def __init__(self, device='cpu', jit=False):
self.device = device
self.jit = jit
# Download and the load default data.
WORD = torchtext.data.Field(include_lengths=True)
UD_TAG = torchtext.data.Field(
init_token="<bos>", eos_token="<eos>", include_lengths=True
)
# Download and the load default data.
train, val, test = torchtext.datasets.UDPOS.splits(
fields=(("word", WORD), ("udtag", UD_TAG), (None, None)),
filter_pred=lambda ex: 5 < len(ex.word) < 30,
)
WORD.build_vocab(train.word, min_freq=3)
UD_TAG.build_vocab(train.udtag)
self.train_iter = torch_struct.data.TokenBucket(train, batch_size=100, device=device)
H = 256
T = 30
NT = 30
self.model = NeuralCFG(len(WORD.vocab), T, NT, H)
if jit:
self.model = torch.jit.script(self.model)
self.model.to(device=device)
self.opt = torch.optim.Adam(self.model.parameters(), lr=0.001, betas=[0.75, 0.999])
for i, ex in enumerate(self.train_iter):
words, lengths = ex.word
self.words = words.long().to(device).transpose(0, 1)
self.lengths = lengths.to(device)
break
class Model:
def __init__(self, device='cpu', jit=False):
self.device = device
self.jit = jit
# Download and the load default data.
WORD = torchtext.data.Field(include_lengths=True)
UD_TAG = torchtext.data.Field(init_token="<bos>",
eos_token="<eos>",
include_lengths=True)
# Download and the load default data.
train, val, test = torchtext.datasets.UDPOS.splits(
fields=(("word", WORD), ("udtag", UD_TAG), (None, None)),
filter_pred=lambda ex: 5 < len(ex.word) < 30,
)
WORD.build_vocab(train.word, min_freq=3)
UD_TAG.build_vocab(train.udtag)
self.train_iter = torch_struct.data.TokenBucket(train,
batch_size=100,
device=device)
H = 256
T = 30
NT = 30
self.model = NeuralCFG(len(WORD.vocab), T, NT, H)
if jit:
self.model = torch.jit.script(self.model)
self.model.to(device=device)
self.opt = torch.optim.Adam(self.model.parameters(),
lr=0.001,
betas=[0.75, 0.999])
def get_module(self):
for ex in self.train_iter:
words, _ = ex.word
words = words.long()
return self.model, (words.to(device=self.device).transpose(0, 1), )
def train(self, niter=1):
losses = []
for i, ex in enumerate(self.train_iter):
if i == niter:
break
self.opt.zero_grad()
words, lengths = ex.word
words = words.long()
params = self.model(words.to(device=self.device).transpose(0, 1))
dist = SentCFG(params, lengths=lengths)
loss = dist.partition.mean()
(-loss).backward()
losses.append(loss.detach())
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 3.0)
self.opt.step()
def eval(self, niter=1):
pass
# Download and the load default data.
train, val, test = UDPOS.splits(
fields=(("word", WORD), ("udtag", UD_TAG), (None, None)),
filter_pred=lambda ex: 5 < len(ex.word) < 30,
)
WORD.build_vocab(train.word, min_freq=3)
UD_TAG.build_vocab(train.udtag)
train_iter = torch_struct.data.TokenBucket(train,
batch_size=100,
device="cuda:0")
H = 256
T = 30
NT = 30
model = NeuralCFG(len(WORD.vocab), T, NT, H)
if args.script:
print("scripting...")
model = torch.jit.script(model)
model.cuda()
opt = torch.optim.Adam(model.parameters(), lr=0.001, betas=[0.75, 0.999])
def train():
# model.train()
losses = []
for epoch in range(2):
for i, ex in enumerate(train_iter):
opt.zero_grad()
words, lengths = ex.word
N, batch = words.shape