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[model] support gemma3 (#7273)
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13
README.md
13
README.md
@ -84,10 +84,10 @@ Choose your path:
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### Day-N Support for Fine-Tuning Cutting-Edge Models
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| Support Date | Model Name |
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| ------------ | ---------------------------------------------------------- |
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| Day 0 | Qwen2.5 / Qwen2-VL / QwQ / QvQ / InternLM3 / MiniCPM-o-2.6 |
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| Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 |
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| Support Date | Model Name |
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| ------------ | ------------------------------------------------------------ |
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| Day 0 | Qwen2.5 / Qwen2.5-VL / Gemma 3 / InternLM 3 / MiniCPM-o-2.6 |
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| Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 |
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## Benchmark
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@ -106,6 +106,8 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
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## Changelog
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[25/03/12] We supported fine-tuning the **[Gemma-3](https://huggingface.co/blog/gemma3)** model.
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[25/02/24] Announcing **[EasyR1](https://github.com/hiyouga/EasyR1)**, an efficient, scalable and multi-modality RL training framework for efficient GRPO training.
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[25/02/11] We supported saving the **[Ollama](https://github.com/ollama/ollama)** modelfile when exporting the model checkpoints. See [examples](examples/README.md) for usage.
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@ -120,7 +122,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
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[25/01/14] We supported fine-tuning the **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** and **[MiniCPM-V-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6)** models. Thank [@BUAADreamer](https://github.com/BUAADreamer)'s PR.
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[25/01/14] We supported fine-tuning the **[InternLM3](https://huggingface.co/collections/internlm/)** models. Thank [@hhaAndroid](https://github.com/hhaAndroid)'s PR.
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[25/01/14] We supported fine-tuning the **[InternLM 3](https://huggingface.co/collections/internlm/)** models. Thank [@hhaAndroid](https://github.com/hhaAndroid)'s PR.
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[25/01/10] We supported fine-tuning the **[Phi-4](https://huggingface.co/microsoft/phi-4)** model.
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@ -229,6 +231,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
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| [DeepSeek R1 (Distill)](https://huggingface.co/deepseek-ai) | 1.5B/7B/8B/14B/32B/70B/671B | deepseek3 |
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| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
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| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma |
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| [Gemma 3](https://huggingface.co/google) | 1B/4B/12B/27B | gemma3 |
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| [GLM-4](https://huggingface.co/THUDM) | 9B | glm4 |
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| [GPT-2](https://huggingface.co/openai-community) | 0.1B/0.4B/0.8B/1.5B | - |
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| [Granite 3.0-3.1](https://huggingface.co/ibm-granite) | 1B/2B/3B/8B | granite3 |
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13
README_zh.md
13
README_zh.md
@ -86,10 +86,10 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
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### 最新模型的 Day-N 微调适配
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| 适配时间 | 模型名称 |
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| ------------ | ---------------------------------------------------------- |
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| Day 0 | Qwen2.5 / Qwen2-VL / QwQ / QvQ / InternLM3 / MiniCPM-o-2.6 |
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| Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 |
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| 适配时间 | 模型名称 |
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| ------------ | ------------------------------------------------------------ |
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| Day 0 | Qwen2.5 / Qwen2.5-VL / Gemma 3 / InternLM 3 / MiniCPM-o-2.6 |
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| Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 |
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## 性能指标
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@ -108,6 +108,8 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
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## 更新日志
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[25/03/12] 我们支持了 **[Gemma-3](https://huggingface.co/blog/gemma3)** 模型的微调。
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[25/02/24] 我们宣布开源 **[EasyR1](https://github.com/hiyouga/EasyR1)**，一个高效可扩展的多模态强化学习框架，支持高效的 GRPO 训练。
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[25/02/11] 我们支持了在导出模型时保存 **[Ollama](https://github.com/ollama/ollama)** 配置文件。详细用法请参照 [examples](examples/README_zh.md)。
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@ -122,7 +124,7 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
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[25/01/14] 我们支持了 **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** 和 **[MiniCPM-V-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6)** 模型的微调。 感谢 [@BUAADreamer](https://github.com/BUAADreamer) 的 PR.
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[25/01/14] 我们支持了 **[InternLM3](https://huggingface.co/collections/internlm/)** 模型的微调。感谢 [@hhaAndroid](https://github.com/hhaAndroid) 的 PR。
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[25/01/14] 我们支持了 **[InternLM 3](https://huggingface.co/collections/internlm/)** 模型的微调。感谢 [@hhaAndroid](https://github.com/hhaAndroid) 的 PR。
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[25/01/10] 我们支持了 **[Phi-4](https://huggingface.co/microsoft/phi-4)** 模型的微调。
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@ -231,6 +233,7 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
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| [DeepSeek R1 (Distill)](https://huggingface.co/deepseek-ai) | 1.5B/7B/8B/14B/32B/70B/671B | deepseek3 |
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| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
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| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma |
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| [Gemma 3](https://huggingface.co/google) | 1B/4B/12B/27B | gemma3 |
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| [GLM-4](https://huggingface.co/THUDM) | 9B | glm4 |
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| [GPT-2](https://huggingface.co/openai-community) | 0.1B/0.4B/0.8B/1.5B | - |
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| [Granite 3.0-3.1](https://huggingface.co/ibm-granite) | 1B/2B/3B/8B | granite3 |
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@ -27,6 +27,7 @@ indent-width = 4
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ignore = [
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"C408", # collection
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"C901", # complex
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"E501", # line too long
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"E731", # lambda function
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"E741", # ambiguous var name
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"D100", # no doc public module
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@ -1,3 +1,20 @@
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# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
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#
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# This code is inspired by the HuggingFace's Transformers library.
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# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llava/processing_llava.py
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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import math
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import re
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@ -5,7 +22,7 @@ from collections.abc import Sequence
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from copy import deepcopy
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from dataclasses import dataclass
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from io import BytesIO
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from typing import TYPE_CHECKING, Optional, TypedDict, Union
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from typing import TYPE_CHECKING, Literal, Optional, TypedDict, Union
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import numpy as np
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import torch
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@ -56,24 +73,63 @@ if TYPE_CHECKING:
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VideoInput = str
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AudioInput = Union[str, NDArray]
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class MMProcessor(ProcessorMixin):
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patch_size: int
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image_seq_length: int
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num_additional_image_tokens: int
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vision_feature_select_strategy: Literal["default", "full"]
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def _get_number_of_features(self, orig_height: int, orig_width: int, height: int, width: int) -> int:
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pass
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def _get_paligemma_token_type_ids(
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imglens: Sequence[int], seqlens: Sequence[int], processor: "ProcessorMixin"
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imglens: Sequence[int], seqlens: Sequence[int], processor: "MMProcessor"
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) -> list[list[int]]:
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r"""Get paligemma token type ids for computing loss.
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It is slightly different with the original token type ids where the prompt part is 0.
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Returns:
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batch_token_type_ids: shape (batch_size, sequence_length)
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batch_token_type_ids: shape (batch_size, seq_length)
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"""
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batch_token_type_ids = []
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for imglen, seqlen in zip(imglens, seqlens):
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image_seqlen = imglen * getattr(processor, "image_seqlen")
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image_seqlen = imglen * processor.image_seq_length
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batch_token_type_ids.append([0] * image_seqlen + [1] * (seqlen - image_seqlen))
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return batch_token_type_ids
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def _get_gemma3_token_type_ids(batch_ids: list[list[int]], processor: "MMProcessor"):
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r"""Get gemma3 token type ids for computing loss.
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Returns:
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batch_token_type_ids: shape (batch_size, seq_length)
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"""
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image_token_id: int = getattr(processor, "image_token_id")
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batch_token_type_ids = []
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for token_ids in batch_ids:
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token_ids = np.array(token_ids)
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token_type_ids = np.zeros_like(token_ids)
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token_type_ids[token_ids == image_token_id] = 1
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batch_token_type_ids.append(token_type_ids.tolist())
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return batch_token_type_ids
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def _make_batched_images(images: Sequence["ImageObject"], imglens: list[int]) -> list[list["ImageObject"]]:
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r"""Make nested list of images."""
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batch_images = []
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for imglen in imglens:
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batch_images.append(images[:imglen])
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images = images[imglen:]
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return batch_images
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@dataclass
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class MMPluginMixin:
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image_token: Optional[str]
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@ -83,7 +139,7 @@ class MMPluginMixin:
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def _validate_input(
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self,
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processor: Optional["ProcessorMixin"],
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processor: Optional["MMProcessor"],
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images: Sequence["ImageInput"],
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videos: Sequence["VideoInput"],
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audios: Sequence["AudioInput"],
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@ -204,7 +260,8 @@ class MMPluginMixin:
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images: Sequence["ImageInput"],
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videos: Sequence["VideoInput"],
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audios: Sequence["AudioInput"],
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processor: "ProcessorMixin",
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processor: "MMProcessor",
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imglens: Optional[list[int]] = None,
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) -> dict[str, "torch.Tensor"]:
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r"""Process visual inputs.
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@ -214,23 +271,34 @@ class MMPluginMixin:
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Returns: (qwen2-vl)
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pixel_values: tensor with shape (num_patches, patch_dim)
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image_grid_thw: tensor with shape (num_images, 3), where the three numbers are time, width, height
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where num_patches == torch.prod(image_grid_thw)
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Returns: (mllama)
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pixel_values: tensor with shape
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(batch_size, max_num_images, max_image_tiles, channels, tile_height, tile_width)
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For example, (2, 1, 4, 3, 560, 560).
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aspect_ratio_ids: tensor with shape (batch_size, max_num_images). For example, (2, 1).
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aspect_ratio_mask: tensor with shape (batch_size, max_num_images, max_image_tiles). For example, (2, 1, 4).
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num_tiles: List[List[int]] with shape (batch_size, num_images_in_batch). For example, (2, 1).
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It holds num_patches == torch.prod(image_grid_thw)
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"""
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image_processor: BaseImageProcessor = getattr(processor, "image_processor", None)
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video_processor: BaseImageProcessor = getattr(processor, "video_processor", image_processor)
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feature_extractor: SequenceFeatureExtractor = getattr(processor, "feature_extractor", None)
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mm_inputs = {}
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if len(images) != 0:
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image_processor: BaseImageProcessor = getattr(processor, "image_processor", None)
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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", 768 * 768),
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image_min_pixels=getattr(processor, "image_min_pixels", 32 * 32),
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)
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if imglens is not None:
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images = _make_batched_images(images, imglens)
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mm_inputs.update(image_processor(images, return_tensors="pt"))
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if len(videos) != 0:
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video_processor: BaseImageProcessor = getattr(
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processor, "video_processor", getattr(processor, "image_processor", None)
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)
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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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@ -244,6 +312,7 @@ class MMPluginMixin:
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mm_inputs.update(video_processor(videos, return_tensors="pt"))
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if len(audios) != 0:
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feature_extractor: SequenceFeatureExtractor = getattr(processor, "feature_extractor", None)
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audios = self._regularize_audios(
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audios,
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sampling_rate=getattr(feature_extractor, "sampling_rate", 16000),
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@ -270,9 +339,9 @@ class BasePlugin(MMPluginMixin):
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images: Sequence["ImageInput"],
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videos: Sequence["VideoInput"],
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audios: Sequence["AudioInput"],
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processor: Optional["ProcessorMixin"],
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processor: Optional["MMProcessor"],
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) -> list[dict[str, str]]:
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r"""Pre-processes input messages before tokenization for VLMs."""
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r"""Pre-process input messages before tokenization for VLMs."""
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self._validate_input(processor, images, videos, audios)
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return messages
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@ -284,9 +353,9 @@ class BasePlugin(MMPluginMixin):
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videos: Sequence["VideoInput"],
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audios: Sequence["AudioInput"],
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tokenizer: "PreTrainedTokenizer",
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processor: Optional["ProcessorMixin"],
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processor: Optional["MMProcessor"],
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) -> tuple[list[int], Optional[list[int]]]:
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r"""Pre-processes token ids after tokenization for VLMs."""
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r"""Pre-process token ids after tokenization for VLMs."""
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self._validate_input(processor, images, videos, audios)
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return input_ids, labels
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@ -299,7 +368,7 @@ class BasePlugin(MMPluginMixin):
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vidlens: Sequence[int],
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audlens: Sequence[int],
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batch_ids: Sequence[list[int]],
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processor: Optional["ProcessorMixin"],
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processor: Optional["MMProcessor"],
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) -> dict[str, Union[list[int], "torch.Tensor"]]:
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r"""Build batched multimodal inputs for VLMs.
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@ -315,11 +384,11 @@ class BasePlugin(MMPluginMixin):
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"""
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self._validate_input(processor, images, videos, audios)
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return {}
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return self._get_mm_inputs(images, videos, audios, processor)
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@dataclass
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class LlavaPlugin(BasePlugin):
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class Gemma3Plugin(BasePlugin):
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@override
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def process_messages(
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self,
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@ -327,19 +396,21 @@ class LlavaPlugin(BasePlugin):
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images: Sequence["ImageInput"],
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videos: Sequence["VideoInput"],
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audios: Sequence["AudioInput"],
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processor: Optional["ProcessorMixin"],
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processor: Optional["MMProcessor"],
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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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image_seqlen = getattr(processor, "image_seqlen") if self.expand_mm_tokens else 1
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messages = deepcopy(messages)
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boi_token: str = getattr(processor, "boi_token")
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full_image_sequence: str = getattr(processor, "full_image_sequence")
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image_str = full_image_sequence if self.expand_mm_tokens else boi_token
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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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content = content.replace(IMAGE_PLACEHOLDER, "{{image}}" * image_seqlen, 1)
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content = content.replace(IMAGE_PLACEHOLDER, "{{image}}", 1)
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num_image_tokens += 1
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message["content"] = content.replace("{{image}}", self.image_token)
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message["content"] = content.replace("{{image}}", image_str)
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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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@ -356,10 +427,53 @@ class LlavaPlugin(BasePlugin):
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vidlens: Sequence[int],
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audlens: Sequence[int],
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batch_ids: Sequence[list[int]],
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processor: Optional["ProcessorMixin"],
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processor: Optional["MMProcessor"],
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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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return self._get_mm_inputs(images, videos, audios, processor)
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mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
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mm_inputs.pop("num_crops", None)
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mm_inputs["token_type_ids"] = _get_gemma3_token_type_ids(batch_ids, processor)
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return mm_inputs
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@dataclass
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class LlavaPlugin(BasePlugin):
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@override
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def process_messages(
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self,
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messages: Sequence[dict[str, str]],
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images: Sequence["ImageInput"],
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videos: Sequence["VideoInput"],
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audios: Sequence["AudioInput"],
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processor: Optional["MMProcessor"],
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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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messages = deepcopy(messages)
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if self.expand_mm_tokens:
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
if "pixel_values" in mm_inputs:
|
||||
height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0]))
|
||||
image_seqlen = (height // processor.patch_size) * (
|
||||
width // processor.patch_size
|
||||
) + processor.num_additional_image_tokens
|
||||
if processor.vision_feature_select_strategy == "default":
|
||||
image_seqlen -= 1
|
||||
else:
|
||||
image_seqlen = 1
|
||||
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while IMAGE_PLACEHOLDER in content:
|
||||
content = content.replace(IMAGE_PLACEHOLDER, "{{image}}" * image_seqlen, 1)
|
||||
num_image_tokens += 1
|
||||
|
||||
message["content"] = content.replace("{{image}}", self.image_token)
|
||||
|
||||
if len(images) != num_image_tokens:
|
||||
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens.")
|
||||
|
||||
return messages
|
||||
|
||||
|
||||
@dataclass
|
||||
@ -371,15 +485,16 @@ class LlavaNextPlugin(BasePlugin):
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
processor: Optional["MMProcessor"],
|
||||
) -> list[dict[str, str]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
num_image_tokens = 0
|
||||
messages = deepcopy(messages)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
if "pixel_values" in mm_inputs:
|
||||
image_sizes = iter(mm_inputs["image_sizes"].tolist())
|
||||
height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0][0]))
|
||||
if self.expand_mm_tokens:
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
if "pixel_values" in mm_inputs:
|
||||
image_sizes = iter(mm_inputs["image_sizes"].tolist())
|
||||
height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0][0]))
|
||||
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
@ -387,7 +502,7 @@ class LlavaNextPlugin(BasePlugin):
|
||||
if self.expand_mm_tokens:
|
||||
orig_height, orig_width = next(image_sizes)
|
||||
image_seqlen = processor._get_number_of_features(orig_height, orig_width, height, width)
|
||||
if getattr(processor, "vision_feature_select_strategy", "default") == "default":
|
||||
if processor.vision_feature_select_strategy == "default":
|
||||
image_seqlen -= 1
|
||||
else:
|
||||
image_seqlen = 1
|
||||
@ -402,21 +517,6 @@ class LlavaNextPlugin(BasePlugin):
|
||||
|
||||
return messages
|
||||
|
||||
@override
|
||||
def get_mm_inputs(
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
imglens: Sequence[int],
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[list[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> dict[str, Union[list[int], "torch.Tensor"]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
return self._get_mm_inputs(images, videos, audios, processor)
|
||||
|
||||
|
||||
@dataclass
|
||||
class LlavaNextVideoPlugin(BasePlugin):
|
||||
@ -427,48 +527,50 @@ class LlavaNextVideoPlugin(BasePlugin):
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
processor: Optional["MMProcessor"],
|
||||
) -> list[dict[str, str]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
num_image_tokens, num_video_tokens = 0, 0
|
||||
messages = deepcopy(messages)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
if "pixel_values" in mm_inputs:
|
||||
image_sizes = iter(mm_inputs["image_sizes"].tolist())
|
||||
height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0][0]))
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while IMAGE_PLACEHOLDER in content:
|
||||
if self.expand_mm_tokens:
|
||||
orig_height, orig_width = next(image_sizes)
|
||||
image_seqlen = processor._get_number_of_features(orig_height, orig_width, height, width)
|
||||
if getattr(processor, "vision_feature_select_strategy", "default") == "default":
|
||||
image_seqlen -= 1
|
||||
else:
|
||||
image_seqlen = 1
|
||||
if self.expand_mm_tokens:
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
if "pixel_values" in mm_inputs:
|
||||
image_sizes = iter(mm_inputs["image_sizes"].tolist())
|
||||
height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0][0]))
|
||||
|
||||
content = content.replace(IMAGE_PLACEHOLDER, "{{image}}" * image_seqlen, 1)
|
||||
num_image_tokens += 1
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while IMAGE_PLACEHOLDER in content:
|
||||
if self.expand_mm_tokens:
|
||||
orig_height, orig_width = next(image_sizes)
|
||||
image_seqlen = processor._get_number_of_features(orig_height, orig_width, height, width)
|
||||
if processor.vision_feature_select_strategy == "default":
|
||||
image_seqlen -= 1
|
||||
else:
|
||||
image_seqlen = 1
|
||||
|
||||
message["content"] = content.replace("{{image}}", self.image_token)
|
||||
content = content.replace(IMAGE_PLACEHOLDER, "{{image}}" * image_seqlen, 1)
|
||||
num_image_tokens += 1
|
||||
|
||||
if "pixel_values_videos" in mm_inputs:
|
||||
if self.expand_mm_tokens:
|
||||
pixel_values_video = to_numpy_array(mm_inputs.get("pixel_values_videos")[0])
|
||||
height, width = get_image_size(pixel_values_video[0])
|
||||
num_frames = pixel_values_video.shape[0] # frame dim is always after batch dim
|
||||
message["content"] = content.replace("{{image}}", self.image_token)
|
||||
|
||||
if self.expand_mm_tokens:
|
||||
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
|
||||
else:
|
||||
video_seqlen = 1
|
||||
else:
|
||||
video_seqlen = 1
|
||||
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while VIDEO_PLACEHOLDER in content:
|
||||
num_video_tokens += 1
|
||||
content = content.replace(VIDEO_PLACEHOLDER, "{{video}}" * video_seqlen, 1)
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while VIDEO_PLACEHOLDER in content:
|
||||
content = content.replace(VIDEO_PLACEHOLDER, "{{video}}" * video_seqlen, 1)
|
||||
num_video_tokens += 1
|
||||
|
||||
message["content"] = content.replace("{{video}}", self.video_token)
|
||||
message["content"] = content.replace("{{video}}", self.video_token)
|
||||
|
||||
if len(images) != num_image_tokens:
|
||||
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens.")
|
||||
@ -478,21 +580,6 @@ class LlavaNextVideoPlugin(BasePlugin):
|
||||
|
||||
return messages
|
||||
|
||||
@override
|
||||
def get_mm_inputs(
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
imglens: Sequence[int],
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[list[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> dict[str, Union[list[int], "torch.Tensor"]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
return self._get_mm_inputs(images, videos, audios, processor)
|
||||
|
||||
|
||||
@dataclass
|
||||
class MiniCPMVPlugin(BasePlugin):
|
||||
@ -503,7 +590,7 @@ class MiniCPMVPlugin(BasePlugin):
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
processor: Optional["MMProcessor"],
|
||||
) -> list[dict[str, str]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
num_image_tokens, num_video_tokens, num_audio_tokens = 0, 0, 0
|
||||
@ -602,7 +689,7 @@ class MiniCPMVPlugin(BasePlugin):
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: "ProcessorMixin",
|
||||
processor: "MMProcessor",
|
||||
**kwargs,
|
||||
) -> dict[str, "torch.Tensor"]:
|
||||
image_processor: BaseImageProcessor = getattr(processor, "image_processor")
|
||||
@ -677,7 +764,7 @@ class MiniCPMVPlugin(BasePlugin):
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[list[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
processor: Optional["MMProcessor"],
|
||||
) -> dict[str, Union[list[int], "torch.Tensor"]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
# image bound
|
||||
@ -745,7 +832,7 @@ class MllamaPlugin(BasePlugin):
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
processor: Optional["MMProcessor"],
|
||||
) -> list[dict[str, str]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
num_image_tokens = 0
|
||||
@ -760,43 +847,6 @@ class MllamaPlugin(BasePlugin):
|
||||
|
||||
return messages
|
||||
|
||||
@override
|
||||
def _get_mm_inputs(
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: "ProcessorMixin",
|
||||
imglens: list[int],
|
||||
) -> dict[str, "torch.Tensor"]:
|
||||
r"""Process visual inputs for mllama because its image processor only accepts List[List[ImageInput]].
|
||||
|
||||
Returns:
|
||||
pixel_values: tensor with shape
|
||||
(batch_size, max_num_images, max_image_tiles, channels, tile_height, tile_width)
|
||||
For example, (2, 1, 4, 3, 560, 560).
|
||||
aspect_ratio_ids: tensor with shape (batch_size, max_num_images). For example, (2, 1).
|
||||
aspect_ratio_mask: tensor with shape (batch_size, max_num_images, max_image_tiles). For example, (2, 1, 4).
|
||||
num_tiles: List[List[int]] with shape (batch_size, num_images_in_batch). For example, (2, 1).
|
||||
|
||||
"""
|
||||
image_processor: BaseImageProcessor = getattr(processor, "image_processor")
|
||||
mm_inputs = {}
|
||||
if len(images) > 0:
|
||||
images = self._regularize_images(
|
||||
images,
|
||||
image_max_pixels=getattr(processor, "image_max_pixels", 768 * 768),
|
||||
image_min_pixels=getattr(processor, "image_min_pixels", 32 * 32),
|
||||
)
|
||||
batch_images = []
|
||||
for image_length in imglens:
|
||||
batch_images.append(images[:image_length])
|
||||
images = images[image_length:]
|
||||
|
||||
mm_inputs.update(image_processor(batch_images, return_tensors="pt"))
|
||||
|
||||
return mm_inputs
|
||||
|
||||
@override
|
||||
def get_mm_inputs(
|
||||
self,
|
||||
@ -807,14 +857,14 @@ class MllamaPlugin(BasePlugin):
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[list[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
processor: Optional["MMProcessor"],
|
||||
) -> dict[str, Union[list[int], "torch.Tensor"]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor, imglens)
|
||||
if mm_inputs:
|
||||
num_tiles = mm_inputs.pop("num_tiles")
|
||||
image_token_id = getattr(processor, "image_token_id")
|
||||
max_image_tiles = getattr(processor.image_processor, "max_image_tiles")
|
||||
image_token_id: int = getattr(processor, "image_token_id")
|
||||
max_image_tiles: int = getattr(processor.image_processor, "max_image_tiles")
|
||||
cross_attention_token_mask = [
|
||||
get_cross_attention_token_mask(input_ids, image_token_id) for input_ids in batch_ids
|
||||
]
|
||||
@ -839,7 +889,7 @@ class PaliGemmaPlugin(BasePlugin):
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
processor: Optional["MMProcessor"],
|
||||
) -> list[dict[str, str]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
num_image_tokens = 0
|
||||
@ -847,10 +897,10 @@ class PaliGemmaPlugin(BasePlugin):
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while IMAGE_PLACEHOLDER in content:
|
||||
content = content.replace(IMAGE_PLACEHOLDER, "{{image}}", 1)
|
||||
content = content.replace(IMAGE_PLACEHOLDER, "", 1)
|
||||
num_image_tokens += 1
|
||||
|
||||
message["content"] = content.replace("{{image}}", "")
|
||||
message["content"] = content
|
||||
|
||||
if len(images) != num_image_tokens:
|
||||
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens.")
|
||||
@ -866,15 +916,15 @@ class PaliGemmaPlugin(BasePlugin):
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
processor: Optional["ProcessorMixin"],
|
||||
processor: Optional["MMProcessor"],
|
||||
) -> tuple[list[int], Optional[list[int]]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
num_images = len(images)
|
||||
image_seqlen = num_images * getattr(processor, "image_seqlen") if self.expand_mm_tokens else 0 # skip mm token
|
||||
image_seqlen = processor.image_seq_length if self.expand_mm_tokens else 0 # skip mm token
|
||||
image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
|
||||
input_ids = [image_token_id] * image_seqlen + input_ids
|
||||
input_ids = [image_token_id] * num_images * image_seqlen + input_ids
|
||||
if labels is not None:
|
||||
labels = [IGNORE_INDEX] * image_seqlen + labels
|
||||
labels = [IGNORE_INDEX] * num_images * image_seqlen + labels
|
||||
|
||||
return input_ids, labels
|
||||
|
||||
@ -888,7 +938,7 @@ class PaliGemmaPlugin(BasePlugin):
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[list[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
processor: Optional["MMProcessor"],
|
||||
) -> dict[str, Union[list[int], "torch.Tensor"]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
seqlens = [len(input_ids) for input_ids in batch_ids]
|
||||
@ -906,33 +956,31 @@ class PixtralPlugin(BasePlugin):
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
processor: Optional["MMProcessor"],
|
||||
) -> list[dict[str, str]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
patch_size = getattr(processor, "patch_size")
|
||||
image_token = getattr(processor, "image_token")
|
||||
image_break_token = getattr(processor, "image_break_token")
|
||||
image_end_token = getattr(processor, "image_end_token")
|
||||
|
||||
num_image_tokens = 0
|
||||
messages = deepcopy(messages)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
if "pixel_values" in mm_inputs:
|
||||
image_sizes = iter(mm_inputs["image_sizes"].tolist())
|
||||
if self.expand_mm_tokens:
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
if "pixel_values" in mm_inputs:
|
||||
image_sizes = iter(mm_inputs["image_sizes"].tolist())
|
||||
image_break_token: str = getattr(processor, "image_break_token")
|
||||
image_end_token: str = getattr(processor, "image_end_token")
|
||||
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while IMAGE_PLACEHOLDER in content:
|
||||
if self.expand_mm_tokens:
|
||||
height, width = next(image_sizes)
|
||||
num_height_tokens = height // patch_size
|
||||
num_width_tokens = width // patch_size
|
||||
replace_tokens = [[image_token] * num_width_tokens + [image_break_token]] * num_height_tokens
|
||||
num_height_tokens = height // processor.patch_size
|
||||
num_width_tokens = width // processor.patch_size
|
||||
replace_tokens = [[self.image_token] * num_width_tokens + [image_break_token]] * num_height_tokens
|
||||
replace_tokens = [item for sublist in replace_tokens for item in sublist] # flatten list
|
||||
replace_tokens[-1] = image_end_token
|
||||
replace_str = "".join(replace_tokens)
|
||||
else:
|
||||
replace_str = image_token
|
||||
replace_str = self.image_token
|
||||
|
||||
content = content.replace(IMAGE_PLACEHOLDER, replace_str, 1)
|
||||
num_image_tokens += 1
|
||||
@ -954,7 +1002,7 @@ class PixtralPlugin(BasePlugin):
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[list[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
processor: Optional["MMProcessor"],
|
||||
) -> dict[str, Union[list[int], "torch.Tensor"]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
@ -971,17 +1019,18 @@ class Qwen2AudioPlugin(BasePlugin):
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
processor: Optional["MMProcessor"],
|
||||
) -> list[dict[str, str]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
bos_token: str = getattr(processor, "audio_bos_token")
|
||||
eos_token: str = getattr(processor, "audio_eos_token")
|
||||
messages = deepcopy(messages)
|
||||
mm_inputs = self._get_mm_inputs([], [], audios, processor)
|
||||
if "feature_attention_mask" in mm_inputs:
|
||||
audio_lengths = mm_inputs["feature_attention_mask"].sum(-1).tolist()
|
||||
|
||||
num_audio_tokens = 0
|
||||
messages = deepcopy(messages)
|
||||
if self.expand_mm_tokens:
|
||||
mm_inputs = self._get_mm_inputs([], [], audios, processor)
|
||||
if "feature_attention_mask" in mm_inputs:
|
||||
audio_lengths = mm_inputs["feature_attention_mask"].sum(-1).tolist()
|
||||
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while AUDIO_PLACEHOLDER in content:
|
||||
@ -1014,7 +1063,7 @@ class Qwen2AudioPlugin(BasePlugin):
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[list[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
processor: Optional["MMProcessor"],
|
||||
) -> dict[str, Union[list[int], "torch.Tensor"]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
return self._get_mm_inputs(images, videos, audios, processor)
|
||||
@ -1072,7 +1121,7 @@ class Qwen2VLPlugin(BasePlugin):
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: "ProcessorMixin",
|
||||
processor: "MMProcessor",
|
||||
) -> dict[str, "torch.Tensor"]:
|
||||
image_processor: BaseImageProcessor = getattr(processor, "image_processor", None)
|
||||
mm_inputs = {}
|
||||
@ -1104,7 +1153,7 @@ class Qwen2VLPlugin(BasePlugin):
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
processor: Optional["MMProcessor"],
|
||||
) -> list[dict[str, str]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
num_image_tokens, num_video_tokens = 0, 0
|
||||
@ -1162,14 +1211,15 @@ class Qwen2VLPlugin(BasePlugin):
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[list[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
processor: Optional["MMProcessor"],
|
||||
) -> dict[str, Union[list[int], "torch.Tensor"]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
fps_per_video = mm_inputs.pop("fps_per_video", [])
|
||||
image_processor: BaseImageProcessor = getattr(processor, "image_processor")
|
||||
temporal_patch_size: int = getattr(image_processor, "temporal_patch_size", 2)
|
||||
if "second_per_grid_ts" in processor.model_input_names and fps_per_video:
|
||||
mm_inputs["second_per_grid_ts"] = [image_processor.temporal_patch_size / fps for fps in fps_per_video]
|
||||
mm_inputs["second_per_grid_ts"] = [temporal_patch_size / fps for fps in fps_per_video]
|
||||
|
||||
return mm_inputs
|
||||
|
||||
@ -1183,45 +1233,45 @@ class VideoLlavaPlugin(BasePlugin):
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
processor: Optional["MMProcessor"],
|
||||
) -> list[dict[str, str]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
num_image_tokens, num_video_tokens = 0, 0
|
||||
messages = deepcopy(messages)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
num_frames = 0
|
||||
has_images = "pixel_values_images" in mm_inputs
|
||||
has_videos = "pixel_values_videos" in mm_inputs
|
||||
if has_images or has_videos:
|
||||
if self.expand_mm_tokens:
|
||||
if has_images:
|
||||
height, width = get_image_size(to_numpy_array(mm_inputs.get("pixel_values_images")[0]))
|
||||
num_frames = 1
|
||||
if self.expand_mm_tokens:
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
if "pixel_values_images" in mm_inputs:
|
||||
height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values_images"][0]))
|
||||
num_frames = 1
|
||||
|
||||
if has_videos:
|
||||
pixel_values_video = to_numpy_array(mm_inputs.get("pixel_values_videos")[0])
|
||||
height, width = get_image_size(pixel_values_video[0])
|
||||
num_frames = pixel_values_video.shape[0] # frame dim is always after batch dim
|
||||
if "pixel_values_videos" in mm_inputs:
|
||||
one_video = to_numpy_array(mm_inputs["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
|
||||
if "pixel_values_images" in mm_inputs or "pixel_values_videos" in mm_inputs:
|
||||
image_seqlen = (height // processor.patch_size) * (
|
||||
width // processor.patch_size
|
||||
) + processor.num_additional_image_tokens
|
||||
video_seqlen = image_seqlen * num_frames
|
||||
if getattr(processor, "vision_feature_select_strategy", "default") == "default":
|
||||
if processor.vision_feature_select_strategy == "default":
|
||||
image_seqlen -= 1
|
||||
else:
|
||||
image_seqlen, video_seqlen = 1, 1
|
||||
else:
|
||||
image_seqlen, video_seqlen = 1, 1
|
||||
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while IMAGE_PLACEHOLDER in content:
|
||||
content = content.replace(IMAGE_PLACEHOLDER, "{{image}}" * image_seqlen, 1)
|
||||
num_image_tokens += 1
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while IMAGE_PLACEHOLDER in content:
|
||||
content = content.replace(IMAGE_PLACEHOLDER, "{{image}}" * image_seqlen, 1)
|
||||
num_image_tokens += 1
|
||||
|
||||
while VIDEO_PLACEHOLDER in content:
|
||||
content = content.replace(VIDEO_PLACEHOLDER, "{{video}}" * video_seqlen, 1)
|
||||
num_video_tokens += 1
|
||||
while VIDEO_PLACEHOLDER in content:
|
||||
content = content.replace(VIDEO_PLACEHOLDER, "{{video}}" * video_seqlen, 1)
|
||||
num_video_tokens += 1
|
||||
|
||||
content = content.replace("{{image}}", self.image_token)
|
||||
message["content"] = content.replace("{{video}}", self.video_token)
|
||||
content = content.replace("{{image}}", self.image_token)
|
||||
message["content"] = content.replace("{{video}}", self.video_token)
|
||||
|
||||
if len(images) != num_image_tokens:
|
||||
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens.")
|
||||
@ -1231,24 +1281,10 @@ class VideoLlavaPlugin(BasePlugin):
|
||||
|
||||
return messages
|
||||
|
||||
@override
|
||||
def get_mm_inputs(
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
imglens: Sequence[int],
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[list[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> dict[str, Union[list[int], "torch.Tensor"]]:
|
||||
self._validate_input(processor, images, videos, audios)
|
||||
return self._get_mm_inputs(images, videos, audios, processor)
|
||||
|
||||
|
||||
PLUGINS = {
|
||||
"base": BasePlugin,
|
||||
"gemma3": Gemma3Plugin,
|
||||
"llava": LlavaPlugin,
|
||||
"llava_next": LlavaNextPlugin,
|
||||
"llava_next_video": LlavaNextVideoPlugin,
|
||||
|
||||
@ -310,6 +310,8 @@ class Template:
|
||||
|
||||
@dataclass
|
||||
class Llama2Template(Template):
|
||||
r"""A template that fuse the system message to first user message."""
|
||||
|
||||
@override
|
||||
def _encode(
|
||||
self,
|
||||
@ -815,10 +817,29 @@ register_template(
|
||||
name="gemma",
|
||||
format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
|
||||
format_assistant=StringFormatter(slots=["{{content}}<end_of_turn>\n"]),
|
||||
format_system=StringFormatter(slots=["{{content}}\n\n"]),
|
||||
format_observation=StringFormatter(
|
||||
slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
|
||||
),
|
||||
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
|
||||
stop_words=["<end_of_turn>"],
|
||||
template_class=Llama2Template,
|
||||
)
|
||||
|
||||
|
||||
# copied from gemma template
|
||||
register_template(
|
||||
name="gemma3",
|
||||
format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
|
||||
format_assistant=StringFormatter(slots=["{{content}}<end_of_turn>\n"]),
|
||||
format_system=StringFormatter(slots=["{{content}}\n\n"]),
|
||||
format_observation=StringFormatter(
|
||||
slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
|
||||
),
|
||||
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
|
||||
stop_words=["<end_of_turn>"],
|
||||
mm_plugin=get_mm_plugin("gemma3", image_token="<image_soft_token>"),
|
||||
template_class=Llama2Template,
|
||||
)
|
||||
|
||||
|
||||
@ -1255,6 +1276,7 @@ register_template(
|
||||
slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
|
||||
),
|
||||
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
|
||||
stop_words=["<end_of_turn>"],
|
||||
mm_plugin=get_mm_plugin(name="paligemma", image_token="<image>"),
|
||||
)
|
||||
|
||||
|
||||
@ -650,11 +650,51 @@ register_model_group(
|
||||
DownloadSource.DEFAULT: "google/gemma-2-27b-it",
|
||||
DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-27b-it",
|
||||
},
|
||||
"Gemma-3-1B": {
|
||||
DownloadSource.DEFAULT: "google/gemma-3-1b-pt",
|
||||
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-1b-pt",
|
||||
},
|
||||
"Gemma-3-1B-Instruct": {
|
||||
DownloadSource.DEFAULT: "google/gemma-3-1b-it",
|
||||
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-1b-it",
|
||||
},
|
||||
},
|
||||
template="gemma",
|
||||
)
|
||||
|
||||
|
||||
register_model_group(
|
||||
models={
|
||||
"Gemma-3-4B": {
|
||||
DownloadSource.DEFAULT: "google/gemma-3-4b-pt",
|
||||
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-4b-pt",
|
||||
},
|
||||
"Gemma-3-12B": {
|
||||
DownloadSource.DEFAULT: "google/gemma-3-12b-pt",
|
||||
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-12b-pt",
|
||||
},
|
||||
"Gemma-3-27B": {
|
||||
DownloadSource.DEFAULT: "google/gemma-3-27b-pt",
|
||||
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-27b-pt",
|
||||
},
|
||||
"Gemma-3-4B-Instruct": {
|
||||
DownloadSource.DEFAULT: "google/gemma-3-4b-it",
|
||||
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-4b-it",
|
||||
},
|
||||
"Gemma-3-12B-Instruct": {
|
||||
DownloadSource.DEFAULT: "google/gemma-3-12b-it",
|
||||
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-12b-it",
|
||||
},
|
||||
"Gemma-3-27B-Instruct": {
|
||||
DownloadSource.DEFAULT: "google/gemma-3-27b-it",
|
||||
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-27b-it",
|
||||
},
|
||||
},
|
||||
template="gemma3",
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
register_model_group(
|
||||
models={
|
||||
"GLM-4-9B": {
|
||||
|
||||
@ -19,6 +19,7 @@ import torch
|
||||
from transformers import (
|
||||
AutoConfig,
|
||||
AutoModelForCausalLM,
|
||||
AutoModelForImageTextToText,
|
||||
AutoModelForSeq2SeqLM,
|
||||
AutoModelForVision2Seq,
|
||||
AutoProcessor,
|
||||
@ -72,7 +73,6 @@ def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
|
||||
Note: including inplace operation of model_args.
|
||||
"""
|
||||
init_kwargs = _get_init_kwargs(model_args)
|
||||
config = load_config(model_args)
|
||||
try:
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_args.model_name_or_path,
|
||||
@ -94,7 +94,7 @@ def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
|
||||
patch_tokenizer(tokenizer, model_args)
|
||||
try:
|
||||
processor = AutoProcessor.from_pretrained(model_args.model_name_or_path, **init_kwargs)
|
||||
patch_processor(processor, config, tokenizer, model_args)
|
||||
patch_processor(processor, tokenizer, model_args)
|
||||
except Exception as e:
|
||||
logger.debug(f"Processor was not found: {e}.")
|
||||
processor = None
|
||||
@ -141,9 +141,11 @@ def load_model(
|
||||
if model_args.mixture_of_depths == "load":
|
||||
model = load_mod_pretrained_model(**init_kwargs)
|
||||
else:
|
||||
if type(config) in AutoModelForVision2Seq._model_mapping.keys(): # assume built-in models
|
||||
if type(config) in AutoModelForVision2Seq._model_mapping.keys(): # image-text
|
||||
load_class = AutoModelForVision2Seq
|
||||
elif type(config) in AutoModelForSeq2SeqLM._model_mapping.keys():
|
||||
elif type(config) in AutoModelForImageTextToText._model_mapping.keys(): # image-text
|
||||
load_class = AutoModelForImageTextToText
|
||||
elif type(config) in AutoModelForSeq2SeqLM._model_mapping.keys(): # audio-text
|
||||
load_class = AutoModelForSeq2SeqLM
|
||||
else:
|
||||
load_class = AutoModelForCausalLM
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
|
||||
# Copyright 2025 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
|
||||
@ -28,7 +28,7 @@ from ...extras import logging
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from transformers import LlavaConfig, PretrainedConfig, PreTrainedModel, ProcessorMixin
|
||||
from transformers import LlavaConfig, PretrainedConfig, PreTrainedModel
|
||||
|
||||
from ...hparams import FinetuningArguments, ModelArguments
|
||||
|
||||
@ -62,6 +62,16 @@ def _register_composite_model(
|
||||
language_model_keys: Optional[list[str]] = None,
|
||||
lora_conflict_keys: Optional[list[str]] = None,
|
||||
):
|
||||
r"""Register a new composite model.
|
||||
|
||||
Args:
|
||||
model_type: model type
|
||||
projector_key: multi_modal_projector
|
||||
vision_model_keys: vision_tower
|
||||
language_model_keys: language_model
|
||||
lora_conflict_keys: None
|
||||
|
||||
"""
|
||||
COMPOSITE_MODELS[model_type] = CompositeModel(
|
||||
model_type=model_type,
|
||||
projector_key=projector_key or "multi_modal_projector",
|
||||
@ -169,39 +179,10 @@ def get_forbidden_modules(config: "PretrainedConfig", finetuning_args: "Finetuni
|
||||
return forbidden_modules
|
||||
|
||||
|
||||
def get_image_seqlen(config: "PretrainedConfig") -> int:
|
||||
r"""Compute 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", processor: "ProcessorMixin") -> int:
|
||||
r"""Compute the patch size of the vit."""
|
||||
patch_size = getattr(config.vision_config, "patch_size", getattr(processor, "patch_size", -1))
|
||||
return patch_size
|
||||
|
||||
|
||||
def get_vision_feature_select_strategy(config: "PretrainedConfig", processor: "ProcessorMixin") -> int:
|
||||
r"""Get the vision_feature_select_strategy."""
|
||||
vision_feature_select_strategy = getattr(
|
||||
config, "vision_feature_select_strategy", getattr(processor, "vision_feature_select_strategy", "default")
|
||||
)
|
||||
return vision_feature_select_strategy
|
||||
|
||||
|
||||
def patch_target_modules(
|
||||
model: "PreTrainedModel", finetuning_args: "FinetuningArguments", target_modules: Sequence[str]
|
||||
) -> list[str]:
|
||||
r"""Freezes vision tower for VLM LoRA tuning."""
|
||||
r"""Freeze vision tower for VLM LoRA tuning."""
|
||||
model_type = getattr(model.config, "model_type", None)
|
||||
if model_type in COMPOSITE_MODELS:
|
||||
forbidden_modules = get_forbidden_modules(model.config, finetuning_args)
|
||||
@ -218,6 +199,11 @@ def patch_target_modules(
|
||||
return target_modules
|
||||
|
||||
|
||||
_register_composite_model(
|
||||
model_type="gemma3",
|
||||
)
|
||||
|
||||
|
||||
_register_composite_model(
|
||||
model_type="llava",
|
||||
)
|
||||
|
||||
@ -33,13 +33,7 @@ from .model_utils.packing import configure_packing
|
||||
from .model_utils.quantization import configure_quantization
|
||||
from .model_utils.rope import configure_rope
|
||||
from .model_utils.valuehead import prepare_valuehead_model
|
||||
from .model_utils.visual import (
|
||||
autocast_projector_dtype,
|
||||
configure_visual_model,
|
||||
get_image_seqlen,
|
||||
get_patch_size,
|
||||
get_vision_feature_select_strategy,
|
||||
)
|
||||
from .model_utils.visual import autocast_projector_dtype, configure_visual_model
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
@ -72,21 +66,16 @@ def patch_tokenizer(tokenizer: "PreTrainedTokenizer", model_args: "ModelArgument
|
||||
|
||||
def patch_processor(
|
||||
processor: "ProcessorMixin",
|
||||
config: "PretrainedConfig",
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
model_args: "ModelArguments",
|
||||
) -> None:
|
||||
setattr(processor, "tokenizer", tokenizer)
|
||||
if getattr(config, "vision_config", None) is not None: # visual models
|
||||
setattr(processor, "image_seqlen", get_image_seqlen(config))
|
||||
setattr(processor, "patch_size", get_patch_size(config, processor))
|
||||
setattr(processor, "image_max_pixels", model_args.image_max_pixels)
|
||||
setattr(processor, "image_min_pixels", model_args.image_min_pixels)
|
||||
setattr(processor, "video_max_pixels", model_args.video_max_pixels)
|
||||
setattr(processor, "video_min_pixels", model_args.video_min_pixels)
|
||||
setattr(processor, "video_fps", model_args.video_fps)
|
||||
setattr(processor, "video_maxlen", model_args.video_maxlen)
|
||||
setattr(processor, "vision_feature_select_strategy", get_vision_feature_select_strategy(config, processor))
|
||||
setattr(processor, "image_max_pixels", model_args.image_max_pixels)
|
||||
setattr(processor, "image_min_pixels", model_args.image_min_pixels)
|
||||
setattr(processor, "video_max_pixels", model_args.video_max_pixels)
|
||||
setattr(processor, "video_min_pixels", model_args.video_min_pixels)
|
||||
setattr(processor, "video_fps", model_args.video_fps)
|
||||
setattr(processor, "video_maxlen", model_args.video_maxlen)
|
||||
|
||||
|
||||
def patch_config(
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user