删除部分代码和文档

gpt2.py

-import os
-import numpy as np
-from transformers import BertTokenizerFast
-import migraphx
-
-# 加载词汇表
-print("INFO: Complete loading the vocabulary")
-vocab_file = os.path.join('./model', 'vocab_shici.txt')
-tokenizer = BertTokenizerFast(vocab_file, sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]")
-
-# 设置最大输入shape
-maxInput={"input":[1,1024]}
-
-# 加载模型
-print("INFO: Parsing and compiling the model")
-model = migraphx.parse_onnx("./model/GPT2_shici.onnx", map_input_dims=maxInput)
-inputName=model.get_parameter_names()[0]
-inputShape=model.get_parameter_shapes()[inputName].lens()
-print("inputName:{0} \ninputShape:{1}".format(inputName,inputShape))
-
-# 编译
-model.compile(t=migraphx.get_target("gpu"), device_id=0)
-
-print('开始和GPT2对诗，输入CTRL + Z以退出')
-while True:
-    try:
-        history = []
-        text = input("user:")
-        text_ids = tokenizer.encode(text, add_special_tokens=False)
-        history.extend(text_ids)
-        input_ids = [tokenizer.cls_token_id] 
-        input_ids.extend(text_ids)
-        input_ids = np.array(input_ids, dtype=np.int64)
-        input_ids = np.expand_dims(input_ids, axis=0) 
-        
-        max_len = 50
-        for _ in range(max_len):
-            # 执行reshape
-            inputShapes = [input_ids.shape[0], input_ids.shape[1]]
-            inputShapeMap={inputName:inputShapes}
-            model.reshape(inputs=inputShapeMap)
-                
-            # 推理
-            result = model.run({inputName: migraphx.argument(input_ids)})
-            logits = [float(x) for x in result[0].tolist()]
-            
-            # 对于[UNK]的概率设为无穷小，模型的预测结果不可能是[UNK]
-            logits[tokenizer.convert_tokens_to_ids('[UNK]')] = -float('Inf')
-            
-            # 排序
-            score = []
-            for index in range((input_ids.shape[1]-1)*22557, input_ids.shape[1]*22557):
-                score.append(logits[index])
-            index_and_score = sorted(enumerate(score), key=lambda x: x[1], reverse=True)
-            
-            # 取概率值最大的作为预测结果
-            next_token = index_and_score[0][0]
-            if next_token == tokenizer.convert_tokens_to_ids('[SEP]'):    # 遇到[SEP]结束标志符，结束循环
-                break
-            history.append(next_token)                                    # 结果存放在response列表中
-            
-            next_token = np.array(next_token, dtype=np.int64)
-            input_ids = np.append(input_ids, next_token)
-            input_ids = np.expand_dims(input_ids, axis=0)
-        
-        text = tokenizer.convert_ids_to_tokens(history)                   
-        print("chatbot:" + "".join(text))
-
-    except KeyboardInterrupt:
-        break

requirement.txt

-os
-numpy
-transformers