Merge branch 'task_head_fix' into 'main'

Only create task heads on last pipeline stage.

See merge request ADLR/megatron-lm!202

megatron/model/classification.py

@@ -17,7 +17,7 @@
 
 import torch
 
-from megatron import get_args, print_rank_0
+from megatron import get_args, print_rank_last
 from megatron import mpu
 from megatron.model.bert_model import bert_attention_mask_func, bert_extended_attention_mask, bert_position_ids
 from megatron.model.language_model import get_language_model
@@ -45,11 +45,12 @@ class ClassificationBase(PipelinedMegatronModule):
                                                          args.num_layers))
 
         # Multi-choice head.
-        self.classification_dropout = torch.nn.Dropout(args.hidden_dropout)
-        self.classification_head = get_linear_layer(args.hidden_size,
-                                                    self.num_classes,
-                                                    init_method)
-        self._classification_head_key = 'classification_head'
+        if mpu.is_pipeline_last_stage():
+            self.classification_dropout = torch.nn.Dropout(args.hidden_dropout)
+            self.classification_head = get_linear_layer(args.hidden_size,
+                                                        self.num_classes,
+                                                        init_method)
+            self._classification_head_key = 'classification_head'
 
     def forward(self, model_input, attention_mask, tokentype_ids=None):
 
@@ -85,9 +86,10 @@ class ClassificationBase(PipelinedMegatronModule):
         state_dict_[self._language_model_key] \
             = self.language_model.state_dict_for_save_checkpoint(
                 destination, prefix, keep_vars)
-        state_dict_[self._classification_head_key] \
-            = self.classification_head.state_dict(
-                destination, prefix, keep_vars)
+        if mpu.is_pipeline_last_stage():
+            state_dict_[self._classification_head_key] \
+                = self.classification_head.state_dict(
+                    destination, prefix, keep_vars)
         return state_dict_
 
     def load_state_dict(self, state_dict, strict=True):
@@ -95,13 +97,14 @@ class ClassificationBase(PipelinedMegatronModule):
 
         self.language_model.load_state_dict(
             state_dict[self._language_model_key], strict=strict)
-        if self._classification_head_key in state_dict:
-            self.classification_head.load_state_dict(
-                state_dict[self._classification_head_key], strict=strict)
-        else:
-            print_rank_0('***WARNING*** could not find {} in the checkpoint, '
-                         'initializing to random'.format(
-                             self._classification_head_key))
+        if mpu.is_pipeline_last_stage():
+            if self._classification_head_key in state_dict:
+                self.classification_head.load_state_dict(
+                    state_dict[self._classification_head_key], strict=strict)
+            else:
+                print_rank_last('***WARNING*** could not find {} in the checkpoint, '
+                                'initializing to random'.format(
+                                    self._classification_head_key))
 
 
 class Classification(ClassificationBase):

megatron/model/multiple_choice.py

@@ -17,7 +17,7 @@
 
 import torch
 
-from megatron import get_args, print_rank_0
+from megatron import get_args, print_rank_last
 from megatron import mpu
 from megatron.model.bert_model import bert_attention_mask_func, bert_extended_attention_mask, bert_position_ids
 from megatron.model.language_model import get_language_model
@@ -44,10 +44,11 @@ class MultipleChoiceBase(PipelinedMegatronModule):
                                                          args.num_layers))
 
         # Multi-choice head.
-        self.multichoice_dropout = torch.nn.Dropout(args.hidden_dropout)
-        self.multichoice_head = get_linear_layer(args.hidden_size, 1,
-                                                 init_method)
-        self._multichoice_head_key = 'multichoice_head'
+        if mpu.is_pipeline_last_stage():
+            self.multichoice_dropout = torch.nn.Dropout(args.hidden_dropout)
+            self.multichoice_head = get_linear_layer(args.hidden_size, 1,
+                                                     init_method)
+            self._multichoice_head_key = 'multichoice_head'
 
     def forward(self, model_input, attention_mask, tokentype_ids=None):
 
@@ -97,9 +98,10 @@ class MultipleChoiceBase(PipelinedMegatronModule):
         state_dict_[self._language_model_key] \
             = self.language_model.state_dict_for_save_checkpoint(
                 destination, prefix, keep_vars)
-        state_dict_[self._multichoice_head_key] \
-            = self.multichoice_head.state_dict(
-                destination, prefix, keep_vars)
+        if mpu.is_pipeline_last_stage():
+            state_dict_[self._multichoice_head_key] \
+                = self.multichoice_head.state_dict(
+                    destination, prefix, keep_vars)
         return state_dict_
 
     def load_state_dict(self, state_dict, strict=True):
@@ -107,13 +109,14 @@ class MultipleChoiceBase(PipelinedMegatronModule):
 
         self.language_model.load_state_dict(
             state_dict[self._language_model_key], strict=strict)
-        if self._multichoice_head_key in state_dict:
-            self.multichoice_head.load_state_dict(
-                state_dict[self._multichoice_head_key], strict=strict)
-        else:
-            print_rank_0('***WARNING*** could not find {} in the checkpoint, '
-                         'initializing to random'.format(
-                             self._multichoice_head_key))
+        if mpu.is_pipeline_last_stage():
+            if self._multichoice_head_key in state_dict:
+                self.multichoice_head.load_state_dict(
+                    state_dict[self._multichoice_head_key], strict=strict)
+            else:
+                print_rank_last('***WARNING*** could not find {} in the checkpoint, '
+                                'initializing to random'.format(
+                                    self._multichoice_head_key))
 
 class MultipleChoice(MultipleChoiceBase):