add back rs to train.py

src/train.py

@@ -98,7 +98,8 @@ def make_data(args, infile, total_lines, outfile, vocab, pos_sample_rates_dict,
 
     if shuffle:
         ts_print('Shuffling training file...', flush=True)
-        subprocess.run(['bash', '-c', 'shuf -o ' + outfile + '<' + outfile])
+        # On Mac, use gshuf instead of shuf
+        subprocess.run(['bash', '-c', 'gshuf -o ' + outfile + '<' + outfile])
         ts_print('Done shuffling training file.', flush=True)
 
     return outfile_len
@@ -197,6 +198,8 @@ def forward(self, context_idxs, target_idxs):
         # Maybe does more computation, though?
         #
         # https://github.com/LakheyM/word2vec/blob/master/word2vec_SGNS_git.ipynb
+        # inner_products = torch.sum(torch.mul(
+        #     context_vectors, target_vectors), dim=1).reshape(-1, 1)
         inner_products = torch.diagonal(
                 context_vectors @ target_vectors.T).reshape(-1, 1)
         return inner_products
@@ -511,6 +514,9 @@ def train(args, device, vocab, train_file, total_train_n, total_lines,
                     # Source: https://www.stat.cmu.edu/~larry/=sml/Opt.pdf p. 5, 12
                     iso_loss_unscaled = torch.linalg.norm(model_vecs_tmp - loaded_vecs)
                     iso_loss = iso_loss_unscaled / len(loaded_vecs)
+                elif args.loss == 'rs':
+                    iso_loss = gh.diffble_rs_distance(model_vecs_tmp,
+                            loaded_vecs, device, args.mode == 'unsupervised')
                 elif args.loss == 'evs':
                     iso_loss = iso.diffble_evs_distance(model_vecs_tmp,
                             loaded_vecs, device)