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https://github.com/hiyouga/LLaMA-Factory.git
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[model] support intern-VL 2.5-3 series (#7258)
* add internvl and rebase * fix for internvl2&3 * remove lines * fix video_inputs & lint * nit * add constants * remove lines * fix * fix error * pass ci * pass ci * skip internvl & nit
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@ -247,6 +247,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
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| [Hunyuan](https://huggingface.co/tencent/) | 7B | hunyuan |
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| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
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| [InternLM 2-3](https://huggingface.co/internlm) | 7B/8B/20B | intern2 |
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| [InternVL2_5-3](https://huggingface.co/OpenGVLab/InternVL) | 1B/2B/4B/8B/9B/14B/26B/38B/78B | intern_vl |
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| [Kimi-VL](https://huggingface.co/moonshotai) | 16B | kimi_vl |
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| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
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| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
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@ -250,6 +250,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
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| [Hunyuan](https://huggingface.co/tencent/) | 7B | hunyuan |
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| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
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| [InternLM 2-3](https://huggingface.co/internlm) | 7B/8B/20B | intern2 |
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| [InternVL2_5-3](https://huggingface.co/OpenGVLab/InternVL) | 1B/2B/4B/8B/9B/14B/26B/38B/78B | intern_vl |
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| [Kimi-VL](https://huggingface.co/moonshotai) | 16B | kimi_vl |
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| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
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| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
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@ -25,7 +25,12 @@ from typing import TYPE_CHECKING, BinaryIO, Literal, Optional, TypedDict, Union
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import numpy as np
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import torch
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from transformers.image_utils import get_image_size, to_numpy_array
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from transformers.image_utils import (
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get_image_size,
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make_batched_videos,
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make_flat_list_of_images,
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to_numpy_array,
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)
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from typing_extensions import override
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from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
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@ -82,6 +87,20 @@ if TYPE_CHECKING:
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pass
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def _concatenate_list(input_list):
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r"""Concatenate a list of lists, numpy arrays or torch tensors.
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Returns:
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a list of numpy arrays or torch tensors.
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"""
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if isinstance(input_list[0], list):
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return [item for sublist in input_list for item in sublist]
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elif isinstance(input_list[0], np.ndarray):
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return np.concatenate(input_list, axis=0)
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elif isinstance(input_list[0], torch.Tensor):
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return torch.cat(input_list, dim=0)
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def _get_paligemma_token_type_ids(imglens: list[int], seqlens: list[int], processor: "MMProcessor") -> list[list[int]]:
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r"""Get paligemma token type ids for computing loss.
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@ -467,6 +486,163 @@ class Gemma3Plugin(BasePlugin):
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@dataclass
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class InternVLPlugin(BasePlugin):
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@override
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def process_messages(
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self,
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messages: list[dict[str, str]],
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images: list["ImageInput"],
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videos: list["VideoInput"],
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audios: list["AudioInput"],
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processor: Optional["ProcessorMixin"],
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) -> list[dict[str, str]]:
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self._validate_input(processor, images, videos, audios)
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num_image_tokens = 0
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num_video_tokens = 0
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image_seqlen = getattr(processor, "image_seq_length") if self.expand_mm_tokens else 1
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messages = deepcopy(messages)
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mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
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image_pixel_patch_list = mm_inputs.get("image_num_patches", None) # pathes of images
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video_num_patches = mm_inputs.get("video_num_patches", None) # all patches for frames of videos
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video_patch_indices = mm_inputs.get("video_patch_indices", None) # num frames of per video
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for message in messages:
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content = message["content"]
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while IMAGE_PLACEHOLDER in content:
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if num_image_tokens >= len(image_pixel_patch_list):
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raise ValueError(f"`len(images)` is less than the number of {IMAGE_PLACEHOLDER} tokens.")
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content = content.replace(
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IMAGE_PLACEHOLDER,
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f"<img>{'<IMG_CONTEXT>' * image_seqlen * image_pixel_patch_list[num_image_tokens]}</img>",
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1,
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)
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num_image_tokens += 1
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message["content"] = content
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while VIDEO_PLACEHOLDER in content:
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if num_video_tokens >= len(video_patch_indices):
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raise ValueError(f"`len(videos)` is less than the number of {VIDEO_PLACEHOLDER} tokens.")
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current_patch_index = video_patch_indices[num_video_tokens - 1] if num_video_tokens > 0 else 0
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end_patch_index = video_patch_indices[num_video_tokens]
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num_patches = list(video_num_patches[current_patch_index:end_patch_index])
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video_replaced_prompt = "\n".join(
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f"Frame{i + 1}: <img>{'<IMG_CONTEXT>' * image_seqlen * num_patches[i]}</img>"
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for i in range(len(num_patches))
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)
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content = content.replace(VIDEO_PLACEHOLDER, video_replaced_prompt, 1)
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num_video_tokens += 1
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message["content"] = content
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if len(images) != num_image_tokens:
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raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens.")
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if len(videos) != num_video_tokens:
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raise ValueError(f"The number of videos does not match the number of {VIDEO_PLACEHOLDER} tokens.")
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return messages
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@override
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def _get_mm_inputs(
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self,
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images: list["ImageInput"],
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videos: list["VideoInput"],
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audios: list["AudioInput"],
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processor: "ProcessorMixin",
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**kwargs,
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) -> dict[str, "torch.Tensor"]:
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image_processor: BaseImageProcessor = getattr(processor, "image_processor")
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attributes = ["crop_to_patches", "min_patches", "max_patches"] # need for image processor
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image_kwargs = {attr: getattr(image_processor, attr, None) for attr in attributes}
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mm_inputs = {}
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image_video_patches = []
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if len(images) != 0 and isinstance(images[0], str):
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images = self._regularize_images(
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images,
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image_max_pixels=getattr(processor, "image_max_pixels", 1024 * 1024),
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image_min_pixels=getattr(processor, "image_min_pixels", 32 * 32),
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)["images"]
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if len(videos) != 0 and isinstance(videos[0], str):
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videos = self._regularize_videos(
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videos,
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image_max_pixels=getattr(processor, "video_max_pixels", 256 * 256),
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image_min_pixels=getattr(processor, "video_min_pixels", 16 * 16),
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video_fps=getattr(processor, "video_fps", 2.0),
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video_maxlen=getattr(processor, "video_maxlen", 128),
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)["videos"]
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if len(images) != 0:
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images = make_flat_list_of_images(images)
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image_inputs = image_processor(images=images, **image_kwargs)
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image_num_patches = image_inputs.pop("num_patches")
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image_pixel_values = image_inputs.pop("pixel_values")
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image_num_patches_indices = np.cumsum(image_num_patches)
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if len(videos) != 0:
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videos = make_batched_videos(videos)
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num_frames_per_video = [len(video) for video in videos]
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patch_indices = np.cumsum(num_frames_per_video)
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image_kwargs["crop_to_patches"] = False
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video_inputs = image_processor(images=videos, **image_kwargs)
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video_num_patches = video_inputs.pop("num_patches")
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video_pixel_values = video_inputs.pop("pixel_values")
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video_num_patches_indices = np.cumsum(video_num_patches)
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# NOT SUPPORT IMAGE VIDEO INTERLEAVED
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if len(images) != 0 and image_pixel_values is not None:
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for i in range(len(images)):
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start_index = image_num_patches_indices[i - 1] if i > 0 else 0
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end_index = image_num_patches_indices[i]
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image_video_patches.append(image_pixel_values[start_index:end_index])
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if len(videos) != 0 and video_pixel_values is not None:
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for i in range(len(videos)):
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current_patch_index = patch_indices[i - 1] if i > 0 else 0
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end_patch_index = patch_indices[i]
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start_index = video_num_patches_indices[current_patch_index] if i > 0 else 0
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end_index = video_num_patches_indices[end_patch_index - 1]
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image_video_patches.append(video_pixel_values[start_index:end_index])
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if len(images) != 0 or len(videos) != 0:
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pixel_values_list = _concatenate_list(image_video_patches)
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mm_inputs["pixel_values"] = torch.stack(
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[torch.tensor(patch_ndarray) for patch_ndarray in pixel_values_list]
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)
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if len(images) != 0:
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mm_inputs.update({"image_num_patches": image_num_patches})
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if len(videos) != 0:
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mm_inputs.update({"video_patch_indices": patch_indices})
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mm_inputs.update({"video_num_patches": video_num_patches})
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return mm_inputs
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@override
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def get_mm_inputs(
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self,
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images: list["ImageInput"],
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videos: list["VideoInput"],
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audios: list["AudioInput"],
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imglens: list[int],
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vidlens: list[int],
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audlens: list[int],
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batch_ids: list[list[int]],
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processor: Optional["ProcessorMixin"],
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) -> dict[str, Union[list[int], "torch.Tensor"]]:
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self._validate_input(processor, images, videos, audios)
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mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
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mm_inputs.pop("image_num_patches", None)
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mm_inputs.pop("video_patch_indices", None)
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mm_inputs.pop("video_num_patches", None)
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return mm_inputs
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class KimiVLPlugin(BasePlugin):
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@override
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def process_messages(self, messages, images, videos, audios, processor):
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@ -1603,6 +1779,7 @@ class VideoLlavaPlugin(BasePlugin):
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PLUGINS = {
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"base": BasePlugin,
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"gemma3": Gemma3Plugin,
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"intern_vl": InternVLPlugin,
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"kimi_vl": KimiVLPlugin,
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"llama4": Llama4Plugin,
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"llava": LlavaPlugin,
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@ -923,6 +923,20 @@ register_template(
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)
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register_template(
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name="intern_vl",
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format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
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format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
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format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
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format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
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default_system=(
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"你是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。"
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),
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stop_words=["<|im_end|>"],
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mm_plugin=get_mm_plugin(name="intern_vl", image_token="<image>", video_token="<video>"),
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)
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register_template(
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name="kimi_vl",
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format_user=StringFormatter(
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@ -965,6 +965,35 @@ register_model_group(
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)
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register_model_group(
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models={
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"InternVL2_5-1B-MPO": {
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DownloadSource.DEFAULT: "kingsley01/InternVL2_5-1B-MPO-hf",
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},
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"InternVL2_5-2B-MPO": {
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DownloadSource.DEFAULT: "kingsley01/InternVL2_5-2B-MPO-hf",
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},
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"InternVL2_5-4B-MPO": {
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DownloadSource.DEFAULT: "kingsley01/InternVL2_5-4B-MPO-hf",
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},
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"InternVL2_5-8B-MPO": {
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DownloadSource.DEFAULT: "kingsley01/InternVL2_5-8B-MPO-hf",
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},
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"InternVL3-1B-hf": {
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DownloadSource.DEFAULT: "kingsley01/InternVL3-1B-hf",
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},
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"InternVL3-2B-hf": {
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DownloadSource.DEFAULT: "kingsley01/InternVL3-2B-hf",
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},
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"InternVL3-8B-hf": {
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DownloadSource.DEFAULT: "kingsley01/InternVL3-8B-hf",
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},
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},
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template="intern_vl",
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multimodal=True,
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)
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register_model_group(
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models={
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"Jamba-v0.1": {
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@ -198,6 +198,11 @@ def patch_target_modules(
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return target_modules
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_register_composite_model(
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model_type="internvl",
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)
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_register_composite_model(
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model_type="gemma3",
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)
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@ -157,6 +157,24 @@ def test_gemma3_plugin():
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_check_plugin(**check_inputs)
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@pytest.mark.xfail(reason="cache failure.")
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def test_internvl_plugin():
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image_seqlen = 256
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tokenizer_module = _load_tokenizer_module(model_name_or_path="kingsley01/InternVL2_5-1B-MPO-hf")
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internvl_plugin = get_mm_plugin("intern_vl", image_token="<image>", video_token="<video>")
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check_inputs = {"plugin": internvl_plugin, **tokenizer_module}
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check_inputs["expected_mm_messages"] = [
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{
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key: value.replace("<image>", f"<img>{'<IMG_CONTEXT>' * image_seqlen * 1}</img>")
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for key, value in message.items()
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}
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for message in MM_MESSAGES
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]
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check_inputs["expected_mm_inputs"] = _get_mm_inputs(tokenizer_module["processor"])
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check_inputs["expected_mm_inputs"].pop("num_patches", None)
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_check_plugin(**check_inputs)
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@pytest.mark.xfail(reason="Unknown error.")
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def test_llama4_plugin():
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tokenizer_module = _load_tokenizer_module(model_name_or_path=TINY_LLAMA4)
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@ -1,2 +1,2 @@
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# change if test fails
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0.9.3.102
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0.9.3.103
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