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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, List, Sequence, Set, Tuple, Union
import torch
import transformers.models
from transformers.activations import ACT2FN
from transformers.utils import logging
from ...extras.logging import get_logger
if TYPE_CHECKING:
from transformers import LlavaConfig, PretrainedConfig, PreTrainedModel
from ...hparams import FinetuningArguments, ModelArguments
logger = get_logger(__name__)
transformers_logger = logging.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
transformers_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") -> None:
r"""
Casts projector output to half precision for fine-tuning quantized VLMs.
"""
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 getattr(model, "quantization_method", None):
model_type = getattr(model.config, "model_type", None)
if model_type in ["llava", "llava_next", "llava_next_video", "paligemma", "video_llava"]:
mm_projector: "torch.nn.Module" = getattr(model, "multi_modal_projector")
elif model_type == "qwen2_vl":
mm_projector: "torch.nn.Module" = getattr(getattr(model, "visual"), "merger")
else:
return
logger.info("Casting multimodal projector outputs in {}.".format(model_args.compute_dtype))
mm_projector.register_forward_hook(_mm_projector_forward_post_hook)
def configure_visual_model(config: "PretrainedConfig") -> None:
r"""
Patches VLMs before loading them.
"""
model_type = getattr(config, "model_type", None)
if model_type in [
"llava",
"llava_next",
"llava_next_video",
"paligemma",
"video_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
def get_forbidden_modules(config: "PretrainedConfig", finetuning_args: "FinetuningArguments") -> Set[str]:
r"""
Freezes vision tower and language model for VLM full/freeze tuning.
"""
model_type = getattr(config, "model_type", None)
forbidden_modules = set()
if model_type in ["llava", "llava_next", "llava_next_video", "paligemma", "video_llava"]:
if finetuning_args.freeze_vision_tower:
forbidden_modules.add("vision_tower")
if finetuning_args.train_mm_proj_only:
forbidden_modules.add("language_model")
elif model_type == "qwen2_vl":
if finetuning_args.freeze_vision_tower:
forbidden_modules.add("visual")
if finetuning_args.train_mm_proj_only:
raise ValueError("Qwen2-VL models do not support `train_mm_proj_only`.")
return forbidden_modules
def get_image_seqlen(config: "PretrainedConfig") -> int:
r"""
Computes the number of special tokens per image.
"""
model_type = getattr(config, "model_type", None)
if model_type == "llava":
image_seqlen = (config.vision_config.image_size // config.vision_config.patch_size) ** 2
if getattr(config, "vision_feature_select_strategy", "default") == "full": # add [CLS] token
image_seqlen += 1
elif model_type == "paligemma":
image_seqlen = config.vision_config.num_image_tokens
else:
image_seqlen = -1
return image_seqlen
def get_patch_size(config: "PretrainedConfig") -> int:
r"""
Computes the patch size of the vit.
"""
patch_size = getattr(config.vision_config, "patch_size", -1)
return patch_size
def get_vision_feature_select_strategy(config: "PretrainedConfig") -> int:
r"""
Get the vision_feature_select_strategy.
"""
vision_feature_select_strategy = getattr(config, "vision_feature_select_strategy", "default")
return vision_feature_select_strategy
def patch_target_modules(
config: "PretrainedConfig", finetuning_args: "FinetuningArguments", target_modules: Sequence[str]
) -> Union[str, List[str]]:
r"""
Freezes vision tower for VLM LoRA tuning.
"""
model_type = getattr(config, "model_type", None)
if finetuning_args.freeze_vision_tower:
if model_type in ["llava", "llava_next", "llava_next_video", "paligemma", "video_llava"]:
return "^(?!.*vision_tower).*(?:{}).*".format("|".join(target_modules))
elif model_type == "qwen2_vl":
return "^(?!.*visual).*(?:{}).*".format("|".join(target_modules))
else:
return target_modules
else:
if model_type == "qwen2_vl":
return "^(?!.*patch_embed).*(?:{}).*".format("|".join(target_modules))
else:
return target_modules
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