Source code for modelzoo.transformers.pytorch.bert.fine_tuning.qa.model

# Copyright 2022 Cerebras Systems.
#
# 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,
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import torch

from modelzoo.transformers.pytorch.bert.bert_finetune_models import (
    BertForQuestionAnswering,
    BertForQuestionAnsweringLoss,
)
from modelzoo.transformers.pytorch.bert.utils import check_unused_model_params


[docs]class BertForQuestionAnsweringModel(torch.nn.Module):
[docs] def __init__(self, params): super().__init__() model_params = params["model"].copy() dropout_rate = model_params.pop("dropout_rate") embedding_dropout_rate = model_params.pop( "embedding_dropout_rate", dropout_rate ) model_kwargs = { "vocab_size": model_params.pop("vocab_size"), "hidden_size": model_params.pop("hidden_size"), "num_hidden_layers": model_params.pop("num_hidden_layers"), "num_heads": model_params.pop("num_heads"), "filter_size": model_params.pop("filter_size"), "nonlinearity": model_params.pop("encoder_nonlinearity"), "pooler_nonlinearity": model_params.pop( "pooler_nonlinearity", None ), "embedding_dropout_rate": embedding_dropout_rate, "dropout_rate": dropout_rate, "attention_dropout_rate": model_params.pop( "attention_dropout_rate" ), "max_position_embeddings": model_params.pop( "max_position_embeddings" ), "layer_norm_epsilon": float(model_params.pop("layer_norm_epsilon")), } self.model = BertForQuestionAnswering(**model_kwargs) self.loss_fn = BertForQuestionAnsweringLoss() check_unused_model_params(model_params)
def forward(self, data): logits, start_logits, end_logits = self.model( input_ids=data["input_ids"], token_type_ids=data["token_type_ids"], attention_mask=data["attention_mask"], ) loss = self.loss_fn(logits, data["labels"], data["label_weights"]) # for prediction inference self.outputs = {"start_logits": start_logits, "end_logits": end_logits} return loss