Add the option for fp32 residual connection (fp32 residual connection...

Add the option for fp32 residual connection (fp32 residual connection machinery still needs to be added)

megatron/arguments.py

@@ -183,6 +183,9 @@ def parse_args(extra_args_provider=None, defaults={},
     # Mixed precision checks.
     if args.fp16_lm_cross_entropy:
         assert args.fp16, 'lm cross entropy in fp16 only support in fp16 mode.'
+    if args.fp32_residual_connection:
+        assert args.fp16, \
+            'residual connection in fp32 only supports in fp16 mode.'
     # Activation checkpointing.
     if args.distribute_checkpointed_activations:
         assert args.checkpoint_activations, \
@@ -435,6 +438,8 @@ def _add_mixed_precision_args(parser):
 
     group.add_argument('--fp16', action='store_true',
                        help='Run model in fp16 mode.')
+    group.add_argument('--fp32-residual-connection', action='store_true',
+                       help='Move residual connections to fp32.')
     group.add_argument('--apply-query-key-layer-scaling', action='store_true',
                        help='Scale Q * K^T by 1 / layer-number. If this flag '
                        'is set, then it will automatically set '

megatron/model/__init__.py

@@ -13,9 +13,27 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+_LAYER_NORM = None
+
+
+def import_layernorm(fp32_residual_connection):
+
+    global _LAYER_NORM
+    if not _LAYER_NORM:
+        if fp32_residual_connection:
+            from .fused_layer_norm import MixedFusedLayerNorm as LayerNorm
+        else:
+            from apex.normalization.fused_layer_norm import FusedLayerNorm as LayerNorm
+        _LAYER_NORM = LayerNorm
+            
+    return _LAYER_NORM
+
+
 from .distributed import *
 from .bert_model import BertModel, BertModelFirstStage, BertModelIntermediateStage, BertModelLastStage
 from .realm_model import ICTBertModel
 from .gpt2_model import GPT2Model, GPT2ModelFirstStage, GPT2ModelIntermediateStage, GPT2ModelLastStage
 from .utils import get_params_for_weight_decay_optimization
 from .language_model import get_language_model
+
+

megatron/model/bert_model.py

@@ -21,7 +21,7 @@ from megatron import get_args
 from megatron import mpu
 from megatron.model.language_model import parallel_lm_logits
 from megatron.model.language_model import get_language_model
-from megatron.model.transformer import LayerNorm
+from megatron.model import import_layernorm
 from megatron.model.utils import openai_gelu, erf_gelu
 from megatron.model.utils import get_linear_layer
 from megatron.model.utils import init_method_normal
@@ -83,6 +83,7 @@ class BertLMHead(MegatronModule):
         self.parallel_output = parallel_output
 
         self.dense = get_linear_layer(hidden_size, hidden_size, init_method)
+        LayerNorm = import_layernorm(args.fp32_residual_connection)
         self.layernorm = LayerNorm(hidden_size, eps=layernorm_epsilon)
         self.gelu = torch.nn.functional.gelu
         if args.openai_gelu:

megatron/model/transformer.py

@@ -21,9 +21,9 @@ import torch.nn.functional as F
 
 from megatron import get_args
 from megatron import mpu
-from megatron.mpu import LayerNorm
 from megatron.module import MegatronModule
 from megatron.checkpointing import get_checkpoint_version
+from megatron.model import import_layernorm
 from megatron.model.fused_softmax import FusedScaleMaskSoftmax
 from megatron.model.fused_bias_gelu import bias_gelu_impl
 from megatron.model.utils import openai_gelu, erf_gelu
@@ -404,6 +404,7 @@ class ParallelTransformerLayer(MegatronModule):
             = args.apply_residual_connection_post_layernorm
 
         # Layernorm on the input data.
+        LayerNorm = import_layernorm(args.fp32_residual_connection)
         self.input_layernorm = LayerNorm(
             args.hidden_size,
             eps=args.layernorm_epsilon)
@@ -500,6 +501,8 @@ class ParallelTransformer(MegatronModule):
         super(ParallelTransformer, self).__init__()
         args = get_args()
 
+        self.fp32_residual_connection = args.fp32_residual_connection
+
         # Store activation checkpoiting flag.
         self.checkpoint_activations = args.checkpoint_activations
         self.checkpoint_num_layers = args.checkpoint_num_layers
@@ -520,6 +523,7 @@ class ParallelTransformer(MegatronModule):
 
         if mpu.is_pipeline_last_stage():
             # Final layer norm before output.
+            LayerNorm = import_layernorm(args.fp32_residual_connection)
             self.final_layernorm = LayerNorm(
                 args.hidden_size,
                 eps=args.layernorm_epsilon)
@@ -564,7 +568,10 @@ class ParallelTransformer(MegatronModule):
 
         if mpu.is_pipeline_first_stage():
             # Data format change to avoid explicit tranposes : [b s h] --> [s b h].
-            hidden_states = hidden_states.transpose(0, 1).contiguous()
+            if self.fp32_residual_connection:
+                hidden_states = hidden_states.transpose(0, 1).contiguous().float()
+            else:
+                hidden_states = hidden_states.transpose(0, 1).contiguous()
 
         if self.checkpoint_activations:
             hidden_states = self._checkpointed_forward(hidden_states,

megatron/model/utils.py

@@ -19,8 +19,8 @@ import math
 
 import torch
 
-from .transformer import LayerNorm
-
+from megatron import get_args
+from megatron.model import import_layernorm
 
 def init_method_normal(sigma):
     """Init method based on N(0, sigma)."""
@@ -65,6 +65,10 @@ def get_params_for_weight_decay_optimization(module):
     """Divide params into with-weight-decay and without-weight-decay groups.
     Layernorms and baises will have no weight decay but the rest will.
     """
+
+    args = get_args()
+    LayerNorm = import_layernorm(args.fp32_residual_connection)
+    
     weight_decay_params = {'params': []}
     no_weight_decay_params = {'params': [], 'weight_decay': 0.0}
     for module_ in module.modules():

megatron/mpu/__init__.py

@@ -41,7 +41,6 @@ from .initialize import get_pipeline_model_parallel_world_size, set_pipeline_mod
 from .initialize import initialize_model_parallel
 from .initialize import model_parallel_is_initialized
 
-from .layers import LayerNorm
 from .layers import ColumnParallelLinear
 from .layers import RowParallelLinear
 from .layers import VocabParallelEmbedding

megatron/mpu/layers.py

@@ -25,16 +25,6 @@ import torch.nn.functional as F
 import torch.nn.init as init
 from torch.nn.parameter import Parameter
 
-try:
-    from apex.normalization.fused_layer_norm import FusedLayerNorm as LayerNorm
-    # Try to use FusedLayerNorm from Apex - this will trigger an error.
-    _ = LayerNorm(8, eps=1e-5)
-
-except Exception as e:
-    print('WARNING: APEX is not installed, using torch.nn.LayerNorm '
-          'instead of apex.normalization.FusedLayerNorm!')
-    from torch.nn import LayerNorm
-
 from .initialize import get_tensor_model_parallel_rank
 from .initialize import get_tensor_model_parallel_world_size
 from .mappings import copy_to_tensor_model_parallel_region

megatron/training.py

@@ -333,16 +333,19 @@ def communicate(tensor_send_next, tensor_send_prev, recv_forward, recv_backward)
     tensor_recv_prev = None
     tensor_recv_next = None
     tensor_shape = (args.seq_length, args.micro_batch_size, args.hidden_size)
+    dtype = args.params_dtype
+    if args.fp32_residual_connection:
+        dtype = torch.float
     if recv_forward:
         tensor_recv_prev = torch.empty(tensor_shape,
                                        requires_grad=True,
                                        device=torch.cuda.current_device(),
-                                       dtype=args.params_dtype)
+                                       dtype=dtype)
     if recv_backward:
         tensor_recv_next = torch.empty(tensor_shape,
                                        requires_grad=True,
                                        device=torch.cuda.current_device(),
-                                       dtype=args.params_dtype)
+                                       dtype=dtype)
 
     # Send tensors in both the forward and backward directions as appropriate.
     torch.distributed.ring_exchange(tensor_send_prev=tensor_send_prev,