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data_wrangle

README.md

FlexLLMGen for Data Wrangling Tasks.

Here we show how to use FlexLLMGen for the data wrangling tasks including entity match (EM), data imputation (DI) and error detection (ED). The implementation follows the fm_data_tasks repo from HazyResearch.

Install

  • Run our install.sh scripts to install additional python library and download the desired datasets.

Examples

  • To check the outcome and verify the result of a data imputation task (e.g., Restaurant on OPT-6.7B), run:

    bash test_single_query_case.sh

  • To test the throughput of FlexLLMGen for a data imputation task (e.g., Restaurant on OPT-6.7B), run:

    bash test_batch_query_case.sh

  • To run the complete tests of all tasks on OPT-6.7B:

    bash test_batch_query_all_opt6.7b.sh

  • To run the complete tests of all tasks on OPT-30B:

    bash test_batch_query_all_opt30b.sh

  • To run the complete tests of all tasks on OPT-175B:

    bash test_batch_query_all_opt175b.sh

Benchmark Results

  • Notice that in this data wrangling tasks, such as entity match (EM), data imputation (DI) and error detection (ED), the input sequences length is very long (from 123 to 1274), but the output length is very short (e.g., 3, 5, or 10). Most of the inference time is spent on prefill phase, so here we report the throughput that includes both input and output tokens as our measurement.

  • We run the experiments on the same setting as the HELM benchmark with a single T4 (16GB) GPU, 200GB of DRAM, and 1.5TB SSD connected by NVMe.

OPT6.7B

Task Tested Samples Input Length Output Length Time (s) Input + Output Throughput (token/s)
EM: Fodors-Zagats 189 744 3 109.556 1281.871
EM: Beer 91 592 3 42.087 1272.360
EM: iTunes-Amazon 109 529 3 59.467 966.178
EM: Walmart-Amazon 200 748 3 126.538 1186.992
EM: Amazon-Google 200 876 3 144.593 1215.828
EM: DBLP-ACM 200 1274 3 207.513 1230.767
EM: DBLP-GoogleScholar 200 1209 3 232.65 1097.78
DI: Restaurant 86 123 5 10.397 984.865
DI: Buy 65 488 10 43.077 739.876
ED: Hospital 200 200 3 30.137 1347.203

OPT30B

Task Tested Samples Input Length Output Length Time (s) Input + Output Throughput (token/s)
EM: Fodors-Zagats 189 744 3 541.550 248.287
EM: Beer 91 592 3 238.58 224.450
EM: iTunes-Amazon 109 529 3 267.639 198.775
EM: Walmart-Amazon 200 748 3 682.635 220.030
EM: Amazon-Google 200 876 3 799.514 219.884
EM: DBLP-ACM 200 1274 3 1119.272 228.184
EM: DBLP-GoogleScholar 200 1209 3 1271.534 190.636
DI: Restaurant 86 123 5 60.310 169.790
DI: Buy 65 488 10 185.882 160.747
ED: Hospital 200 200 3 158.329 256.429

OPT175B

Task Tested Samples Input Length Output Length Time (s) Input + Output Throughput (token/s)
EM: Fodors-Zagats 189 744 3 3928.310 34.228
EM: Beer 91 592 3 1356.786 35.083
EM: iTunes-Amazon 109 529 3 1569.062 33.906
EM: Walmart-Amazon 200 748 3 4171.319 36.008
EM: Amazon-Google 200 876 3 4893.572 35.925
EM: DBLP-ACM 200 1274 3 7624.726 33.496
EM: DBLP-GoogleScholar 200 1209 3 8275.828 29.290
DI: Restaurant 86 123 5 648.762 16.968
DI: Buy 65 488 10 2086.961 14.317
ED: Hospital 200 200 3 1154.133 35.178