ChatGLM-6B模型结构代码解析(单机版)

​ 本文介绍ChatGLM-6B的模型结构，代码来自https://huggingface.co/THUDM/chatglm-6b/blob/main/modeling_chatglm.py。

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【自然语言处理】【大模型】ChatGLM-6B模型结构代码解析(单机版)
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一、激活函数

​ ChatGLM-6B使用的激活函数为GELU，其可以近似实现为：
$$\text{GELU}(x)\approx 0.5x(1+\tanh (\sqrt{\frac{2}{\pi }}(x+0.044715x^3)))$$

@torch.jit.script
def gelu_impl(x):
    """OpenAI's gelu implementation."""
    return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x *
                                       (1.0 + 0.044715 * x * x)))

def gelu(x):
    return gelu_impl(x)

二、GLU层

​ 虽然在实现代码中命名为GLU，但这里实现的还是MLP层：
$$\text{GLU}(X)=\text{GELU}(X{W}_1)W_2$$

class GLU(torch.nn.Module):
    def __init__(self, hidden_size, inner_hidden_size=None,
                 layer_id=None, bias=True, activation_func=gelu, params_dtype=torch.float, empty_init=True):
        super(GLU, self).__init__()
        if empty_init:
            init_method = skip_init
        else:
            init_method = default_init
        self.layer_id = layer_id
        self.activation_func = activation_func

        # Project to 4h.
        self.hidden_size = hidden_size
        if inner_hidden_size is None:
            inner_hidden_size = 4 * hidden_size
        self.inner_hidden_size = inner_hidden_size
        self.dense_h_to_4h = init_method(
            torch.nn.Linear,
            self.hidden_size,
            self.inner_hidden_size,
            bias=bias,
            dtype=params_dtype,
        )
        # Project back to h.
        self.dense_4h_to_h = init_method(
            torch.nn.Linear,
            self.inner_hidden_size,
            self.hidden_size,
            bias=bias,
            dtype=params_dtype,
        )

    def forward(self, hidden_states):
        """
        hidden_states: [seq_len, batch, hidden_size]
        """

        # [seq_len, batch, inner_hidden_size]
        # 投影
        intermediate_parallel = self.dense_h_to_4h(hidden_states)
        # 激活
        intermediate_parallel = self.activation_func(intermediate_parallel)
        # 投影
        output = self.dense_4h_to_h(intermediate_parallel)

        return output

三、位置编码：RoPE

1. 原理

​ 位置编码采用RoPE，推导过程很有启发性，建议去看原文：Transformer升级之路：2、博采众长的旋转式位置编码 - 科学空间。本文仅介绍其实现：

​ 总的来说，RoPE的目标是构建一个位置相关的投影矩阵，使得
$$({\text{R}}_m\text{q}{)}^{\top }({\text{R}}_n\text{k})={\text{q}}^{\top }{\text{R}}_m^{\top }{\text{R}}_n\text{k}={\text{q}}^{\top }{\text{R}}_{n-m}\text{k}$$
其中，$\text{q}$和$\text{k}$分别对应注意力机制中的query和key向量，$m$和$n$代表两个位置，${\text{R}}_i$表示位置$i$处的投影矩阵。下面是作者建议$\text{R}$的形式：
$${\text{R}}_{\theta ,m}^d=\left[\begin{array}{cccccccc}\cos m{\theta }_1& -\sin m{\theta }_1& 0& 0& \dots & 0& 0& \\ \sin m{\theta }_1& \cos m{\theta }_1& 0& 0& \dots & 0& 0& \\ 0& 0& \cos m{\theta }_2& -\sin m{\theta }_2& \dots & 0& 0& \\ 0& 0& \sin m{\theta }_2& \cos m{\theta }_2& \dots & 0& 0& \\ ⋮& ⋮& ⋮& ⋮& \ddots & ⋮& ⋮& \\ 0& 0& 0& 0& \dots & \cos m{\theta }_{d/2}& -\sin m{\theta }_{d/2}& \\ 0& 0& 0& 0& \dots & \sin m{\theta }_{d/2}& \cos m{\theta }_{d/2}& \end{array}\right]$$
其中，$d$是query和key的维度，$\theta$是一个超参数。

通常，$\theta$会设置为
$$\theta =\{{\theta }_i=10000^{\frac{-2(i-1)}{d}},i\in [1,2,\dots ,\frac{d}{2}]\}$$

由于矩阵$\text{R}$非常稀疏，为了提供运算速度，作者也给出了实现方式，以query向量$\text{q}$为例：
$$\left[\begin{array}{c}{q}_0\\ q_1\\ q_2\\ q_3\\ ⋮\\ q_{d-2}\\ q_{d-1}\end{array}\right]\otimes \left[\begin{array}{c}\cos m{\theta }_0\\ \cos m{\theta }_0\\ \cos m{\theta }_1\\ \cos m{\theta }_1\\ ⋮\\ \cos m{\theta }_{d/2-1}\\ \cos m{\theta }_{d/2-1}\end{array}\right]+\left[\begin{array}{c}-q_1\\ q_0\\ -q_3\\ q_2\\ ⋮\\ -q_{d-1}\\ q_{d-2}\end{array}\right]\otimes \left[\begin{array}{c}\sin m{\theta }_0\\ \sin m{\theta }_0\\ \sin m{\theta }_1\\ \sin m{\theta }_1\\ ⋮\\ \sin m{\theta }_{d/2-1}\\ \sin m{\theta }_{d/2-1}\end{array}\right]$$

2. 实现

​ ChatGLM-6B实现采用了PaLM的实现方式，不同于上面的公式：
$$\left[\begin{array}{c}{q}_0\\ ⋮\\ q_{d/2-1}\\ q_{d/2}\\ ⋮\\ q_{d-1}\end{array}\right]\otimes \left[\begin{array}{c}\cos m{\theta }_0\\ ⋮\\ \cos m{\theta }_{d/2-1}\\ \cos m{\theta }_0\\ ⋮\\ \cos m{\theta }_{d/2-1}\end{array}\right]+\left[\begin{array}{c}-q_{d/2}\\ ⋮\\ -q_{d-1}\\ q_0\\ ⋮\\ q_{d/2-1}\end{array}\right]\otimes \left[\begin{array}{c}\sin m{\theta }_0\\ ⋮\\ \sin m{\theta }_{d/2-1}\\ \sin m{\theta }_0\\ ⋮\\ \sin m{\theta }_{d/2-1}\end{array}\right]$$
方便验证，该位置编码仍然满足对称性$({\text{R}}_m\text{q}{)}^{\top }({\text{R}}_n\text{k})={\text{q}}^{\top }{\text{R}}_{n-m}\text{k}$。但是其是如何推导而来的，暂时还没想清楚。

​ 在代码中，RotaryEmbedding负责预先计算sin和cos；rotate_half负责上式第二项中，互换向量的奇偶位以及取负操作；apply_rotary_pos_emb_index则是对输入的query和key注入RoPE。

class RotaryEmbedding(torch.nn.Module):
    def __init__(self, dim, base=10000, precision=torch.half, learnable=False):
        super().__init__()
        # 预先计算好上面的theta
        inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
        inv_freq = inv_freq.half()
        # learnable的效果并没有更好，通常learnable为False
        self.learnable = learnable
        if learnable:
            self.inv_freq = torch.nn.Parameter(inv_freq)
            self.max_seq_len_cached = None
        else:
            self.register_buffer('inv_freq', inv_freq)
            self.max_seq_len_cached = None
            self.cos_cached = None
            self.sin_cached = None
        self.precision = precision

    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
                              error_msgs):
        pass

    def forward(self, x, seq_dim=1, seq_len=None):
        if seq_len is None:
            seq_len = x.shape[seq_dim]
        if self.max_seq_len_cached is None or (seq_len > self.max_seq_len_cached):
            self.max_seq_len_cached = None if self.learnable else seq_len
            t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype)
            # 这里使用了爱因斯坦求和约定，该操作就是t和self.inv_freq的外积
            # freqs中保存了所有的m\theta。e.g. 第一列是0\theta、第二列是1\theta
            freqs = torch.einsum('i,j->ij', t, self.inv_freq)
            # 根据上面的公式，每个\theta都需要两份，所以这里将两个freqs拼接起来
            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
            if self.precision == torch.bfloat16:
                emb = emb.float()

            # [seq_length, 1 (b * np), hn]
            # 计算cos和sin
            cos_cached = emb.cos()[:, None, :]
            sin_cached = emb.sin()[:, None, :]
            if self.precision == torch.bfloat16:
                cos_cached = cos_cached.bfloat16()
                sin_cached = sin_cached.bfloat16()
            if self.learnable:
                return cos_cached, sin_cached
            # 缓存结果，方便重复利用
            self.cos_cached, self.sin_cached = cos_cached, sin_cached
        return self.cos_cached[:seq_len, ...], self.sin_cached[:seq_len, ...]

    def _apply(self, fn):
        if self.cos_cached is not None:
            self.cos_cached = fn(self.cos_cached)
        if self.sin_cached is not None:
            self.sin_cached = fn(self.sin_cached)
        return super()._apply(fn)


def rotate_half(x):
    # x1是x的前半部分，x2是x的后半部分
    x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
    # 前后互换，且后半部分取负
    return torch.cat((-x2, x1), dim=x1.ndim - 1)

@torch.jit.script
def apply_rotary_pos_emb_index(q, k, cos, sin, position_id):
    cos, sin = F.embedding(position_id, cos.squeeze(1)).unsqueeze(2), \
        F.embedding(position_id, sin.squeeze(1)).unsqueeze(2)
    q, k = (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
    return q, k

四、注意力层

1. 原理

​ 二维位置编码。这里仍然采用了GLM-10B的二维位置编码，如下图所示：

输入的样本是$x_1,x_2,x_3,x_4,x_5,x_6$，片段$x_3$和$x_5,x_6$被随机挑选遮蔽掉，原始的输入样本变为$x_1,x_2,[M],x_4,[M]$，这个过程如上图(a)和(b)所示。将三个片段拼接在一起得到模型的输入$x_1,x_2,[M],x_4,[M],[S],x_5,x_6,[S],x_3$，模型的输出则是被遮蔽掉的片段，如上图©所示。这里使用了2种位置编码：第一种编码为整个输入注入位置信息，能够表示遮蔽片段在原始输入中的位置；第二种位置编码则是为遮蔽片段内的tokens输入位置信息。

​ 自注意力机制。标准的自注意力机制为：
$$\begin{array}{rl}Q& =W_{q}X\\ K& =W_{k}X\\ V& =W_{v}X\\ \text{Attention}(Q,K,V,A)& =\text{softmax}(\frac{Q{K}^T}{\sqrt{d_k}})V\end{array}$$
其中，X是输入， $W_q,W_k,W_v$ 分别是query、key、value的投影矩阵。相比于标准的注意力机制，ChatGLM-6B在$Q$和$K$中注意力了RoPE位置信息。多头注意力就是将多个单头注意力的结果拼接起来。
$$\begin{array}{rl}{\text{head}}_i& =\text{Attention}(Q_i,K_i,V_i,A_i)\\ \text{MultiHead}(Q,K,V,A)& =\text{Concat}({\text{head}}_1,\dots ,{\text{head}}_h)W_o\end{array}$$

2. 实现

• 函数attention_fn实现了标准的自注意力机制。

def attention_fn(
        self,
        query_layer,
        key_layer,
        value_layer,
        attention_mask,
        hidden_size_per_partition,
        layer_id,
        layer_past=None,
        scaling_attention_score=True,
        use_cache=False,
):
    # 将传递来的key和value合并至当前的Q和K上(推理场景)
    if layer_past is not None:
        past_key, past_value = layer_past[0], layer_past[1]
        key_layer = torch.cat((past_key, key_layer), dim=0)
        value_layer = torch.cat((past_value, value_layer), dim=0)

    # seqlen, batch, num_attention_heads, hidden_size_per_attention_head
    seq_len, b, nh, hidden_size = key_layer.shape

    if use_cache:
        present = (key_layer, value_layer)
    else:
        present = None
        
    # 对query层进行scaling
    query_key_layer_scaling_coeff = float(layer_id + 1)
    if scaling_attention_score:
        query_layer = query_layer / (math.sqrt(hidden_size) * query_key_layer_scaling_coeff)

    # 注意力分数的输出形状: [batch_size, num_heads, seq_length, seq_length]
    output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0))
    
    # 形状重塑：[seq_length, batch_size, num_heads, head_dim] ->
    # [seq_length, batch_size*num_heads, head_dim]
    query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1)
    key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1)

    matmul_result = torch.zeros(
        1, 1, 1,
        dtype=query_layer.dtype,
        device=query_layer.device,
    )
    
    # 计算非规范化的注意力分数，matmul_result形状为[batch_size*num_head, seq_length,seq_length]
    matmul_result = torch.baddbmm(
        matmul_result,
        query_layer.transpose(0, 1),  # [b * np, sq, hn]
        key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
        beta=0.0,
        alpha=1.0,
    )

    # 重塑形状为：[batch_size,num_head,seq_length,seq_length]
    attention_scores = matmul_result.view(*output_size)
    
    # 对注意分数进行缩放和规范化
    if self.scale_mask_softmax:
        self.scale_mask_softmax.scale = query_key_layer_scaling_coeff
        attention_probs = self.scale_mask_softmax(attention_scores, attention_mask.contiguous())
    else:
        # 对注意力分数进行mask
        if not (attention_mask == 0).all():
            attention_scores.masked_fill_(attention_mask, -10000.0)
        dtype = attention_scores.dtype
        attention_scores = attention_scores.float()
        attention_scores = attention_scores * query_key_layer_scaling_coeff

        attention_probs = F.softmax(attention_scores, dim=-1)

        attention_probs = attention_probs.type(dtype)

    ### 使用注意力分数对value进行加权求和
    output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3))
    # 重塑value的形状
    value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1)
    # 重塑注意力分数的形状
    attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
    # 注意力分数乘以value，得到最终的输出context
    context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))
    context_layer = context_layer.view(*output_size)
    context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
    new_context_layer_shape = context_layer.size()[:-2] + (hidden_size_per_partition,)
    context_layer = context_layer.view(*new_context_layer_shape)

    outputs = (context_layer, present, attention_probs)

    return outputs

• SelfAttention则是为query和key注入RoPE，然后调用attention_fn实现注意力机制。

class SelfAttention(torch.nn.Module):
    def __init__(self, hidden_size, num_attention_heads,
                 layer_id, hidden_size_per_attention_head=None, bias=True,
                 params_dtype=torch.float, position_encoding_2d=True, empty_init=True):
        if empty_init:
            init_method = skip_init
        else:
            init_method = default_init
        super(SelfAttention, self).__init__()

        self.layer_id = layer_id
        self.hidden_size = hidden_size
        self.hidden_size_per_partition = hidden_size
        self.num_attention_heads = num_attention_heads
        self.num_attention_heads_per_partition = num_attention_heads
        # position_encoding_2d：是否使用2维的位置编码
        self.position_encoding_2d = position_encoding_2d
        # RoPE
        self.rotary_emb = RotaryEmbedding(
            self.hidden_size // (self.num_attention_heads * 2)
            if position_encoding_2d
            else self.hidden_size // self.num_attention_heads,
            base=10000,
            precision=torch.half,
            learnable=False,
        )

        self.scale_mask_softmax = None

        if hidden_size_per_attention_head is None:
            self.hidden_size_per_attention_head = hidden_size // num_attention_heads
        else:
            self.hidden_size_per_attention_head = hidden_size_per_attention_head

        self.inner_hidden_size = num_attention_heads * self.hidden_size_per_attention_head

        # query、key、value的投影层
        self.query_key_value = init_method(
            torch.nn.Linear,
            hidden_size,
            3 * self.inner_hidden_size,
            bias=bias,
            dtype=params_dtype,
        )

        self.dense = init_method(
            torch.nn.Linear,
            self.inner_hidden_size,
            hidden_size,
            bias=bias,
            dtype=params_dtype,
        )

    @staticmethod
    def attention_mask_func(attention_scores, attention_mask):
        attention_scores.masked_fill_(attention_mask, -10000.0)
        return attention_scores

    def split_tensor_along_last_dim(self, tensor, num_partitions,
                                    contiguous_split_chunks=False):
        """沿最后一个维度切分tensor
        参数:
            tensor: 输入tensor；
            num_partitions: 切分tensor的数量；
            contiguous_split_chunks: 若为True,切分的块在内存中连续；
        """
        last_dim = tensor.dim() - 1
        last_dim_size = tensor.size()[last_dim] // num_partitions
        tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
        # torch.split并不会默认创建连续的tensor
        if contiguous_split_chunks:
            return tuple(chunk.contiguous() for chunk in tensor_list)

        return tensor_list

    def forward(
            self,
            hidden_states: torch.Tensor,
            position_ids,
            attention_mask: torch.Tensor,
            layer_id,
            layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
            use_cache: bool = False,
            output_attentions: bool = False,
    ):
        """
        hidden_states: [seq_len, batch, hidden_size]
        attention_mask: [(1, 1), seq_len, seq_len]
        """
        # 一次性得到投影的Q、K、V，减少执行矩阵乘法的次数
        # [seq_len, batch, 3 * hidden_size]
        mixed_raw_layer = self.query_key_value(hidden_states)
        
        # 拆分出多头
        # [seq_len, batch, 3 * hidden_size] --> [seq_len, batch, num_attention_heads, 3 * hidden_size_per_attention_head]
        new_tensor_shape = mixed_raw_layer.size()[:-1] + (
            self.num_attention_heads_per_partition,
            3 * self.hidden_size_per_attention_head,
        )
        mixed_raw_layer = mixed_raw_layer.view(*new_tensor_shape)
        # [seq_len, batch, num_attention_heads, hidden_size_per_attention_head]
        # 此时的query_layer、key_layer、value_layer已经是拆分出多头的Q、K、V
        (query_layer, key_layer, value_layer) = self.split_tensor_along_last_dim(mixed_raw_layer, 3)

        if self.position_encoding_2d:
            ## 这里将query和key拆分为两份，分别注入不同的位置信息，然后再拼接在一起
            # 拆分
            q1, q2 = query_layer.chunk(2, dim=(query_layer.ndim - 1))
            k1, k2 = key_layer.chunk(2, dim=(key_layer.ndim - 1))
            # 计算cos和sin值
            cos, sin = self.rotary_emb(q1, seq_len=position_ids.max() + 1)
            position_ids, block_position_ids = position_ids[:, 0, :].transpose(0, 1).contiguous(), \
                position_ids[:, 1, :].transpose(0, 1).contiguous()
            # 将两种位置编码输入到不同的query和key上
            q1, k1 = apply_rotary_pos_emb_index(q1, k1, cos, sin, position_ids)
            q2, k2 = apply_rotary_pos_emb_index(q2, k2, cos, sin, block_position_ids)
            # 拼接注入不同位置信息的query和key，这样query和key中包含了两种位置信息
            query_layer = torch.concat([q1, q2], dim=(q1.ndim - 1))
            key_layer = torch.concat([k1, k2], dim=(k1.ndim - 1))
        else:
            # 普通的RoPE
            position_ids = position_ids.transpose(0, 1)
            cos, sin = self.rotary_emb(value_layer, seq_len=position_ids.max() + 1)
            # [seq_len, batch, num_attention_heads, hidden_size_per_attention_head]
            query_layer, key_layer = apply_rotary_pos_emb_index(query_layer, key_layer, cos, sin, position_ids)

        # [seq_len, batch, hidden_size]
        context_layer, present, attention_probs = attention_fn(
            self=self,
            query_layer=query_layer,
            key_layer=key_layer,
            value_layer=value_layer,
            attention_mask=attention_mask,
            hidden_size_per_partition=self.hidden_size_per_partition,
            layer_id=layer_id,
            layer_past=layer_past,
            use_cache=use_cache
        )

        output = self.dense(context_layer)

        outputs = (output, present)

        if output_attentions:
            outputs += (attention_probs,)

        return outputs  # output, present, attention_probs	

五、GLMBlock

​ GLMBlock的基本结构为：Layer Norm、Self Attention(输入和输出残差连接)、Layer Norm、GLU(输入和输出残差连接)。

class GLMBlock(torch.nn.Module):
    def __init__(
            self,
            hidden_size,
            num_attention_heads,
            layernorm_epsilon,
            layer_id,
            inner_hidden_size=None,
            hidden_size_per_attention_head=None,
            layernorm=LayerNorm,
            use_bias=True,
            params_dtype=torch.float,
            num_layers=28,
            position_encoding_2d=True,
            empty_init=True
    ):
        super(GLMBlock, self).__init__()
        # Set output layer initialization if not provided.

        self.layer_id = layer_id

        # LayerNorm层
        self.input_layernorm = layernorm(hidden_size, eps=layernorm_epsilon)
        # 是否使用2维位置编码
        self.position_encoding_2d = position_encoding_2d
        # 自注意力层
        self.attention = SelfAttention(
            hidden_size,
            num_attention_heads,
            layer_id,
            hidden_size_per_attention_head=hidden_size_per_attention_head,
            bias=use_bias,
            params_dtype=params_dtype,
            position_encoding_2d=self.position_encoding_2d,
            empty_init=empty_init
        )

        # Post Layer Norm层
        self.post_attention_layernorm = layernorm(hidden_size, eps=layernorm_epsilon)
        self.num_layers = num_layers

        # GLU层
        self.mlp = GLU(
            hidden_size,
            inner_hidden_size=inner_hidden_size,
            bias=use_bias,
            layer_id=layer_id,
            params_dtype=params_dtype,
            empty_init=empty_init
        )

    def forward(
            self,
            hidden_states: torch.Tensor,
            position_ids,
            attention_mask: torch.Tensor,
            layer_id,
            layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
            use_cache: bool = False,
            output_attentions: bool = False,
    ):
        """
        hidden_states: [seq_len, batch, hidden_size]
        attention_mask: [(1, 1), seq_len, seq_len]
        """

        # 对输入进行Layer Norm
        # [seq_len, batch, hidden_size]
        attention_input = self.input_layernorm(hidden_states)
        # 自注意力
        attention_outputs = self.attention(
            attention_input,
            position_ids,
            attention_mask=attention_mask,
            layer_id=layer_id,
            layer_past=layer_past,
            use_cache=use_cache,
            output_attentions=output_attentions
        )
        attention_output = attention_outputs[0]
        outputs = attention_outputs[1:]
        # 自注意力的输出和输入残差连接
        alpha = (2 * self.num_layers) ** 0.5
        hidden_states = attention_input * alpha + attention_output
        # Layer Norm
        mlp_input = self.post_attention_layernorm(hidden_states)
        # 全连接层投影
        mlp_output = self.mlp(mlp_input)
        # MLP层的输出和输入残差连接
        output = mlp_input * alpha + mlp_output
        
        if use_cache:
            outputs = (output,) + outputs
        else:
            outputs = (output,) + outputs[1:]

        return outputs  # hidden_states, present, attentions

六、ChatGLMPreTrainedModel

​ ChatGLMPreTrainedModel是ChatGLMModel和ChatGLMForConditionalGeneration，其提供获取注意力mask和position ids。

1. Mask

​ ChatGLM-6B使用的Mask仍然是prefix-LM的Mask，其对于输入的前缀使用双向注意力，对于后续的生成部分则是Causal Mask。下面是ChatGLMPreTrainedModel中的get_masks函数实现：

def get_masks(self, input_ids, device):
    batch_size, seq_length = input_ids.shape
    # context_lengths记录了batch中每个样本的真实长度
    context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids]
    # 生成causal mask，即下三角以及对角线为1，上三角为0
    attention_mask = torch.ones((batch_size, seq_length, seq_length), device=device)
    attention_mask.tril_()
    # 将前缀部分的注意力改为双向
    for i, context_length in enumerate(context_lengths):
        attention_mask[i, :, :context_length] = 1
    attention_mask.unsqueeze_(1)
    attention_mask = (attention_mask < 0.5).bool()
        
    return attention_mask

2. Position_ids

在介绍注意力层的时候，已经介绍过2维的postion_ids了。代码实现中，position_ids就是GLM论文中的Position 1，block_position_ids则是论文中的Position 2。

def get_position_ids(self, input_ids, mask_positions, device, use_gmasks=None):
    """
    input_ids: [batch_size, seq_length]
    mask_positions: [batch_size]，由于GLM系列中会使用[Mask]或[gMask]标志，mask_positions就是指这些标注的具体位置
    """
    batch_size, seq_length = input_ids.shape
    if use_gmasks is None:
        use_gmasks = [False] * batch_size
    # context_lengths：未被padding前，batch中各个样本的长度
    context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids]
    # 2维位置编码
    if self.position_encoding_2d:
        # [0,1,2,...,seq_length-1]
        position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
        # 将原始输入后所有位置的postion id都设置为[Mask]或者[gMask]的位置id
        # (该操作见注意力层对位置编码的介绍)
        for i, context_length in enumerate(context_lengths):
            position_ids[i, context_length:] = mask_positions[i]
        # 原始输入的位置编码全部设置为0，待生成的位置添加顺序的位置id
        # 例如：[0,0,0,0,1,2,3,4,5]
        block_position_ids = [torch.cat((
            torch.zeros(context_length, dtype=torch.long, device=device),
            torch.arange(seq_length - context_length, dtype=torch.long, device=device) + 1
        )) for context_length in context_lengths]
        block_position_ids = torch.stack(block_position_ids, dim=0)
        # 将postion_ids和block_position_ids堆叠在一起，用于后续的参数传入；
        # 在注意力层中，还有将这个position_ids拆分为两部分
        position_ids = torch.stack((position_ids, block_position_ids), dim=1)
    else:
        position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
        for i, context_length in enumerate(context_lengths):
            if not use_gmasks[i]:
                position_ids[i, context_length:] = mask_positions[i]

    return position_ids

七、ChatGLMModel

​ ChatGLMModel基本就是通过上面介绍的各个组件构造最终的模型。原理没什么可介绍了，直接来看代码。下面的代码会将不易于理解模型结构的部分删除掉，因此与原始版本略有不同。

class ChatGLMModel(ChatGLMPreTrainedModel):
    def __init__(self, config: ChatGLMConfig, empty_init=True):
        super().__init__(config)
        if empty_init:
            init_method = skip_init
        else:
            init_method = default_init
        # 保存各类参数
        self.max_sequence_length = config.max_sequence_length
        self.hidden_size = config.hidden_size
        self.params_dtype = torch.half
        self.num_attention_heads = config.num_attention_heads
        self.vocab_size = config.vocab_size
        self.num_layers = config.num_layers
        self.layernorm_epsilon = config.layernorm_epsilon
        self.inner_hidden_size = config.inner_hidden_size
        self.hidden_size_per_attention_head = self.hidden_size // self.num_attention_heads
        self.position_encoding_2d = config.position_encoding_2d
        self.pre_seq_len = config.pre_seq_len
        self.prefix_projection = config.prefix_projection
        # 初始化embedding层
        self.word_embeddings = init_method(
            torch.nn.Embedding,
            num_embeddings=self.vocab_size, embedding_dim=self.hidden_size,
            dtype=self.params_dtype
        )
        self.gradient_checkpointing = False

        def get_layer(layer_id):
            return GLMBlock(
                self.hidden_size,
                self.num_attention_heads,
                self.layernorm_epsilon,
                layer_id,
                inner_hidden_size=self.inner_hidden_size,
                hidden_size_per_attention_head=self.hidden_size_per_attention_head,
                layernorm=LayerNorm,
                use_bias=True,
                params_dtype=self.params_dtype,
                position_encoding_2d=self.position_encoding_2d,
                empty_init=empty_init
            )
        # 堆叠GLMBlock
        self.layers = torch.nn.ModuleList(
            [get_layer(layer_id) for layer_id in range(self.num_layers)]
        )

        # 最后的Layer Norm层
        self.final_layernorm = LayerNorm(self.hidden_size, eps=self.layernorm_epsilon)

    def get_input_embeddings(self):
        return self.word_embeddings

    def set_input_embeddings(self, new_embeddings: torch.Tensor):
        self.word_embeddings = new_embeddings
    @add_start_docstrings_to_model_forward(CHATGLM_6B_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPastAndCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
            self,
            input_ids: Optional[torch.LongTensor] = None,
            position_ids: Optional[torch.LongTensor] = None,
            attention_mask: Optional[torch.Tensor] = None,
            past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
            inputs_embeds: Optional[torch.LongTensor] = None,
            use_cache: Optional[bool] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPast]:
        ### (开始)一些输入输入和参数设置，可以忽略
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape[:2]
        elif inputs_embeds is not None:
            batch_size, seq_length = inputs_embeds.shape[:2]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")
        ### (结束)一些输入输出和参数设置，可以忽略
        
        # embedding层
        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)

        if past_key_values is None:
            past_key_values = tuple([None] * len(self.layers))
            # 获得注意力mask，该功能继承自ChatGLMPreTrainedModel
            if attention_mask is None:
                attention_mask = self.get_masks(
                    input_ids,
                    device=input_ids.device
                )
                
            if position_ids is None:
                MASK, gMASK = self.config.mask_token_id, self.config.gmask_token_id
                seqs = input_ids.tolist()
                # 记录input_ids中是否使用了mask以及mask的位置
                # mask_positions记录每个样本中mask的位置
                # use_gmasks记录是否使用了gMask
                mask_positions, use_gmasks = [], []
                for seq in seqs:
                    mask_token = gMASK if gMASK in seq else MASK
                    use_gmask = mask_token == gMASK
                    mask_positions.append(seq.index(mask_token))
                    use_gmasks.append(use_gmask)
                 # 获得position_ids，该功能继承自ChatGLMPreTrainedModel
                position_ids = self.get_position_ids(
                    input_ids,
                    mask_positions=mask_positions,
                    device=input_ids.device,
                    use_gmasks=use_gmasks
                )

        # [seq_len, batch, hidden_size]
        hidden_states = inputs_embeds.transpose(0, 1)
        presents = () if use_cache else None
        all_self_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None
        if attention_mask is None:
            attention_mask = torch.zeros(1, 1, device=input_ids.device).bool()
        else:
            attention_mask = attention_mask.to(hidden_states.device)
            
        # 模型的前向传播
        for i, layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)
            layer_past = past_key_values[i]

            if self.gradient_checkpointing and self.training:
                layer_ret = torch.utils.checkpoint.checkpoint(
                    layer,
                    hidden_states,
                    position_ids,
                    attention_mask,
                    torch.tensor(i),
                    layer_past,
                    use_cache,
                    output_attentions
                )
            else:
                layer_ret = layer(
                    hidden_states,
                    position_ids=position_ids,
                    attention_mask=attention_mask,
                    layer_id=torch.tensor(i),
                    layer_past=layer_past,
                    use_cache=use_cache,
                    output_attentions=output_attentions
                )

            hidden_states = layer_ret[0]

            if use_cache:
                presents = presents + (layer_ret[1],)

            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_ret[2 if use_cache else 1],)

        # 最终的Layer Norm
        hidden_states = self.final_layernorm(hidden_states)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=presents,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )