issuesFile

代码片段

python minicpm_openai_api.py
# 调用
url = "http://127.0.0.1:9433/v1/chat/completions" # 你的FastAPI服务器地址
file = open("path.jpg", "rb").read()
base64_data = base64.b64encode(file)
base64_str = str(base64_data, 'utf-8')
datas = {
 "model": "暂时无用",
 # "temperature": 10,
 "messages": [
 {
 "role": "user",
 "content": "问题一", 
 "image": base64_str
 },
 # {
 # "role": "assistant",
 # "content": "回答的内容一",
 # },
 # {
 # "role": "user",
 # "content": "问题二",
 # # "image": base64_str
 # },
 ],
 "max_tokens": 300,
}
response = requests.post(url, data=json.dumps(datas))
print(response.json())

trigger_model_path = "output-model-trigger"
arguments_model_path = "output-model-arguments"
sentence_max_length = 200
tokenizer = BertTokenizer.from_pretrained(trigger_model_path)
trigger_model = BertCCRFForTokenClassification.from_pretrained(trigger_model_path)
arguments_model = BertCCRFForTokenClassification.from_pretrained(arguments_model_path)
trigger_model.to("cuda:0")
trigger_model.eval()
arguments_model.to("cuda:0")
arguments_model.eval()
trigger_label_map = trigger_model.config.id2label
arguments_label_map = arguments_model.config.id2label
def get_model_input(sentence):
 token = [101] + tokenizer.convert_tokens_to_ids(tokenizer.tokenize(sentence))[:sentence_max_length - 2] + [102]
 input_ids = np.array([token])
 input_mask = np.array([[1] * len(token)])
 # segment_ids = np.array([[0] * len(token)])
 words = tokenizer.convert_ids_to_tokens(input_ids[0])
 input_ids = torch.from_numpy(input_ids)
 # segment_ids = torch.from_numpy(segment_ids)
 input_mask = torch.from_numpy(input_mask)
 batch = [input_ids, input_mask]
 inputs = {"input_ids": batch[0].cuda(), "attention_mask": batch[1].cuda()}
 return inputs, words
def get_trigger_model_pred(inputs, words, return_pred=False):
 logits = trigger_model(**inputs)[0]
 preds = logits.detach().cpu().numpy()
 preds = np.argmax(preds, axis=2)[0]
 if return_pred:
 return [trigger_label_map[_] for _ in preds]
 tmp_preds, event_type = [], []
 for i, _ in enumerate(preds):
 if trigger_label_map[_] == "O":
 if tmp_preds:
 event_type.append([tmp_preds[0][0].replace("B-", "").replace("I-", ""),
 "".join([_[1] for _ in tmp_preds])])
 tmp_preds = []
 continue
 if "B" in trigger_label_map[_] or "I" in trigger_label_map[_]:
 tmp_preds.append([trigger_label_map[_], words[i]])
 return event_type

def squad_output(prefix, config, init_dict, network, input_tensor):
 """
 Create the squad output
 """
 idims = input_tensor.shape
 assert len(idims) == 5
 B, S, hidden_size, _, _ = idims
 W_out = init_dict[prefix + SQD_W]
 B_out = init_dict[prefix + SQD_B]
 W = network.add_constant((1, hidden_size, 2), W_out)
 dense = network.add_fully_connected(input_tensor, 2, W_out, B_out)
 set_layer_name(dense, prefix, "dense")
 return dense
def multi_class_output(prefix, config, init_dict, network, input_tensor):
 """
 Create the squad output
 """
 labels_num = 12
 idims = input_tensor.shape
 assert len(idims) == 5
 B, S, hidden_size, _, _ = idims
 p_w = init_dict["bert_pooler_dense_kernel"]
 p_b = init_dict["bert_pooler_dense_bias"]
 W_out = init_dict["output_weights"]
 B_out = init_dict["output_bias"]
 # 这里其实可以直接取[CLS]的向量进行后续运算,但是没能实现相关功能,就计算了所有的
 # reshape_ = network.add_slice(input_tensor, [0, 0, 0, 0, 0], [1, 1, 768, 1, 1], [1, 1, 1, 1, 1])
 pool_output = network.add_fully_connected(input_tensor, hidden_size, p_w, p_b)
 pool_data = pool_output.get_output(0)
 tanh = network.add_activation(pool_data, trt.tensorrt.ActivationType.TANH)
 tanh_output = tanh.get_output(0)
 dense = network.add_fully_connected(tanh_output, labels_num, W_out, B_out)
 set_layer_name(dense, prefix, "dense")
 return dense
def ner_output(prefix, config, init_dict, network, input_tensor):
 """
 Create the squad output
 """
 labels_num = 12
 idims = input_tensor.shape
 assert len(idims) == 5
 B, S, hidden_size, _, _ = idims
 W_out = init_dict["project_logits_w"]
 B_out = init_dict["project_logits_b"]
 # 转置在拉平
 W_out = W_out.numpy().reshape((768, labels_num)).transpose((1, 0)).reshape((768*labels_num))
 # add_fully_connected
 # Y:=matmul(X,WT)+bias
 # W = network.add_constant((1, hidden_size, labels_num), W_out)
 pool_output = network.add_fully_connected(input_tensor, labels_num, W_out, B_out)
 pool_data = pool_output.get_output(0)
 dense = network.add_activation(pool_data, trt.tensorrt.ActivationType.TANH)
 set_layer_name(dense, prefix, "dense")
 # 9216 12 (-1, 512, 768, 1, 1) (-1, 512, 12, 1, 1)
 # print(W_out.size, B_out.size, input_tensor.shape, dense.get_output(0).shape)
 # exit()
 return dense