Add query file. Refine print tree.

wit/dataset/meaning_dataset.py

@@ -183,7 +183,7 @@ class MeaningMap:
             self.ms_rank_all[start : start + len],
         )
 
-    def get_tree(self, meaning):  # return meaning all sub items
+    def get_nodetree(self, meaning):  # return meaning all sub items
         def get_tree_node(ms_map, meaning, vocab_size, parent, seqlist):
             ms = ms_map[meaning]
             for m in ms[ms >= 0].tolist():
@@ -200,6 +200,22 @@ class MeaningMap:
         root.seq_node = seqlist
         return root
 
+    def get_tree(self, meaning):
+        def get_tree_list(ms_map, meaning, mlist):
+            ms = ms_map[meaning]
+            mlist.append(meaning)
+            mlist.append(-255)  # level down marker
+            for m in ms[ms >= 0].tolist():
+                if m >= self.vocab_size:
+                    get_tree_list(ms_map, m, mlist)
+                else:
+                    mlist.append(m)
+            mlist.append(-1)  # level up marker
+
+        meaninglist = []
+        get_tree_list(self.ms_map, meaning, meaninglist)
+        return meaninglist
+
     def max_length(self):
         return max(self.ms_len)
 
@@ -314,6 +330,7 @@ class MeaningDataset(Dataset):
         output["labels"] = data.clone()
         output["token_type_ids"] = torch.zeros(data.shape)
         output["val_mask"] = self.get_seq_mask_tensor(idx_list)
+        output["meaning"] = [self.seq_meaning[i] for i in idx_list]
         return output
 
     def get_token(self, idx):  # must equal sequence length
@@ -405,17 +422,6 @@ class BatchGroupMeaningDataloader(Dataset):
     def __getitem__(self, idx):
         return self.meaning_dataset.get_batch(self.indexBatch[idx])
 
-    def get_tree(self, idx):
-        return [self.meaning_dataset.get_tree(i) for i in self.indexBatch[idx]]
-
-    def print_tree(self, idx):
-        idx_list = self.indexBatch[idx]
-        s = "--------------------------------------------------------\n"
-        for i in idx_list:
-            s += self.meaning_dataset.print_tree(i)
-            s += "--------------------------------------------------------\n"
-        return s
-
     def detection_collate(batch):
         return batch[0]
 
@@ -424,7 +430,6 @@ class BatchGroupMeaningDataloader(Dataset):
             self, batch_size=1, num_workers=num_workers, collate_fn=BatchGroupMeaningDataloader.detection_collate
         )
 
-
 if __name__ == "__main__":
 
     md = MeaningDataset(100000, 115200, vocab_size=1024, size=1024, use_cache=False)

wit/inference.py

@@ -12,7 +12,8 @@ import dataset.node_tree as nt
 
 if __name__ == "__main__":
 
-    checkpoint_path = "log/bigger/version_0/checkpoints/epoch=19-step=98720.ckpt"
+    # checkpoint_path = "log/bigger/version_0/checkpoints/epoch=19-step=98720.ckpt"
+    checkpoint_path = "log/bigger/version_1/checkpoints/epoch=14-step=74040.ckpt"
 
     qwen = QwenModule.load_from_checkpoint(checkpoint_path=checkpoint_path)
     qwen.eval()
@@ -21,23 +22,24 @@ if __name__ == "__main__":
     np.random.seed(conf.seed)
     runner = QwenRunner(qwen.llm)
 
-    # batch = torch.tensor([[41]], dtype=torch.int64)
+    # batch = torch.tensor([[11, 0, 3, 7, 15, 8, 10, 7, 14, 13, 1, 12, 13]], dtype=torch.int64)
-    # print(runner.ChatTokens(batch).detach().cpu().numpy()[0])
+    # sorted_logits, sorted_indices = runner.ChatTokens(batch, sample=False)
+    # print(sorted_logits.detach().cpu().numpy())
+    # print(sorted_indices.detach().cpu().numpy())
 
     val = ds.InitValDataset(conf).dataset
     md = val.meaning_dataset
-
     map = md.get_meaning_map()
-
     item = md.get_token(0)
 
-    node = map.get_tree(md.get_meaning(0))
+    node = map.get_nodetree(md.get_meaning(0))
     # node.print()
 
     for i in range(1, len(item)):
         itemm = [item[:i]]
         batch = torch.tensor([item[:i]], dtype=torch.int64)
-        next_token = runner.ChatTokens(batch, sample=False).detach().cpu().numpy()[0]
+        sorted_logits, sorted_indices = runner.ChatTokens(batch, sample=False)
+        next_token = sorted_indices.detach().cpu().numpy()[0][0]
         if item[i] != next_token:
             node.set_seq_prop(i, "ERR_" + str(next_token))
             print(str(item[i]) + "  " + str(next_token) + "  ERROR")

wit/model/modeling_wit.py

@@ -202,12 +202,11 @@ class QwenRunner:
         outputs, loss = self.forwardQWen(input_ids)
         next_token_scores = outputs[:, -1, :]
         next_token_scores = self.repetition_penalty(input_ids, next_token_scores)
-        next_token_scores = self.top_p(next_token_scores)
         if sample:
+            next_token_scores = self.top_p(next_token_scores)
             return self.sample(next_token_scores)
         else:
-            sorted_logits, sorted_indices = torch.sort(next_token_scores, descending=True)
+            return torch.sort(next_token_scores, descending=True)
-            return sorted_indices[0]
 
     @torch.no_grad()
     def Chat(

wit/query_meaning_freq.py

@@ -0,0 +1,37 @@
+import pytorch_lightning as pl
+import torch
+
+from model.qwen_module import QwenModule
+from model.modeling_wit import QwenRunner
+from model.tokenization_qwen import QWenTokenizer
+import numpy as np
+
+import configuration
+import dataset.dataset as ds
+import dataset.node_tree as nt
+
+if __name__ == "__main__":
+
+    checkpoint_path = "log/bigger/version_1/checkpoints/epoch=14-step=74040.ckpt"
+
+    qwen = QwenModule.load_from_checkpoint(checkpoint_path=checkpoint_path)
+    qwen.eval()
+    conf = qwen.config
+    torch.manual_seed(conf.seed)
+    np.random.seed(conf.seed)
+
+    train_dataloader, val_dataloader = ds.InitDataset(conf)
+
+    loader = train_dataloader.dataset
+
+    map = loader.meaning_dataset.get_meaning_map()
+    trees = {}
+    for batch in loader:
+        for m in batch["meaning"]:
+            trees[m] = map.get_tree(m)
+    while True:
+        m = int(input("input meaning: "))
+        total = 0
+        for tree in trees.values():
+            total = total + tree.count(m)
+        print(f"meaning of {m} count as {total}")