Initial commit for untied embeddings

Signed-off-by: MaximumEntropy <sandeep.subramanian.1@umontreal.ca>

megatron/arguments.py

@@ -530,6 +530,8 @@ def _add_network_size_args(parser):
                        dest='bert_binary_head')
     group.add_argument('--num-experts', type=int, default=None,
                        help='Number of Experts in Switch Transformer (None means no Switch)')
+    group.add_argument('--untie-embeddings-and-output-weights', action='store_true',
+                       help='Untie embeddings and output weights.'),
     return parser
 
 

megatron/core/parallel_state.py

@@ -53,6 +53,7 @@ def initialize_model_parallel(
     pipeline_model_parallel_size: int = 1,
     virtual_pipeline_model_parallel_size: Optional[int] = None,
     pipeline_model_parallel_split_rank: Optional[int] = None,
+    untie_embeddings_and_output_weights: bool = False,
 ) -> None:
     """
     Initialize model data parallel groups.
@@ -93,6 +94,9 @@ def initialize_model_parallel(
             pipeline_model_parallel_split_rank is 3, then ranks 0-2
             will be the encoder and ranks 3-7 will be the decoder.
 
+        untie_embeddings_and_output_weights: whether to use separate embedding and output layer.
+                this affects the computation of embedding groups
+
     Let's say we have a total of 16 GPUs denoted by g0 ... g15 and we
     use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize
     the model pipeline. The present function will
@@ -200,13 +204,19 @@ def initialize_model_parallel(
         # Setup embedding group (to exchange gradients between
         # first and last stages).
         if len(ranks) > 1:
-            embedding_ranks = [ranks[0], ranks[-1]]
+            if untie_embeddings_and_output_weights:
+                embedding_ranks = [ranks[0]]
+            else:
+                embedding_ranks = [ranks[0], ranks[-1]]
             position_embedding_ranks = [ranks[0]]
             if pipeline_model_parallel_split_rank is not None:
                 if ranks[pipeline_model_parallel_split_rank] not in embedding_ranks:
-                    embedding_ranks = [ranks[0],
-                                       ranks[pipeline_model_parallel_split_rank],
-                                       ranks[-1]]
+                    if untie_embeddings_and_output_weights:
+                        embedding_ranks = [ranks[0], ranks[pipeline_model_parallel_split_rank]]
+                    else:
+                        embedding_ranks = [ranks[0],
+                                        ranks[pipeline_model_parallel_split_rank],
+                                        ranks[-1]]
                 if ranks[pipeline_model_parallel_split_rank] not in position_embedding_ranks:
                     position_embedding_ranks = [ranks[0],
                                        ranks[pipeline_model_parallel_split_rank]]

megatron/initialize.py

@@ -185,7 +185,8 @@ def _initialize_distributed():
             mpu.initialize_model_parallel(args.tensor_model_parallel_size,
                                            args.pipeline_model_parallel_size,
                                            args.virtual_pipeline_model_parallel_size,
-                                           args.pipeline_model_parallel_split_rank)
+                                           args.pipeline_model_parallel_split_rank,
+                                           args.untie_embeddings_and_output_weights)
             if args.rank == 0:
                 print(f'> initialized tensor model parallel with size '
                       f'{mpu.get_tensor_model_parallel_world_size()}')

megatron/model/gpt_model.py

@@ -57,6 +57,7 @@ class GPTModel(MegatronModule):
         self.pre_process = pre_process
         self.post_process = post_process
         self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy
+        self.untie_embeddings_and_output_weights = args.untie_embeddings_and_output_weights
 
         self.language_model, self._language_model_key = get_language_model(
             num_tokentypes=num_tokentypes,
@@ -90,7 +91,7 @@ class GPTModel(MegatronModule):
         if self.post_process:
             return post_language_model_processing(
                 lm_output, labels,
-                self.word_embeddings_weight(),
+                self.language_model.output_layer.weight if self.untie_embeddings_and_output_weights else self.word_embeddings_weight(),
                 self.parallel_output,
                 self.fp16_lm_cross_entropy)
         else:

megatron/model/language_model.py

@@ -326,8 +326,9 @@ class TransformerLanguageModel(MegatronModule):
                  add_pooler=False,
                  pre_process=True,
                  post_process=True):
-        super(TransformerLanguageModel, self).__init__()
         args = get_args()
+        # TODO: passing share_word_embeddings=False will not work correctly for T5 and embeddings will not be synced. Fix later for T5.
+        super(TransformerLanguageModel, self).__init__(share_word_embeddings=not args.untie_embeddings_and_output_weights)
 
         self.pre_process = pre_process
         self.post_process = post_process
@@ -340,6 +341,7 @@ class TransformerLanguageModel(MegatronModule):
         self.decoder_attn_mask_type = decoder_attn_mask_type
         self.add_pooler = add_pooler
         self.encoder_hidden_state = None
+        self.untie_embeddings_and_output_weights = args.untie_embeddings_and_output_weights
 
         # Embeddings.
         if self.pre_process:
@@ -408,6 +410,14 @@ class TransformerLanguageModel(MegatronModule):
                 self.pooler = Pooler(self.hidden_size, self.init_method)
                 self._pooler_key = 'pooler'
 
+            if self.untie_embeddings_and_output_weights:
+                self.output_layer = tensor_parallel.ColumnParallelLinear(
+                    args.hidden_size,
+                    args.padded_vocab_size,
+                    bias=False, # Setting bias to False always to keep it consistent with embedding tying that also does not have a bias.
+                    init_method=output_layer_init_method)
+                self._output_layer_key = 'output_layer'
+
     def set_input_tensor(self, input_tensor):
         """ See megatron.model.transformer.set_input_tensor()"""
 
@@ -529,6 +539,10 @@ class TransformerLanguageModel(MegatronModule):
                 state_dict_[self._pooler_key] \
                     = self.pooler.state_dict_for_save_checkpoint(prefix=prefix,
                                                                  keep_vars=keep_vars)
+            if self.untie_embeddings_and_output_weights:
+                state_dict_[self._output_layer_key] \
+                    = self.output_layer.state_dict_for_save_checkpoint(prefix=prefix,
+                                                                       keep_vars=keep_vars)
         if self.add_decoder:
             state_dict_[self._decoder_key] \
                 = self.decoder.state_dict_for_save_checkpoint(prefix=prefix,
@@ -584,6 +598,11 @@ class TransformerLanguageModel(MegatronModule):
                     'could not find data for pooler in the checkpoint'
                 self.pooler.load_state_dict(state_dict[self._pooler_key],
                                             strict=strict)
+            if self.untie_embeddings_and_output_weights:
+                assert 'output_layer' in state_dict, \
+                    'could not find data for output_layer in the checkpoint'
+                self.output_layer.load_state_dict(state_dict[self._output_layer_key],
+                                                  strict=strict)
         # Decoder.
         if self.add_decoder:
             assert 'decoder' in state_dict, \

megatron/model/module.py

@@ -71,7 +71,7 @@ class MegatronModule(torch.nn.Module):
         #    the two word_embeddings layers to ensure that every applied weight
         #    update is the same on both stages.
         if mpu.is_pipeline_last_stage() and \
-                not self.pre_process:
+                not self.pre_process and not self.untie_embeddings_and_output_weights:
             assert not mpu.is_pipeline_first_stage()
             self._word_embeddings_for_head_key = 'word_embeddings_for_head'
             # set word_embeddings weights to 0 here, then copy first