Update show and output tokens image.

Readme.md

@@ -12,12 +12,12 @@ for:
 
     hidden_states = inputs_embeds
     for layers : GLMBlock(hidden_states, rotary_pos_emb)
-    hidden_states = RMSNorm(hidden_states)
+    hidden_states = RMSNorm(hidden_states)  # final_layernorm  ->  [6, 1, 4096]
-    hidden_states = hidden_states[-1:] 截取最后一个sequence
+    hidden_states = hidden_states[-1:] 截取最后一个sequence  ->  [1, 1, 4096]
-    lm_logits = self.output_layer(hidden_states)
+    lm_logits = Linear(hidden_states)  ->  [1, 1, 65024]
     lm_logits = lm_logits.transpose(0, 1).contiguous()  -> [1, 1, 65024]
 
-    probs = softmax(lm_logits) -> [1, 65024]
+    probs = softmax(lm_logits) -> [1, 65024]  {Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}
     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

chatglm/modeling_chatglm.py

@@ -110,12 +110,6 @@ class CoreAttention(torch.nn.Module):
 
 
 class SelfAttention(torch.nn.Module):
-    """Parallel self-attention layer abstract class.
-
-    Self-attention layer takes input with size [s, b, h]
-    and returns output of the same size.
-    """
-
     def __init__(self, config: ChatGLMConfig, layer_number, device=None):
         super(SelfAttention, self).__init__()
         self.layer_number = max(1, layer_number)
@@ -237,14 +231,8 @@ class SelfAttention(torch.nn.Module):
                 self.hidden_size_per_attention_head,
             )
         )
-        # ==================================
-        # core attention computation
-        # ==================================
         context_layer = self.core_attention(query_layer, key_layer, value_layer)
-        # =================
+        output = self.dense(context_layer)  # [sq, b, h]
-        # Output. [sq, b, h]
-        # =================
-        output = self.dense(context_layer)
         return output
 
 
@@ -276,7 +264,6 @@ class MLP(torch.nn.Module):
 
         self.activation_func = swiglu
 
-        # Project back to h.
         self.dense_4h_to_h = nn.Linear(
             config.ffn_hidden_size,
             config.hidden_size,
@@ -595,6 +582,7 @@ class ChatGLMForConditionalGeneration(nn.Module):
         isFinished = torch.zeros(
             input_ids.shape[0], dtype=torch.long, device=input_ids.device
         )
+        token_count = 0
         while True:
             input_ids_in = input_ids
             batch_size, seq_length = input_ids_in.shape
@@ -611,8 +599,11 @@ class ChatGLMForConditionalGeneration(nn.Module):
             )
             next_token_logits = logits[:, -1, :]
             probs = nn.functional.softmax(next_token_logits, dim=-1)
-            next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
 
+            # show.DumpTensorToImage(next_token_logits[0], "generated/next_tokens"+str(token_count)+".png")
+            # token_count = token_count + 1
+
+            next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
             # finished sentences should add a padding token to next
             pad_token = pad_token_id * isFinished
             next_tokens = next_tokens * (1 - isFinished) + pad_token

demo.py

@@ -4,12 +4,15 @@ import torch
 from chatglm import ChatGLMForConditionalGeneration
 from chatglm import ChatGLMTokenizer
 
+from tools import show
+
 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,
@@ -39,17 +42,17 @@ tokenizer = ChatGLMTokenizer(*init_inputs, **init_kwargs)
 
 glm = glm.from_pretrained(pretrained_model_name_or_path).half().cuda()
 glm = glm.eval()
-query = "colin"
+query = "你好"
 response, history = glm.chat(tokenizer, query, history=[])
 print(response)
-if response[1:] != " Hello! How can I assist you today":
+if response[1:] != " 你好👋！我是人工智能助手 ChatGLM3-6B，很高兴见到你，欢迎问我任何问题":
     raise ()
 
-query = "你好"
+# query = "colin"
-response, history = glm.chat(tokenizer, query, history=history)
+# response, history = glm.chat(tokenizer, query, history=history)
-print(response)
+# print(response)
-if response[1:] != " 你好！有什么我可以帮助你的吗":
+# if response[1:] != " Hello! How can I assist you today":
-    raise ()
+#     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)

tools/show.py

@@ -5,6 +5,7 @@ import torchvision.transforms.functional as Vision
 import cv2
 import math
 import numpy as np
+import os
 
 
 def DumpTensorToImage(tensor, name, autoPad=True, scale=1.0, auto2d=True):
@@ -14,7 +15,7 @@ def DumpTensorToImage(tensor, name, autoPad=True, scale=1.0, auto2d=True):
     tensor = tensor.float()
     maxv = torch.max(tensor)
     minv = torch.min(tensor)
-    tensor = (((tensor - minv) / (maxv - minv)) * 256).byte().cpu()
+    tensor = (((tensor - minv) / (maxv - minv)) * 255).byte().cpu()
     img = tensor.numpy()
     srp = img.shape
 
@@ -30,3 +31,18 @@ def DumpTensorToImage(tensor, name, autoPad=True, scale=1.0, auto2d=True):
         img = cv2.resize(img, [int(srp[0] * scale), int(srp[1] * scale)])
         srp = img.shape
     cv2.imwrite(name, img)
+
+
+def DumpTensorToLog(tensor, name="log"):
+    shape = tensor.shape
+    f = open(name, "w")
+    data = tensor.reshape([-1]).float().cpu().numpy().tolist()
+    for d in data:
+        f.writelines("%s" % d + os.linesep)
+    f.close()
+
+def DumpTensorToFile(tensor, name="tensor.pt"):
+    torch.save(tensor.cpu(),name)
+
+def LoadTensorToFile(name="tensor.pt"):
+    return torch.load(name)    

tools/test.py

@@ -8,3 +8,6 @@ import torch
 
 radata = torch.randn(127)
 show.DumpTensorToImage(radata, "test.png")
+
+radata = torch.randn(127, 127)
+show.DumpTensorToLog(radata, "test.log")