File size: 11,780 Bytes
d4c5a78 |
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 |
"""
Custom Gemma3 model for token classification with non-causal attention
"""
import torch
import torch.nn as nn
from typing import Optional, Tuple, List, Dict, Any
import types
from transformers import PretrainedConfig, PreTrainedModel
from transformers import Gemma3ForCausalLM
from transformers.models.gemma3.modeling_gemma3 import (
Gemma3Attention,
repeat_kv,
apply_rotary_pos_emb,
ALL_ATTENTION_FUNCTIONS,
Cache,
FlashAttentionKwargs,
)
from transformers.modeling_outputs import TokenClassifierOutput
from transformers.utils import logging
logger = logging.get_logger(__name__)
class Gemma3PunctuationConfig(PretrainedConfig):
"""
Configuration class for Gemma3 punctuation model.
"""
model_type = "cadence_punctuation"
def __init__(
self,
num_labels: int = 31,
classifier_dropout_prob: float = 0.0,
use_non_causal_attention: bool = True,
**kwargs
):
self.num_labels = num_labels
self.classifier_dropout_prob = classifier_dropout_prob
self.use_non_causal_attention = use_non_causal_attention
super().__init__(**kwargs)
def _extract_padding_mask_corrected(
combined_mask_4d: Optional[torch.Tensor],
debug_print: bool = False
) -> Optional[torch.Tensor]:
"""Extract padding mask from combined 4D attention mask."""
if combined_mask_4d is None:
return None
mask_value = torch.finfo(combined_mask_4d.dtype).min
is_key_padding = (combined_mask_4d == mask_value).all(dim=2, keepdim=True)
padding_only_mask = torch.where(
is_key_padding.expand_as(combined_mask_4d),
torch.full_like(combined_mask_4d, mask_value),
torch.zeros_like(combined_mask_4d)
)
return padding_only_mask
def non_causal_eager_attention_forward_with_padding(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
**kwargs: Any,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Non-causal eager attention implementation."""
dropout = kwargs.get("dropout", 0.0)
scaling = kwargs.get("scaling", None)
softcap = kwargs.get("softcap", None)
if scaling is None:
head_dim = getattr(module, "head_dim", query.shape[-1])
scaling = head_dim**-0.5
num_key_value_groups = getattr(module, "num_key_value_groups", 1)
key_states = repeat_kv(key, num_key_value_groups)
value_states = repeat_kv(value, num_key_value_groups)
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
if softcap is not None:
attn_weights = attn_weights / softcap
attn_weights = torch.tanh(attn_weights)
attn_weights = attn_weights * softcap
if attention_mask is not None:
mask_slice = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + mask_slice
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
is_training = getattr(module, "training", False)
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=is_training)
attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
def modified_gemma3_attention_forward_non_causal(
self: Gemma3Attention,
hidden_states: torch.Tensor,
position_embeddings: torch.Tensor,
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Any,
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
"""Modified Gemma3 attention forward for non-causal behavior."""
bsz, q_len, _ = hidden_states.size()
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
query_states = self.q_norm(query_states)
key_states = self.k_norm(key_states)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
cache_kwargs = {
"sin": sin,
"cos": cos,
"cache_position": cache_position,
"sliding_window": self.sliding_window
}
key_states, value_states = past_key_value.update(
key_states, value_states, self.layer_idx, cache_kwargs
)
effective_attn_implementation = self.config._attn_implementation
output_attentions = kwargs.get("output_attentions", False)
if effective_attn_implementation == "sdpa" and output_attentions:
effective_attn_implementation = "eager"
elif effective_attn_implementation == "flash_attention_2" and output_attentions:
effective_attn_implementation = "eager"
padding_only_mask = _extract_padding_mask_corrected(attention_mask)
use_causal_flag = False # Non-causal for punctuation
# Select attention interface
if effective_attn_implementation == "eager":
attention_interface = non_causal_eager_attention_forward_with_padding
elif effective_attn_implementation == "sdpa":
attention_interface = ALL_ATTENTION_FUNCTIONS.get("sdpa", non_causal_eager_attention_forward_with_padding)
elif effective_attn_implementation == "flash_attention_2":
attention_interface = ALL_ATTENTION_FUNCTIONS.get("flash_attention_2", non_causal_eager_attention_forward_with_padding)
else:
attention_interface = non_causal_eager_attention_forward_with_padding
final_attention_mask = padding_only_mask
if final_attention_mask is not None:
final_attention_mask = final_attention_mask.to(query_states.device)
# Prepare kwargs for attention interface
attn_specific_kwargs: Dict[str, Any] = {}
if attention_interface == non_causal_eager_attention_forward_with_padding:
attn_specific_kwargs = {
"dropout": 0.0,
"scaling": self.scaling,
"softcap": getattr(self, "softcap", None)
}
elif effective_attn_implementation == "sdpa":
attn_specific_kwargs = {"is_causal": use_causal_flag}
if output_attentions:
attn_specific_kwargs["output_attentions"] = True
elif effective_attn_implementation == "flash_attention_2":
attn_specific_kwargs = {
"causal": use_causal_flag,
"softcap": getattr(self, "softcap", None),
"dropout": 0.0
}
if output_attentions:
attn_specific_kwargs["output_attentions"] = True
attn_output, attn_weights = attention_interface(
self, query_states, key_states, value_states, final_attention_mask, **attn_specific_kwargs
)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
returned_weights = attn_weights if output_attentions and attn_weights is not None else None
return attn_output, returned_weights
class Gemma3ForTokenClassification(Gemma3ForCausalLM):
"""
Gemma3 model for token classification (punctuation prediction).
Inherits from Gemma3ForCausalLM and replaces the LM head with classification head.
"""
config_class = Gemma3PunctuationConfig
def __init__(self, config):
# Initialize the parent Gemma3ForCausalLM
super().__init__(config)
self.num_labels = config.num_labels
# Replace the lm_head with classification head
# Don't create a separate classifier - just replace lm_head directly
classifier_dropout_prob = getattr(config, 'classifier_dropout_prob', 0.0)
self.lm_head = nn.Sequential(
nn.Dropout(classifier_dropout_prob),
nn.Linear(config.hidden_size, config.num_labels)
)
# Update config for classification
self.config.num_labels = config.num_labels
# Initialize weights for the new head
self.post_init()
# Apply non-causal attention patching if requested
if getattr(config, 'use_non_causal_attention', True):
self._patch_attention_layers()
def _patch_attention_layers(self):
"""Patch attention layers to use non-causal attention."""
count = 0
# The model structure is self.model.layers (inherited from Gemma3ForCausalLM)
if hasattr(self, 'model') and hasattr(self.model, 'layers'):
target_layers = self.model.layers
else:
logger.warning("Could not find model.layers for attention patching")
return
for idx, layer in enumerate(target_layers):
if hasattr(layer, 'self_attn') and isinstance(layer.self_attn, Gemma3Attention):
layer.self_attn.layer_idx = idx
layer.self_attn.forward = types.MethodType(
modified_gemma3_attention_forward_non_causal,
layer.self_attn
)
count += 1
logger.info(f"Patched {count} attention layers for non-causal attention")
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> TokenClassifierOutput:
"""
Forward pass for token classification.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# Call the parent's forward method but get the hidden states instead of logits
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
)
# Get the hidden states from the model output
sequence_output = outputs[0]
# Apply the classification head (which is now self.lm_head)
logits = self.lm_head(sequence_output)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Register the model for AutoModel
from transformers import AutoConfig, AutoModel
AutoConfig.register("cadence_punctuation", Gemma3PunctuationConfig)
AutoModel.register(Gemma3PunctuationConfig, Gemma3ForTokenClassification) |