Qwen2 大模型源码解析 | 极客日志

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Qwen2 大模型源码解析

Qwen2 大模型源码深度解析，涵盖模型初始化配置、RoPE 位置编码实现、注意力机制（GQA）、MLP 激活函数及 RMSNorm 归一化细节。深入剖析模型 Pipeline 结构、权重加载流程、分词器构建以及生成配置（GenerationConfig）。重点讲解推理阶段的采样策略，包括束搜索、贪心搜索、Top-K/P 采样及重复惩罚等 Logits 处理器逻辑，并提供批量推理中的 KV Cache 处理方案。

BackendPro发布于 2025/2/70 浏览

Qwen2 源码解析

本文将深入分析千问模型推理代码，详细解析其内部实现机制。

推理代码示例

 transformers  AutoModelForCausalLM, AutoTokenizer
model_name = 
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=,
    device_map=
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = 
messages = [
    {: , : },
    {: , : prompt},
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=,
    add_generation_prompt=,
)
model_inputs = tokenizer([text], return_tensors=).to(model.device)
generated_ids = model.generate(
    **model_inputs,
     max_new_tokens=,
)
generated_ids = [
    output_ids[(input_ids):]  input_ids, output_ids  (model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=)[]

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "qwen/Qwen2___5-7B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side='left')  # 批量推理，要设置左 padding
batch_prompt = [
    "Give me a short introduction to large language model.",
    "请介绍下您自己？"
         ]
batch_messages = []
for prompt in batch_prompt:
    messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}]  
    batch_messages.append(messages)

text = tokenizer.apply_chat_template(
    batch_messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer(text, padding=True, return_tensors="pt").to(model.device)
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512,
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)

config, kwargs = AutoConfig.from_pretrained(
                pretrained_model_name_or_path,
                return_unused_kwargs=True,
                trust_remote_code=trust_remote_code,
                code_revision=code_revision,
                _commit_hash=commit_hash,
                **hub_kwargs,
                **kwargs,
            )

"""
type(config):
      <class 'transformers.models.qwen2.configuration_qwen2.Qwen2Config'>
"""

model_class = _get_model_class(config, cls._model_mapping)
"""
type(model_class)
     <class 'transformers.models.qwen2.modeling_qwen2.Qwen2ForCausalLM'>
"""

# 1.加载配置文件
with open('config.json', "r", encoding="utf-8") as reader:
         text = reader.read()
config_dict = json.loads(text)
# 2.实例化配置类
config = Qwen2Config(**config_dict)
# 3.实例化模型
model = Qwen2ForCausalLM.from_pretrained(pretrained_model_name_or_path, config=config, **kwarg)
# 即使不写初始化 config 实例代码，PreTrainedModel.from_pretrained 也会自动完成。
model = Qwen2ForCausalLM.from_pretrained(pretrained_model_name_or_path, **kwarg)

  - vocab_size：Qwen2 模型的词汇表大小。定义调用 [`Qwen2Model`] 时 inputs_ids 可以表示的不同 token 的数量。
  - hidden_size：隐藏表示的维度。
  - intermediate_size：MLP 表示的维度。
  - num_hidden_layers：Transformer 编码器中的隐藏层数量。
  - num_attention_heads：Transformer 编码器中每个注意力层的注意力头数量。
  - num_key_value_heads：应使用的 key_value 头的数量以实现分组查询注意力（Grouped Query Attention）。如果 `num_key_value_heads=num_attention_heads`，模型将使用多头注意力（MHA），如果 `num_key_value_heads=1`，模型将使用多查询注意力（MQA），否则使用 GQA。在将多头检查点转换为 GQA 检查点时，每个分组的关键和值头应由该组内的所有原始头的平均池化构建。
  - hidden_act (`str` 或 `function`, *可选*, 默认为 `"silu"`)：解码器中的非线性激活函数（函数或字符串）。
  - max_position_embeddings：模型可能使用的最大序列长度。
  - initializer_range：用于初始化所有权重矩阵的截断正态初始化器的标准差。
  - rms_norm_eps：rms 规范化层使用的 epsilon。
  - use_cache：模型是否应返回最后的 key/values 注意力（并非所有模型都使用）。仅当 `config.is_decoder=True` 时相关。
  - tie_word_embeddings：模型输入和输出词嵌入是否应绑定。
  - rope_theta：RoPE 嵌入的基本周期。
  - use_sliding_window：是否使用滑动窗口注意力。
  - sliding_window：滑动窗口注意力（SWA）窗口大小。如果未指定，将默认为 `4096`。
  - max_window_layers：使用 SWA（滑动窗口注意力）的层数。底部图层使用 SWA，而顶部图层使用全部注意力。
  - attention_dropout：注意力概率的 dropout 比率。

import torch
from torch import nn
class Qwen2RotaryEmbedding(nn.Module):
      def __init__(self, dim = 128 , base = 10000, max_position_embeddings = 2048, device = None):
          super().__init__()
          self.dim = dim
          self.base = base
          self.max_position_embeddings = max_position_embeddings
          inv_freq = 1.0 / (self.base ** (torch.arange(0,self.dim, 2,dtype = torch.int64).float().to(device))  / self.dim )   
          self.register_buffer("inv_freq", inv_freq, persistent=False)
          self._set_cos_sin_cache(
                 seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
                 )   
      def _set_cos_sin_cache(self, seq_len, device, dtype):
            self.max_seq_len_cached = seq_len
            t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
            freqs = torch.outer(t, self.inv_freq)
            # Different from paper, but it uses a different permutation in order to obtain the same calculation
            emb = torch.cat((freqs, freqs), dim=-1)
            self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
            self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)

def rotate_half(x):
    x1 = x[..., :x.shape[-1] //2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2,x1),dim = -1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed

  class Qwen2Attention(nn.Module):
      def __init__(self, config: Qwen2Config, layer_idx: Optional[int] = None):
          super().__init__()
          self.config = config                              # 配置类
          self.layer_idx = layer_idx                        # 模型层 id
          self.hidden_size = config.hidden_size                    # 隐藏层维度 28 * 128
              self.num_heads = config.num_attention_heads          # q 注意力头：28
          self.head_dim = self.hidden_size // self.num_heads       # 注意力头向量：128 
          self.num_key_value_heads = config.num_key_value_heads    # k,v 注意力头：4，广播 28 
          self.num_key_value_groups = self.num_heads // self.num_key_value_heads     # GQA 分 7 组，每组 k,v 共享。
          self.max_position_embeddings = config.max_position_embeddings 
          self.rope_theta = config.rope_theta
          self.is_causal = True
          self.attention_dropout = config.attention_dropout
          if (self.head_dim * self.num_heads) != self.hidden_size:
              raise ValueError(
                  f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
                  f" and `num_heads`: {self.num_heads})."
              )
          self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
          self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
          self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
          self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
  
          self.rotary_emb = Qwen2RotaryEmbedding(
              self.head_dim,
              max_position_embeddings=self.max_position_embeddings,
              base=self.rope_theta,
          )

if layer_idx == 0:
     self._seen_tokens += key_states.shape[-2]
# Update the cache
if len(self.key_cache) <= layer_idx:
    self.key_cache.append(key_states)
    self.value_cache.append(value_states)
else:
    self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
    self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)

 attn_output = torch.nn.functional.scaled_dot_product_attention(
            query_states,
            key_states,
            value_states,
            attn_mask=causal_mask,
            dropout_p=self.attention_dropout if self.training else 0.0,
            is_causal=is_causal,
        )

def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None) -> torch.Tensor:
    L, S = query.size(-2), key.size(-2)
    scale_factor = 1 / math.sqrt(query.size(-1)) if scale is None else scale
    attn_bias = torch.zeros(L, S, dtype=query.dtype)
    if is_causal:
        assert attn_mask is None
        temp_mask = torch.ones(L, S, dtype=torch.bool).tril(diagonal=0)
        attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
        attn_bias.to(query.dtype)
    if attn_mask is not None:
        if attn_mask.dtype == torch.bool:
            attn_bias.masked_fill_(attn_mask.logical_not(), float("-inf"))
        else:
            attn_bias += attn_mask
    attn_weight = query @ key.transpose(-2, -1) * scale_factor
    attn_weight += attn_bias
    attn_weight = torch.softmax(attn_weight, dim=-1)
    attn_weight = torch.dropout(attn_weight, dropout_p, train=True)
    return attn_weight @ value

    class Qwen2MLP(nn.Module):
        def __init__(self, config):
            super().__init__()
            self.hidden_size = config.hidden_size
            self.intermediate_size = config.intermediate_size
            self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
            self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
            self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
            self.act_fn = ACT2FN[config.hidden_act]
    
        def forward(self, hidden_state):
            return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state))

class Qwen2RMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps
    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1,keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)

    Qwen2ForCausalLM(
      (model): Qwen2Model(
        (embed_tokens): Embedding(152064, 3584)
        (layers): ModuleList(
          (0-27): 28 x Qwen2DecoderLayer(
            (self_attn): Qwen2SdpaAttention(
              (q_proj): Linear(in_features=3584, out_features=3584, bias=True)
              (k_proj): Linear(in_features=3584, out_features=512, bias=True)
              (v_proj): Linear(in_features=3584, out_features=512, bias=True)
              (o_proj): Linear(in_features=3584, out_features=3584, bias=False)
              (rotary_emb): Qwen2RotaryEmbedding()
            )
            (mlp): Qwen2MLP(
              (gate_proj): Linear(in_features=3584, out_features=18944, bias=False)
              (up_proj): Linear(in_features=3584, out_features=18944, bias=False)
              (down_proj): Linear(in_features=18944, out_features=3584, bias=False)
              (act_fn): SiLU()
            )
            (input_layernorm): Qwen2RMSNorm((3584,), eps=1e-06)
            (post_attention_layernorm): Qwen2RMSNorm((3584,), eps=1e-06)
          )
        )
        (norm): Qwen2RMSNorm((3584,), eps=1e-06)
      )
      (lm_head): Linear(in_features=3584, out_features=152064, bias=False)
    )
    ```

查看模型参数命名：

```python
 for name, parameters in model.named_parameters():
     print(name)
"""
model.embed_tokens.weight
model.layers.0.self_attn.q_proj.weight
model.layers.0.self_attn.q_proj.bias
model.layers.0.self_attn.k_proj.weight
model.layers.0.self_attn.k_proj.bias
model.layers.0.self_attn.v_proj.weight
model.layers.0.self_attn.v_proj.bias
model.layers.0.self_attn.o_proj.weight
model.layers.0.mlp.gate_proj.weight
model.layers.0.mlp.up_proj.weight
model.layers.0.mlp.down_proj.weight
model.layers.0.input_layernorm.weight
model.layers.0.post_attention_layernorm.weight
...
model.norm.weight
lm_head.weight
"""

def _load_pretrained_model(
        cls,
        model,                # 实例化模型
        state_dict,
        loaded_keys,           # 模型参数的命名
        resolved_archive_file, # 模型权重文件路径,eg suffix.safetensors
        **kwargs)

"""摘自官方部分，仅供参考"""
def set_module_tensor(module, tensor_name, value):
    if "." in tensor_name:
        splits = tensor_name.split(".")
        for split in splits[:-1]:
            new_module = getattr(module, split)
            module = new_module
        tensor_name = splits[-1]
    '''
    举例：origin：
       module      : Qwen2ForCausalLM 
       tensor_name : 'model.embed_tokens.weight'
    执行上述递归后更新为：
       module      : Embedding(152064, 3584) 
       tensor_name : 'weight'
    '''  
    param = module._parameters[tensor_name] # 初始化参数
    param_cls = type(param)  # param 类型：torch.nn.parameter.Parameter'
    # value 是个 <class 'torch.Tensor'> 需要类型转化为<class 'torch.nn.parameter.Parameter'>
    new_value = param_cls(value, requires_grad = param.requires_grad)
    # 赋值替换，更新参数
    module._parameters[tensor_name] = new_value

for shard_file in resolved_archive_file:
    state_dict = load_state_dict(shard_file, is_quantized=is_quantized)
    for param_name, param in state_dict.items():
        set_module_tensor(model, param_name, param)

GenerationConfig {
  "bos_token_id": 151643,
  "do_sample": true,
  "eos_token_id": [
    151645,
    151643
  ],
  "pad_token_id": 151643,
  "repetition_penalty": 1.05,
  "temperature": 0.7,
  "top_k": 20,
  "top_p": 0.8
}

['<|im_start|>', '<|im_end|>', '', '', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '']

AddedToken("<|endoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True)

fast_tokenizer = TokenizerFast.from_file('tokenizer.json')

encodings = fast_tokenizer.encode_batch('您好')
[self._convert_encoding(encoding) for encoding in encodings]

<transformers.generation.logits_process.RepetitionPenaltyLogitsProcessor object at 0x7f55df1d6390>
<transformers.generation.logits_process.TemperatureLogitsWarper object at 0x7f571c31bed0>
<transformers.generation.logits_process.TopKLogitsWarper object at 0x7f571c319cd0>
<transformers.generation.logits_process.TopPLogitsWarper object at 0x7f55db6c3910>

<transformers.generation.stopping_criteria.MaxLengthCriteria object at 0x7f5744a25c50>
<transformers.generation.stopping_criteria.EosTokenCriteria object at 0x7f5744b2ead0

 next_token_scores = logits_processor(input_ids, next_token_logits)
 next_token_scores = logits_warper(input_ids, next_token_scores)
 probs = nn.functional.softmax(next_token_scores, dim=-1)
 next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)

class EosTokenCriteria(StoppingCriteria):
       def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> torch.BoolTensor:
            is_done = torch.isin(input_ids[:, -1], self.eos_token_id)
       return is_done

class MaxLengthCriteria(StoppingCriteria):
       def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> torch.BoolTensor:
            cur_len = input_ids.shape[-1]
            is_done = cur_len >= self.max_length
            return torch.full((input_ids.shape[0],), is_done, device=input_ids.device, dtype=torch.bool)

class RepetitionPenaltyLogitsProcessor(LogitsProcessor):
      def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
            score = torch.gather(scores, 1, input_ids)  
            score = torch.where(score < 0, score * self.penalty, score / self.penalty)
            scores_processed = scores.scatter(1, input_ids, score)
            return scores_processed

class TemperatureLogitsWarper(LogitsWarper):
      def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
            scores_processed = scores / self.temperature
            return scores_processed

class TopKLogitsWarper(LogitsWarper):
       def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
                top_k = min(self.top_k, scores.size(-1))  # Safety check
                # Remove all tokens with a probability less than the last token of the top-k
                indices_to_remove = scores < torch.topk(scores, top_k)[0][..., -1, None]
                scores_processed = scores.masked_fill(indices_to_remove, -float("Inf"))
                return scores_processed

class TopPLogitsWarper(LogitsWarper):
      def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
               sorted_logits, sorted_indices = torch.sort(scores, descending=False)
               cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)

               # Remove tokens with cumulative top_p above the threshold (token with 0 are kept)
               sorted_indices_to_remove = cumulative_probs <= (1 - self.top_p)
               # Keep at least min_tokens_to_keep
               sorted_indices_to_remove[..., -self.min_tokens_to_keep :] = 0

               # scatter sorted tensors to original indexing
               indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
               scores_processed = scores.masked_fill(indices_to_remove, self.filter_value)
               return scores_processed

eos_token_id  = 151643
# kv cache 解码阶段输入:
model_inputs['input_ids']
tensor([[ 77235],
        [151643]], device='cuda:0')
# 输出:
next_tokens
tensor([ 11, 43959], device='cuda:0')

# finished sentences should have their next token be a padding token
# has_eos_stopping_criteria = [1,0],  1 表示未完成，0 表示完成
if has_eos_stopping_criteria:
    next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)

Qwen2 大模型源码解析

Qwen2 源码解析

推理代码示例

批量推理

model 实例化粗探

Qwen2Config 实例化

Qwen2ForCausalLM 实例化

Qwen2Attention

Qwen2MLP

Qwen2RMSNorm

Pipeline

Load_checkpoints 加载权重

GenerationConfig 实例化

AutoTokenizer 实例化

generate 方法

采样策略

EosTokenCriteria

MaxLengthCriteria

repetition_penalty

temperature

top_k

top_p

批量推理

更多推荐文章

相关免费在线工具

Qwen2 大模型源码解析

Qwen2 源码解析

推理代码示例

批量推理

model 实例化粗探

Qwen2Config 实例化

Qwen2ForCausalLM 实例化

Qwen2Attention

Qwen2MLP

Qwen2RMSNorm

Pipeline

Load_checkpoints 加载权重

GenerationConfig 实例化

AutoTokenizer 实例化

generate 方法

采样策略

EosTokenCriteria

MaxLengthCriteria

repetition_penalty

temperature

top_k

top_p

批量推理

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