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[V1] Initial support of multimodal models for V1 re-arch (#10699)

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Signed-off-by: Roger Wang <ywang@roblox.com>
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Roger Wang 2024-12-08 04:50:51 -08:00 committed by GitHub
parent fd57d2b534
commit a11f326528
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11 changed files with 283 additions and 68 deletions
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16
vllm/engine/arg_utils.py
View File

@ -1050,9 +1050,12 @@ class EngineArgs:
# long context (> 32K) models. This is to avoid OOM errors in the
# initial memory profiling phase.
# Chunked prefill is currently disabled for multimodal models by
# default.
if use_long_context and not model_config.is_multimodal_model:
# For multimodal models, chunked prefill is disabled by default in
# V0, but enabled by design in V1
if model_config.is_multimodal_model:
self.enable_chunked_prefill = bool(envs.VLLM_USE_V1)
elif use_long_context:
is_gpu = device_config.device_type == "cuda"
use_sliding_window = (model_config.get_sliding_window()
is not None)
@ -1241,12 +1244,9 @@ class EngineArgs:
Override the EngineConfig's configs based on the usage context for V1.
"""
assert envs.VLLM_USE_V1, "V1 is not enabled"
# TODO (ywang96): Enable APC by default when VLM supports it.
if engine_config.model_config.is_multimodal_model:
logger.warning(
"Prefix caching is currently not supported for multimodal "
"models and has been disabled.")
engine_config.cache_config.enable_prefix_caching = False
# TODO (ywang96): Enable APC by default when VLM supports it.
assert not engine_config.cache_config.enable_prefix_caching
@dataclass

5
vllm/model_executor/models/interfaces.py
View File

@ -36,6 +36,11 @@ class SupportsMultiModal(Protocol):
"""
Returns multimodal embeddings generated from multimodal kwargs
to be merged with text embeddings.
The output embeddings must be one of the following formats:
- A list or tuple of 2D tensors, where each tensor corresponds to
each input image.
- A single 3D tensor, with the batch dimension grouping the 2D tensors.
"""
...

68
vllm/model_executor/models/internvl.py
View File

@ -26,7 +26,7 @@ from vllm.model_executor.models.intern_vit import (InternVisionModel,
InternVisionPatchModel)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
from vllm.multimodal.inputs import NestedTensors
from vllm.multimodal.inputs import NestedTensors, PlaceholderRange
from vllm.multimodal.utils import cached_get_tokenizer
from vllm.sequence import IntermediateTensors
from vllm.utils import is_list_of
@ -52,12 +52,18 @@ class InternVLImagePixelInputs(TypedDict):
Shape:
`(batch_size * num_images * (1 + num_patches), num_channels, height, width)`
"""
patches_per_image: List[int]
"""
List of number of total patches for each image in the batch.
"""
class InternVLImageEmbeddingInputs(TypedDict):
type: Literal["image_embeds"]
data: torch.Tensor
"""Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
data: NestedTensors
"""
A tensor of shape `(num_images, total_image_feature_size, hidden_size)`
or a list of tensors of shape `(total_image_feature_size, hidden_size)`
`hidden_size` must match the hidden size of language model backbone.
"""
@ -349,10 +355,32 @@ class InternVLInputPipeline:
new_prompt = self._expand_image_prompt(prompt, image_feature_sizes,
num_patches)
new_prompt_token_ids = tokenizer.encode(new_prompt)
img_context_token_id = tokenizer.encode(self.img_context_token,
add_special_tokens=False)
assert len(img_context_token_id) == 1, \
(f"Invalid image token '{self.img_context_token}': A valid image "
f"token encodes to a single token ID, got {img_context_token_id}.")
img_context_token_id = img_context_token_id[0]
return token_inputs(prompt=prompt,
prompt_token_ids=new_prompt_token_ids,
multi_modal_data=multi_modal_data)
# Get precise tracking of placeholder positions
token_idx = image_idx = 0
placeholder_ranges = []
while token_idx < len(new_prompt_token_ids):
if new_prompt_token_ids[token_idx] == img_context_token_id:
curr_image_featue_size = image_feature_sizes[image_idx]
placeholder_ranges.append(
PlaceholderRange(offset=token_idx,
length=curr_image_featue_size))
image_idx += 1
token_idx += curr_image_featue_size
else:
token_idx += 1
return token_inputs(
prompt=prompt,
prompt_token_ids=new_prompt_token_ids,
multi_modal_data=multi_modal_data,
multi_modal_placeholders={"image": placeholder_ranges})
def input_mapper(
self,
@ -614,26 +642,46 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
if not isinstance(pixel_values, (torch.Tensor, list)):
raise ValueError("Incorrect type of pixel values. "
f"Got type: {type(pixel_values)}")
patches_per_image = []
for request_pixel_values in pixel_values:
for image_pixel_values in request_pixel_values:
patches_per_image.append(image_pixel_values.shape[0])
# We need to flatten (B, N, P) to (B*N*P),
# so we call flatten_bn twice.
return InternVLImagePixelInputs(
type="pixel_values",
data=self._validate_pixel_values(
flatten_bn(flatten_bn(pixel_values), concat=True)),
)
patches_per_image=patches_per_image)
raise AssertionError("This line should be unreachable.")
def _process_image_input(
self,
image_input: InternVLImageInputs,
) -> torch.Tensor:
) -> Tuple[torch.Tensor]:
if image_input["type"] == "image_embeds":
return image_input["data"]
assert self.vision_model is not None
image_embeds = self.extract_feature(image_input["data"])
patches_per_image = image_input["patches_per_image"]
if len(patches_per_image) == 1:
image_embeds = image_embeds.unsqueeze(0)
return image_embeds
# NOTE: Image embeddings are split into separate tensors for each image
# by the size of each embedding.
feature_size = image_embeds.shape[1]
image_embeds = image_embeds.view(-1,
self.config.text_config.hidden_size)
image_feature_sizes = [
num_patches * feature_size for num_patches in patches_per_image
]
image_embeds = image_embeds.split(image_feature_sizes)
return image_embeds
def _set_visual_token_mask(self, input_ids: torch.Tensor) -> torch.Tensor:
@ -696,13 +744,11 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
"inputs_embeds": inputs_embeds,
}
# Only required if the model is mono-architecture
if self.visual_token_mask is not None:
# overwrite visual_token_mask and img_context_token_id back to None,
# so that this doesn't need to depend on encoder output
forward_kwargs.update(
{"visual_token_mask": self.visual_token_mask})
self.visual_token_mask = None
self.img_context_token_id = None
hidden_states = self.language_model.model(**forward_kwargs)
return hidden_states

72
vllm/model_executor/models/molmo.py
View File

@ -37,7 +37,7 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
from vllm.multimodal.inputs import NestedTensors
from vllm.multimodal.inputs import NestedTensors, PlaceholderRange
from vllm.multimodal.utils import cached_get_tokenizer
from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
SequenceData)
@ -46,12 +46,16 @@ from vllm.transformers_utils.processor import get_processor
from .interfaces import SupportsMultiModal, SupportsPP
from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
maybe_prefix, merge_multimodal_embeddings)
# TODO: hard-coded for now. Consider making it configurable.
VIT_LAYERS = [-2, -9]
NUM_PREFIX_TOKENS = 1
ADDITIONAL_VOCAB_SIZE = 128
DEFAULT_IMAGE_PATCH_TOKEN_ID = 152066
DEFAULT_IM_START_TOKEN_ID = 152067
DEFAULT_IM_END_TOKEN_ID = 152064
DEFAULT_IM_COL_TOKEN_ID = 152065
class MolmoImageInputs(TypedDict):
@ -75,6 +79,11 @@ class MolmoImageInputs(TypedDict):
`(batch_size, num_crops, num_patch)`
"""
image_start_end: Tuple[int, int]
"""Starting and ending index of placeholder
tokens
"""
@dataclass
class VisionBackboneConfig:
@ -918,6 +927,8 @@ def image_input_mapper_for_molmo(
ctx: InputContext,
data: object,
):
if isinstance(data, list):
data = data[0]
return MultiModalKwargs(data)
@ -967,7 +978,22 @@ def dummy_data_for_molmo(ctx: InputContext, seq_len: int,
if "image_masks" in out:
dummy_imgdata["image_masks"] = out["image_masks"]
dummy_imgdata["seq_len"] = torch.tensor(seq_len, dtype=torch.long)
return DummyData(dummy_seqdata, {"image": dummy_imgdata})
size = 0
offset = -1
for i in range(len(token_ids)):
if token_ids[i] in (DEFAULT_IMAGE_PATCH_TOKEN_ID,
DEFAULT_IM_START_TOKEN_ID, DEFAULT_IM_END_TOKEN_ID,
DEFAULT_IM_COL_TOKEN_ID):
if offset < 0:
offset = i
size += 1
dummy_imgdata["image_start_end"] = (offset, offset + size)
return DummyData(seq_data=dummy_seqdata,
multi_modal_data={"image": dummy_imgdata},
multi_modal_placeholders={
"image":
[PlaceholderRange(offset=offset, length=size)]
})
def pad_images(
@ -1055,19 +1081,34 @@ def input_processor_for_molmo(ctx: InputContext, inputs: DecoderOnlyInputs):
if image_masks is not None:
image_data["image_masks"] = image_masks
image_data["seq_len"] = torch.tensor(len(out["input_ids"]),
new_prompt_token_ids = out["input_ids"].tolist()
image_data["seq_len"] = torch.tensor(len(new_prompt_token_ids),
dtype=torch.long)
multi_modal_data = dict(image=image_data)
size = 0
offset = -1
for i in range(len(new_prompt_token_ids)):
if new_prompt_token_ids[i] in (DEFAULT_IMAGE_PATCH_TOKEN_ID,
DEFAULT_IM_START_TOKEN_ID,
DEFAULT_IM_END_TOKEN_ID,
DEFAULT_IM_COL_TOKEN_ID):
if offset < 0:
offset = i
size += 1
image_data["image_start_end"] = (offset, offset + size)
prompt = inputs.get("prompt")
if prompt is None:
prompt = tokenizer.decode(out["input_ids"])
prompt = tokenizer.decode(new_prompt_token_ids)
return token_inputs(
prompt_token_ids=out["input_ids"],
prompt_token_ids=new_prompt_token_ids,
prompt=prompt,
multi_modal_data=multi_modal_data,
multi_modal_placeholders={
"image": [PlaceholderRange(offset=offset, length=size)]
},
)
@ -1113,6 +1154,7 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
) -> Optional[MolmoImageInputs]:
images = kwargs.pop("images", None)
image_masks = kwargs.pop("image_masks", None)
image_start_end = kwargs.pop("image_start_end", None)
if images is None:
return None
@ -1130,6 +1172,7 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
image_input_idx=image_input_idx,
seq_len=seq_len,
image_masks=image_masks,
image_start_end=image_start_end,
)
def _process_image_input(
@ -1178,9 +1221,16 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
# Note: In this original implementation from AI2, the final
# vision_embeddings will be always be the same length
# of input embedddings, which is not very efficient.
# TODO(ywang96): see if this can be optimized.
# of input embeddings.
vision_embeddings = torch.einsum('nd,nm->md', image_features, mat)
# Split by the sizes of the input sequences. For each full embedding,
# extract the actual vision embeddings to be merged.
vision_embeddings = list(vision_embeddings.split(seq_len.tolist()))
for i in range(len(vision_embeddings)):
start, end = image_input['image_start_end'][i]
vision_embeddings[i] = vision_embeddings[i][start:end]
return vision_embeddings
def get_input_embeddings(
@ -1190,7 +1240,11 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
) -> torch.Tensor:
inputs_embeds = self.model.get_input_embeddings(input_ids)
if multimodal_embeddings is not None:
inputs_embeds = inputs_embeds + multimodal_embeddings
inputs_embeds = merge_multimodal_embeddings(
input_ids, inputs_embeds, multimodal_embeddings, [
DEFAULT_IMAGE_PATCH_TOKEN_ID, DEFAULT_IM_START_TOKEN_ID,
DEFAULT_IM_END_TOKEN_ID, DEFAULT_IM_COL_TOKEN_ID
])
return inputs_embeds
def forward(

119
vllm/model_executor/models/pixtral.py
View File

@ -48,6 +48,9 @@ try:
except ImportError:
USE_XFORMERS_OPS = False
PIXTRAL_IMAGE_BREAK_ID = 12
PIXTRAL_IMAGE_END_ID = 13
def get_max_pixtral_image_tokens(ctx: InputContext):
tokenizer = cached_get_tokenizer(
@ -68,7 +71,6 @@ def dummy_data_for_pixtral(ctx: InputContext, seq_len: int,
tokenizer_mode=ctx.model_config.tokenizer_mode)
mm_encoder = tokenizer.mistral.instruct_tokenizer.mm_encoder
patch_size = mm_encoder.mm_config.image_patch_size
image_token_id = mm_encoder.special_ids.img
mm_config = ctx.model_config.multimodal_config
@ -78,8 +80,8 @@ def dummy_data_for_pixtral(ctx: InputContext, seq_len: int,
size = 256
image = Image.new("RGB", (size, size), color=0)
image_feature_size = (size**2) // (patch_size**2)
encoding = tokenizer.instruct.mm_encoder(ImageChunk(image=image))
image_feature_size = len(encoding.tokens)
num_image_tokens = image_feature_size * num_images
seq_data = SequenceData.from_prompt_token_counts(
(image_token_id, num_image_tokens),
@ -101,14 +103,13 @@ def input_mapper_for_pixtral(ctx: InputContext,
Args:
ctx: Context of the loaded model.
data: data potentially containing image/image embeddings to be mapped
to pixel_values in .forward() for a visual QWenLMHeadModel model.
data: data potentially containing PIL images to be processed
and mapped to `images`.
Returns:
MultiModalKwargs containing the stacked normalized images tensor or
image embeddings.
"""
# Early exit if we have provided an image to a language only Qwen model
model_config = ctx.model_config
tokenizer = cached_get_tokenizer(
model_config.tokenizer, tokenizer_mode=model_config.tokenizer_mode)
@ -116,35 +117,67 @@ def input_mapper_for_pixtral(ctx: InputContext,
data_list = data if isinstance(data, list) else [data]
images = []
image_tokens_list = []
for image_data in data_list:
image = ImageChunk(image=image_data)
encoding = tokenizer.instruct.mm_encoder(image)
image = torch.from_numpy(encoding.image).to(device="cuda",
dtype=torch.float16)
images.append(image)
image_tokens_list.append(encoding.tokens)
return MultiModalKwargs({"images": images})
image_tokens = torch.tensor([
token_id for image_tokens in image_tokens_list
for token_id in image_tokens
])
return MultiModalKwargs({"images": images, "image_tokens": image_tokens})
def input_processor_for_pixtral(ctx: InputContext, inputs: DecoderOnlyInputs):
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is not None and "image" in multi_modal_data:
tokenizer = cached_get_tokenizer(
ctx.model_config.tokenizer,
tokenizer_mode=ctx.model_config.tokenizer_mode)
if multi_modal_data is None or "image" not in multi_modal_data:
return inputs
mm_encoder = tokenizer.mistral.instruct_tokenizer.mm_encoder
image_token_id = mm_encoder.special_ids.img
prompt_token_ids = inputs.get("prompt_token_ids")
prompt = inputs.get("prompt")
tokenizer = cached_get_tokenizer(
ctx.model_config.tokenizer,
tokenizer_mode=ctx.model_config.tokenizer_mode)
if image_token_id not in inputs['prompt_token_ids']:
raise ValueError(
f"You've passed {inputs=} without {image_token_id=}"
" Make sure to process your input via mistral_common's"
" tokenizer or pass a chat completion request. For more"
" For more info, see: "
"https://github.com/vllm-project/vllm/issues/8411.")
mm_encoder = tokenizer.mistral.instruct_tokenizer.mm_encoder
image_token_id = mm_encoder.special_ids.img
image_break_id = mm_encoder.special_ids.img_break
image_end_id = mm_encoder.special_ids.img_end
return inputs
if image_token_id not in inputs['prompt_token_ids']:
raise ValueError(
f"You've passed {inputs=} without {image_token_id=}"
" Make sure to process your input via mistral_common's"
" tokenizer or pass a chat completion request. For more"
" For more info, see: "
"https://github.com/vllm-project/vllm/issues/8411.")
# Get precise tracking of placeholder positions
placeholder_ranges = []
curr_offset = -1
curr_length = 0
for i in range(len(prompt_token_ids)):
if prompt_token_ids[i] in (image_token_id, image_break_id):
if curr_offset < 0:
curr_offset = i
curr_length += 1
elif prompt_token_ids[i] == image_end_id:
curr_length += 1
placeholder_ranges.append(
PlaceholderRange(offset=curr_offset, length=curr_length))
curr_offset = -1
curr_length = 0
else:
pass
return token_inputs(prompt=prompt,
prompt_token_ids=prompt_token_ids,
multi_modal_data=multi_modal_data,
multi_modal_placeholders={"image": placeholder_ranges})
@MULTIMODAL_REGISTRY.register_image_input_mapper(input_mapper_for_pixtral)
@ -192,11 +225,29 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
return get_sampler()
def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
image_input = self._parse_and_validate_image_input(**kwargs)
image_input, image_tokens = self._parse_and_validate_image_input(
**kwargs)
if image_input is None:
return None
vision_embeddings = self._process_image_input(image_input)
return vision_embeddings
# NOTE: We patch the outputs of the vision encoder with embeddings
# from `[IMG_BREAK]` and `[IMG_END]` tokens.
image_embeds = self.language_model.get_input_embeddings(image_tokens)
image_token_mask = image_tokens == self.vision_args.image_token_id
image_embeds[image_token_mask] = vision_embeddings
# NOTE: Image embeddings are split into separate tensors for each image
# by the indices of `[IMG_END]` token.
split_indices = torch.where(
image_tokens == PIXTRAL_IMAGE_END_ID)[0] + 1
if len(split_indices) <= 1:
# Do not split, return as tensor of shape [1, fs, hs]
return image_embeds.unsqueeze(0)
image_embeds = image_embeds.tensor_split(split_indices.cpu())
return image_embeds
def get_input_embeddings(
self,
@ -206,8 +257,10 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
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,
self.vision_args.image_token_id)
input_ids, inputs_embeds, multimodal_embeddings, [
self.vision_args.image_token_id, PIXTRAL_IMAGE_END_ID,
PIXTRAL_IMAGE_BREAK_ID
])
return inputs_embeds
def forward(
@ -245,10 +298,11 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
def _parse_and_validate_image_input(
self,
images: Optional[Union[List[List[torch.Tensor]], List[torch.Tensor],
torch.Tensor]] = None
torch.Tensor]] = None,
image_tokens: Optional[torch.Tensor] = None,
) -> Optional[List[torch.Tensor]]:
if images is None:
return None
return None, None
if isinstance(images, torch.Tensor):
# if passed as batch take all images
@ -267,7 +321,16 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
images = flatten_images
return images
if isinstance(image_tokens, torch.Tensor):
# image_tokens are batched
image_tokens = image_tokens.flatten()
elif isinstance(image_tokens, list):
# image_tokens are of different lengths thus passed as a list
image_tokens = torch.cat(image_tokens)
assert image_tokens.dim() == 1
return images, image_tokens
def _process_image_input(self,
image_input: List[torch.Tensor]) -> torch.Tensor:

28
vllm/model_executor/models/utils.py
View File

@ -409,16 +409,42 @@ def merge_multimodal_embeddings(
input_ids: torch.Tensor,
inputs_embeds: torch.Tensor,
multimodal_embeddings: NestedTensors,
placeholder_token_id: int,
placeholder_token_id: Union[int, List[int]],
) -> torch.Tensor:
"""
Merge ``multimodal_embeddings`` into ``inputs_embeds`` by overwriting the
positions in ``inputs_embeds`` corresponding to placeholder tokens in
``input_ids``.
``placeholder_token_id`` can be a list of token ids (e.g, token ids
of img_start, img_break, and img_end tokens) when needed: This means
the order of these tokens in the ``input_ids`` MUST MATCH the order of
their embeddings in ``multimodal_embeddings`` since we need to
slice-merge instead of individually scattering.
For example, if input_ids is "TTTTTSIIIBIIIBIIIETTT", where
- T is text token
- S is image start token
- I is image embedding token
- B is image break token
- E is image end token.
Then the image embeddings (that correspond to I's) from vision encoder
must be padded with embeddings of S, B, and E in the same order of
input_ids for a correct embedding merge.
Note:
This updates ``inputs_embeds`` in place.
"""
if isinstance(placeholder_token_id, list):
placeholder_token_id = torch.tensor(placeholder_token_id,
device=input_ids.device)
return _merge_multimodal_embeddings(
inputs_embeds,
torch.isin(input_ids, placeholder_token_id),
multimodal_embeddings,
)
return _merge_multimodal_embeddings(
inputs_embeds,
(input_ids == placeholder_token_id),

3
vllm/multimodal/inputs.py
View File

@ -96,7 +96,8 @@ class PlaceholderRange(TypedDict):
"""The length of the placeholder."""
NestedTensors = Union[List["NestedTensors"], List[torch.Tensor], torch.Tensor]
NestedTensors = Union[List["NestedTensors"], List[torch.Tensor], torch.Tensor,
Tuple[torch.Tensor, ...]]
"""
Uses a list instead of a tensor if the dimensions of each element do not match.
"""

10
vllm/multimodal/utils.py
View File

@ -535,11 +535,13 @@ def repeat_and_pad_placeholder_tokens(
return new_prompt, new_token_ids, placeholder_ranges
def consecutive_placeholder_ranges(num_items: int,
item_size: int) -> List[PlaceholderRange]:
def consecutive_placeholder_ranges(
num_items: int,
item_size: int,
initial_offset: int = 0) -> List[PlaceholderRange]:
"""Returns a list of consecutive PlaceholderRanges of a fixed size"""
return [
PlaceholderRange(offset=i * item_size, length=item_size)
for i in range(num_items)
PlaceholderRange(offset=initial_offset + i * item_size,
length=item_size) for i in range(num_items)
]

4
vllm/v1/core/scheduler.py
View File

@ -73,12 +73,12 @@ class Scheduler:
# has the Transformer architecture (e.g., ViT).
# FIXME(woosuk): Below are placeholder values. We need to calculate the
# actual values from the configurations.
self.max_num_encoder_input_tokens = 2048
self.max_num_encoder_input_tokens = 16384
# NOTE(woosuk): For the models without encoder (e.g., text-only models),
# the encoder cache will not be initialized and used, regardless of
# the cache size. This is because the memory space for the encoder cache
# is preallocated in the profiling run.
self.encoder_cache_manager = EncoderCacheManager(cache_size=2048)
self.encoder_cache_manager = EncoderCacheManager(cache_size=16384)
def schedule(self) -> "SchedulerOutput":
# NOTE(woosuk) on the scheduling algorithm:

24
vllm/v1/engine/llm_engine.py
View File

@ -1,5 +1,7 @@
from typing import Dict, List, Mapping, Optional, Type, Union
from typing_extensions import TypeVar
from vllm.config import VllmConfig
from vllm.engine.arg_utils import EngineArgs
from vllm.engine.metrics_types import StatLoggerBase
@ -12,7 +14,8 @@ from vllm.outputs import RequestOutput
from vllm.pooling_params import PoolingParams
from vllm.prompt_adapter.request import PromptAdapterRequest
from vllm.sampling_params import SamplingParams
from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs
from vllm.transformers_utils.tokenizer_group import (
BaseTokenizerGroup, init_tokenizer_from_configs)
from vllm.usage.usage_lib import UsageContext
from vllm.v1.engine.core_client import EngineCoreClient
from vllm.v1.engine.detokenizer import Detokenizer
@ -21,6 +24,8 @@ from vllm.v1.executor.gpu_executor import GPUExecutor
logger = init_logger(__name__)
_G = TypeVar("_G", bound=BaseTokenizerGroup, default=BaseTokenizerGroup)
class LLMEngine:
"""Legacy LLMEngine for backwards compatibility."""
@ -169,5 +174,18 @@ class LLMEngine:
def stop_profile(self):
self.engine_core.profile(False)
def get_tokenizer_group(self, group_type):
pass
def get_tokenizer_group(
self,
group_type: Type[_G] = BaseTokenizerGroup,
) -> _G:
tokenizer_group = self.tokenizer
if tokenizer_group is None:
raise ValueError("Unable to get tokenizer because "
"skip_tokenizer_init is True")
if not isinstance(tokenizer_group, group_type):
raise TypeError("Invalid type of tokenizer group. "
f"Expected type: {group_type}, but "
f"found type: {type(tokenizer_group)}")
return tokenizer_group

2
vllm/v1/engine/mm_input_mapper.py
View File

@ -33,7 +33,7 @@ class MMInputMapper:
num_images = len(image_inputs)
for i in range(num_images):
mm_input = self.multi_modal_input_mapper(
{"image": [image_inputs[i]]},
{"image": image_inputs[i]},
mm_processor_kwargs=mm_processor_kwargs,
)
mm_inputs.append(mm_input)