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import torch
from model.memory import BaseMemory
from pytorch_utils.modules import MLP
import torch.nn as nn
from omegaconf import DictConfig
from typing import Dict, Tuple, List
from torch import Tensor
from tqdm import tqdm
import math
class EntityMemory(BaseMemory):
"""Module for clustering proposed mention spans using Entity-Ranking paradigm."""
def __init__(
self, config: DictConfig, span_emb_size: int, drop_module: nn.Module
) -> None:
super(EntityMemory, self).__init__(config, span_emb_size, drop_module)
self.mem_type: DictConfig = config.mem_type
def forward_training(
self,
ment_boundaries: Tensor,
mention_emb_list: List[Tensor],
rep_emb_list: List[Tensor],
gt_actions: List[Tuple[int, str]],
metadata: Dict,
) -> List[Tensor]:
"""
Forward pass during coreference model training where we use teacher-forcing.
Args:
ment_boundaries: Mention boundaries of proposed mentions
mention_emb_list: Embedding list of proposed mentions
gt_actions: Ground truth clustering actions
metadata: Metadata such as document genre
Returns:
coref_new_list: Logit scores for ground truth actions.
"""
assert (
len(rep_emb_list) != 0
), "There are no entity representations, should not happen."
# Initialize memory
coref_new_list = []
mem_vectors, mem_vectors_init, ent_counter, last_mention_start = (
self.initialize_memory(rep=rep_emb_list)
)
for ment_idx, (ment_emb, (gt_cell_idx, gt_action_str)) in enumerate(
zip(mention_emb_list, gt_actions)
):
ment_start, ment_end = ment_boundaries[ment_idx]
if self.config.num_feats != 0:
feature_embs = self.get_feature_embs(
ment_start, last_mention_start, ent_counter, metadata
)
else:
feature_embs = torch.empty(mem_vectors.shape[0], 0, device=self.device)
coref_new_scores = self.get_coref_new_scores(
ment_emb, mem_vectors, mem_vectors_init, ent_counter, feature_embs
)
coref_new_list.append(coref_new_scores)
# Teacher forcing
action_str, cell_idx = gt_action_str, gt_cell_idx
num_ents: int = int(torch.sum((ent_counter > 0).long()).item())
cell_mask: Tensor = (
torch.arange(start=0, end=num_ents, device=self.device)
== torch.tensor(cell_idx)
).float()
mask = torch.unsqueeze(cell_mask, dim=1)
mask = mask.repeat(1, self.mem_size)
## Update memory if action is cluster and memory is not static
if action_str == "c" and self.config.type != "static":
coref_vec = self.coref_update(
ment_emb, mem_vectors, cell_idx, ent_counter
)
mem_vectors = mem_vectors * (1 - mask) + mask * coref_vec
ent_counter[cell_idx] = ent_counter[cell_idx] + 1
last_mention_start[cell_idx] = ment_start
return coref_new_list
def forward(
self,
ment_boundaries: Tensor,
mention_emb_list: List[Tensor],
rep_emb_list: List[Tensor],
gt_actions: List[Tuple[int, str]],
metadata: Dict,
teacher_force: False,
memory_init=None,
):
"""Forward pass for clustering entity mentions during inference/evaluation.
Args:
ment_boundaries: Start and end token indices for the proposed mentions.
mention_emb_list: Embedding list of proposed mentions
metadata: Metadata features such as document genre embedding
memory_init: Initializer for memory. For streaming coreference, we can pass the previous
memory state via this dictionary
Returns:
pred_actions: List of predicted clustering actions.
mem_state: Current memory state.
"""
## Check length of mention_emb_list == gt_action
assert len(mention_emb_list) == len(gt_actions)
# Initialize memory
if memory_init is not None:
mem_vectors, mem_vectors_init, ent_counter, last_mention_start = (
self.initialize_memory(**memory_init, rep=rep_emb_list)
)
else:
mem_vectors, mem_vectors_init, ent_counter, last_mention_start = (
self.initialize_memory(rep=rep_emb_list)
)
pred_actions = [] # argmax actions
coref_scores_list = []
## Tensorized approach for static method
if self.config.type == "static":
batch_size = self.config.batch_size
### Mention Emb list gets batched in batch size
num_batches = len(mention_emb_list) // batch_size + int(
len(mention_emb_list) % batch_size != 0
)
for i in range(num_batches):
print("Batch Number: ", i)
start_idx = i * batch_size
end_idx = min((i + 1) * batch_size, len(mention_emb_list))
num_elements = end_idx - start_idx
if ent_counter.size() == 0:
next_cell_idx, next_action_str = 0, "o"
pred_actions.extend(
[(next_cell_idx, next_action_str) * num_elements]
)
continue
ment_emb_tensor = torch.stack(
mention_emb_list[start_idx:end_idx], dim=0
)
ment_start, ment_end = (
ment_boundaries[start_idx:end_idx, 0],
ment_boundaries[start_idx:end_idx, 1],
)
if self.config.num_feats != 0:
feature_embs = self.get_feature_embs_tensorized(
ment_start, last_mention_start, ent_counter, metadata
) ## [B,D,20]
else:
feature_embs = torch.empty(
ment_start.shape[0], mem_vectors.shape[0], 0, device=self.device
) ## [B,D,20]
coref_new_scores = self.get_coref_new_scores_tensorized(
ment_emb_tensor,
mem_vectors,
mem_vectors_init,
ent_counter,
feature_embs,
)
coref_copy = coref_new_scores.clone().detach().cpu()
coref_scores_list.extend(coref_copy)
assigned_cluster = self.assign_cluster_tensorized(coref_new_scores)
gt_actions_batch = gt_actions[start_idx:end_idx]
if teacher_force:
pred_actions.extend(gt_actions_batch)
else:
pred_actions.extend(assigned_cluster)
else:
for ment_idx, ment_emb in enumerate(mention_emb_list):
if ent_counter.size() == 0:
next_cell_idx, next_action_str = 0, "o"
pred_actions.append((next_cell_idx, next_action_str))
continue
ment_start, ment_end = ment_boundaries[ment_idx]
if self.config.num_feats != 0:
feature_embs = self.get_feature_embs(
ment_start, last_mention_start, ent_counter, metadata
)
else:
feature_embs = torch.empty(
mem_vectors.shape[0], 0, device=self.device
)
coref_new_scores = self.get_coref_new_scores(
ment_emb, mem_vectors, mem_vectors_init, ent_counter, feature_embs
)
coref_copy = coref_new_scores.clone().detach().cpu()
coref_scores_list.append(coref_copy)
pred_cell_idx, pred_action_str = self.assign_cluster(coref_new_scores)
if teacher_force:
next_cell_idx, next_action_str = gt_actions[ment_idx]
pred_actions.append(gt_actions[ment_idx])
else:
next_cell_idx, next_action_str = pred_cell_idx, pred_action_str
pred_actions.append((pred_cell_idx, pred_action_str))
if next_action_str == "c":
coref_vec = self.coref_update(
ment_emb, mem_vectors, next_cell_idx, ent_counter
)
mem_vectors[next_cell_idx] = coref_vec
ent_counter[next_cell_idx] = ent_counter[next_cell_idx] + 1
last_mention_start[next_cell_idx] = ment_start
mem_state = {
"mem": mem_vectors,
"mem_init": mem_vectors_init,
"ent_counter": ent_counter,
"last_mention_start": last_mention_start,
}
return pred_actions, mem_state, coref_scores_list
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