Using learned_emb for training the classifier about diffusion-lm

[XiangLi1999]

Thanks for the question: I realized the code wasn't the latest commit.

          filename = model_args.init_emb  # '/u/scr/nlp/xlisali/predictability/diffusion_models_v3/diff_e2e-tgt_block_rand16_transformer_lr0.0001_2000_cosine_Lsimple_h128_s2_sd101'
          path_save = '{}/random_emb.torch'.format(filename)
          path_learned = '{}/ema_0.9999_200000.pt'.format(filename)
          if model_args.experiment == 'e2e-tgt-pos' and model_args.learned_emb == 'no':
              model.transformer.embeddings.word_embeddings.load_state_dict(torch.load(path_save))
              model.transformer.embeddings.word_embeddings.weight.requires_grad = False
          elif model_args.experiment == 'e2e-tgt-pos' and model_args.learned_emb == 'yes':
              print('loading the learned embeddings')
              learned_embeddings = torch.load(path_learned)['word_embedding.weight']
              model.transformer.embeddings.word_embeddings.weight.data = learned_embeddings.clone()
              model.transformer.embeddings.word_embeddings.weight.requires_grad = False
          elif model_args.experiment == 'e2e-tgt-tree' and model_args.learned_emb == 'no':
              model.transformer.embeddings.word_embeddings.load_state_dict(torch.load(path_save))
              model.transformer.embeddings.word_embeddings.weight.requires_grad = False
          elif model_args.experiment == 'e2e-tgt-tree' and model_args.learned_emb == 'yes':
              print('loading the learned embeddings')
              learned_embeddings = torch.load(path_learned)['word_embedding.weight']
              model.transformer.embeddings.word_embeddings.weight.data = learned_embeddings.clone()
              model.transformer.embeddings.word_embeddings.weight.requires_grad = False
          elif model_args.experiment.startswith('e2e-back') and model_args.learned_emb == 'no':
              model.transformer.wte.load_state_dict(torch.load(path_save))
              model.transformer.wte.weight.requires_grad = False
          elif model_args.experiment.startswith('e2e-back') and model_args.learned_emb == 'yes':
              print('loading the learned embeddings')
              learned_embeddings = torch.load(path_learned)['word_embedding.weight']
              model.transformer.wte.weight.data = learned_embeddings.clone()
              model.transformer.wte.weight.requires_grad = False

I will push a new commit.

[daniellaye]

Hi Lisa, thank you for your response!