Add validate.

wit/query_block_output.py

@@ -5,6 +5,8 @@ from model.light_module import ModelRunner
 from model.modeling_wit import QWenLMHeadModel
 
 import numpy as np
+import pytorch_lightning as pl
+from logger import TBLogger
 
 import math
 import sys
@@ -76,6 +78,25 @@ def get_inference(dataset, meaning):
     node.print()
 
 
+def validate(model, dataloader):
+
+    logger = TBLogger("./log/", name=conf.name)
+    logger.log_hyperparams(configuration.class_to_dict(conf))
+
+    lit_trainer = pl.Trainer(
+        accelerator="cuda",
+        precision=conf.precision,
+        logger=logger,
+        strategy=conf.strategy,
+        max_epochs=conf.max_epochs,
+    )
+
+    dataloader.dataset.meaning_dataset.set_mask([0, 1], [0, -1])
+
+    val = lit_trainer.validate(model, dataloaders=dataloader)
+    print(val)
+
+
 def get_contain_meaning(dataset, meaning):
     map = dataset.get_meaning_map()
     seqs = map.get_maps(contain=meaning)
@@ -133,7 +154,7 @@ def dump_dk(dataset, meaning):
 
 if __name__ == "__main__":
 
-    log_path = "log/bigger/version_0/"
+    log_path = "log/bigger/version_1/"
 
     file = get_latest_file_safe(log_path + "/checkpoints")
     checkpoint_path = log_path + "checkpoints/" + file
@@ -146,18 +167,13 @@ if __name__ == "__main__":
     np.random.seed(conf.seed)
     runner = ModelRunner(qwen.llm)
 
-    train, val = ds.InitDataset(conf)
+    train_loader, val_loader = ds.InitDataset(conf)
-    val = val.dataset
+    val = val_loader.dataset
-    # get_dataset_set_freq(train.dataset)
     md = val.meaning_dataset
     map = md.get_meaning_map()
+    meaning = 2995  # 844 849 995 991
 
-    # seq:844
+    # get_dataset_set_freq(train_loader.dataset)
-    # seq:849
-    # seq:991
-    # seq:995
-    meaning = 995
-    meaning = 2995
 
     # node = map.get_nodetree(meaning)
     # node.print()
@@ -170,3 +186,5 @@ if __name__ == "__main__":
     get_contain_meaning(md, meaning)
 
     dump_dk(md, meaning)
+
+    validate(qwen, val_loader)