mirror of
https://github.com/vllm-project/vllm.git
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113 lines
3.4 KiB
Python
113 lines
3.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import pytest
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import torch
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from transformers import CLIPModel
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from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner
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from ...utils import check_embeddings_close
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HF_TEXT_PROMPTS = [
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"a photo of a stop sign",
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"a photo of a cherry blossom",
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]
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HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts(
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{
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"stop_sign": "",
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"cherry_blossom": "",
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}
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)
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MODELS = ["openai/clip-vit-base-patch32"]
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def _run_test(
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hf_runner: type[HfRunner],
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vllm_runner: type[VllmRunner],
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input_cases: list[tuple[list[str], PromptImageInput]],
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model: str,
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*,
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dtype: str,
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) -> None:
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# NOTE: take care of the order. run vLLM first, and then run HF.
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# vLLM needs a fresh new process without cuda initialization.
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# if we run HF first, the cuda initialization will be done and it
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# will hurt multiprocessing backend with fork method (the default method).
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with vllm_runner(
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model, runner="pooling", dtype=dtype, enforce_eager=True, max_model_len=77
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) as vllm_model:
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vllm_outputs_per_case = [
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vllm_model.embed(input_texts, images=input_images)
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for input_texts, input_images in input_cases
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]
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texts = [HF_TEXT_PROMPTS[0]]
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images = [input_cases[1][1][0]]
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with pytest.raises(ValueError, match="not both"):
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vllm_model.embed(texts, images=images)
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# Should still be able to run subsequent requests
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vllm_model.embed(texts)
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vllm_model.embed([""], images=images)
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with hf_runner(model, dtype=dtype, auto_cls=CLIPModel) as hf_model:
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hf_outputs_per_case = []
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for input_texts, input_images in input_cases:
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all_inputs = hf_model.get_inputs(input_texts, images=input_images)
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hf_outputs = []
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for inputs in all_inputs:
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inputs = hf_model.wrap_device(inputs)
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if "pixel_values" in inputs:
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pooled_output = hf_model.model.get_image_features(
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pixel_values=inputs.pixel_values,
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)
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else:
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pooled_output = hf_model.model.get_text_features(
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input_ids=inputs.input_ids,
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attention_mask=inputs.attention_mask,
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)
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if not isinstance(pooled_output, torch.Tensor):
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pooled_output = pooled_output.pooler_output
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pooled_output = pooled_output.squeeze(0)
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hf_outputs.append(pooled_output.tolist())
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hf_outputs_per_case.append(hf_outputs)
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for hf_outputs, vllm_outputs in zip(hf_outputs_per_case, vllm_outputs_per_case):
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check_embeddings_close(
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embeddings_0_lst=hf_outputs,
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embeddings_1_lst=vllm_outputs,
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name_0="hf",
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name_1="vllm",
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)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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def test_models(
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hf_runner,
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vllm_runner,
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image_assets,
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model: str,
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dtype: str,
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) -> None:
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text_images = [None] * len(HF_TEXT_PROMPTS)
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images = [asset.pil_image for asset in image_assets]
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input_cases = [
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(HF_TEXT_PROMPTS, text_images),
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(HF_IMAGE_PROMPTS, images),
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]
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_run_test(
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hf_runner,
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vllm_runner,
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input_cases, # type: ignore[arg-type]
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model,
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dtype=dtype,
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)
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