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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Evaluation utilities."""
import os
import time
from functools import partial
import torch
from megatron import get_args
from megatron import print_rank_last, is_last_rank
from megatron.core import mpu
from megatron.schedules import get_forward_backward_func
from tasks.finetune_utils import build_data_loader
from tasks.finetune_utils import process_batch
from deepspeed.accelerator import get_accelerator
def accuracy_func_provider(single_dataset_provider):
"""Provide function that calculates accuracies."""
args = get_args()
# Build dataloaders.
datapaths = args.valid_data
dataloaders = []
for datapath in datapaths:
dataset = single_dataset_provider(datapath)
dataloader = build_data_loader(
dataset, args.orig_micro_batch_size, num_workers=args.num_workers,
drop_last=(mpu.get_data_parallel_world_size() > 1))
dataloaders.append((dataset.dataset_name, dataloader))
def metrics_func(model, epoch, output_predictions=False):
print_rank_last('calculating metrics ...')
correct = 0
total = 0
if output_predictions:
assert mpu.get_data_parallel_world_size() == 1
named_predictions = []
names = 'predictions'
for name, dataloader in dataloaders:
output = calculate_correct_answers(name, model, dataloader,
epoch, output_predictions)
if not output_predictions:
correct_ans, total_count = output
else:
correct_ans, total_count, predictions = output
named_predictions.append((name, predictions))
names += '_' + name
correct += correct_ans
total += total_count
if is_last_rank():
percent = 0
if total > 0:
percent = float(correct) * 100.0 / float(total)
print(' >> |epoch: {}| overall: correct / total = {} / {} = '
'{:.4f} %'.format(epoch, correct, total, percent))
if output_predictions and is_last_rank():
assert args.load is not None
filename = os.path.join(args.load, names + '.pt')
torch.save(named_predictions, filename)
return metrics_func
def calculate_correct_answers(name, model, dataloader,
epoch, output_predictions):
"""Calculate correct over total answers and return prediction if the
`output_predictions` is true."""
args = get_args()
forward_backward_func = get_forward_backward_func()
start_time = time.time()
for m in model:
m.eval()
saved_micro_batch_size = args.micro_batch_size
saved_global_batch_size = args.global_batch_size
ds = dataloader.dataset
if hasattr(ds, 'sample_multiplier'):
# If our dataset as a sample_multiplier attribute that means
# each "sample" from the dataset actually has multiple samples
# that will collapse into the batch dimension (for example in
# the RACE dataset that has several options), we need to
# account for that when setting the micro batch size.
sample_multiplier = ds.sample_multiplier
else:
sample_multiplier = 1
micro_batch_size_times_data_parallel = args.orig_micro_batch_size * args.data_parallel_size
num_micro_batches = args.orig_global_batch_size // micro_batch_size_times_data_parallel
def loss_func(output_predictions, labels, output_tensor):
args = get_args()
logits = output_tensor
loss_dict = {}
# Add output predictions.
if output_predictions:
assert False
loss_dict['softmaxes'] = torch.nn.Softmax(dim=-1)(
logits.float()).data.cpu().numpy().tolist()
loss_dict['labels'] = labels.data.cpu().numpy().tolist()
loss_dict['ids'] = batch['uid'].cpu().numpy().tolist()
# Compute the correct answers.
if args.finetune and args.task == 'CoLA':
predicted = torch.argmax(logits, dim=-1)
loss_dict['labels'] = labels.data.cpu().numpy().tolist()
loss_dict['predicted'] = predicted.data.cpu().numpy().tolist()
elif args.finetune and args.task == 'STS-B':
predicted = torch.squeeze(logits)
loss_dict['labels'] = labels.data.cpu().numpy().tolist()
loss_dict['predicted'] = predicted.data.cpu().numpy().tolist()
else:
predicted = torch.argmax(logits, dim=-1)
corrects = (predicted == labels)
# Add to the counters.
loss_dict['total'] = labels.size(0)
loss_dict['correct'] = corrects.sum().item()
return 0, loss_dict
# defined inside to capture output_predictions
def correct_answers_forward_step(batch, model):
try:
batch_ = next(batch)
except BaseException:
batch_ = batch
tokens, types, labels, attention_mask = process_batch(batch_)
# Forward model.
args = get_args()
output_tensor = model(tokens, attention_mask, tokentype_ids=types)
return output_tensor, partial(loss_func, output_predictions, labels)
with torch.no_grad():
# For all the batches in the dataset.
total = 0
correct = 0
labels = []
predicted = []
if output_predictions:
# This option is only possible when data parallel size is 1.
assert mpu.get_data_parallel_world_size() == 1
softmaxes = []
labels = []
ids = []
for _, batch in enumerate(dataloader):
# For evaluation only mode we use drop_last = False to get all the
# samples, which means we might not have a full batch, so we
# adjust batch_size here to actual batch size of data
actual_batch_size = len(batch['label'])
# ... applying sample_multiplier if necessary
args.micro_batch_size = actual_batch_size * sample_multiplier
args.global_batch_size = actual_batch_size * sample_multiplier * num_micro_batches
loss_dicts = forward_backward_func(correct_answers_forward_step, batch, model,
optimizer=None, timers=None, forward_only=True)
for loss_dict in loss_dicts:
if output_predictions:
softmaxes.extend(loss_dict['softmaxes'])
labels.extend(loss_dict['labels'])
ids.extend(loss_dict['ids'])
if args.finetune and args.task in ['CoLA', 'STS-B']:
labels.extend(loss_dict['labels'])
predicted.extend(loss_dict['predicted'])
else:
total += loss_dict['total']
correct += loss_dict['correct']
for m in model:
m.train()
args.micro_batch_size = saved_micro_batch_size
args.global_batch_size = saved_global_batch_size
# Reduce.
if mpu.is_pipeline_last_stage():
if args.finetune and args.task in ['CoLA', 'STS-B']:
if args.task == 'CoLA':
labels = get_accelerator().LongTensor(labels)
predicted = get_accelerator().LongTensor(predicted)
labels_gather = [torch.zeros(len(labels), dtype=torch.long,
device=labels.device) for _ in range(mpu.get_data_parallel_world_size())]
predicted_gather = [torch.zeros(len(predicted), dtype=torch.long,
device=predicted.device) for _ in range(mpu.get_data_parallel_world_size())]
else:
labels = get_accelerator().FloatTensor(labels)
predicted = get_accelerator().FloatTensor(predicted)
labels_gather = [torch.zeros(len(labels), dtype=torch.float,
device=labels.device) for _ in range(mpu.get_data_parallel_world_size())]
predicted_gather = [torch.zeros(len(predicted), dtype=torch.float,
device=predicted.device) for _ in range(mpu.get_data_parallel_world_size())]
torch.distributed.all_gather(labels_gather, labels,
group=mpu.get_data_parallel_group())
torch.distributed.all_gather(predicted_gather, predicted,
group=mpu.get_data_parallel_group())
labels_gather = sum([x.data.cpu().numpy().tolist() for x in labels_gather], [])
predicted_gather = sum([x.data.cpu().numpy().tolist() for x in predicted_gather], [])
# Print on screen.
if args.task == 'CoLA':
from sklearn.metrics import matthews_corrcoef
mcc = matthews_corrcoef(labels_gather, predicted_gather)
elapsed_time = time.time() - start_time
print_rank_last(' > |epoch: {}| metrics for {}: mcc '
'= {} , elapsed time (sec): {:.3f}'.format(
epoch, name, mcc, elapsed_time))
else:
from scipy.stats import pearsonr, spearmanr
pearson_corr = pearsonr(predicted_gather, labels_gather)[0]
spearman_corr = spearmanr(predicted_gather, labels_gather)[0]
corr = (pearson_corr + spearman_corr) / 2
elapsed_time = time.time() - start_time
print_rank_last(' > |epoch: {}| metrics for {}: pearson '
'= {} spearmanr = {} corr = {} elapsed time (sec): {:.3f}'.format(
epoch, name, pearson_corr, spearman_corr,
corr, elapsed_time))
if output_predictions:
return 0, 0, ()
return 0, 0
else:
unreduced = get_accelerator().LongTensor([correct, total])
torch.distributed.all_reduce(unreduced,
group=mpu.get_data_parallel_group())
# Print on screen.
correct_ans = unreduced[0].item()
total_count = unreduced[1].item()
percent = float(correct_ans) * 100.0 / float(total_count)
elapsed_time = time.time() - start_time
print_rank_last(' > |epoch: {}| metrics for {}: correct / total '
'= {} / {} = {:.4f} %, elapsed time (sec): {:.3f}'.format(
epoch, name, correct_ans, total_count,
percent, elapsed_time))
if output_predictions:
return correct_ans, total_count, (softmaxes, labels, ids)
return correct_ans, total_count
if output_predictions:
return 0, 0, ()
return 0, 0
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