Update readme.

Readme.md

@@ -12,16 +12,24 @@ for
 
   hidden_states = inputs_embeds
   for layers : GLMBlock(hidden_states, rotary_pos_emb)
-  hidden_states = self.final_layernorm(hidden_states)
-  hidden_states = hidden_states[-1:]
+  hidden_states = RMSNorm(hidden_states)
+  hidden_states = hidden_states[-1:] 截取最后一个sequence
   lm_logits = self.output_layer(hidden_states)
   lm_logits = lm_logits.transpose(0, 1).contiguous()  -> [1, 1, 65024]
 
   probs = softmax(lm_logits) -> [1, 65024]
-  next_tokens = torch.multinomial(probs, num_samples=1) 采样  -> [1]  1:batch_num 
+  next_tokens = torch.multinomial(probs, num_samples=1)  采样  -> [1]  1:batch_num 
 
   if next_tokens == eos_token_id 推理结束退出循环
 
   input_ids = torch.cat([input_ids, next_tokens)  -> [1, 7]  1:batch_num
 
-response = tokenizer.decode(outputs)
+response = tokenizer.decode(outputs)
+
+## RMSNorm
+
+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
+self.weight -> [4096]
+return (self.weight * hidden_states)  -> [6, 1, 4096]

chatglm/modeling_chatglm.py

@@ -68,6 +68,7 @@ class RMSNorm(torch.nn.Module):
         input_dtype = hidden_states.dtype
         variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
         hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
+        # show.DumpTensorToImage(self.weight, "RMSNorm_weight.png")
         return (self.weight * hidden_states).to(input_dtype)
 
 

tensor.py

@@ -29,3 +29,8 @@ print(x.prod(0))
 print()
 print(x.unsqueeze(1).shape)
 print(x.unsqueeze(1).squeeze(1).shape)
+
+x = torch.tensor([[1, 2], [3, 4]]).to(float)
+print(x.mean(1))
+print(x.mean(0))
+print(x.mean(0, keepdim=True))