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import os
import torch
import sys
import megatron.model.language_model
from megatron import get_args, print_rank_0, get_tokenizer
from megatron.core import mpu
from megatron.checkpointing import fix_query_key_value_ordering
from megatron.checkpointing import get_checkpoint_tracker_filename
from megatron.checkpointing import get_checkpoint_name
from megatron.model.bert_model import bert_position_ids
from megatron.model.enums import AttnMaskType
import megatron.model.utils
from megatron.model.utils import init_method_normal
from megatron.model.utils import scaled_init_method_normal
from .module import MegatronModule
def get_model_provider(only_query_model=False,
only_context_model=False,
biencoder_shared_query_context_model=False,
model_type=None):
def model_provider(pre_process=True,
post_process=True):
"""Build the model."""
print_rank_0('building Bienoder model ...')
model = biencoder_model_provider(only_query_model=only_query_model,
only_context_model=only_context_model,
biencoder_shared_query_context_model=biencoder_shared_query_context_model,
pre_process=pre_process,
post_process=post_process,
model_type=model_type)
return model
return model_provider
def biencoder_model_provider(only_query_model=False,
only_context_model=False,
biencoder_shared_query_context_model=False,
pre_process=True,
post_process=True,
model_type=None):
"""Build the model."""
assert mpu.get_tensor_model_parallel_world_size() == 1 and \
mpu.get_pipeline_model_parallel_world_size() == 1, \
"Model parallel size > 1 not supported for ICT"
print_rank_0('building BiEncoderModel...')
# simpler to just keep using 2 tokentypes since
# the LM we initialize with has 2 tokentypes
model = BiEncoderModel(
num_tokentypes=2,
parallel_output=False,
only_query_model=only_query_model,
only_context_model=only_context_model,
biencoder_shared_query_context_model=\
biencoder_shared_query_context_model,
pre_process=pre_process,
post_process=post_process,
model_type=model_type
)
return model
class BiEncoderModel(MegatronModule):
"""Bert-based module for Biencoder model."""
def __init__(self,
num_tokentypes=1,
parallel_output=True,
only_query_model=False,
only_context_model=False,
biencoder_shared_query_context_model=False,
pre_process=True,
post_process=True,
model_type=None):
super(BiEncoderModel, self).__init__()
args = get_args()
bert_kwargs = dict(
num_tokentypes=num_tokentypes,
parallel_output=parallel_output,
pre_process=pre_process,
post_process=post_process,
model_type=model_type
)
self.biencoder_shared_query_context_model = \
biencoder_shared_query_context_model
assert not (only_context_model and only_query_model)
self.use_context_model = not only_query_model
self.use_query_model = not only_context_model
self.biencoder_projection_dim = args.biencoder_projection_dim
if self.biencoder_shared_query_context_model:
self.model = PretrainedBertModel(**bert_kwargs)
self._model_key = 'shared_model'
self.query_model, self.context_model = self.model, self.model
else:
if self.use_query_model:
# this model embeds (pseudo-)queries - Embed_input in the paper
self.query_model = PretrainedBertModel(**bert_kwargs)
self._query_key = 'query_model'
if self.use_context_model:
# this model embeds evidence blocks - Embed_doc in the paper
self.context_model = PretrainedBertModel(**bert_kwargs)
self._context_key = 'context_model'
def set_input_tensor(self, input_tensor):
"""See megatron.model.transformer.set_input_tensor()"""
# this is just a placeholder and will be needed when model
# parallelism will be used
# self.language_model.set_input_tensor(input_tensor)
return
def forward(self, query_tokens, query_attention_mask, query_types,
context_tokens, context_attention_mask, context_types):
"""Run a forward pass for each of the models and
return the respective embeddings."""
if self.use_query_model:
query_logits = self.embed_text(self.query_model,
query_tokens,
query_attention_mask,
query_types)
else:
raise ValueError("Cannot embed query without the query model.")
if self.use_context_model:
context_logits = self.embed_text(self.context_model,
context_tokens,
context_attention_mask,
context_types)
else:
raise ValueError("Cannot embed block without the block model.")
return query_logits, context_logits
@staticmethod
def embed_text(model, tokens, attention_mask, token_types):
"""Embed a batch of tokens using the model"""
logits = model(tokens,
attention_mask,
token_types)
return logits
def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False):
"""Save dict with state dicts of each of the models."""
state_dict_ = {}
if self.biencoder_shared_query_context_model:
state_dict_[self._model_key] = \
self.model.state_dict_for_save_checkpoint(
prefix=prefix, keep_vars=keep_vars)
else:
if self.use_query_model:
state_dict_[self._query_key] = \
self.query_model.state_dict_for_save_checkpoint(
prefix=prefix, keep_vars=keep_vars)
if self.use_context_model:
state_dict_[self._context_key] = \
self.context_model.state_dict_for_save_checkpoint(
prefix=prefix, keep_vars=keep_vars)
return state_dict_
def load_state_dict(self, state_dict, strict=True):
"""Load the state dicts of each of the models"""
if self.biencoder_shared_query_context_model:
print_rank_0("Loading shared query-context model")
self.model.load_state_dict(state_dict[self._model_key], \
strict=strict)
else:
if self.use_query_model:
print_rank_0("Loading query model")
self.query_model.load_state_dict( \
state_dict[self._query_key], strict=strict)
if self.use_context_model:
print_rank_0("Loading context model")
self.context_model.load_state_dict( \
state_dict[self._context_key], strict=strict)
def init_state_dict_from_bert(self):
"""Initialize the state from a pretrained BERT model
on iteration zero of ICT pretraining"""
args = get_args()
if args.bert_load is None:
print_rank_0("bert_load argument is None")
return
tracker_filename = get_checkpoint_tracker_filename(args.bert_load)
if not os.path.isfile(tracker_filename):
raise FileNotFoundError("Could not find BERT checkpoint")
with open(tracker_filename, 'r') as f:
iteration = int(f.read().strip())
assert iteration > 0
checkpoint_name = get_checkpoint_name(args.bert_load, iteration, False)
if mpu.get_data_parallel_rank() == 0:
print('global rank {} is loading BERT checkpoint {}'.format(
torch.distributed.get_rank(), checkpoint_name))
# Load the checkpoint.
try:
state_dict = torch.load(checkpoint_name, map_location='cpu')
except ModuleNotFoundError:
from megatron.fp16_deprecated import loss_scaler
# For backward compatibility.
print_rank_0(' > deserializing using the old code structure ...')
sys.modules['fp16.loss_scaler'] = sys.modules[
'megatron.fp16_deprecated.loss_scaler']
sys.modules['megatron.fp16.loss_scaler'] = sys.modules[
'megatron.fp16_deprecated.loss_scaler']
state_dict = torch.load(checkpoint_name, map_location='cpu')
sys.modules.pop('fp16.loss_scaler', None)
sys.modules.pop('megatron.fp16.loss_scaler', None)
except BaseException:
print_rank_0('could not load the BERT checkpoint')
sys.exit()
checkpoint_version = state_dict.get('checkpoint_version', 0)
# load the LM state dict into each model
model_dict = state_dict['model']['language_model']
if self.biencoder_shared_query_context_model:
self.model.language_model.load_state_dict(model_dict)
fix_query_key_value_ordering(self.model, checkpoint_version)
else:
if self.use_query_model:
self.query_model.language_model.load_state_dict(model_dict)
# give each model the same ict_head to begin with as well
if self.biencoder_projection_dim > 0:
query_proj_state_dict = \
self.state_dict_for_save_checkpoint()\
[self._query_key]['projection_enc']
fix_query_key_value_ordering(self.query_model, checkpoint_version)
if self.use_context_model:
self.context_model.language_model.load_state_dict(model_dict)
if self.query_model is not None and \
self.biencoder_projection_dim > 0:
self.context_model.projection_enc.load_state_dict\
(query_proj_state_dict)
fix_query_key_value_ordering(self.context_model, checkpoint_version)
class PretrainedBertModel(MegatronModule):
"""BERT-based encoder for queries or contexts used for
learned information retrieval."""
def __init__(self,
num_tokentypes=2,
parallel_output=True,
pre_process=True,
post_process=True,
model_type=None):
super(PretrainedBertModel, self).__init__()
args = get_args()
tokenizer = get_tokenizer()
self.pad_id = tokenizer.pad
self.biencoder_projection_dim = args.biencoder_projection_dim
self.parallel_output = parallel_output
self.pre_process = pre_process
self.post_process = post_process
init_method = init_method_normal(args.init_method_std)
scaled_init_method = scaled_init_method_normal(
args.init_method_std, args.num_layers)
self.language_model, self._language_model_key = megatron.model.language_model.get_language_model(
num_tokentypes=num_tokentypes,
add_pooler=False,
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,
args=args,
model_type=model_type)
if args.biencoder_projection_dim > 0:
self.projection_enc = megatron.model.utils.get_linear_layer(args.hidden_size,
args.biencoder_projection_dim,
init_method,
args.perform_initialization)
self._projection_enc_key = 'projection_enc'
def forward(self, input_ids, attention_mask, tokentype_ids=None):
extended_attention_mask = attention_mask.unsqueeze(1)
position_ids = bert_position_ids(input_ids)
lm_output = self.language_model(input_ids,
position_ids,
extended_attention_mask,
tokentype_ids=tokentype_ids)
# This mask will be used in average-pooling and max-pooling
pool_mask = (input_ids == self.pad_id).unsqueeze(2)
# Taking the representation of the [CLS] token of BERT
pooled_output = lm_output[0, :, :]
# Converting to float16 dtype
pooled_output = pooled_output.to(lm_output.dtype)
# Output.
if self.biencoder_projection_dim:
pooled_output = self.projection_enc(pooled_output)
return pooled_output
def state_dict_for_save_checkpoint(self, 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(
prefix=prefix, keep_vars=keep_vars)
if self.biencoder_projection_dim > 0:
state_dict_[self._projection_enc_key] = \
self.projection_enc.state_dict(prefix=prefix,
keep_vars=keep_vars)
return state_dict_
def load_state_dict(self, state_dict, strict=True):
"""Customized load."""
print_rank_0("loading pretrained weights")
self.language_model.load_state_dict(
state_dict[self._language_model_key], strict=strict)
if self.biencoder_projection_dim > 0:
print_rank_0("loading projection head weights")
self.projection_enc.load_state_dict(
state_dict[self._projection_enc_key], strict=strict)
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