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[V1] V1 Enablement Oracle (#13726)

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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>
This commit is contained in:
Robert Shaw
2025-03-15 01:02:20 -04:00
committed by GitHub
parent 8c0d15d5c5
commit d4d93db2c5
96 changed files with 1537 additions and 512 deletions
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7
tests/models/decoder_only/vision_language/test_awq.py
View File

@@ -108,7 +108,12 @@ def run_awq_test(
@pytest.mark.parametrize("num_logprobs", [5])
@torch.inference_mode()
def test_awq_models(vllm_runner, image_assets, source_model, quant_model,
size_factors, dtype, max_tokens, num_logprobs) -> None:
size_factors, dtype, max_tokens, num_logprobs,
monkeypatch) -> None:
# Test V1: this test hangs during setup on single-scale input.
# TODO: fixure out why and re-enable this on V1.
monkeypatch.setenv("VLLM_USE_V1", "0")
run_awq_test(
vllm_runner,
image_assets,

89
tests/models/decoder_only/vision_language/test_models.py
View File

@@ -9,8 +9,7 @@ from pathlib import PosixPath
import pytest
from packaging.version import Version
from transformers import (AutoModelForImageTextToText, AutoModelForPreTraining,
AutoModelForVision2Seq)
from transformers import AutoModelForPreTraining, AutoModelForVision2Seq
from transformers import __version__ as TRANSFORMERS_VERSION
from vllm.platforms import current_platform
@@ -33,6 +32,16 @@ from .vlm_utils.types import (CustomTestOptions, ExpandableVLMTestArgs,
if current_platform.is_rocm():
os.environ["VLLM_USE_TRITON_FLASH_ATTN"] = "0"
REQUIRES_V0_MODELS = [
# V1 Test: no way to fall back for head_dim = 80
# https://github.com/vllm-project/vllm/issues/14524
"qwen_vl",
"h2ovl",
"blip2",
# V1 Test: not enough KV cache space in C1.
"fuyu",
]
# yapf: disable
COMMON_BROADCAST_SETTINGS = {
"test_type": VLMTestType.IMAGE,
@@ -157,25 +166,25 @@ VLM_TEST_SETTINGS = {
marks=[pytest.mark.core_model, pytest.mark.cpu_model],
),
#### Extended model tests
"aria": VLMTestInfo(
models=["rhymes-ai/Aria"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<|im_start|>user\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n ", # noqa: E501
img_idx_to_prompt=lambda idx: "<fim_prefix><|img|><fim_suffix>\n",
max_model_len=4096,
max_num_seqs=2,
auto_cls=AutoModelForImageTextToText,
single_image_prompts=IMAGE_ASSETS.prompts({
"stop_sign": "<vlm_image>Please describe the image shortly.",
"cherry_blossom": "<vlm_image>Please infer the season with reason.",
}),
multi_image_prompt="<vlm_image><vlm_image>Describe the two images shortly.", # noqa: E501
postprocess_inputs=model_utils.cast_dtype_post_processor("pixel_values"),
stop_str=["<|im_end|>"],
image_size_factors=[(0.10, 0.15)],
max_tokens=64,
marks=[large_gpu_mark(min_gb=64)],
),
# "aria": VLMTestInfo(
# models=["rhymes-ai/Aria"],
# test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
# prompt_formatter=lambda img_prompt: f"<|im_start|>user\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n ", # noqa: E501
# img_idx_to_prompt=lambda idx: "<fim_prefix><|img|><fim_suffix>\n",
# max_model_len=4096,
# max_num_seqs=2,
# auto_cls=AutoModelForImageTextToText,
# single_image_prompts=IMAGE_ASSETS.prompts({
# "stop_sign": "<vlm_image>Please describe the image shortly.",
# "cherry_blossom": "<vlm_image>Please infer the season with reason.", # noqa: E501
# }),
# multi_image_prompt="<vlm_image><vlm_image>Describe the two images shortly.", # noqa: E501
# postprocess_inputs=model_utils.cast_dtype_post_processor("pixel_values"), # noqa: E501
# stop_str=["<|im_end|>"],
# image_size_factors=[(0.10, 0.15)],
# max_tokens=64,
# marks=[large_gpu_mark(min_gb=64)],
# ),
"blip2": VLMTestInfo(
models=["Salesforce/blip2-opt-2.7b"],
test_type=VLMTestType.IMAGE,
@@ -589,7 +598,9 @@ def test_single_image_models(tmp_path: PosixPath, model_type: str,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets):
image_assets: _ImageAssets, monkeypatch):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
runners.run_single_image_test(
tmp_path=tmp_path,
@@ -612,7 +623,9 @@ def test_multi_image_models(tmp_path: PosixPath, model_type: str,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets):
image_assets: _ImageAssets, monkeypatch):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
runners.run_multi_image_test(
tmp_path=tmp_path,
@@ -635,7 +648,9 @@ def test_image_embedding_models(model_type: str,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets):
image_assets: _ImageAssets, monkeypatch):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
runners.run_embedding_test(
model_test_info=model_test_info,
@@ -655,7 +670,9 @@ def test_image_embedding_models(model_type: str,
))
def test_video_models(model_type: str, test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner], vllm_runner: type[VllmRunner],
video_assets: _VideoAssets):
video_assets: _VideoAssets, monkeypatch):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
runners.run_video_test(
model_test_info=model_test_info,
@@ -678,7 +695,10 @@ def test_custom_inputs_models(
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
monkeypatch,
):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
runners.run_custom_inputs_test(
model_test_info=model_test_info,
@@ -701,7 +721,9 @@ def test_single_image_models_heavy(tmp_path: PosixPath, model_type: str,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets):
image_assets: _ImageAssets, monkeypatch):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
runners.run_single_image_test(
tmp_path=tmp_path,
@@ -725,7 +747,9 @@ def test_multi_image_models_heavy(tmp_path: PosixPath, model_type: str,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets):
image_assets: _ImageAssets, monkeypatch):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
runners.run_multi_image_test(
tmp_path=tmp_path,
@@ -749,7 +773,9 @@ def test_image_embedding_models_heavy(model_type: str,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets):
image_assets: _ImageAssets, monkeypatch):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
runners.run_embedding_test(
model_test_info=model_test_info,
@@ -770,7 +796,9 @@ def test_image_embedding_models_heavy(model_type: str,
def test_video_models_heavy(model_type: str, test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
video_assets: _VideoAssets):
video_assets: _VideoAssets, monkeypatch):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
runners.run_video_test(
model_test_info=model_test_info,
@@ -794,7 +822,10 @@ def test_custom_inputs_models_heavy(
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
monkeypatch,
):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
runners.run_custom_inputs_test(
model_test_info=model_test_info,

12
tests/models/decoder_only/vision_language/test_qwen2_vl.py
View File

@@ -14,6 +14,15 @@ from ....conftest import (IMAGE_ASSETS, VIDEO_ASSETS, PromptImageInput,
PromptVideoInput, VllmRunner)
from ...utils import check_logprobs_close
@pytest.fixture(scope="function", autouse=True)
def use_v0_only(monkeypatch):
"""
V1 Test: batch_make_xxxxx_embeddings calls a V0 internal
"""
monkeypatch.setenv('VLLM_USE_V1', '0')
models = ["Qwen/Qwen2-VL-2B-Instruct"]
target_dtype = "half"
@@ -118,6 +127,7 @@ def batch_make_image_embeddings(
return visual(pixel_values_on_device,
grid_thw=image_grid_thw_on_device)
# V1 Test: this calls a V0 internal.
image_embeds = torch.concat(llm.apply_model(get_image_embeds))
# split into original batches
@@ -201,6 +211,7 @@ def batch_make_video_embeddings(
return visual(pixel_values_on_device,
grid_thw=video_grid_thw_on_device)
# V1 Test: this calls a V0 internal.
video_embeds = torch.concat(llm.apply_model(get_image_embeds))
# split into original batches
@@ -253,7 +264,6 @@ def run_embedding_input_test(
processor = AutoProcessor.from_pretrained(model)
# NOTE:
# max_model_len should be greater than image_feature_size
with vllm_runner(model,
task="generate",