【开源实习】bart模型微调

llm/finetune/bart/bart_finetune.py

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+from mindspore import nn, ops, Tensor
+from mindspore.dataset import GeneratorDataset
+from mindnlp.transformers import BartForConditionalGeneration, BartTokenizer
+from mindnlp.engine import Trainer, TrainingArguments
+from datasets import load_dataset
+
+import evaluate
+import mindspore as ms
+
+
+rouge_metric = evaluate.load("rouge")
+# Load dataset and tokenizer
+tokenizer = BartTokenizer.from_pretrained("./bart-base")
+
+dataset = load_dataset("xsum", split="train")
+val_dataset = load_dataset("xsum", split="validation")
+
+
+def preprocess_function(examples):
+    inputs = tokenizer(examples["document"], max_length=512,
+                       truncation=True, padding="max_length")
+    targets = tokenizer(
+        examples["summary"], max_length=128, truncation=True, padding="max_length")
+    inputs["labels"] = targets["input_ids"]
+    return inputs
+
+
+tokenized_data = dataset.map(preprocess_function, batched=True, remove_columns=[
+                             "document", "summary", "id"], num_proc=24)
+tokenized_val_data = val_dataset.map(preprocess_function, batched=True, remove_columns=[
+                                     "document", "summary", "id"], num_proc=24)
+
+
+# Load model
+model = BartForConditionalGeneration.from_pretrained("./bart-base")
+
+
+def create_mindspore_dataset(data, batch_size=8):
+    data_list = list(data)
+
+    def generator():
+        for item in data_list:
+            yield (
+                Tensor(item["input_ids"], dtype=ms.int32),
+                Tensor(item["attention_mask"], dtype=ms.int32),
+                Tensor(item["labels"], dtype=ms.int32)
+            )
+
+    return GeneratorDataset(generator, column_names=["input_ids", "attention_mask", "labels"]).batch(batch_size)
+
+
+def compute_metrics(pred):
+
+    labels_ids = pred.label_ids
+    pred_ids = pred.predictions[0]
+
+    pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
+    labels_ids[labels_ids == -100] = tokenizer.pad_token_id
+    label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)
+
+    rouge_output = rouge_metric.compute(
+        predictions=pred_str,
+        references=label_str,
+        rouge_types=["rouge1", "rouge2", "rougeL", "rougeLsum"],
+    )
+
+    return {
+        "R1": round(rouge_output["rouge1"], 4),
+        "R2": round(rouge_output["rouge2"], 4),
+        "RL": round(rouge_output["rougeL"], 4),
+        "RLsum": round(rouge_output["rougeLsum"], 4),
+    }
+
+
+def preprocess_logits_for_metrics(logits, labels):
+    """
+    防止内存溢出
+    """
+    pred_ids = ms.mint.argmax(logits[0], dim=-1)
+    return pred_ids, labels
+
+
+train_dataset = create_mindspore_dataset(tokenized_data, batch_size=4)
+eval_dataset = create_mindspore_dataset(tokenized_val_data, batch_size=2)
+
+training_args = TrainingArguments(
+    output_dir="./results",
+    evaluation_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=4,
+    per_device_eval_batch_size=2,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    save_total_limit=2,
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    tokenizer=tokenizer,
+    compute_metrics=compute_metrics,
+    preprocess_logits_for_metrics=preprocess_logits_for_metrics,
+)
+
+trainer.train()

llm/finetune/bart/bart_finetune_readme.md

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+## bart模型微调报告
+
+### 任务
+- **任务编号**：#IAUOXU
+- **任务链接**：[【开源实习】bart模型微调](https://gitee.com/mindspore/community/issues/IAUOXU)  
+- **实现内容**：实现了bart模型在XSum数据集上的微调。  
+- **模型**：`facebook/bart-base`  
+- **数据集**：`EdinburghNLP/xsum`
+
+---
+
+### 结果对比
+
+#### **Mindnlp+D910B**
+
+| Epoch | Eval Loss | R1 (ROUGE-1) | R2 (ROUGE-2) | RL (ROUGE-L) | RLsum (ROUGE-Lsum) |
+|------:|----------:|-------------:|-------------:|-------------:|-------------------:|
+| 1     | 0.4504    | 0.5265       | 0.2512       | 0.5003       | 0.5004            |
+| 2     | 0.4481    | 0.5272       | 0.2538       | 0.5026       | 0.5025            |
+| 3     | 0.4440    | 0.5316       | 0.2580       | 0.5061       | 0.5062            |
+
+---
+
+#### **Pytorch+3090**
+
+| Epoch | Eval Loss | R1 (ROUGE-1) | R2 (ROUGE-2) | RL (ROUGE-L) | RLsum (ROUGE-Lsum) |
+|------:|----------:|-------------:|-------------:|-------------:|-------------------:|
+| 1     | 0.4364    | 0.5226       | 0.2432       | 0.4965       | 0.4961            |
+| 2     | 0.4297    | 0.5309       | 0.2547       | 0.5066       | 0.5065            |
+| 3     | 0.4290    | 0.5318       | 0.2563       | 0.5065       | 0.5062            |
+
+---