27.1 大语言模型基础

大语言模型概述

大语言模型（Large Language Models，LLMs）是现代AI系统的核心组件，它们通过在海量文本数据上进行预训练，学习到了丰富的语言知识和推理能力。Claude Code正是基于强大的大语言模型构建的编程助手。

大语言模型的基本概念

1. 什么是大语言模型

大语言模型是一种基于深度学习的AI模型，具有以下特点：

大规模参数 : 模型参数数量从几十亿到数千亿不等
海量训练数据 : 在互联网文本、代码、书籍等大规模数据集上训练
强大的泛化能力 : 能够处理各种自然语言和编程任务
上下文理解 : 能够理解长文本和复杂上下文

2. 大语言模型的架构

示例：简化的大语言模型架构

用户请求： "解释大语言模型的基本架构"

Claude Code 生成的解释：

python

## 大语言模型架构

### Transformer 架构

    bash


    ↓
    嵌入层 (Embedding Layer)
    ↓
    位置编码 (Positional Encoding)
    ↓
    编码器层 (Encoder Layers) × N
    ↓
    解码器层 (Decoder Layers) × N
    ↓
    输出层 (Output Layer)
    ↓
    概率分布

    ```

    ### 核心组件

```python
    #### 1. 自注意力机制 (Self-Attention)

    ```python

    python

    import torch
    import torch.nn as nn
    import math

    class SelfAttention(nn.Module):
        """自注意力机制"""

        def __init__(self, embed_size, heads):
            super(SelfAttention, self).__init__()
            self.embed_size = embed_size
            self.heads = heads
            self.head_dim = embed_size // heads

            assert (self.head_dim * heads == embed_size), "Embed size needs to be divisible by heads"

            self.values = nn.Linear(self.head_dim, self.head_dim, bias=False)
            self.keys = nn.Linear(self.head_dim, self.head_dim, bias=False)
            self.queries = nn.Linear(self.head_dim, self.head_dim, bias=False)

            self.fc_out = nn.Linear(heads * self.head_dim, embed_size)

        def forward(self, values, keys, query, mask):
            N = query.shape[0]
            value_len, key_len, query_len = values.shape[1], keys.shape[1], query.shape[1]

            # 分割成多个头

            values = values.reshape(N, value_len, self.heads, self.head_dim)
            keys = keys.reshape(N, key_len, self.heads, self.head_dim)
            queries = query.reshape(N, query_len, self.heads, self.head_dim)

            # 计算注意力分数

            energy = torch.einsum("nqhd,nkhd->nhqk", [queries, keys])

            # 应用掩码

            if mask is not None:
                energy = energy.masked_fill(mask == 0, float("-1e20"))

            # 计算注意力权重

            attention = torch.softmax(energy / (self.embed_size ** (1/2)), dim=3)

            # 应用注意力权重

            out = torch.einsum("nhql,nlhd->nqhd", [attention, values])

            # 合并多头

            out = out.reshape(N, query_len, self.heads * self.head_dim)

            return self.fc_out(out)

    > **自注意力机制的作用**:
    > - 捕捉序列中的长距离依赖关系
    > - 动态调整不同位置的重要性
    > - 实现并行计算

    #### 2. 前馈神经网络 (Feed-Forward Network)

    ```python

    class FeedForward(nn.Module):
    """前馈神经网络"""

    def __init__(self, embed_size, ff_hidden_size, dropout=0.1):
    super(FeedForward, self).__init__()
    self.linear1 = nn.Linear(embed_size, ff_hidden_size)
    self.linear2 = nn.Linear(ff_hidden_size, embed_size)
    self.dropout = nn.Dropout(dropout)
    self.activation = nn.GELU()

    def forward(self, x):
    x = self.linear1(x)
    x = self.activation(x)
    x = self.dropout(x)
    x = self.linear2(x)
    x = self.dropout(x)
    return x

    **前馈神经网络的作用**:
    > - 增强模型的表达能力
    > - 引入非线性变换
    > - 处理特征交互

    #### 3. Transformer Block

    ```python

    python

    class TransformerBlock(nn.Module):
        """Transformer块"""

        def __init__(self, embed_size, heads, ff_hidden_size, dropout=0.1):
            super(TransformerBlock, self).__init__()
            self.attention = SelfAttention(embed_size, heads)
            self.norm1 = nn.LayerNorm(embed_size)
            self.norm2 = nn.LayerNorm(embed_size)
            self.feed_forward = FeedForward(embed_size, ff_hidden_size, dropout)
            self.dropout = nn.Dropout(dropout)

        def forward(self, value, key, query, mask):

            # 多头注意力 + 残差连接 + 层归一化

            attention = self.attention(value, key, query, mask)
            x = self.dropout(attention) + query
            x = self.norm1(x)

            # 前馈网络 + 残差连接 + 层归一化

            forward = self.feed_forward(x)
            out = self.dropout(forward) + x
            out = self.norm2(out)

            return out

    > **Transformer Block的作用**:
    > - 结合注意力和前馈网络
    > - 残差连接防止梯度消失
    > - 层归一化稳定训练

    ## 大语言模型的训练

    ### 1. 预训练

    ```python
    python

    # 示例：大语言模型预训练

    用户请求：
    "解释大语言模型的预训练过程"

    Claude Code 生成的解释：

    ## 预训练过程

    ### 训练目标

    #### 1. 掩码语言模型 (Masked Language Modeling, MLM)

    ```python

    ```python

    class MaskedLanguageModel(nn.Module):
        """掩码语言模型"""

        def __init__(self, vocab_size, embed_size, num_layers, heads, ff_hidden_size, max_length):
            super(MaskedLanguageModel, self).__init__()
            self.embedding = nn.Embedding(vocab_size, embed_size)
            self.positional_encoding = PositionalEncoding(embed_size, max_length)
            self.transformer_blocks = nn.ModuleList([
                TransformerBlock(embed_size, heads, ff_hidden_size)
                for _ in range(num_layers)
            ])
            self.fc_out = nn.Linear(embed_size, vocab_size)
            self.dropout = nn.Dropout(0.1)

        def forward(self, x, mask):
            # 嵌入 + 位置编码
            x = self.embedding(x)
            x = self.positional_encoding(x)
            x = self.dropout(x)

            # Transformer层
            for block in self.transformer_blocks:
                x = block(x, x, x, mask)

            # 输出层
            out = self.fc_out(x)

            return out

    def mask_tokens(inputs, tokenizer, mlm_probability=0.15):
        """掩码token"""
        labels = inputs.clone()
        probability_matrix = torch.full(labels.shape, mlm_probability)

        special_tokens_mask = [
            tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
        ]
        probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0)

        masked_indices = torch.bernoulli(probability_matrix).bool()
        labels[~masked_indices] = -100

        indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices
        inputs[indices_replaced] = tokenizer.convert_tokens_to_ids(tokenizer.mask_token)

        indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced
        random_words = torch.randint(len(tokenizer), labels.shape, dtype=torch.long)
        inputs[indices_random] = random_words[indices_random]

        return inputs, labels

    > **MLM训练目标**:
    > - 随机掩码输入token的一部分
    > - 预测被掩码的token
    > - 学习双向上下文表示

    #### 2. 因果语言模型 (Causal Language Modeling, CLM)

    ```python

    class CausalLanguageModel(nn.Module):
    """因果语言模型"""

    def __init__(self, vocab_size, embed_size, num_layers, heads, ff_hidden_size, max_length):
    super(CausalLanguageModel, self).__init__()
    self.embedding = nn.Embedding(vocab_size, embed_size)
    self.positional_encoding = PositionalEncoding(embed_size, max_length)
    self.transformer_blocks = nn.ModuleList([
    TransformerBlock(embed_size, heads, ff_hidden_size)
    for _ in range(num_layers)
    ])
    self.fc_out = nn.Linear(embed_size, vocab_size)
    self.dropout = nn.Dropout(0.1)

    def forward(self, x, mask):

     # 嵌入 + 位置编码

    x = self.embedding(x)
    x = self.positional_encoding(x)
    x = self.dropout(x)

     # Transformer层

    for block in self.transformer_blocks:
    x = block(x, x, x, mask)

     # 输出层

    out = self.fc_out(x)

    return out

    def create_causal_mask(size):
    """创建因果掩码"""
    mask = torch.triu(torch.ones(size, size), diagonal=1)
    mask = mask.masked_fill(mask == 1, float('-inf'))
    return mask

    **CLM训练目标**:
    > - 预测下一个token
    > - 学习单向上下文表示
    > - 适用于文本生成任务

    ### 2. 微调

    ```python
    python

    # 示例：大语言模型微调

    用户请求：
    "解释大语言模型的微调过程"

    Claude Code 生成的解释：

    ## 微调过程

    ### 指令微调 (Instruction Tuning)

    class InstructionDataset(torch.utils.data.Dataset):
    """指令数据集"""

    def __init__(self, data, tokenizer, max_length=512):
    self.data = data
    self.tokenizer = tokenizer
    self.max_length = max_length

    def __len__(self):
    return len(self.data)

    def __getitem__(self, idx):
    item = self.data[idx]

     # 格式化指令
    prompt = f"指令: {item['instruction']}\n"
    if 'input' in item and item['input']:
    prompt += f"输入: {item['input']}\n"
    prompt += f"输出: {item['output']}"

     # 编码
    encoded = self.tokenizer(
    prompt,
    max_length=self.max_length,
    padding='max_length',
    truncation=True,
    return_tensors='pt'
    )

    return {
    'input_ids': encoded['input_ids'].squeeze(),
    'attention_mask': encoded['attention_mask'].squeeze(),
    'labels': encoded['input_ids'].squeeze()
    }

    def fine_tune_model(model, train_dataset, val_dataset, config):
    """微调模型"""
     # 数据加载器
    train_loader = torch.utils.data.DataLoader(
    train_dataset,
    batch_size=config['batch_size'],
    shuffle=True
    )

    val_loader = torch.utils.data.DataLoader(
    val_dataset,
    batch_size=config['batch_size'],
    shuffle=False
    )

     # 优化器
    optimizer = torch.optim.AdamW(
    model.parameters(),
    lr=config['learning_rate'],
    weight_decay=config['weight_decay']
    )

     # 学习率调度器
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
    optimizer,
    T_max=config['num_epochs']
    )

     # 训练循环
    for epoch in range(config['num_epochs']):
    model.train()
    total_loss = 0

    for batch in train_loader:
    optimizer.zero_grad()

     # 前向传播
    outputs = model(
    input_ids=batch['input_ids'],
    attention_mask=batch['attention_mask'],
    labels=batch['labels']
    )

    loss = outputs.loss

     # 反向传播
    loss.backward()
    torch.nn.utils.clip_grad_norm_(model.parameters(), config['max_grad_norm'])
    optimizer.step()

    total_loss += loss.item()

     # 验证
    val_loss = evaluate_model(model, val_loader)

     # 学习率调度
    scheduler.step()

    print(f"Epoch {epoch + 1}/{config['num_epochs']}")
    print(f"  Train Loss: {total_loss / len(train_loader):.4f}")
    print(f"  Val Loss: {val_loss:.4f}")

    def evaluate_model(model, val_loader):
    """评估模型"""
    model.eval()
    total_loss = 0

    with torch.no_grad():
    for batch in val_loader:
    outputs = model(
    input_ids=batch['input_ids'],
    attention_mask=batch['attention_mask'],
    labels=batch['labels']
    )
    total_loss += outputs.loss.item()

    return total_loss / len(val_loader)

    **指令微调的作用**:
    - 使模型理解并遵循指令
    - 提高模型在特定任务上的表现
    - 增强模型的交互能力
    ### 3. RLHF (Reinforcement Learning from Human Feedback)

    ```

    python

    # 示例：RLHF训练

    用户请求：
    "解释RLHF训练过程"

    Claude Code 生成的解释：

    ## RLHF训练过程

    ### 训练步骤

    #### 步骤1: 奖励模型训练

    ```python

    ```python

    class RewardModel(nn.Module):
        """奖励模型"""

        def __init__(self, base_model):
            super(RewardModel, self).__init__()
            self.base_model = base_model
            self.reward_head = nn.Linear(base_model.config.hidden_size, 1)

        def forward(self, input_ids, attention_mask):
            outputs = self.base_model(
                input_ids=input_ids,
                attention_mask=attention_mask
            )
            reward = self.reward_head(outputs.last_hidden_state[:, -1, :])
            return reward

    def train_reward_model(reward_model, comparison_data, config):
        """训练奖励模型"""
        optimizer = torch.optim.AdamW(reward_model.parameters(), lr=config['learning_rate'])

        for epoch in range(config['num_epochs']):
            total_loss = 0

            for batch in comparison_data:
                optimizer.zero_grad()

                # 计算两个输出的奖励

                reward_chosen = reward_model(
                    input_ids=batch['chosen_ids'],
                    attention_mask=batch['chosen_mask']
                )

                reward_rejected = reward_model(
                    input_ids=batch['rejected_ids'],
                    attention_mask=batch['rejected_mask']
                )

                # 计算损失

                loss = -torch.log(torch.sigmoid(reward_chosen - reward_rejected)).mean()

                # 反向传播

                loss.backward()
                optimizer.step()

                total_loss += loss.item()

            print(f"Epoch {epoch + 1}, Loss: {total_loss / len(comparison_data):.4f}")

    #### 步骤2: PPO训练

    ````python

    ````python

    def ppo_train(policy_model, reward_model, data, config):
    """PPO训练"""
    optimizer = torch.optim.AdamW(policy_model.parameters(), lr=config['learning_rate'])

    for epoch in range(config['num_epochs']):
    for batch in data:

     # 生成响应

    with torch.no_grad():
    old_log_probs, old_values = generate_response(
    policy_model,
    batch['input_ids'],
    batch['attention_mask']
    )

     # 计算奖励

    rewards = reward_model(
    input_ids=batch['response_ids'],
    attention_mask=batch['response_mask']
    )

     # 计算优势

    advantages = compute_advantages(rewards, old_values)

     # PPO更新

    for _ in range(config['ppo_epochs']):

     # 重新采样

    log_probs, values = generate_response(
    policy_model,
    batch['input_ids'],
    batch['attention_mask']
    )

     # 计算比率

    ratio = torch.exp(log_probs - old_log_probs)

     # 计算PPO损失

    surr1 = ratio * advantages
    surr2 = torch.clamp(ratio, 1 - config['clip_eps'], 1 + config['clip_eps']) * advantages
    policy_loss = -torch.min(surr1, surr2).mean()

     # 价值函数损失

    value_loss = nn.MSELoss()(values, rewards)

     # 总损失

    loss = policy_loss + config['value_coef'] * value_loss

     # 反向传播

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    ```> **RLHF的作用**:
    > - 使模型输出更符合人类偏好
    > - 提高模型的有用性和安全性
    > - 减少有害输出

    ```

    ## 大语言模型的应用

    ### 1. 代码生成

    # 示例：代码生成应用

    用户请求：
    "展示大语言模型在代码生成中的应用"
    Claude Code 生成的示例：

    ## 代码生成应用

    ### 基础代码生成

    ````python
    `python

    def generate_code(prompt, model, tokenizer, max_length=512):
    """生成代码"""
     # 编码输入
    inputs = tokenizer(prompt, return_tensors='pt')

     # 生成代码
    with torch.no_grad():
    outputs = model.generate(
    inputs['input_ids'],
    attention_mask=inputs['attention_mask'],
    max_length=max_length,
    temperature=0.7,
    top_p=0.95,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
    )

     # 解码输出
    generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)

    return generated_code

    # 使用示例
    prompt = """
    编写一个Python函数，实现快速排序算法。
    """

    code = generate_code(prompt, model, tokenizer)
    print(code)

    ```### 代码补全

    ```
    python

    def complete_code(partial_code, model, tokenizer, max_length=256):
        """补全代码"""
        # 编码输入
        inputs = tokenizer(partial_code, return_tensors='pt')

        # 补全代码
        with torch.no_grad():
            outputs = model.generate(
                inputs['input_ids'],
                attention_mask=inputs['attention_mask'],
                max_length=max_length,
                temperature=0.5,
                top_p=0.9,
                do_sample=True,
                pad_token_id=tokenizer.eos_token_id
            )

        # 解码输出
        completed_code = tokenizer.decode(outputs[0], skip_special_tokens=True)

        return completed_code

    # 使用示例
    partial_code = """
    def quick_sort(arr):
        if len(arr) <= 1:
            return arr
        pivot = arr[len(arr) // 2]
    """

    completed_code = complete_code(partial_code, model, tokenizer)
    print(completed_code)

    ### 代码解释

    ````python

    ````python

    def explain_code(code, model, tokenizer, max_length=512):
    """解释代码"""
    prompt = f"""
    请解释以下代码的功能：

    ```python

    ```python

    {code}

    ```

    """

        # 编码输入

        inputs = tokenizer(prompt, return_tensors='pt')

        # 生成解释

        with torch.no_grad():
            outputs = model.generate(
                inputs['input_ids'],
                attention_mask=inputs['attention_mask'],
                max_length=max_length,
                temperature=0.7,
                top_p=0.95,
                do_sample=True,
                pad_token_id=tokenizer.eos_token_id
            )

        # 解码输出

        explanation = tokenizer.decode(outputs[0], skip_special_tokens=True)

        return explanation

    # 使用示例

    code = """
    def quick_sort(arr):
        if len(arr) <= 1:
            return arr
        pivot = arr[len(arr) // 2]
        left = [x for x in arr if x < pivot]
        middle = [x for x in arr if x == pivot]
        right = [x for x in arr if x > pivot]
        return quick_sort(left) + middle + quick_sort(right)
    """

    explanation = explain_code(code, model, tokenizer)
    print(explanation)

    ```

    ```

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714

27.1 大语言模型基础 ​

大语言模型概述 ​

大语言模型的基本概念 ​

1. 什么是大语言模型 ​

2. 大语言模型的架构 ​

示例：简化的大语言模型架构 ​