d4d93db2c5
Signed-off-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com> Co-authored-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com> Co-authored-by: Nicolò Lucchesi <nlucches@redhat.com> Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com> Co-authored-by: Michael Goin <michael@neuralmagic.com>
443 lines
15 KiB
Python
443 lines
15 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import Any, Optional, TypedDict, Union
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import numpy.typing as npt
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import pytest
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import torch
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from PIL import Image
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from vllm.multimodal.image import rescale_image_size
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from vllm.multimodal.video import rescale_video_size, sample_frames_from_video
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from ....conftest import (IMAGE_ASSETS, VIDEO_ASSETS, PromptImageInput,
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PromptVideoInput, VllmRunner)
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from ...utils import check_logprobs_close
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@pytest.fixture(scope="function", autouse=True)
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def use_v0_only(monkeypatch):
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"""
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V1 Test: batch_make_xxxxx_embeddings calls a V0 internal
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"""
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monkeypatch.setenv('VLLM_USE_V1', '0')
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models = ["Qwen/Qwen2-VL-2B-Instruct"]
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target_dtype = "half"
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IMAGE_PLACEHOLDER = "<|vision_start|><|image_pad|><|vision_end|>"
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VIDEO_PLACEHOLDER = "<|vision_start|><|video_pad|><|vision_end|>"
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MODEL_HIDDEN_SIZE = 1536
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def qwen2_vl_chat_template(*query):
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return f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n{''.join(query)}<|im_end|><|im_start|>assistant\n" # noqa: E501
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IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
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"stop_sign":
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qwen2_vl_chat_template(
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IMAGE_PLACEHOLDER,
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"What is the biggest text's content in this image?",
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),
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"cherry_blossom":
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qwen2_vl_chat_template(
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IMAGE_PLACEHOLDER,
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"What is the season shown in this image? ",
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"Reply with a short sentence (no more than 20 words)",
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),
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})
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VIDEO_PROMPTS = VIDEO_ASSETS.prompts({
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"sample_demo_1":
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qwen2_vl_chat_template(
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VIDEO_PLACEHOLDER,
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"Describe this video with a short sentence ",
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"(no more than 20 words)",
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),
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})
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MULTIIMAGE_PROMPT = qwen2_vl_chat_template(
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IMAGE_PLACEHOLDER,
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IMAGE_PLACEHOLDER,
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"Describe these two images separately. ",
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"For each image, reply with a short sentence ",
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"(no more than 10 words).",
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)
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class Qwen2VLPromptImageEmbeddingInput(TypedDict):
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image_embeds: torch.Tensor
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image_grid_thw: torch.Tensor
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class Qwen2VLPromptVideoEmbeddingInput(TypedDict):
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video_embeds: torch.Tensor
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video_grid_thw: torch.Tensor
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def batch_make_image_embeddings(
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image_batches: list[Union[Image.Image, list[Image.Image]]], processor,
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llm: VllmRunner) -> list[Qwen2VLPromptImageEmbeddingInput]:
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"""batched image embeddings for Qwen2-VL
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This will infer all images' embeddings in a single batch,
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and split the result according to input batches.
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image_batches:
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- Single-image batches: `list[Image.Image]`
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- Multiple-image batches: `list[list[Image.Image]]]`
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returns: `list[Qwen2VLPromptImageEmbeddingInput]`
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"""
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image_batches_: list[Any] = image_batches[:]
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# convert single-image batches to multiple-image batches
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for idx in range(len(image_batches_)):
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if not isinstance(image_batches_[idx], list):
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image_batches_[idx] = [image_batches_[idx]]
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assert isinstance(image_batches_[idx], list)
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# append all images into a list (as a batch)
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images: list[Image.Image] = []
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for image_batch in image_batches_:
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images += image_batch
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# image to pixel values
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image_processor = processor.image_processor
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preprocess_result = image_processor \
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.preprocess(images=images, return_tensors="pt") \
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.data
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pixel_values = preprocess_result["pixel_values"]
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image_grid_thw = preprocess_result["image_grid_thw"]
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# pixel values to embeddings & grid_thws
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def get_image_embeds(model):
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with torch.no_grad():
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visual = model.visual
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pixel_values_on_device = pixel_values.to(visual.device,
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dtype=visual.dtype)
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image_grid_thw_on_device = image_grid_thw.to(visual.device,
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dtype=torch.int64)
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return visual(pixel_values_on_device,
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grid_thw=image_grid_thw_on_device)
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# V1 Test: this calls a V0 internal.
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image_embeds = torch.concat(llm.apply_model(get_image_embeds))
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# split into original batches
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result: list[Qwen2VLPromptImageEmbeddingInput] = []
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image_counter = 0
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embed_counter = 0
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for image_batch in image_batches_:
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cur_batch_image_count = len(image_batch)
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merge_size = image_processor.merge_size
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cur_batch_embed_len = sum(
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grid_thw.prod(-1) // merge_size // merge_size
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for grid_thw in image_grid_thw[image_counter:image_counter +
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cur_batch_image_count])
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result.append({
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"image_embeds":
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image_embeds[embed_counter:embed_counter + cur_batch_embed_len],
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"image_grid_thw":
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image_grid_thw[image_counter:image_counter +
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cur_batch_image_count],
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})
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embed_counter += cur_batch_embed_len
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image_counter += cur_batch_image_count
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# ensure we don't lost any images or embeddings
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assert embed_counter == image_embeds.size(0)
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assert image_counter == image_grid_thw.size(0)
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assert len(image_batches) == len(result)
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return result
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def batch_make_video_embeddings(
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video_batches: PromptVideoInput, processor,
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llm: VllmRunner) -> list[Qwen2VLPromptVideoEmbeddingInput]:
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"""batched video embeddings for Qwen2-VL
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A NDArray represents a single video's all frames.
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This will infer all videos' embeddings in a single batch,
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and split the result according to input batches.
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video_batches:
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- Single-video batches: `list[NDArray]`
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- Multiple-video batches: `list[list[NDArray]]`
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"""
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video_batches_: list[Any] = video_batches[:]
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for idx in range(len(video_batches_)):
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if not isinstance(video_batches_[idx], list):
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single_video_batch: list[npt.NDArray] = [video_batches_[idx]]
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video_batches_[idx] = single_video_batch
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assert isinstance(video_batches_[idx], list)
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# append all videos into a list (as a batch)
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videos: list[npt.NDArray] = []
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for video_batch in video_batches_:
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videos += video_batch
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# video to pixel values
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image_processor = processor.image_processor
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preprocess_result = image_processor \
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.preprocess(images=None, videos=videos, return_tensors="pt") \
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.data
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pixel_values = preprocess_result["pixel_values_videos"]
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video_grid_thw = preprocess_result["video_grid_thw"]
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# pixel values to embeddings & grid_thws
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def get_image_embeds(model):
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with torch.no_grad():
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visual = model.visual
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pixel_values_on_device = pixel_values.to(visual.device,
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dtype=visual.dtype)
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video_grid_thw_on_device = video_grid_thw.to(visual.device,
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dtype=torch.int64)
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return visual(pixel_values_on_device,
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grid_thw=video_grid_thw_on_device)
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# V1 Test: this calls a V0 internal.
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video_embeds = torch.concat(llm.apply_model(get_image_embeds))
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# split into original batches
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result: list[Qwen2VLPromptVideoEmbeddingInput] = []
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video_counter = 0
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embed_counter = 0
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for video_batch in video_batches_:
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cur_batch_video_count = len(video_batch)
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merge_size = image_processor.merge_size
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cur_batch_embed_len = sum(
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grid_thw.prod(-1) // merge_size // merge_size
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for grid_thw in video_grid_thw[video_counter:video_counter +
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cur_batch_video_count])
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result.append({
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"video_embeds":
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video_embeds[embed_counter:embed_counter + cur_batch_embed_len],
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"video_grid_thw":
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video_grid_thw[video_counter:video_counter +
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cur_batch_video_count],
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})
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embed_counter += cur_batch_embed_len
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video_counter += cur_batch_video_count
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# ensure we don't lost any videos or embeddings
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assert embed_counter == video_embeds.size(0)
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assert video_counter == video_grid_thw.size(0)
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assert len(video_batches) == len(result)
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return result
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def run_embedding_input_test(
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vllm_runner: type[VllmRunner],
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inputs: list[tuple[list[str], PromptImageInput, PromptVideoInput]],
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model: str,
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*,
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dtype: str,
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max_tokens: int,
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num_logprobs: int,
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mm_limit: int,
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tensor_parallel_size: int,
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distributed_executor_backend: Optional[str] = None,
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):
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"""Inference result should be the same between
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original image/video input and image/video embeddings input.
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"""
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from transformers import AutoProcessor # noqa: F401
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processor = AutoProcessor.from_pretrained(model)
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# max_model_len should be greater than image_feature_size
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with vllm_runner(model,
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task="generate",
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max_model_len=4000,
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max_num_seqs=3,
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dtype=dtype,
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limit_mm_per_prompt={
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"image": mm_limit,
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"video": mm_limit
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},
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tensor_parallel_size=tensor_parallel_size,
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distributed_executor_backend=distributed_executor_backend
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) as vllm_model:
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outputs_per_case_for_original_input = [
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vllm_model.generate_greedy_logprobs(prompts,
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max_tokens,
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num_logprobs=num_logprobs,
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images=images or None,
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videos=videos or None)
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for prompts, images, videos in inputs
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]
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outputs_per_case_for_embeddings_input = [
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vllm_model.generate_greedy_logprobs(
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prompts,
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max_tokens,
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num_logprobs=num_logprobs,
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images=batch_make_image_embeddings(
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images, processor, vllm_model) if images else None,
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videos=batch_make_video_embeddings(
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videos, processor, vllm_model) if videos else None)
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for prompts, images, videos in inputs
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]
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for outputs_for_original_input, \
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outputs_for_embeddings_input \
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in zip(outputs_per_case_for_original_input,
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outputs_per_case_for_embeddings_input):
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check_logprobs_close(
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outputs_0_lst=outputs_for_original_input,
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outputs_1_lst=outputs_for_embeddings_input,
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name_0="original_input",
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name_1="embeddings_input",
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)
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@pytest.mark.core_model
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@pytest.mark.parametrize("model", models)
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@pytest.mark.parametrize(
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"size_factors",
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[
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# Single-scale
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[0.5],
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# Single-scale, batched
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[0.5, 0.5],
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# Multi-scale
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[0.25, 0.5, 0.5],
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],
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)
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@pytest.mark.parametrize("dtype", [target_dtype])
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@pytest.mark.parametrize("max_tokens", [128])
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@pytest.mark.parametrize("num_logprobs", [10])
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def test_qwen2_vl_image_embeddings_input(vllm_runner, image_assets, model,
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size_factors, dtype: str,
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max_tokens: int,
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num_logprobs: int) -> None:
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images = [asset.pil_image for asset in image_assets]
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inputs_per_case: list[tuple[
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list[str], PromptImageInput, PromptVideoInput]] = [(
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[prompt for _ in size_factors],
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[rescale_image_size(image, factor) for factor in size_factors],
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[],
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) for image, prompt in zip(images, IMAGE_PROMPTS)]
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run_embedding_input_test(
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vllm_runner,
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inputs_per_case,
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model,
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dtype=dtype,
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max_tokens=max_tokens,
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num_logprobs=num_logprobs,
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mm_limit=1,
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tensor_parallel_size=1,
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)
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@pytest.mark.core_model
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@pytest.mark.parametrize("model", models)
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@pytest.mark.parametrize(
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"size_factors",
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[
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[],
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# Single-scale
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[0.5],
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# Single-scale, batched
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[0.5, 0.5],
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# Multi-scale
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[0.25, 0.5, 0.5],
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],
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)
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@pytest.mark.parametrize("dtype", [target_dtype])
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@pytest.mark.parametrize("max_tokens", [128])
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@pytest.mark.parametrize("num_logprobs", [10])
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def test_qwen2_vl_multiple_image_embeddings_input(vllm_runner, image_assets,
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model, size_factors,
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dtype: str, max_tokens: int,
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num_logprobs: int) -> None:
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images = [asset.pil_image for asset in image_assets]
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inputs_per_case: list[tuple[list[str], PromptImageInput,
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PromptVideoInput]] = [(
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[MULTIIMAGE_PROMPT for _ in size_factors],
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[[
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rescale_image_size(image, factor)
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for image in images
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] for factor in size_factors],
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[],
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)]
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run_embedding_input_test(
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vllm_runner,
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inputs_per_case,
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model,
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dtype=dtype,
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max_tokens=max_tokens,
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num_logprobs=num_logprobs,
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mm_limit=2,
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tensor_parallel_size=1,
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)
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@pytest.mark.core_model
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@pytest.mark.parametrize("model", models)
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@pytest.mark.parametrize(
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"size_factors",
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[
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# Single-scale
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[0.5],
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# Single-scale, batched
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[0.5, 0.5],
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# Multi-scale
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[0.25, 0.25, 0.5],
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],
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)
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@pytest.mark.parametrize("dtype", [target_dtype])
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@pytest.mark.parametrize("max_tokens", [128])
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@pytest.mark.parametrize("num_logprobs", [10])
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def test_qwen2_vl_video_embeddings_input(vllm_runner, video_assets, model,
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size_factors, dtype: str,
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max_tokens: int,
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num_logprobs: int) -> None:
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num_frames = 4
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sampled_vids = [
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sample_frames_from_video(asset.np_ndarrays, num_frames)
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for asset in video_assets
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]
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inputs_per_case: list[tuple[
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list[str], PromptImageInput, PromptVideoInput]] = [(
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[prompt for _ in size_factors],
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[],
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[rescale_video_size(video, factor) for factor in size_factors],
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) for video, prompt in zip(sampled_vids, VIDEO_PROMPTS)]
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run_embedding_input_test(
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vllm_runner,
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inputs_per_case,
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model,
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dtype=dtype,
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max_tokens=max_tokens,
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num_logprobs=num_logprobs,
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mm_limit=1,
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tensor_parallel_size=1,
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)
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