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# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's Transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llava/modeling_llava.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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
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