Make PyTorch model files independent from each other (#7352)

src/transformers/activations.py

@@ -40,6 +40,10 @@ def gelu_fast(x):
     return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 + 0.044715 * x * x)))
 
 
+def mish(x):
+    return x * torch.tanh(torch.nn.functional.softplus(x))
+
+
 ACT2FN = {
     "relu": F.relu,
     "swish": swish,
@@ -47,6 +51,7 @@ ACT2FN = {
     "tanh": torch.tanh,
     "gelu_new": gelu_new,
     "gelu_fast": gelu_fast,
+    "mish": mish,
 }
 
 

src/transformers/modeling_albert.py

@@ -24,6 +24,7 @@ import torch
 import torch.nn as nn
 from torch.nn import CrossEntropyLoss, MSELoss
 
+from .activations import ACT2FN
 from .configuration_albert import AlbertConfig
 from .file_utils import (
     ModelOutput,
@@ -32,7 +33,6 @@ from .file_utils import (
     add_start_docstrings_to_callable,
     replace_return_docstrings,
 )
-from .modeling_bert import ACT2FN, BertEmbeddings, BertSelfAttention, prune_linear_layer
 from .modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPooling,
@@ -42,7 +42,12 @@ from .modeling_outputs import (
     SequenceClassifierOutput,
     TokenClassifierOutput,
 )
-from .modeling_utils import PreTrainedModel, apply_chunking_to_forward, find_pruneable_heads_and_indices
+from .modeling_utils import (
+    PreTrainedModel,
+    apply_chunking_to_forward,
+    find_pruneable_heads_and_indices,
+    prune_linear_layer,
+)
 from .utils import logging
 
 
@@ -192,33 +197,81 @@ def load_tf_weights_in_albert(model, config, tf_checkpoint_path):
     return model
 
 
-class AlbertEmbeddings(BertEmbeddings):
+class AlbertEmbeddings(nn.Module):
     """
     Construct the embeddings from word, position and token_type embeddings.
     """
 
     def __init__(self, config):
-        super().__init__(config)
-
+        super().__init__()
         self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
         self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size)
         self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size)
-        self.LayerNorm = torch.nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
 
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
+
+    # Copied from transformers.modeling_bert.BertEmbeddings.forward
+    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            input_shape = inputs_embeds.size()[:-1]
+
+        seq_length = input_shape[1]
+
+        if position_ids is None:
+            position_ids = self.position_ids[:, :seq_length]
 
-class AlbertAttention(BertSelfAttention):
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = inputs_embeds + position_embeddings + token_type_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class AlbertAttention(nn.Module):
     def __init__(self, config):
-        super().__init__(config)
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
+            )
 
         self.num_attention_heads = config.num_attention_heads
         self.hidden_size = config.hidden_size
         self.attention_head_size = config.hidden_size // config.num_attention_heads
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
         self.attention_dropout = nn.Dropout(config.attention_probs_dropout_prob)
         self.output_dropout = nn.Dropout(config.hidden_dropout_prob)
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
         self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.pruned_heads = set()
 
+    # Copied from transformers.modeling_bert.BertSelfAttention.transpose_for_scores
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
     def prune_heads(self, heads):
         if len(heads) == 0:
             return

src/transformers/modeling_bert.py

@@ -27,7 +27,7 @@ import torch.utils.checkpoint
 from torch import nn
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from .activations import gelu, gelu_new, swish
+from .activations import ACT2FN
 from .configuration_bert import BertConfig
 from .file_utils import (
     ModelOutput,
@@ -162,16 +162,6 @@ def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
     return model
 
 
-def mish(x):
-    return x * torch.tanh(nn.functional.softplus(x))
-
-
-ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish, "gelu_new": gelu_new, "mish": mish}
-
-
-BertLayerNorm = torch.nn.LayerNorm
-
-
 class BertEmbeddings(nn.Module):
     """Construct the embeddings from word, position and token_type embeddings."""
 
@@ -183,7 +173,7 @@ class BertEmbeddings(nn.Module):
 
         # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
         # any TensorFlow checkpoint file
-        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
         # position_ids (1, len position emb) is contiguous in memory and exported when serialized
@@ -296,7 +286,7 @@ class BertSelfOutput(nn.Module):
     def __init__(self, config):
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, hidden_states, input_tensor):
@@ -372,7 +362,7 @@ class BertOutput(nn.Module):
     def __init__(self, config):
         super().__init__()
         self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, hidden_states, input_tensor):
@@ -528,7 +518,7 @@ class BertPredictionHeadTransform(nn.Module):
             self.transform_act_fn = ACT2FN[config.hidden_act]
         else:
             self.transform_act_fn = config.hidden_act
-        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
 
     def forward(self, hidden_states):
         hidden_states = self.dense(hidden_states)
@@ -605,7 +595,7 @@ class BertPreTrainedModel(PreTrainedModel):
             # Slightly different from the TF version which uses truncated_normal for initialization
             # cf https://github.com/pytorch/pytorch/pull/5617
             module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, BertLayerNorm):
+        elif isinstance(module, nn.LayerNorm):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0)
         if isinstance(module, nn.Linear) and module.bias is not None:

src/transformers/modeling_electra.py

+# coding=utf-8
+# Copyright 2019 The Google AI Language Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch ELECTRA model. """
+
+import math
 import os
 import warnings
 from dataclasses import dataclass
@@ -7,7 +24,7 @@ import torch
 import torch.nn as nn
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from .activations import get_activation
+from .activations import ACT2FN, get_activation
 from .configuration_electra import ElectraConfig
 from .file_utils import (
     ModelOutput,
@@ -16,7 +33,6 @@ from .file_utils import (
     add_start_docstrings_to_callable,
     replace_return_docstrings,
 )
-from .modeling_bert import BertEmbeddings, BertEncoder, BertLayerNorm, BertPreTrainedModel
 from .modeling_outputs import (
     BaseModelOutput,
     MaskedLMOutput,
@@ -25,7 +41,13 @@ from .modeling_outputs import (
     SequenceClassifierOutput,
     TokenClassifierOutput,
 )
-from .modeling_utils import SequenceSummary
+from .modeling_utils import (
+    PreTrainedModel,
+    SequenceSummary,
+    apply_chunking_to_forward,
+    find_pruneable_heads_and_indices,
+    prune_linear_layer,
+)
 from .utils import logging
 
 
@@ -128,18 +150,345 @@ def load_tf_weights_in_electra(model, config, tf_checkpoint_path, discriminator_
     return model
 
 
-class ElectraEmbeddings(BertEmbeddings):
+class ElectraEmbeddings(nn.Module):
     """Construct the embeddings from word, position and token_type embeddings."""
 
     def __init__(self, config):
-        super().__init__(config)
+        super().__init__()
         self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
         self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size)
         self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size)
 
         # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
         # any TensorFlow checkpoint file
-        self.LayerNorm = BertLayerNorm(config.embedding_size, eps=config.layer_norm_eps)
+        self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
+
+    # Copied from transformers.modeling_bert.BertEmbeddings.forward
+    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            input_shape = inputs_embeds.size()[:-1]
+
+        seq_length = input_shape[1]
+
+        if position_ids is None:
+            position_ids = self.position_ids[:, :seq_length]
+
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = inputs_embeds + position_embeddings + token_type_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+# Copied from transformers.modeling_bert.BertSelfAttention with Bert->Electra
+class ElectraSelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=False,
+    ):
+        mixed_query_layer = self.query(hidden_states)
+
+        # If this is instantiated as a cross-attention module, the keys
+        # and values come from an encoder; the attention mask needs to be
+        # such that the encoder's padding tokens are not attended to.
+        if encoder_hidden_states is not None:
+            mixed_key_layer = self.key(encoder_hidden_states)
+            mixed_value_layer = self.value(encoder_hidden_states)
+            attention_mask = encoder_attention_mask
+        else:
+            mixed_key_layer = self.key(hidden_states)
+            mixed_value_layer = self.value(hidden_states)
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+        key_layer = self.transpose_for_scores(mixed_key_layer)
+        value_layer = self.transpose_for_scores(mixed_value_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        if attention_mask is not None:
+            # Apply the attention mask is (precomputed for all layers in ElectraModel forward() function)
+            attention_scores = attention_scores + attention_mask
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.Softmax(dim=-1)(attention_scores)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs = attention_probs * head_mask
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+
+        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+        return outputs
+
+
+# Copied from transformers.modeling_bert.BertSelfOutput
+class ElectraSelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+# Copied from transformers.modeling_bert.BertAttention with Bert->Electra
+class ElectraAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.self = ElectraSelfAttention(config)
+        self.output = ElectraSelfOutput(config)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+        )
+
+        # Prune linear layers
+        self.self.query = prune_linear_layer(self.self.query, index)
+        self.self.key = prune_linear_layer(self.self.key, index)
+        self.self.value = prune_linear_layer(self.self.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=False,
+    ):
+        self_outputs = self.self(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            encoder_hidden_states,
+            encoder_attention_mask,
+            output_attentions,
+        )
+        attention_output = self.output(self_outputs[0], hidden_states)
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        return outputs
+
+
+# Copied from transformers.modeling_bert.BertIntermediate
+class ElectraIntermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.modeling_bert.BertOutput
+class ElectraOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+# Copied from transformers.modeling_bert.BertLayer with Bert->Electra
+class ElectraLayer(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 = ElectraAttention(config)
+        self.is_decoder = config.is_decoder
+        self.add_cross_attention = config.add_cross_attention
+        if self.add_cross_attention:
+            assert self.is_decoder, f"{self} should be used as a decoder model if cross attention is added"
+            self.crossattention = ElectraAttention(config)
+        self.intermediate = ElectraIntermediate(config)
+        self.output = ElectraOutput(config)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=False,
+    ):
+        self_attention_outputs = self.attention(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            output_attentions=output_attentions,
+        )
+        attention_output = self_attention_outputs[0]
+        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
+
+        if self.is_decoder and encoder_hidden_states is not None:
+            assert hasattr(
+                self, "crossattention"
+            ), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
+            cross_attention_outputs = self.crossattention(
+                attention_output,
+                attention_mask,
+                head_mask,
+                encoder_hidden_states,
+                encoder_attention_mask,
+                output_attentions,
+            )
+            attention_output = cross_attention_outputs[0]
+            outputs = outputs + cross_attention_outputs[1:]  # add cross attentions if we output attention weights
+
+        layer_output = apply_chunking_to_forward(
+            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, 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
+
+
+# Copied from transformers.modeling_bert.BertEncoder with Bert->Electra
+class ElectraEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([ElectraLayer(config) for _ in range(config.num_hidden_layers)])
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=False,
+        output_hidden_states=False,
+        return_dict=False,
+    ):
+        all_hidden_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+
+            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,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    output_attentions,
+                )
+            hidden_states = layer_outputs[0]
+            if output_attentions:
+                all_attentions = all_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
+        )
 
 
 class ElectraDiscriminatorPredictions(nn.Module):
@@ -166,7 +515,7 @@ class ElectraGeneratorPredictions(nn.Module):
     def __init__(self, config):
         super().__init__()
 
-        self.LayerNorm = BertLayerNorm(config.embedding_size)
+        self.LayerNorm = nn.LayerNorm(config.embedding_size)
         self.dense = nn.Linear(config.hidden_size, config.embedding_size)
 
     def forward(self, generator_hidden_states):
@@ -177,7 +526,7 @@ class ElectraGeneratorPredictions(nn.Module):
         return hidden_states
 
 
-class ElectraPreTrainedModel(BertPreTrainedModel):
+class ElectraPreTrainedModel(PreTrainedModel):
     """An abstract class to handle weights initialization and
     a simple interface for downloading and loading pretrained models.
     """
@@ -187,6 +536,19 @@ class ElectraPreTrainedModel(BertPreTrainedModel):
     base_model_prefix = "electra"
     authorized_missing_keys = [r"position_ids"]
 
+    # Copied from transformers.modeling_bert.BertPreTrainedModel._init_weights
+    def _init_weights(self, module):
+        """ Initialize the weights """
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
 
 @dataclass
 class ElectraForPreTrainingOutput(ModelOutput):
@@ -306,9 +668,6 @@ ELECTRA_INPUTS_DOCSTRING = r"""
     ELECTRA_START_DOCSTRING,
 )
 class ElectraModel(ElectraPreTrainedModel):
-
-    config_class = ElectraConfig
-
     def __init__(self, config):
         super().__init__(config)
         self.embeddings = ElectraEmbeddings(config)
@@ -316,7 +675,7 @@ class ElectraModel(ElectraPreTrainedModel):
         if config.embedding_size != config.hidden_size:
             self.embeddings_project = nn.Linear(config.embedding_size, config.hidden_size)
 
-        self.encoder = BertEncoder(config)
+        self.encoder = ElectraEncoder(config)
         self.config = config
         self.init_weights()
 

src/transformers/modeling_funnel.py

@@ -63,9 +63,6 @@ FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST = [
     "funnel-transformer/xlarge",  # B10-10-10H1024, no decoder
 ]
 
-
-FunnelLayerNorm = nn.LayerNorm
-
 INF = 1e6
 
 
@@ -163,7 +160,7 @@ class FunnelEmbeddings(nn.Module):
     def __init__(self, config):
         super().__init__()
         self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
-        self.layer_norm = FunnelLayerNorm(config.d_model, eps=config.layer_norm_eps)
+        self.layer_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps)
         self.dropout = nn.Dropout(config.hidden_dropout)
 
     def forward(self, input_ids=None, inputs_embeds=None):
@@ -457,7 +454,7 @@ class FunnelRelMultiheadAttention(nn.Module):
         self.seg_embed = nn.Parameter(torch.zeros([2, n_head, d_head]))
 
         self.post_proj = nn.Linear(n_head * d_head, d_model)
-        self.layer_norm = FunnelLayerNorm(d_model, eps=config.layer_norm_eps)
+        self.layer_norm = nn.LayerNorm(d_model, eps=config.layer_norm_eps)
         self.scale = 1.0 / (d_head ** 0.5)
 
     def relative_positional_attention(self, position_embeds, q_head, context_len, cls_mask=None):
@@ -581,7 +578,7 @@ class FunnelPositionwiseFFN(nn.Module):
         self.activation_dropout = nn.Dropout(config.activation_dropout)
         self.linear_2 = nn.Linear(config.d_inner, config.d_model)
         self.dropout = nn.Dropout(config.hidden_dropout)
-        self.layer_norm = FunnelLayerNorm(config.d_model, config.layer_norm_eps)
+        self.layer_norm = nn.LayerNorm(config.d_model, config.layer_norm_eps)
 
     def forward(self, hidden):
         h = self.linear_1(hidden)

src/transformers/modeling_layoutlm.py

@@ -202,7 +202,7 @@ class LayoutLMSelfOutput(nn.Module):
     def __init__(self, config):
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = LayoutLMLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, hidden_states, input_tensor):
@@ -281,7 +281,7 @@ class LayoutLMOutput(nn.Module):
     def __init__(self, config):
         super().__init__()
         self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = LayoutLMLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, hidden_states, input_tensor):
@@ -441,7 +441,7 @@ class LayoutLMPredictionHeadTransform(nn.Module):
             self.transform_act_fn = ACT2FN[config.hidden_act]
         else:
             self.transform_act_fn = config.hidden_act
-        self.LayerNorm = LayoutLMLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
 
     def forward(self, hidden_states):
         hidden_states = self.dense(hidden_states)

src/transformers/modeling_longformer.py

@@ -22,6 +22,7 @@ import torch.nn as nn
 from torch.nn import CrossEntropyLoss, MSELoss
 from torch.nn import functional as F
 
+from .activations import ACT2FN, gelu
 from .configuration_longformer import LongformerConfig
 from .file_utils import (
     add_code_sample_docstrings,
@@ -29,7 +30,6 @@ from .file_utils import (
     add_start_docstrings_to_callable,
     replace_return_docstrings,
 )
-from .modeling_bert import BertIntermediate, BertLayerNorm, BertOutput, BertPooler, BertPreTrainedModel, BertSelfOutput
 from .modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPooling,
@@ -39,7 +39,6 @@ from .modeling_outputs import (
     SequenceClassifierOutput,
     TokenClassifierOutput,
 )
-from .modeling_roberta import RobertaEmbeddings, RobertaLMHead
 from .modeling_utils import (
     PreTrainedModel,
     apply_chunking_to_forward,
@@ -100,6 +99,95 @@ def _compute_global_attention_mask(input_ids, sep_token_id, before_sep_token=Tru
     return attention_mask
 
 
+# Copied from transformers.modeling_roberta.create_position_ids_from_input_ids
+def create_position_ids_from_input_ids(input_ids, padding_idx):
+    """Replace non-padding symbols with their position numbers. Position numbers begin at
+    padding_idx+1. Padding symbols are ignored. This is modified from fairseq's
+    `utils.make_positions`.
+
+    :param torch.Tensor x:
+    :return torch.Tensor:
+    """
+    # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
+    mask = input_ids.ne(padding_idx).int()
+    incremental_indices = torch.cumsum(mask, dim=1).type_as(mask) * mask
+    return incremental_indices.long() + padding_idx
+
+
+class LongformerEmbeddings(nn.Module):
+    """
+    Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
+    """
+
+    # Copied from transformers.modeling_bert.BertEmbeddings.__init__
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
+
+        # End copy
+        self.padding_idx = config.pad_token_id
+        self.position_embeddings = nn.Embedding(
+            config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
+        )
+
+    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
+        if position_ids is None:
+            if input_ids is not None:
+                # Create the position ids from the input token ids. Any padded tokens remain padded.
+                position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx).to(input_ids.device)
+            else:
+                position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
+
+        # Copied from transformers.modeling_bert.BertEmbeddings.forward
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            input_shape = inputs_embeds.size()[:-1]
+
+        seq_length = input_shape[1]
+
+        if position_ids is None:
+            position_ids = self.position_ids[:, :seq_length]
+
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = inputs_embeds + position_embeddings + token_type_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+    def create_position_ids_from_inputs_embeds(self, inputs_embeds):
+        """We are provided embeddings directly. We cannot infer which are padded so just generate
+        sequential position ids.
+
+        :param torch.Tensor inputs_embeds:
+        :return torch.Tensor:
+        """
+        input_shape = inputs_embeds.size()[:-1]
+        sequence_length = input_shape[1]
+
+        position_ids = torch.arange(
+            self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
+        )
+        return position_ids.unsqueeze(0).expand(input_shape)
+
+
 class LongformerSelfAttention(nn.Module):
     def __init__(self, config, layer_id):
         super().__init__()
@@ -656,11 +744,26 @@ class LongformerSelfAttention(nn.Module):
         return global_attn_output
 
 
+# Copied from transformers.modeling_bert.BertSelfOutput
+class LongformerSelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
 class LongformerAttention(nn.Module):
     def __init__(self, config, layer_id=0):
         super().__init__()
         self.self = LongformerSelfAttention(config, layer_id)
-        self.output = BertSelfOutput(config)
+        self.output = LongformerSelfOutput(config)
         self.pruned_heads = set()
 
     def prune_heads(self, heads):
@@ -697,12 +800,43 @@ class LongformerAttention(nn.Module):
         return outputs
 
 
+# Copied from transformers.modeling_bert.BertIntermediate
+class LongformerIntermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.modeling_bert.BertOutput
+class LongformerOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
 class LongformerLayer(nn.Module):
     def __init__(self, config, layer_id=0):
         super().__init__()
         self.attention = LongformerAttention(config, layer_id)
-        self.intermediate = BertIntermediate(config)
-        self.output = BertOutput(config)
+        self.intermediate = LongformerIntermediate(config)
+        self.output = LongformerOutput(config)
         self.chunk_size_feed_forward = config.chunk_size_feed_forward
         self.seq_len_dim = 1
 
@@ -787,6 +921,48 @@ class LongformerEncoder(nn.Module):
         )
 
 
+# Copied from transformers.modeling_bert.BertPooler
+class LongformerPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+# Copied from transformers.modeling_roberta.RobertaLMHead with Roberta->Longformer
+class LongformerLMHead(nn.Module):
+    """Longformer Head for masked language modeling."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+        self.decoder.bias = self.bias
+
+    def forward(self, features, **kwargs):
+        x = self.dense(features)
+        x = gelu(x)
+        x = self.layer_norm(x)
+
+        # project back to size of vocabulary with bias
+        x = self.decoder(x)
+
+        return x
+
+
 class LongformerPreTrainedModel(PreTrainedModel):
     """An abstract class to handle weights initialization and
     a simple interface for downloading and loading pretrained
@@ -803,7 +979,7 @@ class LongformerPreTrainedModel(PreTrainedModel):
             # Slightly different from the TF version which uses truncated_normal for initialization
             # cf https://github.com/pytorch/pytorch/pull/5617
             module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, BertLayerNorm):
+        elif isinstance(module, nn.LayerNorm):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0)
         if isinstance(module, nn.Linear) and module.bias is not None:
@@ -922,9 +1098,9 @@ class LongformerModel(LongformerPreTrainedModel):
                 f"Expected {config.num_hidden_layers}, given {len(config.attention_window)}"
             )
 
-        self.embeddings = RobertaEmbeddings(config)
+        self.embeddings = LongformerEmbeddings(config)
         self.encoder = LongformerEncoder(config)
-        self.pooler = BertPooler(config)
+        self.pooler = LongformerPooler(config)
 
         self.init_weights()
 
@@ -1121,7 +1297,7 @@ class LongformerForMaskedLM(LongformerPreTrainedModel):
         super().__init__(config)
 
         self.longformer = LongformerModel(config)
-        self.lm_head = RobertaLMHead(config)
+        self.lm_head = LongformerLMHead(config)
 
         self.init_weights()
 
@@ -1218,10 +1394,7 @@ class LongformerForMaskedLM(LongformerPreTrainedModel):
     on top of the pooled output) e.g. for GLUE tasks. """,
     LONGFORMER_START_DOCSTRING,
 )
-class LongformerForSequenceClassification(BertPreTrainedModel):
-    config_class = LongformerConfig
-    base_model_prefix = "longformer"
-
+class LongformerForSequenceClassification(LongformerPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
@@ -1326,10 +1499,7 @@ class LongformerClassificationHead(nn.Module):
     TriviaQA (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """,
     LONGFORMER_START_DOCSTRING,
 )
-class LongformerForQuestionAnswering(BertPreTrainedModel):
-    config_class = LongformerConfig
-    base_model_prefix = "longformer"
-
+class LongformerForQuestionAnswering(LongformerPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
@@ -1457,10 +1627,7 @@ class LongformerForQuestionAnswering(BertPreTrainedModel):
     the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
     LONGFORMER_START_DOCSTRING,
 )
-class LongformerForTokenClassification(BertPreTrainedModel):
-    config_class = LongformerConfig
-    base_model_prefix = "longformer"
-
+class LongformerForTokenClassification(LongformerPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
@@ -1546,10 +1713,7 @@ class LongformerForTokenClassification(BertPreTrainedModel):
     the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
     LONGFORMER_START_DOCSTRING,
 )
-class LongformerForMultipleChoice(BertPreTrainedModel):
-    config_class = LongformerConfig
-    base_model_prefix = "longformer"
-
+class LongformerForMultipleChoice(LongformerPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
 

src/transformers/modeling_lxmert.py

@@ -25,7 +25,7 @@ import torch
 from torch import nn
 from torch.nn import CrossEntropyLoss, SmoothL1Loss
 
-from .activations import gelu, swish
+from .activations import ACT2FN, gelu
 from .configuration_lxmert import LxmertConfig
 from .file_utils import (
     ModelOutput,
@@ -275,11 +275,6 @@ def load_tf_weights_in_lxmert(model, config, tf_checkpoint_path):
     return model
 
 
-ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
-
-LxmertLayerNorm = torch.nn.LayerNorm
-
-
 class LxmertEmbeddings(nn.Module):
     """Construct the embeddings from word, position and token_type embeddings."""
 
@@ -291,7 +286,7 @@ class LxmertEmbeddings(nn.Module):
 
         # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
         # any TensorFlow checkpoint file
-        self.LayerNorm = LxmertLayerNorm(config.hidden_size, eps=1e-12)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, input_ids, token_type_ids=None, inputs_embeds=None):
@@ -385,7 +380,7 @@ class LxmertAttentionOutput(nn.Module):
     def __init__(self, config):
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = LxmertLayerNorm(config.hidden_size, eps=1e-12)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, hidden_states, input_tensor):
@@ -447,7 +442,7 @@ class LxmertOutput(nn.Module):
     def __init__(self, config):
         super().__init__()
         self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = LxmertLayerNorm(config.hidden_size, eps=1e-12)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, hidden_states, input_tensor):
@@ -573,11 +568,11 @@ class LxmertVisualFeatureEncoder(nn.Module):
 
         # Object feature encoding
         self.visn_fc = nn.Linear(feat_dim, config.hidden_size)
-        self.visn_layer_norm = LxmertLayerNorm(config.hidden_size, eps=1e-12)
+        self.visn_layer_norm = nn.LayerNorm(config.hidden_size, eps=1e-12)
 
         # Box position encoding
         self.box_fc = nn.Linear(pos_dim, config.hidden_size)
-        self.box_layer_norm = LxmertLayerNorm(config.hidden_size, eps=1e-12)
+        self.box_layer_norm = nn.LayerNorm(config.hidden_size, eps=1e-12)
 
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
@@ -694,7 +689,7 @@ class LxmertPredictionHeadTransform(nn.Module):
         super(LxmertPredictionHeadTransform, self).__init__()
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
         self.transform_act_fn = ACT2FN[config.hidden_act]
-        self.LayerNorm = LxmertLayerNorm(config.hidden_size, eps=1e-12)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
 
     def forward(self, hidden_states):
         hidden_states = self.dense(hidden_states)
@@ -731,7 +726,7 @@ class LxmertVisualAnswerHead(nn.Module):
         self.logit_fc = nn.Sequential(
             nn.Linear(hid_dim, hid_dim * 2),
             GeLU(),
-            LxmertLayerNorm(hid_dim * 2, eps=1e-12),
+            nn.LayerNorm(hid_dim * 2, eps=1e-12),
             nn.Linear(hid_dim * 2, num_labels),
         )
 
@@ -797,7 +792,7 @@ class LxmertPreTrainedModel(PreTrainedModel):
             # Slightly different from the TF version which uses truncated_normal for initialization
             # cf https://github.com/pytorch/pytorch/pull/5617
             module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, LxmertLayerNorm):
+        elif isinstance(module, nn.LayerNorm):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0)
         if isinstance(module, nn.Linear) and module.bias is not None:

src/transformers/modeling_mobilebert.py

@@ -31,7 +31,7 @@ import torch.nn.functional as F
 from torch import nn
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from .activations import gelu, gelu_new, swish
+from .activations import ACT2FN
 from .configuration_mobilebert import MobileBertConfig
 from .file_utils import (
     ModelOutput,
@@ -40,7 +40,6 @@ from .file_utils import (
     add_start_docstrings_to_callable,
     replace_return_docstrings,
 )
-from .modeling_bert import BertIntermediate
 from .modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPooling,
@@ -155,7 +154,6 @@ class NoNorm(nn.Module):
         return input_tensor * self.weight + self.bias
 
 
-ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish, "gelu_new": gelu_new, "mish": mish}
 NORM2FN = {"layer_norm": torch.nn.LayerNorm, "no_norm": NoNorm}
 
 
@@ -358,10 +356,19 @@ class MobileBertAttention(nn.Module):
         return outputs
 
 
-class MobileBertIntermediate(BertIntermediate):
+class MobileBertIntermediate(nn.Module):
     def __init__(self, config):
-        super().__init__(config)
+        super().__init__()
         self.dense = nn.Linear(config.true_hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
 
 
 class OutputBottleneck(nn.Module):

src/transformers/modeling_reformer.py

@@ -28,7 +28,7 @@ from torch import nn
 from torch.autograd.function import Function
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from .activations import gelu, gelu_fast, gelu_new, swish
+from .activations import ACT2FN
 from .configuration_reformer import ReformerConfig
 from .file_utils import (
     DUMMY_INPUTS,
@@ -55,20 +55,6 @@ REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
 ]
 
 
-def mish(x):
-    return x * torch.tanh(nn.functional.softplus(x))
-
-
-ACT2FN = {
-    "gelu": gelu,
-    "relu": torch.nn.functional.relu,
-    "swish": swish,
-    "gelu_new": gelu_new,
-    "gelu_fast": gelu_fast,
-    "mish": mish,
-}
-
-
 # Define named tuples for nn.Modules here
 LSHSelfAttentionOutput = namedtuple("LSHSelfAttentionOutput", ["hidden_states", "attention_probs", "buckets"])
 LocalSelfAttentionOutput = namedtuple("LocalSelfAttentionOutput", ["hidden_states", "attention_probs"])

src/transformers/modeling_retribert.py

@@ -25,7 +25,7 @@ import torch.utils.checkpoint as checkpoint
 
 from .configuration_retribert import RetriBertConfig
 from .file_utils import add_start_docstrings
-from .modeling_bert import BertLayerNorm, BertModel
+from .modeling_bert import BertModel
 from .modeling_utils import PreTrainedModel
 from .utils import logging
 
@@ -52,7 +52,7 @@ class RetriBertPreTrainedModel(PreTrainedModel):
         """ Initialize the weights """
         if isinstance(module, (nn.Linear, nn.Embedding)):
             module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, BertLayerNorm):
+        elif isinstance(module, nn.LayerNorm):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0)
         if isinstance(module, nn.Linear) and module.bias is not None:

src/transformers/modeling_roberta.py

@@ -22,6 +22,7 @@ import torch
 import torch.nn as nn
 from torch.nn import CrossEntropyLoss, MSELoss
 
+from .activations import ACT2FN, gelu
 from .configuration_roberta import RobertaConfig
 from .file_utils import (
     add_code_sample_docstrings,
@@ -29,7 +30,6 @@ from .file_utils import (
     add_start_docstrings_to_callable,
     replace_return_docstrings,
 )
-from .modeling_bert import ACT2FN, gelu
 from .modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPooling,
@@ -65,15 +65,12 @@ ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
 ]
 
 
-RobertaLayerNorm = torch.nn.LayerNorm
-
-
 class RobertaEmbeddings(nn.Module):
     """
     Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
     """
 
-    # Copied from transformers.modeling_bert.BertEmbeddings.__init__ with Bert->Roberta
+    # Copied from transformers.modeling_bert.BertEmbeddings.__init__
     def __init__(self, config):
         super().__init__()
         self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
@@ -82,7 +79,7 @@ class RobertaEmbeddings(nn.Module):
 
         # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
         # any TensorFlow checkpoint file
-        self.LayerNorm = RobertaLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
         # position_ids (1, len position emb) is contiguous in memory and exported when serialized
@@ -221,12 +218,12 @@ class RobertaSelfAttention(nn.Module):
         return outputs
 
 
-# Copied from transformers.modeling_bert.BertSelfOutput with Bert->Roberta
+# Copied from transformers.modeling_bert.BertSelfOutput
 class RobertaSelfOutput(nn.Module):
     def __init__(self, config):
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = RobertaLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, hidden_states, input_tensor):
@@ -300,12 +297,12 @@ class RobertaIntermediate(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.modeling_bert.BertOutput with Bert->Roberta
+# Copied from transformers.modeling_bert.BertOutput
 class RobertaOutput(nn.Module):
     def __init__(self, config):
         super().__init__()
         self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = RobertaLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, hidden_states, input_tensor):
@@ -465,14 +462,14 @@ class RobertaPreTrainedModel(PreTrainedModel):
     base_model_prefix = "roberta"
     authorized_missing_keys = [r"position_ids"]
 
-    # Copied from transformers.modeling_bert.BertPreTrainedModel._init_weights with Bert->Roberta
+    # Copied from transformers.modeling_bert.BertPreTrainedModel._init_weights
     def _init_weights(self, module):
         """ Initialize the weights """
         if isinstance(module, (nn.Linear, nn.Embedding)):
             # Slightly different from the TF version which uses truncated_normal for initialization
             # cf https://github.com/pytorch/pytorch/pull/5617
             module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, RobertaLayerNorm):
+        elif isinstance(module, nn.LayerNorm):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0)
         if isinstance(module, nn.Linear) and module.bias is not None:
@@ -916,7 +913,7 @@ class RobertaLMHead(nn.Module):
     def __init__(self, config):
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.layer_norm = RobertaLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
 
         self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
         self.bias = nn.Parameter(torch.zeros(config.vocab_size))

src/transformers/modeling_xlnet.py

@@ -25,7 +25,7 @@ from torch import nn
 from torch.nn import CrossEntropyLoss, MSELoss
 from torch.nn import functional as F
 
-from .activations import gelu_new, swish
+from .activations import ACT2FN
 from .configuration_xlnet import XLNetConfig
 from .file_utils import (
     ModelOutput,
@@ -207,12 +207,6 @@ def load_tf_weights_in_xlnet(model, config, tf_path):
     return model
 
 
-ACT2FN = {"gelu": gelu_new, "relu": torch.nn.functional.relu, "swish": swish}
-
-
-XLNetLayerNorm = nn.LayerNorm
-
-
 class XLNetRelativeAttention(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -239,7 +233,7 @@ class XLNetRelativeAttention(nn.Module):
         self.r_w_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
         self.seg_embed = nn.Parameter(torch.FloatTensor(2, self.n_head, self.d_head))
 
-        self.layer_norm = XLNetLayerNorm(config.d_model, eps=config.layer_norm_eps)
+        self.layer_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps)
         self.dropout = nn.Dropout(config.dropout)
 
     def prune_heads(self, heads):
@@ -476,7 +470,7 @@ class XLNetRelativeAttention(nn.Module):
 class XLNetFeedForward(nn.Module):
     def __init__(self, config):
         super().__init__()
-        self.layer_norm = XLNetLayerNorm(config.d_model, eps=config.layer_norm_eps)
+        self.layer_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps)
         self.layer_1 = nn.Linear(config.d_model, config.d_inner)
         self.layer_2 = nn.Linear(config.d_inner, config.d_model)
         self.dropout = nn.Dropout(config.dropout)
@@ -563,7 +557,7 @@ class XLNetPreTrainedModel(PreTrainedModel):
             module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
             if isinstance(module, nn.Linear) and module.bias is not None:
                 module.bias.data.zero_()
-        elif isinstance(module, XLNetLayerNorm):
+        elif isinstance(module, nn.LayerNorm):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0)
         elif isinstance(module, XLNetRelativeAttention):