mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-22 22:02:51 +08:00
85 lines
3.5 KiB
Python
85 lines
3.5 KiB
Python
from typing import TYPE_CHECKING, Tuple
|
|
|
|
import torch
|
|
import transformers.models
|
|
from transformers.activations import ACT2FN
|
|
|
|
from ...extras.logging import get_logger
|
|
|
|
|
|
if TYPE_CHECKING:
|
|
from transformers import LlavaConfig, PretrainedConfig, PreTrainedModel
|
|
|
|
from ...hparams import ModelArguments
|
|
|
|
|
|
logger = get_logger(__name__)
|
|
|
|
|
|
class LlavaMultiModalProjectorForYiVL(torch.nn.Module):
|
|
def __init__(self, config: "LlavaConfig") -> None:
|
|
super().__init__()
|
|
|
|
self.config = config
|
|
if config is None:
|
|
return
|
|
|
|
self.linear_1 = torch.nn.Linear(config.vision_config.hidden_size, config.text_config.hidden_size, bias=True)
|
|
self.linear_2 = torch.nn.LayerNorm(config.text_config.hidden_size, bias=True)
|
|
self.linear_3 = torch.nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=True)
|
|
self.linear_4 = torch.nn.LayerNorm(config.text_config.hidden_size, bias=True)
|
|
self.act = ACT2FN[config.projector_hidden_act]
|
|
|
|
def forward(self, image_features: "torch.Tensor") -> "torch.Tensor":
|
|
hidden_states = self.linear_1(image_features)
|
|
hidden_states = self.linear_2(hidden_states)
|
|
hidden_states = self.act(hidden_states)
|
|
hidden_states = self.linear_3(hidden_states)
|
|
hidden_states = self.linear_4(hidden_states)
|
|
if hidden_states.dtype == torch.float32:
|
|
if torch.is_autocast_enabled():
|
|
target_dtype = torch.get_autocast_gpu_dtype()
|
|
elif hasattr(self.config, "_pre_quantization_dtype"):
|
|
target_dtype = self.config._pre_quantization_dtype
|
|
else:
|
|
target_dtype = self.linear_1.weight.dtype
|
|
|
|
logger.warning_once("The hidden states seems to be silently casted in float32.")
|
|
hidden_states = hidden_states.to(target_dtype)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class LlavaMultiModalProjectorForYiVLForVLLM(LlavaMultiModalProjectorForYiVL):
|
|
def __init__(self, vision_hidden_size: int, text_hidden_size: int, projector_hidden_act: str) -> None:
|
|
super().__init__(config=None)
|
|
|
|
self.linear_1 = torch.nn.Linear(vision_hidden_size, text_hidden_size, bias=True)
|
|
self.linear_2 = torch.nn.LayerNorm(text_hidden_size, bias=True)
|
|
self.linear_3 = torch.nn.Linear(text_hidden_size, text_hidden_size, bias=True)
|
|
self.linear_4 = torch.nn.LayerNorm(text_hidden_size, bias=True)
|
|
self.act = torch.nn.GELU()
|
|
|
|
|
|
def autocast_projector_dtype(
|
|
model: "PreTrainedModel", model_args: "ModelArguments", mm_projector_name: str = "multi_modal_projector"
|
|
) -> None:
|
|
def _mm_projector_forward_post_hook(
|
|
module: "torch.nn.Module", args: Tuple["torch.Tensor"], output: "torch.Tensor"
|
|
) -> "torch.Tensor":
|
|
return output.to(model_args.compute_dtype)
|
|
|
|
if hasattr(model, mm_projector_name) and getattr(model.config, "quantization_method", None):
|
|
logger.info("Casting multimodal projector outputs in {}.".format(model_args.compute_dtype))
|
|
mm_projector: "torch.nn.Module" = getattr(model, mm_projector_name)
|
|
mm_projector.register_forward_hook(_mm_projector_forward_post_hook)
|
|
|
|
|
|
def configure_visual_model(config: "PretrainedConfig") -> None:
|
|
if getattr(config, "model_type", None) == "llava":
|
|
setattr(config, "hidden_size", getattr(config.text_config, "hidden_size", None))
|
|
|
|
if getattr(config, "is_yi_vl_derived_model", None):
|
|
logger.info("Detected Yi-VL model, applying projector patch.")
|
|
transformers.models.llava.modeling_llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVL
|