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"""
Default trainer class for training models
"""
from collections import OrderedDict
from os.path import join
from argparse import ArgumentParser
from tqdm import tqdm
import pandas as pd
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LRScheduler
from .optim import get_optimizer, get_scheduler
from .utils import decode_samples
class OurTrainer():
"""
Basic parent trainer class. Defaults to language modeling.
-> Replacement for Hugging Face Trainer
"""
def __init__(self,
model: nn.Module,
args: ArgumentParser,
train_loader: DataLoader,
eval_loader: DataLoader,
optimizer_and_scheduler: tuple[Optimizer, LRScheduler],
device: torch.device,
wandb, # WandB object
checkpoint_suffix: str = None,
save_checkpoints: bool = True,
save_results: bool = True,
# Custom arguments
optimizer_args: dict = None,
lr_scheduler_args: dict = None,
greater_is_better: bool = False,
metric_for_best_model: str = 'eval/loss',
num_train_epochs: int = 2,
gradient_accumulation_steps: int = 1,
evaluation_strategy: str = 'steps',
load_best_model_at_end: bool = True,
logging_steps: int = 100,
max_steps: int = -1,
eval_steps: int = 100,
max_eval_batches: int = -1,
print_samples: bool = False,
initial_eval: bool = True,
num_save_ckpt_steps: int = 1000,
**kwargs: any):
super().__init__()
self.model = model
self.step = 0 # Total steps taken
self.grad_step = 0 # Total gradient updates
self.compute_loss_backprop = False # Whether we backprop in self.compute_loss
if optimizer_and_scheduler is None:
assert optimizer_args is not None and lr_scheduler_args is not None
self.optimizer = get_optimizer(model=self.model, **optimizer_args)
self.scheduler = get_scheduler(optimizer=self.optimizer, **lr_scheduler_args)
else:
self.optimizer, self.scheduler = optimizer_and_scheduler
try:
self.scheduler_step_after_epoch = 'plateau' in args.lr_scheduler['lr_scheduler_type']
except KeyError:
self.scheduler_step_after_epoch = False
# Dataloaders
self.train_loader = train_loader
self.eval_loader = eval_loader
self.device = device
self.wandb = wandb
# Custom arguments
self.metric_for_best_model = metric_for_best_model
self.num_train_epochs = num_train_epochs
self.gradient_accumulation_steps = gradient_accumulation_steps
self.evaluation_strategy = evaluation_strategy
self.greater_is_better = greater_is_better
self.is_better = (lambda x, y: x > y if greater_is_better else x < y)
self.load_best_model_at_end = load_best_model_at_end
self.logging_steps = logging_steps
self.max_steps = max_steps
self.eval_steps = eval_steps
self.max_eval_batches = max_eval_batches
self.print_samples = print_samples
self.initial_eval = initial_eval
self.num_save_ckpt_steps = num_save_ckpt_steps
# Saving metrics
self.train_metrics = {'train/loss': None,
'train/epoch': None,
'train/step': None}
self.eval_metrics = {metric_for_best_model: None}
self.eval_metrics_by_step = {'eval_step': []} # save all eval metrics
self.criterion = nn.CrossEntropyLoss(reduction='mean')
try:
self.tokenizer = self.train_loader.dataset.tokenizer
except AttributeError:
self.tokenizer = None
self.save_results = save_results
self.results_path = None
self.best_val_metric = 0 if greater_is_better else 1e10
self.best_val_metric_epoch = 0
self.best_val_metric_step = 0
if save_checkpoints: # Also initializes best_val_metrics
self.init_checkpointing(args=args, checkpoint_suffix=checkpoint_suffix)
def train(self) -> nn.Module:
"""
Entire training run
"""
model = self.model
pbar = tqdm(range(self.num_train_epochs), leave=False, colour='white',
desc='Training')
for ix, epoch in enumerate(pbar):
model, early_stopping = self.train_step(model, epoch)
if self.evaluation_strategy == 'epoch':
_eval_metrics = self.eval_step(model, step=self.grad_step)
print(f'Epoch {ix} metrics:', _eval_metrics)
if early_stopping:
break
if self.load_best_model_at_end: # Return best checkpoint
try:
state_dict = torch.load(self.best_val_checkpoint_path)['model_state_dict']
model.load_state_dict(state_dict, strict=False)
print(f'-> Loading best checkpoint from {self.best_val_checkpoint_path}')
except FileNotFoundError as e:
print(e)
print('-> Returning most recent model instead')
return model
def train_step(self, model: nn.Module, epoch: int) -> nn.Module:
"""
Training loop over one epoch
"""
if self.gradient_accumulation_steps is None:
accum_iter = 1
else:
accum_iter = self.gradient_accumulation_steps
model.train()
model.zero_grad()
pbar = tqdm(self.train_loader, leave=False, colour='blue',
desc=f'-> Training (epoch {epoch} / {self.num_train_epochs})')
total_loss = 0
eval_for_step = False
# Initial eval
if self.initial_eval:
print('')
print('-> Initial eval')
self.compute_eval_metrics(model, step=self.grad_step)
# model.to(self.device)
for ix, data in enumerate(pbar):
loss, train_metrics = self.compute_loss(model, data,
sample_idx=ix)
loss /= accum_iter
if not self.compute_loss_backprop:
# loss.backward() did not occur in compute_loss
try:
with torch.autograd.set_detect_anomaly(True):
loss.backward()
except Exception as e:
breakpoint()
if (self.step + 1) % accum_iter == 0: # and self.step != 0:
self.optimizer.step()
if not self.scheduler_step_after_epoch and self.scheduler is not None:
self.scheduler.step()
self.optimizer.zero_grad()
self.grad_step += 1
if not self.compute_loss_backprop:
loss = loss.detach().cpu().item()
self.step += 1
if not isinstance(loss, float):
total_loss += loss.item()
else:
total_loss += loss
desc = f"Training epoch {epoch} | loss: {total_loss / (ix + 1):.3f} | lr: {self.optimizer.param_groups[0]['lr']:.5f}"
desc += f' | gradient step: {self.grad_step}'
for k, v in train_metrics.items():
desc += f' | {k}: {v:.3f}'
pbar.set_description(desc)
# Logging
if (self.grad_step) % (self.logging_steps):
self.train_metrics['train/loss'] = loss.item() if not isinstance(loss, float) else loss
self.train_metrics['train/epoch'] = epoch
self.train_metrics['train/step'] = self.grad_step
self.train_metrics['train/lr'] = self.optimizer.param_groups[0]['lr']
for k, v in train_metrics.items():
self.train_metrics[f'train/{k}'] = v
if self.wandb is not None:
self.wandb.log(self.train_metrics, step=self.grad_step)
if self.evaluation_strategy == 'steps':
if (self.grad_step % self.eval_steps == 0 and self.grad_step > 0 and not eval_for_step):
_eval_metrics = self.eval_step(model, step=self.grad_step)
print(f'Grad Step {self.grad_step} eval metrics:', _eval_metrics)
eval_for_step = True
model.train() # Need to set back to train mode
elif self.grad_step == 0 and self.num_save_ckpt_steps < 1000 and not eval_for_step: # hack for micros
_eval_metrics = self.eval_step(model, step=self.grad_step)
print(f'Grad Step {self.grad_step} eval metrics:', _eval_metrics)
eval_for_step = True
model.train() # Need to set back to train mode
elif self.grad_step % self.eval_steps == 0 and self.grad_step > 0 and eval_for_step:
pass
else:
if self.grad_step > 0:
eval_for_step = False
if self.grad_step == self.max_steps:
early_stopping = True
return model, early_stopping
early_stopping = False
return model, early_stopping
def eval_step(self, model: nn.Module, step: int = None,
**kwargs: any) -> dict[any]:
"""
Evaluation loop over one epoch
"""
with torch.no_grad():
self.eval_metrics = self.compute_eval_metrics(model, step=step, **kwargs)
val_metric = self.eval_metrics[self.metric_for_best_model]
# Save results
if self.wandb is not None: # log to WandB
self.wandb.log(self.eval_metrics, step=self.grad_step)
if self.results_path is not None: # log to local file
self.eval_metrics_by_step['eval_step'].append(step)
for k, v in self.eval_metrics.items():
if k not in self.eval_metrics_by_step:
self.eval_metrics_by_step[k] = [v]
else:
self.eval_metrics_by_step[k].append(v)
# Inefficient, but log for experiments results
pd.DataFrame(self.eval_metrics_by_step).to_csv(self.results_path)
# Save best metric and checkpoint
if self.grad_step % self.eval_steps == 0:
if self.is_better(val_metric, self.best_val_metric):
self.best_val_metric = val_metric
self.best_val_metric_step = self.grad_step
# model.cpu()
torch.save({
'model_state_dict': self.save_trainable_weights(model),
'step': self.grad_step,
self.metric_for_best_model: val_metric
}, self.best_val_checkpoint_path)
print(f'\n-> Saved best model checkpoint to: {self.best_val_checkpoint_path}!')
if self.grad_step % self.num_save_ckpt_steps == 0:
save_path = self.best_val_checkpoint_path.replace('.pt', f'_{self.grad_step}.pt')
torch.save({
'model_state_dict': self.save_trainable_weights(model),
'step': self.grad_step,
self.metric_for_best_model: val_metric
}, save_path)
print(f'\n-> Saved best model checkpoint to: {save_path}!')
if self.scheduler_step_after_epoch and self.scheduler is not None:
self.scheduler.step(val_metric)
return self.eval_metrics
def compute_eval_metrics(self,
model: nn.Module, step: int,
max_batches: int = None,
dataloader: DataLoader = None,
**kwargs: any) -> dict[any]:
"""
One evaluation loop over a validation dataset
"""
max_batches = (self.max_eval_batches if max_batches is None else max_batches)
dataloader = self.eval_loader if dataloader is None else dataloader
pbar = tqdm(dataloader, leave=False, colour='green',
desc=f'Evaluating at step {step}')
model.eval()
step_loss = 0
step_eval_metrics = {}
with torch.no_grad():
for ix, data in enumerate(pbar):
loss, eval_metrics = self.compute_loss(model, data)
if not self.compute_loss_backprop:
loss = loss.item() # otherwise already float
if ix == 0:
step_eval_metrics[self.metric_for_best_model] = [loss]
for k, v in eval_metrics.items():
step_eval_metrics[f'eval/{k}'] = [v]
else:
step_eval_metrics[self.metric_for_best_model].append(loss)
for k, v in eval_metrics.items():
step_eval_metrics[f'eval/{k}'].append(v)
step_loss += loss
desc = f"Evaluating at step {step} | loss: {step_loss / (ix + 1):.3f}"
if self.optimizer is not None:
desc += f" | lr: {self.optimizer.param_groups[0]['lr']:.5f}"
pbar.set_description(desc)
if ix == max_batches:
break
# Average over batches
for k, v in step_eval_metrics.items():
step_eval_metrics[k] = sum(v) / len(v)
print(f'Eval step {step}:', step_eval_metrics)
del loss
torch.cuda.empty_cache()
return step_eval_metrics
def compute_loss(self, model: nn.Module, data: torch.Tensor,
sample_idx: int = None, **kwargs: any,
) -> tuple[torch.Tensor, dict[any]]:
"""
Main method to determine how models are trained.
-> Defaults to next-token prediction / classification,
but override in child classes
Args:
- model: nn.Module, HF model to train
- data: dict[torch.Tensor], HF datasets batch of data
- sample_idx: int, index of batch in dataset
"""
input_keys = {'input_ids', 'attention_mask'}
inputs = {k: v.to(model.device)
for k, v in data.items() if k in input_keys}
outputs = model(**inputs, output_attentions=False, use_cache=False)
outputs = outputs.get('logits')[..., :-1, :].contiguous()
targets = data.get('labels')[..., 1:].contiguous()
# Look at model outputs
if self.print_samples and sample_idx is not None and (sample_idx + 1) % 100 == 0:
decode_samples(outputs, targets, self.tokenizer, sample_idx)
# Flatten and compute cross-entropy loss
outputs = outputs.view(-1, outputs.shape[-1])
targets = targets.view(-1).to(outputs.device)
try:
loss = self.criterion(outputs, targets)
except Exception as e:
print('outputs.shape', outputs.shape)
print('targets.shape', targets.shape)
raise e
targets = targets.cpu()
outputs = outputs.cpu()
return loss, {'ppl': torch.exp(loss).item(), 'seq_len': targets.shape[-1] + 1}
def save_trainable_weights(self, model: nn.Module):
"""
Save checkpoint with only weights actively being trained (e.g., for adapters).
Make sure to later load with model.load_state_dict(state_dict, strict=False)
"""
with torch.no_grad():
state_dict = OrderedDict()
for n, p in model.named_parameters():
if p.requires_grad:
state_dict[n] = p.cpu() # assurance
return state_dict
def init_checkpointing(self,
args: ArgumentParser,
checkpoint_suffix: str) -> None:
"""
Initialize checkpointing attributes
Inputs:
- args: Argparse or HuggingFace TrainingArguments object
- checkpoint_suffix: str to append to checkpoint name
"""
self.best_val_checkpoint_path = f'{join(args.checkpoint_dir, args.run_name)}.pt'
if checkpoint_suffix is not None:
self.best_val_checkpoint_path = self.best_val_checkpoint_path.replace(
'.pt', f'{checkpoint_suffix}.pt')
print(f'-> Saving best model checkpoint to {self.best_val_checkpoint_path}')
if self.save_results:
self.results_path = self.best_val_checkpoint_path.replace(
'.pt', '.csv').replace(args.checkpoint_dir, args.results_dir)
print(f'-> Saving results to {self.results_path}')
# Best metric setup
self.best_val_metric = 0 if self.greater_is_better else 1e10
self.best_val_metric_epoch = 0
self.best_val_metric_step = 0
self.best_train_metric = 0 if self.greater_is_better else 1e10
self.best_train_metric_epoch = 0
self.best_train_metric_step = 0
self.metric_for_best_model = self.metric_for_best_model
if self.metric_for_best_model is not None:
if 'eval' not in self.metric_for_best_model:
self.metric_for_best_model = f'eval/{self.metric_for_best_model}'
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