Feed forward chunking others (#6365)

* Feed forward chunking for Distilbert & Albert

* Added ff chunking for many other models

* Change model signature

* Added chunking for XLM

* Cleaned up by removing some variables.

* remove test_chunking flag
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>

src/transformers/configuration_utils.py

@@ -191,6 +191,7 @@ class PretrainedConfig(object):
         self.pad_token_id = kwargs.pop("pad_token_id", None)
         self.eos_token_id = kwargs.pop("eos_token_id", None)
         self.decoder_start_token_id = kwargs.pop("decoder_start_token_id", None)
+        self.chunk_size_feed_forward = kwargs.pop("chunk_size_feed_forwar", 0)
 
         # task specific arguments
         self.task_specific_params = kwargs.pop("task_specific_params", None)

src/transformers/modeling_albert.py

@@ -43,7 +43,7 @@ from .modeling_outputs import (
     SequenceClassifierOutput,
     TokenClassifierOutput,
 )
-from .modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices
+from .modeling_utils import PreTrainedModel, apply_chunking_to_forward, find_pruneable_heads_and_indices
 
 
 logger = logging.getLogger(__name__)
@@ -69,6 +69,7 @@ def load_tf_weights_in_albert(model, config, tf_checkpoint_path):
     """ Load tf checkpoints in a pytorch model."""
     try:
         import re
+
         import numpy as np
         import tensorflow as tf
     except ImportError:
@@ -286,6 +287,8 @@ class AlbertLayer(nn.Module):
         super().__init__()
 
         self.config = config
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
         self.full_layer_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.attention = AlbertAttention(config)
         self.ffn = nn.Linear(config.hidden_size, config.intermediate_size)
@@ -297,14 +300,20 @@ class AlbertLayer(nn.Module):
         self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, output_hidden_states=False
     ):
         attention_output = self.attention(hidden_states, attention_mask, head_mask, output_attentions)
-        ffn_output = self.ffn(attention_output[0])
-        ffn_output = self.activation(ffn_output)
-        ffn_output = self.ffn_output(ffn_output)
-        ffn_output = self.dropout(ffn_output)
+
+        ffn_output = apply_chunking_to_forward(
+            self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output[0],
+        )
         hidden_states = self.full_layer_layer_norm(ffn_output + attention_output[0])
 
         return (hidden_states,) + attention_output[1:]  # add attentions if we output them
 
+    def ff_chunk(self, attention_output):
+        ffn_output = self.ffn(attention_output)
+        ffn_output = self.activation(ffn_output)
+        ffn_output = self.ffn_output(ffn_output)
+        return ffn_output
+
 
 class AlbertLayerGroup(nn.Module):
     def __init__(self, config):

src/transformers/modeling_bert.py

@@ -424,7 +424,7 @@ class BertLayer(nn.Module):
             outputs = outputs + cross_attention_outputs[1:]  # add cross attentions if we output attention weights
 
         layer_output = apply_chunking_to_forward(
-            self.chunk_size_feed_forward, self.seq_len_dim, self.feed_forward_chunk, attention_output
+            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
         )
         outputs = (layer_output,) + outputs
         return outputs

src/transformers/modeling_distilbert.py

@@ -44,7 +44,12 @@ from .modeling_outputs import (
     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__)
@@ -208,6 +213,8 @@ class FFN(nn.Module):
     def __init__(self, config):
         super().__init__()
         self.dropout = nn.Dropout(p=config.dropout)
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
         self.lin1 = nn.Linear(in_features=config.dim, out_features=config.hidden_dim)
         self.lin2 = nn.Linear(in_features=config.hidden_dim, out_features=config.dim)
         assert config.activation in ["relu", "gelu"], "activation ({}) must be in ['relu', 'gelu']".format(
@@ -216,6 +223,9 @@ class FFN(nn.Module):
         self.activation = gelu if config.activation == "gelu" else nn.ReLU()
 
     def forward(self, input):
+        return apply_chunking_to_forward(self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, input)
+
+    def ff_chunk(self, input):
         x = self.lin1(input)
         x = self.activation(x)
         x = self.lin2(x)

src/transformers/modeling_longformer.py

@@ -41,7 +41,12 @@ from .modeling_outputs import (
     TokenClassifierOutput,
 )
 from .modeling_roberta import RobertaEmbeddings, RobertaLMHead
-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__)
@@ -685,6 +690,8 @@ class LongformerLayer(nn.Module):
         self.attention = LongformerAttention(config, layer_id)
         self.intermediate = BertIntermediate(config)
         self.output = BertOutput(config)
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
 
     def forward(
         self, hidden_states, attention_mask=None, output_attentions=False,
@@ -693,11 +700,17 @@ class LongformerLayer(nn.Module):
         attn_output = self_attn_outputs[0]
         outputs = self_attn_outputs[1:]  # add self attentions if we output attention weights
 
-        intermediate_output = self.intermediate(attn_output)
-        layer_output = self.output(intermediate_output, attn_output)
+        layer_output = apply_chunking_to_forward(
+            self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attn_output
+        )
         outputs = (layer_output,) + outputs
         return outputs
 
+    def ff_chunk(self, attn_output):
+        intermediate_output = self.intermediate(attn_output)
+        layer_output = self.output(intermediate_output, attn_output)
+        return layer_output
+
 
 class LongformerEncoder(nn.Module):
     def __init__(self, config):

src/transformers/modeling_reformer.py

@@ -1369,7 +1369,7 @@ class ChunkReformerFeedForward(nn.Module):
 
     def forward(self, attention_output):
         return apply_chunking_to_forward(
-            self.chunk_size_feed_forward, self.seq_len_dim, self.forward_chunk, attention_output,
+            self.forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output,
         )
 
     def forward_chunk(self, hidden_states):
@@ -1730,7 +1730,7 @@ class ReformerOnlyLMHead(nn.Module):
         self.decoder.bias = self.bias
 
     def forward(self, hidden_states):
-        return apply_chunking_to_forward(self.chunk_size_lm_head, self.seq_len_dim, self.forward_chunk, hidden_states)
+        return apply_chunking_to_forward(self.forward_chunk, self.chunk_size_lm_head, self.seq_len_dim, hidden_states)
 
     def forward_chunk(self, hidden_states):
         hidden_states = self.decoder(hidden_states)

src/transformers/modeling_utils.py

@@ -1519,7 +1519,7 @@ def prune_layer(
 
 
 def apply_chunking_to_forward(
-    chunk_size: int, chunk_dim: int, forward_fn: Callable[..., torch.Tensor], *input_tensors
+    forward_fn: Callable[..., torch.Tensor], chunk_size: int, chunk_dim: int, *input_tensors
 ) -> torch.Tensor:
     """
     This function chunks the :obj:`input_tensors` into smaller input tensor parts of size :obj:`chunk_size` over the
@@ -1529,12 +1529,12 @@ def apply_chunking_to_forward(
     directly applying :obj:`forward_fn` to :obj:`input_tensors`.
 
     Args:
+        forward_fn (:obj:`Callable[..., torch.Tensor]`):
+            The forward function of the model.
         chunk_size (:obj:`int`):
             The chunk size of a chunked tensor: :obj:`num_chunks = len(input_tensors[0]) / chunk_size`.
         chunk_dim (:obj:`int`):
             The dimension over which the :obj:`input_tensors` should be chunked.
-        forward_fn (:obj:`Callable[..., torch.Tensor]`):
-            The forward function of the model.
         input_tensors (:obj:`Tuple[torch.Tensor]`):
             The input tensors of ``forward_fn`` which will be chunked.
     Returns:
@@ -1550,7 +1550,7 @@ def apply_chunking_to_forward(
 
         # implement a chunked forward function
         def forward(self, hidden_states):
-            return apply_chunking_to_forward(self.chunk_size_lm_head, self.seq_len_dim, self.forward_chunk, hidden_states)
+            return apply_chunking_to_forward(self.forward_chunk, self.chunk_size_lm_head, self.seq_len_dim, hidden_states)
     """
 
     assert len(input_tensors) > 0, "{} has to be a tuple/list of tensors".format(input_tensors)

src/transformers/modeling_xlm.py

@@ -50,6 +50,7 @@ from .modeling_utils import (
     PreTrainedModel,
     SequenceSummary,
     SQuADHead,
+    apply_chunking_to_forward,
     find_pruneable_heads_and_indices,
     prune_linear_layer,
 )
@@ -212,8 +213,13 @@ class TransformerFFN(nn.Module):
         self.lin1 = nn.Linear(in_dim, dim_hidden)
         self.lin2 = nn.Linear(dim_hidden, out_dim)
         self.act = gelu if config.gelu_activation else F.relu
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
 
     def forward(self, input):
+        return apply_chunking_to_forward(self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, input)
+
+    def ff_chunk(self, input):
         x = self.lin1(input)
         x = self.act(x)
         x = self.lin2(x)

src/transformers/modeling_xlnet.py

@@ -35,7 +35,14 @@ from .file_utils import (
     add_start_docstrings_to_callable,
     replace_return_docstrings,
 )
-from .modeling_utils import PoolerAnswerClass, PoolerEndLogits, PoolerStartLogits, PreTrainedModel, SequenceSummary
+from .modeling_utils import (
+    PoolerAnswerClass,
+    PoolerEndLogits,
+    PoolerStartLogits,
+    PreTrainedModel,
+    SequenceSummary,
+    apply_chunking_to_forward,
+)
 
 
 logger = logging.getLogger(__name__)
@@ -495,6 +502,8 @@ class XLNetLayer(nn.Module):
         self.rel_attn = XLNetRelativeAttention(config)
         self.ff = XLNetFeedForward(config)
         self.dropout = nn.Dropout(config.dropout)
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
 
     def forward(
         self,
@@ -524,12 +533,18 @@ class XLNetLayer(nn.Module):
         output_h, output_g = outputs[:2]
 
         if output_g is not None:
-            output_g = self.ff(output_g)
-        output_h = self.ff(output_h)
+            output_g = apply_chunking_to_forward(
+                self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, output_g
+            )
+        output_h = apply_chunking_to_forward(self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, output_h)
 
         outputs = (output_h, output_g) + outputs[2:]  # Add again attentions if there are there
         return outputs
 
+    def ff_chunk(self, output_x):
+        output_x = self.ff(output_x)
+        return output_x
+
 
 class XLNetPreTrainedModel(PreTrainedModel):
     """ An abstract class to handle weights initialization and

tests/test_modeling_bert.py

@@ -26,15 +26,15 @@ from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
 if is_torch_available():
     from transformers import (
         BertConfig,
-        BertModel,
-        BertLMHeadModel,
         BertForMaskedLM,
+        BertForMultipleChoice,
         BertForNextSentencePrediction,
         BertForPreTraining,
         BertForQuestionAnswering,
         BertForSequenceClassification,
         BertForTokenClassification,
-        BertForMultipleChoice,
+        BertLMHeadModel,
+        BertModel,
     )
     from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_LIST
 
@@ -370,7 +370,6 @@ 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

@@ -25,15 +25,15 @@ from transformers.testing_utils import require_multigpu, require_torch, slow, to
 
 
 if is_torch_available():
-    import torch
     import numpy as np
+    import torch
 
     from transformers import (
         AdaptiveEmbedding,
         PretrainedConfig,
         PreTrainedModel,
-        BertModel,
         BertConfig,
+        BertModel,
         BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
         MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
         MODEL_FOR_QUESTION_ANSWERING_MAPPING,
@@ -65,7 +65,6 @@ 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, return_labels=False):
@@ -552,9 +551,6 @@ class ModelTesterMixin:
 
     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)

tests/test_modeling_reformer.py

@@ -555,7 +555,6 @@ class ReformerLocalAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest
     test_pruning = False
     test_headmasking = False
     test_torchscript = False
-    test_chunking = True
 
     def prepare_kwargs(self):
         return {
@@ -616,7 +615,6 @@ class ReformerLSHAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest.T
     test_pruning = False
     test_headmasking = False
     test_torchscript = False
-    test_chunking = True
 
     def prepare_kwargs(self):
         return {