v1.0
This commit is contained in:
root
741
model_executor/models/gemma3_mm.py
Normal file
741
model_executor/models/gemma3_mm.py
Normal file
@@ -0,0 +1,741 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
import math
|
||||
from collections.abc import Iterable, Mapping, Sequence
|
||||
from typing import Annotated, Any, Literal, TypeAlias
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from transformers import BatchFeature, Gemma3Config, Gemma3Processor
|
||||
from transformers.models.gemma3.processing_gemma3 import Gemma3ProcessorKwargs
|
||||
|
||||
from vllm.config import VllmConfig
|
||||
from vllm.config.multimodal import BaseDummyOptions
|
||||
from vllm.logger import init_logger
|
||||
from vllm.model_executor.layers.layernorm import GemmaRMSNorm
|
||||
from vllm.model_executor.models.module_mapping import MultiModelKeys
|
||||
from vllm.multimodal import MULTIMODAL_REGISTRY
|
||||
from vllm.multimodal.inputs import (
|
||||
MultiModalDataDict,
|
||||
MultiModalFieldConfig,
|
||||
MultiModalKwargsItems,
|
||||
)
|
||||
from vllm.multimodal.parse import (
|
||||
ImageEmbeddingItems,
|
||||
ImageProcessorItems,
|
||||
ImageSize,
|
||||
MultiModalDataItems,
|
||||
)
|
||||
from vllm.multimodal.processing import (
|
||||
BaseMultiModalProcessor,
|
||||
BaseProcessingInfo,
|
||||
MultiModalPromptUpdates,
|
||||
MultiModalPromptUpdatesApplyResult,
|
||||
PlaceholderFeaturesInfo,
|
||||
PromptReplacement,
|
||||
PromptUpdate,
|
||||
PromptUpdateDetails,
|
||||
replace_token_matches,
|
||||
)
|
||||
from vllm.multimodal.profiling import BaseDummyInputsBuilder
|
||||
from vllm.sequence import IntermediateTensors
|
||||
from vllm.utils.tensor_schema import TensorSchema, TensorShape
|
||||
|
||||
from .interfaces import (
|
||||
MultiModalEmbeddings,
|
||||
SupportsLoRA,
|
||||
SupportsMultiModal,
|
||||
SupportsPP,
|
||||
)
|
||||
from .siglip import SiglipVisionModel
|
||||
from .utils import (
|
||||
AutoWeightsLoader,
|
||||
WeightsMapper,
|
||||
init_vllm_registered_model,
|
||||
maybe_prefix,
|
||||
)
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
class Gemma3ImagePixelInputs(TensorSchema):
|
||||
"""
|
||||
Dimensions:
|
||||
- p: Number of patches total (over each image over each prompt in the
|
||||
batch)
|
||||
- c: Number of channels (3)
|
||||
- h: Height of each patch
|
||||
- w: Width of each patch
|
||||
- bn: Batch size * number of images
|
||||
"""
|
||||
|
||||
type: Literal["pixel_values"] = "pixel_values"
|
||||
|
||||
pixel_values: Annotated[torch.Tensor, TensorShape("p", 3, "h", "w")]
|
||||
|
||||
num_patches: Annotated[torch.Tensor, TensorShape("bn")]
|
||||
|
||||
|
||||
class Gemma3ImageEmbeddingInputs(TensorSchema):
|
||||
type: Literal["image_embeds"] = "image_embeds"
|
||||
image_embeds: Annotated[
|
||||
torch.Tensor,
|
||||
TensorShape("ni", "nf", "hs"),
|
||||
]
|
||||
|
||||
|
||||
Gemma3ImageInputs: TypeAlias = Gemma3ImagePixelInputs | Gemma3ImageEmbeddingInputs
|
||||
|
||||
|
||||
class Gemma3ProcessingInfo(BaseProcessingInfo):
|
||||
def get_hf_config(self):
|
||||
return self.ctx.get_hf_config(Gemma3Config)
|
||||
|
||||
def get_hf_processor(self, **kwargs: object):
|
||||
return self.ctx.get_hf_processor(Gemma3Processor, **kwargs)
|
||||
|
||||
def get_supported_mm_limits(self) -> Mapping[str, int | None]:
|
||||
return {"image": None}
|
||||
|
||||
def _resolve_image_kwargs(
|
||||
self,
|
||||
processor: Gemma3Processor,
|
||||
keys: set[str],
|
||||
) -> dict[str, Any]:
|
||||
image_processor = processor.image_processor
|
||||
kwargs = processor._merge_kwargs(
|
||||
Gemma3ProcessorKwargs,
|
||||
tokenizer_init_kwargs=processor.tokenizer.init_kwargs,
|
||||
)
|
||||
|
||||
images_kwargs = kwargs["images_kwargs"]
|
||||
|
||||
def _resolve_kw(key: str):
|
||||
val = getattr(image_processor, key)
|
||||
if val is None:
|
||||
val = images_kwargs[key]
|
||||
|
||||
return val
|
||||
|
||||
return {k: _resolve_kw(k) for k in keys}
|
||||
|
||||
def get_num_crops(
|
||||
self,
|
||||
*,
|
||||
image_width: int,
|
||||
image_height: int,
|
||||
processor: Gemma3Processor | None,
|
||||
) -> int:
|
||||
if processor is None:
|
||||
processor = self.get_hf_processor()
|
||||
|
||||
images_kwargs = self._resolve_image_kwargs(
|
||||
processor,
|
||||
{
|
||||
"do_pan_and_scan",
|
||||
"pan_and_scan_min_crop_size",
|
||||
"pan_and_scan_max_num_crops",
|
||||
"pan_and_scan_min_ratio_to_activate",
|
||||
},
|
||||
)
|
||||
|
||||
do_pan_and_scan = images_kwargs["do_pan_and_scan"]
|
||||
pan_and_scan_min_crop_size = images_kwargs["pan_and_scan_min_crop_size"]
|
||||
pan_and_scan_max_num_crops = images_kwargs["pan_and_scan_max_num_crops"]
|
||||
pan_and_scan_min_ratio_to_activate = images_kwargs[
|
||||
"pan_and_scan_min_ratio_to_activate"
|
||||
]
|
||||
|
||||
if not do_pan_and_scan:
|
||||
return 0
|
||||
|
||||
logger.warning_once(
|
||||
"`do_pan_and_scan=True` has suboptimal results on V1 "
|
||||
"because of the simplified attention pattern being used."
|
||||
)
|
||||
|
||||
# Based on Gemma3ImageProcessor.pan_and_scan
|
||||
if image_width >= image_height:
|
||||
if image_width / image_height < pan_and_scan_min_ratio_to_activate:
|
||||
return 0
|
||||
|
||||
num_crops_w = min(
|
||||
int(math.floor(image_width / pan_and_scan_min_crop_size)),
|
||||
int(math.floor(image_width / image_height + 0.5)),
|
||||
)
|
||||
|
||||
num_crops_w = max(2, num_crops_w)
|
||||
num_crops_w = min(pan_and_scan_max_num_crops, num_crops_w)
|
||||
num_crops_h = 1
|
||||
else:
|
||||
if image_height / image_width < pan_and_scan_min_ratio_to_activate:
|
||||
return 0
|
||||
|
||||
num_crops_h = min(
|
||||
int(math.floor(image_height / pan_and_scan_min_crop_size)),
|
||||
int(math.floor(image_height / image_width + 0.5)),
|
||||
)
|
||||
|
||||
num_crops_h = max(2, num_crops_h)
|
||||
num_crops_h = min(pan_and_scan_max_num_crops, num_crops_h)
|
||||
num_crops_w = 1
|
||||
|
||||
crop_size_w = int(math.ceil(image_width / num_crops_w))
|
||||
crop_size_h = int(math.ceil(image_height / num_crops_h))
|
||||
|
||||
if min(crop_size_w, crop_size_h) < pan_and_scan_min_crop_size:
|
||||
return 0
|
||||
|
||||
return num_crops_w * num_crops_h
|
||||
|
||||
def get_image_repl(
|
||||
self,
|
||||
*,
|
||||
image_width: int | None,
|
||||
image_height: int | None,
|
||||
num_crops: int | None = None,
|
||||
processor: Gemma3Processor | None,
|
||||
) -> PromptUpdateDetails[str]:
|
||||
if processor is None:
|
||||
processor = self.get_hf_processor()
|
||||
|
||||
boi_token = processor.boi_token
|
||||
|
||||
if num_crops is None:
|
||||
assert image_width is not None and image_height is not None
|
||||
num_crops = self.get_num_crops(
|
||||
image_width=image_width,
|
||||
image_height=image_height,
|
||||
processor=processor,
|
||||
)
|
||||
|
||||
if num_crops == 0:
|
||||
image_text = boi_token
|
||||
else:
|
||||
crops_image_tokens = " ".join(boi_token for _ in range(num_crops))
|
||||
image_text = (
|
||||
f"Here is the original image {boi_token} and here are some "
|
||||
f"crops to help you see better {crops_image_tokens}"
|
||||
)
|
||||
|
||||
repl_full = image_text.replace(boi_token, processor.full_image_sequence)
|
||||
|
||||
tokenizer = processor.tokenizer
|
||||
vocab = tokenizer.get_vocab()
|
||||
image_token_id = vocab[tokenizer.image_token]
|
||||
|
||||
return PromptUpdateDetails.select_token_id(repl_full, image_token_id)
|
||||
|
||||
def get_num_image_tokens(
|
||||
self,
|
||||
*,
|
||||
image_width: int,
|
||||
image_height: int,
|
||||
processor: Gemma3Processor | None,
|
||||
) -> int:
|
||||
if processor is None:
|
||||
processor = self.get_hf_processor()
|
||||
|
||||
num_crops = self.get_num_crops(
|
||||
image_width=image_width,
|
||||
image_height=image_height,
|
||||
processor=processor,
|
||||
)
|
||||
image_seq_len = processor.image_seq_length
|
||||
|
||||
return (num_crops + 1) * image_seq_len
|
||||
|
||||
def get_image_size_with_most_features(self) -> ImageSize:
|
||||
processor = self.get_hf_processor()
|
||||
|
||||
images_kwargs = self._resolve_image_kwargs(
|
||||
processor, {"pan_and_scan_max_num_crops"}
|
||||
)
|
||||
max_num_crops = images_kwargs["pan_and_scan_max_num_crops"]
|
||||
|
||||
# Result in the max possible feature size (h:w = max_num_crops:1)
|
||||
return ImageSize(height=50 * max_num_crops, width=50)
|
||||
|
||||
|
||||
class Gemma3DummyInputsBuilder(BaseDummyInputsBuilder[Gemma3ProcessingInfo]):
|
||||
def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
|
||||
num_images = mm_counts.get("image", 0)
|
||||
|
||||
processor = self.info.get_hf_processor()
|
||||
image_token = processor.boi_token
|
||||
|
||||
return image_token * num_images
|
||||
|
||||
def get_dummy_mm_data(
|
||||
self,
|
||||
seq_len: int,
|
||||
mm_counts: Mapping[str, int],
|
||||
mm_options: Mapping[str, BaseDummyOptions] | None = None,
|
||||
) -> MultiModalDataDict:
|
||||
num_images = mm_counts.get("image", 0)
|
||||
|
||||
target_width, target_height = self.info.get_image_size_with_most_features()
|
||||
|
||||
image_overrides = mm_options.get("image") if mm_options else None
|
||||
|
||||
return {
|
||||
"image": self._get_dummy_images(
|
||||
width=target_width,
|
||||
height=target_height,
|
||||
num_images=num_images,
|
||||
overrides=image_overrides,
|
||||
)
|
||||
}
|
||||
|
||||
|
||||
class Gemma3MultiModalProcessor(BaseMultiModalProcessor[Gemma3ProcessingInfo]):
|
||||
def _call_hf_processor(
|
||||
self,
|
||||
prompt: str,
|
||||
mm_data: Mapping[str, object],
|
||||
mm_kwargs: Mapping[str, object],
|
||||
tok_kwargs: Mapping[str, object],
|
||||
) -> BatchFeature:
|
||||
processed_outputs = super()._call_hf_processor(
|
||||
prompt,
|
||||
mm_data,
|
||||
mm_kwargs,
|
||||
tok_kwargs,
|
||||
)
|
||||
|
||||
# HF processor pops the `num_crops` kwarg, which is needed by vLLM
|
||||
if (images := mm_data.get("images")) is not None:
|
||||
parsed_images = (
|
||||
self._get_data_parser()
|
||||
.parse_mm_data({"image": images})
|
||||
.get_items("image", ImageProcessorItems)
|
||||
)
|
||||
image_sizes = [
|
||||
parsed_images.get_image_size(i) for i in range(len(parsed_images))
|
||||
]
|
||||
hf_processor = self.info.get_hf_processor(**mm_kwargs)
|
||||
|
||||
num_crops = [
|
||||
self.info.get_num_crops(
|
||||
image_width=size.width,
|
||||
image_height=size.height,
|
||||
processor=hf_processor,
|
||||
)
|
||||
for size in image_sizes
|
||||
]
|
||||
processed_outputs["num_patches"] = torch.tensor(num_crops) + 1
|
||||
|
||||
return processed_outputs
|
||||
|
||||
def _get_mm_fields_config(
|
||||
self,
|
||||
hf_inputs: BatchFeature,
|
||||
hf_processor_mm_kwargs: Mapping[str, object],
|
||||
) -> Mapping[str, MultiModalFieldConfig]:
|
||||
num_patches = hf_inputs.get("num_patches", torch.empty(0))
|
||||
|
||||
return dict(
|
||||
pixel_values=MultiModalFieldConfig.flat_from_sizes("image", num_patches),
|
||||
num_patches=MultiModalFieldConfig.batched("image"),
|
||||
image_embeds=MultiModalFieldConfig.batched("image"),
|
||||
)
|
||||
|
||||
def _get_prompt_updates(
|
||||
self,
|
||||
mm_items: MultiModalDataItems,
|
||||
hf_processor_mm_kwargs: Mapping[str, Any],
|
||||
out_mm_kwargs: MultiModalKwargsItems,
|
||||
) -> Sequence[PromptUpdate]:
|
||||
hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
|
||||
image_token = hf_processor.boi_token
|
||||
|
||||
def get_replacement_gemma3(item_idx: int):
|
||||
images = mm_items.get_items(
|
||||
"image", (ImageEmbeddingItems, ImageProcessorItems)
|
||||
)
|
||||
|
||||
if isinstance(images, ImageEmbeddingItems):
|
||||
# For image embedding inputs, only support no crops cases
|
||||
# since it's not supported in hf processor anyway
|
||||
return self.info.get_image_repl(
|
||||
image_width=None,
|
||||
image_height=None,
|
||||
num_crops=0,
|
||||
processor=hf_processor,
|
||||
)
|
||||
|
||||
image_size = images.get_image_size(item_idx)
|
||||
return self.info.get_image_repl(
|
||||
image_width=image_size.width,
|
||||
image_height=image_size.height,
|
||||
processor=hf_processor,
|
||||
)
|
||||
|
||||
return [
|
||||
PromptReplacement(
|
||||
modality="image",
|
||||
target=image_token,
|
||||
replacement=get_replacement_gemma3,
|
||||
)
|
||||
]
|
||||
|
||||
def _apply_token_matches(
|
||||
self,
|
||||
prompt: list[int],
|
||||
mm_prompt_updates: MultiModalPromptUpdates,
|
||||
) -> tuple[list[int], MultiModalPromptUpdatesApplyResult]:
|
||||
token_ids, res = super()._apply_token_matches(prompt, mm_prompt_updates)
|
||||
|
||||
# "\n\n\n" and "\n\n\n\n" are single tokens
|
||||
# Since our replacement can insert "\n\n" next to "\n"
|
||||
# tokens, we have to combine them to be consistent with
|
||||
# the output of the tokenizer
|
||||
tokenizer = self.info.get_tokenizer()
|
||||
vocab = tokenizer.get_vocab()
|
||||
newline_1 = vocab["\n"]
|
||||
newline_2 = vocab["\n\n"]
|
||||
newline_3 = vocab["\n\n\n"]
|
||||
newline_4 = vocab["\n\n\n\n"]
|
||||
|
||||
token_ids = replace_token_matches(
|
||||
token_ids,
|
||||
[newline_1, newline_2],
|
||||
[newline_3],
|
||||
)
|
||||
token_ids = replace_token_matches(
|
||||
token_ids,
|
||||
[newline_2, newline_1],
|
||||
[newline_3],
|
||||
)
|
||||
token_ids = replace_token_matches(
|
||||
token_ids,
|
||||
[newline_2, newline_2],
|
||||
[newline_4],
|
||||
)
|
||||
|
||||
return token_ids, res
|
||||
|
||||
def _find_mm_placeholders(
|
||||
self,
|
||||
new_token_ids: list[int],
|
||||
mm_prompt_updates: MultiModalPromptUpdates,
|
||||
) -> Mapping[str, list[PlaceholderFeaturesInfo]]:
|
||||
# We need to detect "\n\n" inside "\n\n\n" and "\n\n\n\n"
|
||||
tokenizer = self.info.get_tokenizer()
|
||||
vocab = tokenizer.get_vocab()
|
||||
newline_1 = vocab["\n"]
|
||||
newline_2 = vocab["\n\n"]
|
||||
newline_3 = vocab["\n\n\n"]
|
||||
newline_4 = vocab["\n\n\n\n"]
|
||||
|
||||
def get_repl_toks(tok: int) -> list[int]:
|
||||
if tok == newline_3:
|
||||
return [newline_1, newline_2]
|
||||
if tok == newline_4:
|
||||
return [newline_2, newline_2]
|
||||
|
||||
return [tok]
|
||||
|
||||
repl_token_ids = list[int]()
|
||||
repl_orig_idxs = list[int]()
|
||||
for orig_idx, orig_tok in enumerate(new_token_ids):
|
||||
repl_toks = get_repl_toks(orig_tok)
|
||||
repl_token_ids.extend(repl_toks)
|
||||
repl_orig_idxs.extend(orig_idx for _ in range(len(repl_toks)))
|
||||
|
||||
repls = super()._find_mm_placeholders(repl_token_ids, mm_prompt_updates)
|
||||
|
||||
return {
|
||||
modality: [
|
||||
PlaceholderFeaturesInfo(
|
||||
modality=p.modality,
|
||||
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()
|
||||
}
|
||||
|
||||
|
||||
class Gemma3MultiModalProjector(nn.Module):
|
||||
def __init__(self, config: Gemma3Config):
|
||||
super().__init__()
|
||||
|
||||
self.mm_input_projection_weight = nn.Parameter(
|
||||
torch.zeros(
|
||||
config.vision_config.hidden_size, config.text_config.hidden_size
|
||||
)
|
||||
)
|
||||
|
||||
self.mm_soft_emb_norm = GemmaRMSNorm(
|
||||
config.vision_config.hidden_size, eps=config.vision_config.layer_norm_eps
|
||||
)
|
||||
|
||||
self.patches_per_image = int(
|
||||
config.vision_config.image_size // config.vision_config.patch_size
|
||||
)
|
||||
self.tokens_per_side = int(config.mm_tokens_per_image**0.5)
|
||||
self.kernel_size = self.patches_per_image // self.tokens_per_side
|
||||
self.avg_pool = nn.AvgPool2d(
|
||||
kernel_size=self.kernel_size, stride=self.kernel_size
|
||||
)
|
||||
|
||||
def forward(self, vision_outputs: torch.Tensor):
|
||||
batch_size, _, seq_length = vision_outputs.shape
|
||||
|
||||
reshaped_vision_outputs = vision_outputs.transpose(1, 2)
|
||||
reshaped_vision_outputs = reshaped_vision_outputs.reshape(
|
||||
batch_size, seq_length, self.patches_per_image, self.patches_per_image
|
||||
)
|
||||
reshaped_vision_outputs = reshaped_vision_outputs.contiguous()
|
||||
|
||||
pooled_vision_outputs = self.avg_pool(reshaped_vision_outputs)
|
||||
pooled_vision_outputs = pooled_vision_outputs.flatten(2)
|
||||
pooled_vision_outputs = pooled_vision_outputs.transpose(1, 2)
|
||||
|
||||
normed_vision_outputs = self.mm_soft_emb_norm(pooled_vision_outputs)
|
||||
|
||||
projected_vision_outputs = torch.matmul(
|
||||
normed_vision_outputs, self.mm_input_projection_weight
|
||||
)
|
||||
return projected_vision_outputs.type_as(vision_outputs)
|
||||
|
||||
|
||||
@MULTIMODAL_REGISTRY.register_processor(
|
||||
Gemma3MultiModalProcessor,
|
||||
info=Gemma3ProcessingInfo,
|
||||
dummy_inputs=Gemma3DummyInputsBuilder,
|
||||
)
|
||||
class Gemma3ForConditionalGeneration(
|
||||
nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA
|
||||
):
|
||||
merge_by_field_config = True
|
||||
|
||||
packed_modules_mapping = {
|
||||
"qkv_proj": [
|
||||
"q_proj",
|
||||
"k_proj",
|
||||
"v_proj",
|
||||
],
|
||||
"gate_up_proj": [
|
||||
"gate_proj",
|
||||
"up_proj",
|
||||
],
|
||||
}
|
||||
|
||||
hf_to_vllm_mapper = WeightsMapper(
|
||||
orig_to_new_prefix={
|
||||
# mapping for new names in checkpoint saved after transformers v4.52
|
||||
"model.language_model.": "language_model.model.",
|
||||
"model.vision_tower.": "vision_tower.",
|
||||
"model.multi_modal_projector.": "multi_modal_projector.",
|
||||
"lm_head.": "language_model.lm_head.",
|
||||
}
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_placeholder_str(cls, modality: str, i: int) -> str | None:
|
||||
if modality.startswith("image"):
|
||||
return "<start_of_image>"
|
||||
|
||||
raise ValueError("Only image modality is supported")
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
config = vllm_config.model_config.hf_config
|
||||
quant_config = vllm_config.quant_config
|
||||
multimodal_config = vllm_config.model_config.multimodal_config
|
||||
self.config = config
|
||||
self.quant_config = quant_config
|
||||
self.multimodal_config = multimodal_config
|
||||
|
||||
self.vision_tower = SiglipVisionModel(
|
||||
config.vision_config,
|
||||
quant_config,
|
||||
prefix=maybe_prefix(prefix, "vision_tower"),
|
||||
)
|
||||
self.multi_modal_projector = Gemma3MultiModalProjector(config)
|
||||
|
||||
self.language_model = init_vllm_registered_model(
|
||||
vllm_config=vllm_config,
|
||||
hf_config=config.text_config,
|
||||
prefix=maybe_prefix(prefix, "language_model"),
|
||||
architectures=["Gemma3ForCausalLM"],
|
||||
)
|
||||
logit_scale = getattr(config, "logit_scale", 1.0)
|
||||
|
||||
if hasattr(self.language_model, "logits_processor"):
|
||||
# The logits processor can be unset if we're using
|
||||
# automatic conversion to pooling model.
|
||||
self.language_model.logits_processor.scale *= logit_scale
|
||||
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.language_model.make_empty_intermediate_tensors
|
||||
)
|
||||
|
||||
@property
|
||||
def dtype(self):
|
||||
return next(self.parameters()).dtype
|
||||
|
||||
def _parse_and_validate_image_input(
|
||||
self, **kwargs: object
|
||||
) -> Gemma3ImageInputs | None:
|
||||
pixel_values = kwargs.pop("pixel_values", None)
|
||||
num_patches = kwargs.pop("num_patches", None)
|
||||
image_embeds = kwargs.pop("image_embeds", None)
|
||||
|
||||
if pixel_values is not None:
|
||||
image_size = self.config.vision_config.image_size
|
||||
return Gemma3ImagePixelInputs(
|
||||
pixel_values=pixel_values,
|
||||
num_patches=num_patches,
|
||||
resolve_bindings={"h": image_size, "w": image_size},
|
||||
)
|
||||
elif image_embeds is not None:
|
||||
return Gemma3ImageEmbeddingInputs(
|
||||
image_embeds=image_embeds,
|
||||
type="image_embeds",
|
||||
)
|
||||
|
||||
def _image_pixels_to_features(
|
||||
self,
|
||||
vision_tower: SiglipVisionModel,
|
||||
pixel_values: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
return vision_tower(pixel_values)
|
||||
|
||||
def _process_image_input(
|
||||
self,
|
||||
image_input: Gemma3ImageInputs,
|
||||
) -> torch.Tensor | list[torch.Tensor]:
|
||||
if image_input["type"] == "image_embeds":
|
||||
return image_input["image_embeds"]
|
||||
assert self.vision_tower is not None
|
||||
|
||||
pixel_values = image_input["pixel_values"]
|
||||
num_patches = image_input["num_patches"]
|
||||
|
||||
image_features = self._image_pixels_to_features(
|
||||
self.vision_tower,
|
||||
pixel_values,
|
||||
)
|
||||
image_embeds = self.multi_modal_projector(image_features)
|
||||
|
||||
return [e.flatten(0, 1) for e in image_embeds.split(num_patches.tolist())]
|
||||
|
||||
def get_language_model(self) -> torch.nn.Module:
|
||||
return self.language_model
|
||||
|
||||
def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
|
||||
image_input = self._parse_and_validate_image_input(**kwargs)
|
||||
if image_input is None:
|
||||
return []
|
||||
|
||||
return self._process_image_input(image_input)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
**kwargs: object,
|
||||
) -> IntermediateTensors:
|
||||
if intermediate_tensors is not None:
|
||||
inputs_embeds = None
|
||||
|
||||
hidden_states = self.language_model.model(
|
||||
input_ids,
|
||||
positions,
|
||||
intermediate_tensors,
|
||||
inputs_embeds=inputs_embeds,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
return hidden_states
|
||||
|
||||
def prepare_attn_masks(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
mask_dtype: torch.dtype,
|
||||
**kwargs,
|
||||
):
|
||||
kwargs["has_images"] = True
|
||||
# NOTE(woosuk): Here, we distinguish the sequences by the position id 0.
|
||||
# This is a HACK. Fix this.
|
||||
start_indices = (positions == 0).cpu().nonzero()
|
||||
num_seqs = len(start_indices)
|
||||
seq_lens = []
|
||||
for i in range(num_seqs):
|
||||
start_idx = start_indices[i].item()
|
||||
if i < num_seqs - 1:
|
||||
end_idx = start_indices[i + 1].item()
|
||||
else:
|
||||
end_idx = len(input_ids)
|
||||
seq_lens.append(end_idx - start_idx)
|
||||
kwargs["seq_lens"] = seq_lens
|
||||
|
||||
global_attn_masks = []
|
||||
local_attn_masks = []
|
||||
start_idx = 0
|
||||
for seq_len in seq_lens:
|
||||
end_idx = start_idx + seq_len
|
||||
input_token_ids = input_ids[start_idx:end_idx]
|
||||
start_idx = end_idx
|
||||
# Create a global causal mask.
|
||||
global_attn_mask = torch.empty(
|
||||
1,
|
||||
1,
|
||||
seq_len,
|
||||
seq_len,
|
||||
dtype=mask_dtype,
|
||||
device=input_ids.device,
|
||||
)
|
||||
global_attn_mask.fill_(float("-inf"))
|
||||
# Fill the lower triangle with 0.
|
||||
global_attn_mask = global_attn_mask.triu(diagonal=1)
|
||||
|
||||
# Consider the bidirectional attention between image tokens.
|
||||
img_mask = torch.zeros_like(global_attn_mask)
|
||||
img_pos = input_token_ids == self.config.image_token_index
|
||||
img_mask[:, :, :, img_pos] += 1
|
||||
img_mask[:, :, img_pos, :] += 1
|
||||
global_attn_mask = torch.where(img_mask == 2, 0, global_attn_mask)
|
||||
global_attn_masks.append(global_attn_mask)
|
||||
|
||||
sliding_window = self.config.text_config.sliding_window
|
||||
if sliding_window is not None:
|
||||
# Create a local causal mask with sliding window (1024).
|
||||
local_attn_mask = torch.ones_like(global_attn_mask)
|
||||
local_attn_mask = torch.tril(local_attn_mask, diagonal=-sliding_window)
|
||||
local_attn_mask = torch.where(
|
||||
local_attn_mask == 0, global_attn_mask, float("-inf")
|
||||
)
|
||||
local_attn_masks.append(local_attn_mask)
|
||||
kwargs["global_attn_masks"] = global_attn_masks
|
||||
kwargs["local_attn_masks"] = local_attn_masks
|
||||
return kwargs
|
||||
|
||||
def compute_logits(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
) -> torch.Tensor | None:
|
||||
return self.language_model.compute_logits(hidden_states)
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
loader = AutoWeightsLoader(self)
|
||||
return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
|
||||
|
||||
def get_mm_mapping(self) -> MultiModelKeys:
|
||||
"""
|
||||
Get the module prefix in multimodal models
|
||||
"""
|
||||
return MultiModelKeys.from_string_field(
|
||||
language_model="language_model",
|
||||
connector="multi_modal_projector",
|
||||
tower_model="vision_tower",
|
||||
)
|
||||
Reference in New Issue
Block a user