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https://git.datalinker.icu/vllm-project/vllm.git
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139 lines
4.0 KiB
Python
139 lines
4.0 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 typing import Optional
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import pytest
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import torch
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from vllm.multimodal.image import rescale_image_size
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from ...conftest import IMAGE_ASSETS, ImageTestAssets, VllmRunner
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from ..utils import check_logprobs_close
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HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts(
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{
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"stop_sign": "<|im_start|>User\n<image>\nWhat's the content in the center of the image?<|im_end|>\n<|im_start|>Assistant\n", # noqa: E501
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"cherry_blossom": "<|im_start|>User\n<image>\nWhat is the season?<|im_end|>\n<|im_start|>Assistant\n", # noqa: E501
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}
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)
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def run_awq_test(
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vllm_runner: type[VllmRunner],
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image_assets: ImageTestAssets,
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source_model: str,
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quant_model: str,
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*,
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size_factors: list[float],
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dtype: str,
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max_tokens: int,
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num_logprobs: 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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images = [asset.pil_image for asset in image_assets]
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inputs_per_image = [
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(
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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, HF_IMAGE_PROMPTS)
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]
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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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# max_model_len should be greater than image_feature_size
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with vllm_runner(
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source_model,
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max_model_len=4096,
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dtype=dtype,
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tensor_parallel_size=tensor_parallel_size,
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distributed_executor_backend=distributed_executor_backend,
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enforce_eager=True,
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default_torch_num_threads=1,
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) as vllm_model:
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source_outputs_per_image = [
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vllm_model.generate_greedy_logprobs(
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prompts, max_tokens, num_logprobs=num_logprobs, images=images
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)
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for prompts, images in inputs_per_image
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]
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with vllm_runner(
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quant_model,
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quantization="awq",
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max_model_len=4096,
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dtype=dtype,
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tensor_parallel_size=tensor_parallel_size,
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distributed_executor_backend=distributed_executor_backend,
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enforce_eager=True,
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default_torch_num_threads=1,
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) as vllm_model:
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quant_outputs_per_image = [
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vllm_model.generate_greedy_logprobs(
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prompts, max_tokens, num_logprobs=num_logprobs, images=images
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)
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for prompts, images in inputs_per_image
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]
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for source_outputs, quant_outputs in zip(
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source_outputs_per_image, quant_outputs_per_image
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):
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# TODO: Check whether using original CLIPVisionModel can improve
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# consistency against HF
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check_logprobs_close(
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outputs_0_lst=source_outputs,
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outputs_1_lst=quant_outputs,
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name_0="source",
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name_1="awq",
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)
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@pytest.mark.parametrize(
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("source_model", "quant_model"),
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[("OpenGVLab/InternVL2-2B", "OpenGVLab/InternVL2-2B-AWQ")],
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)
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@pytest.mark.parametrize(
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"size_factors",
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[
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# No image
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[],
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# Single-scale
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[1.0],
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# Single-scale, batched
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[1.0, 1.0, 1.0],
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# Multi-scale
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[0.25, 0.5, 1.0],
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],
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)
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@pytest.mark.parametrize("dtype", ["half"])
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@pytest.mark.parametrize("max_tokens", [128])
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@pytest.mark.parametrize("num_logprobs", [5])
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@torch.inference_mode()
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def test_awq_models(
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vllm_runner,
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image_assets,
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source_model,
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quant_model,
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size_factors,
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dtype,
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max_tokens,
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num_logprobs,
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) -> None:
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run_awq_test(
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vllm_runner,
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image_assets,
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source_model,
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quant_model,
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size_factors=size_factors,
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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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tensor_parallel_size=1,
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)
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