Add support for gradient checkpointing in BERT

src/transformers/configuration_bert.py

@@ -90,6 +90,8 @@ class BertConfig(PretrainedConfig):
                 The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
             layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
                 The epsilon used by the layer normalization layers.
+            gradient_checkpointing (:obj:`bool`, optional, defaults to False):
+                If True, use gradient checkpointing to save memory at the expense of slower backward pass.
 
         Example::
 
@@ -121,6 +123,7 @@ def __init__(
         initializer_range=0.02,
         layer_norm_eps=1e-12,
         pad_token_id=0,
+        gradient_checkpointing=False,
         **kwargs
     ):
         super().__init__(pad_token_id=pad_token_id, **kwargs)
@@ -137,3 +140,4 @@ def __init__(
         self.type_vocab_size = type_vocab_size
         self.initializer_range = initializer_range
         self.layer_norm_eps = layer_norm_eps
+        self.gradient_checkpointing = gradient_checkpointing

src/transformers/modeling_bert.py

@@ -22,6 +22,7 @@
 import warnings
 
 import torch
+import torch.utils.checkpoint
 from torch import nn
 from torch.nn import CrossEntropyLoss, MSELoss
 
@@ -391,6 +392,7 @@ def forward(
 class BertEncoder(nn.Module):
     def __init__(self, config):
         super().__init__()
+        self.config = config
         self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])
 
     def forward(
@@ -409,14 +411,31 @@ def forward(
             if output_hidden_states:
                 all_hidden_states = all_hidden_states + (hidden_states,)
 
-            layer_outputs = layer_module(
-                hidden_states,
-                attention_mask,
-                head_mask[i],
-                encoder_hidden_states,
-                encoder_attention_mask,
-                output_attentions,
-            )
+            if getattr(self.config, "gradient_checkpointing", False):
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    attention_mask,
+                    head_mask[i],
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    head_mask[i],
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    output_attentions,
+                )
             hidden_states = layer_outputs[0]
 
             if output_attentions: