基于ChatGLM-6B第一版，要注意还有ChatGLM2-6B以及ChatGLM3-6B
概述
ChatGLM是transformer架构的神经网络模型，因此从transformer结构入手，分析其源码结构。
transformer结构：
位置编码
ChatGLM-6B的位置编码采用的旋转位置编码(RoPB)实现。其源码：
class RotaryEmbedding(torch.nn.Module):
def __init__(self, dim, base=10000, precision=torch.half, learnable=False):
super().__init__()
inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
inv_freq = inv_freq.half()
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)
freqs = torch.einsum('i,j->ij', 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).to(x.device)
if self.precision == torch.bfloat16:
emb = emb.float()
# [sx, 1 (b * np), hn]
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
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)
## 转载请备注出处：https://www.cnblogs.com/zhiyong-ITNote/
激活函数
ChatGLM-6B采用的激活函数是GeLU(高斯误差线性单元)，其源码：
@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)
编码器-解码器(encoder-decoder)
接下来就是编码器解码器结构，如何抓住模型源头来分析？可以从transformers的API入手：
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().to("cuda:1").eval()
print(mode)
## 转载请备注出处：https://www.cnblogs.com/zhiyong-ITNote/
输出：
ChatGLMForConditionalGeneration(
(transformer): ChatGLMModel(
(word_embeddings): Embedding(130528, 4096)
(layers): ModuleList(
(0-27): 28 x GLMBlock(
(input_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
(attention): SelfAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(in_features=4096, out_features=12288, bias=True)
(dense): Linear(in_features=4096, out_features=4096, bias=True)
)
(post_attention_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
(mlp): GLU(
(dense_h_to_4h): Linear(in_features=4096, out_features=16384, bias=True)
(dense_4h_to_h): Linear(in_features=16384, out_features=4096, bias=True)
)
)
)
(final_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
)
(lm_head): Linear(in_features=4096, out_features=130528, bias=False)
)
从脑图的角度来梳理下其结构
其结构图表示如下：
将结构图与最开始的transformer结构图对比来看，两者还是比较符合的。
官方源码中标注了编码器与解码器是一体的，只需要配置参数即可切换为解码器。如下：
