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import unittest
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from torch.autograd import Variable
@dataclass
class CRFOutput:
loss: Optional[torch.tensor]
real_path_score: Optional[torch.tensor]
total_score: torch.tensor
best_path_score: torch.tensor
best_path: torch.tensor
class MaskedCRFLoss(nn.Module):
__constants__ = ["num_tags", "mask_id"]
num_tags: int
mask_id: int
def __init__(self, num_tags: int, mask_id: int = 0):
super().__init__()
self.num_tags = num_tags
self.mask_id = mask_id
self.transitions = nn.Parameter(torch.randn(num_tags, num_tags))
self.start_transitions = nn.Parameter(torch.randn(num_tags))
self.stop_transitions = nn.Parameter(torch.randn(num_tags))
def extra_repr(self) -> str:
s = "num_tags={num_tags}, mask_id={mask_id}"
return s.format(**self.__dict__)
def forward(self, emissions, tags, mask, return_best_path=False):
# emissions: (seq_length, batch_size, num_tags)
# tags: (seq_length, batch_size)
# mask: (seq_length, batch_size)
seq_length, batch_size = tags.shape
mask = mask.float()
# set return_best_path as True always during eval.
# During training it slows things down as best path is not needed
if not self.training:
return_best_path = True
# Compute the total likelihood
total_score, best_path_score, best_path = self.compute_log_partition_function(
emissions, mask, return_best_path=return_best_path
)
if tags is None:
return CRFOutput(None, None, total_score, best_path_score, best_path)
# Compute the likelihood of the real path
real_path_score = torch.zeros(batch_size).to(tags.device)
real_path_score += self.start_transitions[tags[0]] # batch_size
for i in range(1, seq_length):
current_tag = tags[i]
real_path_score += self.transitions[tags[i - 1], current_tag] * mask[i]
real_path_score += emissions[i, range(batch_size), current_tag] * mask[i]
# Transition to STOP_TAG
real_path_score += self.stop_transitions[tags[-1]]
# Return the negative log likelihood
loss = torch.mean(total_score - real_path_score)
return CRFOutput(loss, real_path_score, total_score, best_path_score, best_path)
def compute_log_partition_function(self, emissions, mask, return_best_path=False):
init_alphas = self.start_transitions + emissions[0] # (batch_size, num_tags)
forward_var = init_alphas
forward_viterbi_var = init_alphas
# backpointers holds the best tag id at each time step, we accumulate these in reverse order
backpointers = []
for i, emission in enumerate(emissions[1:, :, :], 1):
broadcast_emission = emission.unsqueeze(2) # (batch_size, num_tags, 1)
broadcast_transmissions = self.transitions.unsqueeze(
0
) # (1, num_tags, num_tags)
# Compute next
next_tag_var = (
forward_var.unsqueeze(1) + broadcast_emission + broadcast_transmissions
)
next_tag_viterbi_var = (
forward_viterbi_var.unsqueeze(1)
+ broadcast_emission
+ broadcast_transmissions
)
next_unmasked_forward_var = torch.logsumexp(next_tag_var, dim=2)
viterbi_scores, best_next_tags = torch.max(next_tag_viterbi_var, dim=2)
# If mask == 1 use the next_unmasked_forward_var else copy the forward_var
# Update forward_var
forward_var = (
mask[i].unsqueeze(-1) * next_unmasked_forward_var
+ (1 - mask[i]).unsqueeze(-1) * forward_var
)
# Update viterbi with mask
forward_viterbi_var = (
mask[i].unsqueeze(-1) * viterbi_scores
+ (1 - mask[i]).unsqueeze(-1) * forward_viterbi_var
)
backpointers.append(best_next_tags)
# Transition to STOP_TAG
terminal_var = forward_var + self.stop_transitions
terminal_viterbi_var = forward_viterbi_var + self.stop_transitions
alpha = torch.logsumexp(terminal_var, dim=1)
best_path_score, best_final_tags = torch.max(terminal_viterbi_var, dim=1)
best_path = None
if return_best_path:
# backtrace
best_path = [best_final_tags]
for bptrs, mask_data in zip(reversed(backpointers), torch.flip(mask, [0])):
best_tag_id = torch.gather(
bptrs, 1, best_final_tags.unsqueeze(1)
).squeeze(1)
best_final_tags.masked_scatter_(
mask_data.to(dtype=torch.bool),
best_tag_id.masked_select(mask_data.to(dtype=torch.bool)),
)
best_path.append(best_final_tags)
# Reverse the order because we were appending in reverse
best_path = torch.stack(best_path[::-1])
best_path = best_path.where(mask == 1, -100)
return alpha, best_path_score, best_path
def viterbi_decode(self, emissions, mask):
seq_len, batch_size, num_tags = emissions.shape
# backpointers holds the best tag id at each time step, we accumulate these in reverse order
backpointers = []
# Initialize the viterbi variables in log space
init_vvars = self.start_transitions + emissions[0] # (batch_size, num_tags)
forward_var = init_vvars
for i, emission in enumerate(emissions[1:, :, :], 1):
broadcast_emission = emission.unsqueeze(2)
broadcast_transmissions = self.transitions.unsqueeze(0)
next_tag_var = (
forward_var.unsqueeze(1) + broadcast_emission + broadcast_transmissions
)
viterbi_scores, best_next_tags = torch.max(next_tag_var, 2)
# If mask == 1 use the next_unmasked_forward_var else copy the forward_var
forward_var = (
mask[i].unsqueeze(-1) * viterbi_scores
+ (1 - mask[i]).unsqueeze(-1) * forward_var
)
backpointers.append(best_next_tags)
# Transition to STOP_TAG
terminal_var = forward_var + self.stop_transitions
best_path_score, best_final_tags = torch.max(terminal_var, dim=1)
# backtrace
best_path = [best_final_tags]
for bptrs, mask_data in zip(reversed(backpointers), torch.flip(mask, [0])):
best_tag_id = torch.gather(bptrs, 1, best_final_tags.unsqueeze(1)).squeeze(
1
)
best_final_tags.masked_scatter_(
mask_data.to(dtype=torch.bool),
best_tag_id.masked_select(mask_data.to(dtype=torch.bool)),
)
best_path.append(best_final_tags)
# Reverse the order because we were appending in reverse
best_path = torch.stack(best_path[::-1])
best_path = best_path.where(mask == 1, -100)
return best_path, best_path_score
class MaskedCRFLossTest(unittest.TestCase):
def setUp(self):
self.num_tags = 5
self.mask_id = 0
self.crf_model = MaskedCRFLoss(self.num_tags, self.mask_id)
self.seq_length, self.batch_size = 11, 5
# Making up some inputs
# emissions = Variable(torch.randn(seq_length, batch_size, num_tags))
# tags = Variable(torch.randint(num_tags, (seq_length, batch_size)))
# mask = Variable(torch.ones(seq_length, batch_size))
self.emissions = torch.randn(self.seq_length, self.batch_size, self.num_tags)
self.tags = torch.randint(self.num_tags, (self.seq_length, self.batch_size))
# mask = torch.ones(seq_length, batch_size)
self.mask = torch.randint(2, (self.seq_length, self.batch_size))
def test_forward(self):
# Checking if forward runs successfully
try:
output = self.crf_model(self.emissions, self.tags, self.mask)
print("Forward function runs successfully!")
except Exception as e:
print("Forward function couldn't run successfully:", e)
def test_viterbi_decode(self):
# Checking if viterbi_decode runs successfully
try:
path, best_path_score = self.crf_model.viterbi_decode(
self.emissions, self.mask
)
print(path.T)
print("Viterbi decoding function runs successfully!")
except Exception as e:
print("Viterbi decoding function couldn't run successfully:", e)
def test_forward_output(self):
# Simple check if losses are non-negative
output = self.crf_model(self.emissions, self.tags, self.mask)
loss = output.loss
self.assertTrue((loss > 0).all())
def test_compute_log_partition_function_output(self):
# Simply checking if the output is non-negative
(
partition,
best_path_score,
best_path,
) = self.crf_model.compute_log_partition_function(self.emissions, self.mask)
self.assertTrue((partition > 0).all())
def test_viterbi_decode_output(self):
print(self.mask.T)
# Check whether the output shape is correct and lies within valid tag range
path, best_path_score = self.crf_model.viterbi_decode(self.emissions, self.mask)
print(path.T)
self.assertEqual(
path.shape, (self.seq_length, self.batch_size)
) # checking dimensions
self.assertTrue(
((0 <= path) | (path == -100)).all() and (path < self.num_tags).all()
) # checking tag validity
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