From 129f0604acb9e8b9cebd2897437324198fa37a0a Mon Sep 17 00:00:00 2001 From: Sam Shleifer Date: Sun, 23 Feb 2020 11:28:48 -0500 Subject: [PATCH] Delete untested, broken Model2LSTM (#2968) --- src/transformers/modeling_encoder_decoder.py | 19 ------------------- 1 file changed, 19 deletions(-) diff --git a/src/transformers/modeling_encoder_decoder.py b/src/transformers/modeling_encoder_decoder.py index 7f1a71f2f2..4c5603b217 100644 --- a/src/transformers/modeling_encoder_decoder.py +++ b/src/transformers/modeling_encoder_decoder.py @@ -18,7 +18,6 @@ import logging import os -import torch from torch import nn from .modeling_auto import AutoModel, AutoModelWithLMHead @@ -294,21 +293,3 @@ class Model2Model(PreTrainedEncoderDecoder): ) return model - - -class Model2LSTM(PreTrainedEncoderDecoder): - @classmethod - def from_pretrained(cls, *args, **kwargs): - if kwargs.get("decoder_model", None) is None: - # We will create a randomly initilized LSTM model as decoder - if "decoder_config" not in kwargs: - raise ValueError( - "To load an LSTM in Encoder-Decoder model, please supply either: " - " - a torch.nn.LSTM model as `decoder_model` parameter (`decoder_model=lstm_model`), or" - " - a dictionary of configuration parameters that will be used to initialize a" - " torch.nn.LSTM model as `decoder_config` keyword argument. " - " E.g. `decoder_config={'input_size': 768, 'hidden_size': 768, 'num_layers': 2}`" - ) - kwargs["decoder_model"] = torch.nn.LSTM(kwargs.pop("decoder_config")) - model = super().from_pretrained(*args, **kwargs) - return model