Created by: wfeagle

• 背景： 预测的时候加入了一些特有的业务逻辑过滤，需要对每一层结果进行处理，因此把整个组网拆开来了，每个组网在一个program下

• 版本、环境信息：    1）PaddlePaddle版本：1.7.2    2）CPU：本地cpu预测    3）系统环境：Python版本2.7

• 问题描述：请详细描述您的问题，同步贴出报错信息、日志/代码关键片段 InvalidArgumentError: The input tensor X's dimensions of MulOp should be larger than x_num_col_dims. But received X's dimensions = 1, X's shape = [0], x_num_col_dims = 1. [Hint: Expected x_dims.size() > x_num_col_dims, but received x_dims.size():1 <= x_num_col_dims:1.] at (/paddle/paddle/fluid/operators/mul_op.cc:68) [operator < mul > error]

Python Call Stacks (More useful to users):

File "/home/disk3/wangfang20/baidu/adx/sublink-tdm/sublink_tdm/python/lib/python2.7/site-packages/paddle/fluid/framework.py", line 2525, in append_op attrs=kwargs.get("attrs", None)) File "/home/disk3/wangfang20/baidu/adx/sublink-tdm/sublink_tdm/python/lib/python2.7/site-packages/paddle/fluid/layer_helper.py", line 43, in append_op return self.main_program.current_block().append_op(*args, **kwargs) File "/home/disk3/wangfang20/baidu/adx/sublink-tdm/sublink_tdm/python/lib/python2.7/site-packages/paddle/fluid/layers/nn.py", line 342, in fc "y_num_col_dims": 1}) File "/home/disk3/wangfang20/baidu/adx/sublink-tdm/sublink_tdm/network.py", line 241, in input_fc_infer bias_attr=fluid.ParamAttr(name="trans.input_fc.bias") File "local_infer.py", line 156, in run_infer input_fc_out = input_net.input_fc_infer(input) File "local_infer.py", line 273, in run_infer(args, path)

local_infer.py

 def run_infer(args, model_path):
    """run infer"""
    logger.info("Infer Begin")
    file_list = [
        str(args.test_files_path) + "/%s" % x
        for x in os.listdir(args.test_files_path)
    ]

    test_reader = TDMDataset().infer_reader(file_list, args.batch_size)

    node2item = get_node2item(args)
#### 这里调用network#####
    input_net = InputTransNet(args)
    input = input_net.input_data()

    place = fluid.CPUPlace()
    exe = fluid.Executor(place)
    path = os.path.join(args.model_files_path, model_path)

    # 各类组网program
    startup_program = fluid.Program()
    # input_embedding
    input_main_program = fluid.Program()
####报错地方#####
    with fluid.program_guard(input_main_program, startup_program):
        input_fc_out = input_net.input_fc_infer(input) 
    fluid.io.load_persistables(
        executor=exe,
        dirname=path,
        main_program=input_main_program)
    logger.info("Load persistables for input_embedding program from \"{}\"".format(path))

network.py

class InputTransNet(object):
    """
    doc
    """
    def __init__(self, args):
        self.input_embed_size = args.query_emb_size
        self.node_embed_size = args.node_emb_size
        self.max_layers = args.layer_size
        self.is_test = args.is_test

    def input_data(self):
        """
        doc
        """
        input_emb = fluid.layers.data(
            name="input_emb",
            shape=[self.input_embed_size],
            dtype="float32",
        )

        return input_emb

 def input_trans_layer(self, input_emb):
        """
        doc
        """
        input_fc_out = fluid.layers.fc(
            input=input_emb,
            size=self.node_embed_size,
            act=None,
            param_attr=fluid.ParamAttr(name="trans.input_fc.weight"),
            bias_attr=fluid.ParamAttr(name="trans.input_fc.bias"),
        )

        input_layer_fc_out = [
            fluid.layers.fc(
                input=input_fc_out,
                size=self.node_embed_size,
                act="tanh",
                param_attr=fluid.ParamAttr(
                    name="trans.layer_fc.weight." + str(i)),
                bias_attr=fluid.ParamAttr(name="trans.layer_fc.bias." + str(i)),
            ) for i in range(self.max_layers)
        ]

        return input_layer_fc_out

def input_fc_infer(self, input_emb):
        """
        doc
        """
        input_fc_out = fluid.layers.fc(
            input=input_emb,
            size=self.node_embed_size,
            act=None,
            param_attr=fluid.ParamAttr(name="trans.input_fc.weight"),
            bias_attr=fluid.ParamAttr(name="trans.input_fc.bias")
        )
        return input_fc_out

    def layer_fc_infer(self, input_fc_out, layer_idx):
        """
        doc
        """
        input_layer_fc_out = fluid.layers.fc(
                                 input=input_fc_out,
                                 size=self.node_embed_size,
                                 act="tanh",
                                 param_attr=fluid.ParamAttr(name="trans.layer_fc.weight." + str(layer_idx)),
                                 bias_attr=fluid.ParamAttr(name="trans.layer_fc.bias." + str(layer_idx))
                             )
        return input_layer_fc_out