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"""
Change the attention of Gemma3 to be bidirectional.
"""
import torch
import torch.nn as nn
from typing import Optional, List, Dict, Any
from functools import partial
from transformers import PretrainedConfig, PreTrainedModel
from transformers import Gemma3ForCausalLM, Gemma3TextConfig
from transformers.models.gemma3.modeling_gemma3 import (
Gemma3Attention,
Gemma3DecoderLayer,
Gemma3TextModel,
)
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)
# ============ Token Classification Model Components ============
class NonCausalGemma3Attention(Gemma3Attention):
"""Gemma3Attention configured for non-causal token classification."""
def __init__(self, config, layer_idx: int):
super().__init__(config, layer_idx)
self.is_causal = False
self.sliding_window = None
class NonCausalGemma3DecoderLayer(Gemma3DecoderLayer):
"""Decoder layer with non-causal attention for token classification."""
def __init__(self, config, layer_idx: int):
super().__init__(config, layer_idx)
self.self_attn = NonCausalGemma3Attention(config, layer_idx)
class Gemma3TokenClassificationModel(Gemma3TextModel):
"""Gemma3 base model configured for token classification."""
_no_split_modules = ["NonCausalGemma3DecoderLayer"]
def __init__(self, config):
super().__init__(config)
if getattr(config, 'use_non_causal_attention', True):
# Replace layers with non-causal versions
self.layers = nn.ModuleList(
[
NonCausalGemma3DecoderLayer(config, layer_idx)
for layer_idx in range(config.num_hidden_layers)
]
)
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values = None,
output_attentions: bool = False,
):
"""Override to create bidirectional attention mask (no causal masking)."""
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None
past_seen_tokens = (
past_key_values.get_seq_length() if past_key_values is not None else 0
)
using_static_cache = isinstance(past_key_values, type(None)) is False and hasattr(past_key_values, 'get_max_length')
dtype, device = input_tensor.dtype, input_tensor.device
min_dtype = torch.finfo(dtype).min
sequence_length = input_tensor.shape[1]
if using_static_cache:
target_length = past_key_values.get_max_length()
else:
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
)
if attention_mask is not None and attention_mask.dim() == 4:
# in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
if attention_mask.max() != 0:
raise ValueError(
"Custom 4D attention mask should be passed in inverted form with max==0`"
)
causal_mask = attention_mask
else:
# KEY CHANGE: Start with zeros (attend to all) instead of min_dtype (mask all)
causal_mask = torch.zeros(
(sequence_length, target_length), dtype=dtype, device=device
)
# REMOVED: Causal masking lines that would make it lower triangular
# if sequence_length != 1:
# causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(
target_length, device=device
) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(
input_tensor.shape[0], 1, -1, -1
)
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = (
causal_mask[:, :, :, :mask_length]
+ attention_mask[:, None, None, :]
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[
:, :, :, :mask_length
].masked_fill(padding_mask, min_dtype)
# Handle SDPA-specific optimizations if needed
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type == "cuda"
and not output_attentions
):
try:
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
causal_mask = AttentionMaskConverter._unmask_unattended(
causal_mask, min_dtype
)
except ImportError:
pass # Fallback for older transformers versions
return causal_mask
class Gemma3ForTokenClassification(Gemma3ForCausalLM):
"""
Gemma3 model for token classification (punctuation prediction).
Uses class-based architecture without monkey patching.
"""
config_class = Gemma3PunctuationConfig
def __init__(self, config):
# Initialize with base Gemma3ForCausalLM structure
super().__init__(config)
self.num_labels = config.num_labels
# Replace the base model with token classification version
if getattr(config, 'use_non_causal_attention', True):
self.model = Gemma3TokenClassificationModel(config)
# Replace the lm_head with classification head
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()
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
# Get hidden states from the model
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,
)
# ============ Model Registration ============
from transformers import AutoConfig, AutoModel
# Register the punctuation config and model
AutoConfig.register("cadence_punctuation", Gemma3PunctuationConfig)
AutoModel.register(Gemma3PunctuationConfig, Gemma3ForTokenClassification)
# ============ Utility Functions ============
def create_token_classification_model(config: Gemma3PunctuationConfig):
"""Create a token classification model with non-causal attention."""
return Gemma3ForTokenClassification(config)
def load_from_pretrained_with_config_detection(model_path: str, **kwargs):
"""
Load model and auto-detect whether it's for token classification or bidirectional tasks
based on the config.
"""
from transformers import AutoConfig
config = AutoConfig.from_pretrained(model_path)
if hasattr(config, 'model_type') and config.model_type == "cadence_punctuation":
# Token classification model
return Gemma3ForTokenClassification.from_pretrained(model_path, config=config, **kwargs)
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