From 273617b86dbe5cd15afb795e994dffc44e09e2df Mon Sep 17 00:00:00 2001 From: thomwolf Date: Thu, 11 Jul 2019 22:45:03 +0200 Subject: [PATCH] update config - fix gpt/gpt-2 from pretrained --- .circleci/config.yml | 2 +- pytorch_transformers/modeling_gpt2.py | 2 +- pytorch_transformers/modeling_openai.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 78358d1188..65e392d2da 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -24,7 +24,7 @@ jobs: - checkout - run: sudo pip install --progress-bar off . - run: sudo pip install pytest codecov pytest-cov - - run: sudo pip install tensorboardX scikit-learn + - run: sudo pip install tensorboardX scikit-learn mock - run: python -m pytest -sv ./pytorch_transformers/tests/ --cov - run: python -m pytest -sv ./examples/ - run: codecov diff --git a/pytorch_transformers/modeling_gpt2.py b/pytorch_transformers/modeling_gpt2.py index 66ff4e7185..29d1cbae42 100644 --- a/pytorch_transformers/modeling_gpt2.py +++ b/pytorch_transformers/modeling_gpt2.py @@ -423,7 +423,7 @@ class GPT2PreTrainedModel(PreTrainedModel): """ num_special_tokens = kwargs.pop('num_special_tokens', None) - model = PreTrainedModel.from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs) + model = super(PreTrainedModel, cls).from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) # Add additional embeddings for special tokens if needed # This step also make sure we are still sharing the output and input embeddings after loading weights diff --git a/pytorch_transformers/modeling_openai.py b/pytorch_transformers/modeling_openai.py index c81d082c70..aa35b163f1 100644 --- a/pytorch_transformers/modeling_openai.py +++ b/pytorch_transformers/modeling_openai.py @@ -431,7 +431,7 @@ class OpenAIGPTPreTrainedModel(PreTrainedModel): num_special_tokens = kwargs.get('num_special_tokens', None) kwargs.pop('num_special_tokens', None) - model = PreTrainedModel.from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs) + model = super(PreTrainedModel, cls).from_pretrained(pretrained_model_name_or_path, pretrained_model_name_or_path, *inputs, **kwargs) # Add additional embeddings for special tokens if needed # This step also make sure we are still sharing the output and input embeddings after loading weights