Moved the encoded_prompts to correct device
alberduris
Lysandre Debut
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.circleci/config.yml
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.coveragerc
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.github/ISSUE_TEMPLATE/---new-benchmark.md
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.github/stale.yml
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.gitignore
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CONTRIBUTING.md
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MANIFEST.in
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deploy_multi_version_doc.sh
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docker/Dockerfile
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docs/Makefile
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docs/README.md
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docs/source/_static/css/Calibre-Light.ttf
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docs/source/benchmarks.md
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docs/source/bertology.rst
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docs/source/conf.py
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docs/source/converting_tensorflow_models.rst
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docs/source/imgs/warmup_constant_schedule.png
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docs/source/index.rst
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docs/source/installation.md
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docs/source/main_classes/configuration.rst
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docs/source/main_classes/model.rst
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docs/source/main_classes/optimizer_schedules.rst
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docs/source/main_classes/processors.rst
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docs/source/main_classes/tokenizer.rst
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docs/source/migration.md
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docs/source/model_doc/albert.rst
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docs/source/model_doc/camembert.rst
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examples/README.md
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examples/benchmarks.py
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examples/contrib/README.md
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examples/contrib/run_camembert.py
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examples/contrib/run_openai_gpt.py
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examples/contrib/run_swag.py
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examples/contrib/run_transfo_xl.py
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examples/distillation/README.md
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examples/distillation/distiller.py
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examples/distillation/grouped_batch_sampler.py
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examples/distillation/lm_seqs_dataset.py
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examples/distillation/requirements.txt
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examples/distillation/run_squad_w_distillation.py
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examples/distillation/scripts/binarized_data.py
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examples/distillation/scripts/extract.py
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examples/distillation/scripts/extract_distilbert.py
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examples/distillation/scripts/token_counts.py
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examples/distillation/train.py
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examples/distillation/training_configs/distilbert-base-uncased.json
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examples/distillation/training_configs/distilgpt2.json
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examples/distillation/utils.py
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examples/mm-imdb/run_mmimdb.py
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examples/mm-imdb/utils_mmimdb.py
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examples/pplm/README.md
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examples/pplm/pplm_classification_head.py
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examples/pplm/run_pplm.py
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examples/pplm/run_pplm_discrim_train.py
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examples/requirements.txt
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examples/run_bertology.py
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examples/run_generation.py
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@ -212,6 +212,7 @@ def main():
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prepare_input = PREPROCESSING_FUNCTIONS.get(args.model_type)
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prompt_text = prepare_input(args, model, tokenizer, prompt_text)
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encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False, return_tensors="pt")
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encoded_prompt = encoded_prompt.to(args.device)
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output_sequences = model.generate(
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input_ids=encoded_prompt,
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