add dolly test code

test/temp_test.py

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+# from transformers import PreTrainedModel
+from transformers.modeling_utils import PreTrainedModel
+import torch
+from torch import nn
+from src_llm.configuration_dolly import DollyConfig
+from src_llm.modeling_dolly import DollyModel
+# model = DollyModel(DollyConfig())
+# model.save_pretrained("./my_model")
+# model.config.save_pretrained("./my_model")
+
+config = DollyConfig.from_pretrained("./my_model")
+model = DollyModel.from_pretrained("./my_model", config=config)
+
+
+# input_ids = torch.tensor([[101, 1024, 102]]).long() 
+# outputs = model(input_ids)
+# print(outputs)
+
+model_size = sum(t.numel() for t in model.parameters())
+print(f"Model Size: {model_size/1000**2:.1f}M parameters")

test/test_dolly.py

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+# from transformers import PreTrainedModel
+from transformers.modeling_utils import PreTrainedModel
+import torch
+from torch import nn
+from src_llm.configuration_dolly import DollyConfig
+from src_llm.modeling_dolly import DollyModel
+#从源码构建dolly模型
+model = DollyModel(DollyConfig())
+model.save_pretrained("weights_llm")
+model.config.save_pretrained("weights_llm")
+model_size = sum(t.numel() for t in model.parameters())
+print(f"Model Size: {model_size/1000**2:.1f}M parameters")
+
+#从配置文件和权重构建dolly模型
+config = DollyConfig.from_pretrained("./my_model")
+model = DollyModel.from_pretrained("./my_model", config=config)
+input_ids = torch.tensor([[101, 1024, 102]]).long() 
+outputs = model(input_ids)
+print(outputs)

test/test_huggingface_transformer.py

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+import transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import LlamaForCausalLM
+from transformers import LlamaConfig
+config = LlamaConfig(
+    vocab_size=32000,  
+    hidden_size=4096,  
+    num_attention_heads=32,  
+    num_hidden_layers=32, 
+    intermediate_size=11008,  
+)
+model = LlamaForCausalLM(config)
+model_size = sum(t.numel() for t in model.parameters())
+print(f"Model Size: {model_size/1000**2:.1f}M parameters")