T5 Gradient Checkpointing

src/transformers/models/t5/configuration_t5.py

@@ -71,6 +71,8 @@ class T5Config(PretrainedConfig):
             the :obj:`"gated-gelu"` feed forward projection. Original T5 uses :obj:`"relu"`.
         use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
             Whether or not the model should return the last key/values attentions (not used by all models).
+        gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            If True, use gradient checkpointing to save memory at the expense of slower backward pass.
     """
     model_type = "t5"
     keys_to_ignore_at_inference = ["past_key_values"]
@@ -93,6 +95,7 @@ def __init__(
         use_cache=True,
         pad_token_id=0,
         eos_token_id=1,
+        gradient_checkpointing=False,
         **kwargs
     ):
         super().__init__(
@@ -116,6 +119,7 @@ def __init__(
         self.initializer_factor = initializer_factor
         self.feed_forward_proj = feed_forward_proj
         self.use_cache = use_cache
+        self.gradient_checkpointing = gradient_checkpointing
 
     @property
     def hidden_size(self):

src/transformers/models/t5/modeling_t5.py

@@ -24,6 +24,7 @@
 import torch.nn.functional as F
 from torch import nn
 from torch.nn import CrossEntropyLoss
+from torch.utils.checkpoint import checkpoint
 
 from ...activations import ACT2FN
 from ...file_utils import (
@@ -323,6 +324,7 @@ def __init__(self, config: T5Config, has_relative_attention_bias=False):
         if self.has_relative_attention_bias:
             self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
         self.pruned_heads = set()
+        self.gradient_checkpointing = getattr(config, "gradient_checkpointing", False)
 
     def prune_heads(self, heads):
         if len(heads) == 0:
@@ -485,6 +487,8 @@ def project(hidden_states, proj_layer, key_value_states, past_key_value):
                 position_bias = torch.zeros(
                     (1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype
                 )
+                if self.training and self.gradient_checkpointing:
+                    position_bias.requires_grad = True
             else:
                 position_bias = self.compute_bias(real_seq_length, key_length)
 
@@ -691,7 +695,11 @@ def forward(
 
         outputs = (hidden_states,)
 
-        outputs = outputs + (present_key_value_state,) + attention_outputs
+        if use_cache:
+            outputs = outputs + (present_key_value_state,) + attention_outputs
+        else:
+            outputs = outputs + attention_outputs
+
         return outputs  # hidden-states, present_key_value_states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
 
 
@@ -947,21 +955,51 @@ def forward(
             if output_hidden_states:
                 all_hidden_states = all_hidden_states + (hidden_states,)
 
-            layer_outputs = layer_module(
-                hidden_states,
-                attention_mask=extended_attention_mask,
-                position_bias=position_bias,
-                encoder_hidden_states=encoder_hidden_states,
-                encoder_attention_mask=encoder_extended_attention_mask,
-                encoder_decoder_position_bias=encoder_decoder_position_bias,
-                layer_head_mask=layer_head_mask,
-                cross_attn_layer_head_mask=cross_attn_layer_head_mask,
-                past_key_value=past_key_value,
-                use_cache=use_cache,
-                output_attentions=output_attentions,
-            )
+            if getattr(self.config, "gradient_checkpointing", False) and self.training:
+                if use_cache:
+                    logger.warn(
+                        "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
+                        "`use_cache=False`..."
+                    )
+                    use_cache = False
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return tuple(module(*inputs, use_cache, output_attentions))
+
+                    return custom_forward
+
+                layer_outputs = checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    extended_attention_mask,
+                    position_bias,
+                    encoder_hidden_states,
+                    encoder_extended_attention_mask,
+                    encoder_decoder_position_bias,
+                    layer_head_mask,
+                    cross_attn_layer_head_mask,
+                    None,  # past_key_value is always None with gradient checkpointing
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask=extended_attention_mask,
+                    position_bias=position_bias,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=encoder_extended_attention_mask,
+                    encoder_decoder_position_bias=encoder_decoder_position_bias,
+                    layer_head_mask=layer_head_mask,
+                    cross_attn_layer_head_mask=cross_attn_layer_head_mask,
+                    past_key_value=past_key_value,
+                    use_cache=use_cache,
+                    output_attentions=output_attentions,
+                )
+
             # layer_outputs is a tuple with:
             # hidden-states, key-value-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
+            if use_cache is False:
+                layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]
             hidden_states, present_key_value_state = layer_outputs[:2]
 
             # We share the position biases between the layers - the first layer store them