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fttrainer.py
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fttrainer.py
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import logging
import numpy as np
import torch
from time import time
from accelerate import Accelerator, PartialState
from accelerate.logging import get_logger
from torch import optim
from tqdm.auto import tqdm
import math
import torch.nn as nn
import os
import torch.distributed as dist
from transformers import get_linear_schedule_with_warmup, get_constant_schedule_with_warmup
from utils import *
from metrics import metrics_to_function
from torch import autograd
class FtTrainer(object):
def __init__(self, args, model, train_data, valid_data, test_data=None):
self.args = args
self.model = model
self.lr = args.learning_rate
self.optimizer_name = args.optim
self.lr_scheduler_type = args.lr_scheduler_type
self.weight_decay = args.weight_decay
self.gradient_accumulation_steps = args.gradient_accumulation_steps
self.epochs = int(args.num_train_epochs)
self.eval_steps = min(int(args.eval_steps), self.epochs)
self.all_metrics = args.metrics.split(",")
self.valid_metric = args.valid_metric
self.max_topk = 0
self.all_metric_name = []
for m in self.all_metrics:
m_name, top_k = m.split("@")
self.max_topk = max(self.max_topk, int(top_k))
if m_name.lower() not in self.all_metric_name:
self.all_metric_name.append(m_name.lower())
self.train_data = train_data
self.valid_data = valid_data
self.test_data = test_data
self.max_steps = self.get_train_steps()
self.warmup_steps = self.args.get_warmup_steps(self.max_steps)
self.optimizer = self._build_optimizer()
self.lr_scheduler = self._get_scheduler()
self.accelerator = Accelerator(
gradient_accumulation_steps = self.gradient_accumulation_steps,
mixed_precision="no"
)
self.model, self.optimizer, self.lr_scheduler, self.train_data, self.valid_data, self.test_data = self.accelerator.prepare(
self.model, self.optimizer, self.lr_scheduler, self.train_data, self.valid_data, self.test_data)
# assert dist.is_initialized(), "Distributed training has not been properly initialized."
self.state = PartialState()
self.world_size = self.state.num_processes
self.device = self.state.device
self.ckpt_dir = args.output_dir
saved_model_dir = "{}".format(get_local_time())
self.ckpt_dir = os.path.join(self.ckpt_dir,saved_model_dir)
ensure_dir(self.ckpt_dir)
self.best_score = 0
self.best_ckpt = "best_model.pth"
self.loss_func = nn.CrossEntropyLoss()
def _build_optimizer(self):
params = self.model.parameters()
optimizer_name = self.optimizer_name
learning_rate = self.lr
weight_decay = self.weight_decay
if optimizer_name.lower() == "adam":
optimizer = optim.Adam(params, lr=learning_rate, weight_decay=weight_decay)
elif optimizer_name.lower() == "sgd":
optimizer = optim.SGD(params, lr=learning_rate, weight_decay=weight_decay)
elif optimizer_name.lower() == "adagrad":
optimizer = optim.Adagrad(
params, lr=learning_rate, weight_decay=weight_decay
)
elif optimizer_name.lower() == "rmsprop":
optimizer = optim.RMSprop(
params, lr=learning_rate, weight_decay=weight_decay
)
elif optimizer_name.lower() == 'adamw_torch':
optimizer = optim.AdamW(
params, lr=learning_rate, weight_decay=weight_decay
)
else:
print(
"Received unrecognized optimizer, set default AdamW optimizer"
)
optimizer = optim.AdamW(
params, lr=learning_rate, weight_decay=weight_decay
)
return optimizer
def _get_scheduler(self):
if self.lr_scheduler_type.lower() == "linear":
lr_scheduler = get_linear_schedule_with_warmup(optimizer=self.optimizer,
num_warmup_steps=self.warmup_steps,
num_training_steps=self.max_steps)
else:
lr_scheduler = get_constant_schedule_with_warmup(optimizer=self.optimizer,
num_warmup_steps=self.warmup_steps)
return lr_scheduler
def get_train_steps(self):
len_dataloader = len(self.train_data)
num_update_steps_per_epoch = len_dataloader // self.gradient_accumulation_steps
num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1)
max_steps = math.ceil(self.epochs * num_update_steps_per_epoch)
return max_steps
def _check_nan(self, loss):
if torch.isnan(loss):
raise ValueError("Training loss is nan")
def _train_epoch(self, epoch_idx):
self.model.train()
total_num = 0
total_loss = 0
iter_data = tqdm(
self.train_data,
total=len(self.train_data),
ncols=100,
desc=set_color(f"Train {epoch_idx}", "pink"),
disable=not self.accelerator.is_main_process,
)
for batch_idx, data in enumerate(iter_data):
with self.accelerator.accumulate(self.model):
labels = data.pop("labels")
total_num += 1
self.optimizer.zero_grad()
logits, id_text_loss = self.model(**data)
loss = self.loss_func(logits, labels) + id_text_loss
self._check_nan(loss)
self.accelerator.backward(loss)
self.optimizer.step()
self.lr_scheduler.step()
total_loss += loss.item()
iter_data.set_postfix(loss=loss.item(), lr=self.lr_scheduler.get_last_lr())
self.accelerator.wait_for_everyone()
return total_loss/total_num
@torch.no_grad()
def test(self, eval_data=None, load_best_model = False, model_file=None):
if eval_data is None:
eval_data = self.test_data
if load_best_model:
checkpoint_file = model_file or os.path.join(self.ckpt_dir, self.best_ckpt)
checkpoint = torch.load(checkpoint_file, map_location=self.device)
if dist.is_initialized():
missing_keys, unexpected_keys = self.model.module.load_state_dict(checkpoint["state_dict"],
strict=False)
else:
missing_keys, unexpected_keys = self.model.load_state_dict(checkpoint["state_dict"], strict=False)
if self.accelerator.is_main_process:
message_output = "Loading model parameters from {}".format(
checkpoint_file
)
print(message_output)
print("missing_keys: ", missing_keys)
print("unexpected_keys: ", unexpected_keys)
self.model.eval()
iter_data = tqdm(
eval_data,
total=len(eval_data),
ncols=100,
desc=set_color(f"Evaluate ", "pink"),
disable=not self.accelerator.is_main_process,
)
total = 0
metrics = {m:0 for m in self.all_metrics}
for batch_idx, data in enumerate(iter_data):
labels = data.pop("labels")
total += len(labels)
scores, _ = self.model(**data)
_metrics = self.evaluate(scores, labels)
for m, v in _metrics.items():
metrics[m] += v
for m in metrics:
metrics[m] = metrics[m] / total
return metrics
def evaluate(self, scores, labels):
metrics = {m:0 for m in self.all_metrics}
_, topk_idx = torch.topk(
scores, self.max_topk, dim=-1
) # B x k
topk_idx = topk_idx.detach().cpu()
labels = labels.detach().cpu()
top_k_labels = torch.gather(labels, dim=1, index=topk_idx).numpy()
pos_nums = labels.sum(dim=1).numpy()
topk_metrics = {}
for m_name in self.all_metric_name:
value = metrics_to_function[m_name](top_k_labels, pos_nums)
topk_metrics[m_name] = value.sum(axis=0)
for m in self.all_metrics:
m_name, top_k = m.split("@")
m_name = m_name.lower()
top_k = int(top_k)
value = topk_metrics[m_name]
metrics[m] = value[top_k - 1]
return metrics
def _save_checkpoint(self, epoch, ckpt_file=None):
ckpt_path = os.path.join(self.ckpt_dir, ckpt_file) if ckpt_file \
else os.path.join(self.ckpt_dir, self.best_ckpt)
state = {
"args": self.args,
"epoch": epoch,
"best_score": self.best_score,
"state_dict": self.accelerator.get_state_dict(self.model),
"optimizer": self.optimizer.state_dict(),
}
torch.save(state, ckpt_path, pickle_protocol=4)
print(
set_color("Saving current", "blue") + f": {ckpt_path}"
)
def _generate_train_loss_output(self, epoch_idx, s_time, e_time, loss):
train_loss_output = (
set_color("epoch %d training", "green")
+ " ["
+ set_color("time", "blue")
+ ": %.2fs, "
) % (epoch_idx, e_time - s_time)
train_loss_output += set_color("train loss", "blue") + ": %.4f" % loss
return train_loss_output + "]"
def train(self,):
cur_eval_step = 0
stop = False
for epoch_idx in range(self.epochs):
self.accelerator.wait_for_everyone()
# train
training_start_time = time()
train_loss = self._train_epoch(epoch_idx)
training_end_time = time()
if dist.is_initialized():
train_loss = torch.tensor(train_loss).to(self.device)
train_loss = self.accelerator.gather(train_loss).mean().item()
if self.accelerator.is_main_process:
train_loss_output = self._generate_train_loss_output(
epoch_idx, training_start_time, training_end_time, train_loss
)
print(train_loss_output)
if (epoch_idx + 1) % self.eval_steps == 0:
metrics = self.test(eval_data=self.valid_data)
if dist.is_initialized():
metrics_list = [None for _ in range(self.world_size)]
dist.all_gather_object(obj=metrics, object_list=metrics_list)
total_metrics = {m:0 for m in self.all_metrics}
for m in self.all_metrics:
for metric_dict in metrics_list:
total_metrics[m] += metric_dict[m]
total_metrics[m] = total_metrics[m] / self.world_size
else:
total_metrics = metrics
if total_metrics[self.valid_metric] > self.best_score:
self.best_score = total_metrics[self.valid_metric]
cur_eval_step = 0
if self.accelerator.is_main_process:
self._save_checkpoint(epoch_idx)
else:
cur_eval_step += 1
if cur_eval_step >= 5:
stop = True
if self.accelerator.is_main_process:
print(str(total_metrics))
self.accelerator.wait_for_everyone()
if stop:
break
test_results=None
if self.test_data is not None:
metrics = self.test(eval_data=self.test_data, load_best_model=True)
if dist.is_initialized():
metrics_list = [None for _ in range(self.world_size)]
dist.all_gather_object(obj=metrics, object_list=metrics_list)
test_results = {m: 0 for m in self.all_metrics}
for m in self.all_metrics:
for metric_dict in metrics_list:
test_results[m] += metric_dict[m]
test_results[m] = test_results[m] / self.world_size
else:
test_results = metrics
return self.best_score, test_results