File size: 11,967 Bytes
97c2d91 |
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 |
# -*- coding: utf-8 -*-
from __future__ import annotations
import math
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.utils.checkpoint
from fla.modules import FusedCrossEntropyLoss, RMSNorm, RotaryEmbedding
from torch.nn import functional as F
from transformers import PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerationConfig
from .configuration_stickbreaking import StickbreakingConfig
class StickbreakingAttention(nn.Module):
"""
Stick-breaking attention mechanism (ICLR 2025)
"""
def __init__(self, config: StickbreakingConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.hidden_size = config.hidden_size
self.num_heads = config.num_heads
self.num_kv_heads = config.num_kv_heads
self.head_dim = self.hidden_size // self.num_heads
self.num_kv_groups = self.num_heads // self.num_kv_heads
self.scale = 1.0 / math.sqrt(self.head_dim)
# Q, K, V projections
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
self.k_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.head_dim, bias=config.attention_bias)
self.v_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.head_dim, bias=config.attention_bias)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
# Optional: RoPE
if config.use_rope:
self.rotary = RotaryEmbedding(
dim=self.head_dim,
base=config.rope_base
)
# Optional: QK norm
if config.qk_norm:
if config.qk_norm_share_param_across_head:
self.q_norm = RMSNorm(hidden_size=self.head_dim, eps=config.norm_eps)
self.k_norm = RMSNorm(hidden_size=self.head_dim, eps=config.norm_eps)
else:
self.q_norm = RMSNorm(hidden_size=self.hidden_size, eps=config.norm_eps)
self.k_norm = RMSNorm(hidden_size=self.num_kv_heads * self.head_dim, eps=config.norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
use_cache: bool = False,
**kwargs
) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor]]]:
batch_size, seq_len, _ = hidden_states.size()
# QKV projections
q = self.q_proj(hidden_states)
k = self.k_proj(hidden_states)
v = self.v_proj(hidden_states)
# Reshape
q = q.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
k = k.view(batch_size, seq_len, self.num_kv_heads, self.head_dim).transpose(1, 2)
v = v.view(batch_size, seq_len, self.num_kv_heads, self.head_dim).transpose(1, 2)
# Optional: RoPE
if self.config.use_rope:
q, k = self.rotary(q, k)
# Optional: QK norm
if self.config.qk_norm:
if self.config.qk_norm_share_param_across_head:
q = self.q_norm(q)
k = self.k_norm(k)
else:
q = self.q_norm(q.transpose(1, 2).contiguous().view(batch_size, seq_len, -1))
k = self.k_norm(k.transpose(1, 2).contiguous().view(batch_size, seq_len, -1))
q = q.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
k = k.view(batch_size, seq_len, self.num_kv_heads, self.head_dim).transpose(1, 2)
# Repeat K, V if using GQA
if self.num_kv_groups > 1:
k = k.repeat_interleave(self.num_kv_groups, dim=1)
v = v.repeat_interleave(self.num_kv_groups, dim=1)
# Stick-breaking attention
from forgetting_transformer.ops.stickbreaking_attention_std import stickbreaking_attention_std
o = stickbreaking_attention_std(
q, k, v,
head_first=True,
sm_scale=self.scale,
normalize=self.config.normalize_attention,
attend_current=self.config.attend_current,
)
# Output projection
o = o.transpose(1, 2).contiguous().view(batch_size, seq_len, self.hidden_size)
o = self.o_proj(o)
return o, None
class StickbreakingMLP(nn.Module):
def __init__(self, config: StickbreakingConfig):
super().__init__()
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size or config.hidden_ratio * config.hidden_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, x):
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
class StickbreakingBlock(nn.Module):
def __init__(self, config: StickbreakingConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.attn_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps)
self.attn = StickbreakingAttention(config, layer_idx)
self.mlp_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps)
self.mlp = StickbreakingMLP(config)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
use_cache: bool = False,
**kwargs
):
# Attention with residual
residual = hidden_states
hidden_states = self.attn_norm(hidden_states)
hidden_states, present_key_value = self.attn(
hidden_states,
attention_mask=attention_mask,
past_key_value=past_key_value,
use_cache=use_cache,
)
hidden_states = residual + hidden_states
# MLP with residual
residual = hidden_states
hidden_states = self.mlp_norm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
return hidden_states, present_key_value
class StickbreakingPreTrainedModel(PreTrainedModel):
config_class = StickbreakingConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["StickbreakingBlock"]
def _init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
class StickbreakingModel(StickbreakingPreTrainedModel):
def __init__(self, config: StickbreakingConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList([
StickbreakingBlock(config, layer_idx)
for layer_idx in range(config.num_hidden_layers)
])
self.norm = RMSNorm(config.hidden_size, eps=config.norm_eps)
self.gradient_checkpointing = False
self.post_init()
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
hidden_states = self.embeddings(input_ids)
for layer in self.layers:
if self.gradient_checkpointing and self.training:
hidden_states, _ = torch.utils.checkpoint.checkpoint(
layer.__call__,
hidden_states,
attention_mask,
None,
use_cache,
)
else:
hidden_states, _ = layer(
hidden_states,
attention_mask=attention_mask,
past_key_value=None,
use_cache=use_cache,
)
hidden_states = self.norm(hidden_states)
return hidden_states
class StickbreakingForCausalLM(StickbreakingPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = StickbreakingModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.post_init()
def get_input_embeddings(self):
return self.model.embeddings
def set_input_embeddings(self, value):
self.model.embeddings = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# Forward through model
hidden_states = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# Compute logits
logits = self.lm_head(hidden_states)
# Compute loss
loss = None
if labels is not None:
if self.config.fuse_cross_entropy:
loss_fct = FusedCrossEntropyLoss(inplace_backward=True, reduction='none')
else:
loss_fct = nn.CrossEntropyLoss(reduction='none')
logits = logits.to(torch.float32)
labels = labels.to(logits.device)
loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
loss = loss.view(*labels.size())
if not return_dict:
output = (logits,)
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=None,
hidden_states=None,
attentions=None,
) |