def setUp(self):
wdic = {
"<MASK>": 0,
"<RARE>": 1,
"the": 10,
"a": 5,
"his": 50,
"abracadabrqmsd--qsdfmqgf-": 6
}
wdic2 = {
"<MASK>": 0,
"<RARE>": 1,
"the": 2,
"a": 3,
"his": 4,
"abracadabrqmsd--qsdfmqgf-": 5,
"qsdfqsdf": 7
}
self.adapted = q.WordLinout(10, worddic=wdic, bias=False)
self.vanilla = q.WordLinout(
10,
worddic=wdic,
weight=self.adapted.lin.weight.detach().numpy(),
bias=False)
self.adapted = self.adapted.adapt(wdic2)
def __init__(self,
rnn_type,
ntoken,
ninp,
nhid,
nlayers,
dropout=0.5,
dropconnect=0.,
tie_weights=False):
super(NewRNNModel, self).__init__()
worddic = dict(zip([str(x) for x in range(ntoken)], range(ntoken)))
dims = [ninp] + [nhid] * nlayers
self.nhid = nhid
self.nlayers = nlayers
self.dims = dims
self.D = worddic
self.states = None
# make layers
self.emb = q.WordEmb(dims[0], worddic=self.D)
self.out = q.WordLinout(dims[-1], worddic=self.D)
self.rnn = self.encodertype(*dims,
bidir=False,
bias=True,
dropout_in=dropout,
dropconnect=dropconnect)
self.rnn.ret_all_states = True
self.dropout = nn.Dropout(p=dropout)
def run(lr=0.001):
x = torch.randint(1, 100, (5, 8, 6), dtype=torch.int64)
y = x[:, 1:, :-1]
y = torch.cat([torch.ones(y.size(0), y.size(1), 1, dtype=y.dtype), y], 2)
y = torch.cat(
[y, torch.randint(1, 100, (y.size(0), 1, y.size(2))).long()], 1)
D = dict(zip(["<MASK>"] + [str(i) for i in range(1, 100)], range(100)))
m = BasicHierarchicalEncoderDecoder(q.WordEmb(10, worddic=D),
q.WordLinout(25, worddic=D), 10,
(20, ), (30, ), (25, ))
pred = m(x, y)
def test_overridden(self):
worddic = "second third fourth fifth"
worddic = dict(zip(worddic.split(), range(len(worddic.split()))))
linout = q.WordLinout(10, worddic=worddic)
l = self.linout.override(linout)
x = Variable(torch.randn(7, 10))
msk = Variable(
torch.FloatTensor([[1, 0, 1, 1, 0, 1, 0]] * 5 +
[[0, 1, 0, 0, 1, 0, 1]] * 2))
y = l(x, mask=msk)
print(y)
def setUp(self):
wdic = {
"<MASK>": 0,
"<RARE>": 1,
"the": 10,
"a": 5,
"his": 50,
"monkey": 6
}
wdic2 = {
"<MASK>": 0,
"<RARE>": 1,
"the": 2,
"a": 3,
"his": 4,
"abracadabrqmsd--qsdfmqgf-": 5,
"qsdfqsdf": 7
}
self.base = q.WordLinout(10, worddic=wdic, bias=False)
self.over = q.WordLinout(10, worddic=wdic2, bias=False)
self.overridden = self.base.override(self.over)
def __init__(self, D, embdim, zdim, startsym, *innerdim, **kw):
super(Decoder, self).__init__()
self.emb = q.WordEmb(embdim, worddic=D)
innerdim = (embdim+zdim,) + innerdim
self.layers = torch.nn.ModuleList(modules=[
q.LSTMCell(innerdim[i-1], innerdim[i]) for i in range(1, len(innerdim))
])
self.linout = q.WordLinout(innerdim[-1], worddic=D)
self.sm = torch.nn.Softmax(-1)
self.maxtime = q.getkw(kw, "maxtime", 100)
self.startid = D[startsym]
self.sm_sample = True
self.zdim = zdim
def __init__(self,
*dims: int,
worddic: dict = None,
bias: bool = True,
dropout: float = 0.,
**kw):
super(RNNLayer_LM, self).__init__(**kw)
self.dims = dims
self.D = worddic
self.states = None
# make layers
self.emb = q.WordEmb(dims[0], worddic=self.D)
self.out = q.WordLinout(dims[-1], worddic=self.D)
self.rnn = self.encodertype(*dims,
bidir=False,
bias=bias,
dropout_in=dropout)
self.rnn.ret_all_states = True
self.dropout = torch.nn.Dropout(p=dropout)
def test_it(self):
x = np.random.randint(0, 100, (1000, 7))
y_inp = x[:, :-1]
y_out = x[:, 1:]
wD = dict((chr(xi), xi) for xi in range(100))
ctx = torch.randn(1000, 8, 30)
decoder_emb = q.WordEmb(20, worddic=wD)
decoder_lstm = q.LSTMCell(20, 30)
decoder_att = q.DotAttention()
decoder_out = q.WordLinout(60, worddic=wD)
decoder_cell = q.DecoderCell(decoder_emb, decoder_lstm, decoder_att,
None, decoder_out)
decoder_tf = q.TFDecoder(decoder_cell)
y = decoder_tf(torch.tensor(x), ctx=ctx)
self.assertTrue(y.size(), (1000, 7, 100))
def run_classify(lr=0.001,
seqlen=6,
numex=500,
epochs=25,
batsize=10,
test=True,
cuda=False,
gpu=0):
device = torch.device("cpu")
if cuda:
device = torch.device("cuda", gpu)
# region construct data
colors = "red blue green magenta cyan orange yellow grey salmon pink purple teal".split(
)
D = dict(zip(colors, range(len(colors))))
inpseqs = []
targets = []
for i in range(numex):
inpseq = list(np.random.choice(colors, seqlen, replace=False))
target = np.random.choice(range(len(inpseq)), 1)[0]
target_class = D[inpseq[target]]
inpseq[target] = "${}$".format(inpseq[target])
inpseqs.append("".join(inpseq))
targets.append(target_class)
sm = q.StringMatrix()
sm.tokenize = lambda x: list(x)
for inpseq in inpseqs:
sm.add(inpseq)
sm.finalize()
print(sm[0])
print(sm.D)
targets = np.asarray(targets)
data = q.dataload(sm.matrix[:-100], targets[:-100], batch_size=batsize)
valid_data = q.dataload(sm.matrix[-100:],
targets[-100:],
batch_size=batsize)
# endregion
# region model
embdim = 20
enc2inpdim = 45
encdim = 20
outdim = 20
emb = q.WordEmb(embdim, worddic=sm.D) # sm dictionary (characters)
out = q.WordLinout(outdim, worddic=D) # target dictionary
# encoders:
enc1 = q.RNNEncoder(embdim, encdim, bidir=True)
enc2 = q.RNNCellEncoder(enc2inpdim, outdim // 2, bidir=True)
# model
class Model(torch.nn.Module):
def __init__(self, dim, _emb, _out, _enc1, _enc2, **kw):
super(Model, self).__init__(**kw)
self.dim, self.emb, self.out, self.enc1, self.enc2 = dim, _emb, _out, _enc1, _enc2
self.score = torch.nn.Sequential(
torch.nn.Linear(dim, 1, bias=False), torch.nn.Sigmoid())
self.emb_expander = ExpandVecs(embdim, enc2inpdim, 2)
self.enc_expander = ExpandVecs(encdim * 2, enc2inpdim, 2)
def forward(self, x, with_att=False):
# embed and encode
xemb, xmask = self.emb(x)
xenc = self.enc1(xemb, mask=xmask)
# compute attention
xatt = self.score(xenc).squeeze(
2) * xmask.float()[:, :xenc.size(1)]
# encode again
_xemb = self.emb_expander(xemb[:, :xenc.size(1)])
_xenc = self.enc_expander(xenc)
_, xenc2 = self.enc2(_xemb,
gate=xatt,
mask=xmask[:, :xenc.size(1)],
ret_states=True)
scores = self.out(xenc2.view(xenc.size(0), -1))
if with_att:
return scores, xatt
else:
return scores
model = Model(40, emb, out, enc1, enc2)
# endregion
# region test
if test:
inps = torch.tensor(sm.matrix[0:2])
outs = model(inps)
# endregion
# region train
optimizer = torch.optim.Adam(q.params_of(model), lr=lr)
trainer = q.trainer(model).on(data).loss(torch.nn.CrossEntropyLoss(), q.Accuracy())\
.optimizer(optimizer).hook(q.ClipGradNorm(5.)).device(device)
validator = q.tester(model).on(valid_data).loss(
q.Accuracy()).device(device)
q.train(trainer, validator).run(epochs=epochs)
# endregion
# region check attention #TODO
# feed a batch
inpd = torch.tensor(sm.matrix[400:410])
outd, att = model(inpd, with_att=True)
outd = torch.max(outd, 1)[1].cpu().detach().numpy()
inpd = inpd.cpu().detach().numpy()
att = att.cpu().detach().numpy()
rD = {v: k for k, v in sm.D.items()}
roD = {v: k for k, v in D.items()}
for i in range(len(att)):
inpdi = " ".join([rD[x] for x in inpd[i]])
outdi = roD[outd[i]]
print("input: {}\nattention: {}\nprediction: {}".format(
inpdi, " ".join(["{:.1f}".format(x) for x in att[i]]), outdi))
def run(lr=0.001,
dropout=0.2,
batsize=50,
embdim=50,
encdim=50,
decdim=50,
numlayers=1,
bidir=False,
which="geo", # "geo", "atis", "jobs"
test=True,
):
settings = locals().copy()
logger = q.log.Logger(prefix="seq2seq_base")
logger.save_settings(**settings)
# region data
nlsm, qlsm, splitidxs = load_data(which=which)
print(nlsm[0], qlsm[0])
print(nlsm._rarewords)
trainloader = q.dataload(nlsm.matrix[:splitidxs[0]], qlsm.matrix[:splitidxs[0]], batch_size=batsize, shuffle=True)
devloader = q.dataload(nlsm.matrix[splitidxs[0]:splitidxs[1]], qlsm.matrix[splitidxs[0]:splitidxs[1]], batch_size=batsize, shuffle=False)
testloader = q.dataload(nlsm.matrix[splitidxs[1]:], qlsm.matrix[splitidxs[1]:], batch_size=batsize, shuffle=False)
# endregion
# region model
encdims = [encdim] * numlayers
outdim = (encdim if not bidir else encdim * 2) + decdim
nlemb = q.WordEmb(embdim, worddic=nlsm.D)
qlemb = q.WordEmb(embdim, worddic=qlsm.D)
nlenc = q.LSTMEncoder(embdim, *encdims, bidir=bidir, dropout_in=dropout)
att = q.att.DotAtt()
if numlayers > 1:
qldec_core = torch.nn.Sequential(
*[q.LSTMCell(_indim, _outdim, dropout_in=dropout)
for _indim, _outdim in [(embdim, decdim)] + [(decdim, decdim)] * (numlayers - 1)]
)
else:
qldec_core = q.LSTMCell(embdim, decdim, dropout_in=dropout)
qlout = q.WordLinout(outdim, worddic=qlsm.D)
qldec = q.LuongCell(emb=qlemb, core=qldec_core, att=att, out=qlout)
class Model(torch.nn.Module):
def __init__(self, _nlemb, _nlenc, _qldec, train=True, **kw):
super(Model, self).__init__(**kw)
self.nlemb, self.nlenc, self._q_train = _nlemb, _nlenc, train
if train:
self.qldec = q.TFDecoder(_qldec)
else:
self.qldec = q.FreeDecoder(_qldec, maxtime=100)
def forward(self, x, y): # (batsize, seqlen) int ids
xemb, xmask = self.nlemb(x)
xenc = self.nlenc(xemb, mask=xmask)
if self._q_train is False:
assert(y.dim() == 2)
dec = self.qldec(y, ctx=xenc, ctxmask=xmask[:, :xenc.size(1)])
return dec
m_train = Model(nlemb, nlenc, qldec, train=True)
m_test = Model(nlemb, nlenc, qldec, train=False)
if test:
test_out = m_train(torch.tensor(nlsm.matrix[:5]), torch.tensor(qlsm.matrix[:5]))
print("test_out.size() = {}".format(test_out.size()))
def setUp(self):
wd = dict(zip(map(lambda x: chr(x), range(100)), range(100)))
self.base = q.WordLinout(50, worddic=wd, bias=False)
self.merg = q.WordLinout(50, worddic=wd, bias=False)
self.linout = self.base.merge(self.merg)
def setUp(self):
worddic = "<MASK> <RARE> first second third fourth fifth sixth"
worddic = dict(zip(worddic.split(), range(len(worddic.split()))))
self.linout = q.WordLinout(10, worddic=worddic, cosnorm=True)
def run_normal_seqvae_toy(
lr=0.001,
embdim=64,
encdim=100,
zdim=64,
batsize=50,
epochs=100,
):
# test
vocsize = 100
seqlen = 12
wD = dict((chr(xi), xi) for xi in range(vocsize))
# region encoder
encoder_emb = q.WordEmb(embdim, worddic=wD)
encoder_lstm = q.FastestLSTMEncoder(embdim, encdim)
class EncoderNet(torch.nn.Module):
def __init__(self, emb, core):
super(EncoderNet, self).__init__()
self.emb, self.core = emb, core
def forward(self, x):
embs, mask = self.emb(x)
out, states = self.core(embs, mask, ret_states=True)
top_state = states[-1][0][:, 0]
# top_state = top_state.unsqueeze(1).repeat(1, out.size(1), 1)
return top_state # (batsize, encdim)
encoder_net = EncoderNet(encoder_emb, encoder_lstm)
encoder = Posterior(encoder_net, encdim, zdim)
# endregion
# region decoder
decoder_emb = q.WordEmb(embdim, worddic=wD)
decoder_lstm = q.LSTMCell(embdim + zdim, encdim)
decoder_outlin = q.WordLinout(encdim, worddic=wD)
class DecoderCell(torch.nn.Module):
def __init__(self, emb, core, out, **kw):
super(DecoderCell, self).__init__()
self.emb, self.core, self.out = emb, core, out
def forward(self, xs, z=None):
embs, mask = self.emb(xs)
core_inp = torch.cat([embs, z], 1)
core_out = self.core(core_inp)
out = self.out(core_out)
return out
decoder_cell = DecoderCell(decoder_emb, decoder_lstm, decoder_outlin)
decoder = q.TFDecoder(decoder_cell)
# endregion
likelihood = Likelihood()
vae = SeqVAE(encoder, decoder, likelihood)
x = torch.randint(0, vocsize, (batsize, seqlen), dtype=torch.int64)
ys = vae(x)
optim = torch.optim.Adam(q.params_of(vae), lr=lr)
x = torch.randint(0, vocsize, (batsize * 100, seqlen), dtype=torch.int64)
dataloader = q.dataload(x, batch_size=batsize, shuffle=True)
trainer = q.trainer(vae).on(dataloader).optimizer(optim).loss(4).epochs(
epochs)
trainer.run()
print("done \n\n")