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######################################################################################
# Copyright (c) muhanzhang, D-VAE, NeurIPS 2019 [GitHub D-VAE]
# Modified by Hayeon Lee, Eunyoung Hyung, MetaD2A, ICLR2021, 2021. 03 [GitHub MetaD2A]
######################################################################################
# import math
# import random
import torch
import json
from torch import nn
import os
from torch.nn import functional as F
import datetime
## Our packages
import gpytorch
import logging
from transfer_nag_lib.DeepKernelGPHelpers import Metric
from transfer_nag_lib.DeepKernelGPModules import StandardDeepGP, ExactGPLayer
from transfer_nag_lib.MetaD2A_mobilenetV3.set_encoder.setenc_models import SetPool
class EncoderFSBO(nn.Module):
def __init__(self, args, graph_config, dgp_arch):
super(EncoderFSBO, self).__init__()
## GP parameters
space="OFA_MBV3"
c, D = 4230, 64
dim = args.nz * 2
rootdir = os.path.dirname(os.path.realpath(__file__))
backbone_params = json.load(open(os.path.join(rootdir, "Setconfig90.json"), "rb"))
backbone_params.update({"dim": dim})
backbone_params.update({"fixed_context_size": dim})
backbone_params.update({"minibatch_size": 256})
backbone_params.update({"n_inner_steps": 1})
backbone_params.update({"output_size_A": dgp_arch})
checkpoint_path = os.path.join(rootdir, "checkpoints", "FSBO-metalearn", f"{space}",
datetime.datetime.now().strftime('meta-%Y-%m-%d-%H-%M-%S-%f'))
backbone_params.update({"checkpoint_path": checkpoint_path})
self.fixed_context_size = backbone_params["fixed_context_size"]
self.minibatch_size = backbone_params["minibatch_size"]
self.n_inner_steps = backbone_params["n_inner_steps"]
self.checkpoint_path = backbone_params["checkpoint_path"]
os.makedirs(self.checkpoint_path, exist_ok=False)
json.dump(backbone_params, open(os.path.join(self.checkpoint_path, "configuration.json"), "w"))
# self.device = torch.device("cpu") # "cuda:0" if torch.cuda.is_available() else "cpu")
self.device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
logging.basicConfig(filename=os.path.join(self.checkpoint_path, "log.txt"), level=logging.DEBUG)
self.config = backbone_params
self.likelihood = gpytorch.likelihoods.GaussianLikelihood()
self.gp = ExactGPLayer(train_x=None, train_y=None, likelihood=self.likelihood, config=self.config,
dims=self.fixed_context_size)
self.mll = gpytorch.mlls.ExactMarginalLogLikelihood(self.likelihood, self.gp).to(self.device)
self.gp.double()
self.likelihood.double()
self.mll.double()
self.mse = nn.MSELoss()
# self.mtrloader = get_meta_train_loader(
# args.batch_size, args.data_path, args.num_sample)
# self.get_tasks()
self.setup_writers()
self.train_metrics = Metric()
self.valid_metrics = Metric(prefix="valid: ")
self.max_n = graph_config['max_n'] # maximum number of vertices
self.nvt = graph_config['num_vertex_type'] if args.search_space == 'ofa' else args.nvt # number of vertex types
self.START_TYPE = graph_config['START_TYPE']
self.END_TYPE = graph_config['END_TYPE']
self.hs = args.hs # hidden state size of each vertex
self.nz = args.nz # size of latent representation z
self.gs = args.hs # size of graph state
self.bidir = True # whether to use bidirectional encoding
self.vid = True
self.input_type = 'DG'
self.num_sample = args.num_sample
if self.vid:
self.vs = self.hs + self.max_n # vertex state size = hidden state + vid
else:
self.vs = self.hs
# 0. encoding-related
self.grue_forward = nn.GRUCell(self.nvt, self.hs) # encoder GRU
self.grue_backward = nn.GRUCell(self.nvt, self.hs) # backward encoder GRU
self.fc1 = nn.Linear(self.gs, self.nz) # latent mean
self.fc2 = nn.Linear(self.gs, self.nz) # latent logvar
# 2. gate-related
self.gate_forward = nn.Sequential(
nn.Linear(self.vs, self.hs),
nn.Sigmoid()
)
self.gate_backward = nn.Sequential(
nn.Linear(self.vs, self.hs),
nn.Sigmoid()
)
self.mapper_forward = nn.Sequential(
nn.Linear(self.vs, self.hs, bias=False),
) # disable bias to ensure padded zeros also mapped to zeros
self.mapper_backward = nn.Sequential(
nn.Linear(self.vs, self.hs, bias=False),
)
# 3. bidir-related, to unify sizes
if self.bidir:
self.hv_unify = nn.Sequential(
nn.Linear(self.hs * 2, self.hs),
)
self.hg_unify = nn.Sequential(
nn.Linear(self.gs * 2, self.gs),
)
# 4. other
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
self.tanh = nn.Tanh()
self.logsoftmax1 = nn.LogSoftmax(1)
# 6. predictor
np = self.gs
self.intra_setpool = SetPool(dim_input=512,
num_outputs=1,
dim_output=self.nz,
dim_hidden=self.nz,
mode='sabPF').to(self.device)
self.inter_setpool = SetPool(dim_input=self.nz,
num_outputs=1,
dim_output=self.nz,
dim_hidden=self.nz,
mode='sabPF').to(self.device)
self.set_fc = nn.Sequential(
nn.Linear(512, self.nz),
nn.ReLU()).to(self.device)
input_dim = 0
if 'D' in self.input_type:
input_dim += self.nz
if 'G' in self.input_type:
input_dim += self.nz
self.pred_fc = StandardDeepGP(backbone_params)
self.mseloss = nn.MSELoss(reduction='sum')
# self.nasbench201 = torch.load(
# os.path.join(args.data_path, 'nasbench201.pt'))
self.data_path = args.data_path
self.pred_fc.to(self.device)
self.inter_setpool.to(self.device)
self.intra_setpool.to(self.device)
self.grue_backward.to(self.device)
self.grue_forward.to(self.device)
self.gate_backward.to(self.device)
self.gate_forward.to(self.device)
self.mapper_backward.to(self.device)
self.mapper_forward.to(self.device)
self.hg_unify.to(self.device)
self.hv_unify.to(self.device)
self.fc1.to(self.device)
self.fc2.to(self.device)
# def get_topk_idx(self, topk=1):
# self.mtrloader.dataset.set_mode('train')
# if self.nasbench201 is None:
# self.nasbench201 = torch.load(
# os.path.join(self.data_path, 'nasbench201.pt'))
# z_repr = []
# g_repr = []
# acc_repr = []
# for x, g, acc in tqdm(self.mtrloader):
# str = decode_igraph_to_NAS_BENCH_201_string(g[0])
# arch_idx = -1
# for idx, arch_str in enumerate(self.nasbench201['arch']['str']):
# if arch_str == str:
# arch_idx = idx
# break
# g_repr.append(arch_idx)
# acc_repr.append(acc.detach().cpu().numpy()[0])
# best = np.argsort(-1 * np.array(acc_repr))[:topk]
# self.nasbench201 = None
# return np.array(g_repr)[best], np.array(acc_repr)[best]
def randomly_init_deepgp(self, ):
self.pred_fc = StandardDeepGP(self.config)
self.likelihood = gpytorch.likelihoods.GaussianLikelihood()
self.gp = ExactGPLayer(train_x=None, train_y=None, likelihood=self.likelihood, config=self.config,
dims=self.fixed_context_size)
self.mll = gpytorch.mlls.ExactMarginalLogLikelihood(self.likelihood, self.gp).to(self.device)
def setup_writers(self, ):
train_log_dir = os.path.join(self.checkpoint_path, "train")
os.makedirs(train_log_dir, exist_ok=True)
# self.train_summary_writer = SummaryWriter(train_log_dir)
valid_log_dir = os.path.join(self.checkpoint_path, "valid")
os.makedirs(valid_log_dir, exist_ok=True)
# self.valid_summary_writer = SummaryWriter(valid_log_dir)
def get_mu_and_std(self, x_support, y_support, x_query, y_query):
if x_support is not None:
x_support.to(self.device)
y_support.to(self.device)
self.gp.set_train_data(inputs=x_support, targets=y_support, strict=False)
self.gp.to(self.device)
self.gp.eval()
self.likelihood.eval()
pred = self.likelihood(self.gp(x_query.to(self.device)))
mu = pred.mean.detach().to("cpu").numpy().reshape(-1, )
stddev = pred.stddev.detach().to("cpu").numpy().reshape(-1, )
return mu, stddev
def predict_finetune(self, z, labels=None, train=False):
if len(labels) > 1:
z = torch.squeeze(z)
if train:
self.gp.set_train_data(inputs=z, targets=labels, strict=False)
y_dist = self.gp(z)
predictions = self.likelihood(y_dist)
return predictions.mean, y_dist
def predict(self, D_mu, G_mu, labels=None, train=False):
input_vec = []
if 'D' in self.input_type:
input_vec.append(D_mu)
if 'G' in self.input_type:
input_vec.append(G_mu)
print(input_vec)
input_vec = torch.cat(input_vec, dim=1)
z = self.pred_fc(input_vec).double()
if train:
self.gp.set_train_data(inputs=z, targets=labels, strict=False)
y_dist = self.gp(z.type(torch.DoubleTensor))
predictions = self.likelihood(y_dist)
return predictions.mean, y_dist
def get_data_and_graph_repr(self, x, g, matrix=False):
input_vec = []
# self.pred_fc.to(self.device)
self.pred_fc.eval()
# self.inter_setpool.to(self.device)
self.inter_setpool.eval()
# self.intra_setpool.to(self.device)
self.intra_setpool.eval()
# self.grue_backward.to(self.device)
self.grue_backward.eval()
# self.grue_forward.to(self.device)
self.grue_forward.eval()
# self.gate_backward.to(self.device)
self.gate_backward.eval()
# self.gate_forward.to(self.device)
self.gate_forward.eval()
# self.mapper_backward.to(self.device)
self.mapper_backward.eval()
# self.mapper_forward.to(self.device)
self.mapper_forward.eval()
# self.hg_unify.to(self.device)
self.hg_unify.eval()
# self.hv_unify.to(self.device)
self.hv_unify.eval()
# self.fc1.to(self.device)
self.fc1.eval()
# self.fc2.to(self.device)
self.fc2.eval()
if 'D' in self.input_type:
input_vec.append(self.set_encode([x for i in range(len(g))]).to(self.device))
if 'G' in self.input_type:
input_vec.append(self.graph_encode(g, matrix=matrix).squeeze())
# print(input_vec)
if len(g) == 1:
input_vec = torch.cat(input_vec, dim=0)
print(input_vec)
else:
input_vec = torch.cat(input_vec, dim=1)
z = self.pred_fc(input_vec)
return z.detach().cpu().numpy().tolist()
def get_device(self):
if self.device is None:
self.device = next(self.parameters()).device
return self.device
def _get_zeros(self, n, length):
return torch.zeros(n, length).to(self.get_device()) # get a zero hidden state
def _get_zero_hidden(self, n=1):
return self._get_zeros(n, self.hs) # get a zero hidden state
def _one_hot(self, idx, length):
if type(idx) in [list, range]:
if idx == []:
return None
idx = torch.LongTensor(idx).unsqueeze(0).t()
x = torch.zeros((len(idx), length)).scatter_(1, idx, 1).to(self.get_device())
else:
idx = torch.LongTensor([idx]).unsqueeze(0)
x = torch.zeros((1, length)).scatter_(1, idx, 1).to(self.get_device())
return x
def _gated(self, h, gate, mapper):
return gate(h) * mapper(h)
def _collate_fn(self, G):
return [g.copy() for g in G]
def _propagate_to(self, G, v, propagator, H=None, reverse=False, gate=None, mapper=None):
# propagate messages to vertex index v for all graphs in G
# return the new messages (states) at v
G = [g for g in G if g.vcount() > v]
if len(G) == 0:
return
if H is not None:
idx = [i for i, g in enumerate(G) if g.vcount() > v]
H = H[idx]
v_types = [g.vs[v]['type'] for g in G]
X = self._one_hot(v_types, self.nvt)
if reverse:
H_name = 'H_backward' # name of the hidden states attribute
H_pred = [[g.vs[x][H_name] for x in g.successors(v)] for g in G]
if self.vid:
vids = [self._one_hot(g.successors(v), self.max_n) for g in G]
gate, mapper = self.gate_backward, self.mapper_backward
else:
H_name = 'H_forward' # name of the hidden states attribute
H_pred = [[g.vs[x][H_name] for x in g.predecessors(v)] for g in G]
if self.vid:
vids = [self._one_hot(g.predecessors(v), self.max_n) for g in G]
if gate is None:
gate, mapper = self.gate_forward, self.mapper_forward
if self.vid:
H_pred = [[torch.cat([x[i], y[i:i + 1]], 1) for i in range(len(x))] for x, y in zip(H_pred, vids)]
# if h is not provided, use gated sum of v's predecessors' states as the input hidden state
if H is None:
max_n_pred = max([len(x) for x in H_pred]) # maximum number of predecessors
if max_n_pred == 0:
H = self._get_zero_hidden(len(G))
else:
H_pred = [torch.cat(h_pred +
[self._get_zeros(max_n_pred - len(h_pred), self.vs)], 0).unsqueeze(0)
for h_pred in H_pred] # pad all to same length
H_pred = torch.cat(H_pred, 0) # batch * max_n_pred * vs
H = self._gated(H_pred, gate, mapper).sum(1) # batch * hs
Hv = propagator(X, H)
for i, g in enumerate(G):
g.vs[v][H_name] = Hv[i:i + 1]
return Hv
def _propagate_from(self, G, v, propagator, H0=None, reverse=False):
# perform a series of propagation_to steps starting from v following a topo order
# assume the original vertex indices are in a topological order
if reverse:
prop_order = range(v, -1, -1)
else:
prop_order = range(v, self.max_n)
Hv = self._propagate_to(G, v, propagator, H0, reverse=reverse) # the initial vertex
for v_ in prop_order[1:]:
self._propagate_to(G, v_, propagator, reverse=reverse)
return Hv
def _get_graph_state(self, G, decode=False):
# get the graph states
# when decoding, use the last generated vertex's state as the graph state
# when encoding, use the ending vertex state or unify the starting and ending vertex states
Hg = []
for g in G:
hg = g.vs[g.vcount() - 1]['H_forward']
if self.bidir and not decode: # decoding never uses backward propagation
hg_b = g.vs[0]['H_backward']
hg = torch.cat([hg, hg_b], 1)
Hg.append(hg)
Hg = torch.cat(Hg, 0)
if self.bidir and not decode:
Hg = self.hg_unify(Hg)
return Hg
def set_encode(self, X):
proto_batch = []
for x in X:
cls_protos = self.intra_setpool(
x.view(-1, self.num_sample, 512)).squeeze(1)
proto_batch.append(
self.inter_setpool(cls_protos.unsqueeze(0)))
v = torch.stack(proto_batch).squeeze()
return v
def graph_encode(self, G, matrix=False):
# encode graphs G into latent vectors
if matrix:
mu = torch.Tensor([decode_igraph_to_NAS201_matrix(g).flatten() for g in G]).to(self.device)
else:
if type(G) != list:
G = [G]
self._propagate_from(G, 0, self.grue_forward, H0=self._get_zero_hidden(len(G)),
reverse=False)
if self.bidir:
self._propagate_from(G, self.max_n - 1, self.grue_backward,
H0=self._get_zero_hidden(len(G)), reverse=True)
Hg = self._get_graph_state(G)
mu = self.fc1(Hg)
# logvar = self.fc2(Hg)
return mu # , logvar
def reparameterize(self, mu, logvar, eps_scale=0.01):
# return z ~ N(mu, std)
if self.training:
std = logvar.mul(0.5).exp_()
eps = torch.randn_like(std) * eps_scale
return eps.mul(std).add_(mu)
else:
return mu