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CL_learner.py
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CL_learner.py
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import os
import torch
from torch.nn import CrossEntropyLoss
from random import sample
import pytorch_lightning as pl
from transformers import (AdamW, GPT2Tokenizer, GPT2LMHeadModel,T5Tokenizer, BartTokenizer, BartForConditionalGeneration, T5ForConditionalGeneration)
from model.adapterGPT2 import GPT2Adapter
from utils.dataloader import get_data_loaders, get_current_task_data, make_loader
from collections import defaultdict
class Seq2SeqToD(pl.LightningModule):
def __init__(self,args):
super().__init__()
if "t5" in args.model_checkpoint:
model = T5ForConditionalGeneration.from_pretrained(args.model_checkpoint)
tokenizer = T5Tokenizer.from_pretrained(args.model_checkpoint, bos_token="[bos]", eos_token="[eos]", sep_token="[sep]")
model.resize_token_embeddings(new_num_tokens=len(tokenizer))
elif "bart" in args.model_checkpoint:
model = BartForConditionalGeneration.from_pretrained(args.model_checkpoint)
tokenizer = BartTokenizer.from_pretrained(args.model_checkpoint, bos_token="[bos]", eos_token="[eos]", sep_token="[sep]")
model.resize_token_embeddings(new_num_tokens=len(tokenizer))
elif "gpt2" in args.model_checkpoint:
if(args.CL == "ADAPTER"):
model = GPT2Adapter.from_pretrained(args.model_checkpoint)
model.add_adapters(bottleneck_size=args.bottleneck_size,adapter_num=args.number_of_adpt)
else:
model = GPT2LMHeadModel.from_pretrained(args.model_checkpoint)
tokenizer = GPT2Tokenizer.from_pretrained(args.model_checkpoint, bos_token="[bos]", eos_token="[eos]", sos_token="[SOS]", sep_token="[sep]",pad_token='[PAD]')
model.resize_token_embeddings(new_num_tokens=len(tokenizer))
self.model = model
self.tokenizer = tokenizer
self.lr = args.lr
self.current_task = 0
self.fisher = defaultdict(list)
self.optpar = defaultdict(list)
self.episodic_mem = defaultdict(list)
self.CL = args.CL
self.reg = args.reg
self.first_task = True
self.model_name = args.model_checkpoint
self.reply_memory = []
self.task_list_seen = []
def set_number_of_tasks(self,n_tasks):
self.n_tasks = n_tasks
def set_up_gem(self):
self.grad_dims = []
for param in self.model.parameters():
self.grad_dims.append(param.data.numel())
dev = next(self.model.parameters()).device
self.grads = torch.Tensor(sum(self.grad_dims), self.n_tasks).to(dev)
def compute_PPL(self,batch,task_id=-1,device='cuda'):
with torch.no_grad():
lm_logits, *_ = self.model(
input_ids=batch["input_id_PPL"].to(device),
attention_mask=None,
labels=None,
task_id=task_id
)
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = batch["output_id_PPL"].to(device)[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss(reduction='none')
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
loss = torch.reshape(loss, shift_labels.size())
return (loss.sum(1)/(loss!=0).sum(1)).tolist()
def training_step(self, batch, batch_idx):
if self.CL == "GEM" and not self.first_task:
dev = next(self.model.parameters()).device
for id_task, (_,task_memory) in enumerate(self.episodic_mem.items()):
batch_mem = sample(task_memory,1)[0] # ==> we sample one batch from episodic memory
self.model.zero_grad()
(loss), *_ = self.model(input_ids=batch_mem["encoder_input"].to(dev),
attention_mask=batch_mem["attention_mask"].to(dev) if "gpt2" not in self.model_name else None,
labels=batch_mem["decoder_output"].to(dev)
)
loss.backward()
store_grad(self.model.parameters, self.grads, self.grad_dims, id_task)
self.model.zero_grad()
elif(self.CL == "AGEM" and not self.first_task):
dev = next(self.model.parameters()).device
batch_mem = sample(self.episodic_mem["all"],1)[0] # ==> we sample one batch from episodic memory
self.model.zero_grad()
(loss), *_ = self.model(input_ids=batch_mem["encoder_input"].to(dev),
attention_mask=batch_mem["attention_mask"].to(dev) if "gpt2" not in self.model_name else None,
labels=batch_mem["decoder_output"].to(dev)
)
loss.backward()
grad_ref = []
for p in self.model.parameters():
if p.requires_grad:
grad_ref.append(p.grad.view(-1))
grad_ref = torch.cat(grad_ref) ## from eq. 10 of AGEM Paper
self.model.zero_grad()
print(batch["encoder_input"].size())
## LOSS ON CURRENT DATA
if(self.CL == "ADAPTER"):
(loss), *_ = self.model(
input_ids=batch["encoder_input"],
attention_mask=batch["attention_mask"],
labels=batch["decoder_output"],
task_id=self.task_list_seen.index(batch["task_id"][0])
)
else:
(loss), *_ = self.model(input_ids=batch["encoder_input"],
attention_mask=batch["attention_mask"],
labels=batch["decoder_output"])
if(self.CL == "AGEM" and not self.first_task):
## Code from https://github.com/GMvandeVen/continual-learning/blob/master/encoder.py#L244
loss.backward()
grad_cur = []
for p in self.model.parameters():
if p.requires_grad:
grad_cur.append(p.grad.view(-1))
grad_cur = torch.cat(grad_cur)
# -check inequality constrain
angle = (grad_cur*grad_ref).sum()
if angle < 0:
# -if violated, project the gradient of the current batch onto the gradient of the replayed batch ...
length_rep = (grad_ref*grad_ref).sum()
grad_proj = grad_cur-(angle/length_rep)*grad_ref
# -...and replace all the gradients within the model with this projected gradient
index = 0
for p in self.model.parameters():
if p.requires_grad:
n_param = p.numel() # number of parameters in [p]
p.grad.copy_(grad_proj[index:index+n_param].view_as(p))
index += n_param
elif self.CL == "GEM" and not self.first_task:
loss.backward()
store_grad(self.model.parameters, self.grads, self.grad_dims, id_task+1)
indx = torch.LongTensor([j for j in range(id_task+1)])
dotp = torch.mm(self.grads.to(dev)[:, id_task].unsqueeze(0), self.grads.to(dev).index_select(1, indx.to(dev)))
if (dotp < 0).sum() != 0:
project2cone2(self.grads.to(dev)[:, id_task].unsqueeze(1), self.grads.to(dev).index_select(1, indx.to(dev)), self.reg)
# copy gradients back
overwrite_grad(self.model.parameters, self.grads.to(dev)[:, id_task], self.grad_dims)
elif self.CL == "L2" and not self.first_task:
dev = next(self.model.parameters()).device
l2_reg = 0
for n,p in self.model.named_parameters():
l = self.reg * (p - self.optpar[n].to(dev)).pow(2)
l2_reg += l.sum()
self.log('l2_reg', l2_reg, on_epoch=True)
loss = loss + l2_reg
elif self.CL == "EWC" and not self.first_task:
dev = next(self.model.parameters()).device
ewc_loss = 0
for n,p in self.model.named_parameters():
## Eq (3) of https://arxiv.org/pdf/1612.00796.pdf
l = self.reg * self.fisher[n].to(dev) * (p - self.optpar[n].to(dev)).pow(2)
ewc_loss += l.sum()
self.log('EWC_reg', ewc_loss, on_epoch=True)
loss = loss + ewc_loss
self.log('train_loss', loss, on_epoch=True)
return loss
def validation_step(self, batch, batch_idx):
if(self.CL == "ADAPTER"):
(loss), *_ = self.model(input_ids=batch["encoder_input"],
attention_mask=batch["attention_mask"],
labels=batch["decoder_output"],
task_id=self.task_list_seen.index(batch["task_id"][0])
)
else:
print(batch["encoder_input"].size())
(loss), *_ = self.model(input_ids=batch["encoder_input"],
attention_mask=batch["attention_mask"],
labels=batch["decoder_output"]
)
self.log('val_loss', loss)
return loss
def configure_optimizers(self):
if(self.CL=="ADAPTER"):
parameters_to_update = [p for n, p in self.named_parameters() if "adapter" in str(n)]
return AdamW(parameters_to_update, lr=self.lr, correct_bias=True)
else:
return AdamW(self.parameters(), lr=self.lr, correct_bias=True)
def backward(self, loss, optimizer, optimizer_idx):
if (self.CL == "GEM" or self.CL == "AGEM") and not self.first_task:
pass
else:
loss.backward()