58 lines
2.3 KiB
Python
58 lines
2.3 KiB
Python
from typing import TYPE_CHECKING, Tuple
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import torch
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import transformers.models
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from transformers.activations import ACT2FN
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from ...extras.logging import get_logger
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if TYPE_CHECKING:
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from transformers import LlavaConfig, PretrainedConfig, PreTrainedModel
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from ...hparams import ModelArguments
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logger = get_logger(__name__)
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class LlavaMultiModalProjector(torch.nn.Module):
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def __init__(self, config: "LlavaConfig"):
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super().__init__()
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self.linear_1 = torch.nn.Linear(config.vision_config.hidden_size, config.text_config.hidden_size, bias=True)
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self.linear_2 = torch.nn.LayerNorm(config.text_config.hidden_size, bias=True)
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self.linear_3 = torch.nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=True)
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self.linear_4 = torch.nn.LayerNorm(config.text_config.hidden_size, bias=True)
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self.act = ACT2FN[config.projector_hidden_act]
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def forward(self, image_features):
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hidden_states = self.linear_1(image_features)
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hidden_states = self.linear_2(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states = self.linear_3(hidden_states)
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hidden_states = self.linear_4(hidden_states)
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return hidden_states
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def autocast_projector_dtype(
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model: "PreTrainedModel", model_args: "ModelArguments", mm_projector_name: str = "multi_modal_projector"
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) -> None:
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def _mm_projector_forward_post_hook(
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module: "torch.nn.Module", args: Tuple["torch.Tensor"], output: "torch.Tensor"
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) -> "torch.Tensor":
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return output.to(model_args.compute_dtype)
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if hasattr(model, mm_projector_name) and getattr(model.config, "quantization_method", None):
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logger.info("Casting multimodal projector outputs in {}.".format(model_args.compute_dtype))
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mm_projector: "torch.nn.Module" = getattr(model, mm_projector_name)
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mm_projector.register_forward_hook(_mm_projector_forward_post_hook)
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def configure_visual_model(config: "PretrainedConfig") -> None:
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if getattr(config, "model_type", None) == "llava":
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setattr(config, "hidden_size", getattr(config.text_config, "hidden_size", None))
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if getattr(config, "is_yi_vl_derived_model", None):
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transformers.models.llava.modeling_llava.LlavaMultiModalProjector = LlavaMultiModalProjector
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