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import torch
import transformers
import jellyfish
from tqdm import tqdm
from transformers import AutoModelForMaskedLM
from transformers.utils import ModelOutput
import numpy as np
from .poet_utils import StropheParams
from torch.utils.data import DataLoader, Dataset
from pytorch_optimizer import SAM
class ValidatorInterface(torch.nn.Module):
"""Pytorch Model Interface. Abstract class for all validators
Args:
torch (_type_): Is child of torch.nn.Module for integration with torch and huggingface
"""
def __init__(self, *args, **kwargs) -> None:
""" Constructor. As child Class needs to construct Parent
"""
super().__init__(*args, **kwargs)
def forward(self, input_ids=None, attention_mask=None, *args, **kwargs):
"""Compute model output and model loss
Args:
input_ids (_type_, optional): Model inputs. Defaults to None.
attention_mask (_type_, optional): Attention mask where padding starts. Defaults to None.
Raises:
NotImplementedError: Abstract class
"""
raise NotImplementedError()
def predict_state(self, input_ids=None, *args, **kwargs):
"""Compute model outputs
Args:
input_ids (_type_, optional): Model inputs. Defaults to None.
Raises:
NotImplementedError: Abstract class
"""
raise NotImplementedError()
def validate_model(self, input_ids=None, *args, **kwargs):
"""Validate model given some labels, Doesn't use loss
Args:
input_ids (_type_, optional): Model inputs. Defaults to None.
Raises:
NotImplementedError: Abstract class
"""
raise NotImplementedError()
class RhymeValidator(ValidatorInterface):
def __init__(self, pretrained_model, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.model = AutoModelForMaskedLM.from_pretrained(pretrained_model, output_hidden_states=True)
self.config = self.model.config
self.model_size = self.config.hidden_size
self.rhyme_regressor = torch.nn.Linear(self.model_size, len(StropheParams.RHYME)) # Common Rhyme Type
self.loss_fnc = torch.nn.CrossEntropyLoss(label_smoothing=0.0, weight=torch.tensor([1, 1, 1.5, 1.5, 1.5, 1.5,
2, 2, 2, 3, 3, 3,
3, 3, 3, 3, 4, 4,
5, 5, 5, 5, 7, 7,
7, 7, 7, 8, 8, 8,
9, 9, 9, 10, 10, 10,
12,12, 12, 12, 12, 12,
15,15,1.5]) )
def forward(self, input_ids=None, attention_mask=None, rhyme=None, *args, **kwargs):
outputs = self.model(input_ids=input_ids, attention_mask=attention_mask, labels=input_ids.type(torch.LongTensor))
last_hidden = outputs['hidden_states'][-1]
rhyme_regression = self.rhyme_regressor((last_hidden[:,0,:].view(-1, self.model_size)))
softmaxed = torch.softmax(rhyme_regression, dim=1)
rhyme_loss = self.loss_fnc(softmaxed, rhyme)
return ModelOutput(loss=rhyme_loss + outputs.loss, model_output=softmaxed)
def predict_state(self, input_ids=None, *args, **kwargs):
outputs = self.model(input_ids=input_ids)
last_hidden = outputs['hidden_states'][-1]
rhyme_regression = self.rhyme_regressor((last_hidden[:,0,:].view(-1, self.model_size)))
softmaxed = torch.softmax(rhyme_regression, dim=1)
return softmaxed
def validate_model(self, input_ids=None, rhyme=None, k:int = 2,*args, **kwargs):
outputs = self.model(input_ids=input_ids)
last_hidden = outputs['hidden_states'][-1]
rhyme_regression = self.rhyme_regressor((last_hidden[:,0,:].view(-1, self.model_size)))
softmaxed = torch.softmax(rhyme_regression, dim=1)
softmaxed = softmaxed.flatten().cpu()
predicted_val = torch.argmax(softmaxed)
predicted_top_k = torch.topk(softmaxed, k).indices
label_val = torch.argmax(rhyme.flatten())
validation_true_val = (label_val == predicted_val).float().sum().numpy()
top_k_presence = 0
if label_val in predicted_top_k:
top_k_presence = 1
levenshtein = jellyfish.levenshtein_distance(StropheParams.RHYME[predicted_val] if StropheParams.RHYME[predicted_val] != None else "", StropheParams.RHYME[label_val] if StropheParams.RHYME[label_val] != None else "")
hit_pred = softmaxed[label_val].detach().numpy()
return {"acc" : validation_true_val,
"top_k" : top_k_presence,
"lev_distance": levenshtein,
"predicted_label" : hit_pred
}
class MeterValidator(ValidatorInterface):
def __init__(self, pretrained_model, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.model = AutoModelForMaskedLM.from_pretrained(pretrained_model, output_hidden_states=True)
self.config = self.model.config
self.model_size = self.config.hidden_size
self.meter_regressor = torch.nn.Linear(self.model_size, len(StropheParams.METER)) # Meter Type
self.loss_fnc = torch.nn.CrossEntropyLoss(label_smoothing=0.0, weight=torch.tensor([1, 1.5, 5, 10, 10, 20, 5, 20, 20, 0]))
def forward(self, input_ids=None, attention_mask=None, metre_ids=None, *args, **kwargs):
outputs = self.model(input_ids=input_ids, attention_mask=attention_mask, labels=input_ids.type(torch.LongTensor))
last_hidden = outputs['hidden_states'][-1]
meter_regression = self.meter_regressor((last_hidden[:,0,:].view(-1, self.model_size)))
softmaxed = torch.softmax(meter_regression, dim=1)
meter_loss = self.loss_fnc(softmaxed, metre_ids)
return ModelOutput(loss=meter_loss + outputs.loss, model_output=softmaxed)
def predict_state(self, input_ids=None, *args, **kwargs):
outputs = self.model(input_ids=input_ids)
last_hidden = outputs['hidden_states'][-1]
meter_regression = self.meter_regressor((last_hidden[:,0,:].view(-1, self.model_size)))
softmaxed = torch.softmax(meter_regression, dim=1)
return softmaxed
def validate_model(self, input_ids=None, metre_ids=None, attention_mask=None, k: int=2,*args, **kwargs):
outputs = self.model(input_ids=input_ids, attention_mask=attention_mask )
last_hidden = outputs['hidden_states'][-1]
meter_regression = self.meter_regressor((last_hidden[:,0,:].view(-1, self.model_size)))
softmaxed = torch.softmax(meter_regression, dim=1)
softmaxed = softmaxed.flatten().cpu()
predicted_val = torch.argmax(softmaxed)
predicted_top_k = torch.topk(softmaxed, k).indices
label_val = torch.argmax(metre_ids.flatten())
validation_true_val = (label_val == predicted_val).float().sum().numpy()
top_k_presence = 0
if label_val in predicted_top_k:
top_k_presence = 1
hit_pred = softmaxed[label_val].detach().numpy()
return {"acc" : validation_true_val,
"top_k" : top_k_presence,
"predicted_label" : hit_pred
}
class YearValidator(ValidatorInterface):
def __init__(self, pretrained_model, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.model = AutoModelForMaskedLM.from_pretrained(pretrained_model, output_hidden_states=True)
self.config = self.model.config
self.model_size = self.config.hidden_size
self.year_era = torch.nn.Linear(self.model_size, len(StropheParams.YEAR))
self.softmax = torch.nn.Softmax(dim=-1)
self.year_val = torch.nn.Linear(self.model_size, 1) # Year Value
self.loss_fnc_era = torch.nn.CrossEntropyLoss(label_smoothing=0.0,weight=torch.tensor([10, 5, 3, 3, 1, 1, 1.5, 2, 5, 0]))
self.loss_fnc_val = torch.nn.L1Loss()
def forward(self, input_ids=None, attention_mask=None, year_bucket=None, year=None, *args, **kwargs):
outputs = self.model(input_ids=input_ids, attention_mask=attention_mask, labels=input_ids.type(torch.LongTensor))
last_hidden = outputs['hidden_states'][-1]
year_val = self.year_val((last_hidden[:,0,:].view(-1, self.model_size)))
year_val_loss = self.loss_fnc_val(year_val, year)
year_era = self.year_era((last_hidden[:,0,:].view(-1, self.model_size)))
year_era = self.softmax(year_era)
year_era_loss = self.loss_fnc_era(year_era, year_bucket)
return ModelOutput(loss=year_val_loss + year_era_loss + outputs.loss, model_output=(year_val, year_era))
def predict_state(self, input_ids=None, *args, **kwargs):
outputs = self.model(input_ids=input_ids)
last_hidden = outputs['hidden_states'][-1]
year_val = self.year_val((last_hidden[:,0,:].view(-1, self.model_size)))
return year_val
def validate_model(self, input_ids=None, year_bucket=None, k: int=2,*args, **kwargs):
outputs = self.model(input_ids=input_ids)
last_hidden = outputs['hidden_states'][-1]
year_val = self.year_val((last_hidden[:,0,:].view(-1, self.model_size)))
if hasattr(self, 'year_era'):
year_era = self.year_era((last_hidden[:,0,:].view(-1, self.model_size)))
year_era = self.softmax(year_era)
year_val = year_val.detach().flatten().cpu().numpy()
if hasattr(self, 'year_era'):
year_era = year_era.detach().flatten().cpu().numpy()
publish_vector = [1/(1 + abs(year - year_val[0])) for year in StropheParams.YEAR[:-1]] + [0]
publish_vector = np.asarray(publish_vector)/np.sum(publish_vector)
# Adding era prediction
if hasattr(self, 'year_era'):
publish_vector+= year_era
publish_vector = torch.tensor( np.asarray(publish_vector)/np.sum(publish_vector))
predicted_val = torch.argmax(publish_vector)
predicted_top_k = torch.topk(publish_vector, k).indices
label_val = torch.argmax(year_bucket.flatten())
validation_true_val = (label_val == predicted_val).float().sum().numpy()
top_k_presence = 0
if label_val in predicted_top_k:
top_k_presence = 1
hit_pred = publish_vector[label_val].detach().numpy()
distance = abs(label_val.numpy() - predicted_val.numpy())
return {"acc" : validation_true_val,
"top_k" : top_k_presence,
"predicted_label" : hit_pred,
"distance" : distance
}
class ValidatorTrainer:
def __init__(self, model: ValidatorInterface, args: dict, train_dataset: Dataset, data_collator, device):
self.model = model
self.args = args
self.epochs = 1 if "epochs" not in args.keys() else args["epochs"]
self.batch_size = 1 if "batch_size" not in args.keys() else args["batch_size"]
self.lr = 5e-5 if "lr" not in args.keys() else args["lr"]
self.weight_decay = 0.0 if "weight_decay" not in args.keys() else args['weight_decay']
self.train_loader = DataLoader(train_dataset, self.batch_size, True, collate_fn=data_collator)
# SAM Values
self.device = device
self.optimizer = SAM(self.model.parameters(), torch.optim.AdamW, lr=self.lr, weight_decay=self.weight_decay)
self.scheduler = transformers.get_constant_schedule_with_warmup(self.optimizer, 4 * len(train_dataset)//self.batch_size)
# GSAM Value
#self.device = device
#self.base_optim = AdamP(self.model.parameters(), lr=self.lr, weight_decay=self.weight_decay)
#self.scheduler = transformers.get_constant_schedule_with_warmup(self.base_optim, len(train_dataset)//self.batch_size)
#self.rho_scheduler= ProportionScheduler( self.scheduler, max_lr=self.lr)
#self.optimizer = GSAM(self.model.parameters(),self.base_optim, self.model, self.rho_scheduler, alpha=0.05)
def train(self):
for epoch in tqdm(range(self.epochs)):
self.model.train()
# SAM Attempt
for step, batch in enumerate(self.train_loader):
# First Pass
loss = self.model(input_ids=batch["input_ids"].to(self.device), attention_mask=batch["attention_mask"].to(self.device),
rhyme = None if batch["rhyme"] == None else batch["rhyme"].to(self.device),
metre_ids = None if batch["metre_ids"] == None else batch["metre_ids"].to(self.device),
year_bucket = None if batch["year_bucket"] == None else batch["year_bucket"].to(self.device),
year = None if batch["year"] == None else batch["year"].to(self.device))['loss']
loss.backward()
self.optimizer.first_step(zero_grad=True)
# Second Pass
loss = self.model(input_ids=batch["input_ids"].to(self.device), attention_mask=batch["attention_mask"].to(self.device),
rhyme = None if batch["rhyme"] == None else batch["rhyme"].to(self.device),
metre_ids = None if batch["metre_ids"] == None else batch["metre_ids"].to(self.device),
year_bucket = None if batch["year_bucket"] == None else batch["year_bucket"].to(self.device),
year = None if batch["year"] == None else batch["year"].to(self.device))['loss']
loss.backward()
self.optimizer.second_step(zero_grad=True)
self.scheduler.step()
# GSAM Attempt
#for step, batch in enumerate(self.train_loader):
# def closure():
# self.optimizer.base_optimizer.zero_grad()
# with torch.enable_grad():
# outputs = self.model(input_ids=batch["input_ids"].to(self.device), attention_mask=batch["attention_mask"].to(self.device),
# rhyme = None if batch["rhyme"] == None else batch["rhyme"].to(self.device),
# metre = None if batch["metre"] == None else batch["metre"].to(self.device))
# loss = torch.nn.functional.cross_entropy(outputs['model_output'].to(self.device),batch['rhyme'].to(self.device) if isinstance(self.model, RhymeValidator) else batch['metre'].to(self.device))
# loss.backward()
# return outputs['model_output'], loss.detach()
# predictions, loss = self.optimizer.step(closure)
# self.scheduler.step()
# self.optimizer.update_rho_t()
#
if step % 100 == 0:
print(f'Step {len(self.train_loader) * epoch + step}, loss : {loss.item()}', flush=True)
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