Fix issue262

.gitignore

@@ -27,3 +27,4 @@ datasets
 qqp
 glm_large_qqp_pytorch
 wandb
+clip_benchmark_datasets/

examples/bminf_generate/glm_generate.py

@@ -0,0 +1,20 @@
+from flagai.model.glm_model import GLMModel
+from flagai.data.tokenizer import Tokenizer
+from flagai.auto_model.auto_loader import AutoLoader
+from flagai.model.predictor.predictor import Predictor
+import torch
+import bminf
+
+model_name = 'GLM-10b-ch'
+loader = AutoLoader("lm", 'GLM-10b-ch', model_dir="./checkpoints/")
+model = loader.get_model()
+tokenizer = loader.get_tokenizer()
+with torch.cuda.device(0):
+    model = bminf.wrapper(model, quantization=False, memory_limit=30 << 39)
+
+tokenizer = Tokenizer.from_pretrained(model_name)
+predictor = Predictor(model, tokenizer)
+
+text = "今天天气不错[gMASK]"
+output = predictor.predict_generate_randomsample(text, out_max_length=10)
+print(text, '\n', output)

flagai/trainer.py

@@ -1009,7 +1009,6 @@ def evaluate(self,
                     labels = data_iterator['labels']
                 else:
                     labels = data_iterator['target_ids']
-                loss_mask = data_iterator['loss_mask']
                 if len(self.metric_methods) != 0:
                     if {metric_tuple[0] for metric_tuple in self.metric_methods} & {"rouge", "bleu"}:
                         batch_preds = torch.argmax(logits.detach(), dim=-1).cpu()