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model_util.py
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model_util.py
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import numpy as np
import os
import torch
import torch.nn.functional as F
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from transformers import Adafactor, AdamW, get_linear_schedule_with_warmup
from transformers import GPT2LMHeadModel
def load_checkpoint(gpt2, checkpoint=None,
prompt_tune=False, head_tune=False, transform_tune=False,
n_prefix=20,
mapping=None):
def convert_to_single_gpu(state_dict):
def _convert(key):
if key.startswith('module.'):
return key[7:]
return key
return {_convert(key):value for key, value in state_dict.items()}
if checkpoint is not None and not prompt_tune and not head_tune and not transform_tune:
assert os.path.exists(checkpoint)
model = GPT2LMHeadModel.from_pretrained(
gpt2,
state_dict=convert_to_single_gpu(torch.load(checkpoint)))
return model
model = GPT2LMHeadModel.from_pretrained(gpt2)
if checkpoint is not None:
assert os.path.exists(checkpoint)
if prompt_tune:
set_extra_embeddings(model, n_prefix=n_prefix)
weight = torch.load(checkpoint)["transformer.wte.new_embed.weight"]
model.transformer.wte.new_embed._load_from_state_dict(
{"weight": weight}, "", None, True, [], [], "")
elif head_tune:
weight = torch.load(checkpoint)["lm_head.my_lm_head.weight"]
set_separate_lm_head(model, mapping=mapping)
model.lm_head.my_lm_head._load_from_state_dict(
{"weight": weight}, "", None, True, [], [], "")
elif transform_tune:
weight = torch.load(checkpoint)["lm_head.transform.weight"]
set_transformed_lm_head(model)
model.lm_head.transform._load_from_state_dict(
{"weight": weight}, "", None, True, [], [], "")
else:
raise NotImplementedError()
return model
def get_dataloader(inputs, batch_size, is_training):
shape = inputs["input_ids"].shape
for k,v in inputs.items():
if k not in ['input_length','output_length']:
assert v.shape==shape
if "labels" in inputs:
dataset = TensorDataset(inputs["input_ids"],
inputs["attention_mask"],
inputs["token_type_ids"],
inputs["labels"])
else:
dataset = TensorDataset(inputs["input_ids"],
inputs["attention_mask"],
inputs["token_type_ids"])
if is_training:
sampler=RandomSampler(dataset)
else:
sampler=SequentialSampler(dataset)
dataloader = DataLoader(dataset, sampler=sampler, batch_size=batch_size)
return dataloader
def get_optimizer_and_scheduler(optimizer_name,
model,
learning_rate=1e-5,
warmup_proportion=0.01,
warmup_steps=50,
weight_decay=0.0,
adam_epsilon=1e-8,
num_training_steps=1000):
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': weight_decay},
{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
#optimizer_grouped_parameters = [p for n, p in named_parameters]
if optimizer_name=="adafactor":
optimizer = Adafactor(optimizer_grouped_parameters,
lr=learning_rate,
relative_step=False,
warmup_init=False)
scheduler = None
elif optimizer_name=="adamw":
optimizer = AdamW(optimizer_grouped_parameters,
lr=learning_rate,
eps=adam_epsilon)
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=warmup_steps,
num_training_steps=num_training_steps)
else:
raise NotImplementedError()
return optimizer, scheduler
class MyEmbedding(torch.nn.Module):
# this is for prompt tuning
def __init__(self, embed, n_prefix):
super().__init__()
self.embed = embed
self.new_embed = torch.nn.Embedding(n_prefix, embed.embedding_dim)
# following Lester et al. 2021 in initializing using the top 5000 random vocabs
indices = np.random.permutation(range(5000))[:n_prefix]
init_weight = self.embed.state_dict()["weight"][indices]
self.new_embed._load_from_state_dict({"weight": init_weight},
"", None, True, [], [], "")
def forward(self, input):
return F.embedding(
input,
torch.cat([self.embed.weight, self.new_embed.weight], 0),
self.embed.padding_idx,
self.embed.max_norm,
self.embed.norm_type,
self.embed.scale_grad_by_freq,
self.embed.sparse)
class MyEmbedding2(torch.nn.Module):
def __init__(self, embed, mapping):
super().__init__()
self.my_embed = torch.nn.Embedding(len(mapping), embed.embedding_dim)
indices = [mapping[i] for i in range(len(mapping))]
init_weight = embed.state_dict()["weight"][indices]
self.my_embed._load_from_state_dict({"weight": init_weight},
"", None, True, [], [], "")
def forward(self, input):
return self.my_embed(input)
class MyLMHead(torch.nn.Module):
def __init__(self, lm_head, mapping):
super().__init__()
self.my_lm_head = torch.nn.Linear(lm_head.in_features, len(mapping), bias=False)
indices = [mapping[i] for i in range(len(mapping))]
init_weight = lm_head.state_dict()["weight"][indices]
self.my_lm_head._load_from_state_dict({"weight": init_weight},
"", None, True, [], [], "")
def forward(self, input):
return self.my_lm_head(input)
class MyLMHeadWithTransform(torch.nn.Module):
def __init__(self, lm_head):
super().__init__()
self.lm_head = lm_head
self.transform = torch.nn.Linear(lm_head.in_features,
lm_head.in_features, bias=False)
init_weight = torch.eye(lm_head.in_features)
self.transform._load_from_state_dict({"weight": init_weight},
"", None, True, [], [], "")
def forward(self, input):
return self.lm_head(self.transform(input))
def set_extra_embeddings(model, n_prefix):
model.transformer.set_input_embeddings(
MyEmbedding(model.transformer.wte, n_prefix))
def set_separate_lm_head(model, mapping):
model.set_output_embeddings(
MyLMHead(model.lm_head, mapping))
def set_separate_embeddings(model, mapping):
model.set_input_embeddings(
MyEmbedding2(model.transformer.wte, mapping))
def set_transformed_lm_head(model):
model.set_output_embeddings(
MyLMHeadWithTransform(model.lm_head))