nama-test4 / similarity_model.py
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import random
import pandas as pd
import numpy as np
from tqdm import tqdm
from copy import copy,deepcopy
from collections import Counter
import torch
from torch import nn
from torch.utils.data import DataLoader
from transformers import get_cosine_schedule_with_warmup,get_linear_schedule_with_warmup, logging
from transformers.modeling_utils import PreTrainedModel
from .match_groups import MatchGroups
from .scoring import score_predicted
from .scoring_model import SimilarityScore
from .embeddings import Embeddings
from .embedding_model import EmbeddingModel
from .configuration import SimilarityModelConfig
logging.set_verbosity_error()
class ExponentWeights():
def __init__(self, config,**kwargs):
self.exponent = config.get("weighting_exponent", 0.5)
def __call__(self,counts):
return counts**self.exponent
class SimilarityModel(PreTrainedModel):
config_class = SimilarityModelConfig
"""
A combined embedding/scorer model that produces Embeddings objects
as its primary output.
- train() jointly optimizes the embedding_model and score_model using
contrastive learning to learn from a training MatchGroups.
"""
def __init__(self, config, **kwargs):
super().__init__(config)
self.embedding_model = EmbeddingModel(config.embedding_model_config, **kwargs)
self.score_model = SimilarityScore(config.score_model_config, **kwargs)
self.weighting_function = ExponentWeights(config.weighting_function_config, **kwargs)
self.config = config
self.to(config.device)
def to(self,device):
super().to(device)
self.embedding_model.to(device)
self.score_model.to(device)
#self.device = device
def save(self,savefile):
torch.save({'metadata': self.config, 'state_dict': self.state_dict()}, savefile)
@torch.no_grad()
def embed(self,input,to=None,batch_size=64,progress_bar=True,**kwargs):
"""
Construct an Embeddings object from input strings or a MatchGroups
"""
if to is None:
to = self.device
if isinstance(input, MatchGroups):
strings = input.strings()
counts = torch.tensor([input.counts[s] for s in strings],device=self.device).float().to(to)
else:
strings = list(input)
counts = torch.ones(len(strings),device=self.device).float().to(to)
input_loader = DataLoader(strings,batch_size=batch_size,num_workers=0)
self.embedding_model.eval()
V = None
batch_start = 0
with tqdm(total=len(strings),delay=1,desc='Embedding strings',disable=not progress_bar) as pbar:
for batch_strings in input_loader:
v = self.embedding_model(batch_strings).detach().to(to)
if V is None:
# Use v to determine dim and dtype of pre-allocated embedding tensor
# (Pre-allocating avoids duplicating tensors with a big .cat() operation)
V = torch.empty(len(strings),v.shape[1],device=to,dtype=v.dtype)
V[batch_start:batch_start+len(batch_strings),:] = v
pbar.update(len(batch_strings))
batch_start += len(batch_strings)
score_model = copy(self.score_model)
score_model.load_state_dict(self.score_model.state_dict())
score_model.to(to)
weighting_function = deepcopy(self.weighting_function)
return Embeddings(strings=strings,
V=V.detach(),
counts=counts.detach(),
score_model=score_model,
weighting_function=weighting_function,
device=to)
def train(self,training_groupings,max_epochs=1,batch_size=8,
score_decay=0,regularization=0,
transformer_lr=1e-5,projection_lr=1e-5,score_lr=10,warmup_frac=0.1,
max_grad_norm=1,dropout=False,
validation_groupings=None,target='F1',restore_best=True,val_seed=None,
validation_interval=1000,early_stopping=True,early_stopping_patience=3,
verbose=False,progress_bar=True,
**kwargs):
"""
Train the embedding_model and score_model to predict match probabilities
using the training_groupings as a source of "correct" matches.
Training algorithm uses contrastive learning with hard-positive
and hard-negative mining to fine tune the embedding model to place
matched strings near to each other in embedding space, while
simulataneously calibrating the score_model to predict the match
probabilities as a function of cosine distance
"""
if validation_groupings is None:
early_stopping = False
restore_best = False
num_training_steps = max_epochs*len(training_groupings)//batch_size
num_warmup_steps = int(warmup_frac*num_training_steps)
if transformer_lr or projection_lr:
embedding_optimizer = self.embedding_model.config_optimizer(transformer_lr,projection_lr)
embedding_scheduler = get_cosine_schedule_with_warmup(
embedding_optimizer,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_training_steps)
if score_lr:
score_optimizer = self.score_model.config_optimizer(score_lr)
score_scheduler = get_linear_schedule_with_warmup(
score_optimizer,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_training_steps)
step = 0
self.history = []
self.val_scores = []
for epoch in range(max_epochs):
global_embeddings = self.embed(training_groupings)
strings = global_embeddings.strings
V = global_embeddings.V
w = global_embeddings.w
groups = torch.tensor([global_embeddings.string_map[training_groupings[s]] for s in strings],device=self.device)
# Normalize weights to make learning rates more general
if w is not None:
w = w/w.mean()
shuffled_ids = list(range(len(strings)))
random.shuffle(shuffled_ids)
if dropout:
self.embedding_model.train()
else:
self.embedding_model.eval()
for batch_start in tqdm(range(0,len(strings),batch_size),desc=f'training epoch {epoch}',disable=not progress_bar):
h = {'epoch':epoch,'step':step}
batch_i = shuffled_ids[batch_start:batch_start+batch_size]
# Recycle ids from the beginning to pad the last batch if necessary
if len(batch_i) < batch_size:
batch_i = batch_i + shuffled_ids[:(batch_size-len(batch_i))]
"""
Find highest loss match for each batch string (global search)
Note: If we compute V_i with dropout enabled, it will add noise
to the embeddings and prevent the same pairs from being selected
every time.
"""
V_i = self.embedding_model(strings[batch_i])
# Update global embedding cache
V[batch_i,:] = V_i.detach()
with torch.no_grad():
global_X = [email protected]
global_Y = (groups[batch_i][:,None] == groups[None,:]).float()
if w is not None:
global_W = torch.outer(w[batch_i],w)
else:
global_W = None
# Train scoring model only
if score_lr:
# Make sure gradients are enabled for score model
self.score_model.requires_grad_(True)
global_loss = self.score_model.loss(global_X,global_Y,weights=global_W,decay=score_decay)
score_optimizer.zero_grad()
global_loss.nanmean().backward()
torch.nn.utils.clip_grad_norm_(self.score_model.parameters(),max_norm=max_grad_norm)
score_optimizer.step()
score_scheduler.step()
h['score_lr'] = score_optimizer.param_groups[0]['lr']
h['global_mean_cos'] = global_X.mean().item()
try:
h['score_alpha'] = self.score_model.alpha.item()
except:
pass
else:
with torch.no_grad():
global_loss = self.score_model.loss(global_X,global_Y)
h['global_loss'] = global_loss.detach().nanmean().item()
# Train embedding model
if (transformer_lr or projection_lr) and step <= num_warmup_steps + num_training_steps:
# Turn off score model updating - only want to train embedding here
self.score_model.requires_grad_(False)
# Select hard training examples
with torch.no_grad():
batch_j = global_loss.argmax(dim=1).flatten()
if w is not None:
batch_W = torch.outer(w[batch_i],w[batch_j])
else:
batch_W = None
# Train the model on the selected high-loss pairs
V_j = self.embedding_model(strings[batch_j.tolist()])
# Update global embedding cache
V[batch_j,:] = V_j.detach()
batch_X = V_i@V_j.T
batch_Y = (groups[batch_i][:,None] == groups[batch_j][None,:]).float()
h['batch_obs'] = len(batch_i)*len(batch_j)
batch_loss = self.score_model.loss(batch_X,batch_Y,weights=batch_W)
if regularization:
# Apply Global Orthogonal Regularization from https://arxiv.org/abs/1708.06320
gor_Y = (groups[batch_i][:,None] != groups[batch_i][None,:]).float()
gor_n = gor_Y.sum()
if gor_n > 1:
gor_X = (V_i@V_i.T)*gor_Y
gor_m1 = 0.5*gor_X.sum()/gor_n
gor_m2 = 0.5*(gor_X**2).sum()/gor_n
batch_loss += regularization*(gor_m1 + torch.clamp(gor_m2 - 1/self.embedding_model.d,min=0))
h['batch_nan'] = torch.isnan(batch_loss.detach()).sum().item()
embedding_optimizer.zero_grad()
batch_loss.nanmean().backward()
torch.nn.utils.clip_grad_norm_(self.parameters(),max_norm=max_grad_norm)
embedding_optimizer.step()
embedding_scheduler.step()
h['transformer_lr'] = embedding_optimizer.param_groups[1]['lr']
h['projection_lr'] = embedding_optimizer.param_groups[-1]['lr']
# Save stats
h['batch_loss'] = batch_loss.detach().mean().item()
h['batch_pos_target'] = batch_Y.detach().mean().item()
self.history.append(h)
step += 1
if (validation_groupings is not None) and not (step % validation_interval):
validation = len(self.validation_scores)
val_scores = self.test(validation_groupings)
val_scores['step'] = step - 1
val_scores['epoch'] = epoch
val_scores['validation'] = validation
self.validation_scores.append(val_scores)
# Print validation stats
if verbose:
print(f'\nValidation results at step {step} (current epoch {epoch})')
for k,v in val_scores.items():
print(f' {k}: {v:.4f}')
print(list(self.score_model.named_parameters()))
# Update best saved model
if restore_best:
if val_scores[target] >= max(h[target] for h in self.validation_scores):
best_state = deepcopy({
'state_dict':self.state_dict(),
'val_scores':val_scores
})
if early_stopping and (validation - best_state['val_scores']['validation'] > early_stopping_patience):
print(f'Stopping training ({early_stopping_patience} validation checks since best validation score)')
break
if restore_best:
print(f"Restoring to best state (step {best_state['val_scores']['step']}):")
for k,v in best_state['val_scores'].items():
print(f' {k}: {v:.4f}')
self.to('cpu')
self.load_state_dict(best_state['state_dict'])
self.to(self.device)
return pd.DataFrame(self.history)
def unite_similar(self,input,**kwargs):
embeddings = self.embed(input,**kwargs)
return embeddings.unite_similar(**kwargs)
def test(self,gold_groupings, threshold=0.5, **kwargs):
embeddings = self.embed(gold_groupings, **kwargs)
if (isinstance(threshold, float)):
predicted = embeddings.unite_similar(threshold=threshold, **kwargs)
scores = score_predicted(predicted, gold_groupings, use_counts=True)
return scores
results = []
for thres in threshold:
predicted = embeddings.unite_similar(threshold=thres, **kwargs)
scores = score_predicted(predicted, gold_groupings, use_counts=True)
scores["threshold"] = thres
results.append(scores)
return results
def load_similarity_model(f,map_location='cpu',*args,**kwargs):
checkpoint = torch.load(f, map_location=map_location, **kwargs)
metadata = checkpoint['metadata']
state_dict = checkpoint['state_dict']
model = SimilarityModel(config=metadata)
model.load_state_dict(state_dict)
return model
#return torch.load(f,map_location=map_location,**kwargs)