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