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- # coding=utf-8
- # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
- #
- # 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.
- """Multiple choice model."""
- import torch
- from megatron import get_args, print_rank_last
- from megatron import mpu
- from megatron.model.enums import AttnMaskType
- from megatron.model.bert_model import bert_extended_attention_mask, bert_position_ids
- from megatron.model.language_model import get_language_model
- from megatron.model.utils import get_linear_layer
- from megatron.model.utils import init_method_normal
- from megatron.model.utils import scaled_init_method_normal
- from .module import MegatronModule
- class MultipleChoice(MegatronModule):
- def __init__(self,
- num_tokentypes=2,
- pre_process=True,
- post_process=True):
- super(MultipleChoice, self).__init__(share_word_embeddings=False)
- args = get_args()
- init_method = init_method_normal(args.init_method_std)
- self.pre_process = pre_process
- self.post_process = post_process
- self.language_model, self._language_model_key = get_language_model(
- num_tokentypes=num_tokentypes,
- add_pooler=True,
- encoder_attn_mask_type=AttnMaskType.padding,
- init_method=init_method,
- scaled_init_method=scaled_init_method_normal(args.init_method_std,
- args.num_layers),
- pre_process=self.pre_process,
- post_process=self.post_process)
- # Multi-choice head.
- if self.post_process:
- self.multichoice_dropout = torch.nn.Dropout(args.hidden_dropout)
- self.multichoice_head = get_linear_layer(args.hidden_size, 1,
- init_method)
- self._multichoice_head_key = 'multichoice_head'
- def set_input_tensor(self, input_tensor):
- """See megatron.model.transformer.set_input_tensor()"""
- self.language_model.set_input_tensor(input_tensor)
- def forward(self, model_input, attention_mask, tokentype_ids=None):
- # [batch, choices, sequence] --> [batch * choices, sequence] -->
- # transformer --> [batch, choices] --> softmax
- # Ensure the shape is [batch-size, choices, sequence]
- assert len(attention_mask.shape) == 3
- num_choices = attention_mask.shape[1]
- # Reshape and treat choice dimension the same as batch.
- attention_mask = attention_mask.view(-1, attention_mask.size(-1))
- extended_attention_mask = bert_extended_attention_mask(attention_mask)
- input_ids = model_input
- # Do the same as attention_mask for input_ids, tokentype_ids
- assert len(input_ids.shape) == 3
- assert len(tokentype_ids.shape) == 3
- input_ids = input_ids.view(-1, input_ids.size(-1))
- tokentype_ids = tokentype_ids.view(-1, tokentype_ids.size(-1))
- position_ids = bert_position_ids(input_ids)
- lm_output = self.language_model(
- input_ids,
- position_ids,
- extended_attention_mask,
- tokentype_ids=tokentype_ids
- )
- if self.post_process:
- _, pooled_output = lm_output
- multichoice_output = self.multichoice_dropout(pooled_output)
- multichoice_logits = self.multichoice_head(multichoice_output)
- # Reshape back to separate choices.
- multichoice_logits = multichoice_logits.view(-1, num_choices)
- return multichoice_logits
- return lm_output
- def state_dict_for_save_checkpoint(self, destination=None, prefix='',
- keep_vars=False):
- """For easy load when model is combined with other heads,
- add an extra key."""
- state_dict_ = {}
- state_dict_[self._language_model_key] \
- = self.language_model.state_dict_for_save_checkpoint(
- destination, prefix, keep_vars)
- if self.post_process:
- state_dict_[self._multichoice_head_key] \
- = self.multichoice_head.state_dict(
- destination, prefix, keep_vars)
- return state_dict_
- def load_state_dict(self, state_dict, strict=True):
- """Customized load."""
- self.language_model.load_state_dict(
- state_dict[self._language_model_key], strict=strict)
- if self.post_process:
- if self._multichoice_head_key in state_dict:
- self.multichoice_head.load_state_dict(
- state_dict[self._multichoice_head_key], strict=strict)
- else:
- print_rank_last('***WARNING*** could not find {} in the checkpoint, '
- 'initializing to random'.format(
- self._multichoice_head_key))
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