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[style] consistent nn. and nn.functional: part 4 examples (#12156)

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* consistent nn. and nn.functional: p4 examples

* restore
This commit is contained in:
Stas Bekman
2021-06-14 12:28:24 -07:00
committed by GitHub
parent 372ab9cd6d
commit 88e84186e5
26 changed files with 130 additions and 126 deletions
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11
examples/research_projects/deebert/run_glue_deebert.py
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@@ -9,6 +9,7 @@ import time
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
@@ -135,11 +136,11 @@ def train(args, train_dataset, model, tokenizer, train_highway=False):
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
model = nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model = nn.parallel.DistributedDataParallel(
model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
)
@@ -190,9 +191,9 @@ def train(args, train_dataset, model, tokenizer, train_highway=False):
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
@@ -255,7 +256,7 @@ def evaluate(args, model, tokenizer, prefix="", output_layer=-1, eval_highway=Fa
# multi-gpu eval
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
model = nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))