Apply meaning data train.

wit/meaning_dataset.py

@@ -31,17 +31,17 @@ class MeaningMap:  # 16777216 1048576 8192
         print("Disk cache miss, build new one.")
 
         mm = np.empty((size, max_subitem), dtype=np.int32)
-        total_level = int(math.log(size / vocab_size, max_subitem))
+        # total_level = int(math.log(size / vocab_size, max_subitem))
 
-        start = [0]
-        end = [vocab_size]
-        shift = vocab_size
-        for i in range(total_level):
-            shift = end[-1]
-            start.append(end[-1])
-            end.append(shift * self.max_subitem)
-        start.append(end[-1])
-        end.append(size)
+        # start = [0]
+        # end = [vocab_size]
+        # shift = vocab_size
+        # for i in range(total_level):
+        #     shift = end[-1]
+        #     start.append(end[-1])
+        #     end.append(shift * self.max_subitem)
+        # start.append(end[-1])
+        # end.append(size)
 
         index = np.arange(0, size)
         mm = np.random.random((size, max_subitem))
@@ -108,7 +108,18 @@ class MeaningDataset(Dataset):
         self.data = []
         meanings = np.random.randint(start, end, size=(size))
         for m in meanings:
-            self.data.append(self.mm.GetSequence(m))
+            sq = self.mm.GetSequence(m)
+            if len(sq) > 1:
+                self.data.append(sq)
+        left = size - len(self.data)
+        while True:
+            if left <= 0:
+                break
+            index = np.random.randint(start, end)
+            sq = self.mm.GetSequence(index)
+            if len(sq) > 1:
+                self.data.append(sq)
+                left = left - 1
 
     def __len__(self):
         return self.size

wit/train.py

@@ -8,10 +8,6 @@ import pytorch_lightning as pl
 import torch
 from torch.utils.data import ConcatDataset, DataLoader, Dataset, random_split, Subset
 
-from transformers import (
-    DefaultDataCollator,
-    set_seed,
-)
 from lit_module import LitModule
 from tokenization_qwen import QWenTokenizer
 from logger import TBLogger
@@ -24,7 +20,7 @@ pretrain_model_name = None  # "qwen/Qwen-1_8B-Chat"
 learning_rate = 0.0001
 use_tril_attention_mask = None
 precision = "32-true"  # "precision:bf16-mixed,16-mixed,32-true"
-train_batch_size = 256
+train_batch_size = 1
 val_batch_size = 1
 num_proc = 8
 max_epochs = 1000
@@ -35,23 +31,30 @@ vocab_size = 256
 
 
 if __name__ == "__main__":
-
-    set_seed(seed)
+    torch.manual_seed(seed)
 
     config = ModelConfig()
     config.vocab_size = vocab_size
-    config.hidden_size = 128  # 128 1024 2048  32
-    config.num_hidden_layers = 6  # 6 12 24  3
-    config.num_attention_heads = 8  # 8 8 16
+    config.hidden_size = 1024  # 128 1024 2048  32
+    config.num_hidden_layers = 12  # 6 12 24  3
+    config.num_attention_heads = 16  # 8 8 16
 
     lit_module = LitModule(pretrain_model_name, learning_rate, config, use_tril_attention_mask)
-
     tokenizer = QWenTokenizer("./wit_b64.tiktoken", "./wit_char.tiktoken")
 
-    raw_dataset = SpecialDataset()
-    # raw_dataset = MeaningDataset(start=65536, end=262133, size=32768, max_subitem=4, vocab_size=vocab_size)
-    train_dataset, val_dataset = random_split(raw_dataset, [0.95, 0.05])
+    # raw_dataset = SpecialDataset()
 
+    level_scale = 4
+    start = vocab_size * level_scale * level_scale
+    raw_dataset = MeaningDataset(
+        start=start,
+        end=start * level_scale,
+        size=start * level_scale * level_scale,
+        max_subitem=level_scale,
+        vocab_size=vocab_size,
+    )
+
+    train_dataset, val_dataset = random_split(raw_dataset, [0.95, 0.05])
     it = iter(train_dataset)
     print("data samples:")
     for i in range(10):
@@ -61,7 +64,6 @@ if __name__ == "__main__":
         train_dataset,
         batch_size=train_batch_size,
         num_workers=num_proc,
-        collate_fn=DefaultDataCollator(),
         persistent_workers=True,
         shuffle=True,
     )
@@ -69,13 +71,13 @@ if __name__ == "__main__":
         val_dataset,
         batch_size=val_batch_size,
         num_workers=num_proc,
-        collate_fn=DefaultDataCollator(),
         persistent_workers=True,
     )
 
     torch.set_float32_matmul_precision("medium")
     lit_trainer = pl.Trainer(
         accelerator="gpu",
+        # devices=[0],
         precision=precision,
         logger=TBLogger("./", default_hp_metric=False),
         strategy=strategy,