T5 speed about fastseq

[JiushengChen]

Thanks for the study! I also checked with a few users, they don't care too much about Python version. But they uses some popular docker images provided by Torch and Nvidia etc. I think most are using Python 3.6. And consider the diff above is not so much. I suggest we just use our existing docker image, which is 3.6.9.

[feihugis]

The expected score is set based on the result without docker. Let me re-run it using docker.

[feihugis]

@JiushengChen I got the following result by using docker on gpu4. It is still different from yours. Which version of Python are you using?

• use Python==3.8.3

Util	Model	Task	Split	BatchSize	Samples	Tokens	Bleu	Rouge	Loss	Perplexity	Runtime(seconds)	Throughput(samples/s)	Throughput(tokens/s)
transformers_v3.0.2	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.44	NA|NA|NA	NA	NA	378	5.3	NA
transformers_v3.0.2	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.44	NA|NA|NA	NA	NA	367	5.4	NA
transformers_v3.0.2	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.44	NA|NA|NA	NA	NA	369	5.4	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_v0.0.3	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.43	NA|NA|NA	NA	NA	275	7.3	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	128	1999	NA	27.42	NA|NA|NA	NA	NA	246	8.1	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.43	NA|NA|NA	NA	NA	279	7.2	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	128	1999	NA	27.42	NA|NA|NA	NA	NA	247	8.1	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.43	NA|NA|NA	NA	NA	285	7.0	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	128	1999	NA	27.42	NA|NA|NA	NA	NA	259	7.7	NA

• use the default python (3.6.9) in the docker

Util	Model	Task	Split	BatchSize	Samples	Tokens	Bleu	Rouge	Loss	Perplexity	Runtime(seconds)	Throughput(samples/s)	Throughput(tokens/s)
transformers_v3.0.2	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.44	NA|NA|NA	NA	NA	407	4.9	NA
transformers_v3.0.2	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.44	NA|NA|NA	NA	NA	407	4.9	NA
transformers_v3.0.2	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.44	NA|NA|NA	NA	NA	406	4.9	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_v0.0.3	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.43	NA|NA|NA	NA	NA	318	6.3	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	128	1999	NA	27.42	NA|NA|NA	NA	NA	296	6.8	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.43	NA|NA|NA	NA	NA	317	6.3	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	128	1999	NA	27.42	NA|NA|NA	NA	NA	298	6.7	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.43	NA|NA|NA	NA	NA	330	6.1	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	128	1999	NA	27.42	NA|NA|NA	NA	NA	294	6.8	NA

I guess the cause is that @replace(...) is not fully compatible with Python-3.6.9 yet and some optimizations did not take into effect. This issue is on my list. Will fix it once I finish the logging issue reported by the user.

[JiushengChen]

I am using docker from docker/Dockerfile. It uses Python 3.6.9 :: Anaconda, Inc. Good to know root cause identified.

[feihugis]

Benchmark results with Docker

• Python-3.6.9

Util	Model	Task	Split	BatchSize	Samples	Tokens	Bleu	Rouge	Loss	Perplexity	Runtime(seconds)	Throughput(samples/s)	Throughput(tokens/s)
transformers_v3.0.2	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.44	NA|NA|NA	NA	NA	402	5.0	NA
transformers_v3.0.2	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.44	NA|NA|NA	NA	NA	403	5.0	NA
transformers_v3.0.2	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.44	NA|NA|NA	NA	NA	399	5.0	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_v0.0.3	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.43	NA|NA|NA	NA	NA	326	6.1	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	128	1999	NA	27.42	NA|NA|NA	NA	NA	278	7.2	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.43	NA|NA|NA	NA	NA	314	6.4	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	128	1999	NA	27.42	NA|NA|NA	NA	NA	280	7.1	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.43	NA|NA|NA	NA	NA	312	6.4	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	128	1999	NA	27.42	NA|NA|NA	NA	NA	278	7.2	NA

• Python-3.8.3

Util	Model	Task	Split	BatchSize	Samples	Tokens	Bleu	Rouge	Loss	Perplexity	Runtime(seconds)	Throughput(samples/s)	Throughput(tokens/s)
transformers_v3.0.2	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.44	NA|NA|NA	NA	NA	388	5.2	NA
transformers_v3.0.2	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.44	NA|NA|NA	NA	NA	385	5.2	NA
transformers_v3.0.2	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.44	NA|NA|NA	NA	NA	379	5.3	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_v0.0.3	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.43	NA|NA|NA	NA	NA	297	6.7	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	128	1999	NA	27.42	NA|NA|NA	NA	NA	268	7.5	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.43	NA|NA|NA	NA	NA	286	7.0	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	128	1999	NA	27.42	NA|NA|NA	NA	NA	252	7.9	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	64	1999	NA	27.43	NA|NA|NA	NA	NA	282	7.1	NA
transformers_v3.0.2+fastseq_v0.0.3	t5-base	wmt_en_ro/raw	val	128	1999	NA	27.42	NA|NA|NA	NA	NA	260	7.7	NA

[feihugis]

@JiushengChen Sounds good! Let's use Python-3.6.9 for our benchmarks and tests.