support num_train_epoches while keep dynamic-step training

examples/test/test_select.yaml

@@ -60,4 +60,4 @@ components_cfg_file: src/dataflex/configs/components.yaml
 component_name: delta_loss  # 选择组件名称，对应 components_cfg_file 中定义的组件
 warmup_step: 4
 update_step: 3
-update_times: 4
+update_times: 4 # Updates per Flex epoch; repeats by num_train_epochs

examples/test/test_weight.yaml

@@ -59,4 +59,4 @@ train_type: dynamic_weight   # 选择训练器类型。可选值包括：
 components_cfg_file: src/dataflex/configs/components.yaml
 component_name: custom  # 选择组件名称，对应 components_cfg_file 中定义的组件
 warmup_step: 1
-train_step: 3 # 总训练步数（包括warm_up） 
+train_step: 3 # Total steps; overrides num_train_epochs

examples/train_lora/selectors/custom.yaml

@@ -57,7 +57,7 @@ components_cfg_file: src/dataflex/configs/components.yaml
 component_name: custom  # 选择组件名称，对应 components_cfg_file 中定义的组件
 warmup_step: 10
 update_step: 10
-update_times: 2
+update_times: 2  # Updates per Flex epoch; repeats by num_train_epochs
 
 ## eval
 # val_size: 0.001
@@ -71,4 +71,4 @@ eval_steps: 10
 # early_stopping_steps: 3
 # early_stopping_min_delta: 0.01
 
-# FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/custom.yaml
+# FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/custom.yaml

examples/train_lora/selectors/delta_loss.yaml

@@ -57,7 +57,7 @@ components_cfg_file: src/dataflex/configs/components.yaml
 component_name: delta_loss  # 选择组件名称，对应 components_cfg_file 中定义的组件
 warmup_step: 10
 update_step: 10
-update_times: 2
+update_times: 2  # Updates per Flex epoch; repeats by num_train_epochs
 
 ## eval
 # val_size: 0.001
@@ -71,4 +71,4 @@ eval_steps: 10
 # early_stopping_steps: 3
 # early_stopping_min_delta: 0.01
 
-# FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/delta_loss.yaml
+# FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/delta_loss.yaml

examples/train_lora/selectors/less.yaml

@@ -57,7 +57,7 @@ components_cfg_file: src/dataflex/configs/components.yaml
 component_name: less  # 选择组件名称，对应 components_cfg_file 中定义的组件
 warmup_step: 10
 update_step: 10
-update_times: 2
+update_times: 2  # Updates per Flex epoch; repeats by num_train_epochs
 
 ## eval
 # val_size: 0.001
@@ -71,4 +71,4 @@ eval_steps: 10
 # early_stopping_steps: 3
 # early_stopping_min_delta: 0.01
 
-# FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/less.yaml
+# FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/less.yaml

examples/train_lora/selectors/loss.yaml

@@ -57,7 +57,7 @@ components_cfg_file: src/dataflex/configs/components.yaml
 component_name: loss  # 选择组件名称，对应 components_cfg_file 中定义的组件
 warmup_step: 10
 update_step: 10
-update_times: 2
+update_times: 2  # Updates per Flex epoch; repeats by num_train_epochs
 
 ## eval
 # val_size: 0.001
@@ -71,4 +71,4 @@ eval_steps: 10
 # early_stopping_steps: 3
 # early_stopping_min_delta: 0.01
 
-# FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/loss.yaml
+# FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/loss.yaml

examples/train_lora/selectors/near.yaml

@@ -57,7 +57,7 @@ components_cfg_file: src/dataflex/configs/components.yaml
 component_name: near  # 选择组件名称，对应 components_cfg_file 中定义的组件
 warmup_step: 10
 update_step: 10
-update_times: 2
+update_times: 2  # Updates per Flex epoch; repeats by num_train_epochs
 
 ## eval
 # val_size: 0.001
@@ -71,4 +71,4 @@ eval_steps: 10
 # early_stopping_steps: 3
 # early_stopping_min_delta: 0.01
 
-# FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/near.yaml
+# FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/near.yaml

examples/train_lora/selectors/nice.yaml

@@ -57,7 +57,7 @@ components_cfg_file: src/dataflex/configs/components.yaml
 component_name: nice # 选择组件名称，对应 components_cfg_file 中定义的组件
 warmup_step: 10
 update_step: 10
-update_times: 2
+update_times: 2  # Updates per Flex epoch; repeats by num_train_epochs
 
 ## eval
 # val_size: 0.001
@@ -71,4 +71,4 @@ eval_steps: 10
 # early_stopping_steps: 3
 # early_stopping_min_delta: 0.01
 
-# FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/nice.yaml
+# FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/nice.yaml

examples/train_lora/selectors/random.yaml

@@ -57,7 +57,7 @@ components_cfg_file: src/dataflex/configs/components.yaml
 component_name: random  # 选择组件名称，对应 components_cfg_file 中定义的组件
 warmup_step: 10
 update_step: 10
-update_times: 2
+update_times: 2  # Updates per Flex epoch; repeats by num_train_epochs
 
 ## eval
 # val_size: 0.001
@@ -71,4 +71,4 @@ eval_steps: 10
 # early_stopping_steps: 3
 # early_stopping_min_delta: 0.01
 
-# FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/random.yaml
+# FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/random.yaml

examples/train_lora/selectors/tsds.yaml

@@ -57,7 +57,7 @@ components_cfg_file: src/dataflex/configs/components.yaml
 component_name: tsds  # 选择组件名称，对应 components_cfg_file 中定义的组件
 warmup_step: 10
 update_step: 10
-update_times: 2
+update_times: 2  # Updates per Flex epoch; repeats by num_train_epochs
 
 ## eval
 # val_size: 0.001

examples/train_lora/weighters/custom.yaml

@@ -57,4 +57,4 @@ train_type: dynamic_weight   # 选择训练器类型。可选值包括：
 components_cfg_file: src/dataflex/configs/components.yaml
 component_name: custom  # 选择组件名称，对应 components_cfg_file 中定义的组件
 warmup_step: 100
-train_step: 500 # 总训练步数（包括warm_up） 
+train_step: 500 # Total steps; overrides num_train_epochs

examples/train_lora/weighters/loss.yaml

@@ -57,4 +57,4 @@ train_type: dynamic_weight   # 选择训练器类型。可选值包括：
 components_cfg_file: src/dataflex/configs/components.yaml
 component_name: loss  # 选择组件名称，对应 components_cfg_file 中定义的组件
 warmup_step: 100
-train_step: 500 # 总训练步数（包括warm_up） 
+train_step: 500 # Total steps; overrides num_train_epochs

skills/how_to_use.md

@@ -59,9 +59,39 @@ A DataFlex training config is a standard LlamaFactory YAML with additional DataF
 | `component_name` | str | Which algorithm to use, matching a key in `components_cfg_file` |
 | `warmup_step` | int | Number of warmup steps before dynamic behavior kicks in |
 | `update_step` | int | Interval (in steps) between dynamic updates |
-| `update_times` | int | Total number of dynamic updates. Use `-1` for continuous updates until training ends |
+| `update_times` | int | For `dynamic_select`, number of dynamic updates per Flex epoch. Use `-1` for dataset-sized epochs with continuous updates |
 | `static_mix` | bool | If `true` with `dynamic_mix`, use fixed proportions (no dynamic updates). Used in DoReMi Step 1 & 3 |
-| `train_step` | int | Total training steps. Required for `dynamic_weight` and `dynamic_mix` with `static_mix: true` |
+| `train_step` | int | Optional total training steps. If set to a positive value, it overrides `num_train_epochs`-derived steps |
+
+### Step and Epoch Semantics
+
+DataFlex dynamic trainers run a step-based training loop internally. Prefer `eval_strategy: "steps"` / `save_strategy: "steps"` or disable them with `"no"` when using dynamic training. Epoch-based evaluation or saving depends on the internal step-to-epoch bookkeeping and may not align with Flex epoch boundaries.
+
+For `dynamic_select`, `num_train_epochs` repeats Flex epochs. One Flex epoch contains:
+
+```text
+warmup_step + update_step * update_times
+```
+
+For example:
+
+```yaml
+warmup_step: 10
+update_step: 10
+update_times: 2
+num_train_epochs: 3
+```
+
+This runs `3 * (10 + 10 * 2) = 90` optimization steps. To keep the old single-Flex-epoch behavior, set:
+
+```yaml
+num_train_epochs: 1.0
+```
+
+If `train_step > 0`, DataFlex uses `train_step` as the exact total number of optimization steps and does not derive total steps from `num_train_epochs`.
+For multi-epoch tests, make sure example configs do not leave a positive `train_step`; pass `train_step=0` on the CLI if you want `num_train_epochs` to control training length.
+
+For `dynamic_weight`, `warmup_step` is a global step threshold. Reweighting starts when `global_step >= warmup_step` and does not reset at epoch boundaries.
 
 ### Data Mixture Fields (for `dynamic_mix` only)
 
@@ -83,12 +113,14 @@ component_name: less       # choices: less, nice, loss, delta_loss, tsds, near,
 warmup_step: 10
 update_step: 10
 update_times: 2
+num_train_epochs: 1.0
 ```
 
 **How it works:**
 1. Warmup phase: train on randomly sampled data for `warmup_step` steps.
 2. At `warmup_step` and every `update_step` steps: pause training, run the selector to pick new samples, rebuild the dataloader.
-3. Total steps = `warmup_step + update_step * update_times`.
+3. One Flex epoch has `warmup_step + update_step * update_times` steps.
+4. Total steps are derived from `num_train_epochs` unless `train_step > 0`.
 
 **Example:**
 ```bash
@@ -138,13 +170,14 @@ train_type: dynamic_weight
 components_cfg_file: src/dataflex/configs/components.yaml
 component_name: loss       # choices: loss, custom
 warmup_step: 100
-train_step: 500
+train_step: 500            # fixed-step example; set to 0 for num_train_epochs-based multi-epoch runs
 ```
 
 **How it works:**
 1. Standard training for `warmup_step` steps (no reweighting).
 2. After warmup: each training step computes per-sample losses and applies the weighting strategy.
-3. Total steps = `train_step`.
+3. `warmup_step` is measured in global optimization steps and does not reset per epoch.
+4. If `train_step > 0`, total steps = `train_step`; otherwise total steps follow the standard `num_train_epochs` calculation.
 
 **Example:**
 ```bash

src/dataflex/train/hparams/dynamic_params.py

@@ -547,15 +547,15 @@ class DynamicFinetuningArguments(
     )
     update_times: int = field(
         default=1,
-        metadata={"help": "Total update times during the whole dynamic training progress for dynamic select or mix training"},
+        metadata={"help": "Update times per Flex epoch for dynamic selection. Use <= 0 for no fixed update count."},
     )
     static_mix: bool = field(
         default=False,
         metadata={"help": "Whether or not to fix the static mix ratio in dynamic mix training."},
     )
     train_step: int = field(
         default=0,
-        metadata={"help": "Only used in dynamic weight trainer and mix trainer (static_mix=True). Total training steps (including warmup)."},
+        metadata={"help": "Optional total training steps. If set, overrides num_train_epochs."},
     )
     freeze_gate: bool = field(
         default=False,
@@ -646,4 +646,4 @@ def __init__(self, early_stopping_patience: int):
                 early_stopping_threshold=min_delta,
             )
 
-    tuner.EarlyStoppingCallback = DataFlexEarlyStoppingCallback
+    tuner.EarlyStoppingCallback = DataFlexEarlyStoppingCallback

src/dataflex/train/trainer/select_trainer.py

@@ -382,7 +382,23 @@ def _inner_training_loop(
             len_dataloader, # 等于数据集长度/worldsize/micro_batchsize
             max_steps,
         ) = self.set_initial_training_values(args, train_dataloader, total_train_batch_size)
-        max_steps = (self.finetuning_args.warmup_step + self.finetuning_args.update_step * self.finetuning_args.update_times)
+        # Issue #49: support num_train_epochs while keeping dynamic-step training
+        epoch_update_steps = None
+        if self.finetuning_args.update_times > 0:
+            epoch_update_steps = max(
+                1,
+                self.finetuning_args.warmup_step + self.finetuning_args.update_step * self.finetuning_args.update_times,
+            )
+        if self.finetuning_args.train_step > 0:
+            max_steps = self.finetuning_args.train_step
+        elif epoch_update_steps is not None:
+            max_steps = int(np.ceil(args.num_train_epochs * epoch_update_steps))
+        else:
+            steps_per_epoch = max(1, int(np.ceil(len(self.train_dataset) / total_train_batch_size)))
+            max_steps = int(np.ceil(args.num_train_epochs * steps_per_epoch))
+            epoch_update_steps = steps_per_epoch
+        if self.finetuning_args.train_step > 0 and args.num_train_epochs != 1:
+            logger.warning("[Dataflex] train_step is set; num_train_epochs will be ignored.")
         epoch_based = False
         logger.info(f"[Dataflex]Set max train steps to {max_steps}")
         logger.info(f"[Dataflex]Set epoch_based = False")
@@ -569,7 +585,6 @@ def _inner_training_loop(
         if args.eval_on_start:
             self._evaluate(trial, ignore_keys_for_eval, skip_scheduler=True)
 
-        # 放弃epoch逻辑，相当于只训练一个epoch，通过step来训练
         current_dataloader = train_dataloader
         epoch = 0
         if self.state.global_step < self.finetuning_args.warmup_step:
@@ -581,7 +596,11 @@ def _inner_training_loop(
         if args.past_index >= 0:
             self._past = None
 
-        steps_in_epoch = max_steps * args.gradient_accumulation_steps
+        total_training_batches = max_steps * args.gradient_accumulation_steps
+        if epoch_update_steps is not None:
+            steps_in_epoch = epoch_update_steps * args.gradient_accumulation_steps
+        else:
+            steps_in_epoch = len_dataloader if len_dataloader is not None else total_training_batches
         self.control = self.callback_handler.on_epoch_begin(args, self.state, self.control)
 
         if resume_from_checkpoint is not None and steps_trained_in_current_epoch == 0:
@@ -603,7 +622,7 @@ def _inner_training_loop(
             remainder = args.gradient_accumulation_steps
         update_step = -1
         # 一个epoch中的模型总更新次数
-        total_updates = steps_in_epoch // args.gradient_accumulation_steps + 1
+        total_updates = total_training_batches // args.gradient_accumulation_steps + 1
         if args.gradient_accumulation_steps == 1:
             total_updates -= 1
         for _ in range(total_updates):
@@ -612,12 +631,22 @@ def _inner_training_loop(
             num_batches = args.gradient_accumulation_steps if update_step != (total_updates - 1) else remainder
 
             batch_samples, num_items_in_batch = self.get_batch_samples(current_iterator, num_batches, args.device)
+            if len(batch_samples) == 0:
+                epoch += 1
+                if hasattr(current_dataloader, "set_epoch"):
+                    current_dataloader.set_epoch(epoch)
+                current_iterator = iter(current_dataloader)
+                batch_samples, num_items_in_batch = self.get_batch_samples(current_iterator, num_batches, args.device)
+                if len(batch_samples) == 0:
+                    self.control.should_training_stop = True
+                    break
             # 遍历当前批次的样本
             for i, inputs in enumerate(batch_samples):
                 step += 1  # 每次迭代时增加全局步数
 
                 # 判断是否达到同步步数，或者是当前epoch的最后一个步数
-                do_sync_step = (step + 1) % args.gradient_accumulation_steps == 0 or (step + 1) == steps_in_epoch
+                is_epoch_end = (step + 1 + steps_skipped) % steps_in_epoch == 0
+                do_sync_step = (step + 1) % args.gradient_accumulation_steps == 0 or is_epoch_end
 
                 # 由于我们使用了预取（prefetching），我们需要手动设置同步梯度
                 self.accelerator.gradient_state._set_sync_gradients(do_sync_step)
@@ -750,26 +779,50 @@ def _inner_training_loop(
                         # learning_rate=learning_rate,
                     )
 
-                    # 动态训练更新
+                    step_in_epoch = (
+                        self.state.global_step % epoch_update_steps
+                        if epoch_update_steps is not None
+                        else self.state.global_step
+                    )
                     if (
+                        step_in_epoch == 0
+                        and epoch_update_steps is not None
+                        and self.state.global_step > 0
+                        and self.state.global_step < max_steps
+                    ):
+                        self.accelerator.wait_for_everyone()
+                        torch.cuda.empty_cache()
+                        if dist.is_initialized():
+                            dist.barrier()
+
+                        warmup_indices = self.selector.warmup(total_warmup_samples, replacement=True)
+                        current_dataloader = self.get_train_dataloader(warmup_indices)
+                        current_iterator = iter(current_dataloader)
+                    elif (
                         self.state.global_step < max_steps and (
-                        self.state.global_step == self.finetuning_args.warmup_step or
-                        (self.state.global_step > self.finetuning_args.warmup_step and
-                        (self.state.global_step - self.finetuning_args.warmup_step) % self.finetuning_args.update_step == 0))
+                        step_in_epoch == self.finetuning_args.warmup_step or
+                        (step_in_epoch > self.finetuning_args.warmup_step and
+                        (step_in_epoch - self.finetuning_args.warmup_step) % self.finetuning_args.update_step == 0))
                     ):
                         self.accelerator.wait_for_everyone()
                         torch.cuda.empty_cache()
                         if dist.is_initialized():
                             dist.barrier()
 
-                        current_update_times = (self.state.global_step - self.finetuning_args.warmup_step) // self.finetuning_args.update_step + 1
+                        current_update_times = (step_in_epoch - self.finetuning_args.warmup_step) // self.finetuning_args.update_step + 1
+                        effective_update_times = self.finetuning_args.update_times
+                        if effective_update_times <= 0 and epoch_update_steps is not None:
+                            effective_update_times = max(
+                                1,
+                                int(np.ceil(max(epoch_update_steps - self.finetuning_args.warmup_step, 0) / self.finetuning_args.update_step)),
+                            )
                         logger.info(f"[Dataflex] Model training paused, starting the {current_update_times}th dynamic data selection...")
                         # 这里传一些特定的selector参数
                         extra_args = dict(
                             optimizer_state=self.optimizer.state,
                             scheduler_state=self.lr_scheduler.state_dict(),
                             current_update_times=current_update_times,
-                            update_times=self.finetuning_args.update_times,
+                            update_times=effective_update_times,
                             tokenizer=self.tokenizer,
                         )
                         new_indices = self.selector.select(
@@ -780,8 +833,8 @@ def _inner_training_loop(
                         )
 
                         # 每个进程根据 local_indices 构造 dataloader
-                        train_loader = self.get_train_dataloader(indices=new_indices)
-                        current_iterator = iter(train_loader)
+                        current_dataloader = self.get_train_dataloader(indices=new_indices)
+                        current_iterator = iter(current_dataloader)
 
                         if self.accelerator.is_main_process:
                             logger.info(f"[Dataflex] Updated dataloader at step {self.state.global_step}, {len(new_indices)} samples in total.")