[vlm] Remove vision language config. (#6089)
Signed-off-by: Xiaowei Jiang <xwjiang2010@gmail.com> Co-authored-by: Roger Wang <ywang@roblox.com>
This commit is contained in:
xwjiang2010
@@ -3,7 +3,6 @@ from typing import List, Optional, Tuple, Type
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import pytest
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from transformers import AutoTokenizer
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from vllm.config import VisionLanguageConfig
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from vllm.multimodal.utils import rescale_image_size
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from vllm.sequence import SampleLogprobs
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@@ -21,49 +20,27 @@ HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
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"USER: <image>\nWhat's in this image?\nASSISTANT:",
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})
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IMAGE_TOKEN_ID = 32000
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def iter_llava_configs(model_name: str):
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image_hw_to_feature_size = {
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(336, 336): 576,
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}
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for (h, w), f in image_hw_to_feature_size.items():
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input_shape = (1, 3, h, w)
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yield (model_name,
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VisionLanguageConfig(image_feature_size=f,
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image_token_id=32000,
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image_input_shape=input_shape))
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model_and_vl_config = [
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*iter_llava_configs("llava-hf/llava-1.5-7b-hf"),
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]
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models = ["llava-hf/llava-1.5-7b-hf"]
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def vllm_to_hf_output(vllm_output: Tuple[List[int], str,
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Optional[SampleLogprobs]],
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vlm_config: VisionLanguageConfig, model_id: str):
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"""Sanitize vllm output to be comparable with hf output.
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The function reduces `input_ids` from 1, 32000, 32000, ..., 32000,
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x1, x2, x3 ... to 1, 32000, x1, x2, x3 ...
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It also reduces `output_str` from "<image><image>bla" to "bla".
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"""
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model: str):
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"""Sanitize vllm output to be comparable with hf output."""
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output_ids, output_str, out_logprobs = vllm_output
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image_token_id = vlm_config.image_token_id
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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image_token_str = tokenizer.decode(image_token_id)
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tokenizer = AutoTokenizer.from_pretrained(model)
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eos_token_id = tokenizer.eos_token_id
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hf_output_ids = [
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token_id for idx, token_id in enumerate(output_ids)
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if token_id != image_token_id or output_ids[idx - 1] != image_token_id
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if token_id != IMAGE_TOKEN_ID or output_ids[idx - 1] != IMAGE_TOKEN_ID
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]
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hf_output_str = output_str \
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.replace(image_token_str * vlm_config.image_feature_size, "")
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assert hf_output_str[0] == " "
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hf_output_str = hf_output_str[1:]
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assert output_str[0] == " "
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hf_output_str = output_str[1:]
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if hf_output_ids[-1] == eos_token_id:
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hf_output_str = hf_output_str + tokenizer.decode(eos_token_id)
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@@ -74,7 +51,7 @@ def run_test(
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hf_runner: Type[HfRunner],
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vllm_runner: Type[VllmRunner],
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image_assets: _ImageAssets,
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model_and_config: Tuple[str, VisionLanguageConfig],
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model: str,
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*,
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size_factors: List[float],
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dtype: str,
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@@ -92,7 +69,6 @@ def run_test(
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Note, the text input is also adjusted to abide by vllm contract.
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The text output is sanitized to be able to compare with hf.
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"""
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model_id, vlm_config = model_and_config
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images = [asset.pil_image for asset in image_assets]
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inputs_per_image = [(
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@@ -106,12 +82,11 @@ def run_test(
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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(model_id,
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with vllm_runner(model,
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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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**vlm_config.as_cli_args_dict()) as vllm_model:
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enforce_eager=True) as vllm_model:
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vllm_outputs_per_image = [
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vllm_model.generate_greedy_logprobs(prompts,
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max_tokens,
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@@ -120,7 +95,7 @@ def run_test(
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for prompts, images in inputs_per_image
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]
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with hf_runner(model_id, dtype=dtype, is_vision_model=True) as hf_model:
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with hf_runner(model, dtype=dtype, is_vision_model=True) as hf_model:
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hf_outputs_per_image = [
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hf_model.generate_greedy_logprobs_limit(prompts,
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max_tokens,
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@@ -136,7 +111,7 @@ def run_test(
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check_logprobs_close(
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outputs_0_lst=hf_outputs,
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outputs_1_lst=[
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vllm_to_hf_output(vllm_output, vlm_config, model_id)
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vllm_to_hf_output(vllm_output, model)
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for vllm_output in vllm_outputs
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],
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name_0="hf",
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@@ -144,7 +119,7 @@ def run_test(
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)
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@pytest.mark.parametrize("model_and_config", model_and_vl_config)
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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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@@ -161,14 +136,13 @@ def run_test(
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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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def test_models(hf_runner, vllm_runner, image_assets, model_and_config,
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size_factors, dtype: str, max_tokens: int,
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num_logprobs: int) -> None:
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def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,
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dtype: str, max_tokens: int, num_logprobs: int) -> None:
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run_test(
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hf_runner,
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vllm_runner,
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image_assets,
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model_and_config,
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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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