Adding gradient checkpointing to GPT2

src/transformers/configuration_gpt2.py

@@ -103,6 +103,8 @@ class GPT2Config(PretrainedConfig):
             :class:`~transformers.GPT2DoubleHeadsModel` and :class:`~transformers.TFGPT2DoubleHeadsModel`.
 
             The dropout ratio to be used after the projection and activation.
+        gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.
 
     Example::
 
@@ -142,6 +144,7 @@ def __init__(
         summary_first_dropout=0.1,
         bos_token_id=50256,
         eos_token_id=50256,
+        gradient_checkpointing=False,
         **kwargs
     ):
         super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
@@ -164,6 +167,7 @@ def __init__(
         self.summary_activation = summary_activation
         self.summary_first_dropout = summary_first_dropout
         self.summary_proj_to_labels = summary_proj_to_labels
+        self.gradient_checkpointing = gradient_checkpointing
 
         self.bos_token_id = bos_token_id
         self.eos_token_id = eos_token_id

src/transformers/modeling_gpt2.py

@@ -15,7 +15,6 @@
 # limitations under the License.
 """PyTorch OpenAI GPT-2 model."""
 
-
 import os
 import warnings
 from dataclasses import dataclass
@@ -624,16 +623,35 @@ def forward(
             if output_hidden_states:
                 all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
 
-            outputs = block(
-                hidden_states,
-                layer_past=layer_past,
-                attention_mask=attention_mask,
-                head_mask=head_mask[i],
-                encoder_hidden_states=encoder_hidden_states,
-                encoder_attention_mask=encoder_attention_mask,
-                use_cache=use_cache,
-                output_attentions=output_attentions,
-            )
+            if getattr(self.config, "gradient_checkpointing", False):
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # checkpointing only works with tuple returns, not with lists
+                        return tuple(output for output in module(*inputs, use_cache, output_attentions))
+
+                    return custom_forward
+
+                outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    layer_past,
+                    attention_mask,
+                    head_mask[i],
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                )
+            else:
+                outputs = block(
+                    hidden_states,
+                    layer_past=layer_past,
+                    attention_mask=attention_mask,
+                    head_mask=head_mask[i],
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=encoder_attention_mask,
+                    use_cache=use_cache,
+                    output_attentions=output_attentions,
+                )
 
             hidden_states, present = outputs[:2]
             if use_cache is True:

src/transformers/trainer.py

@@ -661,8 +661,10 @@ def train(self, model_path: Optional[str] = None, trial: Union["optuna.Trial", D
                 model,
                 device_ids=[self.args.local_rank],
                 output_device=self.args.local_rank,
-                find_unused_parameters=True,
+                find_unused_parameters=not getattr(model.config, "gradient_checkpointing", False),
             )
+        # find_unused_parameters breaks checkpointing as per
+        # https://github.com/huggingface/transformers/pull/4659#issuecomment-643356021
 
         if self.tb_writer is not None:
             self.tb_writer.add_text("args", self.args.to_json_string())

tests/test_modeling_gpt2.py

@@ -88,7 +88,7 @@ def __init__(
         self.bos_token_id = vocab_size - 1
         self.eos_token_id = vocab_size - 1
 
-    def prepare_config_and_inputs(self):
+    def prepare_config_and_inputs(self, gradient_checkpointing=False):
         input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
 
         input_mask = None
@@ -127,6 +127,7 @@ def prepare_config_and_inputs(self):
             bos_token_id=self.bos_token_id,
             eos_token_id=self.eos_token_id,
             return_dict=True,
+            gradient_checkpointing=gradient_checkpointing,
         )
 
         head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
@@ -269,6 +270,15 @@ def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mas
         self.parent.assertEqual(result.loss.shape, ())
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
 
+    def create_and_check_forward_and_backwards(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
+        model = GPT2LMHeadModel(config)
+        model.to(torch_device)
+
+        result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
+        self.parent.assertEqual(result.loss.shape, ())
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+        result.loss.backward()
+
     def create_and_check_double_lm_head_model(
         self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args
     ):
@@ -355,6 +365,10 @@ def test_gpt2_double_lm_head_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_double_lm_head_model(*config_and_inputs)
 
+    def test_gpt2_gradient_checkpointing(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(gradient_checkpointing=True)
+        self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs)
+
     @slow
     def test_model_from_pretrained(self):
         for model_name in GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
@@ -366,33 +380,34 @@ def test_model_from_pretrained(self):
 class GPT2ModelLanguageGenerationTest(unittest.TestCase):
     @slow
     def test_lm_generate_gpt2(self):
-        model = GPT2LMHeadModel.from_pretrained("gpt2")
-        model.to(torch_device)
-        input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device)  # The dog
-        expected_output_ids = [
-            464,
-            3290,
-            373,
-            1043,
-            287,
-            257,
-            2214,
-            1474,
-            262,
-            16246,
-            286,
-            2688,
-            290,
-            2688,
-            27262,
-            13,
-            198,
-            198,
-            464,
-            3290,
-        ]  # The dog was found in a field near the intersection of West and West Streets.\n\nThe dog
-        output_ids = model.generate(input_ids, do_sample=False)
-        self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
+        for checkpointing in [True, False]:
+            model = GPT2LMHeadModel.from_pretrained("gpt2", gradient_checkpointing=checkpointing)
+            model.to(torch_device)
+            input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device)  # The dog
+            expected_output_ids = [
+                464,
+                3290,
+                373,
+                1043,
+                287,
+                257,
+                2214,
+                1474,
+                262,
+                16246,
+                286,
+                2688,
+                290,
+                2688,
+                27262,
+                13,
+                198,
+                198,
+                464,
+                3290,
+            ]  # The dog was found in a field near the intersection of West and West Streets.\n\nThe dog
+            output_ids = model.generate(input_ids, do_sample=False)
+            self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
 
     @slow
     def test_lm_generate_distilgpt2(self):