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Unverified Commit af51d80f authored by Roger Wang's avatar Roger Wang Committed by GitHub
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Revert "[V1] Scatter and gather placeholders in the model runner" (#16075)

parent f5722a50
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docs/source/contributing/model/multimodal.md
View file @ af51d80f
......@@ -860,8 +860,8 @@ prompt_tokens, prompts_length = _tokenize_prompts_with_image_and_batch(
)
```
To assign the vision embeddings to only the image tokens, instead of a string
you can return an instance of {class}`~vllm.multimodal.processing.PromptUpdateDetails`:
To accommodate this, instead of a string you can return an instance of {class}`~vllm.multimodal.processing.PromptUpdateDetails`
with different `full` and `feature` attributes:
```python
hf_config = self.info.get_hf_config()
......@@ -879,9 +879,9 @@ def get_replacement_fuyu(item_idx: int):
image_tokens = ([_IMAGE_TOKEN_ID] * ncols +
[_NEWLINE_TOKEN_ID]) * nrows
return PromptUpdateDetails.select_token_id(
image_tokens + [bos_token_id],
embed_token_id=_IMAGE_TOKEN_ID,
return PromptUpdateDetails(
full=image_tokens + [bos_token_id],
features=image_tokens,
)
```
......@@ -914,9 +914,9 @@ def _get_prompt_updates(
image_tokens = ([_IMAGE_TOKEN_ID] * ncols +
[_NEWLINE_TOKEN_ID]) * nrows
return PromptUpdateDetails.select_token_id(
image_tokens + [bos_token_id],
embed_token_id=_IMAGE_TOKEN_ID,
return PromptUpdateDetails(
full=image_tokens + [bos_token_id],
features=image_tokens,
)
return [
......
docs/source/models/supported_models.md
View file @ af51d80f
......@@ -989,6 +989,9 @@ See [this page](#generative-models) for more information on how to use generativ
<sup>+</sup> Multiple items can be inputted per text prompt for this modality.
:::{important}
To use Gemma3 series models, you have to install Hugging Face Transformers library from source via
`pip install git+https://github.com/huggingface/transformers`.
Pan-and-scan image pre-processing is currently supported on V0 (but not V1).
You can enable it by passing `--mm-processor-kwargs '{"do_pan_and_scan": True}'`.
:::
......
examples/offline_inference/audio_language.py
View file @ af51d80f
......@@ -47,7 +47,7 @@ def run_minicpmo(question: str, audio_count: int) -> ModelRequestData:
model=model_name,
trust_remote_code=True,
max_model_len=4096,
max_num_seqs=2,
max_num_seqs=5,
limit_mm_per_prompt={"audio": audio_count},
)
......
tests/models/decoder_only/audio_language/test_ultravox.py
View file @ af51d80f
......@@ -55,10 +55,7 @@ def server(request, audio_assets):
for key, value in request.param.items()
]
with RemoteOpenAIServer(MODEL_NAME,
args,
env_dict={"VLLM_AUDIO_FETCH_TIMEOUT":
"30"}) as remote_server:
with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
yield remote_server
......
tests/models/decoder_only/vision_language/test_models.py
View file @ af51d80f
......@@ -167,7 +167,7 @@ VLM_TEST_SETTINGS = {
"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=4096,
max_model_len=8192,
max_num_seqs=2,
auto_cls=AutoModelForImageTextToText,
vllm_runner_kwargs={"mm_processor_kwargs": {"crop_to_patches": True}}
......
tests/models/decoder_only/vision_language/test_pixtral.py
View file @ af51d80f
......@@ -176,8 +176,6 @@ def test_chat(
model,
dtype=dtype,
tokenizer_mode="mistral",
load_format="mistral",
config_format="mistral",
max_model_len=max_model_len,
limit_mm_per_prompt=LIMIT_MM_PER_PROMPT,
) as vllm_model:
......@@ -200,14 +198,22 @@ def test_chat(
@large_gpu_test(min_gb=48)
@pytest.mark.parametrize("prompt,expected_ranges",
[(_create_engine_inputs_hf(IMG_URLS[:1]),
[PlaceholderRange(offset=11, length=494)]),
(_create_engine_inputs_hf(IMG_URLS[1:4]), [
PlaceholderRange(offset=11, length=266),
PlaceholderRange(offset=277, length=1056),
PlaceholderRange(offset=1333, length=418)
])])
@pytest.mark.parametrize(
"prompt,expected_ranges",
[(_create_engine_inputs_hf(IMG_URLS[:1]), [{
"offset": 11,
"length": 494
}]),
(_create_engine_inputs_hf(IMG_URLS[1:4]), [{
"offset": 11,
"length": 266
}, {
"offset": 277,
"length": 1056
}, {
"offset": 1333,
"length": 418
}])])
def test_multi_modal_placeholders(vllm_runner, prompt,
expected_ranges: list[PlaceholderRange],
monkeypatch) -> None:
......
tests/models/multimodal/processing/test_llava_next.py
View file @ af51d80f
......@@ -92,8 +92,8 @@ def _validate_image_prompt_replacements_one(
first_placeholder = image_placeholders[0]
# NOTE: There is a BOS token
assert first_placeholder.offset == 1
assert first_placeholder.length == (
assert first_placeholder["offset"] == 1
assert first_placeholder["length"] == (
len(processed_inputs["prompt_token_ids"]) - 1) // num_imgs
except Exception as exc:
......
tests/models/multimodal/processing/test_llava_onevision.py
View file @ af51d80f
......@@ -92,8 +92,8 @@ def _validate_image_prompt_replacements_one(
first_placeholder = image_placeholders[0]
assert first_placeholder.offset == 0
assert first_placeholder.length == len(
assert first_placeholder["offset"] == 0
assert first_placeholder["length"] == len(
processed_inputs["prompt_token_ids"]) // num_imgs
except Exception as exc:
failed_size_excs.append((image_size, exc))
......
tests/models/registry.py
View file @ af51d80f
......@@ -277,9 +277,7 @@ _MULTIMODAL_EXAMPLE_MODELS = {
trust_remote_code=True,
hf_overrides={"architectures": ["GLM4VForCausalLM"]}), # noqa: E501
"H2OVLChatModel": _HfExamplesInfo("h2oai/h2ovl-mississippi-800m",
extras={"2b": "h2oai/h2ovl-mississippi-2b"}, # noqa: E501
max_transformers_version="4.48", # noqa: E501
transformers_version_reason="HF model is not compatible."), # noqa: E501
extras={"2b": "h2oai/h2ovl-mississippi-2b"}), # noqa: E501
"InternVLChatModel": _HfExamplesInfo("OpenGVLab/InternVL2-1B",
extras={"2B": "OpenGVLab/InternVL2-2B"}, # noqa: E501
trust_remote_code=True),
......
tests/multimodal/test_processing.py
View file @ af51d80f
......@@ -785,7 +785,6 @@ def test_find_update_tokens(
item_idx=0,
start_idx=6,
tokens=[32000, 32000],
is_embed=None,
),
],
"pattern_4": [
......@@ -794,7 +793,6 @@ def test_find_update_tokens(
item_idx=0,
start_idx=3,
tokens=[32000],
is_embed=None,
),
],
}
......@@ -809,14 +807,12 @@ def test_find_update_tokens(
item_idx=0,
start_idx=1,
tokens=[32000, 32000],
is_embed=None,
),
PlaceholderFeaturesInfo(
modality="pattern_1",
item_idx=1,
start_idx=5,
tokens=[32000, 32000],
is_embed=None,
),
],
"pattern_3": [
......@@ -825,7 +821,6 @@ def test_find_update_tokens(
item_idx=0,
start_idx=7,
tokens=[1550, 918, 1550],
is_embed=None,
),
],
# No match for pattern_4 as it has lower priority than pattern_1
......@@ -840,14 +835,12 @@ def test_find_update_tokens(
item_idx=0,
start_idx=1,
tokens=[32000, 32000],
is_embed=None,
),
PlaceholderFeaturesInfo(
modality="pattern_1",
item_idx=1,
start_idx=3,
tokens=[32000, 32000],
is_embed=None,
),
],
"pattern_4": [
......@@ -856,7 +849,6 @@ def test_find_update_tokens(
item_idx=0,
start_idx=5,
tokens=[32000],
is_embed=None,
),
],
"pattern_3": [
......@@ -865,7 +857,6 @@ def test_find_update_tokens(
item_idx=0,
start_idx=6,
tokens=[1550, 918, 1550],
is_embed=None,
),
],
}
......
tests/v1/core/test_kv_cache_utils.py
View file @ af51d80f
......@@ -3,7 +3,7 @@
import pytest
import torch
from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
from vllm.multimodal.inputs import MultiModalKwargs
from vllm.sampling_params import SamplingParams
from vllm.utils import sha256
# disable yapf here as it formats differently than isort such that both fail
......@@ -158,10 +158,13 @@ def test_generate_block_hash_extra_keys():
request = make_request(
request_id=0,
prompt_token_ids=[_ for _ in range(20)],
mm_positions=[
PlaceholderRange(offset=0, length=5),
PlaceholderRange(offset=10, length=5),
],
mm_positions=[{
"offset": 0,
"length": 5
}, {
"offset": 10,
"length": 5
}],
mm_hashes=["hash1", "hash2"],
)
......@@ -219,10 +222,13 @@ def test_hash_request_tokens(hash_fn):
request = make_request(
request_id=0,
prompt_token_ids=[_ for _ in range(6)],
mm_positions=[
PlaceholderRange(offset=0, length=3),
PlaceholderRange(offset=3, length=3),
],
mm_positions=[{
"offset": 0,
"length": 3
}, {
"offset": 3,
"length": 3
}],
mm_hashes=["hash1", "hash2"],
)
......@@ -247,19 +253,25 @@ def test_hash_tokens_different_mm_input(hash_fn):
request1 = make_request(
request_id=0,
prompt_token_ids=[_ for _ in range(6)],
mm_positions=[
PlaceholderRange(offset=0, length=3),
PlaceholderRange(offset=3, length=3),
],
mm_positions=[{
"offset": 0,
"length": 3
}, {
"offset": 3,
"length": 3
}],
mm_hashes=["hash1", "hash2"],
)
request2 = make_request(
request_id=1,
prompt_token_ids=[_ for _ in range(6)],
mm_positions=[
PlaceholderRange(offset=0, length=3),
PlaceholderRange(offset=3, length=3),
],
mm_positions=[{
"offset": 0,
"length": 3
}, {
"offset": 3,
"length": 3
}],
mm_hashes=["hash3", "hash2"],
)
block_size = 3
......
vllm/model_executor/models/aya_vision.py
View file @ af51d80f
......@@ -27,7 +27,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
BaseProcessingInfo,
MultiModalFieldConfig,
PromptReplacement, PromptUpdate,
PromptUpdateDetails)
encode_tokens)
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.sequence import IntermediateTensors
......@@ -35,6 +35,7 @@ from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .siglip import SiglipVisionModel
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
maybe_prefix, merge_multimodal_embeddings)
from .vision import scatter_patch_features, select_patch_features
class AyaVisionImagePixelInputs(TypedDict):
......@@ -50,6 +51,13 @@ class AyaVisionImagePixelInputs(TypedDict):
num_patches: torch.Tensor
"""Shape: `(batch_size * num_images)`"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
class AyaVisionMultiModalProjector(nn.Module):
......@@ -127,20 +135,21 @@ class AyaVisionProcessingInfo(BaseProcessingInfo):
def get_max_image_tokens(self) -> int:
hf_processor = self.get_hf_processor()
image_processor = hf_processor.image_processor
image_size = self.get_image_size_with_most_features()
tokenizer = hf_processor.tokenizer
num_patches = self.get_num_patches(
image_width=image_size.width,
image_height=image_size.height,
size=image_processor.size,
min_patches=image_processor.min_patches,
max_patches=image_processor.max_patches,
max_patches=image_processor.max_patches)
image_string = hf_processor._prompt_split_image(num_patches)
x = encode_tokens(
tokenizer,
image_string,
add_special_tokens=False,
)
img_patches_per_tile = (hf_processor.img_size //
hf_processor.patch_size)**2
return num_patches * img_patches_per_tile
return len(x)
def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
return {"image": None}
......@@ -212,6 +221,7 @@ class AyaVisionMultiModalProcessor(
hf_processor = self.info.get_hf_processor(**mm_kwargs)
image_processor = hf_processor.image_processor
hf_config = self.info.get_hf_config()
# HF processor pops the `num_patches` kwarg, which is needed by vLLM
if (images :=
mm_data.get("images")) is not None and '<image>' in prompt:
......@@ -224,7 +234,6 @@ class AyaVisionMultiModalProcessor(
parsed_images.get_image_size(i)
for i in range(len(parsed_images))
]
num_patches = [
self.info.get_num_patches(
image_width=image_size.width,
......@@ -234,6 +243,20 @@ class AyaVisionMultiModalProcessor(
max_patches=image_processor.max_patches)
for image_size in image_sizes
]
image_tokens_list = [
hf_processor._prompt_split_image(num_patch)
for num_patch in num_patches
]
tokenizer = self.info.get_tokenizer()
image_token_ids = [
tokenizer.encode(image_tokens, add_special_tokens=False)
for image_tokens in image_tokens_list
]
embed_is_patch = [
torch.tensor(image_repl_tokens) == hf_config.image_token_index
for image_repl_tokens in image_token_ids
]
processed_outputs["embed_is_patch"] = embed_is_patch
processed_outputs["num_patches"] = torch.tensor(num_patches)
return processed_outputs
......@@ -248,6 +271,7 @@ class AyaVisionMultiModalProcessor(
pixel_values=MultiModalFieldConfig.flat_from_sizes(
"image", num_patches),
num_patches=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"),
image_embeds=MultiModalFieldConfig.batched("image"),
)
......@@ -259,7 +283,6 @@ class AyaVisionMultiModalProcessor(
) -> Sequence[PromptUpdate]:
hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
image_token = hf_processor.image_token
img_patch_token = hf_processor.img_patch_token
image_processor = hf_processor.image_processor
def get_replacement(item_idx: int):
......@@ -271,11 +294,8 @@ class AyaVisionMultiModalProcessor(
image_height=image_size.height,
size=image_processor.size,
min_patches=image_processor.min_patches,
max_patches=image_processor.max_patches,
)
repl = hf_processor._prompt_split_image(num_patches=num_patches)
return PromptUpdateDetails.select_text(repl, img_patch_token)
max_patches=image_processor.max_patches)
return hf_processor._prompt_split_image(num_patches=num_patches)
return [
PromptReplacement(
......@@ -404,6 +424,7 @@ class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
self, **kwargs: object) -> Optional[AyaVisionImagePixelInputs]:
pixel_values = kwargs.pop("pixel_values", None)
num_patches = kwargs.pop("num_patches", None)
embed_is_patch = kwargs.pop("embed_is_patch", None)
image_embeds = kwargs.pop("image_embeds", None)
assert image_embeds is None, "Aya Vision does not support image_embeds."
......@@ -415,13 +436,18 @@ class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
raise ValueError("Incorrect type of num_patches. "
f"Got type: {type(num_patches)}")
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
pixel_values = flatten_bn(pixel_values, concat=True)
num_patches = flatten_bn(num_patches, concat=True)
embed_is_patch = flatten_bn(embed_is_patch)
return AyaVisionImagePixelInputs(
type="pixel_values",
pixel_values=self._validate_pixel_values(pixel_values),
num_patches=num_patches,
embed_is_patch=embed_is_patch,
)
def get_multimodal_embeddings(
......@@ -429,8 +455,11 @@ class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
image_input = self._parse_and_validate_image_input(**kwargs)
if image_input is None:
return None
return self._process_image_input(image_input, **kwargs)
image_features = self._process_image_input(image_input, **kwargs)
return scatter_patch_features(
image_features,
image_input["embed_is_patch"],
)
def get_input_embeddings(
self,
......@@ -442,9 +471,9 @@ class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
inputs_embeds = merge_multimodal_embeddings(
input_ids=input_ids,
inputs_embeds=inputs_embeds,
multimodal_embeddings=multimodal_embeddings,
placeholder_token_id=self.config.image_token_index,
)
multimodal_embeddings=select_patch_features(
multimodal_embeddings),
placeholder_token_id=self.config.image_token_index)
return inputs_embeds
......
vllm/model_executor/models/chameleon.py
View file @ af51d80f
......@@ -162,9 +162,9 @@ class ChameleonMultiModalProcessor(
PromptReplacement(
modality="image",
target=[image_token_id],
replacement=PromptUpdateDetails.select_token_id(
[image_start_id] + image_tokens + [image_end_id],
embed_token_id=image_token_id,
replacement=PromptUpdateDetails(
full=([image_start_id] + image_tokens + [image_end_id]),
features=image_tokens,
),
)
]
......
vllm/model_executor/models/fuyu.py
View file @ af51d80f
......@@ -18,7 +18,7 @@
""" PyTorch Fuyu model."""
import math
from collections.abc import Iterable, Mapping, Sequence
from typing import Literal, Optional, Set, Tuple, TypedDict
from typing import Literal, Optional, Set, Tuple, TypedDict, Union
import torch
import torch.nn as nn
......@@ -43,6 +43,7 @@ from vllm.sequence import IntermediateTensors
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix,
merge_multimodal_embeddings)
from .vision import scatter_patch_features, select_patch_features
# Cannot find the following 2 numbers from hf config.
_IMAGE_TOKEN_ID = 71011
......@@ -65,6 +66,14 @@ class FuyuImagePatchInputs(TypedDict):
flattened just like `flat_data`.
"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
class FuyuProcessingInfo(BaseProcessingInfo):
......@@ -85,7 +94,15 @@ class FuyuProcessingInfo(BaseProcessingInfo):
seq_len: int,
mm_counts: Mapping[str, int],
) -> Mapping[str, int]:
return {"image": self.get_max_image_tokens()}
target_width, target_height = self.get_image_size_with_most_features()
max_ncols, max_nrows = self.get_image_feature_grid_size(
image_width=target_width,
image_height=target_height,
)
max_image_tokens = (max_ncols + 1) * max_nrows
return {"image": max_image_tokens}
def get_image_feature_grid_size(
self,
......@@ -111,32 +128,11 @@ class FuyuProcessingInfo(BaseProcessingInfo):
nrows = math.ceil(image_height / patch_height)
return ncols, nrows
def get_num_image_tokens(
self,
*,
image_width: int,
image_height: int,
) -> int:
ncols, nrows = self.get_image_feature_grid_size(
image_width=image_width,
image_height=image_height,
)
return ncols * nrows
def get_image_size_with_most_features(self) -> ImageSize:
image_processor = self.get_image_processor()
return ImageSize(width=image_processor.size["width"],
height=image_processor.size["height"])
def get_max_image_tokens(self) -> int:
target_width, target_height = self.get_image_size_with_most_features()
return self.get_num_image_tokens(
image_width=target_width,
image_height=target_height,
)
class FuyuDummyInputsBuilder(BaseDummyInputsBuilder[FuyuProcessingInfo]):
......@@ -196,6 +192,19 @@ class FuyuMultiModalProcessor(BaseMultiModalProcessor[FuyuProcessingInfo]):
processed_outputs["image_patches"] = image_patches[0]
# get patch grid size for each image
embed_is_patch = []
for image in images:
ncols, nrows = self.info.get_image_feature_grid_size(
image_width=image.width,
image_height=image.height,
)
mask = torch.tensor(([True] * ncols + [False]) * nrows)
embed_is_patch.append(mask)
processed_outputs["embed_is_patch"] = embed_is_patch
return processed_outputs
def _apply_hf_processor_tokens_only(
......@@ -215,7 +224,8 @@ class FuyuMultiModalProcessor(BaseMultiModalProcessor[FuyuProcessingInfo]):
hf_inputs: BatchFeature,
hf_processor_mm_kwargs: Mapping[str, object],
) -> Mapping[str, MultiModalFieldConfig]:
return dict(image_patches=MultiModalFieldConfig.batched("image"))
return dict(image_patches=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"))
def _get_prompt_updates(
self,
......@@ -242,9 +252,9 @@ class FuyuMultiModalProcessor(BaseMultiModalProcessor[FuyuProcessingInfo]):
image_tokens = ([_IMAGE_TOKEN_ID] * ncols +
[_NEWLINE_TOKEN_ID]) * nrows
return PromptUpdateDetails.select_token_id(
image_tokens + [bos_token_id],
embed_token_id=_IMAGE_TOKEN_ID,
return PromptUpdateDetails(
full=image_tokens + [bos_token_id],
features=image_tokens,
)
return [
......@@ -319,13 +329,20 @@ class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
raise ValueError("Incorrect type of image patches. "
f"Got type: {type(image_patches)}")
embed_is_patch = kwargs.pop("embed_is_patch")
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
image_patches_flat = flatten_bn(image_patches)
embed_is_patch = flatten_bn(embed_is_patch)
return FuyuImagePatchInputs(
type="image_patches",
flat_data=self._validate_pixel_values(
flatten_bn(image_patches_flat, concat=True)),
patches_per_image=[x.size(0) for x in image_patches_flat],
embed_is_patch=embed_is_patch,
)
return None
......@@ -347,7 +364,12 @@ class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
if image_input is None:
return None
return self._process_image_input(image_input)
image_features = self._process_image_input(image_input)
return scatter_patch_features(
image_features,
image_input["embed_is_patch"],
)
def get_input_embeddings(
self,
......@@ -357,11 +379,8 @@ class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
inputs_embeds = self.language_model.get_input_embeddings(input_ids)
if multimodal_embeddings is not None:
inputs_embeds = merge_multimodal_embeddings(
input_ids,
inputs_embeds,
multimodal_embeddings,
_IMAGE_TOKEN_ID,
)
input_ids, inputs_embeds,
select_patch_features(multimodal_embeddings), _IMAGE_TOKEN_ID)
return inputs_embeds
def forward(
......
vllm/model_executor/models/gemma3_mm.py
View file @ af51d80f
......@@ -25,7 +25,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
PlaceholderFeaturesInfo,
PromptReplacement, PromptTargetMatch,
PromptUpdate, PromptUpdateDetails,
find_mm_placeholders,
encode_tokens, find_mm_placeholders,
replace_token_matches)
# yapf: enable
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
......@@ -36,6 +36,7 @@ from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
from .siglip import SiglipVisionModel
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
maybe_prefix, merge_multimodal_embeddings)
from .vision import scatter_patch_features, select_patch_features
logger = init_logger(__name__)
......@@ -53,6 +54,14 @@ class Gemma3ImagePixelInputs(TypedDict):
num_patches: torch.Tensor
"""Shape: `(batch_size * num_images)`"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
Gemma3ImageInputs = Gemma3ImagePixelInputs
......@@ -174,7 +183,7 @@ class Gemma3ProcessingInfo(BaseProcessingInfo):
if processor is None:
processor = self.get_hf_processor()
boi_token = processor.boi_token
image_token = processor.boi_token
num_crops = self.get_num_crops(
image_width=image_width,
......@@ -183,21 +192,19 @@ class Gemma3ProcessingInfo(BaseProcessingInfo):
)
if num_crops == 0:
image_text = boi_token
image_text = image_token
else:
crops_image_tokens = " ".join(boi_token for _ in range(num_crops))
crops_image_tokens = " ".join(image_token
for _ in range(num_crops))
image_text = (
f"Here is the original image {boi_token} and here are some "
f"Here is the original image {image_token} and here are some "
f"crops to help you see better {crops_image_tokens}")
repl_full = image_text.replace(boi_token,
repl_full = image_text.replace(image_token,
processor.full_image_sequence)
repl_features = repl_full.strip("\n")
tokenizer = processor.tokenizer
vocab = tokenizer.get_vocab()
image_token_id = vocab[tokenizer.image_token]
return PromptUpdateDetails.select_token_id(repl_full, image_token_id)
return PromptUpdateDetails(full=repl_full, features=repl_features)
def get_num_image_tokens(
self,
......@@ -206,17 +213,19 @@ class Gemma3ProcessingInfo(BaseProcessingInfo):
image_height: int,
processor: Optional[Gemma3Processor],
) -> int:
if processor is None:
processor = self.get_hf_processor()
num_crops = self.get_num_crops(
tokenizer = self.get_tokenizer()
image_repl = self.get_image_repl(
image_width=image_width,
image_height=image_height,
processor=processor,
)
image_seq_len = processor.image_seq_length
return (num_crops + 1) * image_seq_len
image_repl_tokens = encode_tokens(
tokenizer,
image_repl.features,
add_special_tokens=False,
)
return len(image_repl_tokens)
def get_image_size_with_most_features(self) -> ImageSize:
processor = self.get_hf_processor()
......@@ -292,6 +301,28 @@ class Gemma3MultiModalProcessor(BaseMultiModalProcessor[Gemma3ProcessingInfo]):
]
hf_processor = self.info.get_hf_processor(**mm_kwargs)
image_repl_features = [
self.info.get_image_repl(image_width=size.width,
image_height=size.height,
processor=hf_processor).features
for size in image_sizes
]
tokenizer = self.info.get_tokenizer()
image_repls_feature_tokens = [
tokenizer.encode(image_repl, add_special_tokens=False)
for image_repl in image_repl_features
]
vocab = tokenizer.get_vocab()
image_token_id = vocab[tokenizer.image_token]
embed_is_patch = [
torch.tensor(image_repl_tokens) == image_token_id
for image_repl_tokens in image_repls_feature_tokens
]
processed_outputs["embed_is_patch"] = embed_is_patch
num_crops = [
self.info.get_num_crops(image_width=size.width,
image_height=size.height,
......@@ -313,6 +344,7 @@ class Gemma3MultiModalProcessor(BaseMultiModalProcessor[Gemma3ProcessingInfo]):
pixel_values=MultiModalFieldConfig.flat_from_sizes(
"image", num_crops + 1),
num_crops=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"),
)
def _get_prompt_updates(
......@@ -422,7 +454,6 @@ class Gemma3MultiModalProcessor(BaseMultiModalProcessor[Gemma3ProcessingInfo]):
item_idx=p.item_idx,
start_idx=repl_orig_idxs[p.start_idx],
tokens=p.tokens,
is_embed=p.is_embed,
) for p in placeholders
]
for modality, placeholders in repls.items()
......@@ -541,6 +572,7 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
self, **kwargs: object) -> Optional[Gemma3ImageInputs]:
pixel_values = kwargs.pop("pixel_values", None)
num_crops = kwargs.pop("num_crops", None)
embed_is_patch = kwargs.pop("embed_is_patch", None)
image_embeds = kwargs.pop("image_embeds", None)
assert image_embeds is None, "Gemma3 does not support image_embeds."
if pixel_values is None:
......@@ -554,13 +586,19 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
raise ValueError("Incorrect type of num_crops. "
f"Got type: {type(num_crops)}")
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
pixel_values = flatten_bn(pixel_values, concat=True)
num_crops = flatten_bn(num_crops, concat=True)
embed_is_patch = flatten_bn(embed_is_patch)
return Gemma3ImagePixelInputs(
type="pixel_values",
pixel_values=self._validate_pixel_values(pixel_values),
num_patches=num_crops + 1,
embed_is_patch=embed_is_patch,
)
def _image_pixels_to_features(
......@@ -597,7 +635,12 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
if image_input is None:
return None
return self._process_image_input(image_input)
image_features = self._process_image_input(image_input)
return scatter_patch_features(
image_features,
image_input["embed_is_patch"],
)
def get_input_embeddings(
self,
......@@ -609,7 +652,7 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
inputs_embeds = merge_multimodal_embeddings(
input_ids,
inputs_embeds,
multimodal_embeddings,
select_patch_features(multimodal_embeddings),
self.config.image_token_index,
)
return inputs_embeds
......
vllm/model_executor/models/h2ovl.py
View file @ af51d80f
......@@ -257,7 +257,7 @@ class H2OVLProcessor(BaseInternVLProcessor):
repl_features = IMG_CONTEXT * feature_size
repl_full = IMG_START + repl_features + IMG_END
return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)
return PromptUpdateDetails(full=repl_full, features=repl_features)
def resolve_min_max_num(
self,
......
vllm/model_executor/models/idefics3.py
View file @ af51d80f
......@@ -41,7 +41,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
MultiModalDataItems,
MultiModalFieldConfig,
PromptReplacement, PromptUpdate,
PromptUpdateDetails)
encode_tokens)
# yapf: enable
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.sequence import IntermediateTensors
......@@ -54,6 +54,7 @@ from .interfaces import MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal
from .llama import LlamaModel
from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix,
merge_multimodal_embeddings)
from .vision import scatter_patch_features, select_patch_features
class Idefics3ImagePixelInputs(TypedDict):
......@@ -68,6 +69,14 @@ class Idefics3ImagePixelInputs(TypedDict):
num_patches: torch.Tensor
"""Shape: `(batch_size * num_images)`"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
class Idefics3ImageEmbeddingInputs(TypedDict):
type: Literal["image_embeds"]
......@@ -77,6 +86,14 @@ class Idefics3ImageEmbeddingInputs(TypedDict):
`hidden_size` must match the hidden size of language model backbone.
"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
ImageInputs = Union[Idefics3ImagePixelInputs, Idefics3ImageEmbeddingInputs]
......@@ -258,16 +275,19 @@ class Idefics3ProcessingInfo(BaseProcessingInfo):
image_height: int,
processor: Optional[Idefics3Processor],
) -> int:
if processor is None:
processor = self.get_hf_processor()
num_patches = self.get_num_patches(
tokenizer = self.get_tokenizer()
image_repl = self.get_image_repl(
image_width=image_width,
image_height=image_height,
processor=processor,
)
return num_patches * processor.image_seq_len
image_repl_tokens = encode_tokens(
tokenizer,
image_repl,
add_special_tokens=False,
)
return len(image_repl_tokens)
def get_image_size_with_most_features(self) -> ImageSize:
processor = self.get_hf_processor()
......@@ -344,6 +364,28 @@ class Idefics3MultiModalProcessor(
]
hf_processor = self.info.get_hf_processor(**mm_kwargs)
image_repl_features = [
self.info.get_image_repl(image_width=size.width,
image_height=size.height,
processor=hf_processor)
for size in image_sizes
]
tokenizer = self.info.get_tokenizer()
image_repls_feature_tokens = [
tokenizer.encode(image_repl, add_special_tokens=False)
for image_repl in image_repl_features
]
vocab = tokenizer.get_vocab()
image_token_id = vocab[hf_processor.image_token.content]
embed_is_patch = [
torch.tensor(image_repl_tokens) == image_token_id
for image_repl_tokens in image_repls_feature_tokens
]
processed_outputs["embed_is_patch"] = embed_is_patch
num_patches = [
self.info.get_num_patches(
image_width=size.width,
......@@ -373,6 +415,7 @@ class Idefics3MultiModalProcessor(
"image", num_patches),
image_embeds=MultiModalFieldConfig.batched("image"),
num_patches=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"),
)
def _get_prompt_updates(
......@@ -384,22 +427,17 @@ class Idefics3MultiModalProcessor(
hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
image_token = hf_processor.image_token.content
def get_replacement_idefics3(item_idx: int) -> PromptUpdateDetails:
def get_replacement_idefics3(item_idx: int) -> str:
images = mm_items.get_items("image", ImageProcessorItems)
image_size = images.get_image_size(item_idx)
image_repl = self.info.get_image_repl(
return self.info.get_image_repl(
image_width=image_size.width,
image_height=image_size.height,
processor=hf_processor,
)
return PromptUpdateDetails.select_text(
image_repl,
embed_text=image_token,
)
return [
PromptReplacement(
modality="image",
......@@ -637,6 +675,13 @@ class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
if pixel_values is None and image_embeds is None:
return None
embed_is_patch = kwargs.pop("embed_is_patch")
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
embed_is_patch = flatten_bn(embed_is_patch)
if image_embeds is not None:
if not isinstance(image_embeds, (torch.Tensor, list)):
raise ValueError("Incorrect type of image embeddings. "
......@@ -645,6 +690,7 @@ class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
return Idefics3ImageEmbeddingInputs(
type="image_embeds",
data=flatten_bn(image_embeds, concat=True),
embed_is_patch=embed_is_patch,
)
if pixel_values is not None:
......@@ -672,6 +718,7 @@ class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
pixel_values=self._validate_pixel_values(pixel_values),
pixel_attention_mask=pixel_attention_mask,
num_patches=num_patches,
embed_is_patch=embed_is_patch,
)
raise AssertionError("This line should be unreachable.")
......@@ -707,7 +754,12 @@ class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
if image_input is None:
return None
return self._process_image_input(image_input)
image_features = self._process_image_input(image_input)
return scatter_patch_features(
image_features,
image_input["embed_is_patch"],
)
def get_input_embeddings(
self,
......@@ -719,7 +771,7 @@ class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
inputs_embeds = merge_multimodal_embeddings(
input_ids,
inputs_embeds,
multimodal_embeddings,
select_patch_features(multimodal_embeddings),
self.config.image_token_id,
)
return inputs_embeds
......
vllm/model_executor/models/internvl.py
View file @ af51d80f
......@@ -39,6 +39,7 @@ from vllm.transformers_utils.tokenizer import AnyTokenizer
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
maybe_prefix, merge_multimodal_embeddings)
from .vision import scatter_patch_features, select_patch_features
IMG_START = '<img>'
IMG_END = '</img>'
......@@ -59,6 +60,14 @@ class InternVLImagePixelInputs(TypedDict):
num_patches: torch.Tensor
"""Shape: `(batch_size * num_images)`"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
class InternVLImageEmbeddingInputs(TypedDict):
type: Literal["image_embeds"]
......@@ -410,12 +419,24 @@ class BaseInternVLProcessor(ABC):
torch.tensor([len(item) for item in pixel_values_lst]),
}
tokenizer = self.tokenizer
image_token_id = self.image_token_id
embed_is_patch = list[torch.Tensor]()
for pixel_values in pixel_values_lst:
num_patches = pixel_values.shape[0]
feature_size = num_patches * self.num_image_token
image_repl = self.get_image_repl(feature_size, num_patches)
feature_tokens = tokenizer.encode(image_repl.features,
add_special_tokens=False)
text = [t.replace('<image>', image_repl.full, 1) for t in text]
embed_is_patch.append(
torch.tensor(feature_tokens) == image_token_id)
image_inputs["embed_is_patch"] = embed_is_patch
text_inputs = self.tokenizer(text)
......@@ -439,7 +460,7 @@ class InternVLProcessor(BaseInternVLProcessor):
repl_features = IMG_CONTEXT * feature_size
repl_full = IMG_START + repl_features + IMG_END
return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)
return PromptUpdateDetails(full=repl_full, features=repl_features)
class BaseInternVLProcessingInfo(BaseProcessingInfo):
......@@ -578,6 +599,7 @@ class InternVLMultiModalProcessor(BaseMultiModalProcessor[_I]):
pixel_values_flat=MultiModalFieldConfig.flat_from_sizes(
"image", image_num_patches),
image_num_patches=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"),
image_embeds=MultiModalFieldConfig.batched("image"),
image_token_id=MultiModalFieldConfig.shared("image", num_images),
)
......@@ -809,6 +831,7 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
self, **kwargs: object) -> Optional[InternVLImageInputs]:
pixel_values_flat = kwargs.pop("pixel_values_flat", None)
image_num_patches = kwargs.pop("image_num_patches", None)
embed_is_patch = kwargs.pop("embed_is_patch", None)
image_embeds = kwargs.pop("image_embeds", None)
if pixel_values_flat is None and image_embeds is None:
......@@ -837,14 +860,20 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
raise ValueError("Incorrect type of image_num_patches. "
f"Got type: {type(image_num_patches)}")
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
pixel_values_flat = flatten_bn(pixel_values_flat, concat=True)
image_num_patches = flatten_bn(image_num_patches, concat=True)
embed_is_patch = flatten_bn(embed_is_patch)
return InternVLImagePixelInputs(
type="pixel_values",
pixel_values_flat=self._validate_pixel_values(
pixel_values_flat),
num_patches=image_num_patches,
embed_is_patch=embed_is_patch,
)
raise AssertionError("This line should be unreachable.")
......@@ -890,7 +919,15 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
if image_input is None:
return None
return self._process_image_input(image_input)
image_features = self._process_image_input(image_input)
if image_input["type"] != "pixel_values":
return image_features
return scatter_patch_features(
image_features,
image_input["embed_is_patch"],
)
def get_input_embeddings(
self,
......@@ -904,7 +941,7 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
inputs_embeds = merge_multimodal_embeddings(
input_ids,
inputs_embeds,
multimodal_embeddings,
select_patch_features(multimodal_embeddings),
self.img_context_token_id,
)
return inputs_embeds
......
vllm/model_executor/models/llava.py
View file @ af51d80f
......@@ -32,8 +32,7 @@ from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
ImageSize, MultiModalDataItems)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
BaseProcessingInfo, ProcessingCache,
PromptReplacement, PromptUpdate,
PromptUpdateDetails)
PromptReplacement, PromptUpdate)
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.sequence import IntermediateTensors
......@@ -43,7 +42,8 @@ from .pixtral import PixtralHFEncoderInfo, PixtralHFVisionModel
from .siglip import SiglipVisionModel
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
maybe_prefix, merge_multimodal_embeddings)
from .vision import get_vision_encoder_info
from .vision import (get_vision_encoder_info, scatter_patch_features,
select_patch_features)
class LlavaImagePixelInputs(TypedDict):
......@@ -67,6 +67,14 @@ class PixtralHFImagePixelInputs(TypedDict):
in which case the data is passed as a list instead of a batched tensor.
"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
class LlavaImageEmbeddingInputs(TypedDict):
type: Literal["image_embeds"]
......@@ -335,6 +343,23 @@ class PixtralHFMultiModalProcessor(
for p, (h, w) in zip(pixel_values, image_sizes)
]
hf_config = self.info.get_hf_config()
vision_config = hf_config.vision_config
assert isinstance(vision_config, PixtralVisionConfig)
encoder_info = PixtralHFEncoderInfo(vision_config)
tile_sizes = [
encoder_info.get_patch_grid_size(
image_width=pixel_value.shape[-1],
image_height=pixel_value.shape[-2],
) for pixel_value in processed_outputs["pixel_values"]
]
embed_is_patch = [
torch.tensor(([True] * ncols + [False]) * nrows)
for ncols, nrows in tile_sizes
]
processed_outputs["embed_is_patch"] = embed_is_patch
return processed_outputs
def _get_mm_fields_config(
......@@ -344,6 +369,7 @@ class PixtralHFMultiModalProcessor(
) -> Mapping[str, MultiModalFieldConfig]:
return dict(
pixel_values=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"),
image_embeds=MultiModalFieldConfig.batched("image"),
)
......@@ -378,7 +404,7 @@ class PixtralHFMultiModalProcessor(
tokens = ([image_token_id] * ncols + [image_break_id]) * nrows
tokens[-1] = image_end_id
return PromptUpdateDetails.select_token_id(tokens, image_token_id)
return tokens
return [
PromptReplacement(
......@@ -586,9 +612,17 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
f"Got type: {type(pixel_values)}")
if self.config.vision_config.model_type == "pixtral":
embed_is_patch = kwargs.pop("embed_is_patch")
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
embed_is_patch = flatten_bn(embed_is_patch)
return PixtralHFImagePixelInputs(
type="pixel_values_pixtral",
pixel_values=flatten_bn(pixel_values),
embed_is_patch=embed_is_patch,
)
return LlavaImagePixelInputs(
......@@ -680,7 +714,16 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
if image_input is None:
return None
return self._process_image_input(image_input)
image_features = self._process_image_input(image_input)
if image_input["type"] != "pixel_values_pixtral":
# The path is used for pixtral (V0 only) and llava (V0/V1)
return image_features
return scatter_patch_features(
image_features,
image_input["embed_is_patch"],
)
def get_input_embeddings(
self,
......@@ -692,7 +735,7 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
inputs_embeds = merge_multimodal_embeddings(
input_ids,
inputs_embeds,
multimodal_embeddings,
select_patch_features(multimodal_embeddings),
self.config.image_token_index,
)
return inputs_embeds
......
vllm/model_executor/models/minicpmo.py
View file @ af51d80f
......@@ -40,8 +40,7 @@ from vllm.multimodal.parse import (AudioItem, AudioProcessorItems,
DictEmbeddingItems, ModalityData,
ModalityDataItems, MultiModalDataItems,
MultiModalDataParser)
from vllm.multimodal.processing import (PromptReplacement, PromptUpdate,
PromptUpdateDetails)
from vllm.multimodal.processing import PromptReplacement, PromptUpdate
from vllm.multimodal.profiling import ProcessorInputs
from .minicpmv import (_MAX_FRAMES_PER_VIDEO, MiniCPMV2_6,
......@@ -51,6 +50,7 @@ from .minicpmv import (_MAX_FRAMES_PER_VIDEO, MiniCPMV2_6,
_minicpmv_field_config)
from .utils import (AutoWeightsLoader, cast_overflow_tensors, flatten_bn,
maybe_prefix)
from .vision import scatter_patch_features
CPU_DEVICE = torch.device("cpu")
......@@ -73,6 +73,14 @@ class MiniCPMOAudioFeatureInputs(TypedDict):
which equals to `audio_features.shape[-1]`
"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which audio embeddings correspond
to patch tokens.
Shape: `(batch_size * num_audios, num_embeds)`
"""
class MiniCPMOAudioEmbeddingInputs(TypedDict):
type: Literal["audio_embeds"]
......@@ -85,6 +93,14 @@ class MiniCPMOAudioEmbeddingInputs(TypedDict):
Length of each slice may vary, so pass it as a list.
"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which audio embeddings correspond
to patch tokens.
Shape: `(batch_size * num_audios, num_embeds)`
"""
MiniCPMOAudioInputs = Union[MiniCPMOAudioFeatureInputs,
MiniCPMOAudioEmbeddingInputs]
......@@ -99,6 +115,7 @@ def _minicpmo_field_config(hf_inputs: Mapping[str, torch.Tensor]):
audio_features=MultiModalFieldConfig.batched("audio"),
audio_feature_lens=MultiModalFieldConfig.batched("audio"),
audio_embeds=MultiModalFieldConfig.batched("audio"),
audio_embed_is_patch=MultiModalFieldConfig.batched("audio"),
audio_token_id=MultiModalFieldConfig.shared("audio", num_audios),
)
......@@ -180,7 +197,8 @@ class MiniCPMOProcessingInfo(MiniCPMVProcessingInfo):
pool_step = self.get_default_audio_pool_step()
fbank_feat_in_chunk = 100
cnn_feat_in_chunk = (fbank_feat_in_chunk - 1) // 2 + 1
return (cnn_feat_in_chunk - pool_step) // pool_step + 1
num_audio_tokens = (cnn_feat_in_chunk - pool_step) // pool_step + 1
return num_audio_tokens + 2 # <audio>(<unk>*N)</audio>
def get_max_audio_chunks_with_most_features(self) -> int:
return 30
......@@ -191,7 +209,8 @@ class MiniCPMOProcessingInfo(MiniCPMVProcessingInfo):
def get_audio_len_by_num_chunks(self, num_chunks: int) -> int:
sampling_rate = self.get_default_audio_sampling_rate()
num_tokens_per_chunk = self.get_max_audio_tokens_per_chunk()
# exclude <audio> </audio>
num_tokens_per_chunk = self.get_max_audio_tokens_per_chunk() - 2
return int(num_chunks * sampling_rate / num_tokens_per_chunk) + 1
def get_num_frames_with_most_features(
......@@ -276,6 +295,13 @@ class MiniCPMOMultiModalProcessor(
if isinstance(parsed_audios, MiniCPMOAudioEmbeddingItems):
audio_inputs = {}
audio_lens = [
self.info.get_audio_len_by_num_chunks(
sum(map(len,
parsed_audios.get(i)["audio_embeds"])))
for i in range(len(parsed_audios))
]
else:
audio_inputs = self._base_call_hf_processor(
prompts=[self.info.audio_pattern] * len(parsed_audios),
......@@ -297,7 +323,27 @@ class MiniCPMOMultiModalProcessor(
]
audio_inputs["audio_features"] = unpadded_audio_features
audio_lens = [
parsed_audios.get_audio_length(i)
for i in range(len(parsed_audios))
]
audio_repl_features = [
self.get_audio_prompt_texts(audio_len) for audio_len in audio_lens
]
tokenizer = self.info.get_tokenizer()
audio_repls_feature_tokens = [
tokenizer.encode(audio_repl, add_special_tokens=False)
for audio_repl in audio_repl_features
]
embed_is_patch = [
self.get_embed_is_patch(audio_repl_tokens)
for audio_repl_tokens in audio_repls_feature_tokens
]
audio_inputs["audio_embed_is_patch"] = embed_is_patch
unk_token_id = tokenizer.get_vocab()["<unk>"]
audio_inputs["audio_token_id"] = torch.tensor(unk_token_id)
......@@ -338,10 +384,7 @@ class MiniCPMOMultiModalProcessor(
else:
audio_len = audios.get_audio_length(item_idx)
return PromptUpdateDetails.select_text(
self.get_audio_prompt_texts(audio_len),
"<unk>",
)
return self.get_audio_prompt_texts(audio_len)
return [
*base_updates,
......@@ -670,6 +713,13 @@ class MiniCPMO(MiniCPMV2_6):
assert isinstance(audio_token_id, torch.Tensor)
self.mm_token_ids.add(audio_token_id.flatten().unique().item())
audio_embed_is_patch = kwargs.pop("audio_embed_is_patch")
if not isinstance(audio_embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of audio_embed_is_patch. "
f"Got type: {type(audio_embed_is_patch)}")
audio_embed_is_patch = flatten_bn(audio_embed_is_patch)
if audio_embeds is not None:
if not isinstance(audio_embeds, (torch.Tensor, list)):
raise ValueError("Incorrect type of audio_embeds. "
......@@ -680,6 +730,7 @@ class MiniCPMO(MiniCPMV2_6):
return MiniCPMOAudioEmbeddingInputs(
type="audio_embeds",
audio_embeds=audio_embeds_flat,
embed_is_patch=audio_embed_is_patch,
)
if not isinstance(audio_features, (torch.Tensor, list)):
......@@ -698,6 +749,7 @@ class MiniCPMO(MiniCPMV2_6):
type="audio_features",
audio_features=audio_features_flat,
audio_feature_lens=audio_feature_lens_flat,
embed_is_patch=audio_embed_is_patch,
)
def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
......@@ -729,6 +781,10 @@ class MiniCPMO(MiniCPMV2_6):
if modality == "audios":
audio_input = modalities["audios"]
audio_features = self._process_audio_input(audio_input)
multimodal_embeddings += tuple(audio_features)
multimodal_embeddings += tuple(
scatter_patch_features(
audio_features,
audio_input["embed_is_patch"],
))
return multimodal_embeddings
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