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4bb52fb | 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 | from typing import Any
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 TokenClassifierOutput
from .configuration_sing_probe import SingProbeAttnConfig, SingProbeMlpConfig
class SingProbePreTrainedModel(PreTrainedModel):
base_model_prefix = ""
main_input_name = "hidden_states"
def _init_weights(self, module: nn.Module) -> None:
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.RMSNorm):
nn.init.ones_(module.weight)
@staticmethod
def _validate_input(config: Any, hidden_states: torch.Tensor) -> None:
if hidden_states.shape[-1] != config.input_size:
raise ValueError(
f"input feature size {hidden_states.shape[-1]} does not match "
f"hidden_size * number of tapped layers ({config.input_size})"
)
class SingProbeMlpModel(SingProbePreTrainedModel):
config_class = SingProbeMlpConfig
def __init__(self, config: SingProbeMlpConfig) -> None:
super().__init__(config)
self.fc1 = nn.Linear(config.input_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.num_labels)
self.act_fn = ACT2FN[config.hidden_act]
self.post_init()
def forward(
self,
hidden_states: torch.Tensor,
return_dict: bool | None = None,
**_: Any,
) -> TokenClassifierOutput | tuple[torch.Tensor]:
self._validate_input(self.config, hidden_states)
hidden_states = hidden_states.to(self.fc1.weight.dtype)
hidden_states = self.fc1(hidden_states)
hidden_states = self.act_fn(hidden_states)
logits = self.fc2(hidden_states)
if return_dict is False:
return (logits,)
return TokenClassifierOutput(logits=logits)
class SingProbeAttnModel(SingProbePreTrainedModel):
config_class = SingProbeAttnConfig
def __init__(self, config: SingProbeAttnConfig) -> None:
super().__init__(config)
if config.num_attention_heads < 1 or config.head_dim < 1:
raise ValueError("num_attention_heads and head_dim must be positive")
if config.sliding_window is not None and config.sliding_window <= 0:
raise ValueError("sliding_window must be positive")
self.num_attention_heads = config.num_attention_heads
self.head_dim = config.head_dim
self.projection_size = self.num_attention_heads * self.head_dim
self.proj_q = nn.Linear(config.input_size, self.projection_size, bias=False)
self.proj_k = nn.Linear(config.input_size, self.head_dim, bias=False)
self.proj_v = nn.Linear(config.input_size, self.head_dim, bias=False)
self.o_proj = nn.Linear(self.projection_size, self.projection_size, bias=False)
self.norm = nn.RMSNorm(self.projection_size, eps=1e-6)
self.classifier = nn.Linear(self.projection_size, config.num_labels)
self.post_init()
def _sliding_window_attention(
self,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
) -> torch.Tensor:
seq_len = query.shape[2]
window = self.config.sliding_window
assert window is not None
outputs = []
for start in range(0, seq_len, window):
end = min(start + window, seq_len)
key_start = max(0, start - window + 1)
query_positions = torch.arange(start, end, device=query.device)
key_positions = torch.arange(key_start, end, device=query.device)
relative_positions = query_positions[:, None] - key_positions
attention_mask = (relative_positions >= 0) & (relative_positions < window)
attention_mask = attention_mask[None, None]
query_block = query[:, :, start:end]
key_block = key[:, :, key_start:end]
value_block = value[:, :, key_start:end]
attention_output = F.scaled_dot_product_attention(
query_block,
key_block,
value_block,
attn_mask=attention_mask,
enable_gqa=self.num_attention_heads > 1,
)
outputs.append(attention_output)
return torch.cat(outputs, dim=2)
def forward(
self,
hidden_states: torch.Tensor,
return_dict: bool | None = None,
**_: Any,
) -> TokenClassifierOutput | tuple[torch.Tensor]:
self._validate_input(self.config, hidden_states)
hidden_states = hidden_states.to(self.classifier.weight.dtype)
batch_size, seq_len, _ = hidden_states.shape
query_features = self.proj_q(hidden_states)
query_shape = (batch_size, seq_len, self.num_attention_heads, self.head_dim)
kv_shape = (batch_size, seq_len, 1, self.head_dim)
query = query_features.view(query_shape).transpose(1, 2)
key = self.proj_k(hidden_states).view(kv_shape).transpose(1, 2)
value = self.proj_v(hidden_states).view(kv_shape).transpose(1, 2)
if self.config.sliding_window and self.config.sliding_window < seq_len:
context = self._sliding_window_attention(query, key, value)
else:
context = F.scaled_dot_product_attention(
query=query,
key=key,
value=value,
is_causal=True,
enable_gqa=self.num_attention_heads > 1,
)
context = context.transpose(1, 2).contiguous()
context = context.view(batch_size, seq_len, self.projection_size)
hidden_states = self.o_proj(context) + query_features
logits = self.classifier(self.norm(hidden_states))
if return_dict is False:
return (logits,)
return TokenClassifierOutput(logits=logits)
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