【开源实习】Autoformer模型微调 (#2019)

Author: 4everImmortality

llm/finetune/Autoformer/autoformer_monash_tsf_finetune.md

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+# Autoformer Mindnlp 微调
+
+- Autoformer模型微调任务链接：[【开源实习】autoformer模型微调 · Issue #IAUOTL · MindSpore/community - Gitee.com](https://gitee.com/mindspore/community/issues/IAUOTL)
+- 实现了huggingface/autoformer-tourism-monthly 基准权重 在 [monash_tsf/tourism_monthly] 数据集上的微调
+- base model: [huggingface/autoformer-tourism-monthly · Hugging Face](https://huggingface.co/huggingface/autoformer-tourism-monthly)
+- dataset: [Monash-University/monash_tsf · Datasets at Hugging Face](https://huggingface.co/datasets/Monash-University/monash_tsf)
+
+------
+
+# Requirments
+## Pytorch 
+
+- GPU: RTX 4070ti 12G
+- cuda: 11.8
+- Python version: 3.10
+- torch version: 2.5.0
+- transformers version : 4.47.0
+- accelerate: 0.27.0
+- gluonts: 0.14.0
+- datasets: 2.16.0
+- evaluate: 0.4.0
+- numpy: 1.26.4
+- pandas: 2.1.0
+- scipy: 1.11.0
+
+## Mindspore 启智社区 Ascend910B算力资源
+- Ascend: 910B
+- python: 3.9
+- mindspore: 2.5.0
+- mindnlp: 0.4.1
+- gluonts: 0.16.0
+- datasets: 3.5.0
+- evaluate: 0.4.3
+- numpy: 1.26.4
+- pandas: 2.2.3
+- scipy: 1.13.1
+
+ 
+
+---
+
+## 修改内容
+
+### Ascend
+
+源码中**modeling_autoformer.py**文件中 **padding_mode** = **circular** 改成 **padding_mode** = **replicate**
+
+### CPU/GPU
+
+源码中**modeling_autoformer.py** 922行 roll操作 在gpu和cpu上没有实现
+
+修改源码中922行语句：
+
+```python
+value_states_roll_delay = value_states.roll(shifts=-int(top_k_delays_index[i]), dims=1)
+```
+
+改成
+
+```python
+value_states_roll_delay = custom_roll(
+    value_states,
+    shifts=-int(top_k_delays_index[i].asnumpy())，# 转换为Python整数
+    dim=1
+)
+```
+
+并且在前面添加一个用于替代的roll函数的自定义方法
+
+```python
+def custom_roll(tensor, shifts, dim):
+    """
+    Custom implementation of cyclic shift along specified dimension
+    
+    Args:
+        tensor: Input tensor to be shifted
+        shifts: Number of positions to shift 
+            (positive = right shift, negative = left shift)
+        dim: Dimension index along which to perform shift
+    
+    Returns:
+        Tensor with elements cyclically shifted along specified dimension
+    """
+    # Handle cases where shifts exceed dimension length
+    dim_size = tensor.shape[dim]
+    shifts = shifts % dim_size  # Ensure shifts are within valid range
+    
+    if shifts == 0:
+        return tensor
+    
+    # Split tensor into two parts and swap their order
+    if shifts > 0:
+        # Right shift: keep last 'shifts' elements and move to front
+        part1 = ops.narrow(tensor, dim, 0, dim_size - shifts)
+        part2 = ops.narrow(tensor, dim, dim_size - shifts, shifts)
+    else:
+        # Left shift: keep first '|shifts|' elements and move to end
+        shifts = abs(shifts)
+        part1 = ops.narrow(tensor, dim, shifts, dim_size - shifts)
+        part2 = ops.narrow(tensor, dim, 0, shifts)
+    
+    # Concatenate the reversed parts
+    return ops.cat((part2, part1), dim)
+```
+
+
+
+----
+
+## 微调结果
+
+### Mindspore
+
+| Epoch | Loss              |
+| ----- | ----------------- |
+| 0     | 7.546689510345459 |
+| 1     | 7.772482395172119 |
+| 2     | 7.14789342880249  |
+| 3     | 7.49253511428833  |
+| 4     | 7.337801456451416 |
+| 5     | 6.960692882537842 |
+| 6     | 8.312647819519043 |
+| 7     | 6.90599250793457  |
+| 8     | 7.212374210357666 |
+| 9     | 7.506921291351318 |
+
+------
+
+### Pytorch
+
+| Epoch | Loss               |
+| ----- | ------------------ |
+| 0     | 7.412668228149414  |
+| 1     | 7.8263068199157715 |
+| 2     | 7.839258670806885  |
+| 3     | 8.043777465820312  |
+| 4     | 8.08508586883545   |
+| 5     | 7.503101825714111  |
+| 6     | 7.824302673339844  |
+| 7     | 7.399034023284912  |
+| 8     | 7.122222900390625  |
+| 9     | 7.612663269042969  |