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Unverified Commit 18016a5e authored by Cyrus Leung's avatar Cyrus Leung Committed by GitHub
Browse files

[Bugfix] Fix CI failures for InternVL and Mantis models (#12728) 


Signed-off-by: default avatarDarkLight1337 <tlleungac@connect.ust.hk>
parent 649550f2
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Showing with 79 additions and 412 deletions
tests/models/decoder_only/vision_language/test_models.py
View file @ 18016a5e
...@@ -9,6 +9,7 @@ from pathlib import PosixPath ...@@ -9,6 +9,7 @@ from pathlib import PosixPath
from typing import Type from typing import Type
import pytest import pytest
from packaging.version import Version
from transformers import AutoModelForVision2Seq from transformers import AutoModelForVision2Seq
from transformers import __version__ as TRANSFORMERS_VERSION from transformers import __version__ as TRANSFORMERS_VERSION
...@@ -154,13 +155,7 @@ VLM_TEST_SETTINGS = { ...@@ -154,13 +155,7 @@ VLM_TEST_SETTINGS = {
stop_str=["<|im_end|>"], stop_str=["<|im_end|>"],
image_size_factors=[(0.10, 0.15)], image_size_factors=[(0.10, 0.15)],
max_tokens=64, max_tokens=64,
marks=[ marks=[large_gpu_mark(min_gb=64)],
pytest.mark.skipif(
TRANSFORMERS_VERSION < "4.48.0",
reason="HF model requires transformers>=4.48.0",
),
large_gpu_mark(min_gb=64),
],
), ),
"blip2": VLMTestInfo( "blip2": VLMTestInfo(
models=["Salesforce/blip2-opt-2.7b"], models=["Salesforce/blip2-opt-2.7b"],
...@@ -206,7 +201,7 @@ VLM_TEST_SETTINGS = { ...@@ -206,7 +201,7 @@ VLM_TEST_SETTINGS = {
image_size_factors=[(), (1.0, ), (1.0, 1.0, 1.0), (0.1, 0.5, 1.0)], image_size_factors=[(), (1.0, ), (1.0, 1.0, 1.0), (0.1, 0.5, 1.0)],
marks=[ marks=[
pytest.mark.skipif( pytest.mark.skipif(
TRANSFORMERS_VERSION >= "4.48.0", Version(TRANSFORMERS_VERSION) >= Version("4.48"),
reason="HF model is not compatible with transformers>=4.48.0", reason="HF model is not compatible with transformers>=4.48.0",
) )
], ],
...@@ -339,6 +334,12 @@ VLM_TEST_SETTINGS = { ...@@ -339,6 +334,12 @@ VLM_TEST_SETTINGS = {
auto_cls=AutoModelForVision2Seq, auto_cls=AutoModelForVision2Seq,
vllm_output_post_proc=model_utils.mantis_vllm_to_hf_output, vllm_output_post_proc=model_utils.mantis_vllm_to_hf_output,
patch_hf_runner=model_utils.mantis_patch_hf_runner, 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>=4.48.0",
)
],
), ),
"minicpmv_25": VLMTestInfo( "minicpmv_25": VLMTestInfo(
models=["openbmb/MiniCPM-Llama3-V-2_5"], models=["openbmb/MiniCPM-Llama3-V-2_5"],
......
tests/models/registry.py
View file @ 18016a5e
...@@ -224,8 +224,7 @@ _CROSS_ENCODER_EXAMPLE_MODELS = { ...@@ -224,8 +224,7 @@ _CROSS_ENCODER_EXAMPLE_MODELS = {
_MULTIMODAL_EXAMPLE_MODELS = { _MULTIMODAL_EXAMPLE_MODELS = {
# [Decoder-only] # [Decoder-only]
"AriaForConditionalGeneration": _HfExamplesInfo("rhymes-ai/Aria", "AriaForConditionalGeneration": _HfExamplesInfo("rhymes-ai/Aria"),
min_transformers_version="4.48"),
"Blip2ForConditionalGeneration": _HfExamplesInfo("Salesforce/blip2-opt-2.7b"), # noqa: E501 "Blip2ForConditionalGeneration": _HfExamplesInfo("Salesforce/blip2-opt-2.7b"), # noqa: E501
"ChameleonForConditionalGeneration": _HfExamplesInfo("facebook/chameleon-7b"), # noqa: E501 "ChameleonForConditionalGeneration": _HfExamplesInfo("facebook/chameleon-7b"), # noqa: E501
"ChatGLMModel": _HfExamplesInfo("THUDM/glm-4v-9b", "ChatGLMModel": _HfExamplesInfo("THUDM/glm-4v-9b",
......
tests/multimodal/test_processing.py
View file @ 18016a5e
# SPDX-License-Identifier: Apache-2.0 # SPDX-License-Identifier: Apache-2.0
from contextlib import nullcontext from contextlib import nullcontext
from types import MethodType
from typing import cast from typing import cast
from unittest.mock import MagicMock from unittest.mock import MagicMock
import numpy as np import numpy as np
import pytest import pytest
from transformers import ProcessorMixin
from vllm.config import ModelConfig from vllm.config import ModelConfig
from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal import MULTIMODAL_REGISTRY
...@@ -636,3 +638,70 @@ def test_limit_mm_per_prompt_apply(model_id, num_images, limit, is_valid): ...@@ -636,3 +638,70 @@ def test_limit_mm_per_prompt_apply(model_id, num_images, limit, is_valid):
mm_data=mm_data, mm_data=mm_data,
hf_processor_mm_kwargs={}, hf_processor_mm_kwargs={},
) )
class _ProcessorProxy:
def __init__(self, processor: ProcessorMixin) -> None:
super().__init__()
self.__processor = processor
def __getattr__(self, key: str):
return getattr(self.__processor, key)
def __call__(
self,
text=None,
images=None,
videos=None,
exists=None,
return_tensors=None,
):
return dict(exists=exists)
@pytest.mark.parametrize("model_id", ["Qwen/Qwen2-VL-7B-Instruct"]) # Dummy
# yapf: disable
@pytest.mark.parametrize(
("call_kwargs", "expected_kwargs"),
[
# Should ignore invalid kwargs
({"does_not_exist": 100}, {"exists": None}),
({"exists": 1}, {"exists": 1}),
({"does_not_exist": 100, "exists": 1}, {"exists": 1}),
],
)
# yapf: enable
def test_hf_processor_kwargs(model_id, call_kwargs, expected_kwargs):
model_config = ModelConfig(
model=model_id,
task="auto",
tokenizer=model_id,
tokenizer_mode="auto",
trust_remote_code=False,
seed=0,
dtype="half",
revision=None,
)
processor = MULTIMODAL_REGISTRY.create_processor(
model_config,
tokenizer=cached_get_tokenizer(model_config.tokenizer),
)
orig_get_hf_processor = processor.info.get_hf_processor
def get_hf_processor(self, **kwargs):
assert kwargs == call_kwargs
return _ProcessorProxy(orig_get_hf_processor())
processor.info.get_hf_processor = MethodType(get_hf_processor,
processor.info)
out_kwargs = processor._call_hf_processor(
prompt="",
mm_data={},
mm_kwargs=call_kwargs,
)
assert out_kwargs == expected_kwargs
tests/multimodal/test_processor_kwargs.py deleted 100644 → 0
View file @ 649550f2
# SPDX-License-Identifier: Apache-2.0
from array import array
from typing import Callable, Dict, Mapping, Optional
from unittest.mock import patch
import pytest
import torch
from vllm.inputs import (DecoderOnlyInputs, DummyData, InputContext,
InputRegistry, ProcessorInputs, token_inputs)
from vllm.multimodal import MultiModalRegistry
from vllm.sequence import VLLM_TOKEN_ID_ARRAY_TYPE, SequenceData
from ..models.utils import build_model_context
# Used for fast tests where the model doesn't matter
DUMMY_MODEL_ID = "facebook/opt-125m"
# Used for tests that need a multimodal model
MULTIMODAL_MODEL_ID = "OpenGVLab/InternVL2-2B"
# For mm_processor_kwargs - we test overrides by defining mocks for each place
# it is used, and ensuring that we can pass processor kwargs an override value
# to receive the intended result for things like sequence length etc.
DEFAULT_MAX_DYNAMIC_PATCH = 6
MAX_DYNAMIC_PATCH_OVERRIDE = 4
# Mocks for all of the places that we use the mm_processor_kwargs
# to override values in different callables
@pytest.fixture
def use_processor_mock():
"""Patches the internal model input processor with an override callable."""
def custom_processor(ctx: InputContext,
inputs: DecoderOnlyInputs,
*,
max_dynamic_patch=DEFAULT_MAX_DYNAMIC_PATCH):
# For testing purposes, we don't worry about the prompt
return token_inputs(
prompt_token_ids=[],
mm_processor_kwargs={"max_dynamic_patch": max_dynamic_patch})
with patch("vllm.inputs.registry.InputRegistry._get_model_input_processor",
return_value=custom_processor):
yield
@pytest.fixture
def use_dummy_data_mock():
"""Patches the internal model input processor with an override callable."""
def custom_dummy_data_factory(self,
ctx: InputContext,
seq_len: int,
mm_counts: Mapping[str, int],
*,
max_dynamic_patch=DEFAULT_MAX_DYNAMIC_PATCH):
seq_data = SequenceData(
array(VLLM_TOKEN_ID_ARRAY_TYPE, [0] * max_dynamic_patch))
return DummyData(seq_data, None)
with patch(
"vllm.inputs.registry.InputRegistry._default_dummy_data_factory",
custom_dummy_data_factory):
yield
# Lazy import to avoid CUDA reinitialization error
def mm_model_cls():
from vllm.model_executor.models.internvl import InternVLChatModel
return InternVLChatModel
# lambda whose signature matches max token calcs extra & mapper + extra kwargs
get_max_dynamic_patch = lambda ctx, *, max_dynamic_patch=DEFAULT_MAX_DYNAMIC_PATCH: max_dynamic_patch # noqa: E501
custom_mapper = lambda ctx, data, *, max_dynamic_patch=DEFAULT_MAX_DYNAMIC_PATCH: { # noqa: E501
"pixel_values": torch.zeros(size=(1, max_dynamic_patch + 1, 3, 448, 448))
}
### Tests for default processor logic & mm_processor_kwargs wrapping
def test_default_processor_is_a_noop():
"""Ensure that by default, there is no processor override."""
dummy_registry = InputRegistry()
ctx = build_model_context(DUMMY_MODEL_ID)
processor = dummy_registry.create_input_processor(ctx.model_config)
proc_inputs = token_inputs(prompt_token_ids=[], prompt="")
proc_outputs = processor(inputs=proc_inputs)
assert proc_inputs is proc_outputs
def _get_max_dynamic_patch_info(init_max_dynamic_patch: int,
inference_max_dynamic_patch: int):
"""Get the init / inference kwargs and expected max_dynamic_patch."""
# If we have a value for max_dynamic_patch, pass the override value and make
# sure we get that value as a return-value from out mock processor,
# otherwise fall back to the default value
init_kwargs = None if init_max_dynamic_patch is None else {
"max_dynamic_patch": init_max_dynamic_patch
}
inference_kwargs = None if inference_max_dynamic_patch is None else {
"max_dynamic_patch": inference_max_dynamic_patch
}
if inference_max_dynamic_patch is not None:
expected_seq_count = inference_max_dynamic_patch
elif init_max_dynamic_patch is not None:
expected_seq_count = init_max_dynamic_patch
else:
expected_seq_count = DEFAULT_MAX_DYNAMIC_PATCH
return init_kwargs, inference_kwargs, expected_seq_count
def _get_processed_max_dynamic_patch(
processor: Callable[[ProcessorInputs], ProcessorInputs],
inference_kwargs: Optional[Dict[str, int]],
) -> int:
processed_inputs = processor(
token_inputs(prompt_token_ids=[],
prompt="",
mm_processor_kwargs=inference_kwargs))
assert "type" in processed_inputs
assert processed_inputs["type"] == "token"
assert "mm_processor_kwargs" in processed_inputs
return processed_inputs["mm_processor_kwargs"]["max_dynamic_patch"]
@pytest.mark.parametrize(
"init_max_dynamic_patch,inference_max_dynamic_patch", [
(None, None),
(MAX_DYNAMIC_PATCH_OVERRIDE, None),
(DEFAULT_MAX_DYNAMIC_PATCH, MAX_DYNAMIC_PATCH_OVERRIDE),
])
def test_input_processor_kwargs(use_processor_mock, init_max_dynamic_patch,
inference_max_dynamic_patch):
"""Ensure input processors can use processor kwargs."""
dummy_registry = InputRegistry()
(init_kwargs, inference_kwargs,
expected_seq_count) = _get_max_dynamic_patch_info(
init_max_dynamic_patch, inference_max_dynamic_patch)
ctx = build_model_context(DUMMY_MODEL_ID, mm_processor_kwargs=init_kwargs)
processor = dummy_registry.create_input_processor(ctx.model_config)
max_dynamic_patch_val = _get_processed_max_dynamic_patch(
processor, inference_kwargs)
assert max_dynamic_patch_val == expected_seq_count
@pytest.mark.parametrize(
"mm_processor_kwargs",
[
# Not part of the signature
{
"does_not_exist": 100
},
# Part of the signature, not keyword only
{
"ctx": "something bad"
}
])
def test_processor_with_sad_kwarg_overrides(use_processor_mock,
mm_processor_kwargs):
"""Ensure that input processors filter out invalid mm_processor_kwargs"""
dummy_registry = InputRegistry()
# Should filter out the init time kwargs
ctx = build_model_context(DUMMY_MODEL_ID,
mm_processor_kwargs=mm_processor_kwargs)
processor = dummy_registry.create_input_processor(ctx.model_config)
# Should filter out the inference time kwargs
max_dynamic_patch_val = _get_processed_max_dynamic_patch(
processor, mm_processor_kwargs)
assert max_dynamic_patch_val == DEFAULT_MAX_DYNAMIC_PATCH
### Test overrides for the dummy data
@pytest.mark.parametrize("max_dynamic_patch",
[None, MAX_DYNAMIC_PATCH_OVERRIDE])
def test_dummy_data_kwarg_overrides(use_dummy_data_mock, max_dynamic_patch):
"""Ensure dummy data factories can use processor kwargs."""
mm_processor_kwargs = None if max_dynamic_patch is None else {
"max_dynamic_patch": max_dynamic_patch
}
expected_seq_count = (DEFAULT_MAX_DYNAMIC_PATCH
if max_dynamic_patch is None else max_dynamic_patch)
dummy_registry = InputRegistry()
ctx = build_model_context(DUMMY_MODEL_ID,
mm_processor_kwargs=mm_processor_kwargs)
mm_registry = MultiModalRegistry()
mm_registry.init_mm_limits_per_prompt(ctx.model_config)
# NOTE: seq_len is thrown away here since this will leverage the
# default dummy data factory that we have patched in, whose seq
# len is solely dependent on the value of the mm_processor_kwargs.
dummy_data = dummy_registry.dummy_data_for_profiling(
ctx.model_config, seq_len=-1, mm_registry=mm_registry)
assert len(dummy_data.seq_data.prompt_token_ids) == expected_seq_count
@pytest.mark.parametrize(
"mm_processor_kwargs",
[
# Not part of the signature
{
"does_not_exist": 100
},
# Part of the signature, not keyword only
{
"ctx": "something bad"
}
])
def test_dummy_data_with_sad_kwarg_overrides(use_dummy_data_mock,
mm_processor_kwargs):
"""Ensure the dummy data factory filters out invalid mm_processor_kwargs"""
dummy_registry = InputRegistry()
ctx = build_model_context(DUMMY_MODEL_ID,
mm_processor_kwargs=mm_processor_kwargs)
mm_registry = MultiModalRegistry()
mm_registry.init_mm_limits_per_prompt(ctx.model_config)
# NOTE: seq_len is thrown away here since this will leverage the
# default dummy data factory that we have patched in, whose seq
# len is solely dependent on the value of the mm_processor_kwargs.
dummy_data = dummy_registry.dummy_data_for_profiling(
ctx.model_config, seq_len=-1, mm_registry=mm_registry)
assert len(
dummy_data.seq_data.prompt_token_ids) == DEFAULT_MAX_DYNAMIC_PATCH
### Test overrides for the max token count per multimodal instance
@pytest.mark.parametrize("max_dynamic_patch",
[None, MAX_DYNAMIC_PATCH_OVERRIDE])
def test_max_tokens_kwarg_overrides(max_dynamic_patch):
"""Ensure max token calcs can use processor kwargs."""
mm_processor_kwargs = None if max_dynamic_patch is None else {
"max_dynamic_patch": max_dynamic_patch
}
expected_seq_count = (DEFAULT_MAX_DYNAMIC_PATCH
if max_dynamic_patch is None else max_dynamic_patch)
ctx = build_model_context(MULTIMODAL_MODEL_ID,
task="generate",
trust_remote_code=True,
mm_processor_kwargs=mm_processor_kwargs,
limit_mm_per_prompt={"image": 1})
mm_registry = MultiModalRegistry()
mm_registry.init_mm_limits_per_prompt(ctx.model_config)
# Patch the image registry for phi3v with our lambda that is compatible
# with overrides, then ensure that calling the method correctly echos
# our max_dynamic_patch value back from the mm_processor_kwargs.
with patch.object(
mm_registry._get_plugin("image"),
"_max_mm_tokens",
{mm_model_cls(): get_max_dynamic_patch},
):
max_multimodal_tokens = mm_registry.get_max_multimodal_tokens(
ctx.model_config)
assert expected_seq_count == max_multimodal_tokens
@pytest.mark.parametrize(
"mm_processor_kwargs",
[
# Not part of the signature
{
"does_not_exist": 100
},
# Part of the signature, not keyword only
{
"ctx": "something bad"
}
])
def test_max_tokens_with_sad_kwarg_overrides(mm_processor_kwargs):
"""Ensure that max token calcs filters out invalid mm_processor_kwargs"""
ctx = build_model_context(MULTIMODAL_MODEL_ID,
task="generate",
trust_remote_code=True,
mm_processor_kwargs=mm_processor_kwargs,
limit_mm_per_prompt={"image": 1})
mm_registry = MultiModalRegistry()
mm_registry.init_mm_limits_per_prompt(ctx.model_config)
# Similar before, but since these kwargs get filtered,
# we always get our default value back.
with patch.object(
mm_registry._get_plugin("image"),
"_max_mm_tokens",
{mm_model_cls(): get_max_dynamic_patch},
):
max_multimodal_tokens = mm_registry.get_max_multimodal_tokens(
ctx.model_config)
assert max_multimodal_tokens == DEFAULT_MAX_DYNAMIC_PATCH
### Test overrides for the mapper
@pytest.mark.parametrize(
"max_dynamic_patch",
[DEFAULT_MAX_DYNAMIC_PATCH, MAX_DYNAMIC_PATCH_OVERRIDE])
def test_default_mapper_with_processor_kwargs(image_assets, max_dynamic_patch):
"""Ensure that the mapper processor kwargs can fall back to HF models."""
# NOTE - we don't validate bad inputs for the default mapper, because it's
# through the automodel interface in transformers, so we can't easily
# inspect what kwargs are or are not allowed.
ctx = build_model_context(
MULTIMODAL_MODEL_ID,
task="generate",
trust_remote_code=True,
mm_processor_kwargs={"max_dynamic_patch": max_dynamic_patch},
limit_mm_per_prompt={"image": 1})
mm_registry = MultiModalRegistry()
mm_registry.init_mm_limits_per_prompt(ctx.model_config)
image = image_assets[0].pil_image
mm_inputs = {"image": image}
mapped_inputs = mm_registry.map_input(ctx.model_config, mm_inputs)
# pixel vals should have shape: [batch, max_dynamic_patch+1, ...]
assert mapped_inputs["pixel_values"].shape[1] == max_dynamic_patch + 1
@pytest.mark.parametrize(
"init_max_dynamic_patch,inference_max_dynamic_patch", [
(None, None),
(MAX_DYNAMIC_PATCH_OVERRIDE, None),
(DEFAULT_MAX_DYNAMIC_PATCH, MAX_DYNAMIC_PATCH_OVERRIDE),
])
def test_custom_mapper_kwarg_overrides(image_assets, init_max_dynamic_patch,
inference_max_dynamic_patch):
"""Ensure custom mappers can use processor kwargs."""
(init_kwargs, inference_kwargs,
expected_seq_count) = _get_max_dynamic_patch_info(
init_max_dynamic_patch, inference_max_dynamic_patch)
ctx = build_model_context(MULTIMODAL_MODEL_ID,
task="generate",
trust_remote_code=True,
mm_processor_kwargs=init_kwargs,
limit_mm_per_prompt={"image": 1})
mm_registry = MultiModalRegistry()
mm_registry.init_mm_limits_per_prompt(ctx.model_config)
image = image_assets[0].pil_image
mm_inputs = {"image": image}
# Patch the image registry for phi3v with our lambda that is compatible
# with overrides, then ensure that calling the method correctly echos
# our max_dynamic_patch value back from the mm_processor_kwargs.
mm_registry._get_plugin("image").register_input_mapper(custom_mapper)(
mm_model_cls())
mapped_inputs = mm_registry.map_input(ctx.model_config, mm_inputs,
inference_kwargs)
assert mapped_inputs["pixel_values"].shape[1] == expected_seq_count + 1
@pytest.mark.parametrize(
"mm_processor_kwargs",
[
# Not part of the signature
{
"does_not_exist": 100
},
# Part of the signature, not keyword only
{
"ctx": "something bad"
}
])
def test_custom_mapper_with_sad_kwarg_overrides(image_assets,
mm_processor_kwargs):
"""Ensure that custom mappers filters out invalid mm_processor_kwargs"""
# Should filter out the init time kwargs
ctx = build_model_context(MULTIMODAL_MODEL_ID,
task="generate",
trust_remote_code=True,
mm_processor_kwargs=mm_processor_kwargs,
limit_mm_per_prompt={"image": 1})
mm_registry = MultiModalRegistry()
mm_registry.init_mm_limits_per_prompt(ctx.model_config)
image = image_assets[0].pil_image
mm_inputs = {"image": image}
# Patch the image registry for phi3v with our lambda that is compatible
# with overrides, then ensure that calling the method correctly echos
# our max_dynamic_patch value back from the mm_processor_kwargs.
mm_registry._get_plugin("image").register_input_mapper(custom_mapper)(
mm_model_cls())
# Should filter out the inference time kwargs
mapped_inputs = mm_registry.map_input(
ctx.model_config, mm_inputs, mm_processor_kwargs=mm_processor_kwargs)
assert mapped_inputs["pixel_values"].shape[1] == (
DEFAULT_MAX_DYNAMIC_PATCH + 1)
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