add zeroth selector doc

docs/.vuepress/notes/en/guide.ts

@@ -26,6 +26,7 @@ export const Guide: ThemeNote = defineNoteConfig({
                 'tutorial',
                 'selector_less',
                 'selector_tsds',
+                'selector_zeroth'
             ],
         },
         {

docs/.vuepress/notes/zh/guide.ts

@@ -26,6 +26,7 @@ export const Guide: ThemeNote = defineNoteConfig({
                 'tutorial',
                 'selector_less',
                 'selector_tsds',
+                'selector_zeroth',
             ],
         },
         {

docs/en/notes/guide/selector/selector_zeroth.md

@@ -0,0 +1,145 @@
+---
+title: Zero Order Data Selection
+createTime: 2025/11/03 22:58:52
+permalink: /en/guide/0rfxa64a/
+icon: tabler:chart-dots-3
+---
+# Introduction to the Zeroth Selector
+This document explains how to use the **Zeroth Selector** in the **DataFlex** framework to achieve dynamic selection of training data, thereby enhancing the performance of supervised fine-tuning (SFT). This method is an original selection approach that utilizes co-directional perturbations on the model for differential estimation, thereby obtaining the model's zeroth-order gradient to calculate the effective score of the data.
+
+---
+
+## 1. Method Overview
+The core idea of **Zeroth Selector** is:Based on the **SGD** version of the **influence function**, it measures the correlation between training samples and validation samples through the similarity in zeroth-order gradient directions. It is worth noting that for both training and validation data, the perturbation noise uses the same random seed. Therefore, in the current version, the selection algorithm can directly use the product of **differentials (projected gradients)** to represent gradient inner product similarity. Advantages: 1. Avoids the problem of the **gradient-based influence function**, which can only be computed per sample and cannot be parallelized over data; 2. No backpropagation is required, saving time and GPU memory.
+
+### Mathematical Definition
+Randomly sample $\xi\sim\mathbf{N}(0,I)$ and perturb the model parameters $\theta$,
+$$
+\mathrm{Inf}_{\mathrm{zeroth}}(z,z'):=(\dfrac{f(\theta+\epsilon\xi;z)-f(\theta-\epsilon\xi;z)}{2\epsilon})\cdot(\dfrac{f(\theta+\epsilon\xi;z')-f(\theta-\epsilon\xi;z')}{2\epsilon})
+$$
+
+## 2. Implementation Steps
+
+### Step 1: Environment Installation
+
+```bash
+git clone https://github.com/OpenDCAI/DataFlex.git
+cd DataFlex
+pip install -e .
+pip install llamafactory
+```
+
+---
+### Step 2: Zeroth Selector Parameter Configuration
+**Configuration File Path:**
+```
+DataFlex/src/dataflex/configs/components.yaml
+```
+**Example Configuration:**
+```yaml
+less:
+  name: zeroth
+  params:
+    cache_dir: ../dataflex_saves/zeroth_output
+    seed: 42
+```
+
+**Parameter Description:** 
+* `cache_dir`: The path to save intermediate results, i.e., intermediate difference values.
+* `seed`: Optional, random seed used as the generator seed for sampling noise.
+
+---
+
+### Step 3: Dynamic Training Configuration
+**Configuration File Path:**
+```
+DataFlex/examples/train_lora/selectors/zeroth.yaml
+```
+**Example Configuration:**
+```yaml
+### model
+model_name_or_path: Qwen/Qwen2.5-0.5B-Instruct
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+lora_rank: 16
+lora_alpha: 8
+#deepspeed: examples/deepspeed/ds_z3_config.json  # choices: [ds_z0_config.json, ds_z2_config.json, ds_z3_config.json]
+
+### dataset
+dataset: alpaca_en_demo
+#dataset: flan_v2,cot_data,dolly_data,oasst1_data
+#eval_dataset: mmlu_eval
+template: qwen
+cutoff_len: 4096
+# max_samples: 100000000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 0
+# disable_shuffling: true
+seed: 42
+
+### output
+output_dir: /data1/xlyang/Flex/saves/zeroth/
+logging_steps: 10
+save_steps: 100
+plot_loss: true
+save_only_model: false
+overwrite_output_dir: true
+
+### swanlab
+report_to: none  # choices: [none, wandb, tensorboard, swanlab, 
+
+### train
+per_device_train_batch_size: 2
+gradient_accumulation_steps: 16
+learning_rate: 1.0e-4
+num_train_epochs: 1.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: false
+fp16: true
+ddp_timeout: 180000000
+
+### dynamic_train
+train_type: dynamic_select
+components_cfg_file: src/dataflex/configs/components.yaml
+component_name: zeroth
+warmup_step: 4
+update_step: 3
+update_times: 2
+
+eval_dataset: alpaca_zh_demo
+```
+
+**Parameter Description:**
+
+* `model_name_or_path`: Model name or path for supervised fine-tuning.
+* `dataset`: Training dataset.
+* `output_dir`: Output directory of dynamic fine-tuning (LoRA adapter).
+* `warmup_step`: Number of warmup steps before the first sample selection.
+* `update_step`: Number of steps between each dynamic data selection.
+* `update_times`: Total number of dynamic data selection iterations.
+* `eval_dataset`: Validation dataset.
+
+Both `dataset` and `eval_dataset` can be selected from `DataFlex/data/dataset_info.json` or local JSON files in ShareGPT/Alpaca format. Note: The training set size significantly affects computation cost. Total steps = `warmup_step + update_step × update_times`.
+
+---
+
+### Step 4: Run Training
+
+```bash
+FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/zeroth.yaml
+```
+
+The training process automatically performs dynamic data selection and model updates.
+
+---
+
+## 3. Model Evaluation
+
+It is recommended to use the [DataFlow](https://github.com/OpenDCAI/DataFlow) [Model QA Evaluation Pipeline](https://opendcai.github.io/DataFlow-Doc/zh/guide/2k5wjgls/) for systematic evaluation of the generated model.

docs/zh/notes/guide/selector/selector_zeroth.md

@@ -0,0 +1,159 @@
+---
+title: 零阶优化数据选择器
+createTime: 2025/11/03 22:58:52
+permalink: /zh/guide/wl9tf7o2/
+icon: tabler:chart-dots-3
+---
+# 零阶选择器介绍
+本文档介绍如何在 **DataFlex** 框架中使用 **Zeroth Selector** 实现训练数据的动态选择，从而提升监督微调（SFT）效果。该方法为原创选择方法，利用对模型进行同向扰动进行差分估计，进而得到模型的零阶梯度来计算数据有效分数。
+
+---
+
+## 1. 方法概述
+
+**Zeroth Selector** 的核心思想是：
+基于**SGD**版本的**样本影响函数（Influence Function）**，通过零阶梯度方向的相似性来度量训练样本与验证样本的相关性。值得注意的是，对于训练数据和验证数据，采用的扰动噪声是使用相同的随机数种子生成的，所以目前版本的选择算法可以直接使用**差分（投影梯度）** 的乘积代表梯度内积相似度。优势：1. 避免直接使用**梯度样本影响函数** 只能逐样本进行计算的无法数据并行的问题；2. 不需要反传，节省时间和显存开销。
+
+### 数学定义
+随机采样$\xi\sim\mathbf{N}(0,I)$，对模型参数$\theta$进行扰动，
+$$
+\mathrm{Inf}_{\mathrm{zeroth}}(z,z'):=(\dfrac{f(\theta+\epsilon\xi;z)-f(\theta-\epsilon\xi;z)}{2\epsilon})\cdot(\dfrac{f(\theta+\epsilon\xi;z')-f(\theta-\epsilon\xi;z')}{2\epsilon})
+$$
+
+
+## 2. 实现步骤
+
+### 步骤一：环境安装
+
+```bash
+git clone https://github.com/OpenDCAI/DataFlex.git
+cd DataFlex
+pip install -e .
+pip install llamafactory
+```
+
+---
+
+### 步骤二：Zeroth Selector 参数配置
+
+**配置文件路径：**
+```
+DataFlex/src/dataflex/configs/components.yaml
+```
+
+**示例配置：**
+```yaml
+less:
+  name: zeroth
+  params:
+    cache_dir: ../dataflex_saves/zeroth_output
+    seed: 42
+```
+
+**参数说明：**
+* `cache_dir`: 保存中间结果的缓存路径，即中间差分值。
+* `seed`: 可选，随机种子，用作采样噪声的生成器种子。
+
+---
+
+### 步骤三：动态训练配置
+
+**配置文件路径：**
+
+```
+DataFlex/examples/train_lora/selectors/zeroth.yaml
+```
+
+**示例配置：**
+
+```yaml
+### model
+model_name_or_path: Qwen/Qwen2.5-0.5B-Instruct
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+lora_rank: 16
+lora_alpha: 8
+#deepspeed: examples/deepspeed/ds_z3_config.json  # choices: [ds_z0_config.json, ds_z2_config.json, ds_z3_config.json]
+
+### dataset
+dataset: alpaca_en_demo
+#dataset: flan_v2,cot_data,dolly_data,oasst1_data
+#eval_dataset: mmlu_eval
+template: qwen
+cutoff_len: 4096
+# max_samples: 100000000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 0
+# disable_shuffling: true
+seed: 42
+
+### output
+output_dir: /data1/xlyang/Flex/saves/zeroth/
+logging_steps: 10
+save_steps: 100
+plot_loss: true
+save_only_model: false
+overwrite_output_dir: true
+
+### swanlab
+report_to: none  # choices: [none, wandb, tensorboard, swanlab, 
+
+### train
+per_device_train_batch_size: 2
+gradient_accumulation_steps: 16
+learning_rate: 1.0e-4
+num_train_epochs: 1.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: false
+fp16: true
+ddp_timeout: 180000000
+
+### dynamic_train
+train_type: dynamic_select
+components_cfg_file: src/dataflex/configs/components.yaml
+component_name: zeroth
+warmup_step: 4
+update_step: 3
+update_times: 2
+
+eval_dataset: alpaca_zh_demo
+```
+
+**参数说明：**
+* `model_name_or_path`: 监督微调训练模型的名称或路径。
+* `dataset`: 训练数据集。
+* `output_dir`: 动态微调结果（LoRA 适配器）的输出路径。
+* `warmup_step`: 训练初期第一次训练数据选择前，进行warmup的步数。
+* `update_step`: 每次训练数据动态选择的步数。
+* `update_times`: 数据动态选择的总次数。
+* `eval_dataset`: 验证数据集。
+
+dataset和eval_dataset可选`DataFlex/data/dataset_info.json`中数据，或本地路径下sharegpt或alpaca格式的json数据。注意该方法的情形下，训练集规模会较大影响计算成本。
+
+总步数 = `warmup_step + update_step × update_times`。
+
+---
+
+### 步骤四：运行训练
+
+
+```bash
+FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/zeroth.yaml
+
+```
+训练过程会自动完成动态数据选择与模型更新。
+
+---
+
+
+## 3. 模型评估
+
+推荐使用[DataFlow](https://github.com/OpenDCAI/DataFlow)的[模型QA能力评估流水线](https://opendcai.github.io/DataFlow-Doc/zh/guide/2k5wjgls/)对生成后的模型进行系统性评估。
+