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fix yi vl vllm infer

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Former-commit-id: de54e5d7ec06dd7c20ec82c9ff032fc16cd50244
This commit is contained in:
hiyouga
2024-05-15 19:25:48 +08:00
parent 096677b989
commit 40e3d3fbdd
16 changed files with 113 additions and 24 deletions
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37
src/llmtuner/model/utils/visual.py
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@@ -16,25 +16,51 @@ if TYPE_CHECKING:
logger = get_logger(__name__)
class LlavaMultiModalProjector(torch.nn.Module):
def __init__(self, config: "LlavaConfig"):
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):
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:
@@ -53,5 +79,6 @@ 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):
transformers.models.llava.modeling_llava.LlavaMultiModalProjector = LlavaMultiModalProjector
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