File size: 11,988 Bytes
898e706 9947146 898e706 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutput, MaskedLMOutput
try:
from .configuration_ernierna import ErnieRNAConfig
except ImportError:
from configuration_ernierna import ErnieRNAConfig
class ErnieRNASinusoidalPositionalEmbedding(nn.Module):
def __init__(self, num_positions, embed_dim, padding_idx):
super().__init__()
self.embedding_dim = embed_dim
self.padding_idx = padding_idx
# Table size: need indices up to padding_idx + 1 + num_positions
table_size = padding_idx + 1 + num_positions
self.register_buffer("weights", self._get_embedding(table_size, embed_dim, padding_idx))
@staticmethod
def _get_embedding(num_embeddings, embedding_dim, padding_idx):
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
if embedding_dim % 2 == 1:
emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
if padding_idx is not None:
emb[padding_idx, :] = 0
return emb
def forward(self, input_ids):
mask = input_ids.ne(self.padding_idx).int()
positions = (torch.cumsum(mask, dim=1) * mask).long() + self.padding_idx
return self.weights.index_select(0, positions.view(-1)).view(
input_ids.shape[0], input_ids.shape[1], -1
).detach()
class ErnieRNATwodProj(nn.Module):
def __init__(self, config):
super().__init__()
self.linear1 = nn.Linear(1, 6)
self.linear2 = nn.Linear(6, config.attention_heads)
self.activation_fn = ACT2FN[config.activation_fn]
def forward(self, x):
x = self.linear1(x)
x = self.activation_fn(x)
x = self.linear2(x)
return x
def _compute_pairing_bias(input_ids):
B, T = input_ids.shape
xi = input_ids.unsqueeze(2).expand(B, T, T)
xj = input_ids.unsqueeze(1).expand(B, T, T)
score = torch.zeros(B, T, T, dtype=torch.float32, device=input_ids.device)
score[(xi == 5) & (xj == 6)] = 2.0
score[(xi == 6) & (xj == 5)] = 2.0
score[(xi == 4) & (xj == 7)] = 3.0
score[(xi == 7) & (xj == 4)] = 3.0
score[(xi == 4) & (xj == 6)] = 0.8
score[(xi == 6) & (xj == 4)] = 0.8
return score.unsqueeze(-1) # [B, T, T, 1]
class ErnieRNAAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.embed_dim = config.embed_dim
self.num_heads = config.attention_heads
self.head_dim = self.embed_dim // self.num_heads
assert self.head_dim * self.num_heads == self.embed_dim
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.dropout = nn.Dropout(config.attention_dropout)
def _to_bh_t_hd(self, tensor, tgt_len, bsz):
return tensor.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
def forward(self, x, key_padding_mask=None, twod_bias=None, output_attentions=False):
tgt_len, bsz, _ = x.size()
q = self._to_bh_t_hd(self.q_proj(x), tgt_len, bsz)
k = self._to_bh_t_hd(self.k_proj(x), tgt_len, bsz)
v = self._to_bh_t_hd(self.v_proj(x), tgt_len, bsz)
scale = self.head_dim ** -0.5
q = q * scale
attn_weights = torch.bmm(q, k.transpose(-2, -1)) # [B*H, T, T]
if key_padding_mask is not None:
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, tgt_len)
attn_weights = attn_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")
)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, tgt_len)
if twod_bias is not None:
attn_weights = attn_weights + twod_bias.reshape(bsz * self.num_heads, tgt_len, tgt_len)
# Pre-softmax attention becomes the 2D bias for the next layer
twod_bias_new = attn_weights.view(bsz, self.num_heads, tgt_len, tgt_len)
attn_probs = F.softmax(attn_weights, dim=-1)
attn_weights_out = None
if output_attentions:
attn_weights_out = attn_probs.view(bsz, self.num_heads, tgt_len, tgt_len)
attn_probs = self.dropout(attn_probs)
out = torch.bmm(attn_probs, v)
out = out.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim)
out = self.out_proj(out)
return out, attn_weights_out, twod_bias_new
class ErnieRNALayer(nn.Module):
def __init__(self, config):
super().__init__()
self.self_attn = ErnieRNAAttention(config)
self.self_attn_layer_norm = nn.LayerNorm(config.embed_dim)
self.fc1 = nn.Linear(config.embed_dim, config.ffn_embed_dim)
self.fc2 = nn.Linear(config.ffn_embed_dim, config.embed_dim)
self.final_layer_norm = nn.LayerNorm(config.embed_dim)
self.dropout = nn.Dropout(config.dropout)
self.activation_dropout = nn.Dropout(config.activation_dropout)
self.activation_fn = ACT2FN[config.activation_fn]
def forward(self, x, key_padding_mask=None, twod_bias=None, output_attentions=False):
residual = x
x, attn_weights, twod_bias_new = self.self_attn(
x,
key_padding_mask=key_padding_mask,
twod_bias=twod_bias,
output_attentions=output_attentions,
)
x = self.dropout(x)
x = self.self_attn_layer_norm(residual + x)
residual = x
x = self.activation_fn(self.fc1(x))
x = self.activation_dropout(x)
x = self.fc2(x)
x = self.dropout(x)
x = self.final_layer_norm(residual + x)
return x, attn_weights, twod_bias_new
class ErnieRNAModel(PreTrainedModel):
config_class = ErnieRNAConfig
base_model_prefix = "model"
_supports_sdpa = False
_supports_flash_attn_2 = False
def __init__(self, config):
super().__init__(config)
self.padding_idx = config.padding_idx
self.embed_tokens = nn.Embedding(config.vocab_size, config.embed_dim, padding_idx=config.padding_idx)
self.embed_positions = ErnieRNASinusoidalPositionalEmbedding(
config.max_positions, config.embed_dim, config.padding_idx
)
self.segment_embeddings = nn.Embedding(config.num_segments, config.embed_dim)
self.emb_layer_norm = nn.LayerNorm(config.embed_dim)
self.dropout = nn.Dropout(config.dropout)
self.layers = nn.ModuleList([ErnieRNALayer(config) for _ in range(config.num_layers)])
self.twod_proj = ErnieRNATwodProj(config)
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# HF: 1=attend, 0=pad -> True=padding
if attention_mask is not None:
padding_mask = attention_mask.eq(0)
else:
padding_mask = input_ids.eq(self.padding_idx)
# Zero out padding positions after masking (matches fairseq behavior)
x = self.embed_tokens(input_ids)
# Sinusoidal PE is a float32 buffer; cast to activation dtype for bfloat16 compat.
x = x + self.embed_positions(input_ids).to(x.dtype)
if token_type_ids is not None:
x = x + self.segment_embeddings(token_type_ids)
x = self.emb_layer_norm(x)
if padding_mask.any():
x = x * (~padding_mask).unsqueeze(-1).to(x.dtype)
x = self.dropout(x)
# Compute initial 2D bias from sequence (always float32 as in original)
pairing = _compute_pairing_bias(input_ids) # [B, T, T, 1]
twod_proj_f32 = self.twod_proj.float()
twod_bias = twod_proj_f32(pairing.float()) # [B, T, T, H]
twod_bias = twod_bias.permute(0, 3, 1, 2).contiguous().to(x.dtype) # [B, H, T, T]
# Transpose to [T, B, C] for attention
x = x.transpose(0, 1)
all_hidden_states = []
all_attentions = []
if output_hidden_states:
all_hidden_states.append(x.transpose(0, 1))
key_padding_mask = padding_mask if padding_mask.any() else None
for layer in self.layers:
x, attn_weights, twod_bias = layer(
x,
key_padding_mask=key_padding_mask,
twod_bias=twod_bias,
output_attentions=output_attentions,
)
if output_hidden_states:
all_hidden_states.append(x.transpose(0, 1))
if output_attentions:
all_attentions.append(attn_weights)
x = x.transpose(0, 1) # [B, T, C]
if not return_dict:
return tuple(v for v in [x, tuple(all_hidden_states) or None, tuple(all_attentions) or None] if v is not None)
return BaseModelOutput(
last_hidden_state=x,
hidden_states=tuple(all_hidden_states) if output_hidden_states else None,
attentions=tuple(all_attentions) if output_attentions else None,
)
class ErnieRNALMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.embed_dim, config.embed_dim)
self.layer_norm = nn.LayerNorm(config.embed_dim)
self.activation_fn = ACT2FN[config.activation_fn]
self.decoder = nn.Linear(config.embed_dim, config.vocab_size)
def forward(self, x):
x = self.layer_norm(self.activation_fn(self.dense(x)))
x = self.decoder(x)
return x
class ErnieRNAForMaskedLM(PreTrainedModel):
config_class = ErnieRNAConfig
base_model_prefix = "model"
_supports_sdpa = False
_supports_flash_attn_2 = False
def __init__(self, config):
super().__init__(config)
self.model = ErnieRNAModel(config)
self.lm_head = ErnieRNALMHead(config)
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
out = self.model(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = self.lm_head(out[0])
loss = None
if labels is not None:
loss = F.cross_entropy(
logits.view(-1, self.config.vocab_size),
labels.view(-1),
ignore_index=-100,
)
if not return_dict:
output = (logits,) + out[1:]
return ((loss,) + output) if loss is not None else output
return MaskedLMOutput(
loss=loss,
logits=logits,
hidden_states=out.hidden_states,
attentions=out.attentions,
)
|