Update code.

Readme.md

@@ -32,4 +32,65 @@ 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]
+return (self.weight * hidden_states)  -> [6, 1, 4096]
+
+## MLP
+
+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]
+
+## core_attention
+
+query_layer=query_layer.permute(1, 2, 0, 3)  ->  [1, 32, 6, 128]
+key_layer=key_layer.permute(1, 2, 0, 3)  ->  [1, 32, 6, 128]
+value_layer=value_layer.permute(1, 2, 0, 3)  ->  [1, 32, 6, 128]
+context_layer = scaled_dot_product_attention(query_layer, key_layer, value_layer)  ->  [1, 32, 6, 128]
+    softmax(QK^T/sqrt(in_dim))V
+    att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+    att = F.softmax(att, dim=-1)
+    y = att @ v  ->  (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
+context_layer = context_layer.permute(2, 0, 1, 3)
+context_layer = context_layer.reshape()  ->  [6, 1, 4096]
+
+## self_attention
+
+hidden_states: [s, b, h]
+mixed_x_layer = Linear(hidden_states)  -> [6, 1, 4608]  4608:4096+256+256
+
+(query_layer, key_layer, value_layer) = mixed_x_layer.split  -> [6, 1, 4096], [6, 1, 256], [6, 1, 256]  
+query_layer = query_layer.view  ->  [6, 1, 32, 128]
+key_layer = key_layer.view  ->  [6, 1, 2, 128]
+value_layer = value_layer.view  ->  [6, 1, 2, 128]
+
+query_layer = self.apply_rotary_pos_emb(query_layer, rotary_pos_emb)
+key_layer = self.apply_rotary_pos_emb(key_layer, rotary_pos_emb)
+
+key_layer = key_layer.unsqueeze(-2)  ->  [6, 1, 2, 1, 128]
+key_layer = key_layer.expand  ->  [6, 1, 2, 16, 128]
+key_layer = key_layer.contiguous().view  ->  [6, 1, 32, 128]
+
+value_layer = value_layer.unsqueeze(-2)  ->  [6, 1, 2, 1, 128]
+value_layer = value_layer.expand  ->  [6, 1, 2, 16, 128]
+value_layer = value_layer.contiguous().view  ->  [6, 1, 32, 128]
+
+context_layer = self.core_attention(query_layer, key_layer, value_layer)  ->  [6, 1, 4096]
+return Linear(context_layer)  ->  [6, 1, 4096]
+
+## GLMBlock
+
+ input
+ |   \
+ |   RMSNorm
+ |   self_attention
+ |   dropout
+ |   /
+ Add
+ |  \
+ |  RMSNorm
+ |  mlp
+ |  dropout
+ |  /
+ Add
+
+所有的输出shape都是[6, 1, 4096], 6:sequence_length  1:batch_num  4096:hidden_size

chatglm/modeling_chatglm.py

@@ -4,6 +4,7 @@ import copy
 import os
 import gc
 import json
+import hashlib
 
 import torch
 import torch.utils.checkpoint
@@ -148,28 +149,20 @@ class SelfAttention(torch.nn.Module):
             dtype=config.torch_dtype,
         )
 
-    def apply_rotary_pos_emb(
-        self, x: torch.Tensor, rope_cache: torch.Tensor
-    ) -> torch.Tensor:
+    def apply_rotary_pos_emb(self, x: torch.Tensor, rope: torch.Tensor) -> torch.Tensor:
         # x: [sq, b, np, hn]
         sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3)
-        rot_dim = rope_cache.shape[-2] * 2
-        x, x_pass = x[..., :rot_dim], x[..., rot_dim:]
-        # truncate to support variable sizes
-        rope_cache = rope_cache[:sq]
-        xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2)
-        rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2)
-        x_out2 = torch.stack(
-            [
-                xshaped[..., 0] * rope_cache[..., 0]
-                - xshaped[..., 1] * rope_cache[..., 1],
-                xshaped[..., 1] * rope_cache[..., 0]
-                + xshaped[..., 0] * rope_cache[..., 1],
-            ],
-            -1,
-        )
-        x_out2 = x_out2.flatten(3)
-        return torch.cat((x_out2, x_pass), dim=-1)
+        if rope.size(0) != sq:
+            raise ("Error rotary_pos_emb size")
+        x_rope = x[..., : hn // 2]
+        x_pass = x[..., hn // 2 :]
+        x_rope = x_rope.reshape(sq, -1, np, hn // 4, 1, 2)
+        rope = rope.view(sq, -1, 1, hn // 4, 1, 2)
+        roped1 = x_rope[..., 0] * rope[..., 0] - x_rope[..., 1] * rope[..., 1]
+        roped2 = x_rope[..., 1] * rope[..., 0] + x_rope[..., 0] * rope[..., 1]
+        x_out = torch.cat((roped1, roped2), -1)
+        x_out = x_out.flatten(3)
+        return torch.cat((x_out, x_pass), dim=-1)
 
     def forward(self, hidden_states, rotary_pos_emb):
         # hidden_states: [sq, b, h]

demo.py

@@ -1,11 +1,15 @@
 import json
-
+import torch
 
 from chatglm import ChatGLMForConditionalGeneration
 from chatglm import ChatGLMTokenizer
 
 from transformers import AutoConfig
 
+seed = 1234
+torch.manual_seed(seed)
+torch.cuda.manual_seed_all(seed)
+
 pretrained_model_name_or_path = "../ZhipuAI/chatglm3-6b"
 config, kwargs = AutoConfig.from_pretrained(
     pretrained_model_name_or_path,
@@ -38,9 +42,15 @@ glm = glm.eval()
 query = "colin"
 response, history = glm.chat(tokenizer, query, history=[])
 print(response)
+if response[1:] != " Hello! How can I assist you today":
+    raise ()
+
 query = "你好"
 response, history = glm.chat(tokenizer, query, history=history)
 print(response)
+if response[1:] != " 你好！有什么我可以帮助你的吗":
+    raise ()
+
 # response, history = glm.chat(tokenizer, "你是一个心理学专家，请问晚上睡不着应该怎么办", history=history)
 # print(response)
 

tensor.py

@@ -30,7 +30,20 @@ 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))
+
+print()
+print()
+x = torch.tensor([[1, 2], [3, 4]])
+print(x.flatten(0))
+
+x = torch.tensor([[1, 2], [3, 4]])
+print(torch.stack((x, x), 1))
+print(torch.cat((x, x), 1))
+# So if A and B are of shape (3, 4):
+# torch.cat([A, B], dim=0) will be of shape (6, 4)
+# torch.stack([A, B], dim=0) will be of shape (2, 3, 4)