Update readme.

Readme.md

@@ -28,6 +28,19 @@ response = tokenizer.decode(outputs)
 
 ## RMSNorm
 
+hidden_states   -> [6, 1, 4096]
+ /       \
+|       pow(2)  -> [6, 1, 4096]
+|        |
+|       mean    -> [6, 1, 1]
+|        ↓  
+| rsqrt(   + eps)  -> [6, 1, 1]
+ \   /
+  mul              -> [6, 1, 4096]
+    \     weight   -> [4096]
+     \    /
+      mul          -> [6, 1, 4096]
+
 hidden_states -> [6, 1, 4096]  4096:hidden_size
 variance = hidden_states.pow(2).mean(-1, keepdim=True)  -> [6, 1, 1]
 hidden_states = hidden_states * torch.rsqrt(variance + self.eps) 平方根倒数
@@ -36,12 +49,40 @@ return (self.weight * hidden_states)  -> [6, 1, 4096]
 
 ## MLP
 
+        hidden_states    ->  [6, 1, 4096]
+          Linear         ->  [6, 1, 27392]
+          /    \
+      chunk1   chunk0    ->  [6, 1, 13696]
+         |      |  \
+         |      |  sigmoid
+         |      |  /
+         |      mul
+          \    /
+            mul        ->  [6, 1, 13696]
+           Linear      ->  [6, 1, 4096]
+
 Linear(hidden_states)  no bias  ->  [6, 1, 27392]
 silu (x) = [6, 1, 13696] * sigmoid([6, 1, 13696])
 Linear(intermediate_parallel)  no bias  ->  [6, 1, 4096]
 
 ## self_attention
 
+                        x              -> [6, 1, 4096]
+                        |
+                      Linear           -> [6, 1, 4608]
+                     /  |  \
+[6, 1, 32, 128] <-  q   k   v
+                   /    |    \
+              pos_emb pos_emb \
+                  |     |      \
+                  |   expand  expand   -> [6, 1, 32, 128]
+                   \   /       |
+                    dot        |
+                  softmax     /
+                       \     /
+                         dot           ->  [1, 32, 6, 128]
+                        Linear         ->  [6, 1, 4096]
+
 hidden_states: [s, b, h]
 mixed_x_layer = Linear(hidden_states)  -> [6, 1, 4608]  4608:4096+256+256
 

test_tokenizer.py

@@ -23,9 +23,8 @@ init_kwargs["name_or_path"] = pretrained_model_name_or_path
 tokenizer = ChatGLMTokenizer(*init_inputs, **init_kwargs)
 
 
-aa = tokenizer.build_chat_input("骉")
+a = tokenizer.encode("骉")
-ab = tokenizer.encode("骉")
+b = tokenizer.decode([236,173,140])
-a = tokenizer.decode([236,173,140])