addressed jareds comments

megatron/arguments.py

@@ -97,6 +97,9 @@ def parse_args(extra_args_provider=None, defaults={},
         if args.num_unique_layers < args.num_layers:
             assert args.DDP_impl == 'local', \
                 'torch-DDP does not work with parameters sharing.'
+    # Mixed precision checks.
+    if args.fp16_lm_cross_entropy:
+        assert args.fp16, 'lm cross entropy in fp16 only support in fp16 mode.'
 
     _print_args(args)
     return args

megatron/model/bert_model.py

@@ -115,6 +115,7 @@ def __init__(self, num_tokentypes=2, add_binary_head=True,
         super(BertModel, self).__init__()
         args = get_args()
 
+        self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy
         self.add_binary_head = add_binary_head
         self.parallel_output = parallel_output
         init_method = init_method_normal(args.init_method_std)
@@ -170,7 +171,12 @@ def forward(self, input_ids, attention_mask,
         if lm_labels is None:
             return lm_logits, binary_logits
         else:
-            lm_loss = mpu.vocab_parallel_cross_entropy(lm_logits, lm_labels)
+            if self.fp16_lm_cross_entropy:
+                assert lm_logits.dtype == torch.half
+                lm_loss = mpu.vocab_parallel_cross_entropy(lm_logits, lm_labels)
+            else:
+                lm_loss = mpu.vocab_parallel_cross_entropy(lm_logits.float(),
+                                                           lm_labels)
             return lm_loss, binary_logits
 
 

megatron/model/gpt2_model.py

@@ -40,6 +40,7 @@ def __init__(self, num_tokentypes=0, parallel_output=True):
         args = get_args()
 
         self.parallel_output = parallel_output
+        self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy
 
         self.language_model, self._language_model_key = get_language_model(
             attention_mask_func=gpt2_attention_mask_func,
@@ -79,7 +80,11 @@ def forward(self, input_ids, position_ids, attention_mask, labels=None,
         if labels is None:
             return output
         else:
-            loss = mpu.vocab_parallel_cross_entropy(output, labels)
+            if self.fp16_lm_cross_entropy:
+                assert output.dtype == torch.half
+                loss = mpu.vocab_parallel_cross_entropy(output, labels)
+            else:
+                loss = mpu.vocab_parallel_cross_entropy(output.float(), labels)
             return loss
 
 

pretrain_bert.py

@@ -78,16 +78,12 @@ def forward_step(data_iterator, model):
     timers('batch generator').stop()
 
     # Forward model. lm_labels
-    if args.fp16_lm_cross_entropy:
-        lm_loss_, sop_logits = model(tokens, padding_mask, tokentype_ids=types,
-                                     lm_labels=lm_labels)
-    else:
-        lm_logits, sop_logits = model(tokens, padding_mask, tokentype_ids=types)
-        lm_loss_ = mpu.vocab_parallel_cross_entropy(
-            lm_logits.contiguous().float(), lm_labels.contiguous())
+    lm_loss_, sop_logits = model(tokens, padding_mask,
+                                 tokentype_ids=types,
+                                 lm_labels=lm_labels)
 
-    sop_loss = F.cross_entropy(sop_logits.view(-1, 2).contiguous().float(),
-                               sentence_order.view(-1).contiguous(),
+    sop_loss = F.cross_entropy(sop_logits.view(-1, 2).float(),
+                               sentence_order.view(-1),
                                ignore_index=-1)
 
     lm_loss = torch.sum(

pretrain_gpt2.py

@@ -82,13 +82,8 @@ def forward_step(data_iterator, model):
     timers('batch generator').stop()
 
     # Forward model.
-    if args.fp16_lm_cross_entropy:
-        losses = model(tokens, position_ids, attention_mask, labels=labels)
-    else:
-        output = model(tokens, position_ids, attention_mask)
-        losses = mpu.vocab_parallel_cross_entropy(output.contiguous().float(),
-                                                  labels)
-
+    losses = model(tokens, position_ids, attention_mask, labels=labels)
+
     loss_mask = loss_mask.view(-1)
     loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()