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# Deep learning
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from torch.utils.data import DataLoader
from torch.nn.parallel import DistributedDataParallel as DDP
from fast_transformers.masking import LengthMask
# Standard library
from tqdm import tqdm
import pandas as pd
import numpy as np
import random
import os
class Trainer:
def __init__(
self,
model: torch.nn.Module,
train_data: DataLoader,
optimizer: torch.optim.Optimizer,
save_every: int,
save_checkpoint_path: str,
load_checkpoint_path: str,
config,
) -> None:
self.local_rank = int(os.environ["LOCAL_RANK"])
self.global_rank = int(os.environ["RANK"])
self.model = model.to(self.local_rank)
self.train_data = train_data
self.optimizer = optimizer
self.save_every = save_every
self.epochs_run = 0
self.last_batch_idx = -1
self.save_checkpoint_path = save_checkpoint_path
self.config = config
if os.path.exists(load_checkpoint_path):
print(f"Loading checkpoint at {load_checkpoint_path}...")
self._load_checkpoint(load_checkpoint_path)
self.model = DDP(self.model, device_ids=[self.local_rank])
def _load_checkpoint(self, checkpoint_path):
opt_dict = None
loc = f"cuda:{self.local_rank}"
ckpt_dict = torch.load(checkpoint_path, map_location=loc)
if os.path.exists(os.path.join(self.save_checkpoint_path, 'OPTIMIZER_STATES.pt')):
opt_dict = torch.load(os.path.join(self.save_checkpoint_path, 'OPTIMIZER_STATES.pt'), map_location=loc)
self.model.load_state_dict(ckpt_dict["MODEL_STATE"])
if opt_dict is not None:
self.optimizer.load_state_dict(opt_dict["OPTIMIZER_STATE"])
print('Optimizer states restored!')
self.last_batch_idx = ckpt_dict["last_batch_idx"] if 'last_batch_idx' in ckpt_dict else -1
self.epochs_run = ckpt_dict["EPOCHS_RUN"] + 1 if self.last_batch_idx == -1 else ckpt_dict["EPOCHS_RUN"]
# load RNG states each time the model and states are loaded from checkpoint
if 'rng' in ckpt_dict:
rng = ckpt_dict['rng']
for key, value in rng.items():
if key =='torch_state':
torch.set_rng_state(value.cpu())
elif key =='cuda_state':
torch.cuda.set_rng_state(value.cpu())
elif key =='numpy_state':
np.random.set_state(value)
elif key =='python_state':
random.setstate(value)
else:
print('unrecognized state')
print(f"Resuming training from checkpoint at Epoch {self.epochs_run}.")
def _save_checkpoint(self, epoch, config, last_idx):
# save RNG states each time the model and states are saved
out_dict = dict()
out_dict['torch_state'] = torch.get_rng_state()
out_dict['cuda_state'] = torch.cuda.get_rng_state()
if np:
out_dict['numpy_state'] = np.random.get_state()
if random:
out_dict['python_state'] = random.getstate()
# model states
ckpt_dict = {
"MODEL_STATE": self.model.module.state_dict(),
"EPOCHS_RUN": epoch,
"hparams": vars(config),
"last_batch_idx": last_idx,
"rng": out_dict
}
# optimizer states
opt_dict = {
"OPTIMIZER_STATE": self.optimizer.state_dict(),
}
if last_idx == -1:
filename = f'{str(self.model.module)}_{epoch}.pt'
else:
filename = f'{str(self.model.module)}_{last_idx}_{epoch}.pt'
torch.save(ckpt_dict, os.path.join(self.save_checkpoint_path, filename))
torch.save(opt_dict, os.path.join(self.save_checkpoint_path, 'OPTIMIZER_STATES.pt'))
print(f"Epoch {epoch} | Training checkpoint saved at {os.path.join(self.save_checkpoint_path, filename)}.")
def train(self, max_epochs: int):
for epoch in range(self.epochs_run, max_epochs):
self._run_epoch(epoch)
if self.local_rank == 0:
self._save_checkpoint(epoch, self.config, last_idx=-1)
def _run_epoch(self, epoch):
print(f"[GPU{self.global_rank}] Epoch {epoch} | Batchsize: {self.config.n_batch} | Steps: {len(self.train_data)} | Last batch: {self.last_batch_idx}")
self.train_data.sampler.set_epoch(epoch)
loss_list = pd.Series()
for idx, data in enumerate(tqdm(self.train_data)):
# skip batches
if idx <= self.last_batch_idx:
continue
# run batch
bucket_idx_masked = data[0]
bucket_targets = data[1]
bucket_idx_not_masked = data[2]
loss = self._run_batch(bucket_idx_masked, bucket_targets, bucket_idx_not_masked)
torch.cuda.empty_cache()
# track loss
if self.local_rank == 0:
loss_list = pd.concat([loss_list, pd.Series([loss])], axis=0)
# checkpoint
if self.local_rank == 0 and idx % self.save_every == 0 and idx != 0:
self._save_checkpoint(epoch, self.config, idx)
# WARN: due to job limit time - save loss for each iter
loss_list.to_csv(os.path.join(self.config.save_checkpoint_path, f'training_loss_{idx}_epoch{epoch}.csv'), index=False)
loss_list = pd.Series()
self.last_batch_idx = -1
if self.local_rank == 0:
loss_list.to_csv(os.path.join(self.config.save_checkpoint_path, f'training_loss_epoch{epoch}.csv'), index=False)
def _run_batch(self, bucket_idx_masked, bucket_targets, bucket_idx_not_masked):
raise NotImplementedError
class TrainerEncoderDecoder(Trainer):
def __init__(
self,
model: torch.nn.Module,
train_data: DataLoader,
optimizer: torch.optim.Optimizer,
save_every: int,
save_checkpoint_path: str,
load_checkpoint_path: str,
config,
) -> None:
super().__init__(model, train_data, optimizer, save_every, save_checkpoint_path, load_checkpoint_path, config)
self.criterionC = nn.CrossEntropyLoss(ignore_index=-100)
self.criterionR = nn.MSELoss()
self.optimE = self.optimizer[0]
self.optimD = self.optimizer[1]
self.ngpus_per_node = torch.cuda.device_count()
self.total_batches = len(self.train_data)
self.batch_thresh = int(self.total_batches - (self.total_batches * 0.05 * self.ngpus_per_node))
print('batch_thresh:', self.batch_thresh)
def _load_checkpoint(self, checkpoint_path):
opt_dict = None
loc = f"cuda:{self.local_rank}"
ckpt_dict = torch.load(checkpoint_path, map_location=loc)
if os.path.exists(os.path.join(self.save_checkpoint_path, 'OPTIMIZER_STATES.pt')):
opt_dict = torch.load(os.path.join(self.save_checkpoint_path, 'OPTIMIZER_STATES.pt'), map_location=loc)
self.model.load_state_dict(ckpt_dict["MODEL_STATE"])
if opt_dict is not None:
self.optimizer[0].load_state_dict(opt_dict["OPTIMIZER_STATE_ENCODER"])
self.optimizer[1].load_state_dict(opt_dict["OPTIMIZER_STATE_DECODER"])
print('Optimizer states restored!')
self.last_batch_idx = ckpt_dict["last_batch_idx"] if 'last_batch_idx' in ckpt_dict else -1
self.epochs_run = ckpt_dict["EPOCHS_RUN"] + 1 if self.last_batch_idx == -1 else ckpt_dict["EPOCHS_RUN"]
# load RNG states each time the model and states are loaded from checkpoint
if 'rng' in ckpt_dict:
rng = ckpt_dict['rng']
for key, value in rng.items():
if key =='torch_state':
torch.set_rng_state(value.cpu())
elif key =='cuda_state':
torch.cuda.set_rng_state(value.cpu())
elif key =='numpy_state':
np.random.set_state(value)
elif key =='python_state':
random.setstate(value)
else:
print('unrecognized state')
print(f"Resuming training from checkpoint at Epoch {self.epochs_run}.")
def _save_checkpoint(self, epoch, config, last_idx):
# save RNG states each time the model and states are saved
out_dict = dict()
out_dict['torch_state'] = torch.get_rng_state()
out_dict['cuda_state'] = torch.cuda.get_rng_state()
if np:
out_dict['numpy_state'] = np.random.get_state()
if random:
out_dict['python_state'] = random.getstate()
# model states
ckpt_dict = {
"MODEL_STATE": self.model.module.state_dict(),
"EPOCHS_RUN": epoch,
"hparams": vars(config),
"last_batch_idx": last_idx,
"rng": out_dict
}
# optimizer states
opt_dict = {
"OPTIMIZER_STATE_ENCODER": self.optimizer[0].state_dict(),
"OPTIMIZER_STATE_DECODER": self.optimizer[1].state_dict(),
}
if last_idx == -1:
filename = f'{str(self.model.module)}_{epoch}.pt'
else:
filename = f'{str(self.model.module)}_{last_idx}_{epoch}.pt'
torch.save(ckpt_dict, os.path.join(self.save_checkpoint_path, filename))
torch.save(opt_dict, os.path.join(self.save_checkpoint_path, 'OPTIMIZER_STATES.pt'))
print(f"Epoch {epoch} | Training checkpoint saved at {os.path.join(self.save_checkpoint_path, filename)}.")
def _run_epoch(self, epoch):
print(f"[GPU{self.global_rank}] Epoch {epoch} | Batchsize: {self.config.n_batch} | Steps: {len(self.train_data)}")
self.train_data.sampler.set_epoch(epoch)
loss_list = pd.DataFrame()
for idx, data in enumerate(tqdm(self.train_data)):
bucket_idx_masked = data[0]
bucket_targets = data[1]
bucket_idx_not_masked = data[2]
lossE, lossD = self._run_batch(idx, bucket_idx_masked, bucket_targets, bucket_idx_not_masked)
torch.cuda.empty_cache()
if self.local_rank == 0:
df = pd.DataFrame({
'lossE': [lossE.cpu().item()],
'lossD': [lossD.cpu().item()],
})
loss_list = pd.concat([loss_list, df], axis=0)
if self.local_rank == 0:
loss_list.to_csv(os.path.join(self.config.save_checkpoint_path, f'training_loss_epoch{epoch}.csv'), index=False)
def custom(self, module):
def custom_forward(*inputs):
inputs = module(inputs[0])
return inputs
return custom_forward
def _run_batch(self, batch_idx, bucket_idx_masked, bucket_targets, bucket_idx_not_masked):
self.optimE.zero_grad(set_to_none=True)
self.optimD.zero_grad(set_to_none=True)
can_train_encoder = (batch_idx + 1) <= self.batch_thresh
can_train_decoder = (batch_idx + 1) > self.batch_thresh
padding_idx = 2
errorE = torch.zeros(1).to(self.local_rank)
errorD = torch.zeros(1).to(self.local_rank)
errorE_tmp = .0
errorD_tmp = .0
for chunk in range(len(bucket_idx_masked)):
idx_masked = bucket_idx_masked[chunk].to(self.local_rank)
targets = bucket_targets[chunk].to(self.local_rank)
idx_not_masked = bucket_idx_not_masked[chunk]
idx_not_masked = list(map(lambda x: F.pad(x, pad=(0, self.config.max_len - x.shape[0]), value=2).unsqueeze(0), idx_not_masked))
idx_not_masked = torch.cat(idx_not_masked, dim=0).to(self.local_rank)
mask = (idx_masked != padding_idx)
###########
# Encoder #
###########
if can_train_encoder:
for param in self.model.module.encoder.parameters():
param.requires_grad = True
for param in self.model.module.decoder.parameters():
param.requires_grad = False
# encoder forward
x = self.model.module.encoder.tok_emb(idx_masked)
x = self.model.module.encoder.drop(x)
x = checkpoint.checkpoint(self.custom(self.model.module.encoder.blocks), x)
logits = self.model.module.encoder.lang_model(x)
# loss function
logits = logits.view(-1, logits.size(-1))
targets = targets.view(-1)
errorE_tmp = self.criterionC(logits, targets) / len(bucket_idx_masked)
if chunk < len(bucket_idx_masked)-1:
errorE_tmp.backward()
errorE += errorE_tmp.detach()
else:
errorE += errorE_tmp
###########
# Decoder #
###########
if can_train_decoder:
for param in self.model.module.encoder.parameters():
param.requires_grad = False
for param in self.model.module.decoder.parameters():
param.requires_grad = True
self.model.module.encoder.eval()
# encoder forward
with torch.no_grad():
true_set, true_cte = self.model.module.encoder(idx_masked, mask=mask, inference=True)
# add padding
input_mask_expanded = mask.unsqueeze(-1).expand(true_cte.size()).float()
mask_embeddings = (true_cte * input_mask_expanded)
true_cte = F.pad(mask_embeddings, pad=(0, 0, 0, self.config.max_len - mask_embeddings.shape[1]), value=0)
true_cte = true_cte.view(-1, self.config.max_len*self.config.n_embd)
# decoder forward
pred_set, pred_ids = self.model.module.decoder(true_cte)
# losses
pred_ids = pred_ids.view(-1, pred_ids.size(-1))
true_ids = idx_not_masked.view(-1)
error_ids = self.criterionC(pred_ids, true_ids) / len(bucket_idx_masked)
error_set = self.criterionR(pred_set, true_set) / len(bucket_idx_masked)
errorD_tmp = error_ids + error_set
if chunk < len(bucket_idx_masked)-1:
errorD_tmp.backward()
errorD += errorD_tmp.detach()
else:
errorD += errorD_tmp
if can_train_decoder:
errorD.backward()
self.optimD.step()
elif can_train_encoder:
errorE.backward()
self.optimE.step()
if self.local_rank == 0:
print(f'LossE: {errorE.item()} | LossD: {errorD.item()}')
return errorE, errorD
class TrainerDirectDecoder(Trainer):
def __init__(
self,
model: torch.nn.Module,
train_data: DataLoader,
optimizer: torch.optim.Optimizer,
save_every: int,
save_checkpoint_path: str,
load_checkpoint_path: str,
config,
) -> None:
super().__init__(model, train_data, optimizer, save_every, save_checkpoint_path, load_checkpoint_path, config)
self.criterionC = nn.CrossEntropyLoss(ignore_index=-100)
self.criterionR = nn.MSELoss()
def custom(self, module):
def custom_forward(*inputs):
inputs = module(inputs[0], length_mask=inputs[1])
return inputs
return custom_forward
def _run_batch(self, bucket_idx_masked, bucket_targets, bucket_idx_not_masked):
padding_idx = 2
error = torch.zeros(1).to(self.local_rank)
error_tmp = .0
self.optimizer.zero_grad(set_to_none=True)
for chunk in range(len(bucket_idx_masked)):
idx_masked = bucket_idx_masked[chunk].to(self.local_rank)
targets = bucket_targets[chunk].to(self.local_rank)
idx_not_masked = bucket_idx_not_masked[chunk]
idx_not_masked = list(map(lambda x: F.pad(x, pad=(0, self.config.max_len - x.shape[0]), value=2).unsqueeze(0), idx_not_masked))
idx_not_masked = torch.cat(idx_not_masked, dim=0).to(self.local_rank)
mask = (idx_masked != padding_idx)
# encoder forward
x = self.model.module.encoder.tok_emb(idx_masked)
x = self.model.module.encoder.drop(x)
x = checkpoint.checkpoint(self.custom(self.model.module.encoder.blocks), x, LengthMask(mask.sum(-1), max_len=idx_masked.shape[1]))
# mean pooling
input_masked_expanded = mask.unsqueeze(-1).expand(x.size()).float()
sum_embeddings = torch.sum(x*input_masked_expanded, 1)
sum_mask = torch.clamp(input_masked_expanded.sum(1), min=1e-9)
true_set = sum_embeddings/sum_mask
true_cte = x
del x
torch.cuda.empty_cache()
# add padding
input_mask_expanded = mask.unsqueeze(-1).expand(true_cte.size()).float()
mask_embeddings = (true_cte * input_mask_expanded)
true_cte = F.pad(mask_embeddings, pad=(0, 0, 0, self.config.max_len - mask_embeddings.shape[1]), value=0)
true_cte = true_cte.view(-1, self.config.max_len*self.config.n_embd)
# decoder forward
pred_set, pred_ids = self.model.module.decoder(true_cte)
# losses
pred_ids = pred_ids.view(-1, pred_ids.size(-1))
true_ids = idx_not_masked.view(-1)
error_ids = self.criterionC(pred_ids, true_ids) / len(bucket_idx_masked)
error_set = self.criterionR(pred_set, true_set) / len(bucket_idx_masked)
error_tmp = error_ids + error_set
if chunk < len(bucket_idx_masked)-1:
error_tmp.backward()
error += error_tmp.detach()
else:
error += error_tmp
torch.cuda.empty_cache()
error.backward()
self.optimizer.step()
if self.local_rank == 0:
print(f'Loss: {error.item()}')
return error.item()
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