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general_models.py
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general_models.py
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# -*- coding: utf-8 -*-
#
# general_models.py
#
# Copyright 2020 Amazon.com, Inc. or its affiliates. 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.
#
"""
Graph Embedding Model
1. TransE
2. TransR
3. RESCAL
4. DistMult
5. ComplEx
6. RotatE
"""
import os
import numpy as np
import dgl.backend as F
backend = os.environ.get('DGLBACKEND', 'pytorch')
if backend.lower() == 'mxnet':
from .mxnet.tensor_models import logsigmoid
from .mxnet.tensor_models import get_device
from .mxnet.tensor_models import norm
from .mxnet.tensor_models import get_scalar
from .mxnet.tensor_models import reshape
from .mxnet.tensor_models import cuda
from .mxnet.tensor_models import ExternalEmbedding
from .mxnet.tensor_models import InferEmbedding
from .mxnet.score_fun import *
DEFAULT_INFER_BATCHSIZE = 1024
else:
from .pytorch.tensor_models import logsigmoid
from .pytorch.tensor_models import get_device
from .pytorch.tensor_models import norm
from .pytorch.tensor_models import get_scalar
from .pytorch.tensor_models import reshape
from .pytorch.tensor_models import cuda
from .pytorch.tensor_models import ExternalEmbedding
from .pytorch.tensor_models import InferEmbedding
from .pytorch.score_fun import *
DEFAULT_INFER_BATCHSIZE = 2048
EMB_INIT_EPS = 2.0
class InferModel(object):
def __init__(self, device, model_name, hidden_dim,
double_entity_emb=False, double_relation_emb=False,
gamma=0., batch_size=DEFAULT_INFER_BATCHSIZE):
super(InferModel, self).__init__()
self.device = device
self.model_name = model_name
entity_dim = 2 * hidden_dim if double_entity_emb else hidden_dim
relation_dim = 2 * hidden_dim if double_relation_emb else hidden_dim
self.entity_emb = InferEmbedding(device)
self.relation_emb = InferEmbedding(device)
self.batch_size = batch_size
if model_name == 'TransE' or model_name == 'TransE_l2':
self.score_func = TransEScore(gamma, 'l2')
elif model_name == 'TransE_l1':
self.score_func = TransEScore(gamma, 'l1')
elif model_name == 'TransR':
assert False, 'Do not support inference of TransR model now.'
elif model_name == 'DistMult':
self.score_func = DistMultScore()
elif model_name == 'ComplEx':
self.score_func = ComplExScore()
elif model_name == 'RESCAL':
self.score_func = RESCALScore(relation_dim, entity_dim)
elif model_name == 'RotatE':
emb_init = (gamma + EMB_INIT_EPS) / hidden_dim
self.score_func = RotatEScore(gamma, emb_init)
def load_emb(self, path, dataset):
"""Load the model.
Parameters
----------
path : str
Directory to load the model.
dataset : str
Dataset name as prefix to the saved embeddings.
"""
self.entity_emb.load(path, dataset+'_'+self.model_name+'_entity')
self.relation_emb.load(path, dataset+'_'+self.model_name+'_relation')
self.score_func.load(path, dataset+'_'+self.model_name)
def score(self, head, rel, tail, triplet_wise=False):
head_emb = self.entity_emb(head)
rel_emb = self.relation_emb(rel)
tail_emb = self.entity_emb(tail)
num_head = F.shape(head)[0]
num_rel = F.shape(rel)[0]
num_tail = F.shape(tail)[0]
batch_size = self.batch_size
score = []
if triplet_wise:
class FakeEdge(object):
def __init__(self, head_emb, rel_emb, tail_emb):
self._hobj = {}
self._robj = {}
self._tobj = {}
self._hobj['emb'] = head_emb
self._robj['emb'] = rel_emb
self._tobj['emb'] = tail_emb
@property
def src(self):
return self._hobj
@property
def dst(self):
return self._tobj
@property
def data(self):
return self._robj
for i in range((num_head + batch_size - 1) // batch_size):
sh_emb = head_emb[i * batch_size : (i + 1) * batch_size \
if (i + 1) * batch_size < num_head \
else num_head]
sr_emb = rel_emb[i * batch_size : (i + 1) * batch_size \
if (i + 1) * batch_size < num_head \
else num_head]
st_emb = tail_emb[i * batch_size : (i + 1) * batch_size \
if (i + 1) * batch_size < num_head \
else num_head]
edata = FakeEdge(sh_emb, sr_emb, st_emb)
score.append(F.copy_to(self.score_func.edge_func(edata)['score'], F.cpu()))
score = F.cat(score, dim=0)
return score
else:
for i in range((num_head + batch_size - 1) // batch_size):
sh_emb = head_emb[i * batch_size : (i + 1) * batch_size \
if (i + 1) * batch_size < num_head \
else num_head]
s_score = []
for j in range((num_tail + batch_size - 1) // batch_size):
st_emb = tail_emb[j * batch_size : (j + 1) * batch_size \
if (j + 1) * batch_size < num_tail \
else num_tail]
s_score.append(F.copy_to(self.score_func.infer(sh_emb, rel_emb, st_emb), F.cpu()))
score.append(F.cat(s_score, dim=2))
score = F.cat(score, dim=0)
return F.reshape(score, (num_head * num_rel * num_tail,))
@property
def num_entity(self):
return self.entity_emb.emb.shape[0]
@property
def num_rel(self):
return self.relation_emb.emb.shape[0]
class KEModel(object):
""" DGL Knowledge Embedding Model.
Parameters
----------
args:
Global configs.
model_name : str
Which KG model to use, including 'TransE_l1', 'TransE_l2', 'TransR',
'RESCAL', 'DistMult', 'ComplEx', 'RotatE'
n_entities : int
Num of entities.
n_relations : int
Num of relations.
hidden_dim : int
Dimetion size of embedding.
gamma : float
Gamma for score function.
double_entity_emb : bool
If True, entity embedding size will be 2 * hidden_dim.
Default: False
double_relation_emb : bool
If True, relation embedding size will be 2 * hidden_dim.
Default: False
"""
def __init__(self, args, model_name, n_entities, n_relations, hidden_dim, gamma,
double_entity_emb=False, double_relation_emb=False):
super(KEModel, self).__init__()
self.args = args
self.n_entities = n_entities
self.n_relations = n_relations
self.model_name = model_name
self.hidden_dim = hidden_dim
self.eps = EMB_INIT_EPS
self.emb_init = (gamma + self.eps) / hidden_dim
entity_dim = 2 * hidden_dim if double_entity_emb else hidden_dim
relation_dim = 2 * hidden_dim if double_relation_emb else hidden_dim
device = get_device(args)
self.entity_emb = ExternalEmbedding(args, n_entities, entity_dim,
F.cpu() if args.mix_cpu_gpu else device)
# For RESCAL, relation_emb = relation_dim * entity_dim
if model_name == 'RESCAL':
rel_dim = relation_dim * entity_dim
else:
rel_dim = relation_dim
self.rel_dim = rel_dim
self.entity_dim = entity_dim
self.strict_rel_part = args.strict_rel_part
self.soft_rel_part = args.soft_rel_part
if not self.strict_rel_part and not self.soft_rel_part:
self.relation_emb = ExternalEmbedding(args, n_relations, rel_dim,
F.cpu() if args.mix_cpu_gpu else device)
else:
self.global_relation_emb = ExternalEmbedding(args, n_relations, rel_dim, F.cpu())
if model_name == 'TransE' or model_name == 'TransE_l2':
self.score_func = TransEScore(gamma, 'l2')
elif model_name == 'TransE_l1':
self.score_func = TransEScore(gamma, 'l1')
elif model_name == 'TransR':
projection_emb = ExternalEmbedding(args,
n_relations,
entity_dim * relation_dim,
F.cpu() if args.mix_cpu_gpu else device)
self.score_func = TransRScore(gamma, projection_emb, relation_dim, entity_dim)
elif model_name == 'DistMult':
self.score_func = DistMultScore()
elif model_name == 'ComplEx':
self.score_func = ComplExScore()
elif model_name == 'RESCAL':
self.score_func = RESCALScore(relation_dim, entity_dim)
elif model_name == 'RotatE':
self.score_func = RotatEScore(gamma, self.emb_init)
self.model_name = model_name
self.head_neg_score = self.score_func.create_neg(True)
self.tail_neg_score = self.score_func.create_neg(False)
self.head_neg_prepare = self.score_func.create_neg_prepare(True)
self.tail_neg_prepare = self.score_func.create_neg_prepare(False)
self.reset_parameters()
def share_memory(self):
"""Use torch.tensor.share_memory_() to allow cross process embeddings access.
"""
self.entity_emb.share_memory()
if self.strict_rel_part or self.soft_rel_part:
self.global_relation_emb.share_memory()
else:
self.relation_emb.share_memory()
if self.model_name == 'TransR':
self.score_func.share_memory()
def save_emb(self, path, dataset):
"""Save the model.
Parameters
----------
path : str
Directory to save the model.
dataset : str
Dataset name as prefix to the saved embeddings.
"""
self.entity_emb.save(path, dataset+'_'+self.model_name+'_entity')
if self.strict_rel_part or self.soft_rel_part:
self.global_relation_emb.save(path, dataset+'_'+self.model_name+'_relation')
else:
self.relation_emb.save(path, dataset+'_'+self.model_name+'_relation')
self.score_func.save(path, dataset+'_'+self.model_name)
def load_emb(self, path, dataset):
"""Load the model.
Parameters
----------
path : str
Directory to load the model.
dataset : str
Dataset name as prefix to the saved embeddings.
"""
self.entity_emb.load(path, dataset+'_'+self.model_name+'_entity')
self.relation_emb.load(path, dataset+'_'+self.model_name+'_relation')
self.score_func.load(path, dataset+'_'+self.model_name)
def reset_parameters(self):
"""Re-initialize the model.
"""
self.entity_emb.init(self.emb_init)
self.score_func.reset_parameters()
if (not self.strict_rel_part) and (not self.soft_rel_part):
self.relation_emb.init(self.emb_init)
else:
self.global_relation_emb.init(self.emb_init)
def predict_score(self, g):
"""Predict the positive score.
Parameters
----------
g : DGLGraph
Graph holding positive edges.
Returns
-------
tensor
The positive score
"""
self.score_func(g)
return g.edata['score']
def predict_neg_score(self, pos_g, neg_g, to_device=None, gpu_id=-1, trace=False,
neg_deg_sample=False):
"""Calculate the negative score.
Parameters
----------
pos_g : DGLGraph
Graph holding positive edges.
neg_g : DGLGraph
Graph holding negative edges.
to_device : func
Function to move data into device.
gpu_id : int
Which gpu to move data to.
trace : bool
If True, trace the computation. This is required in training.
If False, do not trace the computation.
Default: False
neg_deg_sample : bool
If True, we use the head and tail nodes of the positive edges to
construct negative edges.
Default: False
Returns
-------
tensor
The negative score
"""
num_chunks = neg_g.num_chunks
chunk_size = neg_g.chunk_size
neg_sample_size = neg_g.neg_sample_size
mask = F.ones((num_chunks, chunk_size * (neg_sample_size + chunk_size)),
dtype=F.float32, ctx=F.context(pos_g.ndata['emb']))
if neg_g.neg_head:
neg_head_ids = neg_g.ndata['id'][neg_g.head_nid]
neg_head = self.entity_emb(neg_head_ids, gpu_id, trace)
head_ids, tail_ids = pos_g.all_edges(order='eid')
if to_device is not None and gpu_id >= 0:
tail_ids = to_device(tail_ids, gpu_id)
tail = pos_g.ndata['emb'][tail_ids]
rel = pos_g.edata['emb']
# When we train a batch, we could use the head nodes of the positive edges to
# construct negative edges. We construct a negative edge between a positive head
# node and every positive tail node.
# When we construct negative edges like this, we know there is one positive
# edge for a positive head node among the negative edges. We need to mask
# them.
if neg_deg_sample:
head = pos_g.ndata['emb'][head_ids]
head = head.reshape(num_chunks, chunk_size, -1)
neg_head = neg_head.reshape(num_chunks, neg_sample_size, -1)
neg_head = F.cat([head, neg_head], 1)
neg_sample_size = chunk_size + neg_sample_size
mask[:,0::(neg_sample_size + 1)] = 0
neg_head = neg_head.reshape(num_chunks * neg_sample_size, -1)
neg_head, tail = self.head_neg_prepare(pos_g.edata['id'], num_chunks, neg_head, tail, gpu_id, trace)
neg_score = self.head_neg_score(neg_head, rel, tail,
num_chunks, chunk_size, neg_sample_size)
else:
neg_tail_ids = neg_g.ndata['id'][neg_g.tail_nid]
neg_tail = self.entity_emb(neg_tail_ids, gpu_id, trace)
head_ids, tail_ids = pos_g.all_edges(order='eid')
if to_device is not None and gpu_id >= 0:
head_ids = to_device(head_ids, gpu_id)
head = pos_g.ndata['emb'][head_ids]
rel = pos_g.edata['emb']
# This is negative edge construction similar to the above.
if neg_deg_sample:
tail = pos_g.ndata['emb'][tail_ids]
tail = tail.reshape(num_chunks, chunk_size, -1)
neg_tail = neg_tail.reshape(num_chunks, neg_sample_size, -1)
neg_tail = F.cat([tail, neg_tail], 1)
neg_sample_size = chunk_size + neg_sample_size
mask[:,0::(neg_sample_size + 1)] = 0
neg_tail = neg_tail.reshape(num_chunks * neg_sample_size, -1)
head, neg_tail = self.tail_neg_prepare(pos_g.edata['id'], num_chunks, head, neg_tail, gpu_id, trace)
neg_score = self.tail_neg_score(head, rel, neg_tail,
num_chunks, chunk_size, neg_sample_size)
if neg_deg_sample:
neg_g.neg_sample_size = neg_sample_size
mask = mask.reshape(num_chunks, chunk_size, neg_sample_size)
return neg_score * mask
else:
return neg_score
def forward_test(self, pos_g, neg_g, logs, gpu_id=-1):
"""Do the forward and generate ranking results.
Parameters
----------
pos_g : DGLGraph
Graph holding positive edges.
neg_g : DGLGraph
Graph holding negative edges.
logs : List
Where to put results in.
gpu_id : int
Which gpu to accelerate the calculation. if -1 is provided, cpu is used.
"""
pos_g.ndata['emb'] = self.entity_emb(pos_g.ndata['id'], gpu_id, False)
pos_g.edata['emb'] = self.relation_emb(pos_g.edata['id'], gpu_id, False)
self.score_func.prepare(pos_g, gpu_id, False)
batch_size = pos_g.number_of_edges()
pos_scores = self.predict_score(pos_g)
pos_scores = reshape(logsigmoid(pos_scores), batch_size, -1)
neg_scores = self.predict_neg_score(pos_g, neg_g, to_device=cuda,
gpu_id=gpu_id, trace=False,
neg_deg_sample=self.args.neg_deg_sample_eval)
neg_scores = reshape(logsigmoid(neg_scores), batch_size, -1)
# We need to filter the positive edges in the negative graph.
if self.args.eval_filter:
filter_bias = reshape(neg_g.edata['bias'], batch_size, -1)
if gpu_id >= 0:
filter_bias = cuda(filter_bias, gpu_id)
neg_scores += filter_bias
# To compute the rank of a positive edge among all negative edges,
# we need to know how many negative edges have higher scores than
# the positive edge.
rankings = F.sum(neg_scores >= pos_scores, dim=1) + 1
rankings = F.asnumpy(rankings)
for i in range(batch_size):
ranking = rankings[i]
logs.append({
'MRR': 1.0 / ranking,
'MR': float(ranking),
'HITS@1': 1.0 if ranking <= 1 else 0.0,
'HITS@3': 1.0 if ranking <= 3 else 0.0,
'HITS@10': 1.0 if ranking <= 10 else 0.0
})
# @profile
def forward(self, pos_g, neg_g, gpu_id=-1):
"""Do the forward.
Parameters
----------
pos_g : DGLGraph
Graph holding positive edges.
neg_g : DGLGraph
Graph holding negative edges.
gpu_id : int
Which gpu to accelerate the calculation. if -1 is provided, cpu is used.
Returns
-------
tensor
loss value
dict
loss info
"""
pos_g.ndata['emb'] = self.entity_emb(pos_g.ndata['id'], gpu_id, True)
pos_g.edata['emb'] = self.relation_emb(pos_g.edata['id'], gpu_id, True)
self.score_func.prepare(pos_g, gpu_id, True)
pos_score = self.predict_score(pos_g)
pos_score = logsigmoid(pos_score)
if gpu_id >= 0:
neg_score = self.predict_neg_score(pos_g, neg_g, to_device=cuda,
gpu_id=gpu_id, trace=True,
neg_deg_sample=self.args.neg_deg_sample)
else:
neg_score = self.predict_neg_score(pos_g, neg_g, trace=True,
neg_deg_sample=self.args.neg_deg_sample)
neg_score = reshape(neg_score, -1, neg_g.neg_sample_size)
# Adversarial sampling
if self.args.neg_adversarial_sampling:
neg_score = F.sum(F.softmax(neg_score * self.args.adversarial_temperature, dim=1).detach()
* logsigmoid(-neg_score), dim=1)
else:
neg_score = F.mean(logsigmoid(-neg_score), dim=1)
# subsampling weight
# TODO: add subsampling to new sampler
#if self.args.non_uni_weight:
# subsampling_weight = pos_g.edata['weight']
# pos_score = (pos_score * subsampling_weight).sum() / subsampling_weight.sum()
# neg_score = (neg_score * subsampling_weight).sum() / subsampling_weight.sum()
#else:
pos_score = pos_score.mean()
neg_score = neg_score.mean()
# compute loss
loss = -(pos_score + neg_score) / 2
log = {'pos_loss': - get_scalar(pos_score),
'neg_loss': - get_scalar(neg_score),
'loss': get_scalar(loss)}
# regularization: TODO(zihao)
#TODO: only reg ent&rel embeddings. other params to be added.
if self.args.regularization_coef > 0.0 and self.args.regularization_norm > 0:
coef, nm = self.args.regularization_coef, self.args.regularization_norm
reg = coef * (norm(self.entity_emb.curr_emb(), nm) + norm(self.relation_emb.curr_emb(), nm))
log['regularization'] = get_scalar(reg)
loss = loss + reg
return loss, log
def update(self, gpu_id=-1):
""" Update the embeddings in the model
gpu_id : int
Which gpu to accelerate the calculation. if -1 is provided, cpu is used.
"""
self.entity_emb.update(gpu_id)
self.relation_emb.update(gpu_id)
self.score_func.update(gpu_id)
def prepare_relation(self, device=None):
""" Prepare relation embeddings in multi-process multi-gpu training model.
device : th.device
Which device (GPU) to put relation embeddings in.
"""
self.relation_emb = ExternalEmbedding(self.args, self.n_relations, self.rel_dim, device)
self.relation_emb.init(self.emb_init)
if self.model_name == 'TransR':
local_projection_emb = ExternalEmbedding(self.args, self.n_relations,
self.entity_dim * self.rel_dim, device)
self.score_func.prepare_local_emb(local_projection_emb)
self.score_func.reset_parameters()
def prepare_cross_rels(self, cross_rels):
self.relation_emb.setup_cross_rels(cross_rels, self.global_relation_emb)
if self.model_name == 'TransR':
self.score_func.prepare_cross_rels(cross_rels)
def writeback_relation(self, rank=0, rel_parts=None):
""" Writeback relation embeddings in a specific process to global relation embedding.
Used in multi-process multi-gpu training model.
rank : int
Process id.
rel_parts : List of tensor
List of tensor stroing edge types of each partition.
"""
idx = rel_parts[rank]
if self.soft_rel_part:
idx = self.relation_emb.get_noncross_idx(idx)
self.global_relation_emb.emb[idx] = F.copy_to(self.relation_emb.emb, F.cpu())[idx]
if self.model_name == 'TransR':
self.score_func.writeback_local_emb(idx)
def load_relation(self, device=None):
""" Sync global relation embeddings into local relation embeddings.
Used in multi-process multi-gpu training model.
device : th.device
Which device (GPU) to put relation embeddings in.
"""
self.relation_emb = ExternalEmbedding(self.args, self.n_relations, self.rel_dim, device)
self.relation_emb.emb = F.copy_to(self.global_relation_emb.emb, device)
if self.model_name == 'TransR':
local_projection_emb = ExternalEmbedding(self.args, self.n_relations,
self.entity_dim * self.rel_dim, device)
self.score_func.load_local_emb(local_projection_emb)
def create_async_update(self):
"""Set up the async update for entity embedding.
"""
self.entity_emb.create_async_update()
def finish_async_update(self):
"""Terminate the async update for entity embedding.
"""
self.entity_emb.finish_async_update()
def pull_model(self, client, pos_g, neg_g):
with th.no_grad():
entity_id = F.cat(seq=[pos_g.ndata['id'], neg_g.ndata['id']], dim=0)
relation_id = pos_g.edata['id']
entity_id = F.tensor(np.unique(F.asnumpy(entity_id)))
relation_id = F.tensor(np.unique(F.asnumpy(relation_id)))
l2g = client.get_local2global()
global_entity_id = l2g[entity_id]
entity_data = client.pull(name='entity_emb', id_tensor=global_entity_id)
relation_data = client.pull(name='relation_emb', id_tensor=relation_id)
self.entity_emb.emb[entity_id] = entity_data
self.relation_emb.emb[relation_id] = relation_data
def push_gradient(self, client):
with th.no_grad():
l2g = client.get_local2global()
for entity_id, entity_data in self.entity_emb.trace:
grad = entity_data.grad.data
global_entity_id =l2g[entity_id]
client.push(name='entity_emb', id_tensor=global_entity_id, data_tensor=grad)
for relation_id, relation_data in self.relation_emb.trace:
grad = relation_data.grad.data
client.push(name='relation_emb', id_tensor=relation_id, data_tensor=grad)
self.entity_emb.trace = []
self.relation_emb.trace = []