Feed forward chunking

src/transformers/configuration_reformer.py

@@ -64,11 +64,6 @@ class ReformerConfig(PretrainedConfig):
                 A chunk size of 0 means that the feed forward layer is not chunked.
                 A chunk size of n means that the feed forward layer processes n < sequence_length embeddings at a time.
                 For more information on feed forward chunking, see `How does Feed Forward Chunking work? <../glossary.html#feed-forward-chunking>`__ .
-            chunk_size_feed_forward (:obj:`int`, optional, defaults to 0):
-                The chunk size of all feed forward layers in the residual attention blocks.
-                A chunk size of 0 means that the feed forward layer is not chunked.
-                A chunk size of n means that the feed forward layer processes n < sequence_length embeddings at a time.
-                For more information on feed forward chunking, see `How does Feed Forward Chunking work? <../glossary.html#feed-forward-chunking>`__ .
             eos_token_id (:obj:`int`, optional, defaults to 2):
                 The token id for the <EOS> token.
             feed_forward_size (:obj:`int`, optional, defaults to 512):
@@ -147,7 +142,6 @@ def __init__(
         axial_pos_shape=[64, 64],
         axial_pos_embds_dim=[64, 192],
         chunk_size_lm_head=0,
-        chunk_size_feed_forward=0,
         eos_token_id=2,
         feed_forward_size=512,
         hash_seed=None,
@@ -202,5 +196,4 @@ def __init__(
         self.axial_pos_embds_dim = tuple(axial_pos_embds_dim)
         self.axial_norm_std = axial_norm_std
         self.chunk_size_lm_head = chunk_size_lm_head
-        self.chunk_size_feed_forward = chunk_size_feed_forward
         self.attn_layers = attn_layers

src/transformers/configuration_utils.py

@@ -64,6 +64,11 @@ class PretrainedConfig(object):
                 2.
             xla_device (:obj:`bool`, `optional`):
                 A flag to indicate if TPU are available or not.
+            chunk_size_feed_forward (:obj:`int`, `optional`, defaults to :obj:`0`):
+                The chunk size of all feed forward layers in the residual attention blocks.
+                A chunk size of :obj:`0` means that the feed forward layer is not chunked.
+                A chunk size of n means that the feed forward layer processes :obj:`n` < sequence_length embeddings at a time.
+                For more information on feed forward chunking, see `How does Feed Forward Chunking work? <../glossary.html#feed-forward-chunking>`__ .
 
         Parameters for sequence generation
             - **max_length** (:obj:`int`, `optional`, defaults to 20) -- Maximum length that will be used by
@@ -160,6 +165,7 @@ def __init__(self, **kwargs):
         self.no_repeat_ngram_size = kwargs.pop("no_repeat_ngram_size", 0)
         self.bad_words_ids = kwargs.pop("bad_words_ids", None)
         self.num_return_sequences = kwargs.pop("num_return_sequences", 1)
+        self.chunk_size_feed_forward = kwargs.pop("chunk_size_feed_forward", 0)
 
         # Fine-tuning task arguments
         self.architectures = kwargs.pop("architectures", None)

src/transformers/modeling_bert.py

@@ -48,7 +48,12 @@
     SequenceClassifierOutput,
     TokenClassifierOutput,
 )
-from .modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
+from .modeling_utils import (
+    PreTrainedModel,
+    apply_chunking_to_forward,
+    find_pruneable_heads_and_indices,
+    prune_linear_layer,
+)
 
 
 logger = logging.getLogger(__name__)
@@ -88,6 +93,7 @@ def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
     """
     try:
         import re
+
         import numpy as np
         import tensorflow as tf
     except ImportError:
@@ -376,6 +382,8 @@ def forward(self, hidden_states, input_tensor):
 class BertLayer(nn.Module):
     def __init__(self, config):
         super().__init__()
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
         self.attention = BertAttention(config)
         self.is_decoder = config.is_decoder
         if self.is_decoder:
@@ -410,11 +418,17 @@ def forward(
             attention_output = cross_attention_outputs[0]
             outputs = outputs + cross_attention_outputs[1:]  # add cross attentions if we output attention weights
 
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
+        layer_output = apply_chunking_to_forward(
+            self.chunk_size_feed_forward, self.seq_len_dim, self.feed_forward_chunk, attention_output
+        )
         outputs = (layer_output,) + outputs
         return outputs
 
+    def feed_forward_chunk(self, attention_output):
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
 
 class BertEncoder(nn.Module):
     def __init__(self, config):

tests/test_modeling_bert.py

@@ -368,6 +368,7 @@ class BertModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    test_chunking = True
 
     def setUp(self):
         self.model_tester = BertModelTester(self)

tests/test_modeling_common.py

@@ -60,6 +60,7 @@ class ModelTesterMixin:
     test_resize_embeddings = True
     test_head_masking = True
     test_missing_keys = True
+    test_chunking = False
     is_encoder_decoder = False
 
     def _prepare_for_class(self, inputs_dict, model_class):
@@ -519,6 +520,29 @@ def check_hidden_states_output(inputs_dict, config, model_class):
 
             check_hidden_states_output(inputs_dict, config, model_class)
 
+    def test_feed_forward_chunking(self):
+        (original_config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
+        if not self.test_chunking:
+            return
+
+        for model_class in self.all_model_classes:
+            torch.manual_seed(0)
+            config = copy.deepcopy(original_config)
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            hidden_states_no_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0]
+
+            torch.manual_seed(0)
+            config.chunk_size_feed_forward = 1
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            hidden_states_with_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0]
+            self.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-3))
+
     def test_resize_tokens_embeddings(self):
         (original_config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
         if not self.test_resize_embeddings:

tests/test_modeling_reformer.py

@@ -291,24 +291,6 @@ def create_and_check_reformer_layer_dropout_seed(
             torch.allclose(next_hidden_states, hidden_states + feed_forward_hidden_states, atol=1e-3,)
         )
 
-    def create_and_check_reformer_feed_forward_chunking(self, config, input_ids, input_mask, choice_labels):
-        torch.manual_seed(0)
-        model = ReformerModel(config=config)
-        model.to(torch_device)
-        model.eval()
-        hidden_states_no_chunk = model(input_ids, attention_mask=input_mask)[0]
-
-        config.chunk_size_lm_head = 1
-        config.chunk_size_feed_forward = 1
-
-        torch.manual_seed(0)
-        model = ReformerModel(config=config)
-        model.to(torch_device)
-        model.eval()
-
-        hidden_states_with_chunk = model(input_ids, attention_mask=input_mask)["last_hidden_state"]
-        self.parent.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-3))
-
     def create_and_check_reformer_feed_backward_chunking(self, config, input_ids, input_mask, choice_labels):
         if not self.is_training:
             return
@@ -517,10 +499,6 @@ def test_reformer_layer_training_dropout(self):
         self.model_tester.create_and_check_reformer_layer_dropout_seed(*config_and_inputs, is_decoder=True)
         self.model_tester.create_and_check_reformer_layer_dropout_seed(*config_and_inputs, is_decoder=False)
 
-    def test_reformer_chunking_forward_equality(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_reformer_feed_forward_chunking(*config_and_inputs)
-
     def test_reformer_chunking_backward_equality(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_reformer_feed_backward_chunking(*config_and_inputs)
@@ -577,6 +555,7 @@ class ReformerLocalAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest
     test_pruning = False
     test_headmasking = False
     test_torchscript = False
+    test_chunking = True
 
     def prepare_kwargs(self):
         return {
@@ -637,6 +616,7 @@ class ReformerLSHAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest.T
     test_pruning = False
     test_headmasking = False
     test_torchscript = False
+    test_chunking = True
 
     def prepare_kwargs(self):
         return {