Refine model of qwen.

qwen/modeling_qwen.py

@@ -204,7 +204,6 @@ class QwenRunner:
         pad_token_id = qwen.config.pad_token_id
 
         unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
-        this_peer_finished = False
         while True:
             outputs = self.forwardQWen(input_ids)
             next_token_scores = outputs[:, -1, :]
@@ -218,11 +217,7 @@ class QwenRunner:
             unfinished_sequences = unfinished_sequences.mul(
                 next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
             )
-
             if unfinished_sequences.max() == 0:
-                this_peer_finished = True
-
-            if this_peer_finished:
                 break
 
         decoded, response, end_reason = decode_tokens(
@@ -233,7 +228,7 @@ class QwenRunner:
             errors="replace",
         )
         history.append((query, response))
-        return response, history, decoded
+        return input_ids[0].cpu().tolist(), history, decoded
 
     def _rotate_half(self, x):
         x = rearrange(x, "... (j d) -> ... j d", j=2)

qwen/research_attention.py

@@ -52,7 +52,7 @@ def Dump_tokens_list(model):
             context_length=0,
             errors="replace",
         )
-        tokens.append(str(token) + ": " + decoded)
+        tokens.append(str(token).zfill(7) + ": " + repr(decoded))
     show.DumpListToFile(tokens, "./temp/qwen_token_list.txt")
 
 
@@ -86,7 +86,6 @@ def DumpQK(query, key, causal_mask, index):
 class ResearchRunner(QwenRunner):
     def __init__(self, model):
         super().__init__(model)
-        self.tokenDecode = []
 
     def attention(self, attention, query, key, value, causal_mask):
         query = query.permute(0, 2, 1, 3)
@@ -100,20 +99,19 @@ class ResearchRunner(QwenRunner):
         attn_output = attention.c_proj(context_layer)
         return attn_output
 
-    def sample(self, next_token_scores):
-        next_tokens = super().sample(next_token_scores)
-        decoded, response, end_reason = decode_tokens(
-            next_tokens,
-            tokenizer,
-        )
-        self.tokenDecode.append(decoded)
-        return next_tokens
-
 runner = ResearchRunner(model)
 
 # 第一轮对话
-response, history, decode_tokens = runner.Chat(tokenizer, "东南亚国家日本的首都是什么市", "日本的首都是")
-print(decode_tokens)
+output_ids, history, decoded = runner.Chat(tokenizer, "东南亚国家日本的首都是什么市", "日本的首都是")
+print(decoded)
+
+tokens = []
+for i, token in enumerate(output_ids):
+    de = tokenizer.decode([token])
+    de = str(i).zfill(3) + " : " + repr(de)
+    tokens.append(de)
+    print(de)
+
 # <|im_start|>system
 # You are a helpful assistant.<|im_end|>
 # <|im_start|>user
@@ -122,8 +120,7 @@ print(decode_tokens)
 # 日本的首都东京。<|im_end|>
 # <|endoftext|>
 
-show.DumpListToFile(runner.tokenDecode, "./temp/token_decode_list.txt")
+show.DumpListToFile(tokens, "./temp/token_decode_list.txt")
 
-
-if decode_tokens.split("\n")[-2] != """日本的首都东京。<|im_end|>""":
+if decoded.split("\n")[-2] != """日本的首都东京。<|im_end|>""":
     raise ()