Detokenization parallelization public repo

[NickNickGo]

Moving #37 here.

[NickNickGo]

transformers_v3.0.2+fastseq_v0.0.4	facebook/bart-large-cnn	cnn_dm.1k/raw	val	32	1024	NA	NA	34.89|14.96|25.30	NA	NA	123	8.3	NA
transformers_v3.0.2+fastseq_v0.0.4	facebook/bart-large-cnn	cnn_dm.1k/raw	val	64	1024	NA	NA	34.92|14.95|25.25	NA	NA	87	11.8	NA
transformers_v3.0.2+fastseq_v0.0.4	facebook/bart-large-cnn	cnn_dm.1k/raw	val	128	1024	NA	NA	34.96|14.98|25.28	NA	NA	82	12.5	NA
transformers_v3.0.2+fastseq_v0.0.4	facebook/bart-large-cnn	cnn_dm.1k/raw	val	32	1024	NA	NA	34.90|14.95|25.30	NA	NA	121	8.5	NA
transformers_v3.0.2+fastseq_v0.0.4	facebook/bart-large-cnn	cnn_dm.1k/raw	val	64	1024	NA	NA	34.93|14.95|25.26	NA	NA	87	11.8	NA
transformers_v3.0.2+fastseq_v0.0.4	facebook/bart-large-cnn	cnn_dm.1k/raw	val	128	1024	NA	NA	34.97|14.96|25.27	NA	NA	81	12.6	NA
transformers_v3.0.2+fastseq_v0.0.4	facebook/bart-large-cnn	cnn_dm.1k/raw	val	32	1024	NA	NA	34.91|14.94|25.25	NA	NA	122	8.4	NA
transformers_v3.0.2+fastseq_v0.0.4	facebook/bart-large-cnn	cnn_dm.1k/raw	val	64	1024	NA	NA	34.93|14.97|25.25	NA	NA	87	11.8	NA
transformers_v3.0.2+fastseq_v0.0.4	facebook/bart-large-cnn	cnn_dm.1k/raw	val	128	1024	NA	NA	34.98|14.96|25.26	NA	NA	81	12.6	NA
Util	Model	Task	Split	BatchSize	Samples	Tokens	Bleu	Rouge	Loss	Perplexity	Runtime(seconds)	Throughput(samples/s)	Throughput(tokens/s)
transformers_v3.0.2+fastseq_v0.0.4	facebook/mbart-large-en-ro	wmt_en_ro/raw	val	64	1984	NA	27.89	NA|NA|NA	NA	NA	274	7.2	NA
transformers_v3.0.2+fastseq_v0.0.4	facebook/mbart-large-en-ro	wmt_en_ro/raw	val	64	1984	NA	27.89	NA|NA|NA	NA	NA	251	7.9	NA
transformers_v3.0.2+fastseq_v0.0.4	facebook/mbart-large-en-ro	wmt_en_ro/raw	val	64	1984	NA	27.89	NA|NA|NA	NA	NA	254	7.8	NA
Util	Model	Task	Split	BatchSize	Samples	Tokens	Bleu	Rouge	Loss	Perplexity	Runtime(seconds)	Throughput(samples/s)	Throughput(tokens/s)
Util	Model	Task	Split	BatchSize	Samples	Tokens	Bleu	Rouge	Loss	Perplexity	Runtime(seconds)	Throughput(samples/s)	Throughput(tokens/s)
transformers_v3.0.2+fastseq_v0.0.4	t5-base	wmt_en_ro/raw	val	64	1984	NA	27.44	NA|NA|NA	NA	NA	162	11.2	NA
transformers_v3.0.2+fastseq_v0.0.4	t5-base	wmt_en_ro/raw	val	128	1984	NA	27.38	NA|NA|NA	NA	NA	107	17.1	NA
transformers_v3.0.2+fastseq_v0.0.4	t5-base	wmt_en_ro/raw	val	64	1984	NA	27.44	NA|NA|NA	NA	NA	134	13.8	NA
transformers_v3.0.2+fastseq_v0.0.4	t5-base	wmt_en_ro/raw	val	128	1984	NA	27.38	NA|NA|NA	NA	NA	106	17.3	NA
transformers_v3.0.2+fastseq_v0.0.4	t5-base	wmt_en_ro/raw	val	64	1984	NA	27.44	NA|NA|NA	NA	NA	129	13.8	NA
transformers_v3.0.2+fastseq_v0.0.4	t5-base	wmt_en_ro/raw	val	128	1984	NA	27.38	NA|NA|NA	NA	NA	107	17.1	NA
Util	Model	Task	Split	BatchSize	Samples	Tokens	Bleu	Rouge	Loss	Perplexity	Runtime(seconds)	Throughput(samples/s)	Throughput(tokens/s)
transformers_v3.0.2+fastseq_v0.0.4	hf.sshleifer.distilbart-cnn-12-6.tar.gz	cnn_dm.1k/raw	val	64	1024	NA	NA	35.18|15.03|25.03	NA	NA	68	15.1	NA
transformers_v3.0.2+fastseq_v0.0.4	hf.sshleifer.distilbart-cnn-12-6.tar.gz	cnn_dm.1k/raw	val	128	1024	NA	NA	35.20|15.14|25.07	NA	NA	64	15.9	NA
transformers_v3.0.2+fastseq_v0.0.4	hf.sshleifer.distilbart-cnn-12-6.tar.gz	cnn_dm.1k/raw	val	64	1024	NA	NA	35.21|15.07|25.00	NA	NA	68	15.1	NA
transformers_v3.0.2+fastseq_v0.0.4	hf.sshleifer.distilbart-cnn-12-6.tar.gz	cnn_dm.1k/raw	val	128	1024	NA	NA	35.19|15.10|25.08	NA	NA	65	15.8	NA
transformers_v3.0.2+fastseq_v0.0.4	hf.sshleifer.distilbart-cnn-12-6.tar.gz	cnn_dm.1k/raw	val	64	1024	NA	NA	35.21|15.04|25.04	NA	NA	67	15.3	NA
transformers_v3.0.2+fastseq_v0.0.4	hf.sshleifer.distilbart-cnn-12-6.tar.gz	cnn_dm.1k/raw	val	128	1024	NA	NA	35.18|15.10|25.06	NA	NA	65	15.8	NA
~

[JiushengChen]

CNN 1k data set is too small now, result is not reliable. Please use full valid set.

[NickNickGo]

transformers_v3.0.2+fastseq_v0.0.4	facebook/bart-large-cnn	cnn_dm/raw	val	32	13344	NA	NA	44.80|21.64|31.17	NA	NA	1763	7.6	NA
transformers_v3.0.2+fastseq_v0.0.4	facebook/bart-large-cnn	cnn_dm/raw	val	64	13312	NA	NA	44.79|21.66|31.18	NA	NA	1174	11.3	NA
transformers_v3.0.2+fastseq_v0.0.4	facebook/bart-large-cnn	cnn_dm/raw	val	128	13312	NA	NA	44.78|21.64|31.16	NA	NA	1075	12.4	NA
transformers_v3.0.2+fastseq_v0.0.4	hf.sshleifer.distilbart-cnn-12-6.tar.gz	cnn_dm/raw	val	64	13312	NA	NA	45.06|21.81|30.91	NA	NA	812	16.4	NA
transformers_v3.0.2+fastseq_v0.0.4	hf.sshleifer.distilbart-cnn-12-6.tar.gz	cnn_dm/raw	val	128	13312	NA	NA	45.05|21.79|30.90	NA	NA	725	18.4	NA

[feihugis]

For bart-large-cnn, why are the numbers of input examples different for different batch_sizes? Could you also paste the result for the baseline?

[NickNickGo]

For bart-large-cnn, why are the numbers of input examples different for different batch_sizes? Could you also paste the result for the baseline?

This is because pytorch dataloader API only supports number of samples to be multiple of batch size. This is why last batch is dropped.
https://pytorch.org/docs/stable/data.html

[NickNickGo]

@JiushengChen For Mbart and T5 , do we have larger dataset? I couldn't find it in benchmark scripts.

[feihugis]

For bart-large-cnn, why are the numbers of input examples different for different batch_sizes? Could you also paste the result for the baseline?

This is because pytorch dataloader API only supports number of samples to be multiple of batch size. This is why last batch is dropped.
https://pytorch.org/docs/stable/data.html

The root cause may be here. Try to change it to be drop_last=False?

[JiushengChen]

@JiushengChen For Mbart and T5 , do we have larger dataset? I couldn't find it in benchmark scripts.

Yes, I have larger data in my local. Please leave out these two, I will update them today.
BTW, looks CI test failed, please take a look.

[NickNickGo]

For bart-large-cnn, why are the numbers of input examples different for different batch_sizes? Could you also paste the result for the baseline?

This is because pytorch dataloader API only supports number of samples to be multiple of batch size. This is why last batch is dropped.
https://pytorch.org/docs/stable/data.html

The root cause may be here. Try to change it to be drop_last=False?

I already did.

[feihugis]

For bart-large-cnn, why are the numbers of input examples different for different batch_sizes? Could you also paste the result for the baseline?

This is because pytorch dataloader API only supports number of samples to be multiple of batch size. This is why last batch is dropped.
https://pytorch.org/docs/stable/data.html

The root cause may be here. Try to change it to be drop_last=False?

I already did.

Could you please explain more? I saw your code here use drop_last=True. I guess that's why the last batch was dropped. Do you mean you have tried drop_last=False but the last batch was still dropped?

[JiushengChen]

same here

[NickNickGo]

Benchmarks on Larger dataset:

Util	Model	Task	Split	BatchSize	Samples	Tokens	Bleu	Rouge	Loss	Perplexity	Runtime(seconds)	Throughput(samples/s)	Throughput(tokens/s)
transformers_v3.0.2+fastseq_v0.0.4	facebook/bart-large-cnn	cnn_dm/raw	val	32	13368	NA	NA	44.80|21.65|31.19	NA	NA	1889	7.1	NA
transformers_v3.0.2+fastseq_v0.0.4	facebook/bart-large-cnn	cnn_dm/raw	val	64	13368	NA	NA	44.80|21.66|31.19	NA	NA	1188	11.3	NA
transformers_v3.0.2+fastseq_v0.0.4	facebook/bart-large-cnn	cnn_dm/raw	val	128	13368	NA	NA	44.78|21.64|31.18	NA	NA	1082	12.4	NA
Util	Model	Task	Split	BatchSize	Samples	Tokens	Bleu	Rouge	Loss	Perplexity	Runtime(seconds)	Throughput(samples/s)	Throughput(tokens/s)
transformers_v3.0.2+fastseq_v0.0.4	hf.sshleifer.distilbart-cnn-12-6.tar.gz	cnn_dm/raw	val	64	13368	NA	NA	45.07|21.81|30.91	NA	NA	810	16.5	NA
transformers_v3.0.2+fastseq_v0.0.4	hf.sshleifer.distilbart-cnn-12-6.tar.gz	cnn_dm/raw	val	128	13368	NA	NA	45.05|21.80|30.90	NA	NA	729	18.3	NA
Util	Model	Task	Split	BatchSize	Samples	Tokens	Bleu	Rouge	Loss	Perplexity	Runtime(seconds)	Throughput(samples/s)	Throughput(tokens/s)
transformers_v3.0.2+fastseq_v0.0.4	facebook/mbart-large-en-ro	wmt_en_ro/raw	val	64	8191	NA	56.19	NA|NA|NA	NA	NA	897	9.1	NA
transformers_v3.0.2+fastseq_v0.0.4	facebook/mbart-large-en-ro	wmt_en_ro/raw	val	64	8191	NA	56.19	NA|NA|NA	NA	NA	884	9.3	NA
transformers_v3.0.2+fastseq_v0.0.4	facebook/mbart-large-en-ro	wmt_en_ro/raw	val	64	8191	NA	56.19	NA|NA|NA	NA	NA	885	9.3	NA
Util	Model	Task	Split	BatchSize	Samples	Tokens	Bleu	Rouge	Loss	Perplexity	Runtime(seconds)	Throughput(samples/s)	Throughput(tokens/s)
transformers_v3.0.2+fastseq_v0.0.4	t5-base	wmt_en_ro/raw	val	64	8191	NA	56.93	NA|NA|NA	NA	NA	425	19.3	NA
transformers_v3.0.2+fastseq_v0.0.4	t5-base	wmt_en_ro/raw	val	128	8191	NA	56.92	NA|NA|NA	NA	NA	350	23.4	NA
transformers_v3.0.2+fastseq_v0.0.4	t5-base	wmt_en_ro/raw	val	64	8191	NA	56.93	NA|NA|NA	NA	NA	436	18.8	NA
transformers_v3.0.2+fastseq_v0.0.4	t5-base	wmt_en_ro/raw	val	128	8191	NA	56.92	NA|NA|NA	NA	NA	362	22.6	NA
transformers_v3.0.2+fastseq_v0.0.4	t5-base	wmt_en_ro/raw	val	64	8191	NA	56.93	NA|NA|NA	NA	NA	442	18.5	NA
transformers_v3.0.2+fastseq_v0.0.4	t5-base	wmt_en_ro/raw	val	128	8191	NA	56.92	NA|NA|NA	NA	NA	340	24.1	NA

[NickNickGo]

Before/After

.	.	.
DistilBart	13.8	18.3
T5	13.8	23.4
BART	11.4	12.4
Mbart	8.9	9.3

[NickNickGo]

For bart-large-cnn, why are the numbers of input examples different for different batch_sizes? Could you also paste the result for the baseline?

This is because pytorch dataloader API only supports number of samples to be multiple of batch size. This is why last batch is dropped.
https://pytorch.org/docs/stable/data.html

The root cause may be here. Try to change it to be drop_last=False?

I already did.

Could you please explain more? I saw your code here use drop_last=True. I guess that's why the last batch was dropped. Do you mean you have tried drop_last=False but the last batch was still dropped?

Synced offline. Included last batch.