image"] == expected_image_tokens
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assert tokens["video"] == 32 * (70 + 2 + 6)
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@pytest.mark.parametrize(
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("limit_mm_per_prompt", "expected_video_tokens"),
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[
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({"video": 1}, 32 * (70 + 2 + 6)),
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({"video": {"count": 1}}, 32 * (70 + 2 + 6)),
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({"video": {"count": 1, "num_frames": 1}}, 1 * (70 + 2 + 6)),
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({"video": {"count": 1, "num_frames": 8}}, 8 * (70 + 2 + 6)),
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({"video": {"count": 1, "num_frames": 32}}, 32 * (70 + 2 + 6)),
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({"video": {"count": 1, "num_frames": 40}}, 32 * (70 + 2 + 6)),
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],
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)
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@pytest.mark.parametrize("model_id", [GEMMA4_MODEL_ID])
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def test_get_mm_max_tokens_per_item_respects_configured_video_num_frames(
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model_id: str,
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limit_mm_per_prompt: Mapping[str, int | Mapping[str, int]],
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expected_video_tokens: int,
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):
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ctx = build_model_context(
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model_id,
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limit_mm_per_prompt=limit_mm_per_prompt,
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)
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processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
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tokens = processor.info.get_mm_max_tokens_per_item(
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seq_len=ctx.model_config.max_model_len,
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mm_counts={"video": 1},
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)
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assert tokens is not None
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assert tokens["image"] == 280
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assert tokens["video"] == expected_video_tokens
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@pytest.mark.parametrize("model_id", [GEMMA4_MODEL_ID])
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def test_get_prompt_updates_respects_nested_max_soft_tokens(model_id: str):
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ctx = build_model_context(
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model_id,
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mm_processor_kwargs={"images_kwargs": {"max_soft_tokens": 560}},
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limit_mm_per_prompt={"image": 1},
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)
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processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
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image = PILImage.new("RGB", (1000, 1000), color="white")
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image_size = image.size
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mm_items = processor.info.parse_mm_data({"image": image})
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prompt_update = processor._get_prompt_updates(mm_items, {}, {})[0]
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replacement = prompt_update.resolve(0).content.full
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expected = processor.info.get_image_repl(
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image_width=image_size[0],
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image_height=image_size[1],
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processor=processor.info.get_hf_processor(),
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max_soft_tokens=560,
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).full
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assert replacement == expected
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@pytest.mark.parametrize("model_id", [GEMMA4_MODEL_ID])
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def test_limit_mm_per_prompt(
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image_assets: ImageTestAssets,
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model_id: str,
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):
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"""Test that limit_mm_per_prompt accurately restricts multiple images."""
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# We only allow 1 image
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ctx = build_model_context(
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model_id,
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mm_processor_kwargs={},
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limit_mm_per_prompt={"image": 1},
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)
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processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
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# Provide 2 images in the prompt
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prompt = "<image><image>"
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# image_assets usually has multiple images
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images = [asset.pil_image for asset in image_assets][:2]
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if len(images) < 2:
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images = [images[0], images[0]]
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mm_data = {"image": images}
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# Expect ValueError when exceeding limit
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with pytest.raises(ValueError, match="At most 1 image"):
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processor(
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prompt,
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mm_items=processor.info.parse_mm_data(mm_data),
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hf_processor_mm_kwargs={},
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)
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# Regression guard for PR #43169 follow-up: the batched Gemma4 vision encoder
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# admitted ``chunk ~= 53`` on a 22 GiB L4 with a 26B AWQ model loaded,
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# allocating 2.43 GiB int64 inside
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# ``F.one_hot(num_classes=position_embedding_size)`` and OOMing because only
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# 2.41 GiB was actually free. The fix sizes ``chunk`` from currently-free GPU
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# memory and counts the ``F.one_hot`` transient as the dominant cost.
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_encoder_chunk = Gemma4ForConditionalGeneration._encoder_chunk
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# Gemma4 vision config default (HF: configuration_gemma4.py).
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_POSITION_EMBEDDING_SIZE = 10240
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# Video frame: max_soft_tokens=70, pooling_kernel_size=2 -> 70 * 4 patches.
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_VIDEO_PATCHES_PER_FRAME = 280
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def test_encoder_chunk_tight_budget_fits_in_free():
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free = 3 * GiB_bytes # L4 22 GiB after 26B AWQ load.
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total = 22 * GiB_bytes
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chunk = _encoder_chunk(
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_VIDEO_PATCHES_PER_FRAME, free, total, _POSITION_EMBEDDING_SIZE
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)
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one_hot_bytes = chunk * _VIDEO_PATCHES_PER_FRAME * 2 * _POSITION_EMBEDDING_SIZE * 8
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assert one_hot_bytes <= free // 2
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def test_encoder_chunk_roomy_gpu_keeps_batching():
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chunk = _encoder_chunk(
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_VIDEO_PATCHES_PER_FRAME,
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60 * GiB_bytes,
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80 * GiB_bytes,
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_POSITION_EMBEDDING_SIZE,
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)
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assert chunk > 8
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def test_encoder_chunk_zero_patches_is_safe():
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assert (
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_encoder_chunk(0, 60 * GiB_bytes, 80 * GiB_bytes, _POSITION_EMBEDDING_SIZE) == 1
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)
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def test_encoder_chunk_zero_position_embedding_size_is_safe():
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# Degenerate config: must not raise ZeroDivisionError.
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assert (
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_encoder_chunk(_VIDEO_PATCHES_PER_FRAME, 60 * GiB_bytes, 80 * GiB_bytes, 0) == 1
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)
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def test_encoder_chunk_no_free_memory_falls_back_to_one():
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assert (
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_encoder_chunk(
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_VIDEO_PATCHES_PER_FRAME, 0, 22 * GiB_bytes, _POSITION_EMBEDDING_SIZE
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)
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== 1
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)
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