File size: 10,811 Bytes
c9912a0 1452010 c9912a0 1452010 c9912a0 1452010 c9912a0 1452010 c9912a0 1452010 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 |
"""
Translation Transformer Model for HuggingFace Hub
"""
import torch
import torch.nn as nn
from transformers import PreTrainedModel, PretrainedConfig
from transformers.modeling_outputs import Seq2SeqLMOutput
from typing import Optional, Tuple, Union
import math
class PositionalEncoding(nn.Module):
"""Positional encoding for transformer"""
def __init__(self, d_model, max_length=5000):
super().__init__()
pe = torch.zeros(max_length, d_model)
position = torch.arange(0, max_length, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
self.register_buffer('pe', pe)
def forward(self, x):
return x + self.pe[:, :x.size(1)]
class TranslationTransformerConfig(PretrainedConfig):
"""Configuration class for TranslationTransformer"""
model_type = "translation_transformer"
def __init__(
self,
vocab_size=32000,
d_model=512,
nhead=8,
num_encoder_layers=6,
num_decoder_layers=6,
dim_feedforward=2048,
dropout=0.1,
pad_token_id=0,
bos_token_id=2,
eos_token_id=3,
max_length=512,
**kwargs
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs
)
self.vocab_size = vocab_size
self.d_model = d_model
self.nhead = nhead
self.num_encoder_layers = num_encoder_layers
self.num_decoder_layers = num_decoder_layers
self.dim_feedforward = dim_feedforward
self.dropout = dropout
self.max_length = max_length
# Required for HuggingFace compatibility
self.is_encoder_decoder = True
self.decoder_start_token_id = bos_token_id
class TranslationTransformerModel(PreTrainedModel):
"""
Encoder-Decoder Transformer for Translation
Compatible with HuggingFace Hub
"""
config_class = TranslationTransformerConfig
base_model_prefix = "translation_transformer"
supports_gradient_checkpointing = True
def __init__(self, config):
super().__init__(config)
self.config = config
# Embeddings
self.embedding = nn.Embedding(
config.vocab_size,
config.d_model,
padding_idx=config.pad_token_id
)
self.pos_encoder = PositionalEncoding(config.d_model, config.max_length)
self.pos_decoder = PositionalEncoding(config.d_model, config.max_length)
# Transformer
self.transformer = nn.Transformer(
d_model=config.d_model,
nhead=config.nhead,
num_encoder_layers=config.num_encoder_layers,
num_decoder_layers=config.num_decoder_layers,
dim_feedforward=config.dim_feedforward,
dropout=config.dropout,
batch_first=True
)
# Output layer
self.fc_out = nn.Linear(config.d_model, config.vocab_size)
# Initialize weights
self.post_init()
def _init_weights(self, module):
"""Initialize weights"""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=0.02)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=0.02)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def get_encoder(self):
"""Return encoder for compatibility"""
return self.transformer.encoder
def get_decoder(self):
"""Return decoder for compatibility"""
return self.transformer.decoder
def generate_square_subsequent_mask(self, sz, device):
"""Generate causal mask for decoder"""
mask = torch.triu(torch.ones(sz, sz, device=device), diagonal=1)
mask = mask.masked_fill(mask == 1, float('-inf'))
return mask
def create_padding_mask(self, seq, pad_token_id):
"""Create padding mask"""
return (seq == pad_token_id)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs
) -> Union[Tuple, Seq2SeqLMOutput]:
"""Forward pass"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
device = input_ids.device
# If labels provided but no decoder_input_ids, shift labels to create decoder_input_ids
if labels is not None and decoder_input_ids is None:
labels_shifted = labels.clone()
labels_shifted[labels_shifted == -100] = self.config.pad_token_id
decoder_input_ids = torch.cat([
torch.full((labels.shape[0], 1), self.config.bos_token_id, dtype=torch.long, device=device),
labels_shifted[:, :-1]
], dim=1)
# Embeddings with scaling
src_emb = self.embedding(input_ids) * math.sqrt(self.config.d_model)
src_emb = self.pos_encoder(src_emb)
tgt_emb = self.embedding(decoder_input_ids) * math.sqrt(self.config.d_model)
tgt_emb = self.pos_decoder(tgt_emb)
# Create masks
tgt_seq_len = decoder_input_ids.size(1)
tgt_mask = self.generate_square_subsequent_mask(tgt_seq_len, device)
src_key_padding_mask = self.create_padding_mask(input_ids, self.config.pad_token_id)
tgt_key_padding_mask = self.create_padding_mask(decoder_input_ids, self.config.pad_token_id)
# Transformer forward pass
output = self.transformer(
src_emb,
tgt_emb,
tgt_mask=tgt_mask,
src_key_padding_mask=src_key_padding_mask,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=src_key_padding_mask
)
# Output projection
logits = self.fc_out(output)
# Calculate loss if labels provided
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (logits,)
return ((loss,) + output) if loss is not None else output
return Seq2SeqLMOutput(
loss=loss,
logits=logits,
)
def prepare_inputs_for_generation(
self,
decoder_input_ids,
past_key_values=None,
attention_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs
):
"""Prepare inputs for generation"""
return {
"input_ids": kwargs.get("input_ids"),
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
}
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
"""Reorder cache for beam search"""
return past_key_values
def generate(
self,
input_ids: torch.LongTensor,
attention_mask: Optional[torch.FloatTensor] = None,
max_length: int = 128,
num_beams: int = 1,
temperature: float = 1.0,
do_sample: bool = False,
top_k: int = 50,
top_p: float = 1.0,
**kwargs
) -> torch.LongTensor:
"""Generate translations"""
device = input_ids.device
batch_size = input_ids.size(0)
# Start with BOS token
decoder_input_ids = torch.full(
(batch_size, 1),
self.config.bos_token_id,
dtype=torch.long,
device=device
)
finished = torch.zeros(batch_size, dtype=torch.bool, device=device)
# Generate tokens one by one
for _ in range(max_length - 1):
outputs = self.forward(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
return_dict=True
)
next_token_logits = outputs.logits[:, -1, :] / temperature
if do_sample:
if top_k > 0:
indices_to_remove = next_token_logits < torch.topk(next_token_logits, top_k)[0][..., -1, None]
next_token_logits[indices_to_remove] = float('-inf')
if top_p < 1.0:
sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
sorted_indices_to_remove = cumulative_probs > top_p
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
next_token_logits[indices_to_remove] = float('-inf')
probs = torch.softmax(next_token_logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
else:
next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
finished = finished | (next_token.squeeze(-1) == self.config.eos_token_id)
next_token[finished] = self.config.pad_token_id
decoder_input_ids = torch.cat([decoder_input_ids, next_token], dim=1)
if finished.all():
break
return decoder_input_ids
# Register the model in the AutoModel registry
from transformers import AutoConfig, AutoModel, AutoModelForSeq2SeqLM
AutoConfig.register("translation_transformer", TranslationTransformerConfig)
AutoModel.register(TranslationTransformerConfig, TranslationTransformerModel)
AutoModelForSeq2SeqLM.register(TranslationTransformerConfig, TranslationTransformerModel) |