wehub-resource-sync
134 lines
4.2 KiB
Python
134 lines
4.2 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from models.conv import ErnieSageV2Conv, GraphSageConv
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class Encoder(nn.Layer):
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"""Base class
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Chose different type ErnieSage class.
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"""
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def __init__(self, config):
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"""init function
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Args:
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config (Dict): all configs.
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"""
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super(Encoder, self).__init__()
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self.config = config
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# Don't add ernie to self, otherwise, there will be more copies of ernie weights
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# self.ernie = ernie
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@classmethod
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def factory(cls, config, ernie):
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"""Classmethod for ernie sage model.
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Args:
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config (Dict): all configs.
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ernie (nn.Layer): the ernie model.
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Raises:
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ValueError: Invalid ernie sage model type.
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Returns:
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Class: real model class.
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"""
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model_type = config.model_type
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if model_type == "ErnieSageV2":
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return ErnieSageV2Encoder(config, ernie)
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else:
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raise ValueError("Invalid ernie sage model type")
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def forward(self, *args, **kwargs):
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raise NotImplementedError
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class ErnieSageV2Encoder(Encoder):
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def __init__(self, config, ernie):
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"""Ernie sage v2 encoder
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Args:
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config (Dict): all config.
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ernie (nn.Layer): the ernie model.
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"""
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super(ErnieSageV2Encoder, self).__init__(config)
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# Don't add ernie to self, otherwise, there will be more copies of ernie weights
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# self.ernie = ernie
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self.convs = nn.LayerList()
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fc_lr = self.config.lr / 0.001
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erniesage_conv = ErnieSageV2Conv(
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ernie,
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ernie.config["hidden_size"],
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self.config.hidden_size,
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learning_rate=fc_lr,
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cls_token_id=self.config.cls_token_id,
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aggr_func="sum",
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)
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self.convs.append(erniesage_conv)
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for i in range(1, self.config.num_layers):
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layer = GraphSageConv(
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self.config.hidden_size, self.config.hidden_size, learning_rate=fc_lr, aggr_func="sum"
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)
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self.convs.append(layer)
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if self.config.final_fc:
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self.linear = nn.Linear(
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self.config.hidden_size, self.config.hidden_size, weight_attr=paddle.ParamAttr(learning_rate=fc_lr)
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)
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def take_final_feature(self, feature, index):
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"""Gather the final feature.
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Args:
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feature (Tensor): the total feature tensor.
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index (Tensor): the index to gather.
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Returns:
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Tensor: final result tensor.
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"""
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feat = paddle.gather(feature, index)
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if self.config.final_fc:
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feat = self.linear(feat)
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if self.config.final_l2_norm:
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feat = F.normalize(feat, axis=1)
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return feat
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def forward(self, graphs, term_ids, inputs):
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"""forward train function of the model.
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Args:
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graphs (Graph List): list of graph tensors.
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inputs (Tensor List): list of input tensors.
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Returns:
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Tensor List: list of final feature tensors.
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"""
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# term_ids for ErnieSageConv is the raw feature.
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feature = term_ids
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for i in range(len(graphs), self.config.num_layers):
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graphs.append(graphs[0])
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for i in range(0, self.config.num_layers):
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if i == self.config.num_layers - 1 and i != 0:
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act = None
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else:
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act = "leaky_relu"
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feature = self.convs[i](graphs[i], feature, act)
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final_feats = [self.take_final_feature(feature, x) for x in inputs]
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return final_feats
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