Support VILA models (#6106)
This commit is contained in:
Zijian
@@ -399,7 +399,7 @@ async def async_request_sglang_generate(
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# NOTE: Some completion API might have a last
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# usage summary response without a token so we
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# want to check a token was generated
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if data["text"]:
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if "text" in data and data["text"]:
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timestamp = time.perf_counter()
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generated_text = data["text"]
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output_len = data["meta_info"]["completion_tokens"]
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@@ -578,6 +578,7 @@ multimodal_model_archs = [
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"KimiVLForConditionalGeneration",
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"InternVLChatModel",
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"Phi4MMForCausalLM",
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"VILAForConditionalGeneration",
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]
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@@ -983,3 +983,9 @@ def match_devstral(model_path: str):
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def match_phi_4_mm(model_path: str):
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if "phi-4-multimodal" in model_path.lower():
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return "phi-4-mm"
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@register_conv_template_matching_function
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def match_vila(model_path: str):
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if re.search(r"vila", model_path, re.IGNORECASE):
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return "chatml"
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@@ -146,7 +146,7 @@ class BaseMultimodalProcessor(ABC):
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request_obj,
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max_req_input_len,
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**kwargs,
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):
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) -> Optional[Dict[str, Any]]:
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pass
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def get_estimated_frames_list(self, image_data):
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@@ -261,7 +261,7 @@ class BaseMultimodalProcessor(ABC):
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def load_mm_data(
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self,
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prompt: str,
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prompt: str | List[int],
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multimodal_tokens: MultimodalSpecialTokens,
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max_req_input_len: int,
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image_data: Optional[list] = None,
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85
python/sglang/srt/managers/multimodal_processors/vila.py
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85
python/sglang/srt/managers/multimodal_processors/vila.py
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@@ -0,0 +1,85 @@
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from typing import Any, Dict, List, Optional, Type, cast
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import torch.nn as nn
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from transformers.configuration_utils import PretrainedConfig
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from transformers.processing_utils import ProcessorMixin
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from transformers.tokenization_utils_base import PreTrainedTokenizerBase
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from sglang.srt.managers.io_struct import (
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EmbeddingReqInput,
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GenerateReqInput,
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ImageDataItem,
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)
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from sglang.srt.managers.multimodal_processors.base_processor import (
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BaseMultimodalProcessor,
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MultimodalSpecialTokens,
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)
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from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
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from sglang.srt.models.vila import VILAForConditionalGeneration
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from sglang.srt.server_args import ServerArgs
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class VILAProcessor(ProcessorMixin):
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"""A stub class for the VILA processor."""
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tokenizer: PreTrainedTokenizerBase
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class VILAMultimodalProcessor(BaseMultimodalProcessor):
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models: List[Type[nn.Module]] = [VILAForConditionalGeneration]
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_processor: VILAProcessor
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def __init__(
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self,
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hf_config: PretrainedConfig,
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server_args: ServerArgs,
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_processor: VILAProcessor,
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) -> None:
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super().__init__(hf_config, server_args, _processor)
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async def process_mm_data_async(
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self,
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image_data: Optional[ImageDataItem | List[ImageDataItem]],
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input_text: str | List[int],
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request_obj: GenerateReqInput | EmbeddingReqInput,
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max_req_input_len: int,
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**kwargs,
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) -> Optional[Dict[str, Any]]:
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if not image_data:
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return None
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if not isinstance(image_data, list):
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image_data = [image_data]
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mm_data = self.load_mm_data(
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prompt=input_text,
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multimodal_tokens=MultimodalSpecialTokens(
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image_token=self._processor.tokenizer.image_token
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),
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max_req_input_len=max_req_input_len,
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image_data=image_data,
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)
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inputs = self.process_mm_data(
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input_text=mm_data.input_text,
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images=mm_data.images,
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)
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image_offsets = self.get_mm_items_offset(
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input_ids=inputs.input_ids[0],
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mm_token_id=cast(int, self._processor.tokenizer.image_token_id),
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)
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mm_items: List[MultimodalDataItem] = [
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MultimodalDataItem(
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modality=Modality.IMAGE,
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image_offsets=image_offsets,
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pixel_values=inputs.pixel_values,
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)
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]
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return dict(
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input_ids=inputs.input_ids[0].tolist(),
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mm_items=mm_items,
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)
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305
python/sglang/srt/models/vila.py
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305
python/sglang/srt/models/vila.py
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@@ -0,0 +1,305 @@
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import logging
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from typing import Any, Dict, Iterable, List, Optional, Tuple, cast
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import Tensor
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from transformers.configuration_utils import PretrainedConfig
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from transformers.modeling_outputs import BaseModelOutputWithPooling
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from transformers.models.qwen2.configuration_qwen2 import Qwen2Config
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from transformers.models.siglip import SiglipVisionConfig, SiglipVisionModel
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import sglang.srt.managers.mm_utils as mm_utils
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import sglang.srt.model_loader.weight_utils as weight_utils
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import sglang.srt.utils as utils
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from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
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from sglang.srt.layers.pooler import Pooler, PoolingType
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.managers.mm_utils import MultiModalityDataPaddingPatternMultimodalTokens
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from sglang.srt.managers.schedule_batch import MultimodalDataItem, MultimodalInputs
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.models.qwen2 import Qwen2ForCausalLM
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logger = logging.getLogger(__name__)
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##### BEGIN COPY configuration.py #####
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class VILAConfig(PretrainedConfig):
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# Class attributes.
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model_type: str = "vila"
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sub_configs: Dict[str, PretrainedConfig] = {
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"text_config": Qwen2Config(),
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"vision_config": SiglipVisionConfig(),
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}
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_auto_class: Optional[str] = "AutoConfig"
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# Configuration for sub-modules.
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text_config: Qwen2Config = Qwen2Config()
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vision_config: SiglipVisionConfig = SiglipVisionConfig()
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# Model configuration.
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hidden_size: int
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image_token_id: int
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mm_hidden_size: int
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mm_projector_type: str
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mm_vision_select_feature: str
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mm_vision_select_layer: int
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video_token_id: int
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def __init__(
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self,
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text_config: Optional[Dict[str, Any]] = None,
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vision_config: Optional[Dict[str, Any]] = None,
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*,
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hidden_size: int = 1536,
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image_token_id: int = 151649,
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mm_hidden_size: int = 1152,
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mm_projector_type: str = "mlp_downsample_3x3_fix",
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mm_vision_select_feature: str = "cls_patch",
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mm_vision_select_layer: int = -2,
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video_token_id: int = 151650,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.text_config = Qwen2Config(**text_config) if text_config else Qwen2Config()
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self.vision_config = (
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SiglipVisionConfig(**vision_config)
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if vision_config
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else SiglipVisionConfig()
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)
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self.hidden_size = hidden_size
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self.image_token_id = image_token_id
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self.mm_hidden_size = mm_hidden_size
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self.mm_projector_type = mm_projector_type
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self.mm_vision_select_feature = mm_vision_select_feature
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self.mm_vision_select_layer = mm_vision_select_layer
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self.video_token_id = video_token_id
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##### END COPY configuration.py #####
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##### BEGIN COPY modeling_vila.py #####
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class DownSample3x3BlockFix(nn.Module):
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def forward(self, x: Tensor) -> Tensor:
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"""
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Args:
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x: The input tensor of shape (batch_size, sequence_length, mm_hidden_size).
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Returns:
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The output tensor of shape (batch_size, image_pad_len, mm_hidden_size * 9).
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"""
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batch_size, sequence_length, hidden_size = x.shape
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feat_size = int(sequence_length**0.5)
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if feat_size**2 != sequence_length:
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raise ValueError(
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f"Cannot take square root: sequence_length {sequence_length} is not a perfect square"
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)
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features = x.reshape(batch_size, feat_size, feat_size, hidden_size)
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pad_after = (3 - feat_size % 3) % 3
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if pad_after > 0:
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features = F.pad(features, (0, 0, 0, pad_after, 0, pad_after))
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feat_size = feat_size + pad_after
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features = features.reshape(
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batch_size, feat_size // 3, 3, feat_size // 3, 3, hidden_size
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)
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features = features.permute(0, 1, 3, 2, 4, 5).contiguous()
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features = features.reshape(batch_size, -1, 9 * hidden_size)
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return features
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class MultimodalProjector(nn.Module):
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layers: nn.Sequential
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def __init__(
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self,
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config: VILAConfig,
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*args,
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**kwargs,
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):
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super().__init__(*args, **kwargs)
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if config.mm_projector_type == "mlp_downsample_3x3_fix":
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self.layers = nn.Sequential(
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DownSample3x3BlockFix(),
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nn.LayerNorm(config.mm_hidden_size * 9),
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nn.Linear(
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config.mm_hidden_size * 9,
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config.mm_hidden_size * 3,
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),
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nn.GELU(),
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nn.LayerNorm(config.vision_config.hidden_size * 3),
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nn.Linear(config.vision_config.hidden_size * 3, config.hidden_size),
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nn.GELU(),
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nn.Linear(config.hidden_size, config.hidden_size),
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)
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else:
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raise NotImplementedError(
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f"Unsupported mm_projector_type: {config.mm_projector_type}"
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)
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self.layers.type(config.torch_dtype)
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@property
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def device(self) -> torch.device:
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return next(self.parameters()).device
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@property
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def dtype(self) -> torch.dtype:
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return next(self.parameters()).dtype
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def forward(self, x: Tensor) -> Tensor:
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"""
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Args:
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x: The input tensor of shape (batch_size, sequence_length, mm_hidden_size).
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Returns:
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The output tensor of shape (batch_size, image_pad_len, hidden_size).
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"""
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return self.layers(x.to(device=self.device, dtype=self.dtype))
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##### END COPY modeling_vila.py #####
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class VILAForConditionalGeneration(nn.Module):
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config: VILAConfig
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quant_config: Optional[QuantizationConfig]
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logits_processor: LogitsProcessor
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pooler: Pooler
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llm: Qwen2ForCausalLM
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mm_projector: MultimodalProjector
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vision_tower: SiglipVisionModel
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def __init__(
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self,
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config: VILAConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.quant_config = quant_config
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self.logits_processor = LogitsProcessor(config)
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self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
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self.llm = Qwen2ForCausalLM(
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config=config.text_config,
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quant_config=quant_config,
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prefix=utils.add_prefix("llm", prefix),
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)
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self.mm_projector = MultimodalProjector(config)
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self.vision_tower = SiglipVisionModel(config.vision_config)
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@property
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def dtype(self) -> torch.dtype:
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return self.config.torch_dtype
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def forward(
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self,
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input_ids: Tensor,
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positions: Tensor,
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forward_batch: ForwardBatch,
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get_embedding: bool = False,
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) -> LogitsProcessorOutput:
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output = mm_utils.general_mm_embed_routine(
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input_ids=input_ids,
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forward_batch=forward_batch,
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language_model=self.llm,
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image_data_embedding_func=self.get_image_feature,
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get_embedding=get_embedding,
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positions=positions,
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)
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return cast(LogitsProcessorOutput, output)
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def get_image_feature(self, mm_input: List[MultimodalDataItem]) -> Tensor:
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pixel_values = cast(Tensor, mm_input[0].pixel_values)
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##### BEGIN COPY modeling_vila.py #####
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vision_tower_output: BaseModelOutputWithPooling = self.vision_tower.__call__(
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pixel_values.to(
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device=self.vision_tower.device, dtype=self.vision_tower.dtype
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),
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output_hidden_states=True,
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)
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mm_projector_input = self._vision_tower_output_to_mm_projector_input(
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vision_tower_output
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)
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image_embedding: Tensor = self.mm_projector.__call__(
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mm_projector_input.to(
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device=self.mm_projector.device, dtype=self.mm_projector.dtype
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)
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)
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##### END COPY modeling_vila.py #####
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return image_embedding
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def load_weights(self, weights: Iterable[Tuple[str, Tensor]]) -> None:
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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if name.startswith("llm."):
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self.llm.load_weights([(name[len("llm.") :], loaded_weight)])
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else:
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param = params_dict[name]
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weight_loader = getattr(
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param, "weight_loader", weight_utils.default_weight_loader
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)
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weight_loader(param, loaded_weight)
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def pad_input_ids(
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self,
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input_ids: List[int],
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image_inputs: MultimodalInputs,
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) -> List[int]:
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pattern = MultiModalityDataPaddingPatternMultimodalTokens(
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token_ids=[self.config.image_token_id],
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)
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return pattern.pad_input_tokens(input_ids, image_inputs)
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##### BEGIN COPY modeling_vila.py #####
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def _vision_tower_output_to_mm_projector_input(
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self,
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vision_tower_output: BaseModelOutputWithPooling,
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) -> Tensor:
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assert vision_tower_output.hidden_states is not None
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selected_layer_hidden_states = vision_tower_output.hidden_states[
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self.config.mm_vision_select_layer
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]
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if self.config.mm_vision_select_feature == "cls_patch":
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return selected_layer_hidden_states
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else:
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raise NotImplementedError(
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f"Unsupported mm_vision_select_feature: {self.config.mm_vision_select_feature}"
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)
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##### END COPY modeling_vila.py #####
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EntryClass = [VILAForConditionalGeneration]
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