[pt/sft] Feature channel loss

docs/source/Instruction/命令行参数.md

@@ -351,6 +351,7 @@ Vera使用`target_modules`, `target_regex`, `modules_to_save`三个参数.
 - check_model: 检查本地模型文件有损坏或修改并给出提示，默认为True。如果是断网环境，请设置为False。
 - 🔥create_checkpoint_symlink: 额外创建checkpoint软链接，方便书写自动化训练脚本。best_model和last_model的软链接路径分别为f'{output_dir}/best'和f'{output_dir}/last'。
 - loss_type: loss类型。默认为None，使用模型自带损失函数。
+- channels : 数据集包含的channel集合。默认为None。结合`--loss_type channel_loss`使用，可参考[这里](https://github.com/modelscope/ms-swift/blob/main/examples/train/plugins/channel_loss.sh)。
 - 🔥packing: 是否使用序列packing提升计算效率，默认为False。当前支持`swift pt/sft`。
   - 注意：使用packing请结合`--attn_impl flash_attn`使用且"transformers>=4.44"，具体查看[该PR](https://github.com/huggingface/transformers/pull/31629)。
   - 支持的多模态模型参考：https://github.com/modelscope/ms-swift/blob/main/examples/train/packing/qwen2_5_vl.sh

docs/source_en/Instruction/Command-line-parameters.md

@@ -360,6 +360,7 @@ Training arguments include the [base arguments](#base-arguments), [Seq2SeqTraine
 - check_model: Check local model files for corruption or modification and give a prompt, default is True. If in an offline environment, please set to False.
 - 🔥create_checkpoint_symlink: Creates additional checkpoint symlinks to facilitate writing automated training scripts. The symlink paths for `best_model` and `last_model` are `f'{output_dir}/best'` and `f'{output_dir}/last'` respectively.
 - loss_type: Type of loss. Defaults to None, which uses the model's built-in loss function.
+- channels：Set of channels included in the dataset. Defaults to None. Used in conjunction with `--loss_type channel_loss`. Refer to [this example](https://github.com/modelscope/ms-swift/blob/main/examples/train/plugins/channel_loss.sh) for more details.
 - 🔥packing: Whether to use sequence packing to improve computational efficiency. The default value is False. Currently supports `swift pt/sft`.
   - Note: When using packing, please combine it with `--attn_impl flash_attn` and ensure "transformers>=4.44". For details, see [this PR](https://github.com/huggingface/transformers/pull/31629).
   - Supported multimodal models reference: https://github.com/modelscope/ms-swift/blob/main/examples/train/packing/qwen2_5_vl.sh

examples/train/plugins/channel_loss.sh

@@ -0,0 +1,32 @@
+# use loss_type channel_loss
+# channels specifies the channels included in the dataset
+# data should have 'channel' field
+# eg.
+# {"channel": "chat",
+#   "messages": [
+#     {"role": "system", "content": "You are a helpful assistant"},
+#     {"role": "user", "content": "What color do you like?"},
+#     {"role": "assistant", "content": "I like blue."}
+#   ]}
+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+    --model Qwen/Qwen2.5-0.5B-Instruct \
+    --dataset '/path/to/channel_dataset' \
+    --train_type full \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --learning_rate 1e-5 \
+    --gradient_accumulation_steps 8 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 1 \
+    --max_length 512 \
+    --output_dir output \
+    --system 'You are a helpful assistant.' \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --loss_type channel_loss \
+    --channels 'chat' 'math' 'code'

swift/llm/dataset/preprocessor/core.py

@@ -20,7 +20,9 @@
 
 
 class RowPreprocessor:
-    standard_keys = ['messages', 'rejected_response', 'label', 'images', 'videos', 'audios', 'tools', 'objects']
+    standard_keys = [
+        'messages', 'rejected_response', 'label', 'images', 'videos', 'audios', 'tools', 'objects', 'channel'
+    ]
 
     def __init__(self,
                  *,
@@ -308,6 +310,7 @@ def __call__(
         dataset = self._cast_pil_image(dataset)
 
         ignore_max_length_error = True if isinstance(dataset, HfDataset) and num_proc > 1 else False
+        keep_columns = ['channel']
         with self._patch_arrow_writer(), safe_ddp_context(None, True):
             try:
                 dataset_mapped = dataset.map(
@@ -316,7 +319,7 @@ def __call__(
                         'strict': strict,
                         'ignore_max_length_error': ignore_max_length_error
                     },
-                    remove_columns=list(dataset.features.keys()),
+                    remove_columns=[col for col in dataset.features.keys() if col not in keep_columns],
                     **map_kwargs)
             except NotImplementedError:
                 pass

swift/llm/template/base.py

@@ -1372,10 +1372,13 @@ def _data_collator(self, batch: List[Dict[str, Any]], *, padding_to: Optional[in
         else:
             inputs_embeds = [b['inputs_embeds'] for b in batch if b.get('inputs_embeds') is not None]
             input_ids = [b['input_ids'] for b in batch if b.get('input_ids') is not None]
+            channel = [b['channel'] for b in batch if b.get('channel') is not None]
             if inputs_embeds:
                 res['inputs_embeds'] = inputs_embeds
             if input_ids:
                 res['input_ids'] = input_ids
+            if channel:
+                res['channel'] = channel
             for key in ['labels', 'loss_scale', 'position_ids', 'token_type_ids']:
                 val = [b[key] for b in batch if b.get(key) is not None]
                 if val:

swift/plugin/loss.py

@@ -5,19 +5,23 @@
 
 import numpy as np
 import torch
+import torch.distributed as dist
 import torch.nn.functional as F
 from accelerate.utils import gather_object
 from torch import nn
 from torch.nn import CrossEntropyLoss, MSELoss
 from transformers.utils import strtobool
 
+from swift.plugin import MeanMetric
+
 
 class LossType:
     loss_scale = 'loss_scale'
     cosine_similarity = 'cosine_similarity'
     contrastive = 'contrastive'
     online_contrastive = 'online_contrastive'
     infonce = 'infonce'
+    channel_loss = 'channel_loss'
 
 
 LOSS_MAPPING = {}
@@ -382,6 +386,61 @@ def online_contrastive_loss(outputs, labels, loss_scale=None, num_items_in_batch
     return loss
 
 
+@register_loss_func(LossType.channel_loss)
+def channel_loss_func(outputs, labels, num_items_in_batch=None, sample_channels=None, trainer=None) -> torch.Tensor:
+    assert sample_channels is not None, 'Data does not have channel field.'
+    channels = trainer.args.channels
+    assert channels is not None, 'Please pass --channels as a hyperparameter.'
+    logits = outputs.logits
+
+    # compute token loss
+    shift_logits = logits[..., :-1, :].contiguous()
+    shift_labels = labels[..., 1:].contiguous()
+    loss_fct = nn.CrossEntropyLoss(reduction='none')
+    flat_logits = shift_logits.view(-1, shift_logits.size(-1))
+    flat_labels = shift_labels.view(-1)
+    token_loss = loss_fct(flat_logits, flat_labels).view_as(shift_labels)  # [batch_size, seq_len-1]
+
+    mask = shift_labels != -100
+
+    state = trainer.state
+    state.local_step += 1
+
+    for ch in set(sample_channels):
+        idx = torch.tensor([s == ch for s in sample_channels], device=logits.device)
+        ch_loss_step = token_loss[idx].masked_select(mask[idx])
+        state.ch_loss_steps.setdefault(ch, []).append(ch_loss_step)
+
+    # At the end of a global step, compute the mean loss for each channel
+    if state.local_step % trainer.args.gradient_accumulation_steps == 0:
+        for ch in channels:
+            ch_loss_steps = state.ch_loss_steps.get(ch, [])
+            loss_sum_tensor = torch.tensor([sum(torch.sum(x) for x in ch_loss_steps)],
+                                           dtype=torch.float32,
+                                           device=logits.device)
+            num_items_tensor = torch.tensor([sum(x.numel() for x in ch_loss_steps)],
+                                            dtype=torch.float32,
+                                            device=logits.device)
+            if dist.is_initialized():
+                dist.all_reduce(loss_sum_tensor, op=dist.ReduceOp.SUM)
+                dist.all_reduce(num_items_tensor, op=dist.ReduceOp.SUM)
+            loss_sum = loss_sum_tensor.item()
+            num_items = num_items_tensor.item()
+            ch_loss = loss_sum / (num_items + 1e-12)
+
+            if ch_loss > 0.0:
+                metric_key = f'loss_{ch}'
+                trainer._custom_metrics.setdefault(metric_key, MeanMetric(nan_value=None)).update(ch_loss)
+            # Reset
+            state.ch_loss_steps[ch] = []
+
+    # return loss
+    total_loss = token_loss.masked_select(mask).sum()
+    total_tokens = mask.sum()
+    return total_loss / num_items_in_batch if num_items_in_batch is not None \
+        else total_loss / (total_tokens.float() + 1e-12)
+
+
 def get_loss_func(loss_type: Optional[str]) -> Optional[Callable]:
     if loss_type is None:
         return None

swift/trainers/arguments.py

@@ -51,6 +51,7 @@ class TrainArgumentsMixin:
     vit_lr: Optional[float] = None
     optimizer: Optional[str] = None
     use_logits_to_keep: Optional[bool] = None
+    channels: List[str] = None
 
     # torchacc
     metric_warmup_step: Optional[float] = 0

swift/trainers/trainers.py

@@ -164,6 +164,16 @@ def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=N
             loss_kwargs['loss_scale'] = loss_scale
             if compute_loss_func is None:
                 compute_loss_func = get_loss_func('loss_scale')
+
+        sample_channels = inputs.pop('channel', None)
+        if sample_channels is not None:
+            state = self.state
+            setattr(state, 'local_step', getattr(state, 'local_step', 0))
+            setattr(state, 'ch_loss_steps', getattr(state, 'ch_loss_steps', {}))
+
+            loss_kwargs['sample_channels'] = sample_channels
+            loss_kwargs['trainer'] = self
+
         if (self.label_smoother is not None or compute_loss_func is not None) and 'labels' in inputs:
             labels = inputs.pop('labels')