[GPT-J] Use the float16 checkpoints in integration tests
#13676
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patil-suraj
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Sep 21, 2021
tests/test_modeling_gptj.py
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| # Marked as @tooslow due to GPU OOM (issue #13676) | ||
| tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B", revision="float16") | ||
| model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16) |
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for this test, I think we could use a smaller random model, since this test does not really test generations but only test the max time constraints. WDYT @LysandreJik @anton-l
LysandreJik
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Sep 21, 2021
tests/test_modeling_gptj.py
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| # Marked as @tooslow due to GPU OOM (issue #13676) | ||
| tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B", revision="float16") | ||
| model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16) |
…ion-tests # Conflicts: # tests/test_modeling_gptj.py
Narsil
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Sep 25, 2021
…ce#13676) * Use fp16 checkpoints * Style * Fix outputs and disable OOM tests * Correct another output * Use a random smaller model for generation tests * repo quickfix * fix gradient checkpointing
stas00
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Oct 12, 2021
…ce#13676) * Use fp16 checkpoints * Style * Fix outputs and disable OOM tests * Correct another output * Use a random smaller model for generation tests * repo quickfix * fix gradient checkpointing
Albertobegue
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Jan 13, 2022
…ce#13676) * Use fp16 checkpoints * Style * Fix outputs and disable OOM tests * Correct another output * Use a random smaller model for generation tests * repo quickfix * fix gradient checkpointing
Albertobegue
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Jan 27, 2022
…ce#13676) * Use fp16 checkpoints * Style * Fix outputs and disable OOM tests * Correct another output * Use a random smaller model for generation tests * repo quickfix * fix gradient checkpointing
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This PR switches GPTJ checkpoints in the integration tests to fp16 to test if they're able to run on our daily CI.
At the moment, fp32 checkpoints are timing out either during model downloads or initialization:
Note that this doesn't guarantee reproducibility of the old tests (some tokens may be different), but it could help with caching the models on the runner to avoid timeouts.