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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.
"""T5 model."""
import torch
from megatron import (
get_args,
mpu
)
from megatron.model.enums import AttnMaskType
from megatron.model.language_model import parallel_lm_logits, get_language_model
from megatron.model.transformer import LayerNorm
from megatron.model.utils import (
openai_gelu,
get_linear_layer,
init_method_normal,
scaled_init_method_normal
)
from .module import MegatronModule
def t5_extended_attention_mask(attention_mask_list):
def attn_mask_postprocess(attn_mask):
# [b, 1, s, s]
extended_attention_mask = attn_mask.unsqueeze(1)
return extended_attention_mask
return [attn_mask_postprocess(attn_mask) for attn_mask in attention_mask_list]
def t5_position_ids(token_ids):
# Create position ids
seq_length = token_ids.size(1)
position_ids = torch.arange(seq_length, dtype=torch.long,
device=token_ids.device)
position_ids = position_ids.unsqueeze(0).expand_as(token_ids)
return position_ids
class T5LMHead(MegatronModule):
"""Masked LM head for T5
Arguments:
mpu_vocab_size: model parallel size of vocabulary.
hidden_size: hidden size
init_method: init method for weight initialization
layernorm_epsilon: tolerance for layer norm divisions
parallel_output: wether output logits being distributed or not.
"""
def __init__(self, mpu_vocab_size, parallel_output):
super(T5LMHead, self).__init__()
args = get_args()
self.bias = torch.nn.Parameter(torch.zeros(mpu_vocab_size))
self.bias.model_parallel = True
self.bias.partition_dim = 0
self.bias.stride = 1
self.parallel_output = parallel_output
def forward(self, hidden_states, word_embeddings_weight):
output = parallel_lm_logits(hidden_states,
word_embeddings_weight,
self.parallel_output,
bias=self.bias)
return output
class T5Model(MegatronModule):
"""T5 Language model."""
def __init__(self,
num_tokentypes=0,
parallel_output=True,
pre_process=True,
post_process=True,
add_encoder=True,
add_decoder=True):
super(T5Model, self).__init__()
args = get_args()
self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy
self.parallel_output = parallel_output
init_method = init_method_normal(args.init_method_std)
scaled_init_method = scaled_init_method_normal(args.init_method_std,
args.num_layers)
self.pre_process = pre_process
self.post_process = post_process
self.add_encoder = add_encoder
self.add_decoder = add_decoder
self.language_model, self._language_model_key = get_language_model(
num_tokentypes=num_tokentypes,
add_pooler=False,
add_encoder=add_encoder,
add_decoder=add_decoder,
encoder_attn_mask_type=AttnMaskType.padding,
init_method=init_method,
scaled_init_method=scaled_init_method,
pre_process=self.pre_process,
post_process=self.post_process)
self.initialize_word_embeddings(init_method_normal)
if self.post_process and self.add_decoder:
self.lm_head = T5LMHead(
self.word_embeddings_weight().size(0),
parallel_output)
self._lm_head_key = 'lm_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,
encoder_input_ids,
decoder_input_ids,
encoder_attn_mask,
decoder_attn_mask,
encoder_decoder_attn_mask,
tokentype_ids=None,
lm_labels=None,
enc_hidden_states=None):
# Converting the attention masks to proper parameter settings
encoder_attn_mask, decoder_attn_mask, encoder_decoder_attn_mask = t5_extended_attention_mask(
[encoder_attn_mask, decoder_attn_mask, encoder_decoder_attn_mask])
encoder_position_ids = t5_position_ids(encoder_input_ids)
decoder_position_ids = t5_position_ids(decoder_input_ids)
lm_output = self.language_model(encoder_input_ids,
encoder_position_ids,
encoder_attn_mask,
decoder_input_ids,
decoder_position_ids,
decoder_attn_mask,
encoder_decoder_attn_mask,
tokentype_ids=tokentype_ids,
enc_hidden_states=enc_hidden_states)
import pdb;pdb.set_trace()
if self.post_process and self.add_decoder:
decoder_output, encoder_output = lm_output
# Output. [s, b, h]
lm_logits = self.lm_head(decoder_output,
self.word_embeddings_weight())
if lm_labels is None:
# [s b h] => [b s h]
return lm_logits.transpose(0,1).contiguous()
else:
# [b s] => [s b]
lm_labels = lm_labels.transpose(0,1).contiguous()
if self.fp16_lm_cross_entropy:
assert lm_logits.dtype == torch.half
lm_loss = mpu.vocab_parallel_cross_entropy(lm_logits, lm_labels)
else:
lm_loss = mpu.vocab_parallel_cross_entropy(lm_logits.float(),
lm_labels)
# [s b] => [b s]
lm_loss = lm_loss.transpose(0,1).contiguous()
return lm_loss
elif self.add_decoder and not self.add_encoder:
decoder_output, encoder_output = lm_output
return decoder_output
else:
encoder_output = lm_output
return encoder_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 and self.add_decoder:
state_dict_[self._lm_head_key] \
= self.lm_head.state_dict_for_save_checkpoint(
destination, prefix, keep_vars)
# Save word_embeddings.
if self.post_process and not self.pre_process and self.add_decoder:
state_dict_[self._word_embeddings_for_head_key] \
= self.word_embeddings.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 and self.add_decoder:
self.lm_head.load_state_dict(state_dict[self._lm_head_key],
strict=strict)
# Load word embeddings.
if self.post_process and not self.pre_process and self.add_decoder:
self.word_embeddings.load_state_dict(
state_dict[self._word_embeddings_for_head_key], strict=strict)
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