# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
RoBERTa: A Robustly Optimized BERT Pretraining Approach.
"""
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.model_parallel.models.transformer import ModelParallelTransformerEncoder
from fairseq.models import register_model, register_model_architecture
from fairseq.models.roberta import (
roberta_base_architecture,
roberta_prenorm_architecture,
RobertaEncoder,
RobertaModel,
)
from fairseq.modules import LayerNorm
try:
from fairseq.model_parallel.megatron.mpu import (
copy_to_model_parallel_region,
gather_from_model_parallel_region,
ColumnParallelLinear,
VocabParallelEmbedding,
)
has_megatron_submodule = True
except (ImportError, ModuleNotFoundError):
has_megatron_submodule = False
logger = logging.getLogger(__name__)
@register_model("model_parallel_roberta")
class ModelParallelRobertaModel(RobertaModel):
def __init__(self, args, encoder):
super().__init__(args, encoder)
self.classification_heads = nn.ModuleDict()
@staticmethod
def add_args(parser):
RobertaModel.add_args(parser)
parser.add_argument(
"--no-final-layer-norm",
action="store_true",
help=(
"don't add final layernorm (only applicable when "
"--encoder-normalize-before=True"
),
)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present
base_architecture(args)
task.source_dictionary.pad_to_multiple_(args.model_parallel_size * 8)
task.target_dictionary.pad_to_multiple_(args.model_parallel_size * 8)
if not hasattr(args, "max_positions"):
args.max_positions = args.tokens_per_sample
if getattr(args, "untie_weights_roberta", False):
raise NotImplementedError(
"--untie-weights-roberta is not supported in model parallel mode"
)
encoder = ModelParallelRobertaEncoder(args, task.source_dictionary)
return cls(args, encoder)
def forward(
self,
src_tokens,
features_only=False,
return_all_hiddens=False,
classification_head_name=None,
**kwargs
):
if classification_head_name is not None:
features_only = True
x, extra = self.encoder(src_tokens, features_only, return_all_hiddens, **kwargs)
if classification_head_name is not None:
x = self.classification_heads[classification_head_name](x)
return x, extra
def register_classification_head(
self, name, num_classes=None, inner_dim=None, **kwargs
):
"""Register a classification head."""
if name in self.classification_heads:
prev_num_classes = self.classification_heads[name].out_proj.out_features
prev_inner_dim = self.classification_heads[name].dense.out_features
if num_classes != prev_num_classes or inner_dim != prev_inner_dim:
logger.warning(
're-registering head "{}" with num_classes {} (prev: {}) '
"and inner_dim {} (prev: {})".format(
name, num_classes, prev_num_classes, inner_dim, prev_inner_dim
)
)
self.classification_heads[name] = ModelParallelRobertaClassificationHead(
self.args.encoder_embed_dim,
inner_dim or self.args.encoder_embed_dim,
num_classes,
self.args.pooler_activation_fn,
self.args.pooler_dropout,
)
class ModelParallelRobertaLMHead(nn.Module):
"""Head for masked language modeling."""
def __init__(self, embed_dim, output_dim, activation_fn, weight=None):
super().__init__()
self.dense = ColumnParallelLinear(embed_dim, embed_dim, gather_output=True)
self.activation_fn = utils.get_activation_fn(activation_fn)
self.layer_norm = LayerNorm(embed_dim)
if weight is None:
weight = nn.Linear(embed_dim, output_dim, bias=False).weight
self.weight = weight
self.bias = nn.Parameter(torch.zeros(output_dim))
def forward(self, features, masked_tokens=None, **kwargs):
# Only project the unmasked tokens while training,
# saves both memory and computation
if masked_tokens is not None:
features = features[masked_tokens, :]
x = self.dense(features)
x = self.activation_fn(x)
x = self.layer_norm(x)
x = copy_to_model_parallel_region(x)
# project back to size of vocabulary with bias
x = F.linear(x, self.weight)
x = gather_from_model_parallel_region(x).contiguous()
x = x + self.bias
return x
class ModelParallelRobertaClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(
self, input_dim, inner_dim, num_classes, activation_fn, pooler_dropout
):
super().__init__()
self.dense = ColumnParallelLinear(input_dim, inner_dim, gather_output=True)
self.activation_fn = utils.get_activation_fn(activation_fn)
self.dropout = nn.Dropout(p=pooler_dropout)
self.out_proj = nn.Linear(inner_dim, num_classes)
def forward(self, features, **kwargs):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = self.activation_fn(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
class ModelParallelRobertaEncoder(RobertaEncoder):
"""RoBERTa encoder."""
def __init__(self, args, dictionary):
super().__init__(args, dictionary)
assert not self.args.untie_weights_roberta
def build_embedding(self, vocab_size, embedding_dim, padding_idx):
return VocabParallelEmbedding(vocab_size, embedding_dim, padding_idx)
def build_encoder(self, args, dictionary, embed_tokens):
return ModelParallelTransformerEncoder(args, dictionary, embed_tokens)
def build_lm_head(self, embed_dim, output_dim, activation_fn, weight):
return ModelParallelRobertaLMHead(embed_dim, output_dim, activation_fn, weight)
@register_model_architecture("model_parallel_roberta", "model_parallel_roberta")
def base_architecture(args):
args.no_final_layer_norm = getattr(args, "no_final_layer_norm", False)
# model parallel RoBERTa defaults to "Pre-LN" formulation
roberta_prenorm_architecture(args)
# earlier versions of model parallel RoBERTa removed the final layer norm
@register_model_architecture("model_parallel_roberta", "model_parallel_roberta_v1")
def model_parallel_roberta_v1_architecture(args):
args.no_final_layer_norm = getattr(args, "no_final_layer_norm", True)
base_architecture(args)
@register_model_architecture(
"model_parallel_roberta", "model_parallel_roberta_postnorm"
)
def model_parallel_roberta_postnorm_architecture(args):
# the original BERT/RoBERTa uses the "Post-LN" formulation
roberta_base_architecture(args)
@register_model_architecture("model_parallel_roberta", "model_parallel_roberta_base")
def model_parallel_roberta_base_architecture(args):
base_architecture(args)
@register_model_architecture("model_parallel_roberta", "model_parallel_roberta_large")
def model_parallel_roberta_large_architecture(args):
args.encoder_layers = getattr(args, "encoder_layers", 24)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
base_architecture(args)