model.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import math
import numpy as np

import paddle.fluid as fluid
import paddle.fluid.layers as layers

from paddlerec.core.utils import envs
from paddlerec.core.model import ModelBase
from paddlerec.core.metrics import RecallK


class Model(ModelBase):
    def __init__(self, config):
        ModelBase.__init__(self, config)

    def _init_hyper_parameters(self):
        self.learning_rate = envs.get_global_env(
            "hyper_parameters.optimizer.learning_rate")
        self.decay_steps = envs.get_global_env(
            "hyper_parameters.optimizer.decay_steps")
        self.decay_rate = envs.get_global_env(
            "hyper_parameters.optimizer.decay_rate")
        self.l2 = envs.get_global_env("hyper_parameters.optimizer.l2")

        self.dict_size = envs.get_global_env(
            "hyper_parameters.sparse_feature_number")
        self.corpus_size = envs.get_global_env("hyper_parameters.corpus_size")

        self.train_batch_size = envs.get_global_env(
            "dataset.dataset_train.batch_size")
        self.evaluate_batch_size = envs.get_global_env(
            "dataset.dataset_infer.batch_size")

        self.hidden_size = envs.get_global_env(
            "hyper_parameters.sparse_feature_dim")
        self.step = envs.get_global_env(
            "hyper_parameters.gnn_propogation_steps")

    def input_data(self, is_infer=False, **kwargs):
        if is_infer:
            bs = self.evaluate_batch_size
        else:
            bs = self.train_batch_size
        items = fluid.data(
            name="items", shape=[bs, -1],
            dtype="int64")  # [batch_size, uniq_max]
        seq_index = fluid.data(
            name="seq_index", shape=[bs, -1, 2],
            dtype="int32")  # [batch_size, seq_max, 2]
        last_index = fluid.data(
            name="last_index", shape=[bs, 2], dtype="int32")  # [batch_size, 2]
        adj_in = fluid.data(
            name="adj_in", shape=[bs, -1, -1],
            dtype="float32")  # [batch_size, seq_max, seq_max]
        adj_out = fluid.data(
            name="adj_out", shape=[bs, -1, -1],
            dtype="float32")  # [batch_size, seq_max, seq_max]
        mask = fluid.data(
            name="mask", shape=[bs, -1, 1],
            dtype="float32")  # [batch_size, seq_max, 1]
        label = fluid.data(
            name="label", shape=[bs, 1], dtype="int64")  # [batch_size, 1]

        res = [items, seq_index, last_index, adj_in, adj_out, mask, label]
        return res

    def net(self, inputs, is_infer=False):
        if is_infer:
            bs = self.evaluate_batch_size
        else:
            bs = self.train_batch_size

        stdv = 1.0 / math.sqrt(self.hidden_size)

        def embedding_layer(input,
                            table_name,
                            emb_dim,
                            initializer_instance=None):
            emb = fluid.embedding(
                input=input,
                size=[self.dict_size, emb_dim],
                param_attr=fluid.ParamAttr(
                    name=table_name, initializer=initializer_instance))
            return emb

        sparse_initializer = fluid.initializer.Uniform(low=-stdv, high=stdv)
        items_emb = embedding_layer(inputs[0], "emb", self.hidden_size,
                                    sparse_initializer)
        pre_state = items_emb
        for i in range(self.step):
            pre_state = layers.reshape(
                x=pre_state, shape=[bs, -1, self.hidden_size])
            state_in = layers.fc(
                input=pre_state,
                name="state_in",
                size=self.hidden_size,
                act=None,
                num_flatten_dims=2,
                param_attr=fluid.ParamAttr(
                    initializer=fluid.initializer.Uniform(
                        low=-stdv, high=stdv)),
                bias_attr=fluid.ParamAttr(
                    initializer=fluid.initializer.Uniform(
                        low=-stdv, high=stdv)))  # [batch_size, uniq_max, h]
            state_out = layers.fc(
                input=pre_state,
                name="state_out",
                size=self.hidden_size,
                act=None,
                num_flatten_dims=2,
                param_attr=fluid.ParamAttr(
                    initializer=fluid.initializer.Uniform(
                        low=-stdv, high=stdv)),
                bias_attr=fluid.ParamAttr(
                    initializer=fluid.initializer.Uniform(
                        low=-stdv, high=stdv)))  # [batch_size, uniq_max, h]

            state_adj_in = layers.matmul(inputs[3],
                                         state_in)  # [batch_size, uniq_max, h]
            state_adj_out = layers.matmul(
                inputs[4], state_out)  # [batch_size, uniq_max, h]

            gru_input = layers.concat([state_adj_in, state_adj_out], axis=2)

            gru_input = layers.reshape(
                x=gru_input, shape=[-1, self.hidden_size * 2])
            gru_fc = layers.fc(input=gru_input,
                               name="gru_fc",
                               size=3 * self.hidden_size,
                               bias_attr=False)
            pre_state, _, _ = fluid.layers.gru_unit(
                input=gru_fc,
                hidden=layers.reshape(
                    x=pre_state, shape=[-1, self.hidden_size]),
                size=3 * self.hidden_size)

        final_state = layers.reshape(
            pre_state, shape=[bs, -1, self.hidden_size])
        seq = layers.gather_nd(final_state, inputs[1])
        last = layers.gather_nd(final_state, inputs[2])

        seq_fc = layers.fc(
            input=seq,
            name="seq_fc",
            size=self.hidden_size,
            bias_attr=False,
            act=None,
            num_flatten_dims=2,
            param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
                low=-stdv, high=stdv)))  # [batch_size, seq_max, h]
        last_fc = layers.fc(input=last,
                            name="last_fc",
                            size=self.hidden_size,
                            bias_attr=False,
                            act=None,
                            num_flatten_dims=1,
                            param_attr=fluid.ParamAttr(
                                initializer=fluid.initializer.Uniform(
                                    low=-stdv, high=stdv)))  # [bathc_size, h]

        seq_fc_t = layers.transpose(
            seq_fc, perm=[1, 0, 2])  # [seq_max, batch_size, h]
        add = layers.elementwise_add(seq_fc_t,
                                     last_fc)  # [seq_max, batch_size, h]
        b = layers.create_parameter(
            shape=[self.hidden_size],
            dtype='float32',
            default_initializer=fluid.initializer.Constant(value=0.0))  # [h]
        add = layers.elementwise_add(add, b)  # [seq_max, batch_size, h]

        add_sigmoid = layers.sigmoid(add)  # [seq_max, batch_size, h]
        add_sigmoid = layers.transpose(
            add_sigmoid, perm=[1, 0, 2])  # [batch_size, seq_max, h]

        weight = layers.fc(
            input=add_sigmoid,
            name="weight_fc",
            size=1,
            act=None,
            num_flatten_dims=2,
            bias_attr=False,
            param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
                low=-stdv, high=stdv)))  # [batch_size, seq_max, 1]
        weight *= inputs[5]
        weight_mask = layers.elementwise_mul(
            seq, weight, axis=0)  # [batch_size, seq_max, h]
        global_attention = layers.reduce_sum(
            weight_mask, dim=1)  # [batch_size, h]

        final_attention = layers.concat(
            [global_attention, last], axis=1)  # [batch_size, 2*h]
        final_attention_fc = layers.fc(
            input=final_attention,
            name="final_attention_fc",
            size=self.hidden_size,
            bias_attr=False,
            act=None,
            param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
                low=-stdv, high=stdv)))  # [batch_size, h]

        # all_vocab = layers.create_global_var(
        #     shape=[items_num - 1],
        #     value=0,
        #     dtype="int64",
        #     persistable=True,
        #     name="all_vocab")
        all_vocab = np.arange(1, self.dict_size).reshape((-1)).astype('int32')
        all_vocab = fluid.layers.cast(
            x=fluid.layers.assign(all_vocab), dtype='int64')

        all_emb = fluid.embedding(
            input=all_vocab,
            param_attr=fluid.ParamAttr(
                name="emb",
                initializer=fluid.initializer.Uniform(
                    low=-stdv, high=stdv)),
            size=[self.dict_size, self.hidden_size])  # [all_vocab, h]

        logits = layers.matmul(
            x=final_attention_fc, y=all_emb,
            transpose_y=True)  # [batch_size, all_vocab]
        softmax = layers.softmax_with_cross_entropy(
            logits=logits, label=inputs[6])  # [batch_size, 1]
        self.loss = layers.reduce_mean(softmax)  # [1]
        acc = RecallK(input=logits, label=inputs[6], k=20)
        self._cost = self.loss

        if is_infer:
            self._infer_results['P@20'] = acc
            self._infer_results['LOSS'] = self.loss
            return

        self._metrics["LOSS"] = self.loss
        self._metrics["Train_P@20"] = acc

    def optimizer(self):
        step_per_epoch = self.corpus_size // self.train_batch_size
        optimizer = fluid.optimizer.Adam(
            learning_rate=fluid.layers.exponential_decay(
                learning_rate=self.learning_rate,
                decay_steps=self.decay_steps * step_per_epoch,
                decay_rate=self.decay_rate),
            regularization=fluid.regularizer.L2DecayRegularizer(
                regularization_coeff=self.l2))
        return optimizer