From a59e063159409cf0e9804c7b2f8602d920a35d73 Mon Sep 17 00:00:00 2001 From: frankwhzhang Date: Fri, 29 May 2020 15:46:26 +0800 Subject: [PATCH] fix mmoe --- models/multitask/mmoe/census_infer_reader.py | 50 ----------- models/multitask/mmoe/config.yaml | 84 +++++++++++-------- .../multitask/mmoe/data/train/train_data.txt | 20 +++++ models/multitask/mmoe/model.py | 61 +++++++------- 4 files changed, 97 insertions(+), 118 deletions(-) delete mode 100644 models/multitask/mmoe/census_infer_reader.py diff --git a/models/multitask/mmoe/census_infer_reader.py b/models/multitask/mmoe/census_infer_reader.py deleted file mode 100644 index fada3990..00000000 --- a/models/multitask/mmoe/census_infer_reader.py +++ /dev/null @@ -1,50 +0,0 @@ -# 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. - -from __future__ import print_function - -from paddlerec.core.reader import Reader - - -class EvaluateReader(Reader): - def init(self): - - pass - - def generate_sample(self, line): - """ - Read the data line by line and process it as a dictionary - """ - - def reader(): - """ - This function needs to be implemented by the user, based on data format - """ - l = line.strip().split(',') - l = list(map(float, l)) - label_income = [] - label_marital = [] - data = l[2:] - if int(l[1]) == 0: - label_income = [1, 0] - elif int(l[1]) == 1: - label_income = [0, 1] - if int(l[0]) == 0: - label_marital = [1, 0] - elif int(l[0]) == 1: - label_marital = [0, 1] - feature_name = ["input", "label_income", "label_marital"] - yield zip(feature_name, [data] + [label_income] + [label_marital]) - - return reader diff --git a/models/multitask/mmoe/config.yaml b/models/multitask/mmoe/config.yaml index e23332cd..9f36f849 100644 --- a/models/multitask/mmoe/config.yaml +++ b/models/multitask/mmoe/config.yaml @@ -12,43 +12,57 @@ # See the License for the specific language governing permissions and # limitations under the License. -evaluate: - reader: - batch_size: 1 - class: "{workspace}/census_infer_reader.py" - test_data_path: "{workspace}/data/train" +workspace: "paddlerec.models.multitask.mmoe" -train: - trainer: - # for cluster training - strategy: "async" +dataset: +- name: dataset_train + batch_size: 1 + type: QueueDataset + data_path: "{workspace}/data/train" + data_converter: "{workspace}/census_reader.py" +- name: dataset_infer + batch_size: 1 + type: QueueDataset + data_path: "{workspace}/data/train" + data_converter: "{workspace}/census_reader.py" - epochs: 3 - workspace: "paddlerec.models.multitask.mmoe" - device: cpu +hyper_parameters: + feature_size: 499 + expert_num: 8 + gate_num: 2 + expert_size: 16 + tower_size: 8 + optimizer: + class: adam + learning_rate: 0.001 + strategy: async - reader: - batch_size: 1 - class: "{workspace}/census_reader.py" - train_data_path: "{workspace}/data/train" +#use infer_runner mode and modify 'phase' below if infer +mode: train_runner +#mode: infer_runner - model: - models: "{workspace}/model.py" - hyper_parameters: - feature_size: 499 - expert_num: 8 - gate_num: 2 - expert_size: 16 - tower_size: 8 - learning_rate: 0.001 - optimizer: adam +runner: +- name: train_runner + class: single_train + device: cpu + epochs: 3 + save_checkpoint_interval: 2 + save_inference_interval: 4 + save_checkpoint_path: "increment" + save_inference_path: "inference" + print_interval: 10 +- name: infer_runner + class: single_infer + init_model_path: "increment/0" + device: cpu + epochs: 3 - save: - increment: - dirname: "increment" - epoch_interval: 2 - save_last: True - inference: - dirname: "inference" - epoch_interval: 4 - save_last: True +phase: +- name: train + model: "{workspace}/model.py" + dataset_name: dataset_train + thread_num: 1 + #- name: infer + # model: "{workspace}/model.py" + # dataset_name: dataset_infer + # thread_num: 1 diff --git a/models/multitask/mmoe/data/train/train_data.txt b/models/multitask/mmoe/data/train/train_data.txt index 992314e4..ba385736 100644 --- a/models/multitask/mmoe/data/train/train_data.txt +++ b/models/multitask/mmoe/data/train/train_data.txt @@ -1,4 +1,24 @@ 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diff --git a/models/multitask/mmoe/model.py b/models/multitask/mmoe/model.py index 03573369..309da6a3 100644 --- a/models/multitask/mmoe/model.py +++ b/models/multitask/mmoe/model.py @@ -22,53 +22,51 @@ class Model(ModelBase): def __init__(self, config): ModelBase.__init__(self, config) - def MMOE(self, is_infer=False): - feature_size = envs.get_global_env("hyper_parameters.feature_size", - None, self._namespace) - expert_num = envs.get_global_env("hyper_parameters.expert_num", None, - self._namespace) - gate_num = envs.get_global_env("hyper_parameters.gate_num", None, - self._namespace) - expert_size = envs.get_global_env("hyper_parameters.expert_size", None, - self._namespace) - tower_size = envs.get_global_env("hyper_parameters.tower_size", None, - self._namespace) - - input_data = fluid.data( - name="input", shape=[-1, feature_size], dtype="float32") + def _init_hyper_parameters(self): + self.feature_size = envs.get_global_env( + "hyper_parameters.feature_size") + self.expert_num = envs.get_global_env("hyper_parameters.expert_num") + self.gate_num = envs.get_global_env("hyper_parameters.gate_num") + self.expert_size = envs.get_global_env("hyper_parameters.expert_size") + self.tower_size = envs.get_global_env("hyper_parameters.tower_size") + + def input_data(self, is_infer=False, **kwargs): + inputs = fluid.data( + name="input", shape=[-1, self.feature_size], dtype="float32") label_income = fluid.data( name="label_income", shape=[-1, 2], dtype="float32", lod_level=0) label_marital = fluid.data( name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0) if is_infer: - self._infer_data_var = [input_data, label_income, label_marital] - self._infer_data_loader = fluid.io.DataLoader.from_generator( - feed_list=self._infer_data_var, - capacity=64, - use_double_buffer=False, - iterable=False) - - self._data_var.extend([input_data, label_income, label_marital]) + return [inputs, label_income, label_marital] + else: + return [inputs, label_income, label_marital] + + def net(self, inputs, is_infer=False): + input_data = inputs[0] + label_income = inputs[1] + label_marital = inputs[2] + # f_{i}(x) = activation(W_{i} * x + b), where activation is ReLU according to the paper expert_outputs = [] - for i in range(0, expert_num): + for i in range(0, self.expert_num): expert_output = fluid.layers.fc( input=input_data, - size=expert_size, + size=self.expert_size, act='relu', bias_attr=fluid.ParamAttr(learning_rate=1.0), name='expert_' + str(i)) expert_outputs.append(expert_output) expert_concat = fluid.layers.concat(expert_outputs, axis=1) - expert_concat = fluid.layers.reshape(expert_concat, - [-1, expert_num, expert_size]) + expert_concat = fluid.layers.reshape( + expert_concat, [-1, self.expert_num, self.expert_size]) # g^{k}(x) = activation(W_{gk} * x + b), where activation is softmax according to the paper output_layers = [] - for i in range(0, gate_num): + for i in range(0, self.gate_num): cur_gate = fluid.layers.fc( input=input_data, - size=expert_num, + size=self.expert_num, act='softmax', bias_attr=fluid.ParamAttr(learning_rate=1.0), name='gate_' + str(i)) @@ -78,7 +76,7 @@ class Model(ModelBase): cur_gate_expert = fluid.layers.reduce_sum(cur_gate_expert, dim=1) # Build tower layer cur_tower = fluid.layers.fc(input=cur_gate_expert, - size=tower_size, + size=self.tower_size, act='relu', name='task_layer_' + str(i)) out = fluid.layers.fc(input=cur_tower, @@ -127,8 +125,5 @@ class Model(ModelBase): self._metrics["AUC_marital"] = auc_marital self._metrics["BATCH_AUC_marital"] = batch_auc_2 - def train_net(self): - self.MMOE() - def infer_net(self): - self.MMOE(is_infer=True) + pass -- GitLab