mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-09-12 16:12:48 +08:00
add llava-next/llava-next-video/video-llava
Former-commit-id: 6642cd501d55a1657678428ef2aa0c9b99b7e83f
This commit is contained in:
BUAADreamer
parent
c576b7ca32
commit
5aa1e847d9
@ -4,6 +4,7 @@ from io import BytesIO
|
||||
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, TypedDict, Union
|
||||
|
||||
import numpy as np
|
||||
from transformers.image_utils import get_image_size, to_numpy_array
|
||||
from typing_extensions import override
|
||||
|
||||
from ..extras.constants import IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
|
||||
@ -173,7 +174,6 @@ class BasePlugin:
|
||||
video_maxlen=getattr(processor, "video_maxlen", 64),
|
||||
)
|
||||
input_dict["videos"] = videos
|
||||
|
||||
if input_dict.get("images", None) is not None or input_dict.get("videos", None) is not None:
|
||||
return image_processor(**input_dict, return_tensors="pt")
|
||||
else:
|
||||
@ -223,50 +223,6 @@ class BasePlugin:
|
||||
return {}
|
||||
|
||||
|
||||
class Idefics2Plugin(BasePlugin):
|
||||
@override
|
||||
def process_messages(
|
||||
self,
|
||||
messages: Sequence[Dict[str, str]],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> List[Dict[str, str]]:
|
||||
self._validate_input(images, videos)
|
||||
num_image_tokens = 0
|
||||
messages = deepcopy(messages)
|
||||
fake_image_token = processor.fake_image_token.content
|
||||
image_str = f"{fake_image_token}{self.image_token * processor.image_seq_len}{fake_image_token}"
|
||||
image_str = image_str * 5
|
||||
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while IMAGE_PLACEHOLDER in content:
|
||||
num_image_tokens += 1
|
||||
content = content.replace(IMAGE_PLACEHOLDER, "{{image}}", 1)
|
||||
content = content.replace("{{image}}", image_str)
|
||||
content = content.replace(f"{fake_image_token}{fake_image_token}", f"{fake_image_token}")
|
||||
message["content"] = content
|
||||
|
||||
if len(images) != num_image_tokens:
|
||||
raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
|
||||
|
||||
return messages
|
||||
|
||||
@override
|
||||
def get_mm_inputs(
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
imglens: Sequence[int],
|
||||
vidlens: Sequence[int],
|
||||
seqlens: Sequence[int],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
|
||||
self._validate_input(images, videos)
|
||||
return self._get_mm_inputs(images, videos, processor)
|
||||
|
||||
|
||||
class LlavaPlugin(BasePlugin):
|
||||
@override
|
||||
def process_messages(
|
||||
@ -319,15 +275,33 @@ class LlavaNextPlugin(BasePlugin):
|
||||
self._validate_input(images, videos)
|
||||
num_image_tokens = 0
|
||||
messages = deepcopy(messages)
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while IMAGE_PLACEHOLDER in content:
|
||||
num_image_tokens += 1
|
||||
content = content.replace(IMAGE_PLACEHOLDER, "{{image}}", 1)
|
||||
if getattr(processor, "patch_size") is None or getattr(processor, "vision_feature_select_strategy") is None:
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while self.image_token in content:
|
||||
num_image_tokens += 1
|
||||
content = content.replace(self.image_token, "{{image}}", 1)
|
||||
else:
|
||||
mm_inputs = self._get_mm_inputs(images, videos, processor)
|
||||
image_sizes = iter(mm_inputs["image_sizes"])
|
||||
height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0][0]))
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while self.image_token in content:
|
||||
image_size = next(image_sizes)
|
||||
orig_height, orig_width = image_size
|
||||
image_seqlen = processor._get_number_of_features(orig_height, orig_width, height, width)
|
||||
if processor.vision_feature_select_strategy == "default":
|
||||
image_seqlen -= 1
|
||||
num_image_tokens += 1
|
||||
print(image_seqlen)
|
||||
content = content.replace(self.image_token, "{{image}}" * image_seqlen, 1)
|
||||
|
||||
message['content'] = content.replace("{{image}}", self.image_token)
|
||||
|
||||
if len(images) != num_image_tokens:
|
||||
raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
|
||||
|
||||
print(messages)
|
||||
return messages
|
||||
|
||||
@override
|
||||
@ -341,8 +315,8 @@ class LlavaNextPlugin(BasePlugin):
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
|
||||
self._validate_input(images, videos)
|
||||
return self._get_mm_inputs(images, videos, processor)
|
||||
|
||||
res = self._get_mm_inputs(images, videos, processor)
|
||||
return res
|
||||
|
||||
class LlavaNextVideoPlugin(BasePlugin):
|
||||
@override
|
||||
@ -357,14 +331,47 @@ class LlavaNextVideoPlugin(BasePlugin):
|
||||
num_image_tokens = 0
|
||||
num_video_tokens = 0
|
||||
messages = deepcopy(messages)
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while IMAGE_PLACEHOLDER in content:
|
||||
num_image_tokens += 1
|
||||
content = content.replace(IMAGE_PLACEHOLDER, "{{image}}", 1)
|
||||
while VIDEO_PLACEHOLDER in content:
|
||||
num_video_tokens += 1
|
||||
content = content.replace(VIDEO_PLACEHOLDER, "{{video}}", 1)
|
||||
if getattr(processor, "patch_size") is None or getattr(processor, "vision_feature_select_strategy") is None:
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while self.image_token in content:
|
||||
num_image_tokens += 1
|
||||
content = content.replace(self.image_token, "{{image}}", 1)
|
||||
while self.video_token in content:
|
||||
num_video_tokens += 1
|
||||
content = content.replace(self.video_token, "{{video}}", 1)
|
||||
else:
|
||||
mm_inputs = self._get_mm_inputs(images, videos, processor)
|
||||
if "pixel_values" in mm_inputs:
|
||||
image_sizes = iter(mm_inputs["image_sizes"])
|
||||
height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0][0]))
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
|
||||
while self.image_token in content:
|
||||
image_size = next(image_sizes)
|
||||
orig_height, orig_width = image_size
|
||||
image_seqlen = processor._get_number_of_features(orig_height, orig_width, height, width)
|
||||
if processor.vision_feature_select_strategy == "default":
|
||||
image_seqlen -= 1
|
||||
num_image_tokens += 1
|
||||
content = content.replace(self.image_token, "{{image}}" * image_seqlen, 1)
|
||||
|
||||
message['content'] = content.replace("{{image}}", self.image_token)
|
||||
|
||||
if "pixel_values_videos" in mm_inputs:
|
||||
one_video = to_numpy_array(mm_inputs.get("pixel_values_videos")[0])
|
||||
height, width = get_image_size(one_video[0])
|
||||
num_frames = one_video.shape[0] # frame dim is always after batch dim
|
||||
image_seqlen = (height // processor.patch_size) * (width // processor.patch_size)
|
||||
video_seqlen = image_seqlen // 4 * num_frames # divide by 4 needed for avg pooling layer
|
||||
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while self.video_token in content:
|
||||
num_video_tokens += 1
|
||||
content = content.replace(self.video_token, "{{video}}", 1)
|
||||
message['content'] = content.replace("{{video}}", self.video_token * video_seqlen)
|
||||
|
||||
if len(images) != num_image_tokens:
|
||||
raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
|
||||
@ -393,6 +400,19 @@ class LlavaNextVideoPlugin(BasePlugin):
|
||||
res.update(video_res)
|
||||
return res
|
||||
|
||||
@override
|
||||
def _regularize_videos(self, videos: Sequence["VideoInput"], **kwargs) -> List[List["ImageObject"]]:
|
||||
r"""
|
||||
Regularizes videos to avoid error. Including reading, resizing and converting.
|
||||
"""
|
||||
videos = super()._regularize_videos(
|
||||
videos,
|
||||
image_resolution=128,
|
||||
video_fps=1.0,
|
||||
video_maxlen=64,
|
||||
)
|
||||
return videos
|
||||
|
||||
|
||||
class PaliGemmaPlugin(BasePlugin):
|
||||
@override
|
||||
@ -561,14 +581,42 @@ class VideoLlavaPlugin(BasePlugin):
|
||||
num_image_tokens = 0
|
||||
num_video_tokens = 0
|
||||
messages = deepcopy(messages)
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while IMAGE_PLACEHOLDER in content:
|
||||
num_image_tokens += 1
|
||||
content = content.replace(IMAGE_PLACEHOLDER, "{{image}}", 1)
|
||||
while VIDEO_PLACEHOLDER in content:
|
||||
num_video_tokens += 1
|
||||
content = content.replace(VIDEO_PLACEHOLDER, "{{video}}", 1)
|
||||
if getattr(processor, "patch_size") is None or getattr(processor, "vision_feature_select_strategy") is None:
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while self.image_token in content:
|
||||
num_image_tokens += 1
|
||||
content = content.replace(self.image_token, "{{image}}", 1)
|
||||
while self.video_token in content:
|
||||
num_video_tokens += 1
|
||||
content = content.replace(self.video_token, "{{video}}", 1)
|
||||
else:
|
||||
mm_inputs = self._get_mm_inputs(images, videos, processor)
|
||||
if "pixel_values_images" in mm_inputs.keys():
|
||||
height, width = get_image_size(to_numpy_array(mm_inputs.get("pixel_values_images")[0]))
|
||||
num_frames = 1
|
||||
|
||||
if "pixel_values_videos" in mm_inputs.keys():
|
||||
one_video = to_numpy_array(mm_inputs.get("pixel_values_videos")[0])
|
||||
height, width = get_image_size(one_video[0])
|
||||
num_frames = one_video.shape[0] # frame dim is always after batch dim
|
||||
|
||||
image_seqlen = (height // processor.patch_size) * (width // processor.patch_size) + 1
|
||||
video_seqlen = num_image_tokens * num_frames
|
||||
if processor.vision_feature_select_strategy == "default":
|
||||
image_seqlen -= 1
|
||||
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while self.image_token in content:
|
||||
num_image_tokens += 1
|
||||
content = content.replace(self.image_token, "{{image}}", 1)
|
||||
while self.video_token in content:
|
||||
num_image_tokens += 1
|
||||
content = content.replace(self.video_token, "{{video}}", 1)
|
||||
|
||||
message["content"] = content.replace("{{image}}", self.image_token * image_seqlen)
|
||||
message["content"] = content.replace("{{video}}", self.video_token * video_seqlen)
|
||||
|
||||
if len(images) != num_image_tokens:
|
||||
raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
|
||||
@ -591,10 +639,22 @@ class VideoLlavaPlugin(BasePlugin):
|
||||
self._validate_input(images, videos)
|
||||
return self._get_mm_inputs(images, videos, processor)
|
||||
|
||||
@override
|
||||
def _regularize_videos(self, videos: Sequence["VideoInput"], **kwargs) -> List[List["ImageObject"]]:
|
||||
r"""
|
||||
Regularizes videos to avoid error. Including reading, resizing and converting.
|
||||
"""
|
||||
videos = super()._regularize_videos(
|
||||
videos,
|
||||
image_resolution=128,
|
||||
video_fps=1.0,
|
||||
video_maxlen=64,
|
||||
)
|
||||
return videos
|
||||
|
||||
|
||||
PLUGINS = {
|
||||
"base": BasePlugin,
|
||||
"idefics2": Idefics2Plugin,
|
||||
"llava": LlavaPlugin,
|
||||
"llava_next": LlavaNextPlugin,
|
||||
"llava_next_video": LlavaNextVideoPlugin,
|
||||
|
||||
@ -686,16 +686,6 @@ _register_template(
|
||||
)
|
||||
|
||||
|
||||
_register_template(
|
||||
name="idefics2",
|
||||
format_user=StringFormatter(slots=["User:{{content}}<end_of_utterance>\nAssistant:"]),
|
||||
format_separator=EmptyFormatter(slots=["\n"]),
|
||||
stop_words=["<end_of_utterance>"],
|
||||
replace_eos=True,
|
||||
mm_plugin=get_mm_plugin(name="idefics2", image_token="<image>"),
|
||||
)
|
||||
|
||||
|
||||
_register_template(
|
||||
name="intern",
|
||||
format_user=StringFormatter(slots=["<|User|>:{{content}}\n<|Bot|>:"]),
|
||||
|
||||
@ -583,23 +583,6 @@ register_model_group(
|
||||
)
|
||||
|
||||
|
||||
register_model_group(
|
||||
models={
|
||||
"Idefics2-Base": {
|
||||
DownloadSource.DEFAULT: "HuggingFaceM4/idefics2-8b-base",
|
||||
},
|
||||
"Idefics2-Chat": {
|
||||
DownloadSource.DEFAULT: "HuggingFaceM4/idefics2-8b",
|
||||
},
|
||||
"Idefics2-Chatty": {
|
||||
DownloadSource.DEFAULT: "HuggingFaceM4/idefics2-8b-chatty",
|
||||
},
|
||||
},
|
||||
template="idefics2",
|
||||
vision=True,
|
||||
)
|
||||
|
||||
|
||||
register_model_group(
|
||||
models={
|
||||
"InternLM-7B": {
|
||||
|
||||
@ -119,15 +119,6 @@ def load_config(model_args: "ModelArguments") -> "PretrainedConfig":
|
||||
Loads model config.
|
||||
"""
|
||||
init_kwargs = _get_init_kwargs(model_args)
|
||||
if "LLaVA-NeXT-Video" in model_args.model_name_or_path:
|
||||
from transformers import CLIPVisionConfig, LlamaConfig, LlavaNextVideoConfig, PretrainedConfig
|
||||
|
||||
official_config = PretrainedConfig.from_pretrained(model_args.model_name_or_path, **init_kwargs)
|
||||
config = LlavaNextVideoConfig(
|
||||
CLIPVisionConfig(**official_config.vision_config), LlamaConfig(**official_config.text_config)
|
||||
)
|
||||
setattr(config, "visual_inputs", True)
|
||||
return config
|
||||
return AutoConfig.from_pretrained(model_args.model_name_or_path, **init_kwargs)
|
||||
|
||||
|
||||
@ -164,11 +155,6 @@ def load_model(
|
||||
load_class = AutoModelForVision2Seq
|
||||
else:
|
||||
load_class = AutoModelForCausalLM
|
||||
if "llava_next_video" == getattr(config, "model_type"):
|
||||
from transformers import LlavaNextVideoForConditionalGeneration
|
||||
|
||||
load_class = LlavaNextVideoForConditionalGeneration
|
||||
|
||||
if model_args.train_from_scratch:
|
||||
model = load_class.from_config(config)
|
||||
else:
|
||||
|
||||
@ -92,7 +92,7 @@ def autocast_projector_dtype(model: "PreTrainedModel", model_args: "ModelArgumen
|
||||
|
||||
if getattr(model, "quantization_method", None):
|
||||
model_type = getattr(model.config, "model_type", None)
|
||||
if model_type in ["llava", "paligemma"]:
|
||||
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")
|
||||
@ -111,9 +111,8 @@ def configure_visual_model(config: "PretrainedConfig") -> None:
|
||||
if model_type in [
|
||||
"llava",
|
||||
"llava_next",
|
||||
"video_llava",
|
||||
"idefics2",
|
||||
"llava_next_video",
|
||||
"video_llava",
|
||||
]: # required for ds zero3 and valuehead models
|
||||
setattr(config, "hidden_size", getattr(config.text_config, "hidden_size", None))
|
||||
|
||||
@ -128,7 +127,7 @@ def get_forbidden_modules(config: "PretrainedConfig", finetuning_args: "Finetuni
|
||||
"""
|
||||
model_type = getattr(config, "model_type", None)
|
||||
forbidden_modules = set()
|
||||
if model_type in ["llava", "paligemma"]:
|
||||
if model_type in ["llava", "llava_next", "llava_next_video", "paligemma", "video_llava"]:
|
||||
if finetuning_args.freeze_vision_tower:
|
||||
forbidden_modules.add("vision_tower")
|
||||
|
||||
@ -170,7 +169,7 @@ def patch_target_modules(
|
||||
"""
|
||||
model_type = getattr(config, "model_type", None)
|
||||
if finetuning_args.freeze_vision_tower:
|
||||
if model_type in ["llava", "paligemma"]:
|
||||
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))
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user