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from typing import List, Tuple, Type, Union
import torch
from nltk import Tree
from torch import Tensor
from torch import nn
from torch.distributions.utils import lazy_property
from torchrua import C, segment_mean, L, Z
from transformers.models.roberta.modeling_roberta import PreTrainedModel, RobertaModel
from tmp.configuration_parserker import ParserkerConfig
Frames = Union[List[Tensor], Tuple[Tensor, ...]]
def diag(tensor: Tensor, offset: int) -> Tensor:
return tensor.diagonal(offset=offset, dim1=1, dim2=2)
def diag_scatter(chart: Tensor, score: Tensor, offset: int) -> None:
chart.diagonal(offset=offset, dim1=1, dim2=2)[::] = score
def left(chart: Tensor, offset: int) -> Tensor:
b, t, _, *size = chart.size()
c, n, m, *stride = chart.stride()
return chart.as_strided(
size=(b, t - offset, offset, *size),
stride=(c, n + m, m, *stride),
)
def right(chart: Tensor, offset: int) -> Tensor:
b, t, _, *size = chart.size()
c, n, m, *stride = chart.stride()
return chart[:, 1:, offset:].as_strided(
size=(b, t - offset, offset, *size),
stride=(c, n + m, n, *stride),
)
def to_hex(x: int, num_bits: int) -> str:
return f'{x:0{(num_bits + 3) // 4}X}'
def bits_to_long(tensor: Tensor) -> Tensor:
*_, num_bits = tensor.size()
index = torch.arange(num_bits, dtype=torch.long, device=tensor.device)
return (tensor << index).sum(dim=-1)
def long_to_bits(tensor: Tensor, num_bits: int) -> Tensor:
index = torch.arange(num_bits, dtype=torch.long, device=tensor.device)
return (tensor[..., None] >> index) & 1
def max(tensor: Tensor, dim: int, keepdim: bool = False) -> Tensor:
return torch.max(tensor, dim=dim, keepdim=keepdim).values
class Semiring(NamedTuple):
zero: float
one: float
add: Callable
mul: Callable
sum: Callable
prod: Callable
Log = Semiring(
zero=-float('inf'),
one=0.,
add=torch.logaddexp,
mul=torch.add,
sum=torch.logsumexp,
prod=torch.sum,
)
Max = Semiring(
zero=-float('inf'),
one=0.,
add=torch.maximum,
mul=torch.add,
sum=max,
prod=torch.sum,
)
def cumsum(tensor: Tensor) -> Tensor:
b, t1, t2, k = tensor.size()
assert t1 == t2, f'{t1} != {t2}'
p1 = tensor.permute(0, 3, 1, 2).triu()
c1 = p1.cumsum(dim=-1)
c2 = c1.flip(dims=[-2]).cumsum(dim=-2).flip(dims=[-2])
p2 = c2.permute(0, 2, 3, 1)
return p2
def cky_partitions(logits: Tensor, token_sizes: Tensor, semiring: Type[Semiring]):
logits = cumsum(logits)
logits = torch.stack([torch.zeros_like(logits), logits], dim=-1)
b, t, _, k, _ = logits.size()
chart = torch.full_like(logits[..., 0, 0], fill_value=semiring.zero, requires_grad=False)
z = diag(logits, offset=0)[..., None].permute([0, 3, 4, 1, 2])
frames = [z]
z = semiring.sum(z, dim=-1)
z = semiring.prod(z, dim=-1)
diag_scatter(chart, z[..., 0], offset=0)
index = torch.arange(t, dtype=chart.dtype, device=chart.device)
for w in range(1, t):
z = diag(logits, offset=w)[..., None].permute([0, 3, 4, 1, 2])
z = z - left(logits, offset=w) - right(logits, offset=w)
z = z / ((1 + index[:w]) * (w - index[:w]))[:, None, None]
frames.append(z)
z = semiring.sum(z, dim=-1)
z = semiring.prod(z, dim=-1)
xyz = semiring.mul(z, semiring.mul(left(chart, offset=w), right(chart, offset=w)))
score = semiring.sum(xyz, dim=-1)
diag_scatter(chart, score, offset=w)
index = torch.arange(b, dtype=torch.long, device=chart.device)
return chart[index, 0, token_sizes - 1], frames
class Distrubition(object):
def __init__(self, logits: Tensor, token_sizes: Tensor) -> None:
super(Distrubition, self).__init__()
self.logits = logits
self.token_sizes = token_sizes
@lazy_property
def log_partitions(self):
partitions, frames = cky_partitions(
logits=self.logits,
token_sizes=self.token_sizes,
semiring=Log,
)
return partitions, frames
@lazy_property
def max(self):
partitions, frames = cky_partitions(
logits=self.logits,
token_sizes=self.token_sizes,
semiring=Max,
)
return partitions, frames
@lazy_property
def marginals(self) -> Frames:
partitions, frames = self.log_partitions
return torch.autograd.grad(
partitions, frames, torch.ones_like(partitions),
create_graph=True, retain_graph=True,
only_inputs=True, allow_unused=True,
)
@lazy_property
def grads(self) -> Frames:
partitions, frames = self.max
return torch.autograd.grad(
partitions, frames, torch.ones_like(partitions),
create_graph=False, retain_graph=False,
only_inputs=True, allow_unused=True,
)
@staticmethod
def gather(marginals: Frames, grads: Frames, spans: Tensor):
b, _, _, k, _ = marginals[0].size()
xs, ys, zs = [], [], []
for w, (x, grad) in enumerate(zip(marginals, grads)):
mask, y = grad.max(dim=-1, keepdim=True)
mask = mask.sum(dim=-2, keepdim=True) > 0
z = diag(spans, offset=w)[..., None, None, None]
xs.append(torch.masked_select(x, mask))
ys.append(torch.masked_select(y, mask))
zs.append(torch.masked_select(z, mask))
xs = torch.cat(xs, dim=0).view((-1, k, 2))
ys = torch.cat(ys, dim=0).view((-1, k))
zs = torch.cat(zs, dim=0)
return xs, ys, zs
@lazy_property
def argmax(self) -> C:
b, t, _, _, _ = self.grads[0].size()
b = torch.arange(b, dtype=torch.long, device=self.grads[0].device)
x = torch.arange(t, dtype=torch.long, device=self.grads[0].device)
y = torch.arange(t, dtype=torch.long, device=self.grads[0].device)
b, x, y = torch.broadcast_tensors(b[:, None, None], x[None, :, None], y[None, None, :])
data = []
for w, grad in enumerate(self.grads):
mask, z = grad.max(dim=-1, keepdim=False)
mask = mask.sum(dim=-1, keepdim=False) > 0
data.append(torch.stack([
torch.masked_select(diag(b, offset=w)[..., None], mask),
torch.masked_select(diag(x, offset=w)[..., None], mask),
torch.masked_select(diag(y, offset=w)[..., None], mask),
torch.masked_select(bits_to_long(z), mask),
], dim=-1))
data = torch.cat(data, dim=0)
b = torch.argsort(data[..., 0], dim=0, descending=False)
return C(data=data[b, 1:], token_sizes=self.token_sizes * 2 - 1)
class HashLayer(nn.Module):
def __init__(self, config: ParserkerConfig) -> None:
super(HashLayer, self).__init__()
self.num_bits = config.num_bits
self.bit_size = (config.hidden_size + config.num_bits - 1) // config.num_bits
self.scale = self.bit_size ** -0.5
self.q_proj = nn.Linear(config.hidden_size, self.num_bits * self.bit_size, bias=True)
self.k_proj = nn.Linear(config.hidden_size, self.num_bits * self.bit_size, bias=True)
def forward(self, q: Tensor, k: Tensor):
q = self.q_proj(q).unflatten(dim=-1, sizes=(self.num_bits, 1, self.bit_size))
k = self.k_proj(k).unflatten(dim=-1, sizes=(self.num_bits, self.bit_size, 1))
return (q[:, :, None] @ k[:, None, :]).flatten(start_dim=-3).transpose(1, 2) * self.scale
class ParserkerModel(PreTrainedModel):
config_class = ParserkerConfig
base_model_prefix = "backbone"
_tied_weights_keys = {}
def __init__(self, config: ParserkerConfig, **kwargs):
super(ParserkerModel, self).__init__(config=config, **kwargs)
self.pad_token_id = config.pad_token_id
self.num_bits = config.num_bits
self.backbone = RobertaModel(config, add_pooling_layer=False)
self.hash_layer = HashLayer(config)
@property
def all_tied_weights_keys(self):
return getattr(self, "_tied_weights_keys", [])
def forward(self, input_ids: Z, duration: Z) -> Tensor:
out = self.backbone.forward(
input_ids=input_ids.left(self.pad_token_id).data,
attention_mask=input_ids.bmask(),
return_dict=True,
)
tensor = L(data=out.last_hidden_state, token_sizes=input_ids.cat().token_sizes)
tensor, token_sizes = tensor.seg(duration, segment_mean).trunc((1, 1))
logits = self.hash_layer(tensor, tensor)
return L(data=logits, token_sizes=token_sizes)
def parse(self, input_ids: Z, duration: C):
logits, token_sizes = self(input_ids, duration)
logits = logits.clone().requires_grad_(True)
dist = Distrubition(logits=logits, token_sizes=token_sizes)
return dist.argmax
def to_tree(self, words, spans) -> Tree:
stack = []
for x, y, z in sorted(spans, key=lambda item: (item[0], -item[1]), reverse=True):
children = []
while len(stack) > 0:
xx, yy, zz = stack.pop()
if x <= xx and yy <= y:
children.append(zz)
else:
stack.append((xx, yy, zz))
break
if len(children) == 0:
children = ['__tok']
stack.append((x, y, Tree(to_hex(z, self.num_bits), children)))
[(_, _, tree)] = stack
for index in range(len(tree.leaves())):
position = tree.leaf_treeposition(index)
tree[position] = words[index]
return tree
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