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Commit 675ba75f authored by zhuwenwen's avatar zhuwenwen
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Merge tag 'v0.8.3' into v0.8.3-ori

parents 5cc98918 296c6572
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Showing with 539 additions and 225 deletions
tests/lora/test_quant_model.py
View file @ 675ba75f
......@@ -37,6 +37,14 @@ else:
]
@pytest.fixture(autouse=True)
def v1(run_with_both_engines_lora):
# Simple autouse wrapper to run both engines for each test
# This can be promoted up to conftest.py to run for every
# test in a package
pass
def do_sample(llm: vllm.LLM,
lora_path: str,
lora_id: int,
......@@ -69,14 +77,6 @@ def do_sample(llm: vllm.LLM,
return generated_texts
@pytest.fixture(autouse=True)
def v1(run_with_both_engines_lora):
# Simple autouse wrapper to run both engines for each test
# This can be promoted up to conftest.py to run for every
# test in a package
pass
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("tp_size", [1])
def test_quant_model_lora(tinyllama_lora_files, num_gpus_available, model,
......
tests/lora/test_transfomers_model.py
View file @ 675ba75f
......@@ -4,6 +4,7 @@ import pytest
import vllm
from vllm.lora.request import LoRARequest
from vllm.platforms import current_platform
from ..utils import create_new_process_for_each_test, multi_gpu_test
......@@ -46,16 +47,6 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
return generated_texts
@pytest.fixture(autouse=True)
def v1(run_with_both_engines_lora):
# Simple autouse wrapper to run both engines for each test
# This can be promoted up to conftest.py to run for every
# test in a package
pass
@pytest.mark.skip_v1
@create_new_process_for_each_test()
def test_ilama_lora(ilama_lora_files):
llm = vllm.LLM(MODEL_PATH,
max_model_len=1024,
......@@ -74,7 +65,8 @@ def test_ilama_lora(ilama_lora_files):
assert output2[i] == EXPECTED_LORA_OUTPUT[i]
@pytest.mark.skip_v1
@pytest.mark.skipif(current_platform.is_cuda_alike(),
reason="Skipping to avoid redundant model tests")
@multi_gpu_test(num_gpus=4)
@create_new_process_for_each_test()
def test_ilama_lora_tp4(ilama_lora_files):
......@@ -96,7 +88,8 @@ def test_ilama_lora_tp4(ilama_lora_files):
assert output2[i] == EXPECTED_LORA_OUTPUT[i]
@pytest.mark.skip_v1
@pytest.mark.skipif(current_platform.is_cuda_alike(),
reason="Skipping to avoid redundant model tests")
@multi_gpu_test(num_gpus=4)
@create_new_process_for_each_test()
def test_ilama_lora_tp4_fully_sharded_loras(ilama_lora_files):
......
tests/metrics/test_metrics.py
View file @ 675ba75f
......@@ -248,8 +248,10 @@ def test_metric_spec_decode(
dtype=dtype,
disable_log_stats=False,
gpu_memory_utilization=0.4,
speculative_model=model,
num_speculative_tokens=k,
speculative_config={
"model": model,
"num_speculative_tokens": k,
},
) as vllm_model:
# Force log interval to be 0 to catch all metrics.
......@@ -300,8 +302,10 @@ def test_metric_spec_decode_interval(
dtype=dtype,
disable_log_stats=False,
gpu_memory_utilization=0.4,
speculative_model=model,
num_speculative_tokens=k,
speculative_config={
"model": model,
"num_speculative_tokens": k,
},
enforce_eager=True,
)
......
tests/model_executor/test_enabled_custom_ops.py
View file @ 675ba75f
......@@ -7,6 +7,10 @@ from vllm.model_executor.custom_op import CustomOp
from vllm.model_executor.layers.activation import (GeluAndMul,
ReLUSquaredActivation,
SiluAndMul)
from vllm.model_executor.layers.fused_moe.fused_moe import (
dispatch_fused_experts_func, dispatch_topk_func,
torch_vllm_inplace_fused_experts, torch_vllm_outplace_fused_experts,
vllm_topk_softmax)
from vllm.model_executor.layers.layernorm import (
RMSNorm, dispatch_cuda_rmsnorm_func, fused_add_rms_norm, rms_norm,
rocm_aiter_fused_add_rms_norm, rocm_aiter_rms_norm)
......@@ -92,6 +96,38 @@ def test_enabled_ops_invalid(env: str):
RMSNorm(1024).enabled()
@pytest.mark.parametrize("use_rocm_aiter", ["0", "1"])
def test_topk_dispatch(use_rocm_aiter: str, monkeypatch):
monkeypatch.setenv("VLLM_ROCM_USE_AITER", use_rocm_aiter)
topk_func = dispatch_topk_func()
if current_platform.is_rocm() and int(use_rocm_aiter):
from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
rocm_aiter_topk_softmax)
assert topk_func == rocm_aiter_topk_softmax
else:
assert topk_func == vllm_topk_softmax
@pytest.mark.parametrize("use_rocm_aiter", ["0", "1"])
@pytest.mark.parametrize("inplace", [True, False])
def test_fused_experts_dispatch(use_rocm_aiter: str, inplace: bool,
monkeypatch):
monkeypatch.setenv("VLLM_ROCM_USE_AITER", use_rocm_aiter)
fused_experts_func = dispatch_fused_experts_func(inplace)
if current_platform.is_rocm() and int(use_rocm_aiter):
from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
rocm_aiter_fused_experts)
assert fused_experts_func == rocm_aiter_fused_experts
elif inplace:
assert fused_experts_func == torch_vllm_inplace_fused_experts
else:
assert fused_experts_func == torch_vllm_outplace_fused_experts
@pytest.mark.parametrize("add_residual", [True, False])
@pytest.mark.parametrize("use_rocm_aiter", ["0", "1"])
@pytest.mark.parametrize("use_rocm_aiter_norm", ["0", "1"])
......
tests/models/decoder_only/language/test_mistral.py
View file @ 675ba75f
......@@ -174,15 +174,8 @@ SAMPLE_JSON_SCHEMA = {
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
max_tokens: int,
num_logprobs: int,
) -> None:
def test_models(hf_runner, vllm_runner, example_prompts, model: str,
dtype: str, max_tokens: int, num_logprobs: int) -> None:
# TODO(sang): Sliding window should be tested separately.
with hf_runner(model, dtype=dtype) as hf_model:
hf_outputs = hf_model.generate_greedy_logprobs_limit(
......@@ -206,14 +199,8 @@ def test_models(
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_mistral_format(
vllm_runner,
example_prompts,
model: str,
dtype: str,
max_tokens: int,
num_logprobs: int,
) -> None:
def test_mistral_format(vllm_runner, example_prompts, model: str, dtype: str,
max_tokens: int, num_logprobs: int) -> None:
with vllm_runner(
model,
dtype=dtype,
......@@ -244,11 +231,8 @@ def test_mistral_format(
@pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
def test_mistral_symbolic_languages(
vllm_runner,
model: str,
dtype: str,
) -> None:
def test_mistral_symbolic_languages(vllm_runner, model: str,
dtype: str) -> None:
with vllm_runner(model,
dtype=dtype,
max_model_len=8192,
......@@ -266,11 +250,7 @@ def test_mistral_symbolic_languages(
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("model",
MISTRAL_FORMAT_MODELS) # v1 can't do func calling
def test_mistral_function_calling(
vllm_runner,
model: str,
dtype: str,
) -> None:
def test_mistral_function_calling(vllm_runner, model: str, dtype: str) -> None:
with vllm_runner(model,
dtype=dtype,
tokenizer_mode="mistral",
......@@ -301,11 +281,8 @@ def test_mistral_function_calling(
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("guided_backend",
["outlines", "lm-format-enforcer", "xgrammar"])
def test_mistral_guided_decoding(
vllm_runner,
model: str,
guided_backend: str,
) -> None:
def test_mistral_guided_decoding(vllm_runner, model: str,
guided_backend: str) -> None:
with vllm_runner(model, dtype='bfloat16',
tokenizer_mode="mistral") as vllm_model:
......
tests/models/decoder_only/vision_language/test_interleaved.py 0 → 100644
View file @ 675ba75f
# SPDX-License-Identifier: Apache-2.0
import pytest
from vllm.assets.image import ImageAsset
from vllm.assets.video import VideoAsset
models = ["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"]
def base_prompt(modalities_str: str) -> str:
return f"<|im_start|>user {modalities_str}\nDescribe what you see from these items.<|im_end|><|im_start|>assistant\n" # noqa: E501
INTERLEAVED_PROMPT = base_prompt("<image><video><image>\n")
NONINTERLEAVED_PROMPT = base_prompt("<image><image><video>\n")
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize("dtype", ["float16"])
@pytest.mark.parametrize("max_tokens", [128])
def test_models(vllm_runner, model, dtype: str, max_tokens: int) -> None:
"""
This is a simple test to check if interleaved and non-interleaved prompts
give the same result.
"""
image_cherry = ImageAsset("cherry_blossom").pil_image.convert("RGB")
image_stop = ImageAsset("stop_sign").pil_image.convert("RGB")
images = [image_cherry, image_stop]
video = VideoAsset(name="sample_demo_1.mp4", num_frames=16).np_ndarrays
inputs = [
(
[INTERLEAVED_PROMPT],
[images],
[video],
),
(
[NONINTERLEAVED_PROMPT],
[images],
[video],
),
]
with vllm_runner(model,
task="generate",
dtype=dtype,
limit_mm_per_prompt={"image": 2},
max_model_len=32768,
max_num_seqs=2,
tensor_parallel_size=1,
enforce_eager=True) as vllm_model:
vllm_outputs_per_case = [
vllm_model.generate_greedy(prompts,
max_tokens,
images=images,
videos=videos)
for prompts, images, videos in inputs
]
all_results = [output[0][1] for output in vllm_outputs_per_case]
outputs = [(total_str, total_str.find("assistant\n") + len("assistant\n"))
for total_str in all_results]
prompt_lengths = [prompt_len for _, prompt_len in outputs]
generated_strs = [
total_str[prompt_len:] for total_str, prompt_len in outputs
]
interleaved_prompt_len, noninterleaved_prompt_len = prompt_lengths
interleaved_output_str, noninterleaved_output_str = generated_strs
# The two prompts are identical except for the order of modality tokens.
assert interleaved_prompt_len == noninterleaved_prompt_len
# The two generated strings should be different because of the
# interleaved modality tokens.
assert interleaved_output_str != noninterleaved_output_str
tests/models/decoder_only/vision_language/test_models.py
View file @ 675ba75f
......@@ -8,9 +8,7 @@ from collections import defaultdict
from pathlib import PosixPath
import pytest
from packaging.version import Version
from transformers import AutoModelForImageTextToText, AutoModelForVision2Seq
from transformers import __version__ as TRANSFORMERS_VERSION
from vllm.platforms import current_platform
from vllm.utils import identity
......@@ -36,8 +34,6 @@ 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",
]
......@@ -126,25 +122,6 @@ VLM_TEST_SETTINGS = {
dtype="bfloat16",
marks=[pytest.mark.skip(reason="vLLM does not support PrefixLM attention mask")], # noqa: E501
),
# TODO(ywang96): Move Qwen2-VL out of core models in favor of Qwen2.5-VL
# once we upgraded to transformers>=4.49.0.
"qwen2_vl": VLMTestInfo(
models=["Qwen/Qwen2-VL-2B-Instruct"],
test_type=(
VLMTestType.IMAGE,
VLMTestType.MULTI_IMAGE,
VLMTestType.VIDEO
),
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: "<|vision_start|><|image_pad|><|vision_end|>", # noqa: E501
video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>", # noqa: E501
max_model_len=4096,
max_num_seqs=2,
auto_cls=AutoModelForVision2Seq,
vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
marks=[pytest.mark.core_model, pytest.mark.cpu_model],
),
"qwen2_5_vl": VLMTestInfo(
models=["Qwen/Qwen2.5-VL-3B-Instruct"],
test_type=(
......@@ -163,26 +140,41 @@ 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.", # noqa: E501
# }),
# multi_image_prompt="<vlm_image><vlm_image>Describe the two images shortly.", # noqa: E501
# 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
stop_str=["<|im_end|>"],
image_size_factors=[(0.10, 0.15)],
max_tokens=64,
marks=[large_gpu_mark(min_gb=64)],
),
"aya_vision": VLMTestInfo(
models=["CohereForAI/aya-vision-8b"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{img_prompt}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>", # noqa: E501
single_image_prompts=IMAGE_ASSETS.prompts({
"stop_sign": "<image>What's the content in the center of the image?", # noqa: E501
"cherry_blossom": "<image>What is the season?", # noqa: E501
}),
multi_image_prompt="<image><image>Describe the two images in detail.", # noqa: E501
max_model_len=8192,
max_num_seqs=2,
auto_cls=AutoModelForImageTextToText,
vllm_runner_kwargs={"mm_processor_kwargs": {"crop_to_patches": True}}
),
"blip2": VLMTestInfo(
models=["Salesforce/blip2-opt-2.7b"],
# TODO: Change back to 2.7b once head_dim = 80 is supported
models=["Salesforce/blip2-opt-6.7b"],
test_type=VLMTestType.IMAGE,
prompt_formatter=lambda img_prompt: f"Question: {img_prompt} Answer:",
img_idx_to_prompt=lambda idx: "",
......@@ -218,12 +210,6 @@ VLM_TEST_SETTINGS = {
hf_output_post_proc=model_utils.deepseekvl2_trunc_hf_output,
stop_str=["<｜end▁of▁sentence｜>", "<｜begin▁of▁sentence｜>"], # noqa: E501
image_size_factors=[(), (1.0, ), (1.0, 1.0, 1.0), (0.1, 0.5, 1.0)],
marks=[
pytest.mark.skipif(
Version(TRANSFORMERS_VERSION) >= Version("4.48"),
reason="HF model is not compatible with transformers>=4.48",
)
],
),
"fuyu": VLMTestInfo(
models=["adept/fuyu-8b"],
......@@ -275,7 +261,8 @@ VLM_TEST_SETTINGS = {
"h2ovl": VLMTestInfo(
models = [
"h2oai/h2ovl-mississippi-800m",
"h2oai/h2ovl-mississippi-2b",
# TODO: Re-enable once head_dim = 80 is supported
# "h2oai/h2ovl-mississippi-2b",
],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<|prompt|>{img_prompt}<|end|><|answer|>", # noqa: E501
......@@ -336,6 +323,7 @@ VLM_TEST_SETTINGS = {
prompt_formatter=lambda vid_prompt: f"<|im_start|>user\n{vid_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
num_video_frames=16,
max_model_len=16384,
hf_model_kwargs=model_utils.llava_onevision_hf_model_kwargs("llava-hf/llava-onevision-qwen2-0.5b-ov-hf"), # noqa: E501
auto_cls=AutoModelForVision2Seq,
vllm_output_post_proc=model_utils.llava_onevision_vllm_to_hf_output,
custom_test_opts=[CustomTestOptions(
......@@ -352,6 +340,7 @@ VLM_TEST_SETTINGS = {
prompt_formatter=lambda vid_prompt: f"USER: {vid_prompt} ASSISTANT:",
num_video_frames=16,
max_model_len=4096,
max_num_seqs=2,
auto_cls=AutoModelForVision2Seq,
vllm_output_post_proc=model_utils.llava_video_vllm_to_hf_output,
),
......@@ -364,12 +353,6 @@ VLM_TEST_SETTINGS = {
auto_cls=AutoModelForImageTextToText,
vllm_output_post_proc=model_utils.mantis_vllm_to_hf_output,
patch_hf_runner=model_utils.mantis_patch_hf_runner,
marks=[
pytest.mark.skipif(
Version(TRANSFORMERS_VERSION) >= Version("4.48"),
reason="HF model is not compatible with transformers>=4.48",
)
],
),
"minicpmv_25": VLMTestInfo(
models=["openbmb/MiniCPM-Llama3-V-2_5"],
......@@ -406,7 +389,7 @@ VLM_TEST_SETTINGS = {
),
"molmo": VLMTestInfo(
models=["allenai/Molmo-7B-D-0924"],
test_type=(VLMTestType.IMAGE),
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=identity,
max_model_len=4096,
max_num_seqs=2,
......@@ -449,6 +432,37 @@ VLM_TEST_SETTINGS = {
vllm_output_post_proc=model_utils.qwen_vllm_to_hf_output,
prompt_path_encoder=model_utils.qwen_prompt_path_encoder,
),
"qwen2_vl": VLMTestInfo(
models=["Qwen/Qwen2-VL-2B-Instruct"],
test_type=(
VLMTestType.IMAGE,
VLMTestType.MULTI_IMAGE,
VLMTestType.VIDEO
),
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: "<|vision_start|><|image_pad|><|vision_end|>", # noqa: E501
video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>", # noqa: E501
max_model_len=4096,
max_num_seqs=2,
auto_cls=AutoModelForVision2Seq,
vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
marks=[pytest.mark.cpu_model],
),
"skywork_r1v": VLMTestInfo(
models=["Skywork/Skywork-R1V-38B"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<｜begin▁of▁sentence｜><｜User｜>\n{img_prompt}<｜Assistant｜><think>\n", # noqa: E501
single_image_prompts=IMAGE_ASSETS.prompts({
"stop_sign": "<image>\nWhat's the content in the center of the image?", # noqa: E501
"cherry_blossom": "<image>\nWhat is the season?",
}),
multi_image_prompt="<image>\n<image>\nDescribe the two images in short.", # noqa: E501
max_model_len=4096,
use_tokenizer_eos=True,
patch_hf_runner=model_utils.skyworkr1v_patch_hf_runner,
marks=[large_gpu_mark(min_gb=80)],
),
### Tensor parallel / multi-gpu broadcast tests
"chameleon-broadcast": VLMTestInfo(
models=["facebook/chameleon-7b"],
......@@ -500,6 +514,7 @@ VLM_TEST_SETTINGS = {
max_model_len=16384,
max_num_seqs=2,
auto_cls=AutoModelForVision2Seq,
hf_model_kwargs=model_utils.llava_onevision_hf_model_kwargs("llava-hf/llava-onevision-qwen2-0.5b-ov-hf"), # noqa: E501
vllm_output_post_proc=model_utils.llava_onevision_vllm_to_hf_output,
custom_test_opts=[CustomTestOptions(
inputs=custom_inputs.multi_image_multi_aspect_ratio_inputs(
......@@ -521,6 +536,22 @@ VLM_TEST_SETTINGS = {
limit_mm_per_prompt={"image": 1},
)],
),
"llama4": VLMTestInfo(
models=["meta-llama/Llama-4-Scout-17B-16E-Instruct"],
prompt_formatter=lambda img_prompt: f"<|begin_of_text|><|header_start|>user<|header_end|>\n\n{img_prompt}<|eot|><|header_start|>assistant<|header_end|>\n\n", # noqa: E501
img_idx_to_prompt=lambda _: "<|image|>",
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
distributed_executor_backend="mp",
image_size_factors=[(.25, 0.5, 1.0)],
hf_model_kwargs={"device_map": "auto"},
max_model_len=8192,
max_num_seqs=4,
dtype="bfloat16",
auto_cls=AutoModelForImageTextToText,
tensor_parallel_size=8,
vllm_runner_kwargs={"gpu_memory_utilization": 0.8},
marks=[large_gpu_mark(min_gb=80), multi_gpu_marks(num_gpus=8)],
),
}
# yapf: enable
......
tests/models/decoder_only/vision_language/vlm_utils/model_utils.py
View file @ 675ba75f
......@@ -104,6 +104,13 @@ def _llava_vllm_to_hf_output(vllm_output: RunnerOutput, model: str,
return hf_output_ids, hf_output_str, out_logprobs
def llava_onevision_hf_model_kwargs(model: str) -> dict:
"""Workaround to fix the sliding window issue in llava_onevision."""
config = AutoConfig.from_pretrained(model)
config.text_config.sliding_window = None
return config.to_dict()
def llava_onevision_vllm_to_hf_output(vllm_output: RunnerOutput,
model: str) -> RunnerOutput:
"""Sanitize vllm output [llava-onevision] to compare with hf output."""
......@@ -376,6 +383,63 @@ def h2ovl_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
return hf_model
def skyworkr1v_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
"""Patches and returns an instance of the HfRunner to use for SkyworkR1V."""
class SkyworkR1VProcessor:
"""A simple processor for SkyworkR1V."""
def __init__(self, hf_runner: HfRunner):
self.num_image_token = hf_runner.model.num_image_token
self.tokenizer = hf_runner.tokenizer
self.config = AutoConfig.from_pretrained(hf_runner.model_name,
trust_remote_code=True)
self.vision_config = self.config.vision_config
self.use_thumbnail = self.config.use_thumbnail
self.min_num = self.config.min_dynamic_patch
self.max_num = self.config.max_dynamic_patch
self.image_size = self.vision_config.image_size
def __call__(self, text: str, images: Union[Image, list[Image]],
**kwargs):
from vllm.model_executor.models.skyworkr1v import (
IMG_CONTEXT, IMG_END, IMG_START,
image_to_pixel_values_skyworkr1v)
images = [images] if isinstance(images, Image) else images
pixel_values = [
image_to_pixel_values_skyworkr1v(
image,
input_size=self.image_size,
min_num=self.min_num,
max_num=self.max_num,
use_thumbnail=self.use_thumbnail,
) for image in images
]
num_patches_list = [
pixel_value.shape[0] for pixel_value in pixel_values
]
pixel_values = torch.cat(pixel_values, dim=0)
for num_patches in num_patches_list:
context_tokens = IMG_CONTEXT * self.num_image_token \
* num_patches
image_tokens = IMG_START + context_tokens + IMG_END
text = text.replace('<image>', image_tokens, 1)
prompt = self.tokenizer(text, return_tensors="pt")
prompt.update({"pixel_values": pixel_values})
return prompt
img_context_token_id = hf_model.tokenizer.convert_tokens_to_ids(
"<IMG_CONTEXT>")
hf_model.model.img_context_token_id = img_context_token_id
hf_model.processor = SkyworkR1VProcessor(hf_model)
hf_model.model.get_output_embeddings = lambda: \
hf_model.model.language_model.get_output_embeddings()
hf_model.model.generate = types.MethodType(_internvl_generate,
hf_model.model)
return hf_model
def internvl_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
"""Patches and returns an instance of the HfRunner to use for InternVL."""
......
tests/models/embedding/language/test_jina_reranker_v2.py 0 → 100644
View file @ 675ba75f
# SPDX-License-Identifier: Apache-2.0
# ruff: noqa: E501
"""Compare the scoring outputs of HF and vLLM models.
Run `pytest tests/models/embedding/language/test_jina_reranker_v2.py`.
"""
import math
import pytest
MODELS = [
"jinaai/jina-reranker-v2-base-multilingual", # Roberta
]
TEXTS_1 = ["Organic skincare products for sensitive skin"]
TEXTS_2 = [
"Organic skincare for sensitive skin with aloe vera and chamomile.",
"New makeup trends focus on bold colors and innovative techniques",
"Bio-Hautpflege für empfindliche Haut mit Aloe Vera und Kamille",
"Neue Make-up-Trends setzen auf kräftige Farben und innovative Techniken",
"Cuidado de la piel orgánico para piel sensible con aloe vera y manzanilla",
"Las nuevas tendencias de maquillaje se centran en colores vivos y técnicas innovadoras",
"针对敏感肌专门设计的天然有机护肤产品",
"新的化妆趋势注重鲜艳的颜色和创新的技巧",
"敏感肌のために特別に設計された天然有機スキンケア製品",
"新しいメイクのトレンドは鮮やかな色と革新的な技術に焦点を当てています",
]
@pytest.fixture(scope="module", params=MODELS)
def model_name(request):
yield request.param
@pytest.mark.parametrize("dtype", ["half"])
def test_llm_1_to_1(vllm_runner, hf_runner, model_name, dtype: str):
text_pair = [TEXTS_1[0], TEXTS_2[0]]
with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model:
hf_outputs = hf_model.predict([text_pair]).tolist()
with vllm_runner(model_name, task="score", dtype=dtype,
max_model_len=None) as vllm_model:
vllm_outputs = vllm_model.score(text_pair[0], text_pair[1])
assert len(vllm_outputs) == 1
assert len(hf_outputs) == 1
assert math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
@pytest.mark.parametrize("dtype", ["half"])
def test_llm_1_to_N(vllm_runner, hf_runner, model_name, dtype: str):
text_pairs = [[TEXTS_1[0], text] for text in TEXTS_2]
with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model:
hf_outputs = hf_model.predict(text_pairs).tolist()
with vllm_runner(model_name, task="score", dtype=dtype,
max_model_len=None) as vllm_model:
vllm_outputs = vllm_model.score(TEXTS_1[0], TEXTS_2)
assert len(vllm_outputs) == 10
assert len(hf_outputs) == 10
assert math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
assert math.isclose(hf_outputs[1], vllm_outputs[1], rel_tol=0.01)
tests/models/embedding/vision_language/test_phi3v.py
View file @ 675ba75f
......@@ -2,6 +2,10 @@
import pytest
import torch.nn.functional as F
from PIL import Image
from vllm.assets.base import get_vllm_public_assets
from vllm.assets.image import VLM_IMAGES_DIR
from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner
from ....utils import large_gpu_test
......@@ -112,6 +116,15 @@ def test_models_image(
(text, asset.pil_image)
for text, asset in zip(HF_IMAGE_PROMPTS, image_assets)
]
# add cases for special_tokens
input_texts_images.append((
"\n<s><|user|>\n <|image_1|>\n\t <s>"
"Represent the given image for classification<|end|>"
"\n<|assistant|>\n",
Image.open(
get_vllm_public_assets(filename="cherry_blossom.jpg",
s3_prefix=VLM_IMAGES_DIR)),
))
input_texts = [text for text, _ in input_texts_images]
input_images = [image for _, image in input_texts_images]
......
tests/models/encoder_decoder/vision_language/test_mllama.py
View file @ 675ba75f
......@@ -212,7 +212,7 @@ def _run_test(
with vllm_runner(model,
dtype=dtype,
max_model_len=4096,
max_num_seqs=2,
max_num_seqs=3,
tensor_parallel_size=tensor_parallel_size,
distributed_executor_backend=distributed_executor_backend,
limit_mm_per_prompt={"image": _LIMIT_IMAGE_PER_PROMPT
......@@ -425,7 +425,6 @@ def test_bnb_regression(
max_model_len=4096,
max_num_seqs=2,
quantization="bitsandbytes",
load_format="bitsandbytes",
)
sampling_params = SamplingParams(
temperature=0,
......
tests/models/multimodal/processing/test_common.py
View file @ 675ba75f
# SPDX-License-Identifier: Apache-2.0
import copy
from functools import partial
from typing import Optional, Union
......@@ -29,7 +28,7 @@ def _test_processing_correctness(
hit_rate: float,
num_batches: int,
simplify_rate: float,
ignore_mm_keys: Optional[list[str]] = None,
ignore_mm_keys: Optional[set[str]] = None,
):
model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id)
model_info.check_available_online(on_fail="skip")
......@@ -145,7 +144,7 @@ def _test_processing_correctness_hf(
baseline_processor: BaseMultiModalProcessor,
cached_processor: BaseMultiModalProcessor,
batch_idx: int,
ignore_mm_keys: Optional[list[str]] = None,
ignore_mm_keys: Optional[set[str]] = None,
):
if model_config.hf_config.model_type in ("mllama", "whisper", "ultravox"):
# For some multimodal models, tokenizer will always add bos_token
......@@ -167,11 +166,12 @@ def _test_processing_correctness_hf(
hf_processor_mm_kwargs={},
)
assert _inputs_equal(
_assert_inputs_equal(
baseline_result,
cached_result,
ignore_mm_keys,
), f"Failed ({batch_idx=}, {prompt=}, {mm_data=})"
ignore_mm_keys=ignore_mm_keys,
msg=f"Failed ({batch_idx=}, {prompt=}, {mm_data=})",
)
baseline_tokenized_result = baseline_processor.apply(
token_prompt,
......@@ -179,11 +179,12 @@ def _test_processing_correctness_hf(
hf_processor_mm_kwargs={},
)
assert _inputs_equal(
_assert_inputs_equal(
baseline_result,
baseline_tokenized_result,
ignore_mm_keys,
), f"Failed ({batch_idx=}, {prompt=}, {mm_data=})"
ignore_mm_keys=ignore_mm_keys,
msg=f"Failed ({batch_idx=}, {prompt=}, {mm_data=})",
)
cached_tokenized_result = cached_processor.apply(
token_prompt,
......@@ -191,11 +192,12 @@ def _test_processing_correctness_hf(
hf_processor_mm_kwargs={},
)
assert _inputs_equal(
_assert_inputs_equal(
cached_result,
cached_tokenized_result,
ignore_mm_keys,
), f"Failed ({batch_idx=}, {prompt=}, {mm_data=})"
ignore_mm_keys=ignore_mm_keys,
msg=f"Failed ({batch_idx=}, {prompt=}, {mm_data=})",
)
def _test_processing_correctness_mistral(
......@@ -206,7 +208,7 @@ def _test_processing_correctness_mistral(
baseline_processor: BaseMultiModalProcessor,
cached_processor: BaseMultiModalProcessor,
batch_idx: int,
ignore_mm_keys: Optional[list[str]] = None,
ignore_mm_keys: Optional[set[str]] = None,
):
images = mm_data.get("image", [])
if not isinstance(images, list):
......@@ -233,16 +235,18 @@ def _test_processing_correctness_mistral(
hf_processor_mm_kwargs={},
)
assert _inputs_equal(
_assert_inputs_equal(
baseline_tokenized_result,
cached_tokenized_result,
ignore_mm_keys,
), f"Failed ({batch_idx=}, {prompt=}, {mm_data=})"
ignore_mm_keys=ignore_mm_keys,
msg=f"Failed ({batch_idx=}, {prompt=}, {mm_data=})",
)
# yapf: disable
@pytest.mark.parametrize("model_id", [
"rhymes-ai/Aria",
"CohereForAI/aya-vision-8b",
"Salesforce/blip2-opt-2.7b",
"facebook/chameleon-7b",
"deepseek-ai/deepseek-vl2-tiny",
......@@ -259,21 +263,24 @@ def _test_processing_correctness_mistral(
"llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
"meta-llama/Llama-3.2-11B-Vision-Instruct",
"TIGER-Lab/Mantis-8B-siglip-llama3",
"mistralai/Pixtral-12B-2409",
"mistral-community/pixtral-12b",
"openbmb/MiniCPM-Llama3-V-2_5",
"openbmb/MiniCPM-o-2_6",
"openbmb/MiniCPM-V-2_6",
"allenai/Molmo-7B-D-0924",
"allenai/Molmo-7B-O-0924",
"nvidia/NVLM-D-72B",
"google/paligemma-3b-mix-224",
"google/paligemma2-3b-ft-docci-448",
"mistralai/Pixtral-12B-2409",
"mistral-community/pixtral-12b",
"Qwen/Qwen-VL-Chat",
"Qwen/Qwen2-VL-2B-Instruct",
"Qwen/Qwen2.5-VL-3B-Instruct",
"Qwen/Qwen2-Audio-7B-Instruct",
"Skywork/Skywork-R1V-38B",
"fixie-ai/ultravox-v0_5-llama-3_2-1b",
"openai/whisper-large-v3",
"google/paligemma-3b-mix-224",
"google/paligemma2-3b-ft-docci-448",
"meta-llama/Llama-4-Scout-17B-16E-Instruct",
])
@pytest.mark.parametrize("hit_rate", [0.3, 0.5, 1.0])
@pytest.mark.parametrize("num_batches", [32])
......@@ -290,7 +297,7 @@ def test_processing_correctness(
# In Ultravox, the audio_features can be different depending on padding
# The slight difference should not be a problem though, since
# attention_mask lets us ignore the difference.
ignore_mm_keys = ['audio_features']
ignore_mm_keys = {"audio_features"}
_test_processing_correctness(
model_id,
......@@ -328,38 +335,26 @@ def test_processing_correctness_phi3v(
)
def _inputs_equal(
def _assert_inputs_equal(
a: MultiModalInputs,
b: MultiModalInputs,
ignore_mm_keys: Optional[list[str]] = None,
*,
ignore_mm_keys: Optional[set[str]] = None,
msg: str = "",
):
return _drop_mm_kwargs_keys(a, ignore_mm_keys) == _drop_mm_kwargs_keys(
b, ignore_mm_keys)
def _drop_mm_kwargs_keys(
result: MultiModalInputs,
ignore_mm_keys: Optional[list[str]] = None,
) -> MultiModalInputs:
"""Drop specified keys from result['mm_kwargs'].
This is mainly to avoid doing exact match of audio_features in ultravox.
Args:
result: Result to drop keys from
ignore_mm_keys: List of keys to ignore, e.g. ['audio_features']
"""
if not ignore_mm_keys:
return result
if 'mm_kwargs' in result:
result = copy.deepcopy(result)
mm_kwargs = result['mm_kwargs']
for key in ignore_mm_keys:
mm_kwargs.pop(key, None)
for items in mm_kwargs._items_by_modality.values():
for item in items:
for key in ignore_mm_keys:
item.pop(key, None)
return result
if ignore_mm_keys is None:
ignore_mm_keys = set()
if msg is None:
assert "mm_kwargs" in a and "mm_kwargs" in b
else:
assert "mm_kwargs" in a and "mm_kwargs" in b, msg
for key in ignore_mm_keys:
a["mm_kwargs"].pop(key, None)
b["mm_kwargs"].pop(key, None)
if msg is None:
assert a == b
else:
assert a == b, msg
tests/models/multimodal/processing/test_h2ovl.py
View file @ 675ba75f
......@@ -10,7 +10,6 @@ from transformers import PretrainedConfig
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.image import rescale_image_size
from vllm.multimodal.processing import BaseMultiModalProcessor
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ....conftest import _ImageAssets
from ...utils import build_model_context
......@@ -156,11 +155,7 @@ def test_processor_override(
mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
limit_mm_per_prompt={"image": len(size_factors)},
)
tokenizer = cached_tokenizer_from_config(ctx.model_config)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=tokenizer,
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
min_num = min_dynamic_patch if dynamic_image_size else 1
......
tests/models/multimodal/processing/test_idefics3.py
View file @ 675ba75f
......@@ -4,7 +4,6 @@ import pytest
from transformers import Idefics3Config
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ....conftest import _ImageAssets
from ...utils import build_model_context
......@@ -29,7 +28,7 @@ def test_processor_override(
num_imgs: int,
kwargs_on_init: bool,
):
"""Ensure input_processor_for_idefics3 handles num_crops properly."""
"""Ensure Idefics3MultiModalProcessor handles num_crops properly."""
# Same as the previous test - don't initialize mm_processor_kwargs
# in this test and assume that the kwargs will be correctly expanded by
# the partial when calling the custom input processor.
......@@ -38,11 +37,7 @@ def test_processor_override(
mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
limit_mm_per_prompt={"image": num_imgs},
)
tokenizer = cached_tokenizer_from_config(ctx.model_config)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=tokenizer,
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
# Build the image str / prompt based on the number of images we pass
......
tests/models/multimodal/processing/test_internvl.py
View file @ 675ba75f
......@@ -10,7 +10,6 @@ from transformers import PretrainedConfig
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.image import rescale_image_size
from vllm.multimodal.processing import BaseMultiModalProcessor
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ....conftest import _ImageAssets
from ...utils import build_model_context
......@@ -113,11 +112,7 @@ def test_processor_override(
mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
limit_mm_per_prompt={"image": len(size_factors)},
)
tokenizer = cached_tokenizer_from_config(ctx.model_config)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=tokenizer,
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
min_num = min_dynamic_patch if dynamic_image_size else 1
......
tests/models/multimodal/processing/test_llama4.py 0 → 100644
View file @ 675ba75f
# SPDX-License-Identifier: Apache-2.0
"""Tests for Llama4's multimodal preprocessing kwargs."""
import pytest
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.transformers_utils.tokenizer import encode_tokens
from ....conftest import _ImageAssets
from ...utils import build_model_context
@pytest.mark.parametrize("model_id",
["meta-llama/Llama-4-Scout-17B-16E-Instruct"])
@pytest.mark.parametrize("mm_processor_kwargs", [{}])
@pytest.mark.parametrize("num_imgs", [1, 5])
@pytest.mark.parametrize("disable_mm_preprocessor_cache", [True, False])
@pytest.mark.parametrize("tokenized_prompt", [True, False])
def test_processor_override(
image_assets: _ImageAssets,
model_id: str,
mm_processor_kwargs: dict,
num_imgs: int,
disable_mm_preprocessor_cache: bool,
tokenized_prompt: bool,
):
"""Ensure llama4 processor works properly."""
ctx = build_model_context(
model_id,
mm_processor_kwargs=mm_processor_kwargs,
limit_mm_per_prompt={"image": num_imgs},
disable_mm_preprocessor_cache=disable_mm_preprocessor_cache,
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
config = processor.info.get_hf_config()
tokenizer = processor.info.get_tokenizer()
hf_processor = processor.info.get_hf_processor()
vocab = tokenizer.get_vocab()
prompt = "<|begin_of_text|><|header_start|>user<|header_end|>" \
+ "<|image|>" * num_imgs \
+ "<|eot|><|header_start|>assistant<|header_end|>"
mm_data = {
"image": [
image_assets[(i % len(image_assets))].pil_image
for i in range(num_imgs)
]
}
if tokenized_prompt:
prompt = encode_tokens(tokenizer, prompt)
processed_inputs = processor.apply(prompt, mm_data, mm_processor_kwargs)
mm_kwargs = processed_inputs["mm_kwargs"]
# place holder replacements
prompt_token_ids = processed_inputs["prompt_token_ids"]
assert prompt_token_ids.count(config.boi_token_index) == num_imgs
assert prompt_token_ids.count(config.eoi_token_index) == num_imgs
assert prompt_token_ids.count(vocab[hf_processor.image_token]) == num_imgs
aspect_ratios = mm_kwargs["aspect_ratios"]
num_x_separators = num_y_separators = 0
for tiles_y, tiles_x in aspect_ratios:
if tiles_x * tiles_y > 1:
num_x_separators += (tiles_x - 1) * tiles_y
num_y_separators += tiles_y
assert prompt_token_ids.count(vocab[hf_processor.tile_token]) \
== num_x_separators
assert prompt_token_ids.count(vocab[hf_processor.tile_global_token]) \
== num_y_separators
# image token offsets
img_locs = processed_inputs["mm_placeholders"].get("image", [])
assert len(img_locs) == num_imgs
assert [img_loc["offset"] for img_loc in img_locs] == \
[i for i, v in enumerate(prompt_token_ids) \
if v == config.boi_token_index]
# patch sizes and masks
assert prompt_token_ids.count(config.image_token_index) \
== sum(img_patch.sum() for img_patch in mm_kwargs["embed_is_patch"])
patch_token_id = vocab[hf_processor.img_patch_token]
num_patches = processed_inputs["prompt_token_ids"].count(patch_token_id)
mm_counts = {"image": num_imgs}
assert num_patches / num_imgs <= \
processor.info.get_mm_max_tokens_per_item(32768, mm_counts)["image"]
num_patches_per_chunk = processor.info.get_patch_per_chunk(
config.vision_config)
assert prompt_token_ids.count(config.image_token_index) \
== mm_kwargs["patches_per_image"].sum() * num_patches_per_chunk
assert mm_kwargs["pixel_values"].shape[0] \
== mm_kwargs["patches_per_image"].sum()
for embed_is_patch, aspect_ratio in zip(mm_kwargs["embed_is_patch"],
mm_kwargs["aspect_ratios"]):
assert embed_is_patch.shape[0] == \
len(tokenizer.encode(
hf_processor._prompt_split_image(
aspect_ratio, num_patches_per_chunk),
add_special_tokens=False))
tests/models/multimodal/processing/test_llava_next.py
View file @ 675ba75f
......@@ -10,7 +10,6 @@ from pqdm.threads import pqdm
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.parse import ImageSize
from vllm.multimodal.processing import BaseMultiModalProcessor
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ...utils import build_model_context
......@@ -40,10 +39,7 @@ def test_processor_max_tokens(model_id):
mm_processor_kwargs=None,
limit_mm_per_prompt={"image": 1},
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
info = processor.info
seen_aspect_ratios = set[float]()
......@@ -139,10 +135,7 @@ def test_processor_prompt_replacements_regression(model_id, num_imgs):
mm_processor_kwargs=None,
limit_mm_per_prompt={"image": num_imgs},
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
image_ratios = [(171, 152), (184, 161), (198, 176), (333, 296), (369, 328),
(488, 183), (2560, 1669)]
......@@ -168,10 +161,7 @@ def test_processor_prompt_replacements_all(model_id, num_imgs):
mm_processor_kwargs=None,
limit_mm_per_prompt={"image": num_imgs},
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
seen_aspect_ratios = set[float]()
image_sizes = list[ImageSize]()
......
tests/models/multimodal/processing/test_llava_onevision.py
View file @ 675ba75f
......@@ -10,7 +10,6 @@ from pqdm.threads import pqdm
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.parse import ImageSize
from vllm.multimodal.processing import BaseMultiModalProcessor
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ...utils import build_model_context
......@@ -41,10 +40,7 @@ def test_processor_max_tokens(model_id):
mm_processor_kwargs=None,
limit_mm_per_prompt={"image": 1},
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
info = processor.info
seen_aspect_ratios = set[float]()
......@@ -139,10 +135,7 @@ def test_processor_prompt_replacements_regression(model_id, num_imgs):
mm_processor_kwargs=None,
limit_mm_per_prompt={"image": num_imgs},
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
image_ratios = [(171, 152), (184, 161), (198, 176), (333, 296), (369, 328),
(488, 183), (2560, 1669)]
......@@ -169,10 +162,7 @@ def test_processor_prompt_replacements_all(model_id, num_imgs):
mm_processor_kwargs=None,
limit_mm_per_prompt={"image": num_imgs},
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
seen_aspect_ratios = set[float]()
image_sizes = list[ImageSize]()
......
tests/models/multimodal/processing/test_phi3v.py
View file @ 675ba75f
......@@ -3,7 +3,6 @@
import pytest
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ....conftest import _ImageAssets
from ...utils import build_model_context
......@@ -30,7 +29,7 @@ def test_processor_override(
num_imgs: int,
kwargs_on_init: bool,
):
"""Ensure input_processor_for_phi3v handles num_crops properly."""
"""Ensure Phi3VMultiModalProcessor handles num_crops properly."""
# Avoid initializing CUDA early
from vllm.model_executor.models.phi3v import _IMAGE_TOKEN_ID
......@@ -39,11 +38,7 @@ def test_processor_override(
mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
limit_mm_per_prompt={"image": num_imgs},
)
tokenizer = cached_tokenizer_from_config(ctx.model_config)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=tokenizer,
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
# Build the image str / prompt based on the number of images we pass
......
tests/models/multimodal/processing/test_qwen2_vl.py
View file @ 675ba75f
......@@ -3,7 +3,6 @@
import pytest
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ....conftest import _ImageAssets
from ...utils import build_model_context
......@@ -34,11 +33,8 @@ def test_processor_override(
mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
limit_mm_per_prompt={"image": num_imgs},
)
tokenizer = cached_tokenizer_from_config(ctx.model_config)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=tokenizer,
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
tokenizer = processor.info.get_tokenizer()
hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
# Build the image str / prompt based on the number of images we pass
......
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