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OV POT integration for QuestionAnswering task #4
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dkurt
reviewed
Jan 13, 2022
dkurt
reviewed
Jan 13, 2022
dkurt
reviewed
Jan 13, 2022
dkurt
reviewed
Jan 13, 2022
dkurt
reviewed
Jan 13, 2022
dkurt
reviewed
Jan 14, 2022
| task: squad | ||
| batch_size : 1 | ||
| max_length : 384 | ||
| calib_size: 50 |
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Is that parameter regulates a number of samples in dataset? BTW, should we specify stat_subset_size as well to specify a number of samples for quantization.
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Yes, actually, stat_subset_size can be specified through the config file as well..I will remove the calib_size parameter since its redundant
dkurt
reviewed
Jan 14, 2022
| @@ -0,0 +1,14 @@ | |||
| model: | |||
| model_ir_path : 'model_ir' | |||
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I'm wondering if we can use cache directory to store IR files. Let me check and add update here.
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There is an error on CI: Please workaround it by torch.sqrt = lambda x: torch.pow(x, 0.5) |
dkurt
reviewed
Feb 9, 2022
| runs-on: ubuntu-18.04 | ||
| strategy: | ||
| matrix: | ||
| transformers-version: [4.9.1, 4.11.0, latest] |
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Let's test only 4.11.0:
python -m pip install transformers==4.11.0 torch==1.9.1
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Hi, @karkadad! Do you mind if I push to this branch to resolve CI issues? |
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@dkurt Please go ahead, thanks |
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What does this PR do?
This PR includes an implementation to integrate OpenVINO POT APIs for the QuestionAnswering task for 8-bit post-training quantization.
@dkurt Please review.
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