clean up multi gpu logic

Author: thomwolf

model_pytorch.py

@@ -177,7 +177,8 @@ class LMHead(nn.Module):
     def __init__(self, model, cfg):
         super(LMHead, self).__init__()
         self.n_embd = cfg.n_embd
-        self.decoder = lambda x: F.linear(x, model.embed.weight)  # Tied weights
+        self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False)
+        self.decoder.weight = model.embed.weight # Tied weights
 
     def forward(self, h):
         # Truncated Language modeling logits (we remove the last token)

train.py

@@ -151,7 +151,7 @@ def run_epoch():
         compute_loss_fct(XMB, YMB, MMB, clf_logits, lm_logits)
         n_updates += 1
         if n_updates in [1000, 2000, 4000, 8000, 16000, 32000] and n_epochs == 0:
-            log()
+            log(save_dir, desc)
 
 
 argmax = lambda x: np.argmax(x, 1)
@@ -194,7 +194,6 @@ def run_epoch():
     parser.add_argument('--clf_pdrop', type=float, default=0.1)
     parser.add_argument('--l2', type=float, default=0.01)
     parser.add_argument('--vector_l2', action='store_true')
-    parser.add_argument('--n_gpu', type=int, default=1)
     parser.add_argument('--opt', type=str, default='adam')
     parser.add_argument('--afn', type=str, default='gelu')
     parser.add_argument('--lr_schedule', type=str, default='warmup_linear')
@@ -223,9 +222,11 @@ def run_epoch():
     desc = args.desc
     data_dir = args.data_dir
     log_dir = args.log_dir
+    submission_dir = args.submission_dir
 
-    # torch.device object used throughout this script TODO add gpu setting
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    n_gpu = torch.cuda.device_count()
+    print("device", device, "n_gpu", n_gpu)
 
     logger = ResultLogger(path=os.path.join(log_dir, '{}.jsonl'.format(desc)), **args.__dict__)
     text_encoder = TextEncoder(args.encoder_path, args.bpe_path)
@@ -259,10 +260,11 @@ def run_epoch():
 
     n_train = len(trY)
     n_valid = len(vaY)
-    n_batch_train = args.n_batch * args.n_gpu
+    n_batch_train = args.n_batch * n_gpu
     n_updates_total = (n_train // n_batch_train) * args.n_iter
 
-    dh_model = DoubleHeadModel(args, clf_token, vocab, n_ctx)
+    if n_gpu > 1:
+        dh_model = DoubleHeadModel(args, clf_token, vocab, n_ctx)
 
     criterion = nn.CrossEntropyLoss(reduce=False)
     model_opt = OpenAIAdam(dh_model.parameters(),