rename files

src/llamafactory/model/model_utils/visual.py

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+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 = ACT2FN[projector_hidden_act]
+
+
+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, "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":  # required for ds zero3 and valuehead models
+        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