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MaskNet.py
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# =========================================================================
# Copyright (C) 2021. Huawei Technologies Co., Ltd. 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 torch
from torch import nn
from fuxictr.pytorch.models import BaseModel
from fuxictr.pytorch.layers import EmbeddingLayer, MLP_Layer
from fuxictr.pytorch.torch_utils import get_activation
class MaskNet(BaseModel):
def __init__(self,
feature_map,
model_id="MaskNet",
gpu=-1,
task="binary_classification",
learning_rate=1e-3,
embedding_dim=10,
dnn_hidden_units=[64,64,64],
dnn_hidden_activations="ReLU",
model_type="SerialMaskNet",
parallel_num_blocks=1,
parallel_block_dim=64,
reduction_ratio=1,
embedding_regularizer=None,
net_regularizer=None,
net_dropout=0,
emb_layernorm=True,
net_layernorm=True,
**kwargs):
super(MaskNet, self).__init__(feature_map,
model_id=model_id,
gpu=gpu,
embedding_regularizer=embedding_regularizer,
net_regularizer=net_regularizer,
**kwargs)
self.embedding_layer = EmbeddingLayer(feature_map, embedding_dim)
if model_type == "SerialMaskNet":
self.mask_net = SerialMaskNet(input_dim=feature_map.num_fields * embedding_dim,
output_dim=1,
output_activation=self.get_output_activation(task),
hidden_units=dnn_hidden_units,
hidden_activations=dnn_hidden_activations,
reduction_ratio=reduction_ratio,
dropout_rates=net_dropout,
layer_norm=net_layernorm)
elif model_type == "ParallelMaskNet":
self.mask_net = ParallelMaskNet(input_dim=feature_map.num_fields * embedding_dim,
output_dim=1,
output_activation=self.get_output_activation(task),
num_blocks=parallel_num_blocks,
block_dim=parallel_block_dim,
hidden_units=dnn_hidden_units,
hidden_activations=dnn_hidden_activations,
reduction_ratio=reduction_ratio,
dropout_rates=net_dropout,
layer_norm=net_layernorm)
if emb_layernorm:
self.emb_norm = nn.LayerNorm([feature_map.num_fields, embedding_dim])
else:
self.emb_norm = None
self.compile(kwargs["optimizer"], loss=kwargs["loss"], lr=learning_rate)
self.reset_parameters()
self.model_to_device()
def forward(self, inputs):
X, y = self.inputs_to_device(inputs)
feature_emb = self.embedding_layer(X)
if self.emb_norm is not None:
feature_emb = self.emb_norm(feature_emb)
y_pred = self.mask_net(feature_emb.flatten(start_dim=1))
return_dict = {"y_true": y, "y_pred": y_pred}
return return_dict
class SerialMaskNet(nn.Module):
def __init__(self, input_dim, output_dim=None, output_activation=None, hidden_units=[],
hidden_activations="ReLU", reduction_ratio=1, dropout_rates=0, layer_norm=True):
super(SerialMaskNet, self).__init__()
if not isinstance(dropout_rates, list):
dropout_rates = [dropout_rates] * len(hidden_units)
if not isinstance(hidden_activations, list):
hidden_activations = [hidden_activations] * len(hidden_units)
self.hidden_units = [input_dim] + hidden_units
self.mask_blocks = nn.ModuleList()
for idx in range(len(self.hidden_units) - 1):
self.mask_blocks.append(MaskBlock(input_dim,
self.hidden_units[idx],
self.hidden_units[idx + 1],
hidden_activations[idx],
reduction_ratio,
dropout_rates[idx],
layer_norm))
fc_layers = []
if output_dim is not None:
fc_layers.append(nn.Linear(self.hidden_units[-1], output_dim))
if output_activation is not None:
fc_layers.append(get_activation(output_activation))
self.fc = None
if len(fc_layers) > 0:
self.fc = nn.Sequential(*fc_layers)
def forward(self, X):
v_out = X
for idx in range(len(self.hidden_units) - 1):
v_out = self.mask_blocks[idx](X, v_out)
if self.fc is not None:
v_out = self.fc(v_out)
return v_out
class ParallelMaskNet(nn.Module):
def __init__(self, input_dim, output_dim=None, output_activation=None, num_blocks=1, block_dim=64,
hidden_units=[], hidden_activations="ReLU", reduction_ratio=1, dropout_rates=0,
layer_norm=True):
super(ParallelMaskNet, self).__init__()
self.num_blocks = num_blocks
self.mask_blocks = nn.ModuleList([MaskBlock(input_dim,
input_dim,
block_dim,
hidden_activations,
reduction_ratio,
dropout_rates,
layer_norm) for _ in range(num_blocks)])
self.dnn = MLP_Layer(input_dim=block_dim * num_blocks,
output_dim=output_dim,
hidden_units=hidden_units,
hidden_activations=hidden_activations,
output_activation=output_activation,
dropout_rates=dropout_rates)
def forward(self, X):
block_out = []
for i in range(self.num_blocks):
block_out.append(self.mask_blocks[i](X, X))
concat_out = torch.cat(block_out, dim=-1)
v_out = self.dnn(concat_out)
return v_out
class MaskBlock(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, hidden_activation="ReLU", reduction_ratio=1,
dropout_rate=0, layer_norm=True):
super(MaskBlock, self).__init__()
self.mask_layer = nn.Sequential(nn.Linear(input_dim, int(hidden_dim * reduction_ratio)),
nn.ReLU(),
nn.Linear(int(hidden_dim * reduction_ratio), hidden_dim))
hidden_layers = [nn.Linear(hidden_dim, output_dim, bias=False)]
if layer_norm:
hidden_layers.append(nn.LayerNorm(output_dim))
hidden_layers.append(get_activation(hidden_activation))
if dropout_rate > 0:
hidden_layers.append(nn.Dropout(p=dropout_rate))
self.hidden_layer = nn.Sequential(*hidden_layers)
def forward(self, X, H):
v_mask = self.mask_layer(X)
v_out = self.hidden_layer(v_mask * H)
return v_out