Loading fairseq/models/transformer.py +2 −4 Changes for fairseq/models/transformer.py: 2 added lines, 4 removed lines. Original line number Diff line number Diff line Loading @@ -244,8 +244,7 @@ class TransformerEncoder(FairseqEncoder): if isinstance(self.embed_positions, SinusoidalPositionalEmbedding): if 'encoder.embed_positions.weights' in state_dict: del state_dict['encoder.embed_positions.weights'] if 'encoder.embed_positions._float_tensor' not in state_dict: state_dict['encoder.embed_positions._float_tensor'] = torch.FloatTensor() state_dict['encoder.embed_positions._float_tensor'] = torch.FloatTensor(1) return state_dict Loading Loading @@ -340,8 +339,7 @@ class TransformerDecoder(FairseqIncrementalDecoder): if isinstance(self.embed_positions, SinusoidalPositionalEmbedding): if 'decoder.embed_positions.weights' in state_dict: del state_dict['decoder.embed_positions.weights'] if 'decoder.embed_positions._float_tensor' not in state_dict: state_dict['decoder.embed_positions._float_tensor'] = torch.FloatTensor() state_dict['decoder.embed_positions._float_tensor'] = torch.FloatTensor(1) for i in range(len(self.layers)): # update layer norms Loading fairseq/modules/sinusoidal_positional_embedding.py +1 −1 Changes for fairseq/modules/sinusoidal_positional_embedding.py: 1 added line, 1 removed line. Original line number Diff line number Diff line Loading @@ -30,7 +30,7 @@ class SinusoidalPositionalEmbedding(nn.Module): embedding_dim, padding_idx, ) self.register_buffer('_float_tensor', torch.FloatTensor()) self.register_buffer('_float_tensor', torch.FloatTensor(1)) @staticmethod def get_embedding(num_embeddings, embedding_dim, padding_idx=None): Loading Loading
fairseq/models/transformer.py +2 −4 Changes for fairseq/models/transformer.py: 2 added lines, 4 removed lines. Original line number Diff line number Diff line Loading @@ -244,8 +244,7 @@ class TransformerEncoder(FairseqEncoder): if isinstance(self.embed_positions, SinusoidalPositionalEmbedding): if 'encoder.embed_positions.weights' in state_dict: del state_dict['encoder.embed_positions.weights'] if 'encoder.embed_positions._float_tensor' not in state_dict: state_dict['encoder.embed_positions._float_tensor'] = torch.FloatTensor() state_dict['encoder.embed_positions._float_tensor'] = torch.FloatTensor(1) return state_dict Loading Loading @@ -340,8 +339,7 @@ class TransformerDecoder(FairseqIncrementalDecoder): if isinstance(self.embed_positions, SinusoidalPositionalEmbedding): if 'decoder.embed_positions.weights' in state_dict: del state_dict['decoder.embed_positions.weights'] if 'decoder.embed_positions._float_tensor' not in state_dict: state_dict['decoder.embed_positions._float_tensor'] = torch.FloatTensor() state_dict['decoder.embed_positions._float_tensor'] = torch.FloatTensor(1) for i in range(len(self.layers)): # update layer norms Loading
fairseq/modules/sinusoidal_positional_embedding.py +1 −1 Changes for fairseq/modules/sinusoidal_positional_embedding.py: 1 added line, 1 removed line. Original line number Diff line number Diff line Loading @@ -30,7 +30,7 @@ class SinusoidalPositionalEmbedding(nn.Module): embedding_dim, padding_idx, ) self.register_buffer('_float_tensor', torch.FloatTensor()) self.register_buffer('_float_tensor', torch.FloatTensor(1)) @staticmethod def get_embedding(num_embeddings, embedding_dim, padding_idx=None): Loading