-
Notifications
You must be signed in to change notification settings - Fork 115
Expand file tree
/
Copy pathmodel.py
More file actions
259 lines (223 loc) · 9.43 KB
/
Copy pathmodel.py
File metadata and controls
259 lines (223 loc) · 9.43 KB
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
import json
import os
from typing import Dict, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from huggingface_hub import snapshot_download
from transformers import AutoModelForCausalLM
INNER_ADAPTER_ALIASES = {
"1layer": "linear_adapter",
"1layer_res": "linear_res_adapter",
"2layer": "adapter",
"2layer_res": "res_adapter",
"2layer_ln_res": "ln_res_adapter",
}
INNER_ADAPTER_TYPES = {
"linear_adapter",
"linear_res_adapter",
"adapter",
"res_adapter",
"ln_res_adapter",
}
OUTER_ADAPTER_ALIASES = {
"1layer": "outer_linear_adapter",
"1layer_res": "outer_linear_res_adapter",
"2layer": "outer_adapter",
"2layer_res": "outer_res_adapter",
"2layer_ln_res": "outer_ln_res_adapter",
}
OUTER_ADAPTER_TYPES = {
"outer_linear_adapter",
"outer_linear_res_adapter",
"outer_adapter",
"outer_res_adapter",
"outer_ln_adapter",
"outer_ln_res_adapter",
}
def resolve_local_pretrained_path(model_name_or_path: str) -> str:
if os.path.isdir(model_name_or_path):
return model_name_or_path
try:
return snapshot_download(model_name_or_path, local_files_only=True)
except Exception:
return model_name_or_path
def normalize_inner_adapter_type(adapter_type: str) -> str:
adapter_type = INNER_ADAPTER_ALIASES.get(adapter_type, adapter_type)
if adapter_type not in INNER_ADAPTER_TYPES:
raise ValueError(f"Unsupported adapter_type: {adapter_type}")
return adapter_type
def normalize_outer_adapter_type(adapter_type: str) -> str:
adapter_type = OUTER_ADAPTER_ALIASES.get(adapter_type, adapter_type)
if adapter_type not in OUTER_ADAPTER_TYPES:
raise ValueError(f"Unsupported outer adapter_type: {adapter_type}")
return adapter_type
class Adapter(nn.Module):
def __init__(self, hidden_size: int, adapter_type: str) -> None:
super().__init__()
adapter_type = normalize_inner_adapter_type(adapter_type)
self.adapter_type = adapter_type
self.is_linear = adapter_type in {"linear_adapter", "linear_res_adapter"}
self.proj1 = nn.Linear(hidden_size, hidden_size)
self.act = nn.GELU() if not self.is_linear else None
self.proj2 = nn.Linear(hidden_size, hidden_size) if not self.is_linear else None
self.use_residual = adapter_type in {"linear_res_adapter", "res_adapter", "ln_res_adapter"}
self.pre_ln = nn.LayerNorm(hidden_size) if adapter_type == "ln_res_adapter" else None
self.post_ln = nn.LayerNorm(hidden_size) if adapter_type == "ln_res_adapter" else None
def forward(self, x: torch.Tensor) -> torch.Tensor:
h = self.pre_ln(x) if self.pre_ln is not None else x
if self.is_linear:
out = self.proj1(h)
else:
out = self.proj2(self.act(self.proj1(h)))
if self.use_residual:
out = x + out
if self.post_ln is not None:
out = self.post_ln(out)
return out
class CrossModelAdapter(nn.Module):
def __init__(self, in_dim: int, out_dim: int, adapter_type: str) -> None:
super().__init__()
adapter_type = normalize_outer_adapter_type(adapter_type)
self.adapter_type = adapter_type
self.in_dim = in_dim
self.out_dim = out_dim
self.is_linear = adapter_type in {"outer_linear_adapter", "outer_linear_res_adapter"}
self.use_ln = adapter_type in {"outer_ln_adapter", "outer_ln_res_adapter"}
self.use_residual = adapter_type in {"outer_linear_res_adapter", "outer_res_adapter", "outer_ln_res_adapter"}
hidden_dim = out_dim * 2 if self.use_ln else out_dim
self.proj1 = nn.Linear(in_dim, hidden_dim)
self.act = nn.GELU() if not self.is_linear else None
self.proj2 = nn.Linear(hidden_dim, out_dim) if not self.is_linear else None
self.ln_source = nn.LayerNorm(in_dim) if self.use_ln else None
self.ln_target = nn.LayerNorm(out_dim) if self.use_ln else None
self.residual_proj = nn.Linear(in_dim, out_dim) if self.use_residual else None
def forward(self, x: torch.Tensor) -> torch.Tensor:
h = self.ln_source(x) if self.ln_source is not None else x
if self.is_linear:
out = self.proj1(h)
else:
out = self.proj2(self.act(self.proj1(h)))
if self.residual_proj is not None:
out = out + self.residual_proj(x)
if self.ln_target is not None:
out = self.ln_target(out)
return out
class LatentReasoningModel(nn.Module):
def __init__(
self,
model_name_or_path: str,
adapter_type: str = "adapter",
torch_dtype: Optional[torch.dtype] = None,
adapter_dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
) -> None:
super().__init__()
resolved_path = resolve_local_pretrained_path(model_name_or_path)
self.model = AutoModelForCausalLM.from_pretrained(
resolved_path,
torch_dtype=torch_dtype,
trust_remote_code=trust_remote_code,
)
for param in self.model.parameters():
param.requires_grad = False
self.model.eval()
hidden_size = self._infer_hidden_size()
self.adapter = Adapter(hidden_size, adapter_type)
base_param = next(self.model.parameters(), None)
base_dtype = base_param.dtype if base_param is not None else torch.float32
self.adapter_dtype = adapter_dtype if adapter_dtype is not None else base_dtype
self.adapter.to(dtype=self.adapter_dtype)
def _infer_hidden_size(self) -> int:
embeddings = self.model.get_input_embeddings()
return embeddings.weight.shape[-1]
def train(self, mode: bool = True) -> "LatentReasoningModel":
super().train(mode)
# Keep the base LLM frozen in eval mode; only adapter is trainable.
self.model.eval()
return self
def save_adapter(self, output_dir: str) -> None:
os.makedirs(output_dir, exist_ok=True)
torch.save(self.adapter.state_dict(), os.path.join(output_dir, "adapter.pt"))
config = {"adapter_type": self.adapter.adapter_type}
with open(os.path.join(output_dir, "adapter_config.json"), "w", encoding="utf-8") as f:
json.dump(config, f, indent=2, sort_keys=True)
@staticmethod
def _masked_mean(values: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
mask = mask.float()
denom = mask.sum().clamp(min=1.0)
return (values * mask).sum() / denom
def _run_adapter(
self,
hidden_states: torch.Tensor,
output_dtype: Optional[torch.dtype] = None,
) -> torch.Tensor:
x = hidden_states
if x.dtype != self.adapter_dtype:
x = x.to(self.adapter_dtype)
out = self.adapter(x)
if output_dtype is not None and out.dtype != output_dtype:
out = out.to(output_dtype)
return out
def _compute_adapter_losses(
self,
hidden_states: torch.Tensor,
target_embeds: torch.Tensor,
mask: torch.Tensor,
) -> Dict[str, torch.Tensor]:
preds = self._run_adapter(hidden_states)
preds_float = preds.float()
targets_float = target_embeds.float()
cosine = 1.0 - F.cosine_similarity(preds_float, targets_float, dim=-1)
mse = (preds_float - targets_float).pow(2).mean(dim=-1)
return {
"cosine": self._masked_mean(cosine, mask),
"mse": self._masked_mean(mse, mask),
}
def forward(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
loss_mask: Optional[torch.Tensor] = None,
latent_mask: Optional[torch.Tensor] = None,
adapter_cos_weight: float = 1.0,
adapter_mse_weight: float = 0.1,
) -> Dict[str, torch.Tensor]:
with torch.no_grad():
input_embeds = self.model.get_input_embeddings()(input_ids)
base_outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
output_hidden_states=True,
use_cache=False,
return_dict=True,
)
hidden_states = base_outputs.hidden_states[-1]
if loss_mask is None:
loss_mask = attention_mask
if latent_mask is None:
latent_mask = loss_mask
latent_mask = latent_mask.to(attention_mask.device)
adapter_cosine_loss = torch.zeros((), device=input_ids.device)
adapter_mse_loss = torch.zeros((), device=input_ids.device)
total_loss = torch.zeros((), device=input_ids.device)
if adapter_cos_weight > 0 or adapter_mse_weight > 0:
realign_pair_mask = latent_mask[:, 1:] * attention_mask[:, :-1]
losses = self._compute_adapter_losses(
hidden_states[:, :-1, :],
input_embeds[:, 1:, :],
realign_pair_mask,
)
adapter_cosine_loss = losses["cosine"]
adapter_mse_loss = losses["mse"]
total_loss = (
adapter_cos_weight * adapter_cosine_loss
+ adapter_mse_weight * adapter_mse_loss
)
return {
"loss": total_loss,
"adapter_cosine_loss": adapter_cosine_loss.detach(),
"adapter_mse_loss": adapter_mse_loss.detach(),
"adapter_cosine_weighted": (adapter_cos_weight * adapter_cosine_loss).detach(),
"adapter_mse_weighted": (adapter_mse_weight * adapter_mse_loss).detach(),
}