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[CI/Build][VLM] Cleanup multiple images inputs model test (vllm-proje…
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…ct#7897)
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@Isotr0py
Isotr0py authored Aug 27, 2024
1 parent cc5b0f6 commit 66cae65
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Showing 2 changed files with 74 additions and 203 deletions.
136 changes: 34 additions & 102 deletions tests/models/test_minicpmv.py
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Original file line number Diff line number Diff line change
@@ -1,14 +1,15 @@
from typing import List, Optional, Tuple, Type
from typing import List, Optional, Tuple, Type, Union

import pytest
import torch
import torch.types
from PIL import Image
from transformers import BatchEncoding

from vllm.multimodal.utils import rescale_image_size
from vllm.sequence import SampleLogprobs

from ..conftest import IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets
from ..conftest import IMAGE_ASSETS, HfRunner, VllmRunner
from .utils import check_logprobs_close

pytestmark = pytest.mark.vlm
Expand All @@ -24,6 +25,11 @@
"(<image>./</image>)\nWhat is the season?<|eot_id|>" \
"<|start_header_id|>assistant<|end_header_id|>\n\n",
})
HF_MULTIIMAGE_IMAGE_PROMPT = \
"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n" \
"(<image>./</image>)\n(<image>./</image>)\n" \
"Describe these images.<|eot_id|>" \
"<|start_header_id|>assistant<|end_header_id|>\n\n"

models = ["openbmb/MiniCPM-Llama3-V-2_5"]

Expand All @@ -46,13 +52,14 @@ def trunc_hf_output(hf_output: Tuple[List[int], str,
def run_test(
hf_runner: Type[HfRunner],
vllm_runner: Type[VllmRunner],
image_assets: _ImageAssets,
inputs: List[Tuple[List[str], Union[List[Image.Image],
List[List[Image.Image]]]]],
model: str,
*,
size_factors: List[float],
dtype: str,
max_tokens: int,
num_logprobs: int,
mm_limit: int,
tensor_parallel_size: int,
distributed_executor_backend: Optional[str] = None,
):
Expand All @@ -65,12 +72,6 @@ def run_test(
Note, the text input is also adjusted to abide by vllm contract.
The text output is sanitized to be able to compare with hf.
"""
images = [asset.pil_image for asset in image_assets]

inputs_per_image = [(
[prompt for _ in size_factors],
[rescale_image_size(image, factor) for factor in size_factors],
) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]

# NOTE: take care of the order. run vLLM first, and then run HF.
# vLLM needs a fresh new process without cuda initialization.
Expand All @@ -82,6 +83,7 @@ def run_test(
max_model_len=4096,
max_num_seqs=1,
dtype=dtype,
limit_mm_per_prompt={"image": mm_limit},
tensor_parallel_size=tensor_parallel_size,
distributed_executor_backend=distributed_executor_backend,
enforce_eager=True) as vllm_model:
Expand All @@ -93,7 +95,7 @@ def run_test(
num_logprobs=num_logprobs,
images=images,
stop_token_ids=stop_token_ids)
for prompts, images in inputs_per_image
for prompts, images in inputs
]

hf_model = hf_runner(model, dtype=dtype, postprocess_inputs=_wrap_inputs)
Expand All @@ -104,7 +106,7 @@ def run_test(
num_logprobs=num_logprobs,
images=images,
tokenizer=tokenizer)
for prompts, images in inputs_per_image
for prompts, images in inputs
]

for hf_outputs, vllm_outputs in zip(hf_outputs_per_image,
Expand Down Expand Up @@ -138,104 +140,26 @@ def run_test(
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,
dtype: str, max_tokens: int, num_logprobs: int) -> None:
images = [asset.pil_image for asset in image_assets]

inputs_per_image = [(
[prompt for _ in size_factors],
[rescale_image_size(image, factor) for factor in size_factors],
) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]

run_test(
hf_runner,
vllm_runner,
image_assets,
inputs_per_image,
model,
size_factors=size_factors,
dtype=dtype,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
mm_limit=1,
tensor_parallel_size=1,
)


HF_MULTIIMAGE_IMAGE_PROMPT = \
"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n" \
"(<image>./</image>)\n(<image>./</image>)\n" \
"Describe these images.<|eot_id|>" \
"<|start_header_id|>assistant<|end_header_id|>\n\n"


def run_multi_image_test(
hf_runner: Type[HfRunner],
vllm_runner: Type[VllmRunner],
image_assets: _ImageAssets,
model: str,
*,
size_factors: List[float],
dtype: str,
max_tokens: int,
num_logprobs: int,
tensor_parallel_size: int,
distributed_executor_backend: Optional[str] = None,
):
"""Inference result should be the same between hf and vllm.
All the image fixtures for the test is under tests/images.
For huggingface runner, we provide the PIL images as input.
For vllm runner, we provide MultiModalDataDict objects
and corresponding MultiModalConfig as input.
Note, the text input is also adjusted to abide by vllm contract.
The text output is sanitized to be able to compare with hf.
"""
images = [asset.pil_image for asset in image_assets]

inputs_per_case = [
([HF_MULTIIMAGE_IMAGE_PROMPT for _ in size_factors],
[[rescale_image_size(image, factor) for image in images]
for factor in size_factors])
]

# NOTE: take care of the order. run vLLM first, and then run HF.
# vLLM needs a fresh new process without cuda initialization.
# if we run HF first, the cuda initialization will be done and it
# will hurt multiprocessing backend with fork method (the default method).

# max_model_len should be greater than image_feature_size
with vllm_runner(model,
max_model_len=4096,
max_num_seqs=1,
limit_mm_per_prompt={"image": len(images)},
dtype=dtype,
tensor_parallel_size=tensor_parallel_size,
distributed_executor_backend=distributed_executor_backend,
enforce_eager=True) as vllm_model:
tokenizer = vllm_model.model.get_tokenizer()
stop_token_ids = [tokenizer.eos_id, tokenizer.eot_id]
vllm_outputs_per_case = [
vllm_model.generate_greedy_logprobs(prompts,
max_tokens,
num_logprobs=num_logprobs,
images=images,
stop_token_ids=stop_token_ids)
for prompts, images in inputs_per_case
]

hf_model = hf_runner(model, dtype=dtype, postprocess_inputs=_wrap_inputs)
with hf_model, torch.no_grad():
hf_outputs_per_case = [
hf_model.generate_greedy_logprobs_limit(prompts,
max_tokens,
num_logprobs=num_logprobs,
images=images,
tokenizer=tokenizer)
for prompts, images in inputs_per_case
]

for hf_outputs, vllm_outputs in zip(hf_outputs_per_case,
vllm_outputs_per_case):
check_logprobs_close(
outputs_0_lst=[
trunc_hf_output(hf_output) for hf_output in hf_outputs
],
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)


@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize(
"size_factors",
Expand All @@ -256,14 +180,22 @@ def run_multi_image_test(
def test_multi_images_models(hf_runner, vllm_runner, image_assets, model,
size_factors, dtype: str, max_tokens: int,
num_logprobs: int) -> None:
run_multi_image_test(
images = [asset.pil_image for asset in image_assets]

inputs_per_case = [
([HF_MULTIIMAGE_IMAGE_PROMPT for _ in size_factors],
[[rescale_image_size(image, factor) for image in images]
for factor in size_factors])
]

run_test(
hf_runner,
vllm_runner,
image_assets,
inputs_per_case,
model,
size_factors=size_factors,
dtype=dtype,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
mm_limit=2,
tensor_parallel_size=1,
)
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