def evaluate(data_eval, model, nsp_loss, mlm_loss, vocab_size, ctx):
"""Evaluation function."""
mlm_metric = MaskedAccuracy()
nsp_metric = MaskedAccuracy()
mlm_metric.reset()
nsp_metric.reset()
eval_begin_time = time.time()
begin_time = time.time()
step_num = 0
local_mlm_loss = local_nsp_loss = 0
total_mlm_loss = total_nsp_loss = 0
local_num_tks = 0
for _, dataloader in enumerate(data_eval):
for _, data in enumerate(dataloader):
step_num += 1
data_list = split_and_load(data, ctx)
loss_list = []
ns_label_list, ns_pred_list = [], []
mask_label_list, mask_pred_list, mask_weight_list = [], [], []
for data in data_list:
out = forward(data, model, mlm_loss, nsp_loss, vocab_size)
(ls, next_sentence_label, classified, masked_id, decoded,
masked_weight, ls1, ls2, valid_length) = out
loss_list.append(ls)
ns_label_list.append(next_sentence_label)
ns_pred_list.append(classified)
mask_label_list.append(masked_id)
mask_pred_list.append(decoded)
mask_weight_list.append(masked_weight)
local_mlm_loss += ls1.as_in_context(mx.cpu())
local_nsp_loss += ls2.as_in_context(mx.cpu())
local_num_tks += valid_length.sum().as_in_context(mx.cpu())
nsp_metric.update(ns_label_list, ns_pred_list)
mlm_metric.update(mask_label_list, mask_pred_list,
mask_weight_list)
# logging
if (step_num + 1) % (args.log_interval) == 0:
total_mlm_loss += local_mlm_loss
total_nsp_loss += local_nsp_loss
log(begin_time, local_num_tks, local_mlm_loss, local_nsp_loss,
step_num, mlm_metric, nsp_metric, None)
begin_time = time.time()
local_mlm_loss = local_nsp_loss = local_num_tks = 0
mlm_metric.reset_local()
nsp_metric.reset_local()
mx.nd.waitall()
eval_end_time = time.time()
total_mlm_loss /= step_num
total_nsp_loss /= step_num
logging.info(
'mlm_loss={:.3f}\tmlm_acc={:.1f}\tnsp_loss={:.3f}\tnsp_acc={:.1f}\t'.
format(total_mlm_loss.asscalar(),
mlm_metric.get_global()[1] * 100, total_nsp_loss.asscalar(),
nsp_metric.get_global()[1] * 100))
logging.info('Eval cost={:.1f}s'.format(eval_end_time - eval_begin_time))
def evaluate(data_eval, model, mlm_loss, vocab_size, ctx, log_interval, dtype,
mlm_weight=1.0, teacher_ce_loss=None, teacher_mse_loss=None, teacher_model=None, teacher_ce_weight=0.0,
distillation_temperature=1.0, log_tb=None):
"""Evaluation function."""
logging.info('Running evaluation ... ')
mlm_metric = MaskedAccuracy()
mlm_metric.reset()
eval_begin_time = time.time()
begin_time = time.time()
step_num = 0
running_mlm_loss = 0
total_mlm_loss = 0
running_teacher_ce_loss = running_teacher_mse_loss = 0
total_teacher_ce_loss = total_teacher_mse_loss = 0
running_num_tks = 0
for _, dataloader in tqdm(enumerate(data_eval), desc="Evaluation"):
step_num += 1
data_list = [[seq.as_in_context(context) for seq in shard]
for context, shard in zip(ctx, dataloader)]
loss_list = []
ns_label_list, ns_pred_list = [], []
mask_label_list, mask_pred_list, mask_weight_list = [], [], []
for data in data_list:
out = forward(data, model, mlm_loss, vocab_size, dtype, is_eval=True,
mlm_weight=mlm_weight,
teacher_ce_loss=teacher_ce_loss, teacher_mse_loss=teacher_mse_loss,
teacher_model=teacher_model, teacher_ce_weight=teacher_ce_weight,
distillation_temperature=distillation_temperature)
(loss_val, next_sentence_label, classified, masked_id,
decoded, masked_weight, mlm_loss_val, teacher_ce_loss_val, teacher_mse_loss_val, valid_length) = out
loss_list.append(loss_val)
ns_label_list.append(next_sentence_label)
ns_pred_list.append(classified)
mask_label_list.append(masked_id)
mask_pred_list.append(decoded)
mask_weight_list.append(masked_weight)
running_mlm_loss += mlm_loss_val.as_in_context(mx.cpu())
running_num_tks += valid_length.sum().as_in_context(mx.cpu())
running_teacher_ce_loss += teacher_ce_loss_val.as_in_context(
mx.cpu())
running_teacher_mse_loss += teacher_mse_loss_val.as_in_context(
mx.cpu())
mlm_metric.update(mask_label_list, mask_pred_list, mask_weight_list)
# logging
if (step_num + 1) % (log_interval) == 0:
total_mlm_loss += running_mlm_loss
total_teacher_ce_loss += running_teacher_ce_loss
total_teacher_mse_loss += running_teacher_mse_loss
log("eval ",
begin_time,
running_num_tks,
running_mlm_loss,
running_teacher_ce_loss,
running_teacher_mse_loss,
step_num,
mlm_metric,
None,
log_interval,
model=model,
log_tb=log_tb)
begin_time = time.time()
running_mlm_loss = running_num_tks = 0
running_teacher_ce_loss = running_teacher_mse_loss = 0
mlm_metric.reset_local()
mx.nd.waitall()
eval_end_time = time.time()
# accumulate losses from last few batches, too
if running_mlm_loss != 0:
total_mlm_loss += running_mlm_loss
total_teacher_ce_loss += running_teacher_ce_loss
total_teacher_mse_loss += running_teacher_mse_loss
total_mlm_loss /= step_num
total_teacher_ce_loss /= step_num
total_teacher_mse_loss /= step_num
logging.info('Eval mlm_loss={:.3f}\tmlm_acc={:.1f}\tteacher_ce={:.2e}\tteacher_mse={:.2e}'
.format(total_mlm_loss.asscalar(), mlm_metric.get_global()[1] * 100,
total_teacher_ce_loss.asscalar(), total_teacher_mse_loss.asscalar()))
logging.info('Eval cost={:.1f}s'.format(eval_end_time - eval_begin_time))
def evaluate(data_eval, model, nsp_loss, mlm_loss, vocab_size, ctx):
"""Evaluation function."""
mlm_metric = MaskedAccuracy()
nsp_metric = MaskedAccuracy()
mlm_metric.reset()
nsp_metric.reset()
eval_begin_time = time.time()
begin_time = time.time()
step_num = 0
# Total loss for the whole dataset
total_mlm_loss = total_nsp_loss = 0
# Running loss, reset when a log is emitted
running_mlm_loss = running_nsp_loss = 0
running_num_tks = 0
for _, dataloader in enumerate(data_eval):
for _, data in enumerate(dataloader):
step_num += 1
data_list = split_and_load(data, ctx)
loss_list = []
ns_label_list, ns_pred_list = [], []
mask_label_list, mask_pred_list, mask_weight_list = [], [], []
# Run inference on the batch, collect the predictions and losses
batch_mlm_loss = batch_nsp_loss = 0
for data in data_list:
out = forward(data, model, mlm_loss, nsp_loss, vocab_size)
(ls, next_sentence_label, classified, masked_id,
decoded, masked_weight, ls1, ls2, valid_length) = out
loss_list.append(ls)
ns_label_list.append(next_sentence_label)
ns_pred_list.append(classified)
mask_label_list.append(masked_id)
mask_pred_list.append(decoded)
mask_weight_list.append(masked_weight)
batch_mlm_loss += ls1.as_in_context(mx.cpu())
batch_nsp_loss += ls2.as_in_context(mx.cpu())
running_num_tks += valid_length.sum().as_in_context(mx.cpu())
running_mlm_loss += batch_mlm_loss
running_nsp_loss += batch_nsp_loss
total_mlm_loss += batch_mlm_loss
total_nsp_loss += batch_nsp_loss
nsp_metric.update(ns_label_list, ns_pred_list)
mlm_metric.update(mask_label_list, mask_pred_list, mask_weight_list)
# Log and reset running loss
if (step_num + 1) % (args.log_interval) == 0:
log(begin_time, running_num_tks, running_mlm_loss, running_nsp_loss,
step_num, mlm_metric, nsp_metric, None)
begin_time = time.time()
running_mlm_loss = running_nsp_loss = running_num_tks = 0
mlm_metric.reset_running()
nsp_metric.reset_running()
mx.nd.waitall()
eval_end_time = time.time()
total_mlm_loss /= step_num
total_nsp_loss /= step_num
logging.info('mlm_loss={:.3f}\tmlm_acc={:.1f}\tnsp_loss={:.3f}\tnsp_acc={:.1f}\t'
.format(total_mlm_loss.asscalar(), mlm_metric.get_global()[1] * 100,
total_nsp_loss.asscalar(), nsp_metric.get_global()[1] * 100))
logging.info('Eval cost={:.1f}s'.format(eval_end_time - eval_begin_time))