File size: 11,932 Bytes
13cea7c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 |
import torch
class PoetModelInterface(torch.nn.Module):
"""Pytorch Model Interface. Abstract class for all Poet model types
Args:
torch (_type_): Is child of torch.nn.Module for integration with torch and huggingface
"""
def __init__(self, *args, **kwargs) -> None:
""" Constructor. As child Class needs to construct Parent
"""
super().__init__(*args, **kwargs)
def forward(self, input_ids=None, labels=None, attention_mask=None, *args, **kwargs):
"""Compute model output and model loss
Args:
input_ids (_type_, optional): Model inputs. Defaults to None.
labels (_type_, optional): Language Model labels. Defaults to None.
attention_mask (_type_, optional): Attention mask where padding starts. Defaults to None.
Raises:
NotImplementedError: Abstract class
"""
raise NotImplementedError()
def generate_forced(self, *args, **kwargs):
"""Generates model output with restriction on inputs and past generation
Raises:
NotImplementedError: Abstract class
"""
raise NotImplementedError()
@staticmethod
def rhyme_like(rhyme:str):
"""DEPRECATED: Check string in rhyme format
Args:
rhyme (str): String with possible rhyme
Returns:
bool: Boolean if string like rhyme
"""
return rhyme.isupper() and len(rhyme) in [4,6]
def save_LM(self, LM_path):
"""Save raw LM
Args:
LM_path (str): Where to store the LM
Raises:
NotImplementedError: Abstract class
"""
raise NotImplementedError()
from transformers import GPT2Config, GPT2Model
from .poet_utils import StropheParams
class ContextModule(torch.nn.Module):
"""Module for understanding poet context
Args:
torch (_type_): Is child of torch.nn.Module for integration with torch and huggingface
"""
def __init__(self, block_count, input_size, n_embd ,output_size,*args, **kwargs) -> None:
"""Construct the underlying small LM for context
Args:
block_count (_type_): LM number of blocks of GPT2Block
input_size (_type_): LM size of input
n_embd (_type_): LM size of hidden layers
output_size (_type_): LM size of output
"""
super().__init__(*args, **kwargs)
self.config = GPT2Config(n_positions=input_size, n_head=(n_embd//(768//12)),n_embd=n_embd,
n_layer=block_count, output_hidden_states=True, output_attentions =True)
self.context_model = GPT2Model(self.config)
self.linear_downscale = torch.nn.Linear(n_embd, output_size)
self.input_size = input_size
self.n_embd = n_embd
self.output_size = output_size
# Context is getting injected from Outside
self.context_ids = None
self.context_attention_mask = None
def forward(self, hidden_states,layer_past=None,*args, **kwargs):
"""Compute Context LM output, Data are injected from outside
Args:
hidden_states (_type_): Current hidden states
layer_past (_type_, optional): Past layer outputs. Defaults to None.
Returns:
_type_: GPT2Block structured output (hidden states, layer past, attention, keys)
"""
down = torch.zeros_like(hidden_states)
model_output = None
# Sometimes there might be no context
if self.context_ids != None:
model_output = self.context_model.forward(input_ids=self.context_ids, attention_mask=self.context_attention_mask)
# Take only the Class token as
down = self.linear_downscale.forward(model_output["hidden_states"][-1][:,0,:].view(-1, self.n_embd))[:, None, :]
return (hidden_states + down,
down[None, :, :, :],
(None if model_output == None else model_output["attentions"],
None))
class PoetTypeModule(torch.nn.Module):
"""Module to classify poet type
Args:
torch (_type_): Is child of torch.nn.Module for integration with torch and huggingface
"""
def __init__(self, block_count, input_size, n_embd,output_size,*args, **kwargs) -> None:
"""Construct LM for poet classification from inputs
Args:
block_count (_type_): LM number of blocks of GPT2Block
input_size (_type_): LM size of input
n_embd (_type_): LM size of hidden layers
output_size (_type_): LM size of output
"""
super().__init__(*args, **kwargs)
self.config = GPT2Config(n_positions=input_size, n_head=(n_embd//(768//12)),n_embd=n_embd,
n_layer=block_count, output_hidden_states=True, output_attentions =True)
self.type_model = GPT2Model(self.config)
self.type_predict = torch.nn.Linear(n_embd, len(StropheParams.YEAR))
self.softmax = torch.nn.Softmax()
self.linear_scale = torch.nn.Linear(len(StropheParams.YEAR), output_size)
self.input_size = input_size
self.n_embd = n_embd
self.output_size = output_size
# Context and labels are getting injected from Outside
self.context_ids = None
self.context_attention_mask = None
self.type_labels=None
# Store for loss for model itself
self.indiv_loss=None
def forward(self, hidden_states,layer_past=None,*args, **kwargs):
"""Compute Classification LM output and loss
Args:
hidden_states (_type_): Current hidden states
layer_past (_type_, optional): Past layer outputs. Defaults to None.
Returns:
_type_: GPT2Block structured output (hidden states, layer past, attention, keys)
"""
type_prob = torch.zeros((hidden_states.shape[0], len(StropheParams.YEAR))).to("cuda" if torch.cuda.is_available() else "cpu")
model_output = None
# Sometimes there might be no context
if self.context_ids != None:
model_output = self.type_model.forward(input_ids=self.context_ids, attention_mask=self.context_attention_mask)
# Only Class token is taken
poet_type = self.type_predict.forward(model_output["hidden_states"][-1][:,0,:].view(-1, self.n_embd))
type_prob = self.softmax.forward(poet_type)
# If type labels are present, inject the true labels to future blocks
if self.type_labels != None:
loss_fct = torch.nn.CrossEntropyLoss()
self.indiv_loss = loss_fct(type_prob, self.type_labels)
type_prob = (self.type_labels.type(torch.FloatTensor)).to("cuda" if torch.cuda.is_available() else "cpu")
linear_up = self.linear_scale.forward(type_prob)
return (hidden_states + linear_up[:, None, :],
linear_up[None, :, None, :],
(None if model_output == None else model_output["attentions"],
None))
from transformers import PreTrainedTokenizerBase
class ModelManipulation:
"""Static Class incorporating methods for Manipulation with LMs
Code Inspired by article: Fine-tuning the English GPT-2 in any language with Hugging Face
Link: https://github.com/piegu/fastai-projects/blob/master/finetuning-English-GPT2-any-language-Portuguese-HuggingFace-fastaiv2.ipynb
"""
@staticmethod
def exchange_embedding(poet_model: PoetModelInterface, new_tokenizer: PreTrainedTokenizerBase, old_tokenizer: PreTrainedTokenizerBase, mirror_imbed:bool=False):
"""Exchange embedding matrixes for GPT2 Models
Args:
poet_model (PoetModelInterface): Model to manipulate with
new_tokenizer (PreTrainedTokenizerBase): New tokenization
old_tokenizer (PreTrainedTokenizerBase): Old tokenization
"""
# Get old Embeddings
if hasattr(poet_model.model, "transformer"):
old_embed_in = poet_model.model.transformer.get_input_embeddings().weight.clone().detach()
else:
old_embed_in = poet_model.model.get_input_embeddings().weight.clone().detach()
old_mean_in = old_embed_in.mean(0)
# Generate new Embedding based on new tokenization
new_embd_in = old_embed_in.new_zeros(new_tokenizer.vocab_size, old_embed_in.size(1))
old_vocab = old_tokenizer.get_vocab()
vocab_hit = 0
# Keep as much from old Embeddings as possible
for w, idx_new in new_tokenizer.get_vocab().items():
idx_old = old_vocab.get(w, -1)
if idx_old >= 0:
new_embd_in[idx_new] = old_embed_in[idx_old]
vocab_hit +=1
else:
new_embd_in[idx_new] = old_mean_in
print(f"Vocab hit rate: {vocab_hit}/{old_tokenizer.vocab_size}")
#Exchange Embeddings and Decoding
new_embd_layer_in = torch.nn.Embedding(new_tokenizer.vocab_size, old_embed_in.size(1))
new_embd_layer_in.weight.data = new_embd_in
if hasattr(poet_model.model, "transformer"):
poet_model.model.transformer.set_input_embeddings(new_embd_layer_in)
else:
poet_model.model.set_input_embeddings(new_embd_layer_in)
new_decoder = torch.nn.Linear( old_embed_in.size(1), new_tokenizer.vocab_size, bias=False)
if hasattr(poet_model.model, "transformer"):
new_decoder.weight = poet_model.model.transformer.wte.weight
else:
new_decoder.weight = poet_model.model.base_model.embeddings.weight
if hasattr(poet_model.model, "lm_head"):
poet_model.model.lm_head = new_decoder
else:
poet_model.model.head = new_decoder
# Update LM config to reflect possible change in vocab size
poet_model.model.config.vocab_size = new_tokenizer.vocab_size
@staticmethod
def exchange_embedding_roberta(metre_model, new_tokenizer: PreTrainedTokenizerBase, old_tokenizer: PreTrainedTokenizerBase):
"""Exchange embedding matrixes for Roberta Models
Args:
poet_model (PoetModelInterface): Model to manipulate with
new_tokenizer (PreTrainedTokenizerBase): New tokenization
old_tokenizer (PreTrainedTokenizerBase): Old tokenization
"""
# Get old Embeddings
old_embed = metre_model.model.get_input_embeddings().weight.clone().detach()
old_mean = old_embed.mean(0)
# Generate new Embedding based on new tokenization
new_embd = old_embed.new_zeros(new_tokenizer.vocab_size, old_embed.size(1))
old_vocab = old_tokenizer.get_vocab()
vocab_hit = 0
# Keep as much from old Embeddings as possible
for w, idx_new in new_tokenizer.get_vocab().items():
idx_old = old_vocab.get(w, -1)
if idx_old >= 0:
new_embd[idx_new] = old_embed[idx_old]
vocab_hit +=1
else:
new_embd[idx_new] = old_mean
print(f"Vocab hit rate: {vocab_hit}/{old_tokenizer.vocab_size}")
#Exchange Embeddings and Decoding
new_embd_layer = torch.nn.Embedding(new_tokenizer.vocab_size, old_embed.size(1))
new_embd_layer.weight.data = new_embd
metre_model.model.set_input_embeddings(new_embd_layer)
new_decoder = torch.nn.Linear( old_embed.size(1), new_tokenizer.vocab_size)
new_decoder.weight = metre_model.model.roberta.embeddings.word_embeddings.weight
metre_model.model.lm_head.decoder = new_decoder
# Update LM config to reflect possible change in vocab size
metre_model.model.config.vocab_size = new_tokenizer.vocab_size
|