【开源实习】chatglm-4模型微调

llm/finetune/chatglm4/finetune_glm4.py

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+import os
+import json
+import pandas as pd
+import numpy as np
+import mindspore
+from mindspore import nn, Tensor
+from mindspore.common.initializer import Normal
+from mindspore.train import Model, LossMonitor, TimeMonitor, Callback
+from mindspore.train.callback import CheckpointConfig, ModelCheckpoint
+from mindspore.nn import AdamWeightDecay
+from mindspore.dataset import GeneratorDataset
+from mindnlp.transformers import AutoTokenizer, AutoModelForCausalLM
+from mindspore import context
+from mindnlp.core import no_grad
+
+# 设置上下文，使用Ascend设备
+context.set_context(device_target="Ascend", device_id=0)
+
+# 输出结果必须保存在该目录
+output_path = "./output"  
+
+def dataset_jsonl_transfer(origin_path, new_path):
+    messages = []
+
+    # 读取旧的JSONL文件
+    with open(origin_path, "r") as file:
+        for line in file:
+            # 解析每一行的json数据
+            data = json.loads(line)
+            context_data = data["text"]
+            category = data["category"]
+            label = data["output"]
+            message = {
+                "instruction": "你是一个文本分类领域的专家，你会接收到一段文本和几个潜在的分类选项，请输出文本内容的正确类型",
+                "input": f"文本:{context_data},类型选型:{category}",
+                "output": label,
+            }
+            messages.append(message)
+
+    with open(new_path, "w", encoding="utf-8") as file:
+        for message in messages:
+            file.write(json.dumps(message, ensure_ascii=False) + "\n")
+
+def process_func(example, tokenizer):
+    MAX_LENGTH = 384 
+    input_ids, attention_mask, labels = [], [], []
+    instruction = tokenizer(
+        f"<|system|>\n你是一个文本分类领域的专家，你会接收到一段文本和几个潜在的分类选项，请输出文本内容的正确类型<|endoftext|>\n<|user|>\n{example['input']}<|endoftext|>\n<|assistant|>\n",
+        add_special_tokens=False,
+    )
+    response = tokenizer(f"{example['output']}", add_special_tokens=False)
+    input_ids = instruction["input_ids"] + response["input_ids"] + [tokenizer.pad_token_id]
+    attention_mask = (
+        instruction["attention_mask"] + response["attention_mask"] + [1]
+    )
+    labels = [-100] * len(instruction["input_ids"]) + response["input_ids"] + [tokenizer.pad_token_id]
+    if len(input_ids) > MAX_LENGTH:  # 做一个截断
+        input_ids = input_ids[:MAX_LENGTH]
+        attention_mask = attention_mask[:MAX_LENGTH]
+        labels = labels[:MAX_LENGTH]
+    return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels}   
+
+# 加载模型和分词器
+model_name = "ZhipuAI/glm-4-9b-chat"
+tokenizer = AutoTokenizer.from_pretrained(model_name, mirror='modelscope')
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    mirror='modelscope',
+    ms_dtype=mindspore.float16
+)
+train_dataset_path = "train.jsonl"
+test_dataset_path = "test.jsonl"
+
+train_jsonl_new_path = "new_train.jsonl"
+test_jsonl_new_path = "new_test.jsonl"
+
+if not os.path.exists(train_jsonl_new_path):
+    dataset_jsonl_transfer(train_dataset_path, train_jsonl_new_path)
+if not os.path.exists(test_jsonl_new_path):
+    dataset_jsonl_new_path(test_dataset_path, test_jsonl_new_path)
+
+# 得到训练集
+train_df = pd.read_json(train_jsonl_new_path, lines=True)
+train_df = train_df[:1000]
+
+# 数据预处理
+train_data = train_df.to_dict(orient='records')
+processed_train_data = []
+for example in train_data:
+    processed_example = process_func(example, tokenizer)
+    processed_train_data.append(processed_example)
+
+# 转换为适合 MindSpore 的格式
+def data_generator(dataset):
+    for item in dataset:
+        yield (
+            np.array(item["input_ids"], dtype=np.int32),
+            np.array(item["attention_mask"], dtype=np.int32),
+            np.array(item["labels"], dtype=np.int32)
+        )
+
+train_dataset = GeneratorDataset(source=lambda: data_generator(processed_train_data), 
+                                column_names=["input_ids", "attention_mask", "labels"])
+
+def forward_fn(input_ids, attention_mask, labels):
+    outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
+    loss = outputs.loss
+    return loss
+
+params_dict = model.parameters_dict()
+trainable_params = [param for param in params_dict.values() if param.requires_grad]
+
+optimizer = AdamWeightDecay(unique_params, learning_rate=3e-4)
+
+train_net = mindspore.nn.TrainOneStepCell(nn.WithLossCell(model, nn.CrossEntropyLoss()), optimizer)
+
+class EvalCallback(Callback):
+    def __init__(self, model, dataset, tokenizer):
+        super(EvalCallback, self).__init__()
+        self.model = model
+        self.dataset = dataset
+        self.tokenizer = tokenizer
+
+    def epoch_end(self, run_context):
+        cb_params = run_context.original_args()
+        eval_results = self.model.eval(self.dataset)
+        print(f"Epoch {cb_params.cur_epoch_num} eval results: {eval_results}")
+
+# 创建模型
+
+model = Model(model, loss_fn=nn.CrossEntropyLoss(), optimizer=optimizer, metrics={'loss'})
+
+config_ck = CheckpointConfig(save_checkpoint_steps=50, keep_checkpoint_max=5)
+ckpoint_cb = ModelCheckpoint(prefix="glm4-9b", directory=output_path, config=config_ck)
+
+model.train(epoch_size=1, train_dataset=train_dataset, callbacks=[TimeMonitor(), LossMonitor(), ckpoint_cb])
+
+test_df = pd.read_json(test_jsonl_new_path, lines=True)[:10]
+test_data = test_df.to_dict(orient='records')
+
+processed_test_data = []
+for example in test_data:
+    processed_example = process_func(example, tokenizer)
+    processed_test_data.append(processed_example)
+
+test_dataset = GeneratorDataset(source=lambda: data_generator(processed_test_data), 
+                               column_names=["input_ids", "attention_mask", "labels"])
+
+eval_results = model.eval(test_dataset)
+print(f"评估结果: {eval_results}")