Refine prepareInput.

qwen/modeling_qwen.py

@@ -198,7 +198,7 @@ class QwenRunner:
     ):
         qwen = self.qwen
         history = copy.deepcopy(history)
-        raw_text, context_tokens = make_context(tokenizer, query, query_assistant, history=history, system=system)
+        raw_text, context_tokens = self.prepareInput(tokenizer, query, query_assistant, history, system)
         input_ids = torch.tensor([context_tokens]).to(next(qwen.parameters()).device)
         eos_token_id_tensor = torch.tensor([qwen.config.eos_token_id]).to(input_ids.device)
         pad_token_id = qwen.config.pad_token_id
@@ -354,6 +354,9 @@ class QwenRunner:
 
         return lm_logits
 
+    def prepareInput(self, tokenizer, query, query_assistant, history, system):
+        return make_context(tokenizer, query, query_assistant, history=history, system=system)
+
     def repetition_penalty(self, input_ids, next_token_scores):
         penalty = self.qwen.config.repetition_penalty
         score = torch.gather(next_token_scores, 1, input_ids)

qwen/research_attention.py

@@ -73,14 +73,16 @@ def Dump_lm_head_weight(model):
 def DumpQK(query, key, causal_mask, index):
     scale_factor = 1 / math.sqrt(query.size(-1))
     attn_weight = query @ key.transpose(-2, -1) * scale_factor
-    attn_weight = torch.softmax(attn_weight, dim=-1)
-    size = query.shape[2]
     attn_mask = torch.ones(causal_mask.shape, dtype=query.dtype, device=query.device)
     attn_mask.masked_fill_(causal_mask.logical_not(), float(0))
-    qk = attn_weight * attn_mask
+    attn_weight = attn_weight * attn_mask
-    qk = qk[0]
+    attn_weight = torch.softmax(attn_weight, dim=-1)
-    prePath = "./temp/" + "q@k_seq_" + str(size) + "_layer_" + str(index) + ".png"
+    size = query.shape[2]
-    show.DumpTensorToImage(qk, prePath, GridValue=255)
+    qk = attn_weight[0]
+    # prePath = "./temp/" + "q@k_seq_" + str(size) + "_layer_" + str(index) + ".png"
+    # show.DumpTensorToImage(qk, prePath, GridValue=255)
+    prePath = "./temp/" + "q@k_sum_seq_" + str(size) + "_layer_" + str(index) + ".png"
+    show.DumpTensorToImage(qk.sum(0), prePath, GridValue=255)
 
 
 class ResearchRunner(QwenRunner):
@@ -99,18 +101,39 @@ class ResearchRunner(QwenRunner):
         attn_output = attention.c_proj(context_layer)
         return attn_output
 
+    def prepareInput(self, tokenizer, query, query_assistant, history, system):
+        start_to = [151644]
+        n_to = [198]
+        end_to = [151645]
+        system_str = "system\nYou are a helpful assistant."
+        user_str = "user\n" + query
+        aassistant_str = "assistant\n" + query_assistant
+
+        system_token = start_to + tokenizer.encode(system_str, allowed_special=set()) + end_to + n_to
+        user_token = start_to + tokenizer.encode(user_str, allowed_special=set()) + end_to + n_to
+        aassistant_token = start_to + tokenizer.encode(aassistant_str, allowed_special=set())
+
+        tokens = system_token + user_token + aassistant_token
+        tokens = user_token + aassistant_token
+        tokens = start_to + tokenizer.encode("user\n你好\nassistant\n", allowed_special=set())
+
+        return "", tokens
+
+
 runner = ResearchRunner(model)
 
 # 第一轮对话
-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")
+# 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)
+
+output_ids, history, decoded = runner.Chat(tokenizer, "你好!!", "")
 print(decoded)
 
 tokens = []
 for i, token in enumerate(output_ids):
     de = tokenizer.decode([token])
-    de = str(i).zfill(3) + " : " + repr(de)
+    de = str(i + 1).zfill(3) + " : " + repr(de)
     tokens.append(de)
-    print(de)
 
 # <|im_start|>system
 # You are a helpful assistant.<|im_end|>
@@ -122,5 +145,9 @@ for i, token in enumerate(output_ids):
 
 show.DumpListToFile(tokens, "./temp/token_decode_list.txt")
 
-if decoded.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 ()
+#     raise ()
+
+
+# normal   (x - mean) / (std + eps) =>  sum(y)==0
+# softmax  exp(x) / sum(exp(x))  =>  0 < y < 1   sum(y)==1