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Update modeling_vlm.py
This commit is contained in:
Aaditya Sharma
GitHub
parent
a74a59f8a9
commit
e23df8fecc
@ -33,8 +33,8 @@ from janus.models.clip_encoder import CLIPVisionTower
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from janus.models.projector import MlpProjector
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class vision_head(torch.nn.Module):
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def __init__(self, params):
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class VisionHead(torch.nn.Module):
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def __init__(self, params: AttrDict):
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super().__init__()
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self.output_mlp_projector = torch.nn.Linear(
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params.n_embed, params.image_token_embed
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@ -44,135 +44,82 @@ class vision_head(torch.nn.Module):
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params.image_token_embed, params.image_token_size
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)
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def forward(self, x):
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.output_mlp_projector(x)
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x = self.vision_activation(x)
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x = self.vision_head(x)
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return x
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def model_name_to_cls(cls_name):
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def model_name_to_cls(cls_name: str) -> type:
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if "MlpProjector" in cls_name:
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cls = MlpProjector
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elif "CLIPVisionTower" in cls_name:
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cls = CLIPVisionTower
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elif "VQ" in cls_name:
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from janus.models.vq_model import VQ_models
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cls = VQ_models[cls_name]
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elif "vision_head" in cls_name:
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cls = vision_head
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# Maintain backward compatibility with existing configs using "vision_head"
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cls = VisionHead
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else:
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raise ValueError(f"class_name {cls_name} is invalid.")
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raise ValueError(f"Invalid class name: {cls_name}")
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return cls
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class VisionConfig(PretrainedConfig):
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class BaseSubConfig(PretrainedConfig):
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model_type: str = "base"
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cls: str = ""
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params: AttrDict = AttrDict()
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.cls = kwargs.get("cls", "")
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if not isinstance(self.cls, str):
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self.cls = self.cls.__name__
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self.params = AttrDict(kwargs.get("params", {}))
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class VisionConfig(BaseSubConfig):
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model_type = "vision"
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cls: str = ""
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params: AttrDict = {}
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.cls = kwargs.get("cls", "")
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if not isinstance(self.cls, str):
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self.cls = self.cls.__name__
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self.params = AttrDict(kwargs.get("params", {}))
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class AlignerConfig(PretrainedConfig):
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class AlignerConfig(BaseSubConfig):
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model_type = "aligner"
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cls: str = ""
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params: AttrDict = {}
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.cls = kwargs.get("cls", "")
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if not isinstance(self.cls, str):
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self.cls = self.cls.__name__
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self.params = AttrDict(kwargs.get("params", {}))
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class GenVisionConfig(PretrainedConfig):
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class GenVisionConfig(BaseSubConfig):
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model_type = "gen_vision"
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cls: str = ""
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params: AttrDict = {}
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.cls = kwargs.get("cls", "")
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if not isinstance(self.cls, str):
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self.cls = self.cls.__name__
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self.params = AttrDict(kwargs.get("params", {}))
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class GenAlignerConfig(PretrainedConfig):
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class GenAlignerConfig(BaseSubConfig):
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model_type = "gen_aligner"
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cls: str = ""
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params: AttrDict = {}
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.cls = kwargs.get("cls", "")
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if not isinstance(self.cls, str):
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self.cls = self.cls.__name__
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self.params = AttrDict(kwargs.get("params", {}))
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class GenHeadConfig(PretrainedConfig):
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class GenHeadConfig(BaseSubConfig):
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model_type = "gen_head"
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cls: str = ""
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params: AttrDict = {}
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.cls = kwargs.get("cls", "")
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if not isinstance(self.cls, str):
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self.cls = self.cls.__name__
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self.params = AttrDict(kwargs.get("params", {}))
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class MultiModalityConfig(PretrainedConfig):
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model_type = "multi_modality"
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vision_config: VisionConfig
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aligner_config: AlignerConfig
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gen_vision_config: GenVisionConfig
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gen_aligner_config: GenAlignerConfig
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gen_head_config: GenHeadConfig
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language_config: LlamaConfig
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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vision_config = kwargs.get("vision_config", {})
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self.vision_config = VisionConfig(**vision_config)
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aligner_config = kwargs.get("aligner_config", {})
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self.aligner_config = AlignerConfig(**aligner_config)
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gen_vision_config = kwargs.get("gen_vision_config", {})
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self.gen_vision_config = GenVisionConfig(**gen_vision_config)
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gen_aligner_config = kwargs.get("gen_aligner_config", {})
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self.gen_aligner_config = GenAlignerConfig(**gen_aligner_config)
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gen_head_config = kwargs.get("gen_head_config", {})
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self.gen_head_config = GenHeadConfig(**gen_head_config)
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self.vision_config = VisionConfig(**kwargs.get("vision_config", {}))
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self.aligner_config = AlignerConfig(**kwargs.get("aligner_config", {}))
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self.gen_vision_config = GenVisionConfig(**kwargs.get("gen_vision_config", {}))
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self.gen_aligner_config = GenAlignerConfig(
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**kwargs.get("gen_aligner_config", {})
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)
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self.gen_head_config = GenHeadConfig(**kwargs.get("gen_head_config", {}))
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language_config = kwargs.get("language_config", {})
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if isinstance(language_config, LlamaConfig):
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self.language_config = language_config
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@ -191,32 +138,31 @@ class MultiModalityCausalLM(MultiModalityPreTrainedModel):
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def __init__(self, config: MultiModalityConfig):
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super().__init__(config)
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vision_config = config.vision_config
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vision_cls = model_name_to_cls(vision_config.cls)
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self.vision_model = vision_cls(**vision_config.params)
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aligner_config = config.aligner_config
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aligner_cls = model_name_to_cls(aligner_config.cls)
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self.aligner = aligner_cls(aligner_config.params)
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gen_vision_config = config.gen_vision_config
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gen_vision_cls = model_name_to_cls(gen_vision_config.cls)
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self.gen_vision_model = gen_vision_cls()
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gen_aligner_config = config.gen_aligner_config
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gen_aligner_cls = model_name_to_cls(gen_aligner_config.cls)
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self.gen_aligner = gen_aligner_cls(gen_aligner_config.params)
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gen_head_config = config.gen_head_config
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gen_head_cls = model_name_to_cls(gen_head_config.cls)
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self.gen_head = gen_head_cls(gen_head_config.params)
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self.gen_embed = torch.nn.Embedding(
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gen_vision_config.params.image_token_size, gen_vision_config.params.n_embed
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# Initialize vision components
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self.vision_model = model_name_to_cls(config.vision_config.cls)(
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**config.vision_config.params
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)
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self.aligner = model_name_to_cls(config.aligner_config.cls)(
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config.aligner_config.params
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)
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language_config = config.language_config
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self.language_model = LlamaForCausalLM(language_config)
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# Initialize generation components
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self.gen_vision_model = model_name_to_cls(config.gen_vision_config.cls)(
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**config.gen_vision_config.params
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)
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self.gen_aligner = model_name_to_cls(config.gen_aligner_config.cls)(
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config.gen_aligner_config.params
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)
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self.gen_head = model_name_to_cls(config.gen_head_config.cls)(
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config.gen_head_config.params
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)
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# Initialize embeddings and language model
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self.gen_embed = torch.nn.Embedding(
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config.gen_vision_config.params.image_token_size,
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config.gen_vision_config.params.n_embed,
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)
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self.language_model = LlamaForCausalLM(config.language_config)
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def prepare_inputs_embeds(
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self,
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@ -225,44 +171,49 @@ class MultiModalityCausalLM(MultiModalityPreTrainedModel):
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images_seq_mask: torch.LongTensor,
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images_emb_mask: torch.LongTensor,
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**kwargs,
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):
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) -> torch.Tensor:
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"""
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Prepares combined text and image embeddings for the language model.
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Args:
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input_ids (torch.LongTensor): [b, T]
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pixel_values (torch.FloatTensor): [b, n_images, 3, h, w]
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images_seq_mask (torch.BoolTensor): [b, T]
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images_emb_mask (torch.BoolTensor): [b, n_images, n_image_tokens]
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assert torch.sum(images_seq_mask) == torch.sum(images_emb_mask)
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input_ids: Token IDs for text inputs, shape [batch_size, seq_len]
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pixel_values: Image tensors, shape [batch_size, num_images, channels, height, width]
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images_seq_mask: Boolean mask indicating image positions in the text sequence,
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shape [batch_size, seq_len]
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images_emb_mask: Boolean mask for valid image tokens per image,
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shape [batch_size, num_images, tokens_per_image]
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Returns:
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input_embeds (torch.Tensor): [b, T, D]
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Combined embeddings tensor of shape [batch_size, seq_len, embedding_dim]
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"""
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bs, n = pixel_values.shape[0:2]
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batch_size, num_images = pixel_values.shape[:2]
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images = rearrange(pixel_values, "b n c h w -> (b n) c h w")
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# [b x n, T2, D]
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images_embeds = self.aligner(self.vision_model(images))
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images_embeds = self.aligner(self.vision_model(images)) # [(b n), tokens, dim]
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# [b x n, T2, D] -> [b, n x T2, D]
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images_embeds = rearrange(images_embeds, "(b n) t d -> b (n t) d", b=bs, n=n)
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# [b, n, T2] -> [b, n x T2]
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# Reshape embeddings and masks
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images_embeds = rearrange(
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images_embeds, "(b n) t d -> b (n t) d", b=batch_size, n=num_images
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)
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images_emb_mask = rearrange(images_emb_mask, "b n t -> b (n t)")
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# [b, T, D]
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input_ids[input_ids < 0] = 0 # ignore the image embeddings
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inputs_embeds = self.language_model.get_input_embeddings()(input_ids)
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# Validate mask compatibility
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assert torch.all(
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images_seq_mask.sum(dim=1) == images_emb_mask.sum(dim=1)
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), "Masks must have matching number of image tokens"
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# replace with the image embeddings
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# Get text embeddings and replace image positions
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input_ids = input_ids.masked_fill(input_ids < 0, 0) # Replace negatives
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inputs_embeds = self.language_model.get_input_embeddings()(input_ids)
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inputs_embeds[images_seq_mask] = images_embeds[images_emb_mask]
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return inputs_embeds
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def prepare_gen_img_embeds(self, image_ids: torch.LongTensor):
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def prepare_gen_img_embeds(self, image_ids: torch.LongTensor) -> torch.Tensor:
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"""Generates image embeddings from token IDs for generation."""
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return self.gen_aligner(self.gen_embed(image_ids))
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# Configuration registration
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AutoConfig.register("vision", VisionConfig)
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AutoConfig.register("aligner", AlignerConfig)
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AutoConfig.register("gen_vision", GenVisionConfig)
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