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Unverified Commit 27174bd4 authored by Sylvain Gugger's avatar Sylvain Gugger Committed by GitHub
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Make PyTorch model files independent from each other (#7352)

parent d161ed16
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Showing with 676 additions and 129 deletions
src/transformers/activations.py
View file @ 27174bd4
......@@ -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
View file @ 27174bd4
......@@ -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
View file @ 27174bd4
......@@ -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
View file @ 27174bd4
# 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
View file @ 27174bd4
......@@ -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
View file @ 27174bd4
......@@ -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
View file @ 27174bd4
......@@ -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
View file @ 27174bd4
......@@ -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
View file @ 27174bd4
......@@ -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
View file @ 27174bd4
......@@ -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
View file @ 27174bd4
......@@ -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
View file @ 27174bd4
......@@ -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
View file @ 27174bd4
......@@ -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):
......
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