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Change dygraph transformer speed to ips #4936

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Change dygraph transformer speed to ips #4936

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67a05a2
change speed to ips; and add reader cost; test=develop
phlrain Nov 4, 2020
f5287ca
Merge branch 'develop' of https://github.com/PaddlePaddle/models into…
phlrain Nov 4, 2020
8e40e77
s to sec; test=develop
phlrain Nov 11, 2020
163c1ad
Merge branch 'develop' of https://github.com/PaddlePaddle/models into…
phlrain Nov 11, 2020
2be6a7c
change seq2seq s to sec; test=develop
phlrain Nov 11, 2020
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2 changes: 1 addition & 1 deletion dygraph/seq2seq/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -180,7 +180,7 @@ def eval(data, epoch_id=0):
batch_times.append(train_batch_cost)
if batch_id > 0 and batch_id % 100 == 0:
print(
"-- Epoch:[%d]; Batch:[%d]; ppl: %.5f, batch_cost: %.5f s, reader_cost: %.5f s, ips: %.5f words/s"
"-- Epoch:[%d]; Batch:[%d]; ppl: %.5f, batch_cost: %.5f s, reader_cost: %.5f s, ips: %.5f words/sec"
% (epoch_id, batch_id, np.exp(total_loss.numpy() /
word_count),
train_batch_cost, total_reader_cost / 100,
Comment on lines +183 to 186
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train_batch_cost需求平均

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13 changes: 10 additions & 3 deletions dygraph/transformer/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -156,10 +156,13 @@ def do_train(args):
batch_id = 0
batch_start = time.time()
interval_word_num = 0.0
total_reader_costs = 0.0
batch_reader_start = time.time()
for input_data in train_loader():
if args.max_iter and step_idx == args.max_iter: #NOTE: used for benchmark
return
batch_reader_end = time.time()
total_reader_costs += batch_reader_end - batch_reader_start

(src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
trg_slf_attn_bias, trg_src_attn_bias, lbl_word,
Expand Down Expand Up @@ -201,13 +204,16 @@ def do_train(args):
logger.info(
"step_idx: %d, epoch: %d, batch: %d, avg loss: %f, "
"normalized loss: %f, ppl: %f, avg_speed: %.2f step/s, "
"words speed: %0.2f words/s" %
"reader cost: %0.2f sec, ips: %0.2f words/sec" %
(step_idx, pass_id, batch_id, total_avg_cost,
total_avg_cost - loss_normalizer,
np.exp([min(total_avg_cost, 100)]),
train_avg_batch_cost, word_speed))
batch_start = time.time()
train_avg_batch_cost,
total_reader_costs / args.print_step, word_speed))

interval_word_num = 0.0
total_reader_costs = 0.0
batch_start = time.time()

if step_idx % args.save_step == 0 and step_idx != 0:
# validation
Expand Down Expand Up @@ -250,6 +256,7 @@ def do_train(args):

batch_id += 1
step_idx += 1
batch_reader_start = time.time()

train_epoch_cost = time.time() - epoch_start
ce_time.append(train_epoch_cost)
Expand Down